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Accurately modeling the DNA sequence preferences of transcription factors ( TFs ) , and using these models to predict in vivo genomic binding sites for TFs , are key pieces in deciphering the regulatory code . These efforts have been frustrated by the limited availability and accuracy of TF binding site motifs , usually represented as position-specific scoring matrices ( PSSMs ) , which may match large numbers of sites and produce an unreliable list of target genes . Recently , protein binding microarray ( PBM ) experiments have emerged as a new source of high resolution data on in vitro TF binding specificities . PBM data has been analyzed either by estimating PSSMs or via rank statistics on probe intensities , so that individual sequence patterns are assigned enrichment scores ( E-scores ) . This representation is informative but unwieldy because every TF is assigned a list of thousands of scored sequence patterns . Meanwhile , high-resolution in vivo TF occupancy data from ChIP-seq experiments is also increasingly available . We have developed a flexible discriminative framework for learning TF binding preferences from high resolution in vitro and in vivo data . We first trained support vector regression ( SVR ) models on PBM data to learn the mapping from probe sequences to binding intensities . We used a novel -mer based string kernel called the di-mismatch kernel to represent probe sequence similarities . The SVR models are more compact than E-scores , more expressive than PSSMs , and can be readily used to scan genomics regions to predict in vivo occupancy . Using a large data set of yeast and mouse TFs , we found that our SVR models can better predict probe intensity than the E-score method or PBM-derived PSSMs . Moreover , by using SVRs to score yeast , mouse , and human genomic regions , we were better able to predict genomic occupancy as measured by ChIP-chip and ChIP-seq experiments . Finally , we found that by training kernel-based models directly on ChIP-seq data , we greatly improved in vivo occupancy prediction , and by comparing a TF's in vitro and in vivo models , we could identify cofactors and disambiguate direct and indirect binding .
Gene regulatory programs are orchestrated by transcription factors ( TFs ) , proteins that coordinate expression of target genes both through direct interaction with DNA and with non-DNA-binding accessory proteins ( cofactors ) . A recent catalog of human and mouse TFs documented almost 900 likely TFs in the human genome , including over 500 with sequence-specific binding to double-stranded DNA [1] . Accurately modeling the DNA sequence preferences of these TFs , and using these sequence preferences in an appropriate way to predict whether the TF can bind a genomic site in vivo , are key pieces in unraveling the regulatory code . For many years , these efforts have been frustrated by the limited availability and quality of TF binding site motifs , usually represented as a position-specific scoring matrix ( PSSM ) or a consensus sequence . These motifs may match thousands of sites in intergenic regions , producing an unreliable list of potential TF target genes . [2] showed that motif hits in yeast could be filtered by TF occupancy profiles measured by ChIP-chip experiments , producing a better quality regulatory map . However , TF occupancy is condition-specific and , in metazoan genomes , cell type-dependent , due to differences in chromatin state , concentrations of cofactors , and other epigenetic determinants . Since it is not feasible to collect occupancy data for all TFs and all possible cellular contexts , we must develop better methods for predicting in vivo occupancy , which will depend in part on improving our models of TF binding preferences . Recently , protein binding microarray technology ( PBM ) has emerged as a new high-throughput technique to obtain more comprehensive data on a TF's in vitro sequence specificities [3] . PBM experiments measure binding of a fluorescently tagged TF or TF binding domain to a carefully designed set of double-stranded DNA probes which cover the space of all possible DNA 10-mers . So far , PBM data has been analyzed by extracting PSSMs or computing rank statistics on probe intensities from the TF binding experiment [3] . Traditional PSSMs may underfit PBM data by failing to capture subtle but detectable sequence preferences . Alternatively , an enrichment score ( E-score ) can be computed for each short sequence pattern , e . g . all possible 8-mers [3] or longer gapped -mer patterns . The collection of 8-mers with high E-scores then constitutes a kind of binding profile , with the E-score value giving a ranking of binding preferences . This representation provides much more information about a TF's DNA sequence affinities than a PSSM , but it is quite unwieldy , as each TF is assigned a list of thousands of scored -mer sequence patterns . Moreover , the E-score approach only implements a rough summarization of the raw probe-level intensity data and , in particular , treats each 8-mer ( or longer gapped ) pattern independently without attempting to exploit sequence similarities between 8-mers . In recent years , there have been numerous successful applications of discriminative machine learning techniques to sequence modeling problems in computational biology ( reviewed in [4] ) , including -mer based string kernel methods that exploit approximate matches of short sequence patterns [5] , [6] . These studies suggest that a more compact and accurate model of TF binding affinities could be learned from PBM data by training on probe sequences with a suitable kernel approach . In the first part of our study , we used a supervised learning strategy to obtain more accurate TF binding preference models from in vitro PBM probe-level data . As a component of this strategy , we developed a novel string kernel for comparing short double-stranded DNA sequences in a manner that captures similarity of potential TF binding sites . This kernel , called the di-mismatch kernel , is a first order Markov mismatch kernel – meaning that it is based on the alphabet of dinucleotides ( see Materials and Methods ) – and extends the -mer based string kernel methods that we and others have used for a wide range of problems involving modeling of biological sequences . In our approach , we used -mer based string kernels for representing the similarity of double-stranded probe sequences on the PBM , and we trained support vector regression ( SVR ) models to directly learn the mapping from probe sequence to binding intensity from PBM training data ( Figure 1 , top ) . The trained models can then be used directly to scan intergenic regions , yielding a predicted occupancy profile ( Figure 1 , bottom ) . To benchmark our approach , we used a large data set of mouse and yeast TFs from three separate studies for which PBM data for two independent probe designs is available . In these cases , we can train SVR models and compute E-scores using data from one PBM probe design and test how well each method predicts the high-intensity probes in the second probe design . We found that our SVR method strongly and consistently outperformed both the E-score and PSSM methods for this in vitro binding prediction task . Moreover , by using SVRs to score yeast intergenic regions as well as mouse and human genomic regions , we were better able to predict genomic occupancy as measured by ChIP-chip and ChIP-seq , compared with a previously described occupancy scoring method based on E-scores or PSSM-based prediction . In the second part of our study , we trained kernel-based SVM models directly on ChIP-seq data , learning to discriminate between ChIP-seq peak and non-peak genomic regions . We call these in vivo models , although the ChIP-seq experiments are performed in cell lines , to distinguish them from PBM-trained in vitro models . We found that the ChIP-derived SVM models significantly improve TF occupancy prediction in mammalian genomes when compared to PBM-derived SVR models . Moreover , our SVM approach outperforms existing PSSM approaches such as Weeder and MDscan [7] , [8] . Finally , we performed a feature analysis to extract -mers contained in both the in vitro and in vivo models . In the latter case , we were able to identify binding information about cofactors and disambiguate direct and indirect binding . These results suggest a strategy for combining discriminatively trained models from in vitro and in vivo data in order to decipher the transcriptional regulatory code .
A PBM experiment provides high resolution data on the binding affinities of a TF , comprising 44K double stranded DNA probes and corresponding measured probe intensities , which quantify the TF binding affinities for the probe sequences . The unique sequence in each probe is a 36-mer , and the probe set is mathematically specified to contain all possible 10-mers as subsequences . We used the probe data as labeled training examples , i . e . pairs , for learning a function that predicts binding intensity from ( 36-mer ) sequences . Since we were not only interested in learning to distinguish between bound and unbound probes but also predicting the range of binding affinities , we used support vector regression ( SVR ) to train our models . To compare pairs of probe sequences for SVR training , we developed a novel string kernel called the di-mismatch kernel , which is a -mer based string kernel adapted to the problem of TF binding models ( see Materials and Methods ) . Briefly , this kernel computes a similarity between probe sequences based on inexact matches to -mer features , allowing up to mismatches , where we count mismatches in the alphabet of dinucleotides . This choice reduces the size of the “mismatch neighborhood” of a given -mer ( i . e . fewer -mers are similar to it ) and favors mismatches that occur consecutively . A typical parameter choice is , i . e . , considering 13-mer sequences , allowing up to 5 mismatches , and operating in the first order alphabet of dinucleotides . We first tested the performance of our SVR models on in vitro binding preferences , in order to establish that they could better capture TF sequence specificities than existing approaches . For 33 yeast and 114 mouse TFs , experimental data for two independent PBM array designs were available [9] , [10] , measuring TF binding against two completely disjoint PBM probe sets . This combined data set provided a perfect cross-validation setting where we trained a model using data from one array design ( “training PBM” ) and then tested the model's ability to predict binding preferences on the other array design's probe sequences ( “test PBM” ) . We benchmarked the SVR models against the E-score approach [3] , using the E-scores for all 8-mer patterns , both contiguous and gappy , as computed and posted on the Uniprobe database [11] . E-scores are modified Wilcoxon rank statistics that assess the enrichment of a given 8-mer sequence pattern in probe sequences at the top of the intensity ranking in a PBM experiment . These scores range from −0 . 5 to 0 . 5 , where scores approaching 0 . 5 indicate that the 8-mer pattern is mostly present in bound probe sequences . In their yeast in vivo predictions , [9] identify high scoring 8-mer patterns to predict TF binding preferences . Therefore , we compared SVR performance to a maximum E-score approach , where each probe sequence in the test PBM is assigned the maximal E-score over the 8-mer patterns it contains , and the E-scores are computed on the training PBM . We call this the E-max score . We note that due to feature selection , our models contain no more than 4 , 000 -mer features . By contrast , for the data set 114 mouse TFs , the average number of -mers with E-scores above 0 . 35 ( the threshold used for reporting the pattern ) was 13 , 300 , with just 10 of the TFs having fewer than 3 , 000 -mers and 64 having more than 10 , 000 -mers . In this sense , the SVR models are more compact than the E-score approach . Because we want the models to predict the most preferred binding sequences , we first validated the results by counting how many of the top 100 predicted probes are in the top 100 highest intensity probes in the test data . Naturally , these scores range from 0 to 100 , with 100 indicating perfect detection of the preferred test probes by the top predictions; we refer to these validation scores as “the detection of the top 100 probes” . For each method and each TF , we averaged the detection rates over the two PBM designs to get one representative score for the TF . To ensure that our model was not specifically tuned to the PBM array data published in the Bulyk lab , we tested our model on another set of yeast PBM arrays published by [12] . Although the PBM array design is intrinsically the same , the probe sequences are different . Two array designs were used to run experiments on 37 yeast TFs and , as before , we perform a cross-validation experiment and compare to the E-max performance by using the published E-scores for this data set . In Figure 2 ( a ) , we show a scatter plot for the three datasets , contrasting SVR to the E-max scores for yeast and mouse PBM data . When a point lies above the diagonal line , the SVR model is better at detecting the top 100 than the E-max approach; we observe that over 80 of the points lie above the diagonal ( 149 out of 184 TFs ) . This performance advantage is not achieved with standard string kernels as the spectrum and regular mismatch kernels . When we tested the mismatch kernel with parameters that intuitively seemed suitable – – we found little improvement over E-max and much weaker performance than the di-mismatch kernel ( see Figure S4 in Text S1 ) . To compare our discriminative model against a standard PSSM motif approach , we also tested the performance of PBM-derived PSSMs for the mouse TF data set [10] . PSSMs for these TFs , derived from PBM probe intensity data using the Seed-and-Wobble algorithm [3] , are available through the Uniprobe database . However , for the Uniprobe motifs , data from both PBM array designs for a TF have been combined to estimate a single PSSM . For a fair comparison in our cross-validation setting , we therefore re-ran the Seed-and-Wobble algorithm on each PBM experiment separately , using parameters similar to those adopted for the published motifs: we used patterns of 8-mers ( allowing two gaps ) in Seed-and-Wobble and then “trimmed” the resulting PSSM to maximize similarity ( as measured by KL divergence ) to the published PSSM . Then we used the PSSM derived from the first array design to test on probe sequences from the second array design , and vice versa . We found that SVR strongly outperforms PSSMs ( Figure 2 ( b ) with wins on 81% of the TFs , while E-max essentially ties the PSSM performance ( E-max wins on 52% of TFs , Figure S1 in Text S1 ) , suggesting that E-max and PSSM approaches are similar and correlated . We note that since the Seed-and-Wobble method uses on E-scores to derive PSSMs , this correlation is perhaps expected . We note that other algorithms for extracting PSSMs from PBMs have also been proposed , including RankMotif++ [13] ( see Figure S3 in Text S1 ) , which was shown to outperform Seed-and-Wobble on a set of five TFs in a similar assessment using cross-validation over probe designs . However , even in this assessment , RankMotif++ did not outperform an 8-mer based method similar to E-max . More precisely , instead of using E-scores , 8-mers were scored by their median training probe intensity or “Z-score” , and test probe sequences were scored by the maximal median intensity over 8-mers , which we can call the “Z-max” approach . For completeness , we did a complete benchmarking of the E-max and Z-max methods and found no significant difference in their performance ( Figure S2 in Text S1 ) . Moreover , we found no significant difference in performance between RankMotif and Seed-and-Wobble , while we found that SVR models significantly outperformed RankMotif ( Figure S3 in Text S1 ) . Therefore , the main conclusion of the previous RankMotif study – namely , that a PSSM method can be competitive with a -mer scoring derived from simple statistics on the probe intensity data – is consistent with our findings . However , we additionally find that supervised discriminative models with SVRs strongly outperform both PSSMs and -mer scoring for the task of predicting in vitro TF binding preferences . Next we used the SVR models trained on in vitro PBM data to predict in vivo occupancy , as measured by chromatin immunoprecipation followed by microarray ( ChIP-chip ) experiments . There are 68 yeast TFs for which PBM data and ChIP-chip data are both available . For each TF , we first computed SVR binding profiles along 6724 intergenic regions ( IGRs ) , each 200–2000 nucleotides in length , using a sliding 36-mer window for scoring . Figure 3 ( a , b ) shows predicted binding profiles for two yeast TFs , Ume6 and Gal4 , along IGRs that they occupy in vivo using different methods: log-odds scores for PBM-derived PSSMs ( gold ) , maximal E-score over a fixed threshold of 0 . 35 ( blue ) ; E-score based occupancy ( black ) , corresponding to the median probe intensity of PBM probes containing the highest-scoring 8-mer pattern [9]; and SVR scores ( green ) . For Ume6 , all methods detect this IGR among the top 200 predictions and seem to agree on the location of the highest and second highest peak . For Gal4 , the SVR profile and the noisier E-score profiles seem to locate a different binding peak than the PBM-derived PSSM , and only the SVR method detects this IGR among the top 200 predictions . While the ChIP-chip data cannot identify the true location ( s ) of the binding sites , we do find increasing enrichment for conservation with increasing SVR score ( Figure S5 in Text S1 ) , with even moderate scoring peaks showing enrichment for conservation relative to background . We then compared the performance of SVR models with previously published results based on the E-score occupancy method of [9] . Following the previous analysis , when TF occupancy data is available for more than one condition , we aggregated the data by assigning each IGR the minimal ChIP-chip -value over conditions ( with Bonferroni correction ) to obtain as comprehensive a list of true positive IGRs as possible . While [9] used an ROC analysis relative to a fixed -value cut-off of 0 . 001 , we found that AUCs for TFs with very few true positive IGRs were not informative for either method . We instead computed the detection of the top 200 IGRs by the top 200 predictions , where the top 200 “bound” IGRs were determined by their -value ranking . For the SVR method , we ranked IGRs by the height of their max peak , while for the E-score occupancy method , we used the scores provided by the authors . Figure 3 ( c ) shows a scatter plot of the detection of the top 200 IGRs by SVR and E-score occupancy . Since chromatin state and interactions with other DNA-binding factors influence in vivo occupancy , we do not expect a TF's sequence signal alone to perfectly correlate with the occupancy data . In fact , similar to the results reported by [9] , prediction of in vivo occupancy is weak to very poor ( fewer than 40 of the top 200 IGRs detected ) by both methods for most TFs . However , for the TFs with the best results by the E-score occupancy method ( 40 top IGRs detected ) , the SVR method outperforms the previous occupancy score method in 8 out of 9 cases , sometimes to a large degree . [9] performed extensive motif analysis to give evidence that indirect binding may account for a part of the TF occupancy signal in yeast . We too hypothesized that interpreting TF occupancy is confounded by indirect or competitive binding . We performed a detailed analysis of potential interactions between TFs and cooperative or competing partners ( see Figure S6 in Text S1 ) , and we found for 26 out of the 68 yeast TFs , the TF's in vivo occupancy is well predicted by the SVR model of a second potential “partner” TF ( Figure S6 in Text S1 ) . As we did for the in vitro cross-validation experiments , we also benchmarked SVR and E-score occupancy against PBM-derived PSSMs from [9] , where we scanned PSSMs across IGR sequences and scored each IGR by its maximum log odds score . Again , we evaluated performance by counting the detection of the top 200 IGRs based in the top 200 predictions , and we found that for the 9 well-predicted TFs , the SVR model outperforms PSSMs ( 6 wins , 1 ties , 2 losses ) while the E-score occupancy performs worse than PSSMs for 5 of these 9 TFs ( Figure S7 in Text S1 ) . We next evaluated the performance of PBM-derived SVR models for the prediction of TF occupancy in mouse and human genomes . We examined seven ChIP-seq experiments conducted in three different cell types: Oct4 , Sox2 , Klf4 , and Esrrb in E14 mouse ES cells [14]; Srf and Gabpa in GM12878 cells ( human EBV-transformed B-lymphocytes ) [15]; and Hnf4a in HepG2 cells [16] . For TFs whose binding domain is not present in the UniPROBE PBM database , we used the most similar binding domain ( s ) with available PBM data: Pou2f3 and Pou2f1 substituting for Oct4; Sox12 and Sox21 for Sox2; Klf7 for Klf4; and Esrra for Esrrb . Both Pou2f3 and Pou2f1 differ from Oct4 by just one residue in their DNA-contact residues , based on homeodomain-DNA contacts determined from the 3D structure for Engrailed [17] , [18] . Sox2 best aligns with Sox21 , but we include Sox12 as well to assess the variability of PBM-derived models for TF domains that are thought to bind similar motifs . We computed the SVR models by carefully selecting the parameters using cross-validation experiments on PBM array data ( see Text S1 ) . For our test data , we selected a set of 1000 confident ChIP-seq peak regions and 1000 “negative” regions selected from flanking sequences . More specifically , we extracted 60bp regions centered around the peaks ( positive examples ) and 60bp regions 300bp away from the peaks ( negative examples ) . Model performance was measured by the area under the ROC curve ( AUC ) , using the maximum SVR prediction score ( over 36-mer windows ) to rank the ChIP-seq 60-mers . We compared our SVR models to the occupancy score derived from E-scores [9] . We also compared to PSSMs extracted from PBM data with the Seed-and-Wobble algorithm [3] , [10] , which are available for download from UniPROBE [11] . Figure 4 ( a ) shows AUC results for all three methods; here , in cases where UniPROBE reports both primary and secondary PSSMs , we show results for the primary motif . We found that SVR outperforms both the PSSM and occupancy score methods in 7 out of 9 cases , where we report results for models trained on two different PBM experiments to predict Oct4 and Sox2 occupancy . There were two TFs , Sox21 and Hnf4a , for which the secondary PSSM outperformed the primary PSSM . However , in each case , the improved AUC ( 0 . 75 and 0 . 70 , respectively ) was still worse than the performance of the PBM-derived SVR . We report two SVR results for Hnf4a , one using the mouse PBM array data present in the UniPROBE database ( Figure 4 ( a ) , Hnf4a , leftmost bar ) , the other using a novel PBM array design developed specifically for human Hnf4a and using the purified full-length protein instead of the DNA-binding domain in the PBM experiment [19] . In the latter dataset , short probe sequences were designed based on known motifs for Hnf4a ( see Figure S8 in Text S1 for contrasting binding profiles of the standard versus custom PBM array ) . [19] published two such PBM array designs and , by combining data from the two arrays and modifying our algorithm to accommodate the 13-mers that comprise this PBM data , we were able to train an SVR model that gave the best predictions of Hnf4a in vivo binding ( Figure 4 ( a ) , Hnf4a , rightmost bar ) . Since PBM arrays are limited to capturing the in vitro binding preferences of transcription factor domains , we hypothesized that additional sequence signals may be present in ChIP-seq data and enable improved prediction performance . We therefore trained support vector machines ( SVMs ) using the standard parameters on 60-mer ChIP-seq peaks ( positive sequences ) and flanking negative sequences . This training procedure potentially allows the SVM to capture sequence information for both the chromatin immunoprecipitated TF and its cis cofactors . We evaluated performance by computing AUCs on the same test sets of 1000 ChIP-seq peaks and 1000 flanking negative sequences using 10-fold cross-validation . For a method comparison , we used two popular motif discovery algorithms , Weeder [7] and MDscan [8] , which determine overrepresented -mer and PSSM motifs , respectively . Again , we tested these methods using 10-fold cross-validation and evaluating AUCs on held-out folds . Weeder performs an exhaustive search for the most overrepresented -mer patterns for a given specified size . We used the algorithm to find the top 50 enriched motifs in the training data , allowing up to one mismatch . To make predictions , we counted the occurrences of these motifs in the test sequences , again allowing up to one mismatch , and used this count to rank the test sequences . We tested -mer lengths 6 , 8 and 10 , and reported results for the best performing model . MDscan identifies overrepresented motifs by iteratively constructing PSSMs and using binding site flanking regions to define a Markov chain background model . We applied the highest scoring PSSM as found by MDscan to the test sequences , using a zero order Markov model based on nucleotide frequencies in the human genome as the background model . ( We did not use a first order Markov background model since we found it slightly decreased PSSM performance for all but one TF . ) We experimented with motif lengths of 8 , 10 12 , 14 and 16 and reported the best results . Figure 4 ( b ) shows results for ChIP-derived SVM models and the motif discovery approaches for the occupancy prediction task . We first note that for 4 out of 6 TFs , the ChIP-derived SVM model significantly outperforms the corresponding PBM-derived SVR model ( s ) . The exceptions are Essrb and Gabpa , where there is little difference in performance between the in vitro and in vivo models . Furthermore , although Weeder and MDscan yielded predictions with AUCs above 0 . 65 for all 7 TFs , the SVM model outperformed both methods in every case that we considered ( Figure 4 ( b ) ) , sometimes by more than 0 . 1 in the AUC score . It is also worth noting that while Weeder and MDscan required parameter tuning , the SVM model parameters were kept fixed . As a final method comparison , we also tested a newer motif discovery algorithm called cERMIT [20] on these data sets , and we again found that the SVM models outperformed the best-performing PSSM returned by cERMIT ( Figure S11 in Text S1 ) . We caution that our sample set of TFs is small: although we did a thorough search for all available ChIP-seq data sets , we found only a small number of TFs with both PBM and ChIP-seq data; moreover , in some cases , the TF domain represented in the PBM experiment is slightly different than in the TF ChIP-seq experiment . It will therefore be important to repeat these experiments on a wider range of TFs once suitable data becomes available . Finally , we evaluated whether there was any advantage to training regression models on ChIP-seq peaks labeled with real-valued occupancy rather that binary classifiers to discriminate between peaks/non-peaks . We found that SVR models trained with real-valued labels gave slightly worse performance in our AUC analysis as compared to SVM models ( see Figure S11 in Text S1 ) . We hypothesize that either ( i ) the currently available ChIP-seq derived occupancy scores are not yet quantitative enough to use to train a regression model or ( ii ) the best predictor of peak height/occupancy score is not the sequence signal itself but chromatin state ( accessibility of the DNA , nucleosome positioning , histone modifications ) . To understand the differences between in vitro and in vivo TF binding models , we developed an approach to examine the sequence information extracted by the SVR/SVM models . It is of course possible to simply examine the top-weighted -mers in the SVR/SVM weight vectors; for example , the 13-mers with highest positive weights in the PBM-derived SVR models often contain subsequences that resemble the Seed-and-Wobble motifs derived from the same data ( Tables S2 and S3 in Text S1 ) . We sought instead to visualize the full -mer content of the model . We first looked at the in vitro models for Oct4 , since the PBM-derived PSSMs for the two selected “nearest neighbor” Pou domains had very different performance , and we wanted to understand the source of the instability . We used a feature analysis procedure to look inside the “black box” of the PBM-derived SVR model for Pou2f3 , the neighbor of Oct4 with the better performing PSSM . The solution of the SVR optimization problem determines a weight vector over the space of 13-mer sequence features; 13-mers with high weights contribute the most to high binding prediction scores . The basic idea is to represent the similarity of -mer features based on their support across the training data and also visually represent the weight of the features in the SVR/SVM model . In this way , we avoid doing too much post hoc summarization of the -mers , and instead we represent the features more as they are used and contribute to the model . To obtain a similarity measure between these features , we represented each 13-mer by the vector of its alignment scores to the training sequences ( see Materials and Methods ) . Intuitively , 13-mers that are close in Hamming distance will be represented by nearby vectors in this representation . After clustering 13-mer features based on this vector representation and projecting to a two-dimensional representation ( see Materials and Methods ) , we identified two clusters of features , shown in Figure 5 ( a ) using stars and circles . The color scheme indicates the SVR weight associated with the 13-mer feature , red for highly weighted features and blue for low weights . The two well-separated clusters suggest that the SVR is learning a primary and secondary motif , similar to results of PSSM-based analysis [10] . We took a 13-mer feature near the centroid of each cluster and expanded each into a PSSM by aligning to the positive training sequences ( see Materials and Methods ) : the “star” cluster is represented by a motif that looks like the canonical Oct4 octamer ( ATGCAAAT ) , but the “circle” cluster is centered on a more degenerate ( TAATT ) motif . To determine the in vivo prediction performance of each cluster independently , we retrained SVR models using the star and circle 13-mer features separately and obtained dramatically different AUCs of 0 . 75 and 0 . 54 , respectively , on the Oct4 ChIP-seq data . The poor in vivo performance of the star cluster of features suggests that the PBM is learning a secondary motif that is not preferred in vivo . The presence of these apparently PBM-specific features only slightly degrades the performance of the full SVR model ( AUC of . 74 ) but may seriously impact PSSM-based methods . For example , the Seed-and-Wobble algorithm identifies a primary motif similar to TAATTA for the other Oct4 nearest neighbor , Pou2f1 ( see Figure S10 in Text S1 ) , which accounts for its poor occupancy prediction . We reiterate the caveat that neither of these Pou domains is in fact Oct4; it is conceivable that the differences between PBM and ChIP binding preferences are due in part to differences in these homeodomains . We next performed a similar feature analysis of the ChIP-derived model for Sox2 , one of the examples where the in vivo model strongly outperformed the in vitro model . Here , 13-mer features from the SVM model are represented by their vector of alignment scores relative to 60bp sequences under ChIP-seq peaks rather than probe sequences . Again , we identified two well-separated clusters , shown using stars and circles in Figure 5 ( b ) . Here , the cluster representative for the “star” cluster can be expanded to a PSSM that closely resembles the Sox2 motif . However , the representative for the “circle” cluster maps to part of the Oct4 octamer motif , indicating that the ChIP-derived model is learning binding information about Sox2's binding partner Oct4 ( Figure 5 ( c ) ) . We hypothesized that this additional cis information may account for part of the improvement of the in vivo model over the PBM-derived model . To quantify this effect , we identified Sox2 bound regions that are not detected by the PBM-trained SVR model but are correctly detected by the ChIP-trained SVM ( Figure S9 ( a ) in Text S1 ) . These 33 60bp regions were 6-fold depleted for the core Sox2 motif TTGT and 3-fold enriched for the core Oct4 motif TGCA . Moreover , 32 out of 33 of these regions were detected as positives by the PBM-trained SVR for Oct4 ( ) . These results suggest that some binding of Sox2 may be indirect via binding of the cofactor Oct4 .
We have presented a flexible new discriminative framework for learning TF binding models from high resolution in vitro and in vivo data . In particular , we showed that SVR models using string kernels outperform existing existing approaches like PSSMs and E-scores for predicting in vitro TF binding preferences as measured by PBM experiments , based on cross-validation experiments across array designs . We also found that PBM-derived SVR models improve in vivo occupancy prediction over PBM-derived PSSMs and E-scores , in particular when ChIP-seq ( as opposed to lower resolution ChIP-chip ) data is available for validation . Furthermore , we saw that by training directly on ChIP-seq , i . e . using ChIP-seq peaks to define positive genomic training sequences and taking non-peak regions as negative sequences , we can significantly improve over PBM-derived models and outperform existing motif discovery methods . We also described a feature analysis procedure for looking inside the “black box” of the trained SVR/SVM models to identify clusters of sequence features that contribute to binding predictions . Importantly , this analysis allowed us to confirm that ChIP-trained SVM models were learning additional sequence signals corresponding to cofactor binding sites . PSSMs have a long history in the analysis of TF binding sites and remain ubiquitous due to their interpretability . However , as we continue to accumulate mammalian PBM data and ChIP-seq data , the more general models that we develop here—i . e . models that do not force a PSSM representation on binding sites and can integrate in vivo sequence signals from both a TF and its cofactors—may be more suitable for representing complex regulatory regions . We anticipate a number of directions for building on this work . First , we can develop strategies to train jointly on PBM and ChIP-seq data for the same TF in order to cleanly disambiguate between direct and indirect binding . Second , as more PBM data becomes available , we can develop multi-task training strategies for modeling the binding preferences of a class of structurally related TFs , using features of the amino acid sequence as well as a -mer representation of probe sequences . Then , given a new TF for which PBM data is not available , the model would extend to predict its binding preferences . Third , we can combine our in vivo TF sequence preference models with data on chromatin state , including histone modifications and DNase I footprinting , using a kernel combination strategy . The goal would be to predict TF target genes in new cell types , given only the chromatin information in the cell type , after training on ChIP-seq data paired with chromatin data in other cell types . Therefore , the flexible sequence-based framework we describe here provides the foundation for the systematic modeling of genome-wide TF occupancy .
We developed a training strategy for our SVR models that involved three key components: ( 1 ) the choice of kernel , which specifies the space of features used to compare pairs of probe sequences; ( 2 ) the sampling procedure for selecting the training sequences , which produces more informative training data and reduces training time; ( 3 ) the feature selection method , which eliminates unimportant features and further improves computational efficiency . Each component is described in more technical detail below . We used the LIBSVM package for the computation of SVR models , keeping the parameter fixed at 0 . 1 for all experiments . Training a kernel method like an SVR on sequence data requires the use of some kind of string kernel , i . e . a similarity measure between sequences that defines an inner product in a corresponding feature space . Various -mer based string kernels have been proposed , including the mismatch kernel [5] , where the feature representation for a sequence amounts to an inexact-matching histogram of -mer counts , allowing up to mismatches in each -mer match ( ) . Here , however , even for small values of the mismatch parameter , this kernel tends to make the “mismatch” neighborhood of a given -mer too large . We therefore developed a novel first order Markov mismatch kernel , called the di-mismatch kernel , that counts mismatching dinucleotides and that inherently favors -mers with consecutive mismatches . Let be a set of unique -mers that occur in the set of training sequences ( PBM probe sequences ) . Given a training sequence of length , we define the set of substrings of length in to beThen may be represented by the feature vectorwhere , and the value is the di-mismatch score between two -mers , which counts the number of matching dinucleotides between and , that number being set to zero if this count falls below the threshold , where is the maximum number of mismatches allowed . This score inherently favors consecutive mismatches , as we show in the following examples . Consider the first pair of 13-mers shown with four non-consecutive mismatches , which results in 6 mismatching dinucleotides out of 12:In contrast , the following pair of 13-mers with four consecutive mismatches leads to a count of 5 mismatching dinucleotides . By enforcing a mismatch parameter of , we induce sparsity in the feature counts and seem to obtain more meaningful “neighborhoods” of the features than the standard mismatch kernel . This procedure appeared to capture the full dynamic range of effective binding while downsampling the large number of unbound probes . Since the PBM arrays are designed to give good coverage of 8-mer patterns ( including gapped patterns ) , we chose parameters that would require at least 8 matching characters between the -mers . Our parameter experiments on one set of yeast PBM arrays [9] indicated to be the best parameter setting , and we used this kernel choice for the in vitro evaluation for most of our reported results . However , one may use a 10-fold cross-validation approach on the training PBM array to perform a grid-search and thereby optimize the choice of the parameters . We used such a strategy for the 7 mammalian in vivo occupancy predictions ( Figure 4 ) , whereby we tested parameters ranging from and where . Much like the mismatch kernel , the computational cost of scoring test sequences with the trained di-mismatch SVM/SVR model is linear with respect to the input sequence length . Every -mer has a non-zero match score to a fixed number of features , and each feature is represented by a weight in the support vector model . Therefore , the contribution of each -mer can be pre-computed , and those with non-zero contribution can be stored in a hash table . Standard PBM arrays typically contain 44K probes , each associated with a binding intensity score , but only few hundred probes indicate some level of TF binding . Using all of the PBM probes as training data would allow the SVR to achieve good training loss simply by learning that most probes have low binding scores . In order to learn sequence information associated with the bound probes , we selected training sequences from the tails of the distribution of the normalized binding intensities . More specifically , we selected the set of “positive” training probes to be those sequences associated with normalized binding intensities ; if the number of such probes was less than 500 , we selected the top 500 probes ranked by their binding signals . The same number of “negative” training probes was selected from the other end of the distribution . This procedure appeared to capture the full dynamic range of effective binding for learning the regression model while downsampling the large number of unbound probes . We also tried sampling “negative” probes from the full intensity distribution ( anywhere outside the positive tail ) , but we found that using the negative tail yielded better results . Careful feature selection can eliminate noisy features and of course reduces computational costs , both in the training and testing of the model . In particular , choosing very infrequent -mers may add noise , and ideally , sequence features should display a preference either for bound or unbound probes . Therefore , we selected the feature set to be those -mers that are over-represented either in the “positive” or “negative” probe class by computing the mean di-mismatch score for each -mer in each class and ranking features by the difference between these means . In all reported results , we used at most 4000 -mers for our models . We processed the ChIP-seq data using the SPP package [21] and extracted the top 1000 peaks to define a gold standard for occupancy . A 60bp window was selected around each of the peaks and used as the positive examples . A 60bp window 300bp to the left of the peak was selected as the negative example . It is informative to be able to use the SVM model to visualize the -mers that contribute to the model . Here our goal is to visually represent both ( i ) the similarity of -mer features based on their support across the training data representing and ( ii ) the contribution ( or weight ) of these -mers to the model . To obtain a similarity measure between -mer features extracted from the trained models , we represented each -mer by a vector of alignment scores against the positive training sequences used to compute the SVR model: we found the optimal ungapped alignment of the -mer to each training sequence and used the number of match positions as the alignment score . Intuitively , sequence-similar -mers will have similar alignment scores across the training examples , so they will be represented by nearby vectors in this representation . However , we are not explicitly modeling sequence dependence but instead relying on co-occurrence of matches of similar -mers . We then performed -means clustering ( = 2 ) on the vectors representing the 13-mer features . Next we used the SVM/SVR weight vector , derived from the solution to the optimization problem , to select the top 500 representatives for each cluster , thereby reducing the rest of our analysis to -mers that contributed significantly to the model . Next , we projected the 1000 -mers to a two-dimensional representation using principal component analysis ( PCA ) , distinguishing cluster members with circles and stars in the plot . The relative significance of each feature is indicated by a color scale ranging from red to blue , for high and low respectively . Finally , we defined a cluster representative for each group by the feature that has the following two properties: ( i ) it is in the top quartile of the weights for that cluster , and ( ii ) it is the closest feature to the cluster centroid . This gives us a cluster representative that is simultaneously close to the true cluster centroid and significant for the model . Finally , to represent a given -mer feature by a motif logo , we selected the top 50 positive training sequences that best aligned with the -mer , extracted the -length sequences that matched the feature , and computed a PSSM . | Transcription factors ( TFs ) are proteins that bind sites in the non-coding DNA and regulate the expression of targeted genes . Being able to predict the genome-wide binding locations of TFs is an important step in deciphering gene regulatory networks . Historically , there was very limited experimental data on the DNA-binding preferences of most TFs . Computational biologists used known sites to estimate simple binding site motifs , called position-specific scoring matrices , and scan the genome for additional potential binding locations , but this approach often led to many false positive predictions . Here we introduce a machine learning approach to leverage new high resolution data on the binding preferences of TFs , namely , protein binding microarray ( PBM ) experiments which measure the in vitro binding affinities of TFs with respect to an array of double-stranded DNA probes , and chromatin immunoprecipitation experiments followed by next generation sequencing ( ChIP-seq ) which measure in vivo genome-wide binding of TFs in a given cell type . We show that by training statistical models on high resolution PBM and ChIP-seq data , we can more accurately represent the subtle DNA binding preferences of TFs and predict their genome-wide binding locations . These results will enable advances in the computational analysis of transcriptional regulation in mammalian genomes . | [
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] | 2010 | High Resolution Models of Transcription Factor-DNA Affinities Improve In Vitro and In Vivo Binding Predictions |
Virulence of the most deadly malaria parasite Plasmodium falciparum is linked to the variant surface antigen PfEMP1 , which is encoded by about 60 var genes per parasite genome . Although the expression of particular variants has been associated with different clinical outcomes , little is known about var gene expression at the onset of infection . By analyzing controlled human malaria infections via quantitative real-time PCR , we show that parasite populations from 18 volunteers expressed virtually identical transcript patterns that were dominated by the subtelomeric var gene group B and , to a lesser extent , group A . Furthermore , major changes in composition and frequency of var gene transcripts were detected between the parental parasite culture that was used to infect mosquitoes and Plasmodia recovered from infected volunteers , suggesting that P . falciparum resets its var gene expression during mosquito passage and starts with the broad expression of a specific subset of var genes when entering the human blood phase .
Malaria is one of the most frequently occurring parasitic diseases worldwide with an estimated 198 million clinical cases in 2013 and a death toll of more than 0 . 5 million [1] . The virulence of the most deadly species of human malaria parasites , Plasmodium falciparum , is directly linked to the variable surface protein PfEMP1 ( P . falciparum erythrocyte membrane protein 1 ) [2 , 3] . Members of the PfEMP1 family enable the parasite to adhere to a large variety of surface receptors on microvasculature linings or in case of pregnancy to the maternal side of the placenta in order to avoid spleen passage and subsequent clearance ( reviewed in [4] ) . Expression switching between different PfEMP1 variants correlates with changes in the antigenic and adhesion phenotype of the parasite [5] . Each parasite possesses about 60 var genes coding for different PfEMP1 variants , which are expressed in a mutually exclusive manner meaning that generally only a single PfEMP1 variant is exposed on the surface of the infected erythrocyte at a time while all other gene copies are silenced [6] . Historically , the global var gene repertoire present in the parasite population was assumed to be highly diverse and the number of variants almost unlimited . But in the recent past , evidence is arising that every parasite genotype is organized similarly and exhibits roughly the same numbers of var gene variants of each subgroup ( A , B , C and E ) defined by PfEMP1 protein domain composition as well as by chromosomal localization , direction of transcription and particular 5’-UTR sequences of their encoding var genes [7–9] . Moreover , the comparison of seven P . falciparum genomes revealed 23 PfEMP1 domain cassettes ( DCs ) , which seem to form conserved recombination and receptor-binding units [10] . Both observations point to a more conserved var gene repertoire than previously assumed . The reference strain NF54 possesses 10 group A var gene copies including the interstrain conserved subfamilies var1 and var3 , which are all located near the end of chromosomes and have a transcriptional direction towards the telomeres . With exception of the short var3 PfEMP1 variants , A-type PfEMP1 proteins have an extended , multiple domain composition and a non CD36-binding head structure consisting of a DBLα1 and a CIDRα1/β/γ/δ domain . Their pattern of expression has been linked to severe disease outcome [11–20] . The 37 member group B var genes are located closest to the telomere and transcribed towards the centromere and their expression has been associated with both severe and mild malaria [13 , 15 , 19–21] . Recently , expression of A- and B-type var genes encoding the interstrain conserved PfEMP1 DCs 8 and 13 , which bind the endothelial receptor EPCR , as well as the DC number 5 , known to mediate PECAM1 binding , were linked to severe malaria in young children [18 , 22 , 23] . Interestingly , some genes are chimeras of group B and A or C var genes and are thought to represent intermediate groups between the major groupings [8] . In the NF54 genome , 4 and 9 members form these intermediate groups B/A and B/C , respectively , which all have a 5’-UTR characteristic for B-type var genes . In contrast to B/A genes , which are very similar in location and transcriptional orientation to group B genes , the chromosomal characteristics of group B/C genes are in common with group C genes . The 13 group C genes are located at chromosome internal clusters , transcribed towards the telomeres and possess a C-type 5’-UTR . C-type PfEMP1 variants are known to be expressed in parasites causing asymptomatic infection and in long-term in vitro cultivated parasites [11 , 13 , 24–27] . Most B- and C-type PfEMP1 proteins have a 4-domain extracellular structure including a CD36-binding head structure consisting of a DBLα0 and a CIDRα2–6 domain plus another DBL and CIDR domain [7 , 10] . Despite this large repertoire of variant surface proteins , the vast majority of P . falciparum infections do not lead to severe disease , suggesting that parasite sequestration is a well-adapted process and increased parasite transmission to mosquitoes outweighs losses due to host death . Hence , PfEMP1 facilitates repeated and long-lasting infections of the human host even after repeated exposures . But , remarkably , particular PfEMP1 subtypes appear to be specialized for infection of malaria naïve hosts where the interaction of PfEMP1 with endothelial and circulating cells directly causes obstruction of blood circulation , which contributes along with immunopathology to organ failure . In malaria endemic areas , severe malaria mainly affects young children under the age of five lacking a sufficient immune response from previous Plasmodium infections [28] . Therefore , a better understanding of the var gene expression in malaria naïve individuals and of the mechanisms that control the expression of particular PfEMP1 types is of particular importance . Three previous studies analyzed var gene expression in three experimentally infected naive individuals [29–31] . Peters et al . found a single dominant B-type var transcript in two volunteers , whereas Lavstsen et al . and Wang et al . detected a more broad activation of most var genes at the early onset of the blood infection . Based on these results two different strategies used by the malaria parasite to initiate an infection in the human host are currently discussed in the scientific community . The first model suggests that the parasites may use an ordered hierarchical var gene expression program , meaning that most of the parasites express a single var type in the first generation after egress from the liver . In the following replication cycles the parasite switches to other var gene variants determined by the intrinsic rate for each gene to be turned on or off . This would provide an efficient mechanism to evade the host's immune system in concordance with protection of the remaining PfEMP1 variants from unnecessary exposure to the immune system [29 , 32] . The second concept postulates the early exploration of the suitability of the available host sequestration receptors . Accordingly , the parasite population released from the liver expresses all var genes and later on selective forces favor the survival of parasites expressing certain PfEMP1 variants with the best adhesion properties and for which the human host has no pre-existing variant-specific immunity [30] . To clarify these contradicting concepts the study presented here made use of a controlled human malaria infection ( CHMI ) trial in which volunteers were injected with aseptic , purified , cryopreserved P . falciparum NF54 sporozoites ( PfSPZ Challenge ) of a single production lot produced under cGMP [33] and var gene expression was analyzed in samples from 18 malaria naïve hosts at the early onset of blood infection . The data presented here reveal a strategy , which favors the expression of a broad subset of var gene variants from subtelomeric locations while repressing var variants that are conserved between strains or that are located at chromosome internal sites . Moreover , we clearly show for the first time that ex vivo var gene expression patterns differ significantly from the expression profile of the parasite culture that was used to produce the PfSPZ Challenge lot used for CHMI , indicating that the expression of subtelomeric var genes is specifically turned on in vivo . Therefore our data support aspects of both postulated models: the parasite population seems to activate a whole subset of var genes allowing for exploration of the situation in the host which is shaped by available host receptors ( e . g . CSA in pregnant women ) and pre-existing variant specific immunity , whereas a different subset of var genes remains infrequently expressed .
Our study was carried out in the frame of a dose-finding CHMI trial , in which malaria-naïve volunteers were infected with increasing doses of PfSPZ Challenge [33] . All individuals underwent CHMI with PfSPZ from the same lot of PfSPZ Challenge either by intravenous ( iv , n = 24 ) or intradermal ( id , n = 6 ) injections of 50 to 3 , 200 sporozoites . Samples from a subset of the volunteers inoculated with 200 to 3 , 200 sporozoites by intravenous injection ( n = 15 ) or 2 , 500 sporozoites by intradermal injection ( n = 3 ) were assessed ( Table 1 ) . All volunteers were examined every 12 hours from day 5 after PfSPZ injection and were treated immediately once their thick blood smear became parasite positive by microscopy . On average , the subset of volunteers included for var transcript analysis became parasite positive by thick blood smear ~12 days after infection ( mean = 12 . 0; standard deviation ( SD ) : ±1 . 2 days post infection ) with parasitemias ranging between 2 . 5 and 54 parasites per μl of blood ( mean = 9 . 1; SD: ±10 . 1 parasites/μl ) ( Table 1 ) . In concordance with previous CHMI studies liver stage development took about 6 days and parasite kinetics measured by qPCR in this study suggest that parasites of the third generation after liver release were generally detected at day 11 and 12 post infection [30 , 33 , 34] . Accordingly , samples from volunteers infected intravenously either with 800 or 3 , 200 PfSPZ contained third generation blood phase parasites , whereas volunteer 02 . 1 , the only one infected with 200 PfSPZ , and volunteers infected intradermally with 2 , 500 PfSPZ had fourth and fifth generation blood phase parasites ( day 13–15 ) ( Table 1 ) . The blood from 18 infected volunteers was preserved for transcript profiling of the entire NF54 var gene repertoire at the day of patent infection defined as presence of ring stage parasites in the thick blood smear . Analysis of var gene expression profiles in individual volunteers showed that transcripts of all var gene variants were detectable during the early blood stage NF54 infection . Interestingly , the individual gene expression profiles seemed to be very similar between the volunteer samples and the variability of the expression values for each var gene was mainly caused by differences in total var expression levels between the samples ( Figs 1A and S1 ) . In all samples a B-type variant was expressed at the highest level , mostly the variants MAL6P1 . 1/PF3D7_0632800 ( IDs 08 . 1 , 08 . 2 , 08 . 4 , 25 . 1 , 32 . 2 , 32 . 5 and 32 . 9 ) or PFD0005w/PF3D7_0400100 ( IDs 02 . 1 , 08 . 3 , 25 . 2 , 25 . 3 , 32 . 1 , 32 . 3 and 32 . 7 ) ( Figs 1A and 1B and S2 ) . Across all patients , the highest transcript levels were detected for MAL6P1 . 1/PF3D7_0632800 ( median = 988 . 4; interquartile range ( IQR ) : 479 . 2–1 , 535 . 9 ) , followed by PFD0005w/PF3D7_0400100 ( median = 964 . 5; IQR: 430 . 0–1 , 523 . 7 ) , PF07_0139/PF3D7_0733000 ( median = 814 . 9; IQR: 558 . 2–1 , 173 . 1 ) , PFI1830c/PF3D7_0937800 ( median = 695 . 8; IQR: 496 . 6–883 . 7 ) and PF08_0142/PF3D7_0800100 ( median = 618 . 8; IQR: 464 . 0–667 . 7 ) ( S2 Fig ) . All these genes are categorized as group B var genes , reflecting the dominant expression of group B var genes in vivo ( Fig 1C ) . Conversely , transcript abundance of the var gene groups C and E showed some of the lowest expression levels detected . Within group A , the highest relative expression value was always detected for the EPCR-binding variant PFD0020c/PF3D7_0400400 ( median = 500 . 5; IQR: 351 . 1–793 . 4 ) , while the conserved group A var1 pseudogene PFE1640w/PF3D7_0533100 and the three var3 genes PFA0015c/PF3D7_0100300 , PFI1820w/PF3D7_0937600 and PFF0020c/PF3D7_0600400 are among the 12 genes with lowest expression values ( Figs 1A–1C and S2 ) . A comparison of the expression levels between subtelomerically located var genes with those located on internal chromosome clusters revealed a significant difference between both gene subsets ( Wilcoxon rank-sum test; p = <0 . 0001 ) ( Fig 1D ) . Overall , transcript profiles showed high positive correlations between all patients , irrespective of infection route and dose ( Figs 1E and S1 ) . Furthermore , the observed expression profiles highly correlated with the expression profile obtained from a single volunteer in a previous study by Wang et al . [30] ( Spearman’s Rank Correlation coefficient = 0 . 77 , p < 0 . 001 ) ( S3 Fig ) . In contrast , the observed var gene expression patterns showed no correlation with the data obtained from a previous study in which var gene activation from a null-var background induced by promoter-titration was monitored in an attempt to mimic the early onset of infection in vitro [35] ( S3 Fig ) . In summary , the var gene expression pattern in NF54 parasites at the early onset of blood infection seems to be remarkably uniform at the level of both var gene groups and single var genes . In contrast to the high abundance of transcripts of a restricted subset of var genes which we previously observed in established clinical malaria cases [32] , the expression profiles in parasites from all volunteers at this early stage of infection are rather broad without a single gene clearly dominating the infection ( Fig 1A and 1B ) . One interesting outlier from the general expression profile is the pattern detected in volunteer 02 . 1 , in which the intermediate group B/A gene MAL6P1 . 4/PF3D7_0632500 was not expressed and transcript abundance of the neighboring B-type var gene MAL6P1 . 1/PF3D7_0632800 was significantly reduced in comparison to all other volunteer samples . Analysis of the genomic DNA by qPCR showed that this was due to a loss of the MAL6P1 . 4/PF3D7_0632500 gene from the entire parasite population and a partial loss of the MAL6P1 . 1/PF3D7_0632800 variant , which was only present in the genome of about 2 . 5% of the parasites in volunteer 02 . 1 ( S4 Fig ) . In addition to patient samples obtained at the day of first positive thick blood smear , samples were collected from 8 volunteers at time points with sub-microscopic parasitemia , prior to patent infection . Var transcript profiles were assessed on blood samples obtained either at day 9 ( volunteers 32 . 1 , 32 . 3 , 32 . 5 , 32 . 7 ) or day 11 post infection ( volunteers 08 . 3 , 08 . 4 , 25 . 3 , 32 . 9 ) , thus , 1 or 2 days before the infected volunteer became microscopically positive ( Fig 2A ) . Hence , the expression profiling only detected highly expressed variants because parasite load in these samples was extremely low ( Fig 2A and 2B ) . qPCR confirmed high expression of B-type var genes and the A-type var gene PFD0020c/PF3D7_0400400 in these early samples ( Fig 2A–2C ) . The expression profiles showed positive correlations between the two parasite generations in vivo ( Fig 2D , Spearman’s Rank correlation ) suggesting a stable var gene expression pattern during the first few blood phase parasite replication cycles at least in this group of malaria naïve individuals . In order to investigate whether mosquito passage results in reprogramming of the var gene transcription pattern , we compared ex vivo var gene expression profiles in the volunteers with the var gene expression profiles in the parental parasite line used to produce the PfSPZ Challenge injected into the subjects . For this purpose , two vials of frozen NF54 parasites from the Sanaria Master Cell Bank RKV01-092505 ( MCB ) were independently thawed and cultured and var gene expression levels were analyzed after 6 , 8 and 21 parasite replication cycles , respectively . The results indicate that the in vitro-adapted pre-mosquito NF54 line stably expressed exclusively the var2csa gene PFL0030c/PF3D7_1200600 ( median = 9 , 012 . 7; IQR: 8 , 525 . 2–10 , 804 . 4 ) ( Fig 3A and 3B ) . Given the abundant expression of var2csa in the parental NF54 parasites and the monoallelic expression of var genes [6 , 36] it was unsurprising that direct comparisons of transcript levels between the parental NF54 parasite line MCB and parasites recovered from the infected volunteers revealed increased expression in vivo of the entire gene family except var2csa ( Fig 4A ) . The elevated expression of the var groups in the infected volunteer samples was significant; A ( p <0 . 0001 ) including the var3 subfamily ( p = 0 . 0028 ) , B/A ( p <0 . 0001 ) , B ( p <0 . 0001 ) , B/C ( p <0 . 0001 ) and C ( p <0 . 0001 ) . The only exception was the decreased expression of the group E var2csa gene ( p = 0 . 0045 ) and the var1 subfamily pseudogene PFE1640w/PF3D7_0533100 was expressed at very low , unchanging levels in both NF54 and volunteers ( Fig 4B ) . Thus , the var gene expression profiles of the ex vivo patient samples revealed substantial changes in comparison to the pre-mosquito NF54 parasites , which indicates epigenetic reprogramming of var genes during mosquito and/or liver passage .
Analyses of the role of PfEMP1 in malaria pathogenesis and protection are hampered by the high sequence variability of the encoding var genes . Hence , quantitative expression data of the entire var gene repertoire from P . falciparum patient isolates are not well documented . The most common strategy for analyzing var gene expression in parasite strains with variable genomic background uses degenerate primer pairs targeting conserved sequence blocks for semi-quantitative RT-PCR or qPCR approaches . Using this strategy a higher frequency of A- and B-type PfEMP1 transcripts was detected in malaria patients suffering from severe disease in comparison to mild or asymptomatic malaria [11–21] . In contrast to malaria patients who have already developed clinical symptoms of the disease and parasites ran through an unknown number of cycles , parasites from CHMI studies are only allowed to progress through a few , well-characterized replication cycles in vivo before the volunteers have to be treated . Accordingly , patients infected in CHMI studies have very low levels of parasitemia and do not develop complications ( reviewed in [34] ) . Using samples from such a CHMI study we performed the first in depth quantitative analysis of var gene expression on 18 volunteer samples at the early onset of blood infection . All volunteers were infected with the NF54 strain and the same cryopreserved PfSPZ lot was used for intravenous or intradermal injection . In agreement with the expression of all or most of the NF54 var gene repertoire in parasites from recently infected volunteers express , there is also evidence that parasites tend to express many var genes rather than a single dominant variant in individuals with low levels of naturally acquired immunity [12 , 32 , 37] . Interestingly , genes possessing A- and B-type 5’-UTRs revealed a significant higher expression level in comparison to genes with C- or E-type 5’-UTR . Moreover , the expression level also seems to be influenced by the chromosomal position of the gene since the subtelomerically located A- , B/A- and B-type genes show higher expression levels than centromerically located genes of the B/C and C groups . This observation is in line with previously described differences in on- and off-rates for subtelomerically versus centromerically located var gene variants indicating that subtelomerically located variants have a much higher expression dynamic [25 , 36 , 38 , 39] . Lowest transcript abundances in vivo were detected for centromeric genes with C-type 5'-UTR . The only exceptions are the var gene variants of the var1 and var3 subfamilies , which show a very low expression level despite their A-type promoter sequence and their subtelomeric location . Together with the E-type var2csa ( PFL0030c/PF3D7_1200600 ) gene all interstrain conserved var gene variants reveal only minor transcript abundances in all volunteers analyzed . The first study on var gene expression in ex vivo samples of 3D7-infected volunteers applied a semi-quantitative RT-PCR strategy followed by sequencing of a large number of clones . On day 12 . 5 or 13 . 5 post infection most of the detected transcripts belonged to subtelomeric var genes of group A and B , consistent with the results presented here [29] . In contrast to the broad expression of most var genes detected in our study , a single B-type PfEMP1 transcript , PF11_0007/PF3D7_1100100 , was found most frequently and clearly dominated the expression profile in both volunteers analyzed by Peters and colleagues . One possible explanation for this divergent observation is the susceptibility of the semi-quantitative RT-PCR method to primer and cloning bias , which can result in overestimation of transcript frequencies . In another study from Lavstsen et al . parasites from six infected volunteers were analyzed by qPCR after approximately a month of in vitro-cultivation . In line with our data , all parasite lines established from first-generation parasites after liver release showed a similar var gene expression pattern and transcripts of all var genes could be detected [31] . In contrast to the data obtained in our study , 9 of the 10 lowest transcribed genes were classified as A or B/A var gene , which may be the result of the higher expression dynamic of these genes and/or the in vitro-cultivation of the parasites before analysis . More recently , NF54 parasites isolated from a Dutch volunteer in another CHMI study were analyzed using a more accurate qPCR approach [30] . Due to the very low parasitemia in CHMI studies the authors were able to analyze transcript abundances of the full NF54 var repertoire in a single blood sample only taken at day 11 after infection . Interestingly , apart from slight differences in frequencies , they observed exactly the same pattern of var gene transcription at the early onset of blood infection and , remarkably , the highest transcript levels were observed for the same group B var gene variant , MAL6P1 . 1/PF3D7_0632800 ( synonym PFF1595c ) . Moreover , the three least polymorphic var gene subfamilies var1 , var2csa and var3 were also among the 10 lowest transcript levels in the Dutch volunteer . The positive correlation between both in vivo data sets was highly significant ( S3 Fig ) . These highly reproducible results indicate that an intrinsic var gene expression program of the NF54 parasite exists which seems to drive higher levels of transcription of A- and B-type 5'-UTR controlled genes with the exception of the conserved var1 and var3 genes at the onset of blood infection . Later on , growth advantages of parasites best adapted to their host’s adhesion surfaces may select for parasites expressing particular var gene variants , subsequently resulting in the typical , dominant expression of one or a few var genes as we observed in natural infections . But whether this var gene expression pattern is a general strategy used by all parasite strains to establish human blood infections waits to be confirmed . Further work is also needed to address the question whether var gene expression patterns differ between parasites from experimentally infected malaria-naïve humans and those who already underwent natural or experimental P . falciparum infections and , accordingly , possess a pre-formed immune response against PfEMP1 variants expressed in previous infection ( s ) . Another study reversibly silenced all endogenous var genes by promoter titration to mimic the moment when parasites enter the human blood phase [35] . In agreement with our data , two weeks after drug removal these “null-var” parasites with erased epigenetic memory from previous in vitro generations broadly activated all var genes . However , although A- and B-type variants are also activated , these parasites tend to express predominantly group C var genes , which becomes even more clearly after one and three month of cultivation . Accordingly , central var genes are the most highly activated genes in vitro , which is in clear contrast to the expression pattern we observe in vivo where subtelomerically located var genes dominate . Accordingly , Spearman’s rank correlation analysis showed no significant correlation between the data set from Fastman et al . and our in vivo data ( S3 Fig ) . Maybe the observed pattern in vivo is the result of resetting plus in vivo selection while the resetting of the var gene repertoire by promoter titration is the result of the resetting alone . Additionally , because Fastman et al . found highly activated B and C-type var genes adjacent to rarely activated genes they concluded that the probability of var genes to be turned on or off are independent of their chromosomal location or promoter type per se and rather seems to be associated with intrinsic properties of each gene . Although we also found expression level differences between adjacent gene variants , e . g . PFI1820 and PFI1830 , our ex vivo data show a clear association of the subtelomeric position as well as A- and , especially , B-type promoters with higher expression level . The comparison between var gene expression in the pre-mosquito NF54 parasites and in the ex vivo patient samples revealed substantial changes indicating a kind of resetting of the var gene expression program during mosquito and/or liver passage of parasites . Pre-mosquito MCB parasites predominantly transcribed the var2csa gene PFL0030c/PF3D7_1200600 and this gene was also among the most abundant transcripts in Sanaria’s Working Cell Bank lot SAN02-073009 , which was derived from the MCB and used to produce the PfSPZ Challenge lot for this particular volunteer infection ( personal communication Matthias Frank ) , which is in clear contrast to the broad expression pattern of the subtelomeric var gene groups in the patient samples . Previous studies have indicated that mosquito passage leads to a shift in the transcribed var profile towards expression of subtelomeric genes [29] and promiscuous var transcription [30] . By using a parental parasite that appeared to exclusively express var2csa and robust and exhaustive methods to quantitate the entire var repertoire , we prove conclusively for the first time that the expressed var repertoire is dramatically reset following mosquito passage . Two mechanisms could explain these results: either the var epigenetic program is altered following passage through the mosquito leading to erasure of epigenetic memory and activation of subtelomeric var genes by first generation merozoites; or parental parasites expressing var2csa were selected against after exiting asexual replication in vitro . Although the latter explanation is possible it would mean that nearly all of the parental parasites would either fail to passage through the mosquito or to establish the infection in the human host . In fact , some parasites transcribing var2csa express no PfEMP1 on the surface of the infected erythrocyte , because var2csa is often repressed at the level of translation [40] . Those parasites fail to adhere to endothelial receptors and would be rapidly cleared by the spleen . Resetting of the epigenetic var program following mosquito passage is supported by the observation that release of artificial repression of the entire var repertoire in asexual parasites leads to promiscuous var activation within the population [35] , similar to the var profiles following mosquito passage that we and Wang et al . observed [30] . Furthermore , in NF54 sporozoites var gene expression is largely silenced , which is in agreement with a resetting of the var expression program during mosquito passage [41] . Our findings synthesize these various studies into a complete picture of mosquito passage causing epigenetic reprogramming to allow expression of any member of the var repertoire but with an apparent bias towards the subtelomeric var genes . Whether this bias was the consequence of phenotype selection in the first rounds of asexual replication remains unknown . Interestingly , the modification of variable antigen expression by vector transmission has recently also been shown for the rodent malaria parasite P . chabaudi [42] . Spence and colleagues showed that the high virulence of serially blood-passaged parasites is attenuated by parasite transmission through mosquitos and this correlates with altered expression of the cir ( chabaudi interspersed repeats ) multi-gene family . Moreover , the activation of 114 cir variants ( 57% ) after mosquito passage of P . chabaudi parasites closely reflects the broad activation pattern of all or most subtelomerically located var gene variants at the onset of blood infection in malaria naïve volunteers . Although the pir ( Plasmodium interspersed repeats ) multi-gene family is unique to P . vivax , P . knowlesi and the rodent clade of malaria parasites without any significant sequence similarity in the P . falciparum genome [43] , the reprogramming of antigenic variant gene expression by vector transmission seems to be universal in the Plasmodium genus [29 , 42 , 44 , 45] . In summary , our data from this study clearly show differences in var gene expression before mosquito passage of the NF54 strain and early onset of blood infections , providing novel evidence for an epigenetic reprogramming or resetting of virulence gene expression during parasite transmission as proposed previously [29 , 32 , 42] .
The ethics committee of the University Clinic and the Medical Faculty of the University of Tübingen approved the study and the U . S . Food and Drug Administration Agency ( FDA ) provided regulatory oversight . Investigation of the var gene expression pattern in early blood stage infection was an exploratory objective of the trial . The study was conducted according to the principles of the Declaration of Helsinki in its 6th revision as well as International Conference on Harmonization–Good Clinical Practice ( ICH-GCP ) guidelines . The study registration code with ClinicalTrials . gov is NCT01624961 . All volunteers , aged 18 to 45 years , provided written informed consent and understanding of the study and procedures was assessed with a quiz [33] . At the Institute of Tropical Medicine in Tübingen , Germany , healthy , malaria-naïve volunteers were infected with aseptic , purified , cryopreserved NF54 sporozoites ( PfSPZ Challenge ) from a single lot manufactured by Sanaria Inc . , USA [33] . Blood samples for thick blood smears were taken daily from the onset of merozoite release at 5 days after sporozoite injection . Blood samples for var transcription profiling were taken at a 48 hours interval . Sampling continued until parasites were detected in thick blood smears when anti-malaria treatment was initiated . Then , erythrocytes from all infected volunteers were separated from leukocytes by Lymphoprep ( Axis-Shield ) gradient centrifugation followed by filtration through Plasmodipur filter ( EuroProxima ) . In total , 4 . 5–9 . 0 ml of packed red blood cells were obtained from 18 volunteers at the day of first positive P . falciparum parasitemia by thick blood smear and from 8 volunteers at one time point before the thick blood smear was positive ( 1–2 days ) . Two frozen vials ( A and B ) of NF54 parasites from Sanaria’s MCB lot RKV01-092505 were separately thawed and maintained in culture using a protocol adopted from Trager and Jensen [46] . A hematocrit of 5% was adjusted using human O+ red blood cells and the parasite culture medium was supplemented with 10% heat-inactivated human serum . For DNA purification , MCB parasites were cultivated for 6 parasite replication cycles to obtain a higher yield . Genomic DNA was prepared from ring stage parasites established from both cell stocks using the QIAamp DNA Blood Mini Kit ( Qiagen ) . For RNA purification ring stage parasites from MCB vials A and B were taken after 6 , 8 and 21 parasite replication cycles after thawing , respectively . One cycle of invasion before harvesting , parasite growth was synchronized twice at an interval of 6 hours using sorbitol [47] . Cell pellets of MCB and of the leukocyte depleted patient blood samples were rapidly lysed in 10 volumes pre-warmed TRIzol ( Invitrogen ) and stored at -80°C . RNA purification was performed according to the manufacturers instruction of the PureLink RNA Kit ( Life Technologies ) including DNase treatment on the column . Absence of DNA was checked using 50 ng RNA as template for a qPCR run with the sbp1 primer pair . If necessary , DNasing was repeated until the sample was free of any DNA contamination by qPCR . Afterwards , cDNA was synthetized with the SuperScript III Reverse Transcriptase ( Invitrogen ) primed with random hexamers ( Invitrogen ) at 50°C for 1 hour . As possible , a cDNA synthesis reaction without enzyme was performed and analyzed in parallel by qPCR . Quantitative amplification was conducted in the ABI PRISM 7900HT Sequence Detection System ( Applied Biosystems ) using the SDS software version 2 . 3 ( Applied Biosystems ) . Template was mixed with the SYBR Green PCR Master Mix ( Applied Biosystems ) and 0 . 3 μM forward and reverse primer in a final volume of 10 μl . Reactions were incubated at 50°C for 2 min , at 95°C for 10 min , then subjected to 40 cycles of 95°C for 15 s and 60°C for 1 min and a subsequent melting step ( 60–95°C ) . The specificity of each primer pair was confirmed after each qPCR run by dissociation curve analysis . Analysis was performed using sbp1 ( PFE0065w/PF3D7_0501300 ) to normalize and gDNA from MCB parasites to calibrate the individual var gene expression data . Relative quantification of the NF54 var repertoire by 2-ΔΔCt analysis was performed using a previously described primer set [48] supplemented with new primer pairs for PF07_0049/PF3D7_0712000 , PFL1950w/PF3D7_1240300 , PFE0005w/PF3D7_0500100 and PF07_0051/PF3D7_0712600 . Furthermore , primer pairs targeting the housekeeping genes fructose-bisphosphate aldolase ( PF14_0425/PF3D7_1444800 ) , seryl-tRNA synthetase ( PF07_0073/PF3D7_0717700 ) and arginyl-tRNA synthetase ( PFL0900c/PF3D7_1218600 ) were included ( S1 Table ) . Relative expression data were corrected for amplification efficiency of each primer pair , which was determined by dilution of a single gDNA from 3D7 over 5 logs of concentration ( S1 Table ) . Expression and correlation heat maps were generated using Multiple Experiment Viewer ( MeV ) . The expression plots were programmed with Stata version 14 . In all expression dot plots a scale break of the y-axis was introduced at 2 , 000 due to the huge difference of the relative expression values between gene groups . Spearman’s rank correlation was applied to assess whether gene expression patterns between parasites isolated from different infected volunteers or between different parasite generations from the same infected volunteer were comparable . Correlation coefficients ( R-values ) were displayed in a heat-map , where color codes define the correlation levels ( Figs 1E and 2D ) . Furthermore , Spearman’s rank correlation was used to compare the gene expression patterns observed in this study with the results obtained by Wang et al . [30] and Fastman et al . [35] ( S3C Fig ) . To compare var gene expression levels between infected volunteers , and the pre-mosquito cell line MCB differences in the median gene expressions were calculated for each individual var gene variant . Therefore , the median ratio was calculated . A value of 1 indicates no difference , >1 higher gene expression in the infected volunteers and a value <1 higher gene expression in the MCB cell line ( Fig 4A ) . To describe the expression of genes with different chromosomal localizations or per var gene group observations within respective groups were pooled and the median expression along with interquartile rage ( IQR ) was calculated . Differences in gene expressions between the respective groups were tested via a Wilcoxon rank sum test . This analysis was applied to assess differences in gene expression levels of subtelomerically versus centromerically located var gene variants in the volunteer samples taken at the day of first microscopically detectable parasitemia ( Fig 1D ) . Furthermore , expression level differences of var gene groups ( i . e . , A , A var3 and var1 subfamilies , B/A , B , B/C , C , E ) and control genes between volunteer samples at the day of patent infection versus the pre-mosquito parasite line MCB were also tested via Wilcoxon rank sum test ( Fig 4B ) . Bonferroni corrected significance level was used to account for multiple comparisons . Since comparison was done among 9 var gene groups , the p-value was corrected by the multiplication with 9 . All analyses were done using STATA 14 ( College Station , TX: StataCorp LP ) or GraphPad Prism 4 . | Parasites of the species Plasmodium falciparum , which are responsible for the most severe forms of malaria , escape from the human immune response by antigenic variation . A repertoire of 60 var genes codes for a broad range of different variant antigens presented on the surface of infected erythrocytes . These antigens mediate adhesion to blood vessels , thereby disturbing the blood microcirculation and causing life-threatening organ dysfunctions . To better understand antigenic variation in vivo we analyzed the var gene expression profiles in blood samples from 18 malaria naïve volunteers , who were experimentally infected with cryopreserved sporozoites isolated from Anopheles mosquitoes . Our in-depth analysis revealed a broad , but remarkably uniform expression pattern of a specific set of var gene variants . Moreover , the results clearly show that this var gene expression program is specifically activated after mosquito transmission of the parasite . These findings are of particular importance for our understanding of the strategy of malaria parasites to establish and maintain infections in the human host . | [
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] | 2016 | Mosquito Passage Dramatically Changes var Gene Expression in Controlled Human Plasmodium falciparum Infections |
Proteorhodopsins are globally abundant photoproteins found in bacteria in the photic zone of the ocean . Although their function as proton pumps with energy-yielding potential has been demonstrated , the ecological role of proteorhodopsins remains largely unexplored . Here , we report the presence and function of proteorhodopsin in a member of the widespread genus Vibrio , uncovered through whole-genome analysis . Phylogenetic analysis suggests that the Vibrio strain AND4 obtained proteorhodopsin through lateral gene transfer , which could have modified the ecology of this marine bacterium . We demonstrate an increased long-term survival of AND4 when starved in seawater exposed to light rather than held in darkness . Furthermore , mutational analysis provides the first direct evidence , to our knowledge , linking the proteorhodopsin gene and its biological function in marine bacteria . Thus , proteorhodopsin phototrophy confers a fitness advantage to marine bacteria , representing a novel mechanism for bacterioplankton to endure frequent periods of resource deprivation at the ocean's surface .
Proteorhodopsins ( PRs ) are membrane-embedded , light-driven proton pumps , which generate a chemiosmotic potential by translocating protons across an energy-transducing membrane [1]–[3] . This proton gradient can subsequently be used for production of biologically available energy in the form of adenosine triphosphate ( ATP ) , the basic energy currency conserved among living beings , and/or for fueling motility and enhancing solute transport across the membrane [2] , [3] . The discovery of PR in marine bacteria revealed a possible role of non–chlorophyll-based phototrophy in biogeochemical carbon cycling and energy fluxes in the ocean [1] . Consistent with their inferred ecological importance , PRs are highly abundant and exceedingly genetically diverse in aquatic environments [4]–[10] . The large genetic diversity of PRs suggests that they could potentially display an array of physiological and ecological functions [2] , [11] , [12] . However , there is a striking lack of knowledge concerning which biological function PRs fulfill and how they contribute to the success of PR-containing bacteria in the marine environment . Survival and reproduction are the main components that determine fitness , a fundamental concept in ecology . For marine bacteria , the molecular mechanisms that contribute to the variation in these components are poorly understood . Previous studies have focused on the effects of PR on reproduction . In a flavobacterial strain containing PR , light-stimulated growth in seawater was observed [13] . However , similar experiments with other marine bacteria containing PR—Flavobacteria or members of the ubiquitous SAR11 or SAR92 clades—revealed no detectable effect of light on growth [10] , [14] , [15] . Nevertheless , Lami et al . [16] recently showed that PR expression in SAR11 and Flavobacteria was up-regulated in the presence of light and could be correlated with the abundance of PR genes . Taken together , these findings suggest that PR may involve fitness components other than growth . Thus , in this work , we explored the consequences of PR phototrophy for survival of marine bacteria . Vibrio species are widespread marine bacteria and are frequently referred to as metabolically versatile heterotrophs [17] . Undoubtedly , the most well-known member of the genus is V . cholerae , the etiological agent of the disease cholera . Vibrios are typically found associated with detritus particles , algae or zooplankton , as commensals or pathogens on higher organisms , or as free-living populations in the water column [18] , [19] . Nevertheless , the potential for phototrophy using PR or other light-harvesting mechanisms has not previously been reported for any member of the genus . We investigated the ecological response to light in a proteorhodopsin-containing member of the genus Vibrio . This representative of marine bacteria showed enhanced survival during starvation when exposed to light compared to darkness . Moreover , mutational analysis provided a direct link between the proteorhodopsin gene and the light response that conferred an increased ecological fitness .
A PR-encoding gene was identified from the whole-genome sequence of strain AND4 , isolated from surface waters of the Andaman Sea . Phylogenetic analysis of the 16S rRNA gene as well as comparative genome analyses showed that AND4 is a member of the Gammaproteobacteria genus Vibrio ( Figure 1 and Table 1 ) . The AND4 PR shares a sequence similarity of 87% over 269 amino acid residues with the PR encoded in the publicly available genome sequence of V . harveyi strain BAA-1116 . To our knowledge , the PR gene has as yet not been found in any other member of the genus Vibrio . PRs from the isolates AND4 and BAA-1116 both contain Leu in position 105 , which fine tunes the PR light absorption peak towards green light ( absorption maximum 535 nm , Figure S1 ) , thereby adapting to the dominant light conditions prevailing in surface seawater [5] , [20] , [21] . All essential amino acid residues of the energy transducing rhodopsins are conserved ( Figure S2 ) , and the protein photocycle has a half-life of approximately 50 ms ( Figure S1 ) , as would be expected for a proton pump [22] . In AND4 and BAA-1116 , the PR gene and the genes required for synthesis of the chromophore retinal , crtEIBY and blh [2] , [23] , were found at the same genetic locus ( Figure 2 ) . Phylogenetic analysis of the Vibrio PR amino acid sequences showed that , in contrast to the 16S rRNA gene placing AND4 and BAA-1116 among the Gammaproteobacteria , PRs in these bacteria clustered with PRs in Alphaproteobacteria ( Figure 1 ) . Moreover , the retinal biosynthesis genes have an ancestry that is divergent from the flanking genes ( Table S1 ) . This strongly suggests that the genes for PR and its chromophore have been acquired as a linked set of genes through lateral gene transfer from relatively distantly related bacteria , as has recently been suggested for other marine bacteria [2] , . Lateral gene transfer may involve mobile genetic elements since transposase genes are found flanking the PR , crtEIBY , and blh genes in both BAA-1116 and AND4 ( Figure 2 ) . The transposase gene closest to the PR gene in AND4 was truncated and showed best matches to transposases in V . anguillarum 775 , V . parahaemolyticus AQ3776 and V . cholerae 91 , with percent similarities of 83%–87% . Several of the transposase genes in the genomes of AND4 and BAA-1116 are part of the IS903 subfamily of the IS5 family , which frequently are part of compound transposons in Vibrio species [26] . This indicates that AND4 and BAA-1116 share , or have shared , with other vibrios the mechanisms for lateral gene transfer . McCarren and DeLong [25] recently suggested that diverse marine bacteria may have acquired and retained the PR gene because it confers a competitive advantage in an otherwise resource-depleted surface ocean . However , there are no reported studies demonstrating how PR genes that have been putatively acquired through lateral gene transfer could impact on the life strategy of its carrier . To explore this possibility , we investigated the growth and survival of AND4 in light and dark using a suite of approaches , where light refers to photosynthetically active radiation . Growth experiments with AND4 in rich medium showed no differences in cell yields for light ( continuous light , 133 µmol photons m−2 s−1 ) and dark conditions ( Figure 3A , inset ) . Also upon transfer of cells from rich medium to sterile and particle-free natural seawater with low concentrations of organic and inorganic nutrients , an increase of cell numbers within the first 2 d was observed ( Figure 3A ) . Notably , epifluorescence microscopy images of AND4 cultures showed that most of the observed increase in cell numbers was due to reductive division rather than growth , i . e . , cell numbers increased , but total biomass did not because each cell decreased in size ( Figure 3B ) . This decrease in cell size is a well-described characteristic of vibrios ( and many other bacteria ) exposed to starvation , being an important strategy for optimizing cellular energetic efficiency when resources become limited [27] . After 10 d of incubation , bacterial numbers decreased in all cultures , but remained 2 . 5 times higher in the light compared to darkness . This finding strongly suggests that PR phototrophy can improve the survival of marine bacteria during periods of starvation in seawater . Next , we monitored the development of optical densities ( ODs ) and bacterial numbers of AND4 grown in rich medium , washed , resuspended in sterile seawater , and exposed to four different light conditions ( Figure 4A ) . In the dark , OD values steeply decreased during the first 13 d of starvation , whereas cultures exposed to light decreased more slowly , with the difference in ODs between the light treatments and darkness changing significantly over time ( F = 5 . 81 , df = 18 , 24 , p = 0 . 0079 ) . After 7–13 d of starvation , OD values were 40%–60% higher for the treatments with continuous high light and with 16∶8 h light∶dark cycles ( 133 and 150 µmol photons m−2 s−1 , respectively; corresponding to light intensities in oceanic upper mixed-surface layers ) , when compared with treatments maintained in the dark . Concomitantly , the effect of light in the low-light treatment ( continuous light , at 6 µmol photons m−2 s−1; corresponding to light intensities at the lower limit of the photic zone ) was less pronounced . In parallel with the initial decrease in OD , bacterial numbers peaked on day 3 in the high-light treatments , and thereafter decreased in all treatments . Nevertheless , bacterial numbers remained nearly twice as high for the high-light intensity treatments ( Figure 4A , inset ) . These results again imply that PR can increase the survival rates of bacteria during starvation under light conditions corresponding to those found in the surface ocean . To establish that the PR gene conveys this light-enhanced survival during starvation in AND4 , we constructed a strain where the PR gene had been removed by an in-frame deletion of the near-complete PR gene ( AND4 Δprd ) . In contrast with the wild-type behavior ( Figures 4A , 5A , and 5B ) , starvation experiments with the Δprd strain showed no differences in ODs or bacterial numbers between light and dark conditions ( Figures 4B , 6A , and 6B ) . However , when the mutant was complemented with the prd gene in trans , the wild-type phenotype was regained ( Figure 4C; significant day-by-light treatment interaction; F = 13 . 21 , df = 8 , 24 , p = 0 . 0083 ) . Moreover , we analyzed the ability of the wild-type and Δprd strains to recover growth during 5 h incubation in rich medium after increasing periods of starvation ( Figures 5 and 6 ) . Although little difference in growth recovery was observed after 1 . 5 d of starvation , after 5 d , the wild-type bacteria exposed to light during starvation grew to 3- to 6-fold higher densities when compared to bacteria starved in darkness ( Figure 5 ) . No differences in recovery were detected in the Δprd strain , irrespective of the history of light-exposed or dark incubations ( Figure 6 ) . These growth recovery experiments on wild-type and modified AND4 strains thus confirm that the increased rates of survival and the ability to actively respond to improved growth conditions are a direct consequence of having the light energy–harvesting potential of PR . Genetic inventories of the world's ocean have revealed that the potential for harvesting light energy by means of PR phototrophy is found in a diverse variety of marine bacteria , encompassing organisms with very different life strategies and physiologies [14] , [15] , [28] . For example , members of the SAR11 clade are free-living bacteria with a range of cellular and physiological adaptations allowing them to minimize the consequences of starvation in oligotrophic waters . Their PRs are highly expressed , which may be a contributing factor to obtain positive net growth in seawater [14] , [16] . For particle-attached bacteria or commensals/pathogens , PR phototrophy could also be important , but on a more irregular basis , during phases of starvation survival between particle or host colonization events . Irrespective of life strategy , the ability to survive starvation while maintaining the potential to proliferate is an essential trait for any evolutionarily successful organism [29] . Our results demonstrate that PR phototrophy represents a physiological mechanism that imparts an improved ability to survive when resources are scarce . This , thus , represents a substantial widening of the phototrophic properties known for marine bacteria in general , and vibrios in particular . Vibrio sp . AND4 , studied here , is closely related to organisms that are known pathogens on higher organisms ( e . g . , V . harveyi BAA-1116 and V . parahaemolyticus ) and requires nutrient-rich seawater for growth . AND4 and BAA-1116 are so far the only genome-sequenced members of the genus Vibrio that contain the PR gene . For BAA-1116 , it is still unknown whether the gene is functional , although the high PR gene region synteny and PR amino acid sequence similarity may suggest that its function is similar to that in AND4 . An important challenge for the future will be to unveil the physiological status and growth capacity of other PR-containing bacteria , both cultivated species , and major taxa in the marine environment , and to what extent PR phototrophy may alleviate starvation and/or contribute to other physiological processes in key species . Given the enhanced fitness observed in the present study , the acquisition and maintenance of the PR gene may be highly advantageous in the competitive marine environment , potentially influencing bacterioplankton community composition and population dynamics in the ocean's surface .
AND4 was isolated from surface water ( 2-m depth ) in the Andaman Sea ( 7° 48′ 0″ N , 98° 12′ 36″ E ) in December 1996 by spreading a 100-µl seawater aliquot on Marine Agar 2216 ( Difco ) . After initial isolation and purification , the isolate was stored in glycerol ( 20% final concentration ) at −70°C . Whole-genome sequencing of AND4 was carried out by the J . Craig Venter Institute ( JCVI ) through the Gordon and Betty Moore Foundation initiative in Marine Microbiology . The draft genome sequence consists of 143 contigs representing ten scaffolds . The genome sequence was obtained using a Sanger/pyrosequencing hybrid method [30] . Our genome analysis is based on open reading frames predicted and annotated using JCVI's prokaryotic annotation pipeline ( genome sequence available at https://moore . jcvi . org/moore/ ) . All automatically annotated genes of interest were inspected and verified manually by BLAST , COG , PFAM , and TIGRFAM analyses . The genome sequence ( accession no . ABGR00000000; annotation added by the National Center for Biotechnology Information prokaryotic genomes automatic annotation pipeline group ) , 16S rRNA gene sequence ( accession no . AF025960 ) , and proteorhodopsin amino acid sequence ( accession no . ZP_02194911 ) of Vibrio sp . AND4 are publicly available in the GenBank database . For the phylogenetic tree of 16S rRNA genes shown in Figure 1A , a multiple alignment was generated using the software package ClustalW ( Version 1 . 83 ) . The alignment was edited with Gblocks ( Version 0 . 91b ) to identify conserved regions . The tree was constructed based on a Jukes-Cantor distance matrix and the Neighbor-Joining method using the PHYLIP package ( Version 3 . 68 ) . The sequence of Polaribacter sp . MED152 ( DQ481463 ) served as outgroup . GenBank accession numbers are given in parentheses . The scale bar represents Jukes-Cantor distances ( nucleotide substitutions per base position ) . Filling of circles at nodes represents matching topology with a maximum likelihood tree constructed with RAxML [31] and a neighbor-joining tree ( default parameters ) constructed with the ARB software package [32] . The proteorhodopsin amino acid sequence tree in Figure 1B was created from a multiple sequence alignment in ClustalW using the PHYLIP software package and the Kimura distance matrix and the neighbor-joining method . The alignment was edited with Gblocks ( Version 0 . 91b ) to identify conserved regions with a minimum block of five . The scale bar represents the Kimura distances ( number of amino acid substitutions per base position ) . Circles at nodes represent matching topologies with a maximum likelihood tree constructed with RAxML [31] using the PROTGAMMABLOSUM62F amino acid model . The sequence of Polaribacter sp . MED152 ( EAQ40925 ) served as the outgroup . The number of transposase genes was obtained by BLASTP hits against the ISFinder dataset ( http://www-is . biotoul . fr ) and an E-value<10−10 and sequence identity values >35% . To obtain measures of AND4 proteorhodopsin absorption maximum and photolysis rates , cell-free expression of AND4 prd was carried out by cloning from genomic DNA into the TOPO vector pEXP5NT ( Invitrogen ) using the sense and antisense primers 5′-ATGAAAAACCAAGTTGAAAAGATAACA-3′ and 5′-TTACGCATCCTGACTCTCGG-3′ , respectively . This generated a prd construct with an N-terminal 6xhistidine tag followed by a Tev protease cleavage site preceding the AND4 coding sequence . prd was expressed with an in-house cell-free expression system based on Escherichia coli S12 extract , essentially according to a combination of protocols described in Kim et al . [33] and Torizawa et al . [34] . Expression was performed in batch format at 34°C in the presence of 0 . 1% Brij35 and 5 µg ml−1 all-trans retinal for 2 h at 800 rpm . The resulting reaction mix was centrifuged at 13 , 000×g for 5 min , and the supernatant was bound in batch mode to TALON resin ( Clontech ) pre-equilibrated with buffer A ( phosphate-buffered saline containing 10 mM imidazole and 0 . 05% Brij35 ) . After 1 h incubation at 4°C , the resin was transferred to a gravity-flow column , washed with 20 column volumes of buffer A , and eluted with buffer B ( buffer A with 150 mM imidazole ) . The resulting eluate was passed through a PD10 column to change the buffer to phosphate-buffered saline containing 0 . 05% Brij35 , and then concentrated with VivaSpin 6 ultrafiltration tubes with a MWCO of 30 , 000 . For flash-photolysis experiments , the buffer was changed to either 25 mM CAPS ( pH 10 ) , 0 . 05% Brij35 , or 25 mM MES ( pH 5 . 5 ) , 0 . 05% Brij35 by repeated dilution and concentration cycles in the ultrafiltration tube . To create a null mutation in the PR gene , an in-frame deletion was made by allelic exchange using the suicide vector pDM4 as described by Milton et al . [35] with a few minor changes . Plasmid pDM4-rdp-AD , which carries a mutated allele of the PR gene that encodes the first seven amino acids fused to the last eight amino acids of the gene , was introduced into Vibrio sp . strain AND4 by conjugation . After the mating , selection for a Vibrio strain carrying the plasmid in the chromosome was done using Trypticase soy agar containing 1% sodium chloride , 200 µg ml−1 carbenicillin , and 15 µg ml−1 chloramphenicol . To complete the allelic exchange , direct selection on Trypticase soy agar containing 5% sucrose for a strain that had lost the sacB gene carried on pDM4-rdp-AD was done as described previously . The in-frame deletion was confirmed by sequencing a PCR-amplified DNA fragment of the deleted chromosomal locus . Primers used for the overlap PCR to create the mutated allele were as follows: PR-A-5′-GGACTAGTGGTTACTGGACACAA , PR-B-5′-AACCAAGTTGAAAAGGCAACCTCCGAGAGT , PR-C-5′-CTTTTCAACTTGGTTTTTCATAAT , and PR-D-5′-CTCGAGCTCCAGGGGAGATAGGTT . To complement the deletion mutation , the wild-type prd gene was expressed in trans from the plasmid pMMB-prd-wt . To construct pMMB-prd-wt , the prd gene was amplified by PCR using KOD polymerase and the primers Rdp-5′-CTCGAGCTCCGTTAAAAGTGAGACTAT and Rdp-3′-CGCGGATCCTGGAAAGAGGGACAGAGA . An 890-bp fragment was gel purified , digested with SacI and BamHI , and ligated to pMMB207 [36] , which was similarly digested . The resulting plasmid , pMMB-prd-wt , was mobilized into the Δprd mutant via conjugation . For the rich-medium growth experiment ( Figure 3 , inset ) , ZoBell medium ( 5 g of peptone [Bacto Peptone; BD] and 1 g of yeast extract [Bacto Yeast Extract; Difco] in 800 ml of Skagerrak seawater and 200 ml MilliQ water ) was 8-fold diluted in sterile-filtered and autoclaved Skagerrak seawater . Triplicate cultures were incubated at 16°C under an artificial light source of 133 µmol photons m−2 s−1 . Triplicate dark control bottles were covered with aluminum foil . In all experiments , artificial light was provided by fluorescent lamps ( L 36W/865 , Lumilux , Osram ) emitting photosynthetically active radiation , not the full spectrum of sunlight . For the experiment with natural seawater ( Figure 3 ) , water was collected in the Skagerrak Sea and filter sterilized through 0 . 2 µm-pore-size membrane filters ( Supor 200 ) and autoclaved . Each culture contained 250 ml of seawater ( in 500-ml blue-cap glass bottles ) , and received a final concentration of 100 , 2 . 1 , and 0 . 3 µM dissolved organic carbon ( in the form of ZoBell medium ) , nitrogen ( NH4Cl ) , and phosphate ( Na2HPO4 ) , respectively . All material in contact with the samples was acid rinsed with 1 M HCl and extensively washed with MilliQ-water prior to use . Cultures were inoculated with AND4 bacteria previously grown overnight in rich medium ( i . e . , to early stationary phase ) . Duplicate light cultures were incubated at 16°C under an artificial light source of 133 µmol photons m−2 s−1 , and duplicate dark controls were covered with aluminum foil . For the starvation experiments , AND4 cells were grown overnight in 200 ml of rich medium ( in 500-ml blue-cap glass bottles ) . Cells were harvested through centrifugation at 4 , 000 rpm for 10 min . Cell pellets were washed twice with sterile seawater ( filter sterilized through 0 . 2 µm-pore-size membrane filters and autoclaved ) , resuspended in seawater , and distributed into Erlenmeyer flasks , with 75 ml of seawater–cells mix in each . Experiments with the wild-type AND4 included duplicate flasks for the high-light intensity treatment at 133 µmol photons m−2 s−1 , the 16∶8 h light∶dark cycle treatment at 150 µmol photons m−2 s−1 , and the low-light treatment at 6 µmol photons m−2 s−1 . Duplicate dark controls were completely covered with aluminum foil . The experiment was carried out at 16°C . Experiments with the Δprd strain and the Δprd strain complemented with the prd gene in trans included duplicate or triplicate flasks for each of the strains in the light ( continuous light at 133 µmol photons m−2 s−1 ) and in dark controls . In a separate experiment , growth recovery experiments with the wild-type and Δprd AND4 strains were performed after 35 , 131 , and 203 h of starvation in light or darkness . Two hundred microliters of each starved culture were inoculated in 50-ml Falcon tubes that contained 25 ml of ZoBell medium based on Skagerrak seawater . Recovery cultures were incubated at room temperature in the dark and were monitored for 5 h . Samples for optical density ( OD ) were measured at 600 nm using a bench top spectrophotometer ( Beckman DU 640 ) . Samples for bacterial numbers were fixed with 0 . 2 µm-pore-size filtered formaldehyde ( 4% , final concentration ) , stained with SYBR Gold ( 1∶100 dilution , Molecular Probes ) , filtered onto black 0 . 2 µm-pore-size polycarbonate filters ( Poretics , Osmonics Inc . ) , and counted by epifluorescence microscopy within 48 h . Alternatively , samples for bacterial numbers were fixed with 0 . 2 µm-pore-size filtered formaldehyde ( 4% , final concentration ) , and stored frozen at −70°C until analysis by flow cytometry using a FACSCalibur flow cytometer after staining with Syto13 [37] . The effect of light on AND4 during starvation was analyzed by repeated-measures analysis of variance . Analyses were performed with PROC GLM in SAS 8 . 2 , using type 3 sums of squares . | It is estimated that marine microscopic algae—phytoplankton—are responsible for half of the Earth's photosynthesis . As much as half of the surface ocean bacteria have proteorhodopsins , which are membrane proteins that allow harvesting of energy from sunlight , implying a potentially significant role of non–chlorophyll-based phototrophy in oceanic carbon cycling and energy flux . Functional evidence for specific roles for proteorhodopsins in native marine bacteria and the marine environment remains surprisingly scarce . One reason for this is the lack of marine bacteria ( containing proteorhodopsin genes ) that can be maintained in laboratory culture and that are tractable to genetic manipulation . In this study , we show that a proteorhodopsin-containing member of the widespread marine genus Vibrio displays light-enhanced survival during starvation in seawater . Furthermore , growth recovery experiments showed that bacteria starving in the light could more rapidly respond to improved growth conditions than those incubated in the dark . We generated a proteorhodopsin deficient Vibrio strain and used it to confirm that light-dependent survival of starvation was mediated by the proteorhodopsin . Proteorhodopsin phototrophy thus provides a physiological mechanism that allows surface ocean bacteria to manage an environment where resource availability fluctuates markedly . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Material",
"and",
"Methods"
] | [
"marine",
"and",
"aquatic",
"sciences/microbiology",
"genetics",
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] | 2010 | Proteorhodopsin Phototrophy Promotes Survival of Marine Bacteria during Starvation |
Triatoma dimidiata is one of the most significant vectors of Chagas disease in Central America and Colombia , and , as in most species , its pattern of genetic variation within and among populations is strongly affected by its phylogeographic history . A putative origin from Central America has been proposed for Colombian populations , and high genetic differentiation among three biographically different population groups has recently been evidenced . Analyses based on putatively neutral markers provide data from which past events , such as population expansions and colonization , can be inferred . We analyzed the genealogies of the nicotinamide adenine dinucleotide dehydrogenase 4 ( ND4 ) and the cytochrome oxidase subunit 1-mitochondrial genes , as well as partial nuclear ITS-2 DNA sequences obtained across most of the eco-geographical range in Colombia , to assess the population structure and demographic factors that may explain the geographical distribution of T . dimidiata in this country . The population structure results support a significant association between genetic divergence and the eco-geographical location of population groups , suggesting that clear signals of demographic expansion can explain the geographical distribution of haplotypes of population groups . Additionally , empirical date estimation of the event suggests that the population's expansion can be placed after the emergence of the Panama Isthmus , and that it was possibly followed by a population fragmentation process , perhaps resulting from local adaptation accomplished by orographic factors such as geographical isolation . Inferences about the historical population processes in Colombian T . dimidiata populations are generally in accordance with population expansions that may have been accomplished by two important biotic and orographic events such as the Great American Interchange and the uplift of the eastern range of the Andes mountains in central Colombia .
A population's demographic history as well as phylogeographic inferences are usually accessed by studying the reconstructed genealogical histories of individual genes ( gene trees ) sampled from different populations [1]–[3]: Studying patterns of genetic variation in a geographical context via gene trees can contribute considerably to our understanding of what factors have influenced geographical population structure and species divergence [4] , [5] . Coalescent theory [6] is applied to studies relating to the haplotype frequency , genealogy , and geographical distribution of populations , and has been applied as a useful focus for understanding many events that may have occurred in the past across the demographic history of populations ( e . g . , population expansion , bottlenecks , vicariance , and migration ) . Triatoma dimidiata is considered the major vector of Chagas disease in several Central American countries as well as in various regions of Ecuador and Colombia [7] . Across its distribution in Colombia , T . dimidiata occupies a great diversity of habitats , including sylvatic habitats such as palm trees and hollow trees in northern regions , or rock piles , as well as intradomiciliary synanthropic habitats mostly in the country's central Andean departments [8] . Previous studies have suggested that T . dimidiata shows a strong and significant genetic structure related to its original eco-geographical regions in Colombia [9] , which , albeit weakly , correlates with an isolation-by-distance model [10] . A preliminary paper on the genetic diversity and population differentiation of T . dimidiata in Colombia was assessed using DNA sequence analysis of the nicotinamide adenine dinucleotide dehydrogenase 4 ( ND4 ) mitochondrial gene , which interestingly suggested a high genetic interpopulation differentiation within Colombia [9] . However , because the sample evaluated was rather small ( n = 40 ) , representing only a minimal area of the species distribution , a more exhaustive genetic analysis of several communities of Colombian T . dimidiata was performed by using a microsatellite as well as cytochrome c oxidase subunit 1 ( CO1 ) gene [10] . Here , three major clusters with distinct ecological attributes were distinguished . These three clusters were termed: ( i ) Inter-Andean Valleys ( IAV ) , harboring a population group located in central Colombia , where T . dimidiata shows more epidemiological relevance and apparently high flow between synanthropic and sylvatic habitats; ( ii ) the Caribbean Plains ( CP ) population group , the most widely distributed group from the Caribbean coast to the lowlands of the Central Andean Cordillera , occupying mainly sylvatic habitats; and ( iii ) the Sierra Nevada de Santa Marta ( SNSM ) mountain population group , located in the northwestern zone of Colombia , occupying exclusively sylvatic habitats such as palm trees , although a few individuals have also been found sporadically visiting indigenous dwellings and have also been implicated in human Trypanosoma cruzi infections [11] , [12] . In a phylogeographic context , according to the evidence addressed by molecular analyses of ITS-2 [13] , cytochrome b ( cyt b ) , and ND4 genes [14] , Colombian T . dimidiata populations are considered a differentiated form derived from Central American conspecific populations ( in fact , it might be considered an additional subspecies or species within T . dimidiata sensu lato , according to the authors ) [7] , [13]–[16] . Under this hypothesis , Colombian populations are thought to have originated from an ancient population introduced through the Isthmus of Panama [13] after its emergence between 1 . 9 and 3 . 8 mya [14] , therefore undergoing a wide geographical expansion at a later time that gave birth to the current population structure . Consequently , the aim of the present study was to assess the population structure and history as factors explaining the geographical distribution of population groups of T . dimidiata in Colombia as well as their position in the phylogeographic picture proposed for the species so far . Analysis of the population genetics in the context of the geographic structure suggests demographic processes that occurred in the past . Thus , while the pattern of variation in mtDNA haplotypes allows one to identify geographical distribution differentiation among groups of haplotypes in several populations , it also supports inferences on demographic events that occurred in the past , such as geographical range expansion and population size , according to coalescence theory . In this way , the change in population size through genealogy is reflected by a haplotype network with a star shape [17] , an excess of rare mutations resulting in an excess of low-frequency haplotype presence [18] , and a unimodal mismatch nucleotide distribution [19] , [20] . In this study , we broaden the knowledge of the spatial structure of the three population groups of T . dimidiata in Colombia by analyzing ND4 gene nucleotide sequences obtained from 228 specimens in 22 localities; subsequently , several historical demographic tests were conducted using ND4 combined with previously reported CO1 nucleotide sequences [10] and ITS-2 rDNA . Finally , we also explored the phylogeographic pattern of Colombian populations by including the available ITS-2 and ND4 sequences of Central American and Mexican conspecific populations . Knowledge of population dynamics issues such as geographical dispersion and individual migration between extradomiciliary and domiciliary ecotopes is essential for predicting the success of vector control and surveillance strategies against Chagas disease . In this sense , study of the population structure and demographic history in the most relevant vectors is required for the design of more effective intervention strategies .
A total of 228 sequences for the ND4 ( 624-bp ) gene as well as 42 partial sequences ( 252 bp ) for ITS-2 were obtained ( Table 1 ) . Individuals were collected in intradomiciliary , peridomiciliary , and sylvatic ecotopes of 22 municipalities from ten departments in Colombia ( Table 1 and Figure 1 ) . Bug captures were carried out in 2003–2009 in collaboration with local personnel from the Ministry of Health . Sylvatic samples were collected with live-baited traps [21] . Domiciliary and peridomiciliary collections were made using the traditional manual collection method using a dislodging spray [22] and capture by homeowners . Captures from palm trees were obtained through palm dissection as described elsewhere [23] , with consent previously obtained from the landowners . All specimens were identified to the species level using Lent and Wygodzinsky's typological key [24] and kept in 70% ethanol until being processed for DNA extraction . Genomic DNA was obtained from four legs of each insect or from the thorax muscle when necessary ( i . e . , in old , dead , dry bugs , or those that had lost their legs ) . DNA extraction was performed according to a previously reported mosquito DNA-extraction protocol [25] . For each specimen , a 614-bp fragment of the ND4 gene was PCR-amplified using ND4-F ( 5′-TCAACATGAGCCCTTGGAAG-3′ ) and ND4-R ( 5′-TAATTCGTTGTCATGGTAATG-3′ ) primers [9] . PCR reactions for the mitochondrial gene were conducted in a final volume of 35 µl using a 30-ng DNA template , 1× PCR buffer ( 0 . 1 M Tris-HCl , 0 . 5 M KCl , and 0 . 015 M MgCl2 , pH 8 . 3 ) , 250 µM dNTP , 0 . 016 µM of each primer , 5 mM MgCl2 , and 2 U of Taq DNA polymerase ( Promega® ) . The fragments were amplified with the following thermal cycling conditions: 95°C for 5 min; 35 cycles of 94°C for 30 s , 50°C for 30 s , and 72°C for 60 s; 72°C for 10 min . Because no reproducible and unspecific amplifications in Colombian T . dimidiata specimens were obtained when universal primers 5 . 8S and 28T [16] were used , we employed the T . dimidiata-specific primers TdITSF ( 5′-TGGAAATTTTCTGTTGTCCACA-3′ ) and TdITS-2R ( 5′-CTTGCTTTATACAACAAGAAGTA-3′ ) [26] for PCR amplification of a 252-bp fragment of ITS-2 rDNA . PCR reactions were conducted in a final volume of 35 µl using 30-ng DNA templates , 1× PCR buffer ( 0 . 1 M Tris-HCl , 0 . 5 M KCl , and 0 . 015 M MgCl2 , pH 8 . 3 ) , 250 µM dNTP , 0 . 025 µM of each primer , 3 mM MgCl2 , and 2 U of Taq DNA polymerase ( Promega® ) . After an initial denaturation of 95°C for 5 min , PCR reactions comprised 35 cycles at 95°C for 30 s , 60°C for 30 s , and 72°C for 30 s , followed by a final extension of 72°C for 7 min [26] . All PCR products were sent to Macrogen Inc . , Seoul , Korea , for DNA purification and sequencing service . For all samples , sequencing was conducted in both forward and reverse directions . Forward and reverse sequences from specimens were used to generate a consensus sequence with a previous pairwise alignment using the CLUSTALW algorithm [27] implemented in Bioedit v . 7 . 0 . 5 [28] . Posterior multiple sequence alignment for each DNA marker was performed using the CLUSTALW algorithm [27] . In the complete data set for ND4 and ITS-2 , we evaluated the nucleotide diversity ( π ) , number of haplotypes ( h ) , and haplotype diversity ( Hd ) using DnaSP v . 5 . 10 [29] . The genetic differentiation among Colombian geographical samples was assessed by Fst comparison , and both nucleotide and haplotype diversity levels were estimated using Hudson's statistics Kst and Hst [30] , defining the statistical significance ( p<0 . 001 ) with a permutation test of 1 , 000 replicates . A median joining ( MJ ) haplotype network was used to examine inter-haplotype relationships among the 155 haplotypes of the 228 ND4 sequences as well as for 17 partial ITS-2 haplotypes using default parameters in Network 4 . 6 . 0 . 0 software ( http://www . fluxus-engineering . com ) . Spatial analysis of molecular variance ( SAMOVA ) was performed to estimate the structure among population groups according to pairwise geographical distances between geographical locations by Fct statistical calculations using SAMOVA v . 1 . 0 [31] . Fct values were estimated for simulated population groups from k = 2 to k = 4 in 1 , 000 iterations of the data set , which corresponds to the number of eco-geographical regions of T . dimidiata populations suggested in Colombia plus or minus one . The maximized Fct was selected according to the highest significant ( p<0 . 001 ) value . An interpolation-based graphical method was employed to generate a three-dimensional genetic landscape shape ( GLS ) within the Alleles in Space ( AIS ) program [32] . This analysis provides a visual perspective of the spatial distribution of the genetic structure over landscapes , with peaks in areas where pairwise genetic distances between haplotypes from each geographical location are high , and valleys where genetic distances between individuals are low ( the x- and y-axes represent latitude and longitude , whereas the z-axis represents genetic distances ) [32] . Georeferenced coordinates ( Universal Transverse Mercator system ) were provided for each individual and analyzed for the ND4 sequences . Additionally , we performed a spatial autocorrelation analysis to test whether there were significant correlations ( based on Vendramin et al . [33] correlation index V ) between average pairwise genetic distances of haplotypes ( Ay ) in each spatial class defined according to geographical distances among geographical locations ( y ) . This analysis is illustrated by a distogram where Ay takes on a value of 0 when all individuals within distance class y are genetically identical , and takes on a value of 1 when all individuals in distance class y are completely dissimilar . Spatial autocorrelation analysis was performed on 10 spatial classes with unequal distance and equal sample size ( approximately 20 observations per class ) in the AIS software [32] . Likewise , to test whether the inter-geographical location structure fits an isolation by distance model , we performed a Mantel test [34] on the pairwise genetic and geographical distance matrices , and the statistical significance ( p<0 . 001 ) was assessed by a permutation test of 1 , 000 replicates . The demographic history of the Colombian T . dimidiata was investigated by comparison of mismatch distributions of pairwise nucleotide differences under an expected constant and fluctuating population size in 10 , 000 generations of coalescent simulations using DNaSP v . 5 [29] . The distribution of mismatch pairwise nucleotide differences was obtained for ND4 haplotypes , as well as combined with CO1 haplotypes ( n = 86; sequence size , 1 , 016 bp ) , and for partial ITS-2 sequences assuming free recombination . Parameters for a sudden demographic expansion were estimated using the sum of squares deviation ( SSD ) [35] and Harpending's raggedness index ( Rag ) [36] implemented in Arlequin v 3 . 1 [37] . Tests for neutrality were also assessed for access to the demographic history . Fu's Fs [38] and the Ramos-Onsins and Rozas R2 [39] statistics for detecting population growth were estimated under coalescent simulations with 10 , 000 generations using DNAsp v . 5 software [29] . To visualize the effective breeding population size ( Ne ) fluctuation over time , a Bayesian skyline plot ( BSP ) analysis [40] was performed as implemented in the BEAST 1 . 6 package and Tracer v1 . 5 . 1 [41] . The starting trees were tested initially for Colombian T . dimidiata using ND4 haplotypes , and combined ND4 and CO1 genes , and then tested for additional haplotype sequences of Central American and Mexican conspecifics for the ND4 ( n = 39 ) gene and ITS-2 ( n = 66; Table S1 ) . Trees were obtained using the maximum likelihood ( under GTR+G ) substitution model after checking according to the Akaike criterion [42] implemented in the jModelTest software [43] with an uncorrelated lognormal relaxed molecular clock assuming one generation per year , as reported for Colombian T . dimidiata individuals [44] . Two separate runs of BMCMC were performed , and a simulated population size ( ESS ) greater than 200 was obtained using a chain length of 10×106 , assuming 10 stepwise control points as the number of coalescent groups . The complete data set of ITS-2 ( n = 83 ) and ND4 ( n = 194 ) haplotypes including Central American and Mexican conspecifics was used to perform a Bayesian inference ( BI ) Markov Chain Monte Carlo ( BMCMC ) approach as implemented in the BEAST v . 1 . 6 . 1 package [41] . The topologies were inferred from the GTR+G substitution model and the model parameters ( base frequencies , transition/transversion ratio , rate variation shape parameter ) were derived empirically . Metropolis coupling was used with two chains per analysis . BMCMC was run for 10×106 generations , with a sampling frequency of 1 , 000 . Two independent trees were combined using LogCombiner v . 1 . 6 . 1 , and convergence of parameters in the Bayesian analyses was assessed with Tracer v . 1 . 5 , after discarding a 10% burn-in . Finally , a majority rule consensus tree ( >0 . 75 posterior probability node support ) was calculated from all trees sampled using TreeAnnotator v . 1 . 6 . 1 in the BEAST v . 1 . 6 . 1 package [41] . Additionally , a maximum likelihood ( ML ) tree was estimated using the GTRCAT approximation of substitution model , and the best knowledge likelihood tree ( BKLT ) was selected via bootstrapping ( 10 , 000 replicates ) in RAxML-VI-HPC v . 2 . 2 . 3 [45] . ML tree nodes showing bootstrap support of more than 75% were considered as well supported . Topologies were edited with the FigTree v . 1 . 3 . 1 software ( http://tree . bio . ed . ac . uk ) . The overall topological match score and a well-supported node match score between IB and ML topologies for both ND4 and ITS-2 markers were calculated using Compare2Trees software [46] . All nucleotide sequences are available with GenBank accession codes for ND4: KC489309–KC489463 and ITS-2: KC489292–KC489308 .
The ND4 gene analysis at both haplotype and nucleotide diversity levels showed a statistically significant differentiation index among the three eco-geographical groups ( Kst = 0 . 235; p<0 . 001 and Hst = 0 . 0166; p<0 . 001 ) . Moreover , the ITS-2 marker indicated low variability , mostly in the SNSM region ( Table 2 ) . From this result , we consider that ND4 offers a better resolution for exploring the spatial structure of Colombian T . dimidiata populations , and therefore ITS-2 was excluded from these analyses . Thus the overall Fst value ( 0 . 482; p<0 . 05 ) using ND4 indicates high genetic differentiation among eco-geographical groups . Pairwise Fst was 0 . 592 ( p<0 . 05 ) between CP and SNSM , 0 . 588 ( p<0 . 05 ) between IAV and CP , and 0 . 512 ( p<0 . 05 ) between IAV and SNSM . A large number of unique ND4 haplotypes ( h = 155 ) , which are likely to be rare or recent in a population , were widely distributed in the haplotype network according to the three eco-geographical groups suggested ( Figure 2A; [10] ) , whereas for partial ITS-2 haplotypes ( h = 17; Table S2 ) a clear star-shaped network with no differentiation among haplotypes from a particular eco-geographical region was observed ( Figure 2B ) . Despite large numbers of hypothetical haplotypes ( median vectors that suggest both unsampled or extinct haplotypes ) observed in the ND4 network , those haplotypes from sampled sites of each eco-geographical region were closely related in almost all cases ( Figure 2A and Table 1 ) . Out of 155 ND4 haplotypes found ( Table S3 ) , only 10 were apparently more closely grouped with an unassigned eco-geographical region than indicated , suggesting possible gene flow among regions or retained haplotypes of an ancient origin ( Table 1 and Figure 1 ) . Spatial analysis of molecular variance performed to assess the substructure within T . dimidiata indicated a significant maximized Fct at k = 3 ( 0 . 485; p<0 . 001 ) , supporting the eco-geographical structure previously evidenced . Collection sites from the Boyacá , Santander , and Huila departments comprised the first group ( congruent with the IAV region ) , sites from Bolívar , Antioquia , Norte de Santander , Córdoba , and Cesar formed the second group ( congruent with the CP region ) , and Magdalena and La Guajira sites formed the third ( the SNSM region ) . Genetic landscape shape interpolation analysis showed that the spatial distribution of haplotype diversity across Colombia was not uniform , as indicated by the presence of peaks and valleys ( Figure 3 ) . The lowest pairwise genetic distances in T . dimidiata geographical locations were detected across the CP region , and the highest in both IAV and SNSM ( Figure 3 ) . Spatial autocorrelation analysis showed a low and nonsignificant autocorrelation value for the full data set ( V = 0 . 01; p>0 . 05 ) , suggesting that nonsignificant clustering of the haplotypes ( based on pairwise genetic distance ) within each of the 10 spatial classes tested could be inferred ( for instance , genetic distances between haplotypes from a spatial class 100 m apart is not significantly lower than estimated between haplotypes ∼425 m apart , as expected under an isolation-by-distance model , see Figure 4A ) . The bimodal shape observed in the distogram intersect the mean genetic distance value ( 0 . 032 ) around the central geographical distances of the classes , and showed a significant autocorrelation when spatial classes ranking between ∼525 m and ∼625 m ( Figure 4A ) . This results indicates that pairwise genetic differences can be higher than average at both shorter and longer geographical distances , and lower at moderate geographical distances ( Figure 4A ) . Furthermore , genetic distances between haplotypes separated about ∼525 m are significantly lower than those haplotypes separated by ∼625 m ( Figure 4A ) . These results could reflect the geographical range of gene flow among the three eco-geographical population groups described , although further specific analyses of intra- and inter-regional gene flow must be performed . Spatial autocorrelation analysis results were congruent with those of Mantel's test , in which the correlation index between genetic and geographical distances among individuals of the geographical locations analyzed was not significant ( r = 0 . 098; p>0 . 05 ) , indicating no isolation-by-distance model fit that could explain the geographical structure of T . dimidiata in Colombia ( Figure 4B ) . The mismatch distribution of ND4 , ND4+CO1 , and of partial ITS-2 rDNA were fit to expect a mismatch distribution with a fluctuating population size ( Figure 5 ) . The goodness of fit of the mismatch distribution between the observed and expected results – under population expansion – was identified ( ND4: Rag = 0 . 003 , p>0 . 05; SSD = 0 . 003 , p>0 . 05; ND4+CO1: Rag = 0 . 001 , p>0 . 05; SSD = 0 . 003 , p>0 . 05; and ITS-2: Rag = 0 . 019 , p>0 . 05; SSD = 0 . 002 , p>0 . 05 ) . Thus , the possibility that the expansion model fits cannot be rejected . In addition , significant values for Fs and R2 in the neutrality tests used for detecting population expansion were found in the three sequence data sets , indicating clear signals of population growth in T . dimidiata in Colombia ( Table 3 ) . The Bayesian skyline plot ( BSP ) of ND4 , and combined ND4 and CO1 genes , indicated that T . dimidiata in Colombia seems to have gone through an effective population increase ranging from 1 to ∼4 mya before the present ( Figure 6A and 6B ) . Moreover , the BSP for the ND4 gene including Central American and Mexican isolates supported the suggested population increase approximately 2 to 3 mya before the present ( Figures 6C ) , and the BSP for partial ITS-2 including Central American and Mexican isolates indicated that the population increase was approximately 1 . 5 mya ( Figure 6D ) . The overall topological match score between IB and ML approaches was moderate for both ND4 ( 65% ) and ITS-2 ( 49 . 6% ) , but high match node scores ( >75% ) were obtained between them for the well-supported clades observed using both markers ( Figure 7 ) . This result indicates incomplete congruence of phylogenies was obtained by the IB and ML approaches using both ND4 and ITS-2 markers , but high congruence of the monophyletic clades comprising the T . dimidiata genetic groups [13] , [14] . The topologies built using the partial ITS-2 showed three main clades ( Figure 7A ) , congruent with the previously reported monophyletic clades using a complete ITS-2 sequence , termed groups 1 , 2 , and 3 [13] . Within group 1 , a low node match score between the IB and ML approaches as well as low node support ( posterior probability <0 . 7 and bootstrap values <75% , data not shown ) was observed between Colombian and most Central American haplotypes , which were previously suggested as subgroups 1A and 1B , respectively [13] . This indicates that an inconclusive monophyletic status can be assigned to Colombian and Central American isolates . Moreover , in the ND4 phylogeny four well-supported monophyletic clades showing a high match node score ( >95% ) were found ( Figure 7B ) . The four clades included haplotypes belonging to the suggested groups III , II , and I [14] plus a secondary clade within group I harboring haplotypes of the Colombian IAV region ( Figure 7B ) .
The results reported herein on the genetic structure among the three eco-geographical regions IAV , CP , and SNSM are in agreement with previous studies discussing several epidemiological considerations [9] , but they also provide additional information on the spatial picture of this genetic diversity and its possible origin . Pairwise comparison between population groups at both the haplotype ( Hst ) and nucleotide ( Kst ) diversity levels indicates that the differential distribution of genetic variability is placed among the IAV , CP , and SNSM regions . Additionally , the results of spatial genetic structure analyses ( Fct , genetic landscape shape , spatial autocorrelation , and the Mantel test ) make at least two main conclusions possible: ( i ) there is a heterogenic geographical distribution of genetic diversity and ( ii ) none of the correlations among genetic divergence and geographical distance explains the geographical group structure of a population . Spatial interpolation of genetic diversity in Colombia shows a bimodal curve-shaped distribution with SNSM and IAV having higher diversities than the CP region , indicating that several microevolutionary processes have been involved in the genetic diversity divergence among population regions , where possible disruptive segregation of ancestral populations of T . dimidiata could be one of many probable causes . An additional observation can be made about this distribution . While T . dimidiata populations in SNSM and IAV occupy mostly diverse mountain and premountain ecosystems located around 1 , 000 masl , CP is for the most part composed of extended lowlands with dry and warm zones at lower altitudes , given that the departments of the CP region extend from the Colombia–Panama border to the foothills of the west mountain range of the northern Andes . The current eco-geographical structure of Colombian T . dimidiata cannot be understood separately from its history . Unlike the previous hypothesis on the possible isolation of population groups due to geographical distance explaining the genetic differences identified [10] , the present results further suggest that the spatial genetic structure in Colombian populations could be the consequence of a recent ( after ∼4 mya ) sudden increase in population size and range occurring after the Great American Interchange ( >4 . 5 mya ) , contemporary with the rapid uplift of the Eastern Cordillera of the Andes mountain range in Colombia ( dating between 2 and 5 mya ) [49] , where local adaptations of populations shaped T . dimidiata diversity . With the emergence of the Isthmus of Panama , an important paleozoogeographical event occurred as a product of the Great American Interchange between North America via Central America to South America and vice versa for fauna during the Piacenzian ( or after early Miocene [48] ) age . We suggest that as consequence of this process , Central American populations colonized the Colombia–Panama border regions , inhabiting mostly hollow trees and palm trees , spreading across the Colombian Caribbean Coast Plains , SNSM , and IAV . Afterward , the concordance of ( 1 ) the allopatric separation between the two main population groups ( SNSM-CP and IAV ) and ( 2 ) the uplift of the Eastern Cordillera of the Andes mountains in Colombia as well as the SNSM mountain areas ( dating between 2 and 11 mya ) , dates the beginning of the eco-geographical structure detected in T . dimidiata . We also note that an alternative phylogeographical hypothesis indicating a northern South American origin could be postulated for the Colombian IAV region T . dimidiata populations based on the high genetic diversity . Although Ecuadorian populations are considered to have been passively introduced from Nicaragua [13] , [14] , southern and central IAV Colombian sylvatic specimens inhabit diverse ecotopes ( hollow trees and rock piles ) and their high genetic diversity could indicate originating foci for this Colombian population group ( or a sibling species according to Monteiro et al . , 2013 ) in the Central Andean Valleys around Colombia . Although an unquestionably close phylogenetic relationship between T . dimidiata sensu lato and the phyllosoma species complex from Mexico [16] supports the Central America origin of this species , we consider further hypotheses should not be completely disregarded . Moreover , a more extensive phylogeographic picture including Andean Triatoma species such as T . maculata and T . dispar should be explored , and additional studies on other factors such as niche differentiation must be considered . | The Chagas disease vector Triatoma dimidiata is one of the most important vectors in America , owing to its wide genetic and epidemiological heterogeneity . Colombian T . dimidiata populations occupy eclectic sylvatic ecotopes , but have also been found in dwellings infected with Trypanosoma cruzi , and therefore it is considered ( along with Rhodnius prolixus ) the most important vector in several departments . The current study explores the population structure history of Colombian populations by means of a molecular coalescence approach . The results indicate that the historical population processes in T . dimidiata in Colombia are in accordance with population expansions that may have been accomplished by two important biotic and orographic events such as the Great American Interchange and the uplift of the eastern range of the Andes Mountains in central Colombia . The genetic history as well as the heterogeneity of the populations could be reflected in different responses of the populations to vector control interventions; thus , a local level of entomological vigilance should be implemented to evaluate the intervention results in each region . | [
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] | 2014 | Molecular Evidence of Demographic Expansion of the Chagas Disease Vector Triatoma dimidiata (Hemiptera, Reduviidae, Triatominae) in Colombia |
The plasticity of the human nervous system allows us to acquire an open-ended repository of sensorimotor skills in adulthood , such as the mastery of tools , musical instruments or sports . How novel sensorimotor skills are learned from scratch is yet largely unknown . In particular , the so-called inverse mapping from goal states to motor states is underdetermined because a goal can often be achieved by many different movements ( motor redundancy ) . How humans learn to resolve motor redundancy and by which principles they explore high-dimensional motor spaces has hardly been investigated . To study this question , we trained human participants in an unfamiliar and redundant visually-guided manual control task . We qualitatively compare the experimental results with simulation results from a population of artificial agents that learned the same task by Goal Babbling , which is an inverse-model learning approach for robotics . In Goal Babbling , goal-related feedback guides motor exploration and thereby enables robots to learn an inverse model directly from scratch , without having to learn a forward model first . In the human experiment , we tested whether different initial conditions ( starting positions of the hand ) influence the acquisition of motor synergies , which we identified by Principal Component Analysis in the motor space . The results show that the human participants’ solutions are spatially biased towards the different starting positions in motor space and are marked by a gradual co-learning of synergies and task success , similar to the dynamics of motor learning by Goal Babbling . However , there are also differences between human learning and the Goal Babbling simulations , as humans tend to predominantly use Degrees of Freedom that do not have a large effect on the hand position , whereas in Goal Babbling , Degrees of Freedom with a large effect on hand position are used predominantly . We conclude that humans use goal-related feedback to constrain motor exploration and resolve motor redundancy when learning a new sensorimotor mapping , but in a manner that differs from the current implementation of Goal Babbling due to different constraints on motor exploration .
The paradigmatic example of motor acquisition is infant motor development . Humans are born without a complete innate knowledge of how their bodies are configured . They lack motor coordination and have to learn how to control their hands , heads , feet , etc . , by exploratory activity . It has been observed that infants undergo a phase in their development during which they seem to perform random movements [10 , 11] . Piaget’s view was that motor development is organized in distinct phases and that this presumed random movement phase might serve to learn an inverse kinematic model of the body . That is , he suggested that during this random movement learning phase , infants do not perform purposeful actions . According to this hypothesis , only in a second phase , when the inverse model has been learned , infants start performing goal-directed action [10] . This idea has inspired learning algorithms in Cognitive and Developmental Robotics . From a computational perspective , the problem of inverse motor model learning can be seen as a search problem , i . e . , the search for a control policy or inverse model that links the behavioural goals ( states in goal space ) to available actions ( states in motor space ) . So-called Motor Babbling algorithms [e . g . , 12] solve this problem in a very straightforward , brute-force manner inspired by the traditional view on infant development described above: In an initial ‘infant’ stage , robots perform random actions ( Motor Babbling ) to uniformly and exhaustively sample the motor space . They observe and memorize the consequences of the action to derive a generalized rule for action-outcome mappings ( forward model ) . This model can then , in a second phase , be inverted ( at least locally ) for control . The resulting inverse model is used to perform goal-directed actions in the ‘adult’ robot . Motor Babbling algorithms have also been proposed as generative models of infant motor acquisition [12 , 13] , in line with Piaget’s stage-wise view , which furthermore is compatible with the traditional view on computational inverse model learning , i . e . , that a feedforward model has to exist first to acquire an inverse model second [14 , 15] . However , in human development , Piaget’s view that motor learning is prevalently driven by random exploration has been criticized [11] in view of empirical evidence that infants perform goal-directed action right from the outset of motor learning [11 , 16] , which results in complicated learning dynamics , as it has been shown in elaborate experiments [17] . Furthermore , it has been observed that the first successful reaching actions in infants appear to be executed fully feedforward [18 , 19] , which would require the existence of an inverse model to start with . These results speak against the traditional view of a stage-wise motor acquisition process with a random exploration phase ( forward model learning ) followed by a phase of inverse model generation and purposeful action , and in favour of a direct acquisition of the inverse model while behaving . Goal Babbling is a computational approach inspired by this latter idea that infant motor acquisition might be goal-directed right from the start . It has recently been proposed by Rolf , Steil & Gienger [8 , 9] as a model for learning of an inverse model while behaving ( direct inverse model learning ) . Thereby , the exhaustive random sampling phase characteristic of Motor Babbling can be avoided and there is no need to distinguish explicit data collection and exploitation phases anymore . Related goal directed exploration schemes for motor learning that share the idea to guide exploration by goal-related feedback have also been proposed by others [20] . In Goal Babbling , a robot’s exploration of the motor space is not just random . Instead , it is constrained and guided by goals . A starting position ( home posture ) seeds the search for a successful policy . The expansion of the motor repertoire around the home posture occurs through random perturbations that are reinforced in the direction that is most associated with behavioural success: Goal-directed feedback ( reinforcement ) favours the future use of actions that are associated with a reduction in a goal-related error signal . A robot thereby locally expands its action repertoire around an initially minimal inverse model control policy that maps all goals onto a single motor command , which keeps the robot resting at the home posture . Goal-irrelevant parts of the motor space or parts that contain solutions that are redundant to the initially learned one are thus never explored ( see Method Sect Goal Babbling Simulations and [8 , 9] for details of the Goal Babbling algorithm ) . In a reinforcement learning context , Goal Babbling for inverse model learning can be seen as a one-step reinforcement learning problem , where distance to the goal can be reinterpreted as a reward [21] , but for a very large number of goals , which distinguishes inverse model learning from standard reinforcement learning problems . Standard episodic reinforcement learning algorithms , for instance , aim at learning a temporal control policy to solve a particular defined task or task sequence with a potentially distant reward . Many successful computational algorithms for this type of problem have been proposed in recent years [22] . They are heavily applied in robotics and have been linked to human reinforcement learning . However , these reinforcement learning algorithms , though related to Goal Babbling , are not easily applicable to the problem of direct inverse model learning from scratch discussed here . There is ample evidence [5] that ( adult ) human motor control relies on both forward and inverse models and the question how inverse models are learned is an open research question . Other computational models for learning inverse models online have been proposed recently , but again require the prior existence of a working feedback controller ( see the review in [23] ) . In all these approaches , additional measures have to be taken to deal with redundancy in the action-goal mapping . In summary , neither standard theories of model learning nor ( episodic ) reinforcement tackles the problem of acquiring an inverse model for a novel sensorimotor mapping in a redundant domain that we focus on here . In the robotics domain , Goal Babbling is particularly useful for platforms that , like humans , have many and redundant Degrees of Freedom ( DoFs ) with non-linear dependencies between them [24] . Other inverse model learning approaches like Motor Babbling struggle to learn the inverse models for such platforms . Goal Babbling as a model for infant motor skill acquisition [25] has already been shown to reproduce the dynamics of the so-called U-shaped sequence of disappearance and reappearance of a skill for pre-reaching to balls presented to infants of early age [17] . Here , we study Goal Babbling as a computational model for adult human motor learning . The experiment we present here investigates whether learning dynamics similar to Goal Babbling might occur when humans learn an unfamiliar and redundant sensorimotor mapping from scratch: It tests for Goal Babbling-like motor exploration ( i . e . , local goal-directed expansion of the action repertoire , no distinction between data collection and exploitation phases ) in a motor skill acquisition task with adult volunteers ( see Introduction Sects Motor Skill Acquisition in Human Adults and Do adult humans use error feedback to guide motor skill acquisition ? ) . Human adults who have already mastered control over their bodies can also learn new high-dimensional and redundant motor tasks , when interacting with objects such as musical instruments , tools , toys , or sports equipment . This kind of motor skill acquisition differs from motor development in the sense that it involves adding a new layer on top of the already familiar control of one’s body . However , the requirements posed on the neural mechanisms of learning are similar: The interaction with the object brings about sensory effects in response to motor actions , such as sounds when playing instruments , forces when operating tools , or visual effects when playing computer games . The rules that connect sensory effects with movements are initially unfamiliar . To master control of the interaction , humans have to explore the oftentimes high-dimensional and non-linear mapping from states in motor space to states in goal space and to constrain and memorize the non-unique inverse mapping from goal states to motor states , much like during infant motor development . We here test whether Goal Babbling simulations can qualitatively predict the dynamics and outcome of motor skill acquisition in adult humans , which would suggest that humans might use similar principles for motor exploration and inverse model learning . It has been suggested that motor skill acquisition involves the formation of Motor Synergies , i . e . , of patterns of DoF co-activation that can serve as motor primitives and reduce the dimensionality of a motor control problem [7 , 26] . In many tasks that involve redundant DoFs , motor behaviour has been shown to be reducible to the linear combination of just two or three spatio-temporal movement components ( synergies ) , for instance , kicking behaviour in frogs [27] , human object grasping [28] or even full body walking and reaching [29] . The question of redundancy resolution can therefore also be understood as a question of synergy formation . We here use this approach , that is , we measure motor organization and dimensionality reduction by synergy formation , which we define as principal components in the motor space ( see Method Sect Data Analysis ) . This approach has also been previously used to describe redundancy resolution in robotic Goal Babbling [30] . The focus of our work is on human adult learning of a new , redundant inverse sensorimotor mapping from goals to actions . A number of principles have been shown to influence motor learning of redundant motor control problems . For instance , participants adapt behaviour optimally according to simple , goal-related cost functions [2 , 31] as well as to achieve dynamic stability of control [32 , 33] . Here , we were interested primarily in principles of motor exploration and whether they follow more the traditional view ( which we here call “Motor Babbling” ) , where a random exploration phase is followed by a goal-directed action phase , or the integrated process view that underlies the Goal Babbling approach . Do humans use goal-related feedback right from the start to guide the exploration of a motor space and the continuous formation of synergies as in Goal Babbling ? In particular , we were interested in testing the following predictions from Goal Babbling: In summary , Motor Babbling predicts distinguishable phases of exploration and goal-directed behaviour as well as learning outcomes that do not depend on the condition ( home posture ) . Goal Babbling predicts an integrated exploration , learning and goal-directed action process , where task-related feedback for action guides and biases the learning process and outcome for inverse model learning . To test these predictions , we trained human participants in an unfamiliar and redundant sensorimotor control task ( Fig 1 and Method Sects Task and Experimental Setup and Procedure ) . We also qualitatively compared the results to those of a population of simulated agents that were trained on the same task using the Goal Babbling learning approach ( Method Sects Goal Babbling Simulations ) , to illustrate the Goal Babbling predictions more concretely . For these agents , the task was to set the joint angles of a three DoF planar arm ( Fig 1A ) to reach to targets in a quarter segment of the circular reach space ( Fig 1B , bottom , and Method Sect Task ) . Human participants were trained in the same task but the input variables were presented in an unfamiliar transformation to avoid that participants might rely on prior knowledge , e . g . , about physics or joint kinematics , which can facilitate motor learning [34] . Participants therefore controlled the joint angles of the task by the elevation of the left index , right index , and right middle fingers , unaware that this was internally interpreted as joint angle settings ( Method Sect Experimental Setup and Procedure ) . Moreover , the positions of targets and reach-endpoints in goal space were presented as static , deforming ellipses ( Method Sect Experimental Setup and Procedure ) to avoid that the solution learned is constrained by principles of spatial processing , as it is the case in human motor skill acquisition of spatial control tasks . For instance , Mosier et al . [35] have shown that participants tend to learn solutions that generate rectilinear movements of a cursor in visual space . Sailer et al . [36] have investigated hand-eye coordination in a similar task and have found that with increasing mastery of the task , participants first learned to perform predictive eye movements and later saccades to a target , which demonstrates remapping not only between hand movements and cursor movements but also alignment with oculomotor control . By presenting the targets and reach-endpoints as static , deforming ellipses , we aimed at reducing such biases inherent to spatial tasks . In summary , the mapping from finger movements to elliptic shapes was , mathematically speaking , that of reaching with a three DoF planar arm ( Fig 1 ) . However , participants experienced this as deforming ellipses with up-down finger movements ( Method Sect Experimental Setup and Procedure ) . The task we chose is redundant: There are infinitely many DoF configurations to reach each target . Especially targets that are close to the origin of the goal space can be reached from many parts of the motor space ( Fig 1B , bottom ) . Note that the degree of redundancy , here measured as number of different configurations to reach a particular target in a discretized version of the task is non-homogeneous and strongly varying across the target space ( Fig 1B , bottom , colour coding ) . We analysed redundancy resolution by performing Principal Component Analysis ( PCA ) on the reach-endpoints in motor space in a test phase with no feedback . Thereby , we could identify DoF co-activation patterns ( synergies ) in the learned inverse mappings from goal states to motor states that can reduce the dimensionality of a complex , redundant control problem and are a sign of motor organization ( Method Sect Data Analysis ) . We compared the learning dynamics and outcome starting from two different initial positions ( home postures H1 and H2; Fig 1B , top ) . These home postures reach to the same target in goal space but are located in different parts of the motor space ( Fig 1B , top ) . In particular , in H1 the reach-endpoint is located on top of the q2 DoF , which modulates the effectiveness of this joint . The different home postures predict substantial differences in learning dynamics and outcome for predictions P2 and P3 . If human learning is sensitive to the home posture in the ways predicted , this would indicate that also humans use local , goal-related feedback to structure exploration behaviour right from the start . The Goal Babbling simulations results are presented alongside the results from the human experiment in Sect Results for illustrative purposes .
Participants learned to perform the task in both conditions within the time allocated ( 70 minutes ) . They acquired an inverse model of the trained mapping over the course of the 24 training blocks , which allowed them to move the reach-endpoints on average closer to the target than at the beginning of training and above chance in the test blocks without feedback ( Fig 2B ) . A two-way repeated-measures ANOVA ( rmANOVA ) on the average distance to the targets ( error ) with factors Time ( block number ) and Condition ( H1 , H2 ) shows a main effect of the factor Time ( F ( 23 , 940 ) = 10 . 9 , p≪0 . 001 ) , confirming that participants learned across blocks . There are neither a significant main effect of the factor Condition ( F ( 1 , 959 ) = 0 . 01 , p = 0 . 944 ) nor an interaction between the factors Condition and Time ( F ( 23 , 959 ) = 0 . 5 , p = 0 . 507; full ANOVA table in S1 Table ) . We had hypothesised that participants might learn faster starting in home posture H2 , as it was the case for the Artificial agents ( Fig 2A ) . For the agents , the extended q3 joint in H2 ( Fig 1B ) made it easier to expand the goal space ( cf . Results Sect Relative use of DoFs in synergies ) , so even after one block of learning , the reach error was much lower than in condition H1 . However , there was no such condition-dependent difference in learning speed in the human population ( Fig 2B and 2C ) . Moreover , the human participants’ performance error remained at a higher level than in the Goal Babbling simulations ( Fig 2A ) . Also , the test performance error ( Fig 2B ) converged at a higher value earlier than the training error ( Fig 2C ) . This was likely due to proprioceptive and motor noise when executing the intended actions . More detailed results about reach performance during test can be found in S1 Fig ( individual learning curves for participants ) , S2 Fig and S3 Fig ( Distribution of reach-endpoints for individual test targets in agents ( S2 Fig ) and participants ( S3 Fig ) ) and in S4 Fig ( performance across time for different targets ) . Motor skill acquisition in human adults involves adding a new layer of motor control on top of the already existing control of the body in space . In the current experiment , existing motor synergies for finger movements and biomechanical constraints of the hand likely had an effect on motor skill acquisition . To decrease these effects , we chose to randomize the assignment of fingers to task DoFs and counter-balanced this assignment across conditions . To get an idea of the strength of such biasing effects of human morphology , we performed two additional PCAs on the reach-endpoints pooled across participants , to compare the amount of motor organization in the biological morphology motor space with the amount of motor organization in the task motor space . That is , in a first PCA , we looked for motor synergies for physical finger control ( human morphology ) and in a second PCA , we looked for motor synergies for task DoF control ( q1 , q2 and q3 ) . More variance explained by the first synergies ( PCs ) indicates more organization of motor control . Fig 8 shows the variance explained by PC1 , PC2 and PC3 in both analyses . In all test blocks , the first two synergies of finger movement ( PC1 and PC2 , human morphology ) can explain approximately 80% of the overall variance throughout the experiment ( Fig 8 , left ) . This confirms that there was above-chance motor organization in finger control , i . e . , participants used patterns of finger co-activation . Thus , human morphology influenced motor organization throughout the experiment . In the task motor space , the variance explained by the first two synergies ( PC1 and PC2 ) started at a similar level but then increased across the learning phase ( Fig 8 , right ) , to the point that the first two synergies explain 90% of the variance at the end of the experiment . The learned motor organization in task motor space is stronger than the organization in the previously existing morphological motor space .
We here investigated whether humans use goal-related feedback to explore an unfamiliar , redundant , and non-linear visuomotor mapping and whether they use such feedback to constrain the inverse model from goal states to motor states right from the start of exposure . Little is known to date about the dynamics of inverse model learning in human motor control and in robotics . To this end , we tested whether initial state ( home posture ) influences human behaviour in ways predicted by the Goal Babbling learning rule , an inverse model learning rule for robotics that works even for high-dimensional robotic platforms . We observed that learned solutions rely on the use of motor synergies from the first block onwards that keep being refined throughout the experiment ( prediction P1 , Results Sect Motor Synergy Formation ) and that the solutions are biased to remain close to the training home posture in motor space ( prediction P2 , Results Sect Location of solutions ) . There is also a trend for the home posture to bias exploratory behaviour ( prediction P3 , Results Sect Relative use of DoFs in synergies ) : DoFs that are locally less effective appear to be initially favoured , which suggests that goal-related feedback structures exploration behaviour right from the start , but in different ways than predicted . Altogether , these results show that motor exploration is guided by goal-related local feedback throughout the experiment , as is the case in the Goal Babbling learning rule , not random in an initial exploration phase and structured in a later goal-oriented action phase , as Motor Babbling models assume [12 , 13] . Despite these similarities between human behaviour and Goal Babbling simulations , there are also important qualitative differences between simulated agents and human participants . In the Goal Babbling simulations , small variations around the home posture cause a gradual expansion of the motor repertoire away from the home posture into the direction of behavioural success . This gradual expansion is strongly structured by goal-related feedback . By contrast , the human participants in our study initially explored the space by performing large ( Fig 7 ) and less structured ( Fig 3 ) movements , that is , their exploration was more random than in the agent population . It is important to note that , though more random than in the agent , exploration was still more structured than would be expected of random Motor Babbling: even after one block of experimentation ( 80 s ) , movements were already organized in synergies ( P1 ) and were confined in motor space ( P2 ) . There is no evidence for a transition from a random exploration phase to a structured behaviour phase , as predicted by Motor Babbling models , even though performance was still poor and learning continued for another 70 minutes . Therefore , the randomness in human behaviour can be better described as exploratory noise with a large magnitude than as Motor Babbling . It is however theoretically possible that Motor Babbling behaviour might have occurred within the first block ( 80s ) ; our experiment was not designed to identify learning processes that might take place in a sub-minute time scale . Even if this were the case , learning that was guided by goal-directed feedback was clearly the dominant process over the course of the experiment . This experiment shows that direct inverse model learning can happen also when adult humans learn new unfamiliar and redundant sensorimotor mappings . From this result , we cannot rule out that humans might learn new sensorimotor mappings in a more phase-wise manner in different scenarios . We also did not explicitly seek a comparison with other theories or computational models of motor acquisition that have no predictions for inverse model learning . For instance , some of our results might also be explained by a nearest-neighbour reinforcement learning strategy , though the continuous nature of the task and the fact that participants and robots comfortably generalize also to targets outside the learned range of targets ( See S4 Fig ) speaks against that possibility . There are still a lot of open questions about how humans learn new and redundant sensorimotor mappings and we hope that our result and paradigm give researchers a new angle on this problem . One question that remains is why participants had a more random initial exploration strategy . A possible explanation is that they had to dynamically infer a suitable learning rate in a way that the agents did not . That is , the initially large exploratory movements might have served the purpose to estimate a reasonable range of finger movements in this task . The agents in the chosen implementation of Goal Babbling were bound to a fixed , task-specific learning rate that was set by their engineer to be low enough to avoid destabilization of control yet high enough for learning to converge in a reasonable amount of time . The freedom to autonomously derive and adapt the learning rate gives the human participants more autonomy to successfully learn in a larger range of learning scenarios compared to the chosen implementation of Goal Babbling . Attempts to include such active learning behaviour also in robotics have recently been made , for instance , in forward ( Motor Babbling ) exploration [37] and in Goal Babbling [38] , where the motor space can be explored through so-called direction sampling in a larger-scale manner without explicitly setting the goals beforehand . Another possible explanation for the differences in motor exploratory strategy is that the motor task given to the human participants is not in all aspects a typical example of human inverse modelling . For instance , it does not harbour any danger of injury , pain or behavioural breakdown , which makes a conservative learning rate unnecessary and encourages a ‘riskier’ exploratory strategy . In many situations where humans acquire new motor skills and specifically during infant development , large exploratory movements might be more dangerous or injury-prone and a more careful motor exploration might be performed . Independent of their origin , the differences in exploratory strategy might explain why DoF-effectiveness has the opposite effect on humans as it does on simulated agents for motor synergy learning ( prediction P3 , Results Sect Relative use of DoFs in synergies ) . In an effort to stabilize an unstable control system , it would make sense for humans to attenuate DoFs whose movements have drastic sensory consequences and instead rely more on DoFs whose movements are less affected by motor noise . Agents learning an inverse model by Goal Babbling instead are eager to ‘escape’ the narrow range of target reaches they are initially able to perform and learn according to a cost function that minimizes effort ( Method Sect Goal Babbling Simulations ) . Goal Babbling thus favours more effective DoFs and DoFs with more noise [30] ( see also [39] for a similar trend in human behaviour in a different motor learning task ) . Overall , the magnitude of differences between conditions in the human population were small compared to the Goal Babbling simulations . Again , the noisier exploratory strategy is a possible reason for this: If the local search spaces are larger for humans , they are more likely to overlap , especially given that the task used is not really high-dimensional , compared to other tasks used for the study of redundancy resolution in human motor learning [28 , 35 , 40 , 41] . In our task , an exhaustive sampling of the three DoF motor space , as proposed in Motor Babbling , would still be possible . Our rationale for keeping the motor space comparably low-dimensional is that it allowed us to analyse the role of specific DoFs in motor skill acquisition in an intelligible manner that is easy to interpret . Moreover , previous studies on learning unfamiliar sensorimotor mappings used existing motor synergies as building blocks for learning , which we tried to avoid by randomizing the assignment between fingers and task DoFs across participants . Another possible source of differences between agent and human participant behaviour lies in differences in the task . For instance , in the setup , there is the baseline posture q* ( Methods Sect Setup , Fig 7B left ) as an across-condition salient posture . The baseline posture is achieved when participants hold their fingers level on the visual reference line and which is located between the two home postures H1 and H2 in motor space . It is well possible that this baseline posture attracts motor exploration in both conditions . This might have diminished the effect of home posture in the human population , especially as keeping the fingers in the baseline posture also corresponds to a comfortable hand posture for the participants ( fingers kept level in the plane of the hand mount ) . Also , the visual display of finger position feedback throughout the experiment might have emphasised the role of visual feedback in ways not usually encountered in motor skill acquisition . There are many factors that distinguish the experiment with human participants from the agent simulations , so the comparative analysis should not be overstrained . We were mostly interested in a qualitative comparison of the two types of system , whether their learning can be structured into phases of exploration and goal-directed action and how initial state and goal-related feedback influence the solutions learned . Little is known to date about how humans explore unfamiliar motor spaces and how they constrain redundant sensorimotor mappings . Motor Babbling approaches struggle to explain how inverse models might be learned in high-dimensional and redundant systems , where a random and exhaustive exploration phase is not feasible . Goal Babbling exploits goal-related feedback for motor exploration and learning and thus makes inverse model learning in high-dimensional platforms possible . We could show that , in the present experiment , like in Goal Babbling , participants use local goal-related feedback to guide exploratory behaviour and resolve motor redundancy right from the start of the experiment . Even if the exploratory behaviour differs between artificial agents and human participants , who use a larger and less structured initial range of movements , the learning in both cases can be best explained as an integrated process , where goal-oriented action and inverse model learning mutually bootstrap and bias one another , not as a stage-wise process where the sensorimotor mappings are learned first and then put to use in a second , distinct phase of learning and goal-oriented action .
The experiment was approved by the ethics committee of the Bielefeld University and was conducted according to the principles expressed in the Declaration of Helsinki . Informed written consent was obtained from the participants . Both humans and simulated agents were trained in the same planar reaching task with a three DoF planar arm ( Fig 1A ) . The three segments l1 , l2 and l3 of the arm are of length l1 = 0 . 55 , l2 = 0 . 225 and l3 = 0 . 225 ( length in arbitrary spatial units ) . No joint limits were implemented: The joint angles that define a posture of the arm ( q1 , q2 , q3 ) could be set to any value in [0 , 2π [ ( angles in radians ) . Two different home postures H1 and H2 were defined as starting conditions for learning ( Fig 1B; H1 = ( ¾ π , 1 . 99 , π ) , blue; H2 = ( 1 . 51 , 1 . 99 , 0 ) , red ) . These home postures reach to the same point ( -0 . 39 , 0 . 39 ) in goal space but are located in different parts of the motor space ( Fig 1B ) . The home postures are located at the edge of an array of 16 training targets that are distributed in a regular manner ( in polar coordinates ) in a quarter segment of the arm’s reach space ( Fig 1B; distances from origin: [0 . 25 , 0 . 45 , 0 . 65 , 0 . 85]; angles: [¼ π , 5/12 π , 7/12 π , ¾ π] ) . Nine additional targets were used as test targets ( Fig 1B ) ; five within the training array ( polar coordinates: ( 0 . 35 , 2/3 π ) , ( 0 . 75 , 2/3 π ) , ( 0 . 55 , ½ π ) , ( 0 . 35 , 4/3 π ) , ( 0 . 75 , 4/3 π ) ) and four just outside the training array ( polar coordinates: ( 0 . 55 , 1/6 π ) , ( 0 . 15 , ½ π ) , ( 0 . 95 , ½ π ) , ( 0 . 55 , 5/6 π ) ) . A population of 100 simulated agents was trained on the task using the Goal Babbling procedure , which we here describe only briefly . For technical details of the procedure , please refer to [24] or to pages 65–70 of [42] . The goal space and motor space for the simulated agents were identical to those used in the experiment with human participants ( Method Sect Task ) . That is , the forward kinematics are given by ( x , y ) =f ( q ) ( 1 ) where q∈R3 are the joint angles . Goal Babbling aims at learning the inverse model: q=g ( x , y ) ( 2 ) which maps desired outcomes ( hand positions ) to actions ( joint angle configurations ) such that ( x , y ) ≈ f ( g ( x , y ) ) for a set of targets X , Y* in the goal space . Training and test targets were identical to those used in the experiment with human participants ( see visualization in Fig 1 ) . A local linear map with learning rate η = 0 . 2 is used [42 , 43] as learning model for the inverse function . Training starts at the home posture H1 or H2 ( cf . Method Sect Task ) , depending on condition . The agent is trained using the training targets ( see Fig 9C ) . Training involved moving the hand from a given training target x , y* ( tn ) to the next training target x , y* ( tn+1 ) in 25 steps . That is , 24 sub-targets were generated to transition from the current training target x , y* ( tn ) to the next training target x , y* ( tn+1 ) by linear interpolation in the goal space . The agent tried to reach these targets by utilizing the respective current estimate of the inverse function q^ ( t ) . After each step in each trial the estimate of the inverse function is immediately updated ( online learning ) . In each trial , exploratory noise e was added to the outcome of the local linear map estimate of the inverse function q^ ( t ) to generate novel behaviour and gradually expand the explored region of the motor space . The noise e follows a slow and bounded random walk of linear perturbations as defined in [42] , p . 68 ( Equations 6 . 8–6 . 10 with parameter values σ = 0 . 5 , σΔ = 0 . 005 ) . The amount of noise was equal for all joints qi . Implementing exploratory noise as a random walk ensures both continuity of behaviour ( to avoid instability ) and ensures that , over time , previously unknown parts of the motor space are explored . A training block involved the presentation of 40 training targets that were selected at random from the set defined in Method Sect Task . The home posture was chosen as a target with probability 0 . 1 . Considering the 24 sub-targets on the way to the next training target , a total of 1000 parameter updates were thus performed during learning in one block . The agents were trained in ten subsequent blocks . Note that these blocks have no special meaning in the learning algorithm . They are solely distinguished for evaluation purposes , i . e . , to assess the current estimate at regular intervals using the nine test targets that are also shown in Fig 9C . During these test blocks , the learning dynamics were disabled . All simulation results show the average and variance over a population of 100 agents , which differ according to the random exploration scheme ( exploratory noise ) described above . These results are presented merely to qualitatively illustrate the kind of behaviour to be expected from Goal Babbling-like learning in the participants , as it is known from the literature and reflected in the experimental hypotheses . We therefore do not phrase or test any hypotheses about the behaviour of the simulated agents or how it quantitatively compares to the behaviour exhibited by human participants . To avoid that participants use prior knowledge about arm kinematics , space or physics to learn the task , we applied an unintuitive and unfamiliar transformation to the DoFs in motor space ( joint angles q1 , q2 , q3 ) and the feedback about target and reach-endpoint position ( x and y coordinates ) . Participants controlled joint angles qi by elevation of the left index , right index and right middle fingers ( Fig 9A ) . They received visual feedback about reach-endpoint and target as deformation of two static ellipses ( Fig 9A ) . That is , formally speaking , participants were controlling movements of a three DoF planar arm but their experience of the experiment was that of moving the fingers up and down to deform an ellipse . The assignment of fingers to DoFs of the simulated planar arm was randomized across participants and conditions ( see Procedure ) . S1 Video shows a participant performing the task for illustration . The following variables were analysed to assess differences in test performance between the two conditions and across time . In 1 . 4% of the cases , the motion sensor did not register the position of all three fingers . These data points ( 175 in total ) were discarded and treated as missing data in all the analyses below . For most of the variables , we tested for differences using two-way rmANOVAs to test for the effects of the factors Time ( block number or bin-of-block number ) and Condition ( H1 , H2 ) . For the factor Condition , we decided to use the finger mapping as grouping variable , which was counter-balanced across participants ( cf . Procedure ) rather than the participant identity . Especially for synergy learning , finger mapping had a strong biasing effect ( Fig 8 left ) , which seemed more important to consider in the analysis than participant identity ( both was not possible ) . The ANOVA tables are reported as supporting tables S1 Table , S2 Table , S3 Table and S4 Table . Partial eta-squared ( ηP2 ) was used as a measure of statistical effect size as it is established in the literature , even if generalized eta-squared ( ηG2 ) is regarded by some as preferable [46] . | Even in adulthood , humans can learn to master new motor skills with unfamiliar mappings between desired goals or sensations and corresponding movements , such as playing tennis or musical instruments . To master a new skill involves the resolution of motor redundancy; that is a selection from many possible movements that all achieve the same goal . Here , we trained participants in a redundant and unfamiliar task that mapped their hand movements to shapes , in order to investigate which of the many possible redundant solution participants learn . The results show that local task feedback , which depends on the starting posture of the hand , influences participants’ motor learning . We qualitatively compared the experimental results to computer simulations of artificial agents that learned the same task by Goal Babbling , i . e . , a motor learning approach used in robotics , to assess if humans might learn the task following similar principles . Both the simulated agents and the participants show sensitivity to goal-directed feedback during learning , but they use different strategies to explore the movement space . We conclude that human motor learning and redundancy resolution is guided by local goal feedback , like in Goal Babbling , but that differences in motor exploration lead to different learning outcomes . | [
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] | 2019 | Goal-related feedback guides motor exploration and redundancy resolution in human motor skill acquisition |
Different strains and species of the soil phytopathogen Agrobacterium possess the ability to transfer and integrate a segment of DNA ( T-DNA ) into the genome of their eukaryotic hosts , which is mainly mediated by a set of virulence ( vir ) genes located on the bacterial Ti-plasmid that also contains the T-DNA . To date , Agrobacterium is considered to be unique in its capacity to mediate genetic transformation of eukaryotes . However , close homologs of the vir genes are encoded by the p42a plasmid of Rhizobium etli; this microorganism is related to Agrobacterium , but known only as a symbiotic bacterium that forms nitrogen-fixing nodules in several species of beans . Here , we show that R . etli can mediate functional DNA transfer and stable genetic transformation of plant cells , when provided with a plasmid containing a T-DNA segment . Thus , R . etli represents another bacterial species , besides Agrobacterium , that encodes a protein machinery for DNA transfer to eukaryotic cells and their subsequent genetic modification .
The Rhizobiales order contains many species of plant-associated bacteria , such as the related genera Agrobacterium and Rhizobium . Phylogenetic analyses based on 16S rDNA sequences led to the idea that Agrobacterium and Rhizobium could be regrouped into one genus [1] . Yet their lifestyles are very different . Agrobacterium comprises species that are often , but not always [2 , 3] , pathogenic and can genetically transform their host plant cells by transferring a segment of their own plasmid , the T-DNA , and induce neoplastic growths that synthesize small molecules used as nutrients by the bacteria [4–6] . This Agrobacterium capability to modify genetically their host cells is widely used in research and biotechnology for generating transgenic plants [7] as well as fungi [8] . In contrast , Rhizobium belongs to a group of very diverse symbiotic bacteria ( collectively termed rhizobia ) that form nitrogen-fixing nodules on the roots of legume plants [9–12] . Rhizobium and Agrobacterium species have complex genomes composed of one or two chromosomes and several plasmids [13–16]; the chromosomes are designed as “core” components defining the species as opposed to the “accessory” components that are the plasmids [17] . The outcome of interactions of these bacteria with plants is essentially determined by large specialized plasmids , the tumor inducing ( Ti ) plasmid for Agrobacterium , and symbiotic ( pSym ) plasmid for Rhizobium . Indeed , introducing an Agrobacterium Ti plasmid into some rhizobia species resulted in virulent bacteria capable of inducing tumors in host plants [18] . In general , rhizobia species are known to gain T-DNA transfer ability only when provided with the virulence ( vir ) genes [4 , 5] of the Agrobacterium Ti plasmid [19 , 20] . Rhizobium , therefore , is thought to possess chromosomal , but not plasmid-based factors required for plant genetic transformation , and because of that lack endogenous DNA transfer capacity . Intriguingly , however , many Rhizobium species harbor different sets of homologs of the Agrobacterium vir genes; specifically , R . etli carries a complete set of vir genes [15 , 21] whereas the closely related R . leguminosarum lacks such degree of homology . Here , we show that R . etli can independently mediate functional DNA transfer and stable genetic transformation of plant cells , when provided with a plasmid containing a T-DNA segment . Thus , R . etli represents another bacterial species , in addition to Agrobacterium , capable of genetic modification of plants .
Sequencing of the R . etli CFN42 genome revealed that it encodes a complete set of virulence ( Vir ) proteins encoded by the vir genes [15 , 22] . Indeed , Fig 1A shows that all the essential Vir proteins encoded by the p42a plasmid of R . etli exhibit a high level of homology with their counterparts from different Agrobacterium Ti plasmids , except for the VirD3 and VirD5 proteins , which are non-essential for DNA transfer . Phylogenetic analysis demonstrated that the Vir proteins of R . etli and Agrobacterium are very close to each other , as exemplified for VirE2 ( Fig 1B ) . In contrast , the putative Vir protein orthologs of R . leguminosarum only share a relatively weak homology , i . e . , usually less than 40% identity , with Agrobacterium . Fig 1C shows that , within the p42a plasmid of R . etli , the vir genes are grouped in a cluster , forming a virulence region that is similar in many ways with the vir region of Agrobacterium Ti-plasmids , but it also displays some notable differences . Specifically , the organization of the “core” of the vir region—the virA , virB , virG , virC , virD , and virE operons—is nearly identical , but the order of the virD and virE operons is inverted in R . etli . In addition , in R . etli , the virB2 coding sequence is not part of the virB operon , but is located at a distant locus on the same plasmid , and two virF homologs are present , virF1 and virF2 , which are related to the virF genes from octopine ( A6 ) and nopaline ( C58 ) Agrobacterium strains , respectively . The presence of many transposase insertion sequences in the vicinity of the vir cluster of R . etli [15] may explain the rearrangements in the organization of the vir region . In R . leguminosarum , the organization of the vir region located on the pRL7 plasmid appears to be scrambled , with several operons having been duplicated ( see the pRL7 map in the KEGG database , http://www . genome . jp/kegg/ ) . Although other Rhizobium species , such as R . mesoamericanum and R . tropici , contain homologs of several vir genes ( S1 Table ) , a high level of homology with all essential vir genes is found only in R . etli . Whereas a complete vir region is present in the R . etli p42a plasmid which is homologous to the Agrobacterium vir genes , we could not detect homologies to any of the Agrobacterium T-DNA sequences; specifically , our search for T-DNA-specific oncogenes and opine synthesis genes and for the T-DNA border sequences did not yield significant homology . To examine potential functionality of the vir genes of R . etli , we introduced into R . etli cells a plasmid that harbors a T-DNA sequence with reporter genes gfp or gus-int , and selection gene nptII but lacks any vir sequences . This strain was then tested for its ability to promote transient T-DNA expression in plant cells and generate stably transformed transgenic plants , and compared to A . tumefaciens EHA105 , one of the standard strains for plant genetic transformation [24] . After infiltration of Nicotiana benthamiana leaves with R . etli , expression of both GFP ( Fig 2A and 2B ) and β–glucuronidase ( GUS ) reporters ( Fig 2C ) was consistently observed in the inoculated plant tissues , although expression levels with R . etli were about ten times lower than those with A . tumefaciens ( Fig 2B ) . Thus , R . etli was able to transfer to plant cells DNA that subsequently could be expressed . In contrast , in similar experiments performed with R . leguminosarum , transient expression of the reporter gfp or gus-int genes was never observed ( Fig 2 ) . That R . leguminosarum—which is very closely related to R . etli , except for the vir region—is unable to effect genetic transformation suggests that it is the vir genes that are required for the T-DNA transfer by R . etli . We tested this notion directly using R . etli carrying p42a with virG or virE2 genes mutated by insertion of a promoterless gusA gene [21] . PCR-based analysis using primers specific for gusA and virG and virE2 showed that R . etli cells with the mutated p42a plasmids , i . e . , p42a virGmut and p42a virE2mut , indeed , contained the mutagenic sequences inserted in the sense orientation within the virG and virE2 genes . Specifically , Fig 3A shows that the reverse primer , corresponding to the 3’-end of gusA , and forward primers , corresponding to the 5’-ends of virG and virE2 , amplified fragments of ca . 2 . 3 Kb ( lane 1 ) and 2 . 8 Kb ( lane 5 ) for the virGmut and virE2mut mutants , respectively , but not for the wild-type genes in the same strains , i . e . , for virE2 in the virGmut strain ( lane 2 ) and for virG in the virE2mut strain ( lane 4 ) . As expected , no gusA sequences were detected in the wild-type p42a plasmid ( Fig 3A , lanes 7 , 8 ) whereas all samples contained bacterial chromosomal DNA ( Fig 3A , lanes 3 , 6 . 9 ) . Neither of these plasmids was able to promote transfer and transient expression of the gfp reporter gene ( Fig 3B ) . In control experiments with R . etli carrying the wild-type p42a , the gfp reporter was transferred to plant cells , resulting in expression of its protein product ( Fig 3B , see also Fig 2A ) . These observations suggest that the T-DNA transfer mediated by R . etli relies on and requires its vir genes . For stable genetic transformation , tobacco ( N . tabacum ) leaf discs were inoculated with R . etli or A . tumefaciens harboring a plasmid with the selection gene nptII encoding resistance to kanamycin as well as the gfp marker gene in its T-DNA . Regenerating plantlets were observed after four weeks incubation under kanamycin selection , which indicates stable genetic transformation ( Fig 4A and 4B ) . Consistent with the transient T-DNA expression data , the genetic transformation efficiency mediated by R . etli was much lower than with A . tumefaciens ( compare Fig 4A to Fig 4B ) . Confirming stable transgene expression in the regenerated plants , GFP was observed in a typical nucleocytoplasmic pattern in virtually all cells in leaves of one-month-old transgenic plants generated using R . etli ( Fig 4C ) . Finally , we confirmed the actual presence of the T-DNA within the genome of these transformed plants . Genomic DNA was isolated from two independent stable transgenic lines , designated TL1 and TL2 , and from a wild-type , untransformed plant and analyzed by Southern blot hybridization . Specifically , the DNA samples were digested with EcoRI and hybridized them with a probe corresponding to the T-DNA right border-proximal nos promoter region of the T-DNA of pBin19-RCS1-GFP that has no recognition sites for EcoRI . Fig 5A shows that no hybridization signal was detected in the DNA from the wild-type plant ( lane 1 ) whereas T-DNA-specific signal was present in the DNA of both transgenic TL1 and TL2 lines ( lanes 2 , 3 , asterisks ) , suggesting a single integration site of the T-DNA within the genome of each of the tested plants . When we similarly digested purified pBin19-RCS1-GFP DNA , which has only one EcoRI recognition site in its entire sequence , a 11 . 9-kb band , corresponding to the linearized plasmid , was observed ( Fig 5B , lane 1 ) . Additional negative controls , which probed EcoRI-digested wild-type and transgenic plant DNA with sequences specific for the p42a plasmid ( Fig 5B , lanes 2 , 3 ) or for the R . etli chromosome ( Fig 5B , lanes 5 , 6 ) did not yield any signal . As expected , positive controls detected specific signals using undigested p42a DNA hybridized to the p42a-specific probe ( Fig 5B , lane 4 ) and undigested R . etli chromosomal DNA hybridized with the chromosome-specific probe ( Fig 5B , lane 7 ) . Taken together , the stable expression of two marker genes , gfp and nptII , and the physical presence of the transforming DNA in the plant genomic DNA , indicate that the T-DNA was indeed integrated into the plant genome .
Our results demonstrate that R . etli within its p42a plasmid contains a complete and functional vir region , encoding a set of Vir proteins able to mediate functional T-DNA transfer into plant cells . Whereas it has been known that the vir genes from Agrobacterium can function in several rhizobia species [18 , 19] , this is the first time that an endogenous virulence system encoded by a non-Agrobacterium species is shown to be functional in DNA transfer and stable genetic transformation . The virE2 and virG mutants , which render R . etli unable to promote genetic transformation , previously have been shown to have no effect on formation of nitrogen-fixing nodules or on nodulation competitiveness [21] . Thus , the vir genes likely fulfill a function unrelated to symbiosis . Two factors might account for the presence of a functional vir region in R . etli . First , the ability to transform host plant cells may have been widespread among bacterial species in the past , and not restricted to the Agrobacterium genus . That we could not identify T-DNA-like sequences in R . etli suggests that Rhizobium-mediated plant transformation does not occur at present , although it cannot be ruled out that other Rhizobium strains , not yet sequenced , harbor a T-DNA . Furthermore , proteins from other rhizobia , such as Mesorhizobium loti R7A ( see S1 Table for Vir protein sequence homologies with M . loti R7A ) , can be recognized by the Agrobacterium VirB/D4 type IV secretion system ( T4SS ) and exported to plant cells [25] , suggesting that T4SS could substitute for the type III secretion system ( T3SS ) during effector protein translocation in some rhizobia species . Thus , the VirB/D4 T4SS encoded by p42a could also function to translocate protein effectors in R . etli . Second , because the p42a plasmid is transmissible between Rhizobium and Agrobacterium [21] , this plasmid may belong to an “interspecies plasmid pool” , and R . etli may function as a “vector” for p42a which is then transferred to Agrobacterium and only then used for plant genetic transformation . It would be interesting to examine whether quorum sensing signals that activate conjugative transfer of plasmids between Agrobacterium cells also induce conjugation between Rhizobium and Agrobacterium . Indeed , in natural Agrobacterium populations , Ti-plasmids are not present in all cells [2 , 3] , but , in response to bacterial and plant signals via a quorum sensing mechanism , conjugative plasmid transfer can be activated [26] . The need to identify or even generate non-Agrobacterium bacterial species that could be used as a vector for plant genetic transformation has been emphatically articulated [27] . First , a non-Agrobacterium vector might be more efficient in some hosts that are difficult to transform by Agrobacterium . Indeed , although the efficiency of R . etli mediated transformation of Nicotiana species was very low compared to Agrobacterium , R . etli might be more efficient with other plant species , such as its native hosts . Second , several aspects of plant genetic transformation methods are legally limited by existing patents , and using a different bacterial species may help to circumvent these limitation and avoid litigation [28] . In conclusion , we demonstrate that R . etli , a symbiotic Rhizobium species different from the phytopathogenic Agrobacterium , contains the complete molecular machinery able to transfer DNA to the plant genome , which has implications for evolution and origin of the Agrobacterium virulence system as well as for potential utilization in biotechnology .
Protein sequences were compared using the blastp program ( PubMed ) ; the percentages of identity of full sequences were calculated as the percentage of identity corrected by the query cover percentage . VirE2 phylogenetic tree was generated using MEGA version 6 [29] , via the minimum evolution method . The KEGG database release 71 . 0 ( http://www . genome . jp/kegg/ ) was used to design schematic maps for the different vir regions . R . etli CE3 , a streptomycin-resistant isolate of the CFN42 strain , and R . leguminosarum bv . viciae strain 3841 ( kindly provided by Dr . Russell Carlson , University of Georgia , Athens ) were grown in TY medium ( 5 g . L-1 tryptone , 3 g . L-1 yeast extract , and 10 mM CaCl2 ) . A . tumefaciens strain EHA105 , derived from nopaline wild-type strain C58 . C1 , was grown as described [30] . T-DNA containing plasmids were introduced into these Agrobacterium and Rhizobium strains using the classical CaCl2 protocol , with minor modifications in the case of Rhizobium [31] . R . etli strains , carrying p42a with mutated virG and virE2 genes were described previously [21] . For transient expression of GFP , pCB302T-GFP was obtained by inserting the gfp expression cassette from pSAT1-EGFP-C1 [32] into the AgeI-BglII sites of pCB302T-MCS [33] , derived from pCB302 [34] . For transient expression of GUS , pBISN1 [35] , carrying an expression cassette for a gus reporter gene with a plant intron sequence ( gus-int ) , was used . For stable transformation , the multiple cloning site of pPZP-RCS1 [36] was first introduced into the EcoRI-HindIII sites of pBin19 [37] , forming pBin19-RCS1 . Then , the gfp expression cassette from pSAT1-EGFP-C1 was inserted into the AscI site of pBin19-RCS1 , resulting in pBin19-RCS1-GFP carrying both nptII and gfp expression cassettes in its T-DNA segment . Agrobacterium and Rhizobium strains carrying pCB302T-GFP or pBISN1 were grown 24–48 h at 28°C , and infiltrated into intact N . benthamiana leaves as described [38] . The bacterial suspension was first adjusted to OD600nm 0 . 6 and then diluted 20 or 50 times before infiltration . Reporter gene expression was monitored three days after infiltration . For detection of GUS expression , leaf discs were excised from the infiltrated zone and subjected to the histochemical assay as described [39] . GFP expression was observed under a Zeiss LSM 5 Pascal confocal microscope at low magnification with a 10x objective; the number of GFP-expressing cells per cm2 of infiltrated leaf surface was counted as described [38] . Stable genetic transformation was performed using N . tabacum cv . Turk and Agrobacterium and Rhizobium strains carrying pBin19-RCS1-GFP in the classical leaf disc protocol [40] . Transgenic plantlets were selected on MS regeneration medium ( 30 g . L-1 sucrose , 8 g . L-1 agar , 10 mg . L-1 BAP , 1 mg . L-1 NAA ) supplemented with 50 mg . L-1 timentin and 50 mg . L-1 kanamycin . Images of regenerated transgenic plantlets were recorded after 4 weeks of incubation on the regeneration/selection medium , using a Leica MZ FLIII stereoscope . Regenerated plantlets were then placed on rooting medium ( 30 g . L-1 sucrose , 8 g . L-1 agar ) supplemented with 25 mg . L-1 kanamycin for one month before GFP expression in the leaves was analyzed by confocal microscopy as described above , but with a 40x objective . Total DNA was extracted from cultures of R . etli harboring p42a , p42a virGmut , or p42a virE2mut [21] and PCR-amplified for 32 cycles using the primer pairs 5’ATGAAAGGTGAACGGTTGAAACAC3’/5’CCGGAATTCTCATTGTTTGCCTCCCTGCTGC3’ specific for virG ( RHE_PA00053 ) and gusA , 5’ATGGATCCGAAAAGCGAAGACAAT3’/5’CCGGAATTCTCATTGTTTGCCTCCCTGCTGC3’ specific for virE2 ( RHE_PA00061 ) and gusA , or 5’CTCCTGCGTGTCCTGATTGGC3’/5’AGCGGCGCGACGAACGTGAC3’ specific for a 320-bp segment of the R . etli chromosome between positions 109 , 451 and 109 , 770 . Before proceeding with this analysis , we determined the orientation of the mutagenic gusA insertion in the virG and virE2 genes . We showed that , with forward primers corresponding to the 5’-ends of virG and virE2 , a PCR product was observed only with the reverse primer corresponding to the 3’-end of gusA , but not with the primer corresponding to its 5’-end ( S1 Fig ) , which reflects the sense orientation of gusA both within virG and virE2 . Total genomic DNA of wild type and transgenic tobacco plants was purified using the DNeasy plant DNA extraction kit ( Qiagen ) according to the manufacturer’s instructions . The purified DNA ( 10 μg ) was digested with EcoR1 ( New England Biolab ) overnight . The digested DNA was resolved on a 1 . 0% agarose gel for 6 hours at 60 V , and DNA was transferred onto a nylon charged membrane with alkali transfer buffer [41] . For the T-DNA-specific probe , we used a 300-bp segment of the nopaline synthase ( nos ) promoter of the T-DNA region of pBin19-RCS1-GFP amplified using the primer pair 5’CAATATATCCTGTCAAACACTGATAG3’/5’GAAATATTTGCTAGCTGATAGTGAC3’; this probe fragment did not contain recognition sites for EcoRI . For the p42a-specific probe , we used a 240-bp segment of the virB5 gene amplified using the primer pair 5’5’ATGCATGAGCTCATGAAGATGTCGAGACTAGTTAC3’/5’AAAGGATCCCCTCGTGGCGGGATACTGG3’ . For the R . etli genomic probe , we used a 320-bp segment of the chromosome between positions 109 , 451 and 109 , 770 amplified using the primer pair 5’CTCCTGCGTGTCCTGATTGGC3’/5’AGCGGCGCGACGAACGTGAC3’ . For Southern blot analysis of transgenic plants ( Fig 5A ) , agarose gel electrophoresis , blotting , and detection were performed at Lofstrand Labs Ltd . ( Gaithersburg , MD ) using a 32P labeled the T-DNA-specific probe ( 3 . 26 x 106 dpm/ml of hybridization buffer in a total volume of 50 ml ) . The hybridization was carried out for 3 days at 68°C; after washes , the membrane was autoradiographed for 17 hours with an intensifier screen at -80°C . For control experiments ( Fig 5B ) , biotinylated probes were prepared using the biotin decalabel DNA labeling kit ( Thermo Scientific ) ; hybridization and detection were performed using the Phototope Star kit ( NEB ) according to the manufacturer’s instructions . Based on the 4 . 5 Gb size of the complex allotetraploid genome of N . tabacum [42] , ca . 4 kb size of the T-DNA region of pBin19-RCS1-GFP , the DNA size-to-mass conversion ratio of 978 Mb = 1 pg ( http://ebook2 . worldlibrary . net/articles/C-value ) , and at least one T-DNA insertion per genome , we estimated that 10 μg of total transgenic plant DNA would contain ca . 9 pg of T-DNA , which is well within the detection range of the classical Southern blot analysis [43] . For comparable controls , we utilized 100 pg of purified pBin19-RCS1-GFP , 50 pg of p42a DNA , and 1 ng of R . etli chromosomal DNA . | Since the discovery of gene transfer from Agrobacterium to host plants in the late 1970s , this bacterial pathogen has been widely used in research and biotechnology to generate transgenic plants . Agrobacterium’s infection process relies on a set of virulence proteins that mediate the transfer of a segment of its own DNA ( T-DNA ) into the host cell genome . To date , Agrobacterium is believed to be the only prokaryote with the capability of cross-kingdoms gene transfer . However , homologs of the Agrobacterium’s virulence proteins are found in some symbiotic plant-associated bacterial species , belonging to the Rhizobium genus . Here we show that one of these species , Rhizobium etli , encodes a complete set of virulence proteins and is able to mediate transfer and integration of DNA into host-plant cell genome , when provided with a T-DNA . This is the first time that a bacterium-to-plant DNA transfer machinery encoded by a non-Agrobacterium species is shown to be functional . | [
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] | 2016 | A Functional Bacterium-to-Plant DNA Transfer Machinery of Rhizobium etli |
C-to-U editing of transcripts in plant organelles is carried out by small ( <400 kD ) protein complexes called editosomes . Recognition of the proper C target for editing is mediated by pentatricopeptide repeat ( PPR ) containing proteins that recognize cis-elements . Members of two additional gene families , the RIP/MORF and ORRM families , have each been found to be required for editing of particular sets of Cs in mitochondria and/or chloroplasts . By co-immunoprecipitation of the chloroplast editing factor ORRM1 , followed by mass spectrometry , we have now identified a member of the RanBP2 type zinc fingers ( pFAM00641 ) protein family that is required for editing of 14 sites in chloroplasts and affects editing efficiency of another 16 chloroplast C targets . In yeast two-hybrid assays , OZ1 ( Organelle Zinc finger 1 ) interacts with PPR site recognition factors whose cognate sites are affected when OZ1 is mutated . No interaction of OZ1 with the chloroplast editing factors RIP2 and RIP9 was detected; however , OZ1 interacts with ORRM1 , which binds to RIP proteins , allowing us to build a model for the chloroplast RNA editosome . The RNA editosomes that act upon most chloroplast C targets are likely to contain a PPR protein recognition factor , either RIP2 or RIP9 , ORRM1 , and OZ1 . The organelle zinc finger editing factor family ( OZ ) contains 4 members in Arabidopsis , three that are predicted to be targeted to chloroplasts and one to mitochondria . With the identification of OZ1 , there are now 4 nuclear-encoded protein families known to be essential for plant organelle RNA editing .
In vascular plants , specific cytidines are converted to uridines by RNA editing in the chloroplast transcripts [1–3] . A typical land plant modifies 30 to 40 C targets in chloroplasts , usually changing the encoded amino acid , and also acts upon hundreds of Cs in plant mitochondria [4 , 5] . The process is believed to be a correction mechanism to restore functional mRNAs in chloroplasts and mitochondria , whose genomes have undergone otherwise detrimental T-to-C changes [6] . The composition of the molecular machines that carry out plant organellar RNA editing , the editosomes , is not yet fully understood . Editosomes are found between the 200 and 400 kD markers on size exclusion columns [7] . Specificity of editing is achieved through the recognition of a cis-element 5’ adjacent to the editable cytidine by a pentatricopeptide repeat ( PPR ) motif-containing protein [8–10] . Recognition codes that match particular PPRs with nucleotides within the bound RNA region have been proposed [11 , 12] . Multiple PPRs in editing factors are followed by a so-called E domain and many PPR protein editing factors also contain a C-terminal DYW domain [13] . The DYW domain exhibits sequence motifs characteristic of cytidine deaminases [14] and is required for editing activity of some PPR-DYW proteins but is dispensable in others [15–20] . Attempts to demonstrate deaminase activity of purified DYW domains have failed so far [19 , 21] . Nevertheless , it remains possible that a DYW domain providing an enzymatic activity needed to deaminate cytidine to uridine could be present in all editosomes even if not on all PPR recognition factors . A protein named DYW1 , which lacks any PPRs , was found to be required for editing of a chloroplast C target that is recognized by a PPR-E factor that lacks a DYW domain [22] . Mutating conserved residues characteristic of deaminases in DYW1 or in the DYW domains of QED1 and RARE1 results in impaired editing [23 , 24] . In addition to the large PPR protein family that provides site-specific recognition , members of two other plant protein families have been identified as components of editosomes , the RIP/MORF family and the ORRM family [7 , 25 , 26] . As each of these additional proteins are needed for efficient editing of some C targets but not others , editosomes that act upon particular C targets differ not only in the site-specific PPR protein recognition factor they contain , but also in which members of these additional families comprise the protein complex . The chloroplast editing factor ORRM1 contains both a RIP domain and an RRM ( RNA Recognition Motif ) domain [25] . The ORRM1 protein belongs to a distinct clade of RRM-containing proteins , and the RRM domain by itself is able to provide RNA editing activity to orrm1 mutants [25] . In order to identify components of chloroplast editosomes that contain ORRM1 , Arabidopsis thaliana orrm1 mutants were complemented with an epitope-tagged ORRM1 protein . A candidate ORRM1-interacting protein , encoded by At5g17790 , was identified in immunoprecipitates . Through analysis of mutant and silenced tissue , we demonstrated that the candidate protein is a novel chloroplast editing factor . The protein , which we have named OZ1 ( Organelle Zinc finger 1 ) , belongs to the RanBP2 type zinc finger protein family , and is required for editing of 14 sites in chloroplasts and affects editing efficiency of another 16 chloroplast C targets . OZ1 is a member of an Arabidopsis protein family that encodes three additional proteins predicted to be targeted to chloroplasts or mitochondria . Identification of OZ1 as a chloroplast editing factor implicates a previously unsuspected class of zinc finger-containing proteins as potentially involved in RNA editing or other aspects of plant organelle RNA metabolism .
Preliminary experiments demonstrated that an epitope tag placed at the C-terminus of ORRM1 disrupted its function ( S1 Fig A and B ) . We therefore produced an ORRM1 expression vector with a RecA transit sequence followed by three tandem FLAG tags fused with a strepII tag ( 3FS tag ) ( S1 Fig A and C ) . This construct , designated , RecA-3FS-mORRM1 , resulted in significant increase of editing of matK C640 , from 11% to 20% , following transfections of orrm1 protoplasts ( S1C Fig ) . The low complementation level can be largely attributed to the size of the vector used in this assay . While the plasmid harboring RecA-RRM is around 6kb ( S1 Fig ) , the N-terminal tagged ORRM1 is integrated into a binary vector around 14kb , and plasmids over 10 kb are known to exhibit lower transfection efficiency [27] . We investigated whether the epitope-tagged protein ( Fig . 1A ) could restore editing in transgenic plants obtained by root transformation of orrm1 mutant plants . Transgenic plants of normal phenotype were obtained and RNA was extracted for use in editing assays . Editing extent of matK C640 , ndhB C872 and ndhG C50 , which exhibit decreased editing in orrm1 , was examined by bulk sequencing ( Fig . 1B ) . Editing of all three sites was restored to wild-type level in the RecA-3FS-mORRM1 transgenic plants . Total leaf proteins were used to perform ORRM1 immunoprecipitation ( IP ) . Wild-type Arabidopsis , Columbia ecotype , was included as a negative control for comparison in order to eliminate non-specific binding proteins . We observed that the affinity of the strepII tag on ORRM1 to streptactin resin was poor , which was probably due to its internal position caused by the N-terminal fusion of the FLAG tag ( Fig . 1A ) . Therefore we used only anti-FLAG antibodies for immunoprecipitation . As is shown in Fig . 2 , the anti-FLAG antibody recognizes one band from the transgenic plant samples , but none in the wild-type sample . The unique band’s electrophoretic mobility is slightly slower than that expected for the predicted 42 kD size of the tagged ORRM1 , possibly due to post-translational modifications . Anti-FLAG resins retained almost all tagged ORRM1 protein from the extract ( Fig . 2A ) . The elutions from both ORRM1 and negative control were separated by a SDS-PAGE gel and silver stained . The bait , 3FS-mORRM1 , is clearly seen in the transgenic plant IP but missing in the Col negative control ( Fig . 2B ) . The immunoprecipitates were subjected to MS/MS mass spectrometry in order to identify ORRM1-binding proteins . The protein encoded by At5g17790 was selected for further investigation because after the ORRM1 peptides , it had the largest number of matches in MS/MS spectra and was not detected in the negative controls . S1 Table describes the peptides detected that resulted in the identification of the At5g17790 as a candidate ORRM1-interacting protein . At5g17790 contains two tandem C2X10C2 zinc finger domains [28] called RanBP2 type zinc fingers ( X2GDWICX2CX3NFARRX2CXRCX2-PRPEX2; pFAM00641 ) , which were characterized in the Ran Binding Protein 2 ( RanBP2 ) . Ran is a small GTPase and RanBP2 is a nucleoporin that binds Ran via the zinc finger motifs . This gene previously was identified as mutated in a variegated Ds insertional mutant of Arabidopsis thaliana Landsberg erecta , but the cause of the chloroplast developmental aberration was not determined [28] . We obtained one T-DNA insertional line in A . thaliana ecotype Columbia from ABRC , SAIL_358_H03 ( Fig . 3A ) . In contrast to the mutant in the Landsberg ecotype , the homozygous Columbia mutant showed a uniform yellow phenotype as a young seedling , as shown in Fig . 3B . Subsequent growth on sucrose media result in the appearance of light green , non-variegated leaves ( Fig . 3C ) . These older mutant seedlings could be transferred to soil , where the pale green leaves were able to support autotrophic growth ( Fig . 3D ) . The protein encoded by At5g17790 was given the name OZ1 ( Organelle Zinc finger 1 ) . RNA from 4-week-old oz1–1 homozygous mutants and the siblings was extracted and the editing extent was examined by bulk sequencing as shown in Fig . 4A . The oz1 mutation causes altered editing of various chloroplast C targets . For example , editing of rpoA C200 and ndhB C872 is completely lost in oz1–1 while editing of rpoB C338 is partially disrupted ( Fig . 4A ) . No obvious effect was observed on psbE C214 editing in the oz1–1 mutant . On the contrary , at rpoC1 C488 , the mutant editing level is up-regulated compared to the wild-type ( Fig . 4A ) . Because poisoned primer extension ( PPE ) is a more sensitive method to measure editing extent than bulk sequencing [4 , 7] , all chloroplast editing sites were assayed both in oz1 and its siblings using PPE ( Fig . 4B and Table 1 ) . The oz1–1 allele is clearly recessive because no significant editing difference is seen between heterozygotes and wild-type plants . Editing of rpoA C200 is 0% in oz1–1 by PPE , which confirmed the result from bulk sequencing . ndhB C746 editing dropped from 97% in wild-type to 68% in the mutant . Editing of rpoC1 C488 increases from 25% in wild type to 58% in the mutant , in agreement with the bulk sequencing data . The assay data for the complete set of chloroplast sites is shown in Table 1 . 14 sites have major loss of editing ( >90% decrease in editing ) in oz1–1 and 15 other sites have significantly decreased editing ( >5% , P<0 . 05 ) . Although editing defects are massive , editing events on the same transcript are not all affected in the same pattern by oz1 , hence the editing defects are unlikely to be a secondary effect caused by some change in a transcript itself . For example , ndhD C2 and ndhD C878 lost over 90% of the wild-type editing extent in oz1–1 , but ndhD C383 is not affected at all . On the contrary , the sites recognized by the same PPR protein are largely affected in the same way by oz1–1 mutation . ndhD C878 and ndhB C467 share the same PPR recognition factor CRR28 and both of them lose over 90% editing in oz1–1 ( Table 1 ) . Likewise , editing of both ndhB C836 and ndhG C50 ( controlled by OTP82 ) and editing of both rpoA C200 and clpP C559 ( recognized by CLB19 ) are similarly affected . ndhF C290 ndhB C1481 and psbZ C50 are all recognized by OTP84 , and in oz1–1 , all sites exhibit mild defects in editing ( 5%-20% ) ( Table 1 ) . Given that OZ1 is immunoprecipitated by ORRM1 , we also compared the OZ1-dependent sites and ORRM1-dependent sites to examine if these two factors participate in the same editing events . Indeed , editing efficiencies of the 14 OZ1-dependent sites are all severely affected in the orrm1 mutant . Many other sites mildly affected by the oz1–1 mutation are also orrm1-dependent ( Table 1 ) . 8 sites are controlled only by OZ1 but not by ORRM1 . Taken together , OZ1 is a genuine editing factor for the majority of C targets in chloroplasts . Since a second T-DNA mutant in the coding region of OZ1 was not available , we performed Virus Induced Gene Silencing ( VIGS ) to transiently silence OZ1 expression in young Arabidopsis seedlings . To monitor the silencing efficiency , a GFP co-silencing marker harbored in the VIGS construct was used [29] . Agrobacteria carrying either the OZ1/GFP co-silencing construct or the GFP silencing construct alone were inoculated into 2 week-old 35S::GFP expressing Arabidopsis seedlings . After growth in long days for 5 more weeks , the editing extents in RNA from GFP-silenced leaves and from uninoculated plants were analyzed by PPE . There were no differences between leaves of GFP-silenced plants and untreated plants ( Fig . 5 ) . ndhB C836 editing extent decreased from 97% in the untreated control to 47% in OZ1 silenced plants ( P<0 . 01 ) . rpoA C200 editing extent dropped from 74% in untreated control to 29% in OZ1 silenced plants ( P<0 . 001 ) . These results agree with the data from oz1–1 mutants , in which editing is abolished at both sites . The residual editing in the silenced plants is probably caused by incomplete depletion of OZ1 protein . Although the young oz1–1 mutant has a severely defective phenotype , the plants gradually recover some chlorophyll . In a 6-week- old oz1–1 plant , the old leaves remain pale yellow while the new leaves are light green ( Fig . 3C ) . To investigate whether editing defects are rescued in the light green leaves , editing of RNA extracted from both the pale yellow leaves and the light green leaves were analyzed by bulk sequencing . Although pigmentation has been partially recovered in light green leaves , plastid editing is still defective in those leaves compared to wild type Col ( Fig . 6 ) . No obvious difference in editing between yellow and green leaves was observed . This finding indicates that the defects in editing are not due to a pleiotropic effect caused by some other chloroplast developmental problem in yellow chlorophyll-deficient leaves . Green leaves were therefore used to prepare protoplasts . OZ1 was cloned into a pSAT4a vector to create 35S::OZ1 , a plant transient expression vector driven by a 35S promoter for transfections of oz1–1 protoplasts . A chloroplast targeted YFP construct ( 35S::cpYFP ) was included as a negative control . Monitoring of transfection efficiency of the YFP constructs by microscopy indicated expression of YFP in over 50% of the protoplasts . RNA was extracted from protoplasts two days after the transfection and analyzed by PPE to examine the editing efficiency ( Fig . 7 ) . No significant difference in editing was seen between the untransfected control and the 35S::cpYFP-transfected control . Introduction of 35S::OZ1 significantly increases the editing level for all the sites we tested . rpoA C200 increased from 3% to 21% , ndhB C836 from 19% to 31% and rps12- ( i1 ) C58 from 3% to 15% . This confirms that the editing defects in the oz-1–1 mutant can be reduced by introduction of OZ1 . Because of the poor growth of the homozygous oz1 mutant plant ( Fig . 3D ) , we decided to transform the heterozygous plant by floral dipping with a construct expressing OZ1 under the control of a 35S promoter . Genotyping the transgenic plants growing on a selectable plate allowed us to recover several independent plants homozygous mutant for the endogenous oz1 alleles but expressing the OZ1 transgene . The introduction of a functional OZ1 complements the editing defect in all the transgenic plants assayed ( Fig . 8A ) . Positional effects on the transgene are known to affect expression and likely resulted in the range of responses in the transformed plants . For example , in different plants , rpoA C200 editing extent ranged from 13%-65% ( Fig . 8A ) . The restoration of editing extent in some transgenic mutant plants is much more pronounced than with the transient expression in the oz1 mutant protoplasts , e . g . 89% vs . 31% for ndhB C836 , and reaches almost the level observed in the wild-type plant . In addition to reverting the editing defects , the introduction of OZ1 in planta also suppress the yellow phenotype observed in the mutant plant ( Fig . 8B ) . The reversion of both editing and phenotypic defects by expression of a functional OZ1 demonstrates the role of this protein in both phenomena . A yeast two-hybrid ( Y2H ) assay was employed to examine the interaction between OZ1 and ORRM1 . Both OZ1 and ORRM1 are plastid-targeted proteins , so the predicted transit peptide sequences were removed from each before cloning them into AD/BD fusion constructs . As shown in Fig . 9A , OZ1 interacts with ORRM1 in yeast . The interaction is not affected by the position of the fusion protein since both AD-OZ1/BD-ORRM1 and its reciprocal pair AD-ORRM1/BD-OZ1 showed interaction , implicating a genuine interaction between these two proteins . ORRM1 was further divided into nORRM1 and cORRM1 , encompassing the RIP-RIP and the RRM domain respectively . nORRM1 but not cORRM1 interacts with OZ1 , indicating the RIP-RIP domain actually mediates the interaction with OZ1 ( Fig . 9B ) . We suspected that OZ1 might also interact with other components of chloroplast editosomes in addition to ORRM1 , such as additional PPR site recognition factors and members of the RIP/MORF protein family . In order to determine whether OZ1 can dimerize and/or interact with other components of the editing complex , we performed a series of Y2H assays . OZ1 fused to either AD or BD does not show any auto-activation for HIS and ADE reporters , while yeast with AD-OZ1/BD-OZ1 is able to grow on histidine and adenine deficient media , indicating self-interaction ( Fig . 10A ) . OZ1 also interacts with OTP82 and CRR28 , as shown in Fig . 10B , a result expected from the effect of the oz1–1 mutation on C targets controlled by OTP82 and CRR28 ( Table 1 ) . OZ1 exhibits a weaker interaction with RIP1; fewer colonies are seen in the RIP1/OZ1 combination ( Fig . 10B ) . However , no interaction was observed between OZ1 and RIP2 or RIP9 ( Fig . 10B ) , even though RIP2 and RIP9 are essential for editing of a large number of chloroplast C targets . We considered the possibility that OZ1 associates with RIP2 and RIP9 via ORRM1 . To test this hypothesis , we performed a Y2H assay for ORRM1 and RIP proteins ( Fig . 10C ) . Both RIP1 and RIP2 can interact with ORRM1 . RIP9 fused with the GAL4 binding domain strongly autoactivates HIS and ADE reporters , so RIP9 was not tested in this experiment . ORRM1 with the GAL4 activation domain shows no autoactivation ( Fig . 9A ) . Our data is consistent with ORRM1 as a mediator of interaction between OZ1 and RIP2 within the chloroplast editosome . Three highly similar RanBP2 zinc finger proteins were found in Arabidopsis protein database in a BLAST search with OZ1 . In 2004 , these proteins were reported to comprise a four-member gene family of unknown function [28]; however , changes in gene models result in a new alignment ( Fig . 11 ) . The protein sequence alignment by T-coffee shows presence of multiple highly conserved regions in the N-terminal portion of the protein , past the predicted transit sequences , with various numbers of zinc finger motifs and more variable C-terminal regions ( Fig . 11 ) . In order to investigate the subcellular location of OZ1 , we fused the N-terminal sequences encoding 100 amino acids to yellow fluorescent protein ( YFP ) and transfected protoplasts . Confocal microscopic imaging revealed that OZ1-YFP is located in the chloroplasts at punctate loci ( Fig . 12 ) . Previously , the entire coding region of OZ1 ( VAR3 ) was fused to GFP and observed to be located within chloroplasts at punctate loci [28] . OZ1 was also detected in chloroplast nucleoid preparations by mass spectrometry ( ppdb . tc . cornell . edu ) . In addition , Target P predicts all of the other OZ1 family members to be organelle targeted [30] , one in mitochondria and two in plastids ( S2 Table ) . Proteomics studies have also found OZ4 ( At1g48570 ) in both chloroplast nucleoids and stroma ( ppdb . ts . cornell . edu ) . Except for the zinc finger motif , no other annotated domain or motif was found in the OZ1 family . In order to find hidden uncharacterized motifs , motif scanning was performed using MEME against all four members to look for motifs between 15aa to 70aa . Five motifs were returned ( Fig . 13 ) . The zinc finger domain has 4 characteristic cysteine residues . As shown in Fig . 13 , the zinc finger motif is shared by all four members , but the number of repeats varies . OZ1 and OZ2 ( At1g55040 ) contain two zinc finger motifs , while OZ3 ( At1g70650 ) has three and OZ4 ( At1g48570 ) has four . The regions preceding the zinc finger motifs are relatively highly conserved , briefly spanning 3 distinct domains . The region downstream of the zinc finger domains is quite variable . OZ1 has three repeats of motif 5 , which is either missing or poorly conserved in the other members ( S2 Fig ) . Portions of motif 5 were previously identified as three “long repeats” in At5g17790 [28] . We performed homology searches to determine whether orthologs of the four Arabidopsis OZ family members could be detected in other well-characterized plant genomes . We were able to identify putative orthologs for all 4 genes in poplar , grape , rice , and maize . Moss and Selaginella , which exhibit chloroplast RNA editing , also encode OZ-like proteins but with a lower similarity ( Fig . 14 ) . We could not detect proteins similar to the OZ family in Chlamydomonas or Volvox , where editing does not occur .
Because a second coding region mutant in OZ1 was not available , we performed transient silencing , transient complementation , and stable complementation to verify the function of OZ1 in editing . Given that VIGS can only knock down gene expression , the editing level of the OZ1-dependent sites were reduced but not totally abolished . Introduction of a 35S::OZ1 construct into mutant protoplasts or into transgenic plants greatly increased the editing extents of the editing defective sites , demonstrating that the editing defects seen in the oz1 mutant can be attributed to loss of OZ1 . The absence of OZ1 results in reduced editing efficiency at most of the affected C targets , but editing of rpoC1 C488 is increased . Possibly , the loss of OZ1 results in reduction of sequestration of an editing factor needed for rpoC1 C488 that is present in limiting amounts when OZ1 is present . If OZ1 is not needed for editing of rpoC1 C488 , its loss could make available more of an unknown editing factor needed for efficient rpoC1 C488 editing . Although 14 chloroplast C targets have major loss of editing with nine sites showing no detectable editing by PPE , and editing efficiencies of 16 other editing sites are significantly altered , the oz1 mutant can survive on sucrose as a yellow seedling and then undergo sufficient chloroplast development to support autotrophic growth . Even in the green , fully photosynthetic leaves , the editing defects are still observed . Most of the sites at which editing is abolished in the oz1–1 mutant are in non-coding regions or in NADH dehydrogenase genes that are not needed in low light growth chamber conditions . The virescent phenotype could be largely due to the complete loss of editing of rpoA C200 . Absence of editing at this site also occurs when the PPR editing factor gene CLB19 is mutated [15] , and the phenotype of oz1–1 is similar to the clb19 mutant . rpoA encodes a subunit of the plastid-encoded RNA polymerase ( PEP ) ; loss of editing at this particular site results in defective PEP and yellow seedling phenotype in early developmental stages . However , mutants survive defects in PEP and later partially recover because of the presence of the nuclear-encoded plastid polymerase that can remedy some of the impaired gene expression . The phenotype of oz1–1 differs from the previously characterized Ds insertion mutant var3 in At5g17790 , which was found to exhibit variegated leaves [28] . The action of OZ1 is clearly site-specific , because C targets that reside on the same transcript are differently affected . For example , ndhD C878 has a major loss of editing while ndhD C383 is barely affected . Which editing sites are affected is likely determined by the editing factors with which OZ1 interacts . C targets that share PPR recognition factors are similarly affected in the oz1 mutant . In Y2H assays , OZ1 binds to CRR28 and OTP82 , PPR proteins that are required for editing of sites that also require OZ1 . Furthermore , OZ1 interacts with ORRM1 , and all 14 severely affected chloroplast sites are also affected in the orrm1 mutant . OZ1 interacts with RIP1 , though the interaction is not as strong as that with ORRM1 . We did not observe direct interaction of OZ1 with RIP2 or RIP9 . However , ORRM1 can bind to RIP1 and RIP2 . Previously we reported interaction between the RIP-RIP domain of ORRM1 and CRR28 and OTP82 [25] . Another group determined that RIP2 and RIP9 interact with CRR28 [31] . RIP2 and RIP9 have been reported to interact with PPO1 , protoporphyrinogen IX oxidase 1 , which is required for efficient editing of a number of chloroplast sites [31] . However , PPO1 does not interact with CRR28 or other PPR editing factors , and it is presently unknown whether PPO1 also interacts with either ORRM1 or OZ1 [31] . All of the interaction data , taken together , is consistent with the presence of multi-component editing complexes that contain ORRM1 , OZ1 , and at least one RIP protein and a PPR protein , at unknown stoichiometry . An example of the model for the editosome acting upon ndhD C878 , drawn according to the yeast two-hybrid data , is shown in Fig . 15 . Some complexes are also likely to contain PPO1 , but we cannot place this protein into our diagram until its interaction with ORRM1 and OZ1 is investigated in the future . Three proteins that share high similarity with OZ1 are also predicted to be targeted to organelles according to Target P ( S2 Table ) . OZ1 contains three long repeats at its C terminus; these domains of unknown function are less conserved in other family members . The only well-documented and most significant domain found in this family is the Ran binding protein 2 type zinc finger motif , which is a conserved 30-amino-acid consensus ( X2GDWICX2CX3NFARRX2CXRCX2-PRPEX2; pFAM00641 ) characterized in RAN binding protein 2 ( RanBP2 ) and other nucleoporins . OZ1 contains two tandem RanBP2 zinc-finger domains while other members have various number of this domain . It is highly possible that other members of the OZ family have similar role in chloroplast or mitochondrial RNA editing . Such redundancy could explain the residual editing for some sites in the oz1 mutant Chloroplast RNA editing in vitro has been shown to be Zn2+ dependent [32] . Zinc binding is characteristic of cytidine deaminases , and the DYW domain , which contains cytidine deaminase motifs and is present on a subset of PPR protein editing factors , has been shown to bind zinc ions [16 , 23] . The requirement for zinc in plant organelle RNA editing has been thought to be due to the need for cytidine deaminase activity . The discovery of OZ1 implicates the OZ family as another possible source of the zinc requirement for editing to occur . Further experiments will be needed to determine whether the zinc fingers present in the OZ family actually bind zinc and whether they are important in RNA and/or protein binding in the RNA editosome .
The T-DNA insertional A . thaliana Columbia ecotype mutant SAIL_358_H03 in the OZ1 gene was obtained from the Arabidopsis Biological Resource Center ( https://abrc . osu . edu/ ) . After 3 days of stratification , the seeds were placed onto petri dishes containing Murashige-Skoog medium in a 25o room with 14 hour day length . Mutant plants and the wild-type siblings were then transferred into soil after 7 weeks on tissue culture medium . Leaves from 4 week- old and 8 week- old plants were collected for further analysis . The ORRM1 coding sequence was cloned using primer pair ORRM1_1F and ORRM1_R_WO from previous constructs . The sequence was first cloned into PCR8/GW/TOPO ( Life Technologies , Carlsbad , CA ) , and then shuttled into a modified PBI121 vector with a 3XFLAG-strepII C-terminal tag . Alternatively , coding sequence of the ORRM1 mature form ( without the predicted 54aa transit peptide ) was fused to a N terminal 3XFLAG-strepII tag sequence and an artificial transit peptide sequence from RecA in an overlapping PCR using primer pairs RecA_F , RecA_R , 3FS_F , 3FS_R , ORRM1_163F and ORRM1_R . This chimeric gene was cloned into PCR8 vector first and then the PBI121 vector using LR ClonaseII ( Life Technologies , Carlsbad , CA ) . The C-terminal tag on the vector was eliminated by the endogenous stop codon of ORRM1 in the sequence . A RecA-RRM construct was described previously . The OZ1 coding sequence was cloned using primer pair OZ1_F and OZ1_R from A . thaliana cDNA . The PCR product was first ligated to PCR8/GW/TOPO and then transferred into the destination vectors pSAT4a and pAUL13 [33] to create a transient expression construct and a stable complementation construct , respectively . All oligonucleotides used in this study are shown in S3 Table . Protoplasts from orrm1 or oz1 mutants were prepared following the protocol from Jen Sheen’s lab [27] . Light green leaves from the oz1 mutant were used for protoplast preparation . 10 μg of plasmid DNA was used to transfect 2x104 cells . The transfected protoplasts were incubated in the dark at room temperature for 3 days ( orrm1 complementation ) or 1 day ( oz1 complementation ) before harvest . An OZ1 gene-specific region was selected from the CATMA database [34] and amplified using primer pair OZ1_VIGS_F and OZ1_VIGS_R . The fragment was first integrated into PCR8/GW/TOPO and then into the silencing vector PTRV2/GW/GFP by an LR reaction . Agrobacteria harboring the silencing construct were used to infiltrate 2 week-old Arabidopsis seedlings that expressed GFP driven by 35S promoter as previously described [7] . 5 weeks after infiltration , silencing efficiency was monitored by the expression of the co-silenced GFP in each individual . Silenced plants , which exhibited a dark red color from stem to leaf under UV light , were collected for further analysis . The 3XFLAG-strepII-ORRM1ΔCTP and OZ1 constructs in PBI121 and PAUL13 , respectively , were used to transform the Agrobacterium GV3101 strain . A standard root transformation protocol was followed to transform mutant orrm1 roots . The roots were first induced on Callus Inducing Medium ( CIM ) for 2 days and then infected with Agrobacteria in liquid media [35] . Roots were incubated on CIM for another 2 days until they were overgrown by Agrobacteria and then bacteria were removed by several washing steps with liquid CIM containing timentin and carbenicillin . Roots were then cut into 0 . 5mm pieces and put onto Shoot Inducing Medium ( SIM ) containing 100mg/L Basta for selection . After the shoots grew out , they were removed from the calli and transferred onto a Root Inducing Medium ( RIM ) [35] . Fully grown transgenic plants with healthy roots were then transferred into soil . Because the root transformation was not successful with the oz1 mutant , a standard floral dip protocol was used to transform a heterozygous plant . 10 g of leaves of each line were ground in liquid nitrogen into fine powder . Total leaf protein was extracted using grinding buffer ( 150mM NaCl , 50mM Tris-HCl pH7 . 4 , 1mM EDTA , 0 . 2%NP-40 and 1x cocktail protease inhibitor ( Sigma-Aldrich , St . Louis , MO ) . The extract was cleared by 13 , 000 rpm centrifugation , 0 . 45 μm filtration and then 30 minutes incubation with unconjugated agarose beads ( Vector Laboratories , Burlingame , CA ) to minimize non-specific binding . 200 μl anti-FLAG agarose resins ( Sigma-Aldrich , St . Louis , MO ) were first blocked with 4%BSA before 2 hours incubation with around 30 μg pre-cleared protein extract . A washing step was done using washing buffer ( 150mM NaCl , 50mM Tris-HCl pH7 . 4 , 1mM EDTA , 0 . 2% NP-40 ) . The IP was eluted with elution buffer ( 2M MgCl2 , 50mM Tris-HCl ph8 . 0 , 150mM NaCl , 0 . 5%CHAPS ) . The final sample was prepared using SDS-PAGE sample prep kit ( Thermo Scientific , Waltham , MA ) . 1% of the IP samples were loaded onto an Any kD MINI-PROTEAN TGX precast gel ( Bio-Rad , Hercules , CA ) followed by standard procedures of immunoblotting . α-FLAG-M2-HRP ( Sigma-Aldrich , St . Louis , MO ) was used to detect FLAG-tagged proteins . 10% of the IP samples were separated on a 10% polyacrylamide gel before being subjected to silver staining compatible to mass spectrometry . Co-purifying proteins with FLAG-tagged ORMM1 were identified by tandem mass spectrometry using a nanoLC-LTQ-Orbitrap instrument , followed by database searching with MASCOT against TAIR10 [36] . DNA contaminants were removed from RNA samples by TURBO DNase ( Life Technologies , Carlsbad , CA ) . The cDNA was reverse transcribed from the RNAs using the pooled reverse primers as previously described [7] . PCR products harboring the editing sites were either bulk Sanger-sequenced or subjected to PPE assay [29 , 37] . Mature OZ1 coding sequence ( without the N-terminal predicted 33 aa transit peptide ) was amplified using primer pair OZ1_100F and OZ1_R from cDNA and cloned into PCR8/GW/TOPO ( Life Technologies , Carlsbad , CA ) . Mature RIP2 and RIP9 coding sequences were amplified using primer pairs RIP2_133F and RIP2_R , RIP9_175F and RIP9_R from A . thaliana cDNA , respectively . PCR products were first cloned into PCR8/GW/TOPO and then pGADT7GW and pGBKT7GW destination vectors through homologous recombination by LR clonaseII ( Life Technologies , Carlsbad , CA ) . RIP1 , RARE1 , CRR28 , OTP82 , ORRM1 constructs produced for Y2H assays were previously described [7] . Empty vectors were used as negative controls . Two mating types of the PJ69–4 yeast strain , a and α , were used . Single transformants were obtained by transformation while double transformants were produced through mating . Yeast harboring testing pairs were grown in leucine and tryptophan deficient media overnight before they were diluted with water to OD600 0 . 5 , 0 . 05 , or 0 . 005 . 10 μl of each dilution was spotted onto leucine- , tryptophan- , histidine- , adenine-deficient media plates . Growth results were collected after 3 days incubation at 30°C . The first 100 codons of OZ1 was amplified from cDNA with primers listed in S3 Table , and cloned into PCR8/GW/TOPO ( Life Technologies , Carlsbad , CA ) . The fragment was subsequently transferred to the pEXSG-YFP Gateway destination vector [38] by recombination using LR Clonase II ( Life Technologies , Carlsbad , CA ) , creating a gene encoding a YFP fusion protein driven by a tandem CaMV 35S promoter . Protoplasts were prepared from leaves of 3-week old Arabidopsis accession Ler and transfected as described above . Images were acquired 3 days after transfection using a Zeiss 710 confocal microscope at the Cornell Biotechnology Resource Center ( BRC ) . | Transcripts encoding chloroplast and mitochondrial proteins of flowering plants are profoundly affected by RNA editing . In Arabidopsis , over 600 genomically-encoded Cs are modified to Us in organelle transcripts , altering the encoded amino acids and creating stop and start codons . Pentatricopeptide proteins are known to bind to cis-elements near C targets of editing and chloroplast RNA editing also requires members of two additional protein families . Nevertheless , not all protein components of the editosome have been identified . We now report the discovery of a member of fourth gene family essential for chloroplast RNA editing: OZ1 , member of a family of Arabidopsis RanBP2-type zinc finger proteins . Identifying all of the proteins in the RNA editosome is critical for understanding the mechanism behind the remarkable specificity of C-to-U editing . | [
"Abstract",
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] | [] | 2015 | A Zinc Finger Motif-Containing Protein Is Essential for Chloroplast RNA Editing |
The immunocytes that regulate papillomavirus infection and lesion development in humans and animals remain largely undefined . We found that immunocompetent mice with varying H-2 haplotypes displayed asymptomatic skin infection that produced L1 when challenged with 6×1010 MusPV1 virions , the recently identified domestic mouse papillomavirus ( also designated “MmuPV1” ) , but were uniformly resistant to MusPV1-induced papillomatosis . Broad immunosuppression with cyclosporin A resulted in variable induction of papillomas after experimental infection with a similar dose , from robust in Cr:ORL SENCAR to none in C57BL/6 mice , with lesional outgrowth correlating with early viral gene expression and partly with reported strain-specific susceptibility to chemical carcinogens , but not with H-2 haplotype . Challenge with 1×1012 virions in the absence of immunosuppression induced small transient papillomas in Cr:ORL SENCAR but not in C57BL/6 mice . Antibody-induced depletion of CD3+ T cells permitted efficient virus replication and papilloma formation in both strains , providing experimental proof for the crucial role of T cells in controlling papillomavirus infection and associated disease . In Cr:ORL SENCAR mice , immunodepletion of either CD4+ or CD8+ T cells was sufficient for efficient infection and papillomatosis , although deletion of one subset did not inhibit the recruitment of the other subset to the infected epithelium . Thus , the functional cooperation of CD4+ and CD8+ T cells is required to protect this strain . In contrast , C57BL/6 mice required depletion of both CD4+ and CD8+ T cells for infection and papillomatosis , and separate CD4 knock-out and CD8 knock-out C57BL/6 were also resistant . Thus , in C57BL/6 mice , either CD4+ or CD8+ T cell-independent mechanisms exist that can protect this particular strain from MusPV1-associated disease . These findings may help to explain the diversity of pathological outcomes in immunocompetent humans after infection with a specific human papillomavirus genotype .
Papillomaviruses ( PV ) are DNA tumor viruses that infect stratified squamous epithelia of the skin and mucous membranes of humans and many other vertebrate species [1] . PV infections are species-restricted and region-restricted , in that only part of the skin and mucous membranes of the host species of a given PV is permissive for productive infection [2] . More than 150 human PV ( HPV ) genotypes ( types ) have been identified . These viruses can induce long-term infection that , depending on the virus type and its human host , may not cause lesions , may induce benign lesions ( warts or papillomas ) , or may lead to the development of anogenital carcinomas , most notably cervical and oropharyngeal cancers . Certain cutaneous HPV types have also been implicated in the pathogenesis of some epidermal squamous cell cancers in genetically predisposed or immunocompromised individuals [3] . Although neutralizing antibodies against the viral capsid proteins are sufficient to prevent PV infections , cell-mediated immunity is generally thought to control the infections once they become established . For instance , individuals with underlying T cell deficiencies , but not B cell deficiencies , often have difficulties in controlling and clearing HPV-induced neoplasia [4] . However , it has been difficult to provide experimental support for this concept or to determine which particular subset ( s ) of immunocytes are responsible for these activities . Although studies of animal PVs , such as bovine PV ( BPV ) , cottontail rabbit PV ( CRPV ) , and canine PV , have contributed to our understanding of PV biology [reviewed in 5] , the limited immunological reagents available for these species have hampered critical investigations of immune regulation of PV infection with these systems . The recent identification of MusPV1 ( also designated “MmuPV1” ) , which is the first domestic mouse PV , provides an excellent opportunity to investigate the immune mechanisms that control PV infection in a mammalian species whose immunology has been well characterized . MusPV1 was found initially in an inbred NMRI-Foxn1nu/Foxn1nu nude laboratory mouse colony [6] . These immunodeficient mice spontaneously developed papillomas at cutaneous surfaces near the mucocutaneous junctions of nose and mouth , from which the virus was isolated . Subsequent studies reported that papillomas could be induced by experimental infection with MusPV1 in immunodeficient B6 . Cg-Foxn1nu/Foxn1nu , Foxn1nu/Foxn1nu and SCID SHO mice [7] , [8] . We also determined that athymic NCr nu/nu ( nude ) mice could be infected with in vitro synthesized full-length genomic DNA of MusPV1 , giving rise to non-regressing cutaneous papillomas from which high titers of authentic , infectious MusPV1 virions were isolated and serially passaged [9] . In contrast to these reports of MusPV1 in immunodeficient mice , the study of MusPV1 in immunocompetent mice has been limited . The initial report of MusPV1 noted that when cell-free extracts from papillomas in the immunodeficient mice were used to inoculate the dorsal skin of S/RV/Cri-ba/ba mice , which have an unknown mutation that results in thin short hair , they induced small papules at most injection sites . These lesions , which were not characterized further , took at least three weeks to develop , and regressed spontaneously by 8 weeks post-inoculation [6] . Subsequent efforts to induce lesions in C57BL/6J mice were unsuccessful [7] . Thus establishment and further characterization of MusPV1 infection and disease in an immunocompetent setting were warranted . In this study we aimed at characterizing MusPV1 infection in different immunocompetent murine strains and sought to determine the key immunologic players primarily responsible for control of cutaneous PV infection and papilloma induction . Our results revealed asymptomatic MusPV1 infection in these immunocompetents and demonstrated that profound immunosuppression can render these strains that had various H-2 haplotypes susceptible to MusPV1-induced papilloma formation of the skin . This is reminiscent of infection with HPV of genus beta that also predominantly induces asymptomatic skin infections in immunocompetent individuals but can induce visible lesions after immunosuppression . The observed differences in the efficiency of papilloma outgrowth between the individual murine strains corresponded partially to their previously reported susceptibility to chemical-induced skin papillomatosis [summarized in 10] , suggesting that there may be similarities between their relative susceptibility to papilloma formation induced by MusPV1 and to chemical carcinogens . We further provide clear experimental proof that T cell functions are required for control of PV infection and disease and reveal striking differences in the protective capacities of T cell subsets among murine strains , including CD4-mediated effector mechanisms .
Several inbred immunocompetent strains of mice , namely FVB/NCr , BALB/cAnNCr , DBA/2NCr , A/JCr , C57BL/6NCr ( C57BL/6 ) , 129S6/SvEv , C3H/HeJCr , and as well the outbred Cr:ORL SENCAR ( SENCAR ) were evaluated for their susceptibility to papilloma induction by MusPV1 ( Table 1 ) . The inbred strains have a range of H-2 haplotypes and a range of reported susceptibility to chemical carcinogen-induced skin papillomas ( Table 1 ) [10]–[12] . DBA/2NCr and BALB/cAnNCr are both H2d , but vary in their sensitivity to chemical carcinogenesis , as is also true for the two H2b strains , C57BL/6 and 129S6/SvEv . The SENCAR strain was included because it was selectively bred for high susceptibility to skin tumor induction by chemical carcinogens . All strains were infected with 6×1010 MusPV1 virions per animal on pre-scarified tail skin , using a previously optimized procedure for PV infection of mouse skin [9] , [13] , and followed for 4 months . During this period , no papilloma outgrowth was observed in any strain , although the same preparation and dose of MusPV1 virions consistently induced large papillomas in immunodeficient athymic NCr nude mice within one month of inoculation ( data not shown ) . To determine whether immunosuppression renders these mouse strains susceptible to MusPV1-induced papilloma formation , animals were treated systemically with the immunosuppressant CsA , which can inhibit T cell activation and lymphokine production and is routinely used in humans for prevention of transplant rejection [14] , [15] . CsA treatment was initiated one week prior to infection with 4 . 2×1010 MusPV1 virions and continued for additional 4 weeks , during which the mice were evaluated for papilloma formation . The results uncovered a hierarchy of susceptibility to MusPV1-induced papillomas among the strains ( Table 1 and Figure 1A–H ) . SENCAR and FVB/NCr mice were highly susceptible . Most ( 6/8; 75% ) of the SENCAR mice ( Figure 1A ) developed large and raised papillomas , with a mean length of 8 . 2 mm . All of the FVB/NCr mice ( Figure 1B ) inoculated with MusPV1 developed papillomas ( 8/8; 100% ) , which were moderately elevated , with a mean length of 7 . 6 mm . The BALB/cAnNCr , A/JCr , and 129S6/SvEv strains displayed intermediate susceptibility for papilloma development . For BALB/cAnNCr ( Figure 1C ) , 5/8 animals developed papillomas , which were smaller than those in SENCAR and FVB/NCr , with a mean length of 4 . 6 mm . The comparable numbers for A/JCr mice ( Figure 1D ) were 4/8 and 2 . 9 mm , respectively , while those for 129S6/SvEv ( Figure S1A ) were 2/4 and 2 . 5 mm , respectively . By contrast , CsA-treated C57BL/6 , DBA/2NCr , and C3H/HeJCr mice were comparatively resistant to MusPV1-induced papillomas . None of the virally inoculated C57BL/6 ( Figure 1E ) and DBA/2NCr mice ( Figure S1B ) developed papillomas ( 0/8 and 0/4 , respectively , both 0% ) , and only one C3H/HeJCr mouse ( Figure 1F ) developed a lesion ( 1/8; 12 . 5% ) , and its length was only 2 mm . The papillomas that did develop in the tested strains were attributable to the combination of CsA treatment and MusPV1 infection , as littermates inoculated with the same amount of MusPV1 virions in the absence of CsA or mock inoculation with CsA treatment did not result in papilloma formation ( Figure 1G , 1H; SENCAR shown as representative strain ) . The lesions that developed were histologically verified to be papillomas with numerous koilocytes in the epithelium ( Figure 1I; SENCAR shown as representative ) , and extracts of the lesions contained infectious MusPV1 virions that induced papillomas , in athymic NCr nude mice , whose morphology was identical to those previously reported for MusPV1 in this immunologically impaired strain ( Figure 1J ) [9] . Papillomas in the CsA-treated/MusPV1-inoculated immunocompetent strains grew progressively and did not regress during the period of CsA administration . However , after cessation of CsA administration , the lesions completely regressed within several weeks ( Figure 1K and 1L; same SENCAR mouse at a 6-week interval ) . The length of time to regression depended upon the size of the lesion , with smaller ones tending to regress sooner than larger ones ( Figure S1C ) . Therefore , the immunosuppressive phenotype resulting from CsA treatment was required for maintenance of the papillomas , in addition to their induction . In a rabbit model , persistent PV genome expression was reported at mucosal sites of infection following lesion-regression [16] . Subsequent immunosuppression led to an increase in viral copy numbers and even to reappearance of small lesions , consistent with reactivation of latent infection [17] . To investigate latency of MusPV1 , all mouse strains , except for DBA/2NCr and 129S6/SvEv , that had been inoculated with 6×1010 virions per animal , were subjected to CsA administration 4 months later for a period of 4 weeks ( corresponding to 5 months post-infection ) . After this period , papilloma outgrowth was not observed in any of the animals ( data not shown ) . MusPV1 E1∧E4 spliced transcripts , a marker for PV infection [9] , [18] , [19] , and viral genomes were undetectable in the skin tissues taken from the inoculation sites ( Figure S2 ) , suggesting that MusPV1 infection in cutaneous tissues was efficiently cleared by a fully functioning murine immune system prior to immunosuppression and therefore do not persist long-term in a latent state in immunocompetent mice . When susceptibility to MusPV1-induced papillomas was considered in the context of the H-2 haplotype of the mice , surprisingly , there was little correlation ( Table 1 ) . For example , although C57BL/6 and 129S6/SvEv are both H2b , the former strain was resistant to MusPV1-induced papilloma formation , while the latter strain had intermediate susceptibility . In addition , DBA/2NCr was resistant , while BALB/cAnNCr had intermediate sensitivity , although both strains are H2d . By contrast , we noted some correlation between the observed susceptibility to MusPV1-induced papillomas and their previously reported susceptibility to chemical carcinogenesis ( Table 1 ) [10] , suggesting that common mechanisms may , in part , control the relative susceptibility to MusPV1-induced papilloma formation and to chemical carcinogens . However , this correlation is based on different published studies on the strain-specific susceptibility to chemical carcinogenesis [summarized in 10] and is not complete , as DBA/2NCr is reported to be susceptible to chemical carcinogenesis , but MusPV1 inoculation did not produce papillomas , and the sensitivity of 129S6/SvEv to MusPV1 may be less than its reported sensitivity to chemical carcinogenesis . Further experimentation is required to validate the partial correlation between strain-specific susceptibility to papillomatosis and chemical carcinogens . Next , we investigated the impact of varying doses of MusPV1 virions on representatives of the most sensitive and the most resistant strains , SENCAR and C57BL/6 , respectively . Serial titrations of the inocula revealed that 2–3 weeks after infection with very high doses of 1×1012 MusPV1 virions per animal , the majority of the SENCAR mice developed small lesions at the site of infection ( covering about 1 cm of the length of the tails ) ( Table S1 ) , even in the absence of immunosuppression . Sporadic papilloma outgrowth was observed after infection with 1×1011 MusPV1 virions . All lesions spontaneously regressed within 1–2 weeks after formation ( corresponding to 3–5 weeks post-infection ) . Consistently , lesions did not arise with lower amounts of inocula ranging from 1×1010 to 1×108 virions . The transient papillomas in the immunocompetent SENCAR mice obtained after inoculation with 1×1012 MusPV1 virions were morphologically and histologically similar to papillomas that formed under CsA immunosuppression and to those previously reported [9] ( Figure S3A and S3B ) and contained low , but detectable MusPV1 E1∧E4 spliced transcripts , a marker for infection ( Figure S3C , lane designated M ) . In the epithelium of these papillomas , expression of the major capsid protein L1 was demonstrated by immunofluorescent microscopy ( Figure S3D , E ) . Cell extracts derived from these papillomas contained infectious MusPV1 virions that were able to induce papilloma formation on the tails of athymic nude NCr mice after experimental transmission ( Figure S3F ) , indicating that MusPV1 could be propagated in immunocompetent SENCAR mice . For C57BL/6 mice no tumor formation was observed , even at very high doses of 1×1012 MusPV1 virions , further supporting the higher resistance of this particular strain to MusPV1 pathogenesis ( Figure S3G ) . To investigate whether qualitative and/or quantitative differences in infection parameters were responsible for the observed variation in papilloma formation between strains , the relative level of MusPV1 E1∧E4 spliced transcripts were determined in skin tissues taken 4 weeks after infection with 4 . 2×1010 MusPV1 virions ( corresponding to 5 weeks of CsA administration ) from the virally inoculated sites of all strains , except for DBA/2NCr and 129S6/SvEv ( Figure 1M and Table 1 ) . Skin necropsies were taken from all animals within the remaining six tested strains , including those mice that lacked visible lesions . To ensure comparability , specimens of approximately the same size were processed , and the levels of the viral transcripts were adjusted to level of endogenous beta-actin transcripts from the same sample ( Figure 1M , the lanes designated CM ) . The resulting ratios corresponded well to the macroscopic appearance of the lesions . High levels of MusPV1 E1∧E4 copies relative to beta-actin levels were detected in tissues of CsA-treated/MusPV1-infected SENCAR and FVB/NCr mice . In CsA-treated/MusPV1-infected BALB/cAnNCr and A/JCr mice , the lower levels of E1∧E4/beta-actin reflected the less pronounced and smaller lesions observed in these intermediately susceptible strains . Consistent with the lack of visible papillomas , even lower levels of MusPV1 E1∧E4 spliced transcripts were detected in infected C57BL/6 and C3H/HeJCr mice treated with CsA . In tissues taken from MusPV1-infected littermates that had not received CsA , the levels of E1∧E4 spliced transcripts per copy beta-actin were low , but detectable , in all strains ( Figure 1M , lanes designated M ) , but were not detected in mock-infected mice ( Figure 1M , lanes designated 0 ) . Thus MusPV1 established persistent asymptomatic infections in all of the immunocompetent mouse strains . Immunofluorescent microscopy was used to examine the relative expression of the major capsid protein L1 in these tissues . SENCAR was used as the high susceptibility strain , BALB/cAnNCr as the intermediate one , and C57BL/6 as the resistant one . Given the correlation between the susceptibility of these strains to papilloma formation and their relative level of E1∧E4 spliced transcripts , it was anticipated that a similar correlation would be seen for L1 expression . However , there was abundant L1 staining in the epithelium of all CsA-treated/MusPV1-infected animals of all three strains ( Figure 1N–P ) , including the resistant C57BL/6 mice , which did not develop papillomas . Similar to previous reports in athymic NCr nude mice [9] , punctate L1 expression was found in the cytoplasm of keratinocytes in the basal and lower spinous layers of the epithelium , while nuclear L1 expression was confined to the upper spinous and granular layers , suggesting active virion production in these more differentiated epithelial layers . Additionally , some L1 positive squames that presumably enclose matured virions prior to shedding were found in these tissues . L1 expression was restricted to the epithelial compartment , although seemingly positive staining could occasionally be observed below the basement membrane due to the ( trans- ) sectioning of the papilloma's deregulated architecture . L1 staining was not detected at sites of lesion regression following cessation of CsA treatment ( data not shown ) or in the skin of untreated MusPV1-infected mice at this time point , regardless of the strain ( Figure 1Q–S ) . It remains to be determined whether CsA treatment deregulates L1 protein expression to a greater degree than that of the E1∧E4 spliced transcripts , or if a difference in sensitivities between the two assays might explain the disparate results . Interestingly , L1 was readily detected in infected sites on day 14 after inoculation in untreated mice of all three strains , raising the possibility that adaptive immune responses arising between two and four weeks may regulate late gene expression and thereby virion production ( data not shown ) . The recruitment of T cells to the infected tissue was evaluated in the SENCAR mice , which developed large papillomas with CsA treatment . Tissue taken 4 weeks after inoculation with 4 . 2×1010 MusPV1 virions ( corresponding to 5 weeks of CsA administration ) was analyzed for the presence of CD4+ and CD8+ T cells . By immunofluorescent staining ( IFS ) , higher numbers of both T cell subsets were present in the MusPV1-infected tissue , independent of whether the mice had been treated with CsA , although L1 expression was only detected in the infected CsA-treated mice ( Figure 2A and 2B ) . Many CD4+ T cells infiltrated both the epithelium and the underlying dermis in the papillomatous tissue of the CsA-treated mice and the non-papillomatous tissue of the untreated mice ( Figure 2A and 2C ) , in contrast to the limited number of CD4+ T cells in the mock-infected control skin ( Figure 2E ) . Similarly , numerous CD8+ lymphocytes were found in the papillomas of CsA-treated/MusPV1-infected mice and in the macroscopically unchanged MusPV1-infected skin of untreated animals ( Figure 2B and 2D ) . However , the CD8+ T cells were localized predominantly in the epithelium and were sparse in the dermis . Only a few widely spaced intraepithelial CD8+ T cells were found in the mock-infected control skin ( Figure 2F ) . Consistent with these findings , quantification of CD4+ and CD8+ T cell numbers in the infected skin tissues ( Figure 2G ) by flow cytometry confirmed that MusPV1 induced strong recruitment of the T cells even in CsA-treated mice . Viral infection without CsA resulted in a 15 . 3- and 12 . 8-fold increase in CD4+ and CD8+ T cell numbers , respectively , compared to mock-infected littermates . Similarly , viral infection with CsA treatment resulted in an increase in CD4+ and CD8+ T cells that was , respectively , 9 . 3- and 7 . 8-fold higher when compared with mock-infected mice not treated with CsA , or was , respectively , 31 . 1- and 15-fold higher , compared to mock-infected/CsA-treated controls . Thus , MusPV1 infection efficiently recruits both T cell subsets to the site of infection , even in the presence of CsA . However , when the same mice were analyzed for their CD4+ and CD8+ T cell levels in blood ( Figure 2H ) , spleen ( Figure 2I ) , and draining lymph nodes ( Figure 2J ) , the impact of MusPV1 infection was much less pronounced . Although some MusPV1-dependent differences seen in the CD4+ or CD8+ T cells in these systemic compartments were statistically significant , there was less than a two-fold difference for each comparison . To investigate the role of T cells as key effectors responsible for controlling MusPV1 infection and/or papilloma formation , we studied the consequences of MusPV1 infection following antibody-induced depletion of specific T cell populations in two mouse strains: SENCAR , which had been found to be highly sensitive to papilloma formation following CsA treatment , and C57BL/6 , which had been found to be resistant . The SENCAR results are presented in this section and the C57BL/6 results in the next section . In the first experiment , the CD3+ T cell population , which includes most T cell subsets , was specifically removed from the SENCAR mice by systemic administration of a monoclonal antibody ( mAb ) recognizing murine CD3 . Depletion was started at various time points , from one week prior to viral infection ( day −7 ) to 7 weeks ( day +49 ) after infection , and it was maintained for seven weeks for all groups . The mice were infected on day 0 with 7 . 3×1010 MusPV1 virions , and the number and size of lesions were determined for each group after 7 weeks of immunodepletion ( Figure 3A–H ) . Depletion starting 1 week prior to infection ( day −7; Figure 3A ) resulted in the development of large papillomas ( mean length = 13 mm ) in all 5 mice in this group . When depletion was started on the day of infection ( day 0; Figure 3B ) or 1 week after infection ( day +7; Figure 3C ) , most mice developed papillomas ( 4/5 in both groups ) , but their size was somewhat smaller ( mean length = 10 . 5 mm for both groups ) . The efficiency of papilloma formation was markedly reduced ( 2/5 ) when depletion was started 1 month post-infection ( day +28; mean length = 4 mm; Figure 3D ) . No papillomas were seen when depletion was started 7 weeks after infection ( day +49; 0/5; Figure 3E ) , suggesting that MusPV1 infection is effectively cleared prior to this time . MusPV1 inoculation after administration of an isotype control ( Figure 3F ) or without mAb addition ( Figure 3G ) did not induce papilloma formation . The papillomas in the CD3-depleted mice were histologically verified ( data not shown ) , and virions extracted from these lesions were able to cause papillomas in athymic NCr nude mice ( Figure 3I ) , thus demonstrating that depletion of CD3+ T cells enables the complete viral lifecycle . Throughout the papillomatous tissues taken after 7 weeks of depletion ( corresponding to 6 weeks post-infection ) from the CD3-depleted mice ( Figure 3J and 3K; representative of group day −7 shown ) , exceptionally large amounts of MusPV1 L1 protein were detected by IFS , and CD4+ ( Figure 3J ) and CD8+ ( Figure 3K ) T cells were absent , as expected , since these T cells are CD3+ . In contrast , skin tissues taken from isotype-administered controls ( Figure 3L and 3M ) lacked MusPV1 L1 protein , and CD4+ ( Figure 3L ) and CD8+ ( Figure 3M ) T lymphocytes were readily detectable . No L1 protein and only isolated T cells were observed in mock-infected controls ( data not shown ) . Taken together , these findings indicate that , in SENCAR mice , T cells are obligatory for controlling MusPV1 infection and associated papillomatosis . T cell-mediated responses either clear MusPV1 infection within 7 weeks post-inoculation or control it by a mechanism that does not permit reactivation after their removal . We next determined whether depletion of either the CD4+ or the CD8+ T cell population alone , by administration of anti-CD4 or anti-CD8 mAbs , respectively , would be sufficient to induce sensitivity to MusPV1-induced papillomas in the SENCAR mice . The immunodepletion was started one week prior to infection with 5 . 1×109 MusPV1 virions , and the depleted state was monitored in the animals' blood prior to infection ( day −1 ) and every 2 weeks by flow cytometric analysis ( Figure S4 ) . At six weeks post-infection , the majority of the CD4-depleted SENCAR mice had developed papillomas ( 7/9; 78% ) ( Figure 4A ) . Similarly , there were papillomas in 7/9 ( 78% ) of the CD8-depleted mice ( Figure 4B ) at this time point . MusPV1-infected controls with ( Figure 4C ) and without ( Figure 4D ) isotype depletion did not develop lesions . The mean length of the lesions was similar in the CD4-depleted and CD8-depleted groups , being 10 . 7 mm vs . 12 . 7 mm , respectively ( Figure 4E ) . The fact that removal of either subset allowed papilloma outgrowth suggests that cooperation between CD4+ and CD8+ T cells is required for effective control of infection and lesion development in SENCAR mice . As expected , MusPV1 L1 protein was present in skin tissues taken at this time point from both CD4- ( Figure 4F and 4G ) and CD8-depleted ( Figure 4H and 4I ) animals . The L1 staining pattern and the intensity were similar to the pattern observed in CsA-treated/MusPV1-infected and athymic NCr nude mice [9] , with punctate , cytoplasmic L1 expression in the lower epithelium and nuclear L1 positivity in the upper epithelial layers . Compared to the results obtained after CD3+ T cell depletion , L1 expression seemed less pronounced , but this difference may be due to the lower amount of virus used for the initial inoculation . Depletion of the targeted T cell subset at the site of infection was associated with the apparent absence of the depleted subset but with no loss in the infiltration by the non-depleted subset , indicating the neither subset was needed for recruitment of the other ( Figure 4F–I ) . After 5 weeks of depletion ( corresponding to 4 weeks post-infection ) , when the growth of papillomas was clearly visible , lymphocytes from each group ( 4 mice/group ) were isolated from the site of infection ( Figure 4J ) , and the blood ( Figure 4K ) , spleen ( Figure 4L ) and draining lymph nodes ( Figure 4M ) for analysis by flow cytometry . The results in the skin confirmed the validity of the microscopy results , and demonstrated the efficient and specific depletion of the targeted subpopulation in each compartment at this time point . T cell depletion experiments analogous to those described for the SENCAR mice were performed in C57BL/6 mice . MAb-induced depletion of CD3+ T cells was initiated one week prior to infection with 5 . 3×1010 MusPV1 virions , and the depleted state was monitored in the animals' blood one day prior to infection and every second week ( Figure S5 ) . In parallel , mAb-induced depletion was initiated , for the same length of time , for the CD4+ T cells , the CD8+ T cells , and both the CD4+ and the CD8+ T cells ( CD4+8 depletion ) , and the depletion monitored similarly ( Figure S5 ) . After 7 weeks of depletion ( corresponding to 6 weeks post-infection ) , all of the CD3-depleted animals ( 14/14 ) inoculated with MusPV1 had developed papillomas ( 100% ) ( Figure 5A ) , similar to the results observed in the SENCAR mice . This finding was somewhat unexpected , given that continuous CsA treatment of the C57BL/6 mice had not led to the development of MusPV1-induced papillomas ( Figure 1E ) . On the other hand , the mice with depletion of only CD4+ T cells ( 0/5 ) ( Figure 5B ) or of only CD8+ T cells ( 0/5 ) ( Figure 5C ) did not develop papillomas , which is in contrast to the results with the SENCAR mice under the same conditions . However , combined CD4+8 depletion in the C57BL/6 mice did lead to MusPV1-induced papillomas ( 10/10 ) ( Figure 5D ) . When crude skin extracts taken from the mice 6 weeks after infection were evaluated by western blotting for the presence of L1 protein , it was only detected in the two groups that developed papillomas , CD3 depletion and the combined CD4+8 depletion ( Figure 5E ) . Given that the positive results with C57BL/6 mice were seen with CD3 depletion or combined CD4+8 depletion induced by mAb , further analysis was focused on depletion induced by these mAb . At 6 weeks after virus inoculation , high levels of MusPV1 E1∧E4 spliced transcripts , as a measure of persistent infection , were found in infected skin tissues of CD3- and combined CD4+8-depleted animals ( Figure 5F ) , the two depletion conditions that resulted in papillomas . In contrast , no E1∧E4 spliced transcripts were detected in CD4- or in CD8-depleted mice , demonstrating the effective control of MusPV1 infection in these animals . When the expression of L1 protein at the inoculated skin sites was examined by IFS , the results corroborated the results observed with the E1∧E4 spliced transcripts . L1 was abundantly expressed in the cytoplasm and nuclei of infected keratinocytes in the characteristic pattern of MusPV1 in mice with CD3 depletion and CD4+8 depletion , but not with CD4 depletion or CD8 depletion ( Figure 5G–5N ) . As expected , no CD4+ cells were seen in skin sites from CD3-depleted , CD4+8-depleted , or CD4-depleted mice , although they were detected in CD8-depleted mice ( Figure 5G , 5M , 5I and 5K ) . Conversely , no CD8+ cells were seen in skin sites from CD3-depleted , CD4+8-depleted , or CD8-depleted mice , although they were detected in CD4-depleted mice ( Figure 5H , 5N , 5L and 5J ) . Quantification of T cell infiltrates in the infected skin sites ( Figure 5O ) and the blood ( Figure 5P ) , spleen ( Figure 5Q ) , and draining lymph nodes ( Figure 5R ) by flow cytometric analyses further confirmed the efficiency and specificity of the T cell depletion . To address whether C57BL/6 mice genetically engineered to be knock-outs ( KO ) for CD4+ or CD8+ T cells might be susceptible to papilloma induction by MusPV1 , these KO mice were inoculated with 9 . 4×1010 MusPV1 virions . However , as had been true of the mice with mAb-induced depletion of these individual T cell subsets , the CD4 KO mice ( 0/5 ) ( Figure 6A ) and the CD8 KO mice ( 0/5 ) ( Figure 6B ) were resistant to papilloma formation when observed for 3 months , as were wild-type ( wt ) C57BL/6 controls ( 0/5 ) ( Figure 6C ) , and did not produce detectable levels of L1 protein in crude skin extracts at this time point ( Figure 6D ) . However , analysis of MusPV1 E1∧E4 spliced transcripts , as a measure of infection , at earlier time points ( Figure 6E ) revealed substantially higher numbers of MusPV1 E1∧E4 spliced transcripts relative to beta-actin in CD4 KO mice in the first three weeks post-infection , compared to wt C57BL/6 and CD8 KO mice . The relative MusPV1 E1∧E4 levels in the CD4 KO mice peaked at week 2 post-infection , showing 36-fold and 27-fold higher values than in wt and CD8-deficient C57BL/6 mice , respectively , gradually decreased thereafter and became undetectable at week 6 post infection . Very low numbers of MusPV1 E1∧E4 transcripts could be detected within the first 2 week after infection in wt C57BL/6 mice and were undetectable 3 weeks after infection . In contrast the levels of MusPV1 E1∧E4 transcripts in the CD8 KO mice remained uniformly very low , but detectable , throughout the experiment , suggesting that CD4+ T cells are specifically involved in early , presumably innate , immune responses that initially control viral gene expression . CD1d-deficient C57BL/6 mice , which selectively lack the natural killer ( NK ) -T cell population , also failed to form papillomas after inoculation with 6 . 8×1010 MusPV1 virions and a 3 . 5 month observation period ( Figure 6F ) , indicating that ablation of this subset by itself is not sufficient to permit papilloma formation , in contrast to the crucial role of T cells .
In this study , we have determined that a variety of immunocompetent mouse strains are resistant to papilloma induction by MusPV1 , although a limited degree of virus expression can be detected at 4 weeks after infection . However , immunosuppression induced by CsA uncovered a strain-dependent hierarchy in the degree of susceptibility to papilloma formation and virus production . Papilloma formation correlated more closely with expression of E1∧E4 spliced transcripts than with expression of the major L1 capsid protein , as clinically normal virally inoculated skin sites in the resistant C57BL/6 mouse had relatively high levels of L1 , but low E1∧E4 levels . Inoculation with what we assume are super-physiological amounts of MusPV1 virions ( 1×1012 virions ) seems to overcome early control of infection in the highly susceptible SENCAR strain , allowing for transient papilloma formation . In the more resistant C57BL/6 strain , papilloma outgrowth was not observed after this stringent challenge , supporting the conclusion that this strain is better able to control infection than SENCAR mice . To date , strong circumstantial evidence supports the role of cell-mediated immunity , especially T cells , in controlling and eliminating established PV infection and neoplasia . Evidence for the pivotal role of T cells has emerged from studies of humans infected with the human immunodeficiency virus [20] . In these individuals , higher prevalence of HPV infection , especially of the anogenital tract , viral persistence , and very often the presence of multiple types , has been observed . An important role for T cells is also supported by observations that iatrogenic immunosuppressed transplant recipients have high rates of extensive viral warts , HPV-associated anogenital cancers , and non-melanoma skin cancer [3] . The current study demonstrated that treatment with CsA , whose predominant activity is against T cells and which is routinely used in humans , led to papilloma formation and persistent MusPV1 infection in most , but not all , of the murine strains tested . However , the finding that depletion of CD3+ T cells rendered the mice susceptible to papilloma formation and exuberant viral infection , even in C57BL/6 mice that were resistant after CsA treatment , provides direct experimental evidence for the critical importance of T cell function in the control of MusPV1 infection . The available data with other PV systems have not defined the T cell subpopulation ( s ) that is the key player responsible for virus control and papilloma regression . Observational studies in humans have found that regression of anogenital warts is accompanied by a massive infiltration of CD4+ lymphocytes , both within the papilloma's stroma and the epithelium [21] . However , intraepithelial CD8+ T cells have also been associated with regression of cervical intraepithelial lesions [22] , [23] . T cell infiltrates , consisting of predominantly CD4+ T cells but also containing CD8+ T cells , have been described in regressing mucosal lesions caused by BPV type 4 [24] , canine oral PV [25] , and rabbit oral PV [26] . In cutaneous papillomas caused by CRPV , a mostly CD8+ T cell infiltration of the epithelium , with very few accompanying CD4+ T cells , was demonstrated [27] . In our murine system , MusPV1 infection of the sensitive SENCAR mouse induced a dense CD4+ T cell infiltrate in the dermal and the epithelial compartment , as well as an intraepithelial one composed of CD8+ T cells , whether the animals developed papillomas as a result of the immunosuppressive CsA treatment or had clinically normal skin because they had not been immunosuppressed . Thus , the infiltrate was similar , independent of whether the mouse developed lesions , indicating that functional properties of the T cells , rather than epithelial trafficking , were primarily affected by CsA treatment . The availability of many immunological reagents for the domestic mouse made it possible to critically evaluate individual T cell subsets in the control of PV infection and associated neoplastic disease . In SENCAR , depletion of either CD4+ or CD8+ T cells allowed papillomatosis . By contrast , combined depletion of CD4+ and CD8+ T cells was necessary for papilloma development in C57BL/6 . These discrepant results were confirmed using genetically modified C57BL/6 mice deficient in either CD4+ or CD8+ T cells . Clearance of virally-infected epithelial cells and regression of epithelial neoplasia have most often been attributed to CD8+ T cells , especially cytotoxic ones . However , in many well-characterized systems , CD4+ T help is necessary both for induction of primary CD8+ T cell responses and for their proliferation , activation , and differentiation into effector cytotoxic T lymphocytes ( CTL ) [28]–[30] . In the absence of CD4+ T help , CD8+ T cells often fail to acquire antiviral effector functions , including the ability to produce antiviral cytokines , such as interferon ( IFN ) -γ and tumor necrosis factor ( TNF ) -α , and cytotoxic molecules , such as perforin and granzymes . In this scenario , CD4+ T cells would contribute to protection indirectly , rather than directly providing a critical T cell effector function . However , the T cell subset depletion and knock-out data in C57BL/6 data make it clear that protective antiviral CD8+ T cell responses to a PV infection can develop in the absence of CD4+ T help , and that CD4+ T cell-dependent effector functions can control PV infection in the absence of CD8+ T cell functions . There is an increasing body of evidence that CD4+ T helper-independent CD8+ CTL responses can be elicited by some pathogens , such as ectromelia virus [31] , influenza virus [32] , lymphocytic choriomeningitis virus , dengue virus [33] , and Listeria monocytogenes [34] . However , CD4+ T cells were required for efficient local recruitment of herpes simplex virus-specific CD8+ T cells to the vaginal epithelium in a murine model of herpes virus infection [35] . It was therefore unexpected that , in both C57BL/6 and SENCAR mice , CD8+ T cells infiltrated the skin sites inoculated with MusPV1 whether or not the mice contained CD4+ T cells , and vice versa . The discrepant conclusions between our study and the murine herpes virus infection report [35] may be attributable to substantial differences in the two experimental systems , most notably the adoptive transfer of transgenic CD8+ T cells in the herpes virus study and the de novo generation of the T cells by in situ virus infection in the current study . Various mechanisms for CD8+ T cell activation in the absence of CD4+ T cell help have been proposed , such as direct signaling through CD40 present on antigen-presenting cells [36] , [37] , up-regulation of CD40L on dendritic cells to enhance CD8+ T cell responses via direct engagement of CD40 on activated CD8+ T cells [38] , and NK cell-derived IFN-γ [39] . These bypass mechanisms may also be active in C57BL/6 mice and explain the observed CD4-independent suppression of MusPV1-induced papillomatosis . CD4+ T cells could independently control PV infection by either a cytokine-mediated mechanism or by direct cytotoxicity [40] . Several recent studies have reported that cytolytic CD4+ T lymphocytes , which may represent a new CD4+ lineage , can contribute to the control of certain viral infections [40]–[42] . In C57BL/6 mice , lymphochoriomeningitis virus-specific CD4+ T cells are capable of in vivo cytolytic killing of peptide pulsed MHC II-positive lymphocytes [43] . However , none of these studies have demonstrated complete protection from productive infection and disease in the absence of CD8+ T cells . Cytolysis by CD4+ T cells would likely require a direct interaction of the T cells with MHC class II molecules on the infected keratinocytes ( the only cell type that is normally infected by PVs ) . We did detect CD4+ T cells within the epithelium at sites of infection , in addition to being present in the dermis , and keratinocytes can express class II molecules under certain inflammatory conditions , e . g . in response to ( IFN ) -γ [44] . However , we failed to detect MHC class II expression on keratinocytes at sites of infection , both when the infection was being controlled by the immune system and when it was not ( unpublished data ) . Therefore we currently favor the hypotheses that CD4+ T cells are activated by cross-presentation of viral antigens by professional antigen presenting cells , and PV infection is controlled via soluble factors produced by the CD4+ T cells . Although the details of immune recognition of MusPV1 remain to be determined , it seems likely that T cells control MusPV1 infection by both innate and adaptive immune mechanisms . The timing of papilloma regression after suspending CsA treatment and of spontaneous regression after high dose challenge in the SENCAR mice is consistent with induction of an antigen-specific adaptive response . However , the early control viral gene transcription in C57BL/6 mice at week 1–2 , that is maintained in the CD8 KO mice but lost in the CD4 KO mice ( Figure 6E ) , strongly suggests that CD4-mediated innate immune responses also plays a role in controlling infection . Our findings raise several additional interesting issues . One is what may account for the different requirements for protection in C57BL/6 and SENCAR by CD4+ and CD8+ T cells . One possibility is that both the CD4+ and the CD8+ T cells in C57BL/6 are more potent in their ability to confer resistance than in SENCAR , making the presence of one of the T cell subsets sufficient to keep C57BL/6 resistant . This situation could arise if there were a single factor common to both T cell subsets that is more potent in C57BL/6 than in SENCAR , or if there were separate CD4+-specific and CD8+-specific factors . Alternatively , there could be a factor that , although it is extrinsic to CD4+ and CD8+ T cells , can cooperate with either subset in making a mouse resistant to papillomatosis . The observed strain-dependent difference could then be explained if the activity of this putative non-CD4/non-CD8 factor ( s ) is sufficiently more potent in C57BL/6 than in SENCAR that it can cooperate with CD4+ or CD8+ T cells to confer resistance in C57BL/6 but not in SENCAR . A second issue is the partial correlation between the reported strain-dependent susceptibility to chemical-induced papillomatosis and the robustness of persistent infection and papillomatosis after CsA treatment found in this study . The observed partial correlations may be due to yet undetermined strain-specific genetic factors allowing for control of skin tumor susceptibility/resistance . The effects of immunosuppression on 7 , 12-dimethylbenz[a]anthracene ( DMBA ) /phorbol ester 12-O-tetradecanoylphorbol 13-acetate ( TPA ) tumorigenesis have been studied to a limited degree . In C3H/HeN , compared with wt mice , CD8 KO mice had an increased number of tumors , but numbers were decreased in CD4 KO mice [45] . In contrast to C3H/HeN , in FVB/N mice , CD8 KO mice had a decreased number of tumors [46] . It might be informative to examine the role of CD4+ and CD8+ cells in DMBA/TPA tumorigenesis in C57BL/6 . A third issue is whether the characteristics of MusPV1 infection documented herein make it an attractive model of any HPVs . At present MusPV1 infection appears to most closely resemble that of genus beta HPVs . These cutaneous HPVs have been found in immunocompetent individuals in clinically normal skin and plucked hairs where they predominantly induce asymptomatic skin infections [47] , [48] , similar to MusPV1 infection described herein . However , after immunosuppression , beta HPVs could be more frequently detected , tended to display higher viral loads , and most importantly induced visible lesions after immunosuppression [49]–[51] . The presence of beta-HPVs has been implicated as a causal factor in the increased risk for development of non-melanoma skin cancers in immunosuppressed transplant recipients . Thus , MusPV1 infection may provide a model to study the impact of a cutaneous PV type on the pathogenesis of non-melanoma skin cancer in an immunosuppressed and in an immunocompetent setting .
The following mice ( H-2 haplotypes are given in parentheses ) were obtained from the National Institutes of Health or The Jackson Laboratories ( Bar Harbor , MN ) : immunocompetent outbred Cr:ORL SENCAR; immunocompetent inbred strains FVB/NCr ( H2q ) , BALB/cAnNCr ( H2d ) , DBA/2NCr ( H2d ) , A/JCr ( H2a ) , C57BL/6NCr ( C57BL/6 ) ( H2b ) , 129S6/SvEv ( H2b ) , C3H/HeJCr ( H2k ) ; immunodeficient athymic NCr nu/nu; CD4- , CD8- , CD1d-deficient C57BL/6 . The mice , all females aged 6–10 weeks , were housed and handled in strict accordance to the National Institutes of Health guidelines for the use and care of live animals . Experimental protocols were approved by the National Cancer Institute's Animal Care and Use Committee ( Permit Number LCO 027 ) . Crude extracts of papillomatous tissues were prepared as previously described [9] , and MusPV1 virions purified from the extracts by Optiprep gradient centrifugation as detailed on the laboratory website ( http://home . ccr . cancer . gov/Lco ) [52] . In the purified preparations , MusPV1 viral copy numbers were quantified by real-time PCR [9] after liberation of encapsidated DNA from the viral capsids with proteinase K . The presence of the MusPV1 major capsid protein L1 in the purified fractions or in the crude tissue extracts was determined by Western blot using a polyclonal rabbit immune serum raised against MusPV1 L1 virus-like particles , at a dilution of 1∶1000 [9] . In vivo infection with purified MusPV1 virions was performed on pre-scarified skin of the animals' tails as previously published [9] , [13] . The tail was chosen , as it represents a location that is highly permissive for MusPV1 infection [9] . The tail also has the advantage over the equally permissive muzzle skin , in that extensive lesional growth does not cause obvious distress to the animals . The skin on the animals' backs was not tested due to its minimal susceptibility to MusPV1 infection in athymic NCr nude mice using the same technique of inoculation [9] . For systemic immunosuppression , CsA ( Sandimmune Inject . , Novartis ) was diluted under sterile conditions with PBS and administered subcutaneously to the animals five times per week at a dose of 75 mg/kg body weight in a volume of 0 . 1 ml . Treatment was started one week prior to infection and maintained for additional 4 weeks post-infection for a total of 5 weeks . In vivo depletion of T cells was achieved by intraperitoneal administration of mAbs ( all BioXCell ) : anti-mouse CD4 ( clone GK1 . 5 ) , anti-mouse CD8a ( clone 53-6 . 72 ) , anti-mouse CD3 ( clone 17A2 ) in a dose of 0 . 5 mg per mouse in a volume of 0 . 1 ml . Rat IgG2b ( clone LTF-2 ) or rat IgG2a ( clone 2A3 ) mAbs were used as appropriate isotype controls . Depletion was performed on three consecutive days starting at indicated time points and the depleted state maintained by administration of mAbs twice a week for a period of 7 weeks . Depletion was verified by flow cytometry analyses as described below . The CsA experiments ( n = 4–5 animals per experimental group ) were repeated twice for FVB/NCr , A/JCr and C3H/HeJCr mice , three times for BALB/cAnNCr and C57BL/6 mice , and 17 times for Cr:ORL SENCAR . The experiments using DBA/2NCr and 129S6/SvEv mice were tested only once as these haplotypes ( H2d and H2b ) were already represented by BALB/cAnNCr and C57BL/6 mice , respectively . The depletion experiments in Cr:ORL SENCAR mice ( n = 9 per group ) and in C57BL/6 mice ( n = 5 per group ) were repeated twice . The experiments employing C57BL/6 KO mice ( n = 5 per group ) were repeated twice . For detection of MusPV1 E1∧E4 spliced transcripts as a measure for initial infection , total RNA was isolated from tail skin necropsies using TRI Reagent ( Molecular Research Center Inc . ) , treated with DNAse I ( Qiagen ) , and reverse-transcribed into cDNA using the SuperScript III First-Strand Synthesis System ( Invitrogen ) , following the manufacturers' instructions . Real-time PCR using primers and probe specific for MusPV1 E1∧E4 spliced transcripts was performed in an ABI PRISM 7900HT Sequence Detection System , as previously reported [9] . The results were correlated to the endogenous control , beta-actin ( LifeTechnologies ) , in the same samples . Quantification of MusPV1 genome copy numbers were performed using MusPV1-forward and MusPV1-reverse primers and compared to defined amounts of re-ligated MusPV1 genome as standards , as described previously [9] . Skin necropsies were snap frozen in Tissue-Tek OCT Compound freezing medium ( Sakura Finetek USA Inc . ) and HE- and IFS performed on ethanol-fixed tissue sections of 6 µm thickness [9] , [13] . For detection of MusPV1 L1 protein , sections were stained with a rabbit polyclonal immune serum directed against MusPV1 L1 at a dilution of 1∶4000 and detected with either an Alexa Fluor 488 or an Alexa Fluor 594-conjugated donkey anti-rabbit secondary antibody ( both Life Technologies ) , as indicated . Co-stainings with either directly conjugated Alexa Fluor 488-anti-mouse CD4 or Alexa Fluor 488-anti-mouse CD8a antibodies ( both Biolegend; dilution 1∶100 ) were performed to detect CD4+ or CD8+ T cells , respectively . To determine localization of MusPV1 L1 in relation to basal keratinocytes , sections were co-stained with a phycoerythrin-conjugated anti-CD49f antibody ( integrin alpha 6 , BD Biosciences ) . Nuclei were visualized by mounting sections with ProLong Gold antifade reagent containing 4′ , 6-diamidino-2-phenylindole ( DAPI ) ( LifeTechnologies ) . All microscopy analyses were performed on a Zeiss LSM 510 UV system and color levels of images were processed equally in Adobe Photoshop across experiments . Spleens , iliac and inguinal lymph nodes , and tail skin were harvested from CO2 euthanized mice . To obtain single cell suspensions , spleens and lymph nodes were enzymatically digested with DNAse I ( 0 . 2 mg/ml; Roche ) with and without Collagenase A ( 0 . 5 mg/ml; Roche ) , respectively , and processed as described previously [53] . Skin tissues were cut into fine pieces and incubated with collagenase IV ( 2 mg/ml; Worthington Biochemical Corp . ) plus DNAse I ( 0 . 1 mg/ml ) in RPMI 1640 , supplemented with 10% fetal bovine serum and 1% penicilin/streptomycin , for 1 hr at 37°C . All cell suspensions were passed through 70 µM nylon mesh filters ( BD Falcon ) prior to lysis of erythrocytes with Ammonium-Chloride-Potassium Buffer ( ACK; Lonza ) . Blood was collected in heparinized tubes and erythrocytes removed by ACK lysis . After blocking of Fc receptors by incubation with anti-mouse CD16/CD32 antibody ( BD Pharmingen ) , surface-stainings were performed on single cell suspensions from tissues and blood using anti-mouse CD4-PerCP/Cy5 . 5 ( clone RM 4-5; BD Pharmingen ) and anti-mouse CD8a-FITC labeled antibodies ( clone YTS; Abcam ) . Cells were fixed with Cytofix/Cytoperm ( BD Biosciences ) and acquisitions of flow cytometric data performed on a FACSCanto with FACSDiva software ( BD Biosciences ) [53] . The FlowJo software was used for analyses . Statistical analyses ( Mann Whitney tests ) were performed using the GraphPad Prism Software 6 . 00 for Windows . | Infection with papillomaviruses can cause benign warts ( papillomas ) on skin and mucosae of humans and animals but also malignancies , especially anogenital carcinomas and , in genetically predisposed or immunocompromised individuals , cutaneous squamous cell cancers . Control and clearance of these viruses are thought to be mediated by the cellular immune system , however experimental determination for the necessary cellular effector ( s ) is lacking . The recently identified mouse papillomavirus ( MusPV1 , also designated “MmuPV1” ) is known to induce papilloma formation on skin of immunodeficient mice . However , its pathogenesis in immunocompetent mice is unclear . Our study shows that in an immunocompetent setting , MusPV1 generally causes asymptomatic skin infections , but no lesion outgrowth . Visible papillomas were consistently observed after profound immunosuppression in some , but not other , strains of mice . By selective removal of distinct cellular immune populations and employing genetically modified mice , we could show that T cells are pivotal for controlling MusPV1 infection and disease . We further show that surprising differences in the T cell subsets are required for protection in different strains of immunocompetent mice . This implies that unanticipated effector mechanisms can control virus infection and pathogenesis in specific genetic backgrounds . The findings may help to explain the wide of range of pathologic outcomes of infection by a specific human papillomavirus type in immunocompetent people . | [
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] | 2014 | Strain-Specific Properties and T Cells Regulate the Susceptibility to Papilloma Induction by Mus musculus Papillomavirus 1 |
To understand the regulation of tissue-specific gene expression , the GTEx Consortium generated RNA-seq expression data for more than thirty distinct human tissues . This data provides an opportunity for deriving shared and tissue specific gene regulatory networks on the basis of co-expression between genes . However , a small number of samples are available for a majority of the tissues , and therefore statistical inference of networks in this setting is highly underpowered . To address this problem , we infer tissue-specific gene co-expression networks for 35 tissues in the GTEx dataset using a novel algorithm , GNAT , that uses a hierarchy of tissues to share data between related tissues . We show that this transfer learning approach increases the accuracy with which networks are learned . Analysis of these networks reveals that tissue-specific transcription factors are hubs that preferentially connect to genes with tissue specific functions . Additionally , we observe that genes with tissue-specific functions lie at the peripheries of our networks . We identify numerous modules enriched for Gene Ontology functions , and show that modules conserved across tissues are especially likely to have functions common to all tissues , while modules that are upregulated in a particular tissue are often instrumental to tissue-specific function . Finally , we provide a web tool , available at mostafavilab . stat . ubc . ca/GNAT , which allows exploration of gene function and regulation in a tissue-specific manner .
Tissue-specificity , in which cells perform different functions despite possessing identical DNA , is achieved partially through tissue-dependent mechanisms of gene regulation , including epigenetic modification and transcriptional and post-transcriptional regulation [1–3] . These complex programs of control produce different gene expression programs across tissues , with most genes showing statistically significant differential expression [4 , 5] . These differences can have significant consequences: tissue-specific genes are especially likely to be drug targets [6] and tissue-specific transcription factors are especially likely to be implicated in complex diseases [2 , 7 , 8] . Understanding these differences is also essential for understanding pleiotropic genes , and for interpreting studies in which genomics data can only be collected for an accessible or a proxy tissue ( such as use of blood in studying psychiatric disorders [9–11] ) . Tissue-specific mechanisms of control may be captured by co-expression networks , in which two genes are connected if their expression levels are correlated across a set of individuals . In such a setting , genetic or environmental differences across individuals serve as small perturbations to the underlying regulatory network , resulting in correlation between genes’ expression levels that are consistent with regulatory relationships . Co-expression networks provide insight into cellular activity as genes that are co-expressed often share common functions [12] , and such networks have been widely used to study disease [13–15] . The Genotype-Tissue Expression ( GTEx ) consortium dataset [16] provides an opportunity to study such co-expression networks for an unprecedented number of human tissues simultaneously . However , many of the profiled tissues have fewer than a dozen samples , too few to accurately infer the tens of millions of parameters that would define a co-expression or regulatory network . One solution would be to combine all available samples and learn a single consensus network for all tissues , but this would offer no insight into tissue-specificity . On the other hand , inferring each network independently ignores tissue commonalities: tissue networks share far more links than would be expected by chance , and learning links across multiple tissues is less noisy than learning links using a single tissue , even when using the same number of total samples [12] . Here , we use a novel algorithm , GNAT ( Gene Network Analysis Tool ) , to simultaneously construct co-expression networks for 35 distinct human tissues . Using a hierarchy which encodes tissue similarity , our approach learns a network for each tissue , encouraging tissues that are nearby in the hierarchy to have similar networks . Hierarchical transfer learning has been shown to improve power and accuracy in previous work [5 , 6 , 17 , 18] . We propose a novel hierarchical model along with a parameter optimization method designed for large-scale data , and apply it to the GTEx data . We show that our method infers networks with higher cross-validated likelihood than networks learned on each tissue independently or a single network learned on all tissues . Our method is applicable to any dataset in which sample relationships can be described by a hierarchy—for example , multiple cancer cell lines or species in a phylogenetic tree . The complete code for our method is available as S1 Data . We analyze the resulting networks to make several novel observations regarding principles of tissue-specificity . We propose multiple metrics for identifying genes that are important in defining tissue identity , and demonstrate that such genes are disproportionately essential genes . We show that tissue-specific transcription factors , which are central hubs in our networks , link to genes with tissue-specific functions , which in turn display higher expression levels . We identify 1 , 789 gene modules that are enriched for Gene Ontology functions , and show that enriched modules that are upregulated within a tissue are often instrumental to tissue function . We also show that modules which occur across tissues are especially likely to be enriched for Gene Ontology functions , and that these functions tend to be those which are essential to all tissues . The results presented here , including all the networks and gene modules , can be interactively queried through our web tool [19]; the genes and modules identified provide a basis for future investigation .
The goal of our algorithm was to construct co-expression networks that captured both tissue-dependent and tissue-shared relationships between genes . In order to increase statistical power and accuracy when inferring such relationships in tissues with limited sample sizes , it used a two-stage transfer learning framework to construct networks for all tissues simultaneously . The first stage of the algorithm constructed a hierarchy over the tissues . The second stage optimized the network for each tissue using a method that encouraged fidelity to the expression data , sparsity in the networks , and similarity between networks that were nearby in the hierarchy . 1 . Learning a hierarchy . A tissue hierarchy was constructed using agglomerative hierarchical clustering on the mean gene expression levels for the 35 tissues ( Fig 1 ) . Since the rest of the algorithm was independent of the construction of the hierarchy , the method would also work with a hierarchy based on prior knowledge or on some other measure of dataset similarity . 2 . Learning networks based on the hierarchy . We modeled the network for each tissue in the hierarchy using a Gaussian Markov Random Field ( GMRF ) , a standard model in computational biology and image processing [20–22] . GMRFs model gene expression with a multivariate Gaussian distribution; we projected the samples for each gene onto a Gaussian ( Methods ) so this modeling assumption was reasonable . GMRFs are parameterized by an inverse covariance matrix S ( k ) ( where k denotes the kth tissue ) whose zero entries indicate pairs of genes that have expression levels which are conditionally independent given the expression levels of the other genes . These entries correspond exactly to direct connections between genes in the GMRF; other genes may still be connected through longer paths in the network . To encourage zero entries and diminish the number of links in the network , GMRFs maximize the convex Gaussian log likelihood plus an L1 sparsity penalty: n ( k ) 2 ( log det S ( k ) - t r ( S ( k ) Σ ( k ) ) ) - λ s ( k ) ‖ S ( k ) ‖ 1 where n ( k ) is the number of samples and Σ ( k ) the empirical covariance matrix for the genes in tissue k , and λ s ( k ) is a sparsity parameter . The sparsity makes the networks more interpretable and computationally tractable . We extended this method by constraining the matrices S ( k ) in tissues that were nearby in the hierarchy to have similar entries , creating similar networks , using an L2 penalty that penalized differences between the S ( k ) . We used an L2 penalty rather than an L1 penalty because it allowed us to develop a fast parallel algorithm for optimizing the objective function ( Methods ) . This transfer learning framework proved especially valuable for tissues with very few samples , for which we would otherwise lacked sufficient statistical power to infer co-expression networks . For example , we had only about two dozen samples for each of the 13 brain tissues in the GTEx dataset—too few to learn networks with 50 million parameters—but because all the brain tissues were closely related in our hierarchy , by adaptively sharing samples for related brain tissues we were able to make more robust estimates of co-expression . We provide a schematic illustration of our algorithm in Fig 2 . Previous work suggests the promise of using transfer learning to learn multiple genetic networks [18 , 20 , 21 , 23]; hierarchical models have also been used more broadly throughout biology , for example to study phylogenies [24] . [18] used prior knowledge of a hierarchy of cancer cell types to learn a network for each cell type . Their method , however , relied on a hand-specified hierarchy , which would only be feasible if the number of datasets was smaller than the 35 in the GTEx dataset , and though successful in simulation was never shown to improve on prior methods on real data . [20] and [21] learn networks for multiple datasets using shrinkage between precision matrices , although they do not use a hierarchy and simply use a single shrinkage parameter . Additionally , none of these methods were designed to work on the large number of tissues included in the GTEx dataset , because such data has not been previously available . Importantly , our choice of optimization objective allows parallel optimization of all 35 tissue networks , which is critical for scaling to a large number of tissues . In contrast , the methods described in [18] and [20] cannot be easily parallelized and thus will not scale to the GTEx dataset , as we confirmed by testing their code on simulations with 35 tissues but far fewer genes than we use in our analysis ( n = 10 versus n = 9998 ) . Adapting our algorithm to the scale of the GTEx data required several further methodological innovations ( Methods ) . For example , selecting a sparsity parameter for each of the 35 datasets using cross validation would have been prohibitively slow , so we developed a faster heuristic . We used 5-fold cross-validation to evaluate our algorithm: for each tissue , we randomly divided our samples into five groups , learned networks based on samples from four of the five groups , and measured the accuracy of each network ( quantified by the log likelihood on the held out test data ) using the remaining group . We compared the performance of our method to two baselines: learning a network for each tissue independently , or learning a single network for all tissues . We observed a higher log likelihood on the held out test set using our approach as compared to the two baselines on three different gene sets of increasing sizes ( Fig 3 ) , indicating that the transfer learning approach resulted in a more robust estimation of the networks . We confirmed the accuracy of our learned networks in two ways . First , we evaluated agreement with two previous datasets . When we compared our networks to the co-expression database COEXPRESdb [25] , pairs of genes we predicted to be linked had expression levels that were 2 . 6 times as correlated as genes we did not predict to be linked ( p < 10−6 , 2-sample KS test ) . To analyze tissue-specificity , we also compared our networks to TS-CoExp [12] , which provides lists of tissue-specific co-expressed genes . Genes we predicted to be linked in a tissue were 10 . 5 times more likely to be linked in the corresponding TS-CoExp tissue than genes we did not predict to be linked ( p < 10−6 , χ2 test ) . Links in the TS-CoExp database that were specific to a tissue were 2 . 1 times more likely to appear in our networks for the tissue than links in the TS-CoExp database that were not specific to that tissue ( p < 10−6 , χ2 test ) . ( We compared all these numbers to the baseline of the learning the networks independently , which yielded slightly higher agreement with TS-CoExp and virtually equivalent agreement with COEXPRESdb . We speculate that the higher agreement with TS-CoExp is due to the fact that the TS-CoExp networks were also learned on tissues independently . ) Second , using Gene Ontology [26] , we found that genes that were linked in our networks were likely to represent functionally coherent interactions: across all tissues , genes that shared a Gene Ontology function were linked to each other 94% more often than were genes that did not share a function ( p < 10−6 , t-test ) . ( Gene Ontology annotations were downloaded January 2012; for enrichment analysis , we only considered functional categories with 30–300 annotations . ) Tissue-specific transcription factors ( tsTFs ) are important in defining tissue-specific phenotypes and mutations affecting tsTFs are enriched in loci associated with disease [2 , 27] . We used our networks to analyze the role tsTFs play in tissue specificity using a collection of 203 known tsTFs ( S2 Table ) and 88 general TFs ( gTFs ) defined in [8] . We provide a schematic illustration of important conclusions of our analysis in Fig 4 and a tabular summary in Table 1 . Well-connected genes ( also known as “hubs” ) are especially likely to be essential genes [28] . To quantify a measure of “hubness” , we computed the betweenness centrality [29] in our networks for each gene . Both general and tissue-specific TFs had higher average hubness scores than the average gene ( p < . 001 , p = . 023 , respectively ) , highlighting the importance of TFs in our networks . tsTFs were higher expressed in tissues they were specific to ( p < . 001 , bootstrap; S1 Fig ) , and tsTFs that showed the largest expression increases in tissues they were specific to were especially likely to be essential genes as defined in [30] ( 16 of the top 20 tsTFs as compared to 115/203 tsTFs overall , p = . 005 , Fisher’s exact test; this enrichment was not sensitive to the choice of 20 as the cutoff ) . tsTFs which showed tissue-specific increases in expression tended to also show increases in hubness ( Spearman p = 3 ⋅ 10−4 ) ( S2 Fig ) . To investigate how tsTFs interacted with genes with tissue-specific functions , we defined thirteen sets of tissue-specific function genes ( tsFXNGs ) using Gene Ontology annotations of gene function ( S3 Table ) . Importantly , in our networks , tsTFs showed clear signs of preferentially connecting to and upregulating genes with tissue-specific functions . Across all tissues , tsTFs were 58% more likely to be linked to genes with tissue-specific functions than they were to be linked to other genes ( p < 10−6 , binomial test ) . Genes with tissue-specific functions that were connected to tsTFs were higher expressed on average than either a ) genes with tissue-specific functions that were not connected to tsTFs or b ) genes with non tissue-specific functions that were connected to tsTFs ( p < 10−6 , t-test ) . ( For a list of the tsTFs linked to the largest numbers of tissue-specific genes , see S4 and S5 Tables ) . This underscores the important role that tfTFs play in upregulating genes with tissue-specific functions . Perhaps as a consequence of this upregulation , tsFXNGs were higher expressed in the tissues they were specific to than in the tissues they were not specific to ( p < . 001 , bootstrap ) . ( We note that because our analysis is correlative and our networks are undirected , further analysis is needed to conclusively establish directed regulatory relationships . ) Strikingly , in contrast to tsTFs , tsFXNGs were less hubby than the average gene . This was especially surprising given that , across all tissues , higher-expressed genes tended to be more hubby ( p < . 001 , linear regression ) . However , our finding is consistent with prior research showing that tissue-specific proteins have fewer interactions than widely expressed proteins [31] . One possible explanation is that tsFXNGs lie at the periphery of our networks because they have specialized functions , acting as final nodes in pathways . To gain further insights into genes that were important to tissue specificity , at each internal node in our tissue hierarchy ( representing a point where one group of tissues split into two ) we examined genes that differed in hubness most dramatically between the two tissue groups . We first sorted all genes by the difference in their hubness in brain and non-brain tissues . The highest three scoring genes have all been previously shown to play important roles in the brain: ACTL6A , a chromatin remodeling factor which is required for the development of neural progenitors [32 , 33]; VRK2 , a gene implicated in schizophrenia [34]; and the Huntington’s gene , HTT . Notably , three of the four genes HTT was most often linked to in brain tissues are themselves associated with neurological disorders: RNF123 to major depression [35] , MTHFR to neural tube defects [36] and dementia [37]; MECP2 to Rett syndrome [38] . HTT has been found to interact directly with MECP2 [39] . Several other tissue-specific hubs proved interesting ( S6 Table ) . For example , the genes which increased most in hubness in the two skin tissues were APOE , which has been linked with skin lesions known as xanthomas [40] ( although it is more famous because of its link with Alzheimer’s ) and CERS3 [41] , which when mutated causes congenital ichthyosis , a skin disease . Similarly , in the testis , the top-two ranked tissue-specific hubs were DDX3Y and KDM5D , both Y-chromosome linked genes which function in spermatogenesis [42–44] . To identify tissue-specific and tissue-shared gene modules , we used the affinity propagation algorithm [45] to group genes into modules for each of the tissue networks . The average number of genes per module was 18 , with the largest module containing 56 genes; there were 548 modules per tissue on average . 1 , 789 modules were enriched for Gene Ontology functions ( Fisher’s exact test with Bonferroni correction p < . 05 ) ; all enriched modules can be viewed online [19] . Functionally enriched modules upregulated in a given tissue were often instrumental to tissue-specific function ( S7 Table ) . In the blood , for example , the most upregulated enriched module ( henceforth , the “top module” ) was enriched for T cell receptor complex expression ( Fig 5 ) ; in the skin , for epidermis development; in the testis , for chromosome segregation; in the muscle and heart for muscle-related functions; and in various brain tissues for glutamate receptor activity , chloride channel activity , and regulation of axonogenesis . Given the plausibility of these functions , these modules represent useful candidates for future investigation . Curiously , genes that were members of enriched clusters were less hubby than genes that were not in every tissue ( p < . 001 , t-test ) . This discrepancy was so pronounced that we originally noticed it by visual examination of the networks in our web tool . One explanation would be that these enriched modules , like tsFXNGs , lie at the peripheries of networks because they act as the final steps in functional pathways . Top modules also revealed more complex relationships between tissues . For example , immune-related modules were found not only in the blood , but also in lung and digestive tissues . ( We note that there is some possibility of sample contamination , with the collected lung tissue including some blood cells . On the other hand , previous research [5] has found that the lung has similar gene expression patterns to immune tissues like the spleen and thymus , perhaps indicating the importance of immune function in the lung . ) The top module in suprapubic skin , enriched for mitosis , was also upregulated in other tissues where cells divide frequently , including the testis , the stomach , the esophagus , and the colon . Our analysis also revealed upregulation of tissue-specific modules in “similar” tissues: the top module in one tissue was often upregulated in tissues nearby in the hierarchy . For all brain tissues , top modules were dramatically upregulated in all other brain tissues as well , but not in non-brain tissues ( Fig 6 ) . The top module in the heart atrium , related to “structural constituent of muscle” was unsurprisingly upregulated in the muscle and heart ventricle as well . We also identified a number of modules that were conserved in most tissues , representing ubiquitous functions shared by all cells . For each module in each tissue , we measured the degree to which the module was conserved by calculating the average fraction of links that were present among its genes in other tissues: f = 1 K ∑ j = 1 K n k n , where K was the total number of tissues , nk was the number of links between genes in the module in the kth tissue , and n was the number of links had the module been fully connected . When we sorted modules by f ( filtering out modules with fewer than 10 genes , which tended to have high interlink fractions ) we found that the top 50 modules were much more likely than the average module to be significantly enriched for a Gene Ontology function ( 78% vs 11% ) , and were dominated by functions related to chromosome segregation or the cell cycle , capacities essential for almost every tissue . When we sorted functions by the degree to which their enriched modules were conserved , we found that 8 of the 10 most conserved functions were general to almost every tissue , relating to cell division or cell signaling: “phosphatidylinositol-mediated signaling” , “mitotic cell cycle spindle assembly checkpoint” , “chromosome segregation” , “cell cycle” , “transport” , “cytokinesis” , “M phase of mitotic cell cycle” , and “chromosome , centromeric region” .
We present an algorithm that infers genetic networks in a collection of tissues , using a hierarchy to share data between tissues with many samples and tissues with few , and show that this sharing increases the accuracy with which we infer the networks . We use an objective function that can be optimized over all tissues in parallel , allowing our algorithm to scale to the GTEx dataset , and propose several further innovations that increase scalability . Our algorithm has broad applicability to any dataset of hierarchically related samples: species in a phylogenetic tree or cell lineages in a tumor , for example . We then conduct a detailed analysis of the genetic networks in 35 human tissues , searching for principles underlying both the unity and diversity of tissue function . We find that unity arises from modules that persist across tissues , which are not only disproportionately likely to be enriched for Gene Ontology functions , but for functions like mitosis that are shared across virtually every tissue . We show that previously discovered general transcription factors , which act across many tissues , tend to be hubs in our networks . At the same time , we find strong evidence of functional specialization among tissues ( Fig 4 ) . tsTFs , which tend to be hubs in our networks , play instrumental roles: they preferentially connect to genes with tissue-specific functions , and these genes show higher expression levels . Strikingly , genes with tissue-specific functions lie at the peripheries of our networks , as do genes within enriched clusters; one explanation for this is that these genes act as the final steps in pathways instrumental to tissue-specific function . Finally , modules enriched for Gene Ontology functions that are upregulated within a tissue are often instrumental to tissue-specific function , and provide intriguing candidates for biological investigation . As the availability of biological data increases , statistical network analysis will continue to reveal both important general principles by which networks accomplish their functions , and specific hypotheses worth investigating .
Genome-wide gene expression data for 1 , 606 samples across 43 unique tissues was collected by the GTEx consortium using RNA-sequencing; we used version phs000424 . v3 . p1 of the data . We confined our analysis to tissues with expression data for at least ten samples , resulting in a total of 1 , 559 samples and 35 tissues ( S1 Table ) . GO annotations were downloaded from www . geneontology . org on January 28th , 2012 . All IEA annotations were excluded , and then all remaining GO categories with 20–300 annotated genes ( any annotation type except IEA ) were included in the analysis . No filter was placed on the ontology . For each read count ni in each sample , we computed the normalized read count ri = log2 ( 2 + C ⋅ ni/n ) where n was the total number of reads in the sample and C was the FPKM normalization constant , 5 ⋅ 107 . Because GMRFs are designed for Gaussian data , we projected all samples for each transcript for each tissue onto a Gaussian with variance 1 . The GTEx dataset contained expression levels for 52 , 576 different transcripts , which would have produced a prohibitively large covariance matrix . We filtered down the set of transcripts to a more computationally tractable size . Since transcripts would have to show variation in expression levels to have meaningful patterns in correlation , we first filtered out all probes that were zero or constant across any tissue by requiring that genes show non-zero expression in at least 1/5 of samples in a tissue . We then selected a set of transcripts as follows: we repeatedly looped over all tissues , and for each tissue selected the transcript which corresponded to a gene which showed the highest relative expression in that tissue , was annotated in Gene Ontology , and was not already included in the genes selected . ( We defined relative expression in a tissue to be the difference between the gene’s mean expression in that tissue and the gene’s mean expression across all tissues divided by the variance of the gene’s expression ) . We continued this process until we had obtained 9 , 998 genes . ( This number was produced by choosing a threshold of 10 , 000 genes , which represented a compromise between representing the entire dataset and achieving computational tractability , and removing two genes which did not have unique names . ) This process yielded a set of genes with diverse tissue-specific functions ( since each tissue contributed many genes which showed high relative expression in that tissue ) . We confirmed that our algorithm also produced improvements over the baseline algorithms in two smaller gene sets containing roughly 2 , 000 genes: one selected using the method described above , and one selected using the genes that showed the largest variance across tissues . Given a hierarchy of K tissues , our algorithm learned a precision matrix for each node in the hierarchy , including the K leaf nodes S ( 1 ) , S ( 2 ) , … , S ( K ) ( which corresponded directly to tissues ) and the K − 1 internal nodes S ( K+1 ) , … , S ( 2K−1 ) . Denote by S p k the parent of node k . Then the optimization objective was max S ( k ) , k = 1 , … , 2 K - 1 ∑ k = 1 K ( n ( k ) 2 ( log det S ( k ) - t r ( S ( k ) Σ ( k ) ) ) - λ s ( k ) ‖ S ( k ) ‖ 1 ) - λ p ∑ k = 1 2 K - 2 ‖ S ( k ) - S p ( k ) ‖ 2 2 S ( k ) ⪰ 0 , k = 1 , 2 , … , K where λ s ( k ) were the k L1 sparsity penalties ( chosen for each dataset as described below ) and λp was the L2 penalty that encouraged S ( k ) to be similar to its parent S p ( k ) ( constant for all tissues ) . In other words , for the leaf nodes , our optimization objective included the Gaussian log likelihood term , a sparsity penalty on the off-diagonal elements , and an L2 parent similarity term; for the internal nodes , there was only an L2 similarity term . While this optimization objective was convex , the inverse precision matrices had tens of millions of entries and optimizing all 2K − 1 matrices simultaneously would have been very slow . Instead , we used an iterative algorithm: given a hierarchy , the full optimization procedure was as follows: For each dataset k = 1 , … , K , learn an initial S ( k ) by maximizing n ( k ) 2 ( log det S ( k ) − t r ( S ( k ) Σ ( k ) ) ) − λ s ( k ) ‖ S ( k ) ‖ 1 . In other words , initialize by solving the graphical lasso problem for each dataset independently . Until convergence: Optimize the internal matrices , S ( k ) , k = K + 1 , … , 2K − 1 , holding the leaf matrices fixed; because all relevant terms of the objective were quadratic , this was analytic and essentially instantaneous . ( We note that this would not be true if an L1 penalty were used rather than an L2 penalty . ) Optimize the leaf matrices , S ( k ) , k = 1 , … , K , holding the internal matrices fixed; each leaf matrix was independent of the others given its parent , so this was done in parallel . Optimization was performed using the L1General [46] and glasso [47] packages . To ensure that the size of the entries in S were comparable across tissues and between internal and external nodes , prior to each iteration we normalized each S such that all S had the same mean absolute value of diagonal elements and the same mean absolute value of nonzero off-diagonal elements . To expedite this potentially lengthy process of choosing a sparsity parameter λ s ( k ) for each of 35 tissues , we used a heuristic rather than using the traditional cross-validation for every single tissue . We confirmed that our heuristic produced similar results to cross validation . [48] found the BIC penalty effective in selecting the sparsity parameter for graphical lasso: log ( n ) ‖S ( k ) ‖0 , where ‖S ( k ) ‖0 is the number of non-zero off-diagonal entries of S ( k ) . This suggests setting λ s ( k ) to a value that makes the L1 penalty equal to the BIC penalty: λ s ( k ) = l o g ( n ( k ) ) / s ‾ , where s ‾ is the mean absolute value of the nonzero off-diagonal entries in the optimized precision matrix . Substantiating this , we found that log ( n ( k ) ) was tightly correlated in both simulated and actual data with the optimal L1 penalty , and also outperformed the n ( k ) suggested by [49] . This appears to beg the question of how to estimate s ‾ without doing the actual optimization; however , we found that s ‾ was tightly correlated in both simulations and in the GTEx datasets with Σ ‾ , the mean size of the entries in the empirical covariance matrix . Similarly , l o g ( n ( k ) ) / Σ ‾ ( k ) was tightly correlated in both simulations and actual data with λ s ( k ) . Thus , we can select λ s ( k ) for all K datasets by using parameter search to select λ s ( 1 ) , λ s ( 2 ) , … λ s ( i ) , where i is much smaller than K; we then do a regression of the optimized λ s ( k ) s on l o g ( n ( k ) ) / Σ ‾ ( k ) , and use that fit to compute the remaining λ s ( k ) . We confirm that this method works on both simulated precision matrices and the GTEx dataset . For the GTEx dataset , using i = 5 yields λ ( k ) within 17% of the values selected by cross-validation on average; i = 3 yields values within 26% , acceptable discrepancies given the coarseness of parameter search . Most algorithms for solving the graphical lasso problem with p genes are O ( p3 ) , making optimization intractable for 9 , 998 genes . If the optimal solution were block diagonal , with block sizes p1 , … , pk , optimization could be performed in O ( ∑ i = 1 k p i 3 ) , as noted in [50] and [51] . Unfortunately , we found that the criterion these papers provide for determining whether the problem decomposes requires too large a sparsity parameter to be practically useful . Instead , we used an approximate eigenvector-based diagonalization similar to that described in [52]: for each tissue , we computed a matrix C ( k ) , with C i j ( k ) = m a x ( 0 , Σ i j ( k ) − λ s ( k ) ) 2 . We then computed the weighted sum of the matrices: S = ∑ k = 1 K n ( k ) C ( k ) , and partitioned S into approximate connected components using the principal eigenvector as described in [52] . ( To ensure that all components had tractable size , we set a maximum component size of 500 genes and recursively partitioned components until they fell below this threshold . ) We confirmed that this approximate solution had a higher test log likelihood than that obtained by choosing a sparsity parameter sufficiently large to make an exact solution tractable . Because the L1 optimization algorithm and our initializations are stochastic , the final optimized networks may vary slightly from run to run . However , we verified that our results were not overly sensitive to repeated runs of the algorithm , to parameter settings , or to which samples we used by examining two modified networks: one optimized using a subset of 4/5 of the samples and one optimized using λp = 2 as opposed to λp = 4 . We found that both modified networks were highly enriched for links in our actual network; links in the actual network were more than 100 times as likely as random links to be found in the modified networks . In modified networks , we tested a number of the network properties reported above . First , we verified that we still saw statistically significant correlations with the external datasets COEXPRESdb and TS-CoExp . Second , we verified that tissue-specific genes , and genes with shared functions , still showed statistically significant tendencies to be linked to each other . Finally , we verified that tsTFs still showed a statistically significant tendency to be linked to genes with tissue-specific functions . The robustness of all these conclusions made us confident that the conclusions reported above are unlikely to be due to which samples in the dataset are used , the values of the parameters , or variations in the initialization of the algorithm , although specific links in the networks may change . We also analyzed the proportion of links that were conserved across different conditions . We compared networks calculated using our chosen value of λp = 4 to those learned with different values of λp ( S9 Table ) ; 89% of links were conserved between networks learned with λp = 4 , λp = 2 , and 98% between networks learned with λp = 4 , λp = 8 . A somewhat lower proportion ( 75% ) of links were conserved between λp = 4 , λp = 0 , implying that the use of a similarity penalty may be more important than the exact size of the similarity penalty . We also compared the networks learned on all samples to the networks learned using a subset of 4/5 of the samples; 38% of the links were conserved in the average tissue . Given the sparsity of the networks , all these proportions are more than 100 times what random chance would predict . However , because specific links can change depending on which samples are used , the broad conclusions of our analysis are more robust than any particular link we predict . | Cells in different tissues perform very different functions with the same DNA . This requires tissue-specific gene expression and regulation; understanding this tissue-specificity is often instrumental to understanding complex diseases . Here , we use tissue-specific gene expression data to learn tissue-specific gene regulatory networks for 35 human tissues , where two genes are linked if their expression levels are correlated . Learning such networks accurately is difficult because of the large number of possible links between genes and small number of samples . We propose a novel algorithm that combats this problem by sharing data between similar tissues and show that this increases the accuracy with which networks are learned . We provide a web tool for exploring these networks , enabling users to pose diverse queries in a gene- or tissue-centric manner , and facilitating explorations into gene function and regulation . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Sharing and Specificity of Co-expression Networks across 35 Human Tissues |
Hookworm infection is considered one of the most important poverty-promoting neglected tropical diseases , infecting 576 to 740 million people worldwide , especially in the tropics and subtropics . These blood-feeding nematodes have a remarkable ability to downmodulate the host immune response , protecting themselves from elimination and minimizing severe host pathology . While several mechanisms may be involved in the immunomodulation by parasitic infection , experimental evidences have pointed toward the possible involvement of regulatory T cells ( Tregs ) in downregulating effector T-cell responses upon chronic infection . However , the role of Tregs cells in human hookworm infection is still poorly understood and has not been addressed yet . In the current study we observed an augmentation of circulating CD4+CD25+FOXP3+ regulatory T cells in hookworm-infected individuals compared with healthy non-infected donors . We have also demonstrated that infected individuals present higher levels of circulating Treg cells expressing CTLA-4 , GITR , IL-10 , TGF-β and IL-17 . Moreover , we showed that hookworm crude antigen stimulation reduces the number of CD4+CD25+FOXP3+ T regulatory cells co-expressing IL-17 in infected individuals . Finally , PBMCs from infected individuals pulsed with excreted/secreted products or hookworm crude antigens presented an impaired cellular proliferation , which was partially augmented by the depletion of Treg cells . Our results suggest that Treg cells may play an important role in hookworm-induced immunosuppression , contributing to the longevity of hookworm survival in infected people .
Human hookworm infection is mainly caused by the blood-feeding nematodes Ancylostoma duodenale and Necator americanus , which infects 576 to 740 million people worldwide , especially in the tropics and subtropics [1] , [2] , [3] . Hookworm is considered one of the thirteen poverty-promoting neglected tropical diseases , and the second most important parasitic infection of humans [4] . This infection takes a particularly devastating toll on the most vulnerable of the world's population , including children , productive men , and women of childbearing age [5] , [6] , [7] . Persistent blood and serum protein loss attributable to chronic hookworm infection are associated with anemia , malnutrition , and growth/cognitive retardation , resulting in the annually loss of tens of millions of disability adjusted life-years ( DALYs ) [8] . Despite its overall impact over the global public health system , the immunomodulatory mechanisms associated with hookworm survival on host's intestine in face of an immunologically hostile environment are not yet fully understood . Human hookworm infection is a longstanding , chronic infection with complex life cycle stages and host-parasite interactions . Although this parasitic infection may seem unnoticed by the immune system , an intense T helper ( Th ) 2 phenotype immune response is mounted by the host against this helminthic infection [9] . In recent years , evidence has accumulated that the immune response to hookworms may not be a simple polarized and putatively protective Th2 response , but rather a mixture of Th1/Th2 responses , presenting significant levels of interferon gamma and IL-12 production [10] . In fact , hookworms have a remarkable ability to downmodulate the host immune response , protecting themselves from elimination and minimizing severe host pathology . The parasite may promote its survival by excreting/secreting a panel of molecular immunosuppressive agents and , possibly , by stimulating the appearance of regulatory T-cell populations . Among the most striking aspects already described of this downregulation is the ablation of parasite specific T cell proliferative responses ( “hyporesponsiveness” ) [11] . Indeed , hookworm infection in animal models has been classically associated with impaired lymphocyte proliferation , functional defects in antigen presentation/processing and increased secretion of nitric oxide [12] . Recently , Fujiwara et al . have also shown that human dendritic cells differentiation and maturation may also be downmodulated by these worms , contributing to the T cell hyporesponsive state observed in individuals chronically infected with N . americanus [13] . The discovery of regulatory T lymphocytes ( Treg ) that are actively involved in maintaining immune tolerance has recently led to new insights into mechanisms of tolerance breakdown and/or immunoregulation in human diseases , including those resulting from allergic , autoimmune , or infectious causes [14] . These cells have been shown to suppress cellular immune responses through direct contact with immune effector cells and by the production of regulatory cytokines , including TGF-β and IL-10 [15] , [16] . In fact , Geiger et al . have shown that hookworm infection is accompanied by elevated levels hookworm antigen-specific IL-10 production dependent on parasite stage , as well as significantly higher levels of CD4+/CD25+ T-cells [11] . While studies in experimental models have provided evidence for increased FOXP3+ ( forkhead box P3 transcription factor ) Treg function during different helminth infections [17] , [18] , [19] , the role of Tregs cells in human hookworm infection is still poorly understood and has not been addressed . To investigate Treg activity in human hookworm infection , we have evaluated Treg frequencies , function and immune responses to hookworm antigens in N . americanus-infected individuals from a rural area of Minas Gerais state ( Brazil ) . In the present study we described an augmentation of circulating CD4+CD25+FOXP3+ regulatory T cells in hookworm-infected individuals compared with healthy non-infected donors . We have also demonstrated by flow cytometry that infected individuals present higher levels of circulating Treg cells expressing CTLA-4 ( cytotoxic T lymphocyte antigen 4 ) , GITR ( glucocorticoid-induced tumor necrosis factor receptor ) , IL-10 , TGF-β and IL-17 . Additionally , we showed that hookworm crude antigen stimulation reduces the number of CD4+CD25+FOXP3+ T regulatory cells co-expressing IL-17 in infected individuals . Furthermore , PBMCs from infected individuals pulsed with excreted/secreted products or hookworm crude antigens presented an impaired cellular proliferation , which was augmented after the depletion of Treg cells . Taken together , our results suggest that Treg cells may play an important role in hookworm-induced immunosuppression , contributing to the longevity of hookworm survival in infected people .
The study was conducted in areas endemic for N . americanus in the Northeast Minas Gerais State , Brazil . Ten volunteers between the ages of 18 and 76 were recruited over the course of two months ( Table 1 ) . These volunteers reside in areas of moderate N . americanus transmission and presented with low to moderate ( up to 872 epg ) intensity of Necator infection . Individuals were selected on the basis of not having any other helminth infection ( mono-infected after analysis of 6 slides of Kato-Katz fecal thick-smear and Baermann-Moraes techniques ) . The presence of Necator infection was determined by formalin–ether sedimentation and , if positive , two more stool samples were analyzed by the Kato–Katz fecal thick-smear technique and parasite load was expressed as eggs per gram of feces ( epg ) [20] . Ten hookworm-naive individuals were enrolled as healthy non-infected individuals from Belo Horizonte , Minas Gerais State , Brazil , where no transmission occurs . None of these individuals had a history of Necator infection and all presented with egg-negative stool ( 6 slides of Kato-Katz fecal thick-smear and Baermann-Moraes techniques ) and no specific antibodies to Necator crude antigen extracts . The geographic areas included in this study are not endemic for tissue-dwelling helminth infections . Furthermore , the nutritional status of non-infected volunteers ( controls ) was similar to those presented by hookworm-infected individuals as determined by anthropometric measurements . The nutritional status of adults was determined using the absolute body mass index and classified as eutrophic ( 18 . 5–24 . 9 kg/m2 ) , underweight ( <18 . 5 kg/m2 ) or overweight ( ≥25 kg/m2 ) [21] . Approximately 24 mL of blood was collected in heparinized tubes for separation of peripheral blood mononuclear cells ( PBMC ) and 4 mL of blood in EDTA tubes for the immunological assays described below . The study was approved by the Ethical Committee on Research of Universidade Federal de Minas Gerais ( COEP ) ( Protocol #ETIC0449 . 0 . 203 . 000-09 ) . Written consent was obtained from all individuals prior to enrollment in this study . Ancylostoma ceylanicum adult worms were obtained from purpose-bred hamsters maintained at the Universidade Federal de Minas Gerais according to a protocol approved by the Committee for Animal Experimentation of Universidade Federal de Minas Gerais ( Protocol# 66/08 ) . All animals procedures were performed under the guidelines from COBEA ( Brazilian College of Animal Experimentation ) and strictly followed the Brazilian law for “Procedures for Scientific Use of Animals” ( 11 . 794/2008 ) . For preparation of excreted-secreted ( ES ) antigens , worms were removed from the intestines of euthanized hamsters , washed several times in phosphate-buffered saline ( PBS ) , and then cultured overnight in RPMI 1640 containing 100 U/ml penicillin G sodium , 100 µg/ml streptomycin sulfate , and 0 . 25 µg/ml amphotericin B ( all reagents from Sigma-Aldrich , St . Louis , MO ) at 37°C with 5% CO2 in a humidified incubator . The ES products were concentrated using microconcentration filter units with a 10-kDa-cutoff membrane ( Millipore , Bedford , MA ) . Adult worm crude extract was prepared by direct maceration of parasites using a tissue grinder and further rupture using a cell disruptor ( Sonifier Cell Disruptor , Branson Sonic Power Co . , Danbury , CT , USA ) in PBS for 1 min at 40 Watts , in an ice bath . The procedure was repeated five times , with 1 min intervals between disruptions . All of the antigen preparations used in cell cultures were passed through a 0 . 22 µm low-protein binding syringe filter ( Millipore , USA ) , and the resulting protein concentration was determined using a BCA protein assay kit ( Pierce , USA ) . Hookworm antigen preparations were tested negative for endotoxin content by the Limulus lysate assay ( sensitivity of 0 . 06 U/ml; Cambrex , USA ) , and stored in aliquots at −80°C . Whole blood was collected in Vacutainer tubes containing EDTA ( Becton Dickinson , USA ) and 100 µL samples were mixed in tubes with 2 µL of undiluted monoclonal antibodies PerCP anti-human CD4 ( clone BNI3 ) , FITC anti-human CD25 ( clone M-A251 ) and APC anti-human FOXP3 ( clone 236A ) ( all from BD Pharmingen , USA ) . After adding the antibodies , the cells were incubated in the dark for 30 minutes at room temperature . Following incubation , erythrocytes were lysed using 2 mL of FACS Lysing Solution ( BD Biosciences , USA ) and washed twice with 2 mL of phosphate-buffered saline containing 0 . 01% sodium azide and 0 . 5% bovine serum albumin ( SIGMA , USA ) . Intracellular staining was performed after cell fixation in formaldehyde ( 4% ) and permeabilization with saponin buffer ( 0 . 5% ) ( Sigma , USA ) for 15 minutes . Cells were washed twice with 2 mL of phosphate-buffered saline containing 0 . 01% sodium azide and 0 . 5% bovine serum albumin ( SIGMA , USA ) and incubated for 30 minutes with 2 µL of undiluted monoclonal antibodies PE anti-human IL-10 ( clone JES3-9D7 ) , PE anti-human TGF-β ( clone TB21 ) , PE anti-human IL-17 ( clone 64CAP17 ) , PE anti-human CTLA-4 ( clone BNI3 ) and PE anti-human GITR ( clone eBioAITR ) ( all from BD Pharmingen , USA ) . After incubation and washing with PBS with 0 . 01% sodium azide and 0 . 5% bovine serum albumin , the cells were the fixed in 200 µL of fixative solution ( 10 g/L paraformaldehyde , 1% sodium-cacodylate , 6 . 65 g/L sodium chloride ) . Phenotypic analyses were performed by flow cytometry with a FACScalibur flow cytometer ( BD Biosciences , USA ) . Data were collected on 30 , 000 events ( gated by forward and side scatter ) and analyzed using CellQuest® software ( BD Biosciences , USA ) . Whole blood was stimulated in vitro with ES and crude antigens ( 5 µg/well ) in RPMI 1640 media supplemented with 1 . 6% L-glutamine ( Sigma , USA ) , 3% antibiotic-antimycotic ( Invitrogen , USA ) , 5% of heat inactivated AB+ human serum ( Sigma , USA ) , for 24 hours at 37°C with 5% CO2 . Unstimulated cultures were used as negative controls . During the last 4 hours of culture , Brefeldin A ( Sigma , USA ) ( 10 µg/mL ) was added to the cultures . Phenotypic analyses were performed by flow cytometry after staining using the same antibody panel described for ex vivo immunophenotyping assays . Data were collected on 30 , 000 events ( gated by forward and side scatter ) and analyzed using CellQuest® software ( BD Biosciences , USA ) . Peripheral blood mononuclear cells ( PBMC ) were isolated by Ficoll-Hypaque ( GE Healthcare , USA ) using density gradient centrifugation ( Sigma , USA ) . Cells were then washed and resuspended at 5×106 cells/mL in RPMI 1640 medium ( Invitrogen , USA ) , supplemented with 5% heat-inactivated human AB serum ( Sigma , USA ) , 2 mM of L-glutamine ( Sigma , USA ) , 50 U/mL of penicillin , and 50 g/mL of streptomycin ( Invitrogen , USA ) . CD4+CD25+ T cells were purified from PBMCs using the CD4+CD25+ regulatory T cell isolation kit and a QuadroMACS cell separator ( both from Miltenyi Biotec , USA ) , according to the protocol provided by the manufacturer . In brief , cells were suspended in PBS supplemented with 2 mM EDTA and 0 . 5% BSA ( Sigma , USA ) at a density of 107 cells in 90 µL of buffer and 10 µl of biotin-Ab mixture . Cells were incubated at 4°C for 10 minutes . Then , 20 µL of anti-biotin microbeads was added and incubated for 15 min at 4°C . CD4+ cells were eluted and resuspended at a density of 107 cells in 90 µL of buffer and 10 µL of CD25 microbeads . The cells were then incubated at 4°C for 15 min and further separated by a magnetic field . CD4+CD25+ T cell fraction retained in the column was eluted by removing the column from the magnetic field and flushing out the cells with 1 mL elution buffer ( PBS with 2 mM EDTA and 0 . 5% BSA ) . CD4+CD25+ T cells were washed and used immediately . The purity of CD4+CD25+ T cells after purification reached up to 95% ( data not shown ) ( Supplementary Figure S1 ) . CD4+CD25−/low T cells and CD4− cells together were considered as Treg-depleted PBMCs ( dPBMCs ) and used in functional assays . Stimulation assays were performed in duplicates and mitogen and antigens were added at previously determined concentrations known to result in optimal proliferation [22] . The mitogen phytoemagglutinin-l ( PHA–L ) ( Sigma , USA ) was used for polyclonal stimulation of peripheral blood mononuclear cells ( PBMCs ) . Crude and excreted-secreted ( ES ) antigens from adult Ancylostoma ceylanicum were employed for hookworm specific cellular stimulation at a final concentration of 5 µg/well . For the analysis of the effect of CD4+CD25+ T cells on cellular proliferation , two different experiments were performed . Firstly , PBMCs ( 106 cells/mL in PBS/1% BSA ) were co-labeled with 0 . 4 mM CFDA-SE ( carboxyfluorescein diacetate succinimidyl ester , Vybrant™ CFDA-SE Cell Tracer Kit , Molecular Probes , USA ) and PE-conjugated anti-human CD8 ( clone UCTH-4 ) or PE-Cy5 anti-human CD4 ( clone RPA-T4 ) ( BD Pharmingen , USA ) for 10 minutes at room temperature . The previously purified CD4+CD25+ T cells were incubated at ratio of 1∶10 with autologous CFDA-SE-labeled PBMCs pulsed with hookworm antigens , for 96 hours at 37°C with 5% CO2 atmosphere . In a second experiment , Treg-depleted PBMCs were co-labeled with CFDA-SE/CD4 or CD8 and pulsed with hookworm antigens , as previously described . The cell proliferative response of both experiments was assessed using a FACScan® cytometer ( Becton Dickinson , USA ) and CellQuest® software ( BD Biosciences , USA ) . Analysis of CFDA-SE proliferation was performed as previously described [23] . The cytokines IL-2 , IFN-γ , IL-10 , and IL-5 were detected and quantified in cell supernatants by commercially available sandwich ELISA kits ( R&D Systems , USA ) . Assays were performed according to the manufacturer's instructions . Biotin-labeled detection antibodies were used , revealed with streptavidin-HRP ( Amersham Biosciences , USA ) and OPD substrate system ( Sigma ) . The colorimetric reaction was read in an automated ELISA microplate reader at 492 nm . Calculations of chemokine/cytokine concentrations from mean optical density values were interpolated from the standard curve using 5-parameter curve fitting software ( SOFTmax® Pro 5 . 3 , Molecular Devices , USA ) . Results were achieved in pg/mL and the detection limits were as follows: 15 . 6 pg/mL for IL-2; 3 . 9 pg/mL for IFN-γ; 23 . 4 pg/mL for IL-10 and IL-5 . Samples with values above the top of the standard curve were retested at 1/10 or 1/100 dilutions in RPMI 1640 , and the chemokine/cytokine levels were recalculated . The one-sample Kolmogorov-Smirnoff test was used to determine whether variability followed a normal distribution pattern . The Mann-Whitney U test was used to determine the differences ( p value<0 . 05 ) of non-parametric variables between Necator-infected individuals and non-infected individuals . The maximum residual test ( Grubb's test ) was used to detect possible outliers . All statistics were carried out using Prism 5 . 0 for Windows ( GraphPad Software Inc . , USA ) .
Regulatory T cells were identified by flow cytometry as CD4+ T cells expressing both CD25 and FOXP3 marker ( Figure 1A ) and are reported as frequency ( Figure 1B ) and absolute numbers of cells per mm3 ( Figure 1C ) . Analysis of PBMCs from Necator-infected individuals showed a significant increase in frequency ( p<0 . 0001 , Figure 1C ) and absolute numbers ( p = 0 . 0018 , Figure 1B ) of circulating CD4+CD25+FOXP3+ T cells ( 21 . 4±15 . 4% , 477 . 4±131 . 8 cells/mm3 ) when compared with non-infected naive individuals ( 2 . 3±0 . 6% , 78 . 9±11 . 2 cells/mm3 ) . Once observed the elevated number of Treg cells in the peripheral blood of hookworm infected donors , we further characterized this cell population by evaluating the expression of molecules and cytokines associated with cell modulation . Surface expression of the GITR molecule and intracellular expression of CTLA-4 , IL-10 , TGF-β and IL-17 cytokines , were assessed by flow cytometry . Infection of N . americanus significantly increased the proportion of cells expressing CTLA-4 ( p = 0 . 0002 ) and GITR ( p<0 . 0001 ) . Flow cytometric analysis also showed a significant augmentation of CD4+CD25+FOXP3+ cells producing IL-10 ( p<0 . 0001 ) , TGF-β ( p<0 . 0001 ) and IL-17 ( p = 0 . 0003 ) in hookworm infected individuals ( Figure 2 ) . Similar results were found when frequency of cells were analyzed ( Supplementary Figure S2 ) . The expression of analyzed surface and intracellular markers was determined by median intensity of fluorescence in order to obtain the absolute expression level per cell basis . Conversely to the increase in the absolute numbers of Treg subpopulations , no differences in the expression of IL-10 , TGF-β , IL-17 , CTLA-4 and GITR , between infected and non-infected individuals were observed ( data not shown ) . In order to determine the possible effect of hookworm antigens on the expression of cell surface markers ( CTLA-4 and GITR ) and cytokines ( IL-17 , TGF-β , and IL-10 ) by CD4+CD25+FOXP3+ regulatory T cells in N . americanus-infected donors , whole blood cultures were stimulated with either hookworm crude antigen or ES products . When crude antigen was added to the in vitro cultures , the percentage and the absolute counts of CD4+CD25+FOXP3+ regulatory T cells co-expressing IL-17 was significantly reduced ( p<0 . 0030 ) ( Figure 3 ) . Although there was a tendency in increase on the expression of IL-17 in ES stimulated blood it was not statistically significant ( p = 0 . 3562 ) ( Figure 3 ) . No differences in the proportion of cells expressing CTLA-4 , GITR , TGF-β , IL-10 , ( Supplementary Figure S3 ) and median intensity of fluorescence were seen after hookworm antigen stimulation ( p>0 . 05 for all ) . Chronic human N . americanus infection is classically associated with a profound ablation of cell proliferation . In order to determine the possible effect of Treg cells on the immune response during hookworm infection , functional assays were designed to evaluate whether CD4+CD25+FOXP3+ Treg cells could modulate the in vitro cellular proliferation of CD4+ and CD8+ lymphocytes after parasite antigen stimulation . Hookworm antigen-stimulated PBMCs from infected individuals showed a naturally impaired proliferative response , which was not further suppressed by co-incubation with Tregs ( data not shown ) . However , in vitro cultures , where Tregs cells were depleted ( dPBMC ) , showed a significant increase on the CD4+ cell proliferative response induced by crude ( p = 0 . 0039 ) and ES ( p = 0 . 0012 ) hookworm antigenic stimulation ( Figure 4A ) . Interestingly , depletion of Treg cells significantly augmented the proliferation of CD8+ PBMCs of infected donors in response to hookworm ES products ( p = 0 . 0039 ) , but not to crude antigen ( Figure 4B ) . The depletion of Tregs elicited the increase of IL-2 and lower levels of IL-10 in supernatants of dPBMCs with or without antigenic stimulation although statistical significance was not achieved ( Supplementary Figure S4 ) . No differences were also observed for IFN-γ and IL-5 ( Supplementary Figure S4 ) after CD4+CD25+ depletion . These results suggest that T regulatory cells do have the capacity to modulate the in vitro proliferative response in hookworm-infected patients . Additional experiments using PBMCs from control individuals demonstrated the absence of cell proliferative response after antigenic stimulation , which remains unaltered by the add-back or depletion of Tregs ( Supplementary Figure S5 ) . No differences on cell proliferative response of PBMCs from both infected and control individuals were observed in cultures after stimulation with the mitogen PHA–L .
CD4+CD25+FOXP3+ regulatory T cells ( Tregs ) constitute a minor subpopulation of CD4+ T-cells , which play an important role in controlling the extent of the immune-mediated pathology and maintaining immunological self-tolerance and immune homeostasis [24] , [25] . These cells suppress the activation and proliferation of CD4+ and CD8+ T cells by direct contact of with effector T cells or secretion of immunoregulatory cytokines , such as IL-10 and TGF-β [16] , [26] . Moreover , the balance of Treg cell–dependent immunomodulation may lead to enhanced pathogen survival and , in some cases , their long-term persistence [27] . In fact , one of the hallmarks of chronic helminth infections is induction of T-cell hyporesponsiveness and bystander suppression [28] . While the mechanisms involved in the immunomodulation by parasitic infection may be multiple , some experimental evidences have pointed toward the possible involvement of natural and inducible Treg in downregulating effector T-cell responses upon chronic infection . Over the past four decades [29] , [30] , several studies have attempted to describe the role of regulatory T cells in parasitic diseases ( reviewed in [16] , [27] ) , including leishmaniasis , schistosomiasis , malaria and lymphatic filariasis . However , a limited number of studies have focused on the currently known Treg dynamics and functional capacity in human helminth infections . Evidence for Treg activity in human chronic helminth infections has been firstly provided by T cell clones generated from onchocerciasis patients [31] and recently described for geohelminth infection in humans [32] . Several studies in animal models of filariasis [33] and schistosomiasis [34] , [35] , demonstrated that Treg phenotype populations develop following infection , whilst in infection with the murine gastrointestinal nematode Heligmosomoides polygyrus [36] , functional regulation by CD4+CD25+ T cells suppresses the bystander response to an allergic provocation . In the present study we describe the role of CD4+CD25+FOXP3+ T cells in human N . americanus infection . To explore cellular immune mechanisms underlying classic hookworm-induced T cell hyporesponsive state , we have analyzed Treg frequencies in peripheral blood and performed in vitro Treg depletion and add-back experiments with PBMC isolated from N . americanus-infected individuals from a rural area of Minas Gerais State , Brazil . We initially showed that hookworm-infected individuals present a significant increase of circulating Treg cells in peripheral blood compared to non-infected healthy volunteers , as previously demonstrated in other nematode infections [18] , [19] , [37] , [38] , [39] . Of note , while the expansion of CD4+CD25+FOXP3+cells is observed in infected individuals , no differences were observed in the expression of all markers associated with cell suppression , including FOXP3 . Such increase in the absolute number of circulating FOXP3+ Treg cells might be driven as a direct consequence of the infection . In fact , the expression of FOXP3+ in naïve T cells can be elicited by excretory-secretory products of nematodes , resulting in induced de novo FOXP3+ expression and active suppressor cells [17] . Interestingly , hookworm-infected individuals also presented with significant lower levels of circulating lymphocytes , which may be partially explained by the inhibition of effector T cells emergence during the inductive phase of the immune response in the secondary lymphoid tissues by IL-10-independent mechanisms [40] . It is possible that increased Treg activity may trigger modulation of host immune response and consequently facilitate hookworm prolonged survival . On the other hand , increased Treg responses might also account for limitation of exacerbated infection-induced tissue pathology , which would be ultimately beneficial to the host . However , it would not limit the blood loss or anemia induced by the infection . A variety of potential mediators of Treg activity that could contribute to the suppression of the host's immune response have been identified , including GITR [41] , [42] , CTLA-4 [43] , FOXP3 [44] , [45] , and the anti-inflammatory cytokines IL-10 and TGF-β [46] , [47] , [48] . In the current study , a significant increase of circulating CD4+CD25+FOXP3+ lymphocytes , co-expressing GITR , CTLA-4 , IL-10 , TGF-β or IL-17 , was demonstrated in N . americanus-infected donors , compared to non-exposed volunteers . Nevertheless , a higher expression of these markers on per cell basis has not been observed in hookworm-infected donors in relation to healthy individuals . Both GITR and CTLA-4 molecules are constitutively expressed on cell surface of natural Tregs [27] and are regulated by FOXP3 expression [49] , [50] . Initial studies related to the effects of GITR signaling on Treg cells indicated that interaction of this receptor with anti-GITR antibody or GITR ligand ( GITRL ) lead to an apparent abrogation of suppressive activity of Tregs [41] , [42] , [51] . Indeed , treatment of Trichuris muris infected mice with anti-GITR resulted in an earlier worm expulsion [19] . Although not essential for the T cell suppressor activity [50] , the engagement of GITR promotes proliferation of Tregs [50] , [52] and potential enhancement of their suppressive function [51] . The significant increase of circulating CD4+CD25+FOXP3+GITR+ and CD4+CD25+FOXP3+CTLA-4+ in Necator-infected donors might partially reflect the concomitant augmentation of Tregs . Similarly , mice experimentally infected H . polygyrus or Litomosoides sigmodontis also presented a prominent increase of GITR and CTLA-4 expression [39] , [53] . The inhibitory receptor CTLA-4 presents partial homology to CD28 molecule and interacts to the same ligands , CD80 and CD86 , with a much higher affinity [54] . The suppressive effect of CTLA-4 is associated with the reduced IL-2 production and IL-2 receptor expression , and by arresting T cells at the G1 phase of the cell cycle [55] , [56] . Moreover , CTLA-4 expressing Treg cells induce the expression of the enzyme indoleamine 2 , 3-dioxygenase ( IDO ) by antigen-presenting cells which degrades tryptophan , and the lack of this essential amino acid inhibits T cell activation and promotes T cell apoptosis [57] . In helminth infections , such as lymphatic filariasis , the expansion of CTLA-4+ T cell populations in was associated with suppressed T cell function [58] . The increased number of circulating GITR+ and CTLA-4+ Treg cells in hookworm-infected individuals suggests that these cells might play a suppressive role on host immune regulation . Although it has long been recognized that IL-10-producing T cells could be generated in vivo during parasitic infection [53] , it is only recently that the concept has emerged that specialized subsets of regulatory T cells contribute to this regulatory network [16] . In the current study , hookworm-infected individuals presented a significant augmentation of CD4+CD25+FOXP3+cells producing the anti-inflammatory cytokines IL-10 and TGF-β . In fact , a prominent rise in IL-10 secretion was demonstrated in ES antigen-stimulated PBMC cultures during primary experimental and natural human hookworm infection [22] , [59] . It is well known that IL-10 and TGF-β are naturally produced by Tregs [27] and are required to induce FOXP3 expression [60] . Although it is not clear whether or how precisely hookworm infection influences the production these anti-inflammatory cytokines , lower levels of IL-10 were observed in supernatants of cultures after Treg depletion , which might support the possibility that that N . americanus induced-Tregs contribute as an important source of their production . In this study , a significant increase of circulating CD4+CD25+FOXP3+cells producing the IL-17 was observed in infected donors , corroborating previous studies on other parasitic diseases [15] , [17] , [60] . Noteworthy , a recent study demonstrated that human hookworm products are able to influence the pro-inflammatory Th17 pathway , promoting a significant decrease in IL-17 production in the mouse infection model [61] . Moreover , it has been suggested that worm infection could block mucosal IL-23 and IL-17 secretion , leading to an important mechanism of control of inflammatory responses [62] . In fact , Ruyssers et al . also observed that helminth antigens could reduce the expression of IL-17 in both colon and mesenteric lymph node T cells [63] . Interestingly , a significant reduction in CD4+CD25+FOXP3+IL-17+ T cells after hookworm crude antigen stimulation was also demonstrated , suggesting the possible secretion of this cytokine or downmodulation of IL-17 expression after cell restimulation . Indeed , Elliott et al . showed that mouse colonization with the helminth H . polygyrus reduces IL-17A mRNA expression by mesenteric lymph node ( MLN ) cells and inhibits IL-17 production by cultured lamina propria mononuclear cells and MLN cells [64] . Moreover , the co-expression of FOXP3 and IL-17 may indicate the transient status of CD4+ lymphocytes from hookworm-infected individuals between Treg ( FOXP3 ) and Th17 ( IL-17 ) profiles , where the development of either pathway of differentiation is driven by TGF-β and IL-6 [65] . The decrease of IL-17 after antigenic stimulation might imply the differentiation of these transient cells in truly effector Tregs . Nonetheless , our results suggest that hookworm products are able to induce an immunomodulated microenvironment at the site of infection . Although recent evidences demonstrate the local suppression of Th1 and Th17 inflammatory cytokines during hookworm infection [66] , the role of IL-17 in hookworm-induced Tregs still remains to be addressed . Based in our results , we demonstrated that hookworm infected individuals present a significant augmentation of activated Treg cells in the peripheral blood , observed by increased numbers of Treg subpopulations expressing cell surface molecules and mediators associated with suppression of immune responses . These cells might contribute to the suppressive effect of this parasitic infection , leading to reduction of antigen-specific proliferative responses previously demonstrated in human populations and animal models [13] , [67] . Indeed , chronic human N . americanus infection has classically been associated with a profound ablation of cell proliferation , which may even extend to other infectious agents and mitogens ( “bystander effect” ) [22] , [68] . In the present study , we have demonstrated by functional assays that both T CD4+ and CD8+ cell proliferative responses to either ES or hookworm crude antigen were increased after Treg depletion , followed by an increase of IL-2 secretion and lower levels of IL-10 , although not statistically significant , further implying that these cells may exert a specific immunomodulatory effect during persistent hookworm infection . Recently , Cuéllar et al . showed that coincubation of mouse splenic T cells with dendritic cells pulsed with the hookworm antigen Ac-TMP-1 induced their differentiation into CD4+ and , particularly , CD8+CD25+FOXP3+ T cells that expressed IL-10 [69] . These cells were able to suppress proliferation of naive and activated CD4+ T cells by TGF-β-dependent ( CD4+ suppressors ) or independent ( CD8+ suppressors ) mechanisms . However , while we showed that the expansion and suppressive effects of Tregs are prominent during chronic hookworm infection , no changes in the number of T cells nor in the absolute counts of regulatory T cells were observed in the human primo-infection with N . americanus [70] . Nonetheless , the data in the present work suggest that hookworms exploit Treg cells to facilitate its own survival by dampening host immune response . In conclusion , hookworm-induced Treg activity may be able to control and divert selective proliferative and cytokine responses to numerous disorders , such as intestinal inflammation , airways inflammation/hyper-reactivity , diabetes , and multiple sclerosis . While it is known that ES products from nematodes may stimulate T reg cells [17] , only few studies have demonstrate the immunomodulatory properties of hookworm-derived antigens [38] , [71] . Considering the recent availability of transcriptomic data sets for hookworm species [72] , [73] , further studies are still required to identify specific antigens directly associated with host's immune suppression , leading to the understanding of mechanisms used by the parasite to skew the immune response in its favour and the possible discovery of several promising candidate vaccine antigens . While our results shed light on the patent mechanisms of immunosuppression present in hookworm infection , it is important to mention that the sample size and age variation of our studied population might be considered as possible limiting factors of our study . Moreover , although effort was made to match the nutritional status of all participants ( endemic and non-endemic areas ) , unfortunately perfect matching of control individuals and infected patients by age and sex was not always possible . The absence of negative individuals from endemic areas as controls was preferred once it is not possible to guarantee the absence of infection in these donors ( due to the limited sensitivity of fecal exams or long pre-patency period ) . Moreover , it has been previously shown that the immunological status of helminth-infected patients remain unaltered after anthelmintic treatment for several months [71] . Nonetheless , this study was designed to minimize potential confounders , which could mask the immunological assessment of hookworm infection . Therefore , our data should be further validated by large immunoepidemiological surveys to be conducted in endemic areas . | The hookworm infection is characterized by the long-term survival of the parasite and the concomitant modulation of the host immunity . Among several mechanisms that may account for the suppression of T cell response , we here described the presence and role of T regulatory cells ( also known as Tregs ) in the human hookworm infection . Tregs are a minor subpopulation of CD4+ T-cells , which also express specific cell markers that allow its further identification ( CD25 and FOXP3 ) . Our results showed that hookworm infection induce an augmentation of Tregs in the peripheral blood , followed by the higher levels of circulating Treg cells expressing several markers and cytokines associated with cell regulation ( CTLA-4 , GITR , IL-10 , TGF-β and IL-17 ) . We also demonstrated that in vitro depletion of Tregs partially enhanced the naturally impaired cellular proliferation of lymphocytes from infected individuals after antigenic stimulation . Our results suggest that Treg cells may play an important role in hookworm-induced immunosuppression , contributing to the longevity of hookworm survival in infected people . | [
"Abstract",
"Introduction",
"Materials",
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"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"immunity",
"immunology",
"biology",
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"parasitology"
] | 2011 | Induction of CD4+CD25+FOXP3+ Regulatory T Cells during Human Hookworm Infection Modulates Antigen-Mediated Lymphocyte Proliferation |
Filamentous fungi that thrive on plant biomass are the major producers of hydrolytic enzymes used to decompose lignocellulose for biofuel production . Although induction of cellulases is regulated at the transcriptional level , how filamentous fungi sense and signal carbon-limited conditions to coordinate cell metabolism and regulate cellulolytic enzyme production is not well characterized . By screening a transcription factor deletion set in the filamentous fungus Neurospora crassa for mutants unable to grow on cellulosic materials , we identified a role for the transcription factor , VIB1 , as essential for cellulose utilization . VIB1 does not directly regulate hydrolytic enzyme gene expression or function in cellulosic inducer signaling/processing , but affects the expression level of an essential regulator of hydrolytic enzyme genes , CLR2 . Transcriptional profiling of a Δvib-1 mutant suggests that it has an improper expression of genes functioning in metabolism and energy and a deregulation of carbon catabolite repression ( CCR ) . By characterizing new genes , we demonstrate that the transcription factor , COL26 , is critical for intracellular glucose sensing/metabolism and plays a role in CCR by negatively regulating cre-1 expression . Deletion of the major player in CCR , cre-1 , or a deletion of col-26 , did not rescue the growth of Δvib-1 on cellulose . However , the synergistic effect of the Δcre-1; Δcol-26 mutations circumvented the requirement of VIB1 for cellulase gene expression , enzyme secretion and cellulose deconstruction . Our findings support a function of VIB1 in repressing both glucose signaling and CCR under carbon-limited conditions , thus enabling a proper cellular response for plant biomass deconstruction and utilization .
Bioconversion of lignocellulosic biomass to simple sugars holds great promise in next-generation biofuel production and relies on a complex repertoire of proteins for enzymatic deconstruction of plant cell walls [1] . Many filamentous fungi have evolved to utilize cellulosic materials and are capable of producing a wide spectrum of enzymes , but only a few species have been harnessed for industrial usage [2] . Further improvement in fungal cellulolytic enzyme production is desired to make biofuel production cost-competitive , but this relies on a better understanding of the molecular basis of networks involved in carbon sensing and regulatory aspects associated with induction of gene expression of hydrolytic enzymes [3] . Cellulolytic enzyme production and secretion is a unique attribute of filamentous fungi , and efforts to identify important factors in enzyme production led to the discovery of a number of transcriptional activators and repressors . For example , the transcription factor XlnR/XYR1 positively regulates expression of cellulase and hemicellulase genes in Aspergillus niger and Trichoderma reesei , respectively [4]–[7] . In Neurospora crassa , the transcription factors CLR1 and CLR2 are essential for growth on cellulose and are required for expression of a ∼212 gene regulon that is induced in response to cellodextrins , such as cellobiose [8] , [9] ( Figure 1 ) . In Aspergillus nidulans and A . oryzae , a clr-2 homolog , called clrB/manR , respectively , is also essential for cellulase gene expression and activity [8] , [10] , [11] . Additional transcriptional regulators that promote expression of some genes encoding hydrolytic enzymes have also been identified , including mcmA in A . nidulans [12] , clbR in A . aculeatus [13] , and aceII and bglR in T . reesei [14] , [15] . In addition to induction , cellulase gene expression is also subject to carbon catabolite repression ( CCR ) , which functions when a favorable carbon source , such as glucose , is present [3] , [16] , [17] . The most well-characterized transcription factor involved in CCR in filamentous fungi is CreA/CRE1 . Deletion of creA/cre-1 alleviates some aspects of CCR for cellulolytic enzyme expression in Aspergilli [18]–[22] , T . reesei [23]–[25] , Penicillium decumbens [2] and N . crassa [26] , [27] . In A . nidulans , repression by CreA occurs both by binding to promoters of hemicellulase genes as well as repressing expression of transcriptional activators [28] . Other factors including creB/cre2 , creC , creD , lim1 , and aceI were also reported to promote CCR in different fungal species via unknown mechanisms [29]–[37] . The strength of CCR is tuned by glucose sensing and signaling , although crosstalk between these two regulatory systems is not well understood . In N . crassa , RCO3 , a predicted sugar transporter was proposed to function as a glucose sensor [38] , [39] . In A . nidulans , phosphorylation of glucose triggers CreA repression [29] , [40] . In Magnaporthe oryzae , trehalose-6-phosphate synthase ( Tps1 ) promotes glucose metabolism and CCR through inhibition of Nmr ( nitrogen metabolite repression ) proteins ( Nmr1 , Nmr2 , Nmr3 ) [41] . Downstream , a multidrug and toxin extrusion pump , Mdt1 , promotes citrate efflux to relieve CCR . To what extent these mechanisms are shared among cellulolytic fungi and whether they all converge to regulate CreA/CRE1-mediated CCR is currently unclear . N . crassa is an early colonizer of burnt vegetation [42] , [43] , grows robustly on plant biomass and secretes a broad spectrum of enzymes to degrade plant cell walls [44] , [45] . By screening the N . crassa near-full genome deletion strain set [46] for growth on Avicel ( crystalline cellulose ) , we identified a transcription factor , vib-1 , that is essential for cellulose utilization . VIB1 ( vegetative incompatibility blocked ) is a p53-like transcription factor that is conserved among filamentous ascomycete fungi . Characterized as a mediator of nonself recognition and cell death in N . crassa [47] , [48] , VIB1 is also required for extracellular protease secretion in response to both carbon and nitrogen starvation [48] . Here , we demonstrated that vib-1 functions upstream of cellulolytic gene induction and its absence leads to a weak induction of clr-2 and cellulase genes but increased expression of genes predicted to function in CCR . Functional analysis of one such predicted transcription factor gene , col-26 , an N . crassa bglR homolog , showed that COL26 regulates glucose sensing/metabolism and which is separate from CRE1-mediated CCR . Deletion of both col-26 and cre-1 leads to a synergistic effect in rescuing Δvib-1 utilization of cellulose and cellulolytic activity . Our data support a function for VIB1 in repression of glucose signaling and CCR and which is critical for fungal utilization of plant biomass .
Screening of a transcription factor deletion set of N . crassa strains [46] for ability to deconstruct crystalline cellulose showed that a strain carrying a deletion of the vib-1 gene ( FGSC11309 ) failed to grow on Avicel ( Figure 2A ) . Since functional vib-1 is required for extracellular protease secretion in response to carbon and nitrogen starvation in N . crassa [48] , [49] , we hypothesized that the Δvib-1 mutant might be unable to respond to complex extracellular carbon sources . In support of this hypothesis , the Δvib-1 mutant also exhibited slow growth on xylan . Growth defects were accompanied by barely detectable extracellular enzyme activity towards crystalline cellulose and low extracellular xylanase activity ( Figure 2B and S1A ) . In contrast , the Δvib-1 mutant accumulated a similar amount of mycelial biomass as the WT strain when inoculated into minimal media containing simple sugars ( sucrose , cellobiose or xylose ) ( Figure S1B ) . The introduction of an ectopic copy of vib-1 ( Pvib-1 ) completely restored the growth defects on Avicel of the Δvib-1 mutant , as well as the secretome and cellulolytic enzyme activity of culture supernatants ( Figure 2B ) . To test the hypothesis that the role of VIB1 in cellulose utilization is conserved in other filamentous fungi , especially in fungi used in industrial production of cellulolytic enzymes , we carried out complementation tests using the vib-1 ortholog from T . reesei ( EGR52133; Trvib1 ) ; TrVIB1 and N . crassa VIB1 share 49% amino acid identity . Constitutive expression of Trvib1 in a N . crassa Δvib-1 mutant fully restored the growth and cellulolytic enzyme activity ( Figure 2B ) . The Trvib1 strain also recapitulated most of the secretome of N . crassa WT and Pvib-1 strains on Avicel ( Figure 2C ) . These results suggest that vib-1 is functionally conserved for the utilization of cellulose in filamentous ascomycete fungi . The Δvib-1 mutant shows an inappropriate temporal and spatial conidiation pattern . These phenotypes are correlated with differential localization of VIB1-GFP in vegetative hyphae versus conidiophores [48] . As conidiation is regulated by glucose limitation [50] , we assessed whether differential localization of VIB1 was also associated with cellulose utilization . We examined a strain in which we replaced the resident vib-1 gene with a functional vib-1-gfp construct . Nuclear localization of VIB1-GFP was observed in hyphae and localization was independent of carbon source , either following a shift to Avicel for 2 hrs ( Figure S1C ) or after prolonged growth . Previous comparative RNA-seq analysis of WT revealed that 212 genes are significantly differentially expressed under cellulose conditions , a gene set referred to as the “Avicel regulon” [8] . To determine whether the defect in cellulase secretion and activity in the Δvib-1 mutant was due to failure to induce cellulase gene expression versus a defect in cellulase secretion , we assessed genome wide expression differences via RNA-seq between the WT and Δvib-1 strains following a shift for 4 hrs from sucrose medium to either carbon-free or Avicel medium . Of the 212 genes in the Avicel regulon , 91 genes were expressed at a significantly lower level in the Δvib-1 mutant versus WT under Avicel conditions ( cutoff: Padj <0 . 05 and fold change >2; Table S1 ) . This gene set includes the essential cellulase transcription factor gene , clr-2 and 43 carbohydrate-active enzymes ( CAZy ) from 27 different families ( Carbohydrate Active Enzymes database: http://www . cazy . org/ ) [51] . CLR1 and CLR2 are strictly required for full expression of 140 genes within the Avicel regulon [8] , [10]; 62 of these genes were identified in the 91-gene set that showed low expression in the Δvib-1 mutant . Although expression of clr-1 was not significantly different from WT in the Δvib-1 mutant , the expression of clr-2 was significantly reduced ( FPKM: 107±43 in WT; 66±27 in Δvib-1 for clr-1 versus 171±10 in WT; 39±14 in Δvib-1 for clr-2 ) ( Table S1 ) . Importantly , constitutive expression of clr-2 ( Pc clr-2 ) in minimal medium without cellulosic inducers recapitulates the response of N . crassa to crystalline cellulose , including the secretion of active cellulolytic enzymes [10] . The reduced transcription of clr-2 in the Δvib-1 mutant ( Table S1 ) suggested that constitutive expression of clr-2 might suppress the cellulose utilization defect in the Δvib-1 mutant . To test this hypothesis , we constructed a Pc clr-2; Δvib-1 strain and evaluated its ability to secrete cellulases and utilize Avicel in comparison to the Pc clr-2 , the Δvib-1 and WT strains . In support of our hypothesis , the Pc clr-2; Δvib-1 strain showed restoration of protein secretion and cellulolytic activity to near WT levels ( Figure 3A ) . Although the clr-2 expression levels in the Pc clr-2 strain were at a similar level to a WT strain after a 4 hr shift to Avicel ( Figure S4 ) , the Pc clr-2; Δvib-1 mutant showed a ∼3 . 5 fold increase in clr-2 expression level under the same conditions . To evaluate the functions of VIB1 versus CLR2 in regulating the 91 genes in the Avicel-regulon , we generated RNA-seq data from the Pc clr-2; Δvib-1 mutant that was shifted for 4 hrs from sucrose medium to carbon-free medium and compared it to previously obtained data for Pc clr-2 [10] . Analysis of genes encoding different CAZy family proteins revealed a similar pattern of expression between the Pc clr-2 and the Pc clr-2; Δvib-1 strains ( Figure 3B ) , consistent with our hypothesis that VIB1 functions upstream of clr-2 in response to cellulose . To differentiate whether any Avicel-regulon genes that showed decreased expression levels in Δvib-1 mutant were due to the vib-1 deletion rather than a low level of clr-2 expression , we performed hierarchical clustering of expression patterns of the Avicel-regulon genes across 6 RNA-seq experiments ( WT and Δvib-1 shifted to no carbon or Avicel and the Pc clr-2 and the Pc clr-2; Δvib-1 strains shifted to no carbon ) ; 4 major expression groups were identified ( Figure 3C and Table S1 ) . Two groups ( group 1 and 3 ) were CLR2-regulon genes that were vib-1 independent . Group 1 consisted of 54 genes whose expression was fully induced by constitutive expression of clr-2 regardless of the presence or absence of vib-1 , including 33 of the 43 CAZy proteins ( Table S1 ) . Group 3 consisted of 8 genes whose expression was partially induced by clr-2 , but still in a vib-1-independent manner . The fourth group of genes included 14 vib-1 modulated genes . These genes were partially induced in Δvib-1 on Avicel , but remained repressed in both the Pc clr-2 and the Pc clr-2; Δvib-1 strains under no carbon conditions ( Table S1 ) . Expression of these genes is likely induced by the cellulolytic cascade pathways upstream of CLR2 or other components present in commercial Avicel preparations , such as a low concentration of hemicellulose [9] . The second group consisted of 15 genes that were induced by constitutive clr-2 expression under no carbon conditions but in vib-1-dependent manner . This gene set included a pectate lyase ( NCU06326 ) , a BNR/Asp-box repeat protein predicted to have exo-α-L-1 , 5-arabinanase activity ( NCU09924 ) , a β−xylosidase ( NCU09923/gh3-7 ) , an extracellular β−1 , 4-D-glucosidase ( NCU04952/gh3-4 ) , a β−1 , 3-glucosidase ( NCU09904 ) , a starch binding domain-containing protein ( NCU08746 ) , a LysM domain-containing protein ( NCU05319 ) , a putative methyltransferase ( NCU05501 ) , and 6 hypothetical proteins . Six genes in this set encode proteins predicted to enter the secretory pathway ( Table S1 ) . Our epistasis experiments indicated that vib-1 functions upstream of clr-2 , suggesting that VIB1 could be involved in signal molecule processing that leads to CLR1 activation and thus clr-2 expression ( Figure 1 ) . In N . crassa , a strain carrying deletions of genes encoding two extracellular β-glucosidases and an intracellular β-glucosidase ( Δ3βG ) , recapitulates the cellulolytic response when the Δ3βG strain is exposed to cellobiose [9] . These data indicate that cellobiose ( or a derivative ) functions as a cellulose signal that results in the induction of cellulolytic genes and subsequent secretion of cellulase enzymes . This cellobiose-induced cellulase gene expression and secretion is dependent upon functional clr-2 gene , as the Δ3βG; Δclr-2 mutant is unable to produce cellulolytic enzymes in response to Avicel or cellobiose ( unpublished data ) . We therefore asked if VIB1 plays a role in induction via signal processing . To test this hypothesis , we created a Δ3βG; Δvib-1 quadruple mutant and asked whether the Δ3βG; Δvib-1 mutant could induce cellulase gene expression in response to cellobiose . Following a switch from sucrose to either no carbon , or 0 . 2% cellobiose , or 2% Avicel for 4 hrs , the induction of two major cellulase genes , cbh-1/NCU07340 and gh5-1/NCU00762 were significantly induced in the Δ3βG; Δvib-1 and the Δ3βG strains , but not in the Δvib-1 strain ( p<0 . 05 ) ( Figure 4A ) . The restoration of cellulase gene expression in the Δ3βG; Δvib-1 strain when exposed to cellobiose was accompanied by enzyme production and activity . Similar to the Δ3βG mutant , the Δ3βG; Δvib-1 strain accumulated biomass more slowly on cellobiose than WT or the Δvib-1 mutant due to the slow conversion of cellobiose to glucose ( 0 . 51±0 . 11 g/L and 0 . 63±0 . 0 g/L for the Δ3βG and the Δ3βG; Δvib-1 strains , respectively , versus 3 . 83±0 . 19 g/L and 3 . 62±0 . 11 g/L for WT and Δvib-1 , respectively ) . However , despite less biomass accumulation , both the Δ3βG and the Δ3βG; Δvib-1 strains showed significantly more enzyme activity than WT and the Δvib-1 strains on 2% cellobiose ( Figure 4B ) . When grown on medium containing 2% Avicel as a sole carbon source , the Δ3βG; Δvib-1 strain showed significantly higher enzyme activity than Δvib-1 ( Figure S2 ) . These expression and activity data indicate VIB1 does not play a role in signal processing or signal transduction mechanisms that lead to activation of CLR1 and transcription of the cellulase activator , CLR2 . In addition to induction , cellulolytic enzyme production requires proper nutrient sensing and relief from carbon catabolite repression ( CCR ) ( reviewed in [17] , [52] ) . We therefore hypothesized that the Δvib-1 mutant might be defective in either nutrient sensing and/or relieving CCR in response to Avicel . To test this hypothesis , we first compared RNA-seq data of the Δvib-1 mutant when shifted from sucrose to carbon-free media versus a shift from sucrose to Avicel media . This comparison revealed 770 differentially expressed genes ( cutoff: Padj<0 . 01 and fold change >2 ) ( Table S2 ) . We then compared how these genes were expressed in WT under no carbon versus Avicel conditions using a previously published RNA-seq dataset [8] . Hierarchical clustering analysis of expression patterns of these 770 genes revealed three gene clusters ( Figure 5 ) ( Table S2 ) . The first cluster contained 237 genes whose expression pattern was similar between the Δvib-1 and WT strains . This gene set was expressed at low levels under no carbon conditions but induced to higher levels upon exposure to Avicel . This group contained 51 CAZy proteins , clr-1 and clr-2 , all three cellodextrin transporters ( cdt-1 , cdt-2 , and cbt-1 ) [44] , [53] , [54] and 102 hypothetical proteins . This gene set overlapped the WT Avicel-regulon for 143 genes , suggesting that cellulosic induction still occurred in the Δvib-1 mutant albeit at a low level . The second cluster consisted of 173 genes whose expression pattern was also similar between WT and the Δvib-1 strain . However , in contrast to the first gene set , the expression level of these 173 genes was higher under carbon-free conditions . This set included 7 CAZy proteins , three conidiation-specific proteins ( NCU08769/con-6 , NCU07325/con-10 , NCU09235/con-8 ) , a high affinity glucose transporter/NCU08152 , and 103 hypothetical proteins . Genes in this cluster may encode proteins that function in a general response to carbon starvation . The third cluster consisted of 360 genes whose expression pattern between no carbon and Avicel conditions was different in the Δvib-1 mutant as compared to the WT strain . This gene set showed consistently higher expression in the Δvib-1 mutant on Avicel medium as compared to carbon-free medium ( Figure 5 ) . Only 7 genes encoding CAZy proteins were in this set and 169 genes were annotated as hypothetical . An enrichment in the categories of metabolism and energy , particularly , degradation of glycine ( p = 2 . 37e-03 ) , nitrogen , sulfur and selenium metabolism ( p = 8 . 00e-03 ) , purine nucleotide/nucleoside/nucleobase catabolism ( p = 2 . 49e-05 ) , isoprenoid metabolism ( p = 8 . 63e-04 ) , respiration ( p = 3 . 34e-04 ) , metal binding ( p = 6 . 18e-04 ) , and mitochondrial transport ( p = 2 . 94e-03 ) was observed . These data suggested that the Δvib-1 mutant was improperly responding to carbon-limited conditions as compared to a WT strain . Within the gene set that showed increased expression level in the Δvib-1 mutant on Avicel were genes involved in CCR . This gene set included cre-1/NCU08807 , creD/NCU03887 , creB/NCU08378 and bglR/NCU07788 ( Table S2 ) . Although the role of cre-1 in CCR and cellulose utilization is established in N . crassa [26] , [27] , the function of the creB and creD homologs in cellulolytic enzyme production were uncharacterized . In N . crassa , NCU07788/BglR was previously characterized in a transcription factor deletion screen and was named col-26 for its colonial phenotype on minimal sucrose medium [46] . To determine whether homologs of the CCR genes that showed increased expression in the Δvib-1 mutant play a role in cellulose deconstruction , we first measured protein concentration and cellulase enzyme activity in supernatants from the Δcol-26 , ΔNCU08378/creB , and ΔNCU03887/creD mutants grown on Avicel for 7 days: none of the mutants showed significantly different cellulase activity than WT ( Figure S3 ) . To test if these genes are involved in CCR , we evaluated resistance of WT and the mutants to 2-deoxy-glucose ( 2-DG ) . The compound 2-DG is an analogue of glucose that cannot be metabolized and is often used to select for , or evaluate , impairment of CCR and glucose repression in filamentous fungi [39] , [55]–[57] . In strains with functional CCR , 2-DG is phosphorylated , thus activating CCR , resulting in the inability of the strain to grow on alternative carbon sources; strains with impaired CCR are insensitive to 2-DG exposure . When 2% cellobiose and 0 . 2% 2-DG were used as carbon sources , only the Δcre-1 and the Δcol-26 mutants showed 2-DG resistance , which was more obvious when Avicel instead of cellobiose was used as a carbon source ( Figure 6A ) . These data implicated COL26 in CCR in N . crassa . To confirm the role of COL26 in CCR , we tested CCR functionality using allyl alcohol ( AA ) . As reported for M . oryzae [41] , when CCR is impaired , alcohol dehydrogenase is expressed and will convert AA into toxic acrylaldehyde . Thus , strains with impaired CCR exhibit AA sensitivity , while strains with functional CCR are insensitive . As predicted , the Δcre-1 mutant was sensitive to AA , but the Δcol-26 mutant , similar to WT , was insensitive ( Figure 6B ) . These data indicated that CCR was still functional in the Δcol-26 mutant . To reconcile the different results for the Δcol-26 mutant with respect to CCR , we analyzed growth of the Δcre-1 and the Δcol-26 mutants on different simple carbon sources . When grown on MM media with 2% glucose , fructose , sucrose , or cellobiose as the sole carbon source , the Δcre-1 mutant accumulated a similar amount of biomass to the WT strain ( Figure 7A ) . However , the Δcol-26 mutant exhibited a severe growth defect on glucose , fructose and sucrose , consistent with its colonial designation [46] , but only a moderate growth defect on cellobiose ( Figure 7A ) . The fact that the Δcol-26 mutant grew much better on cellobiose as compared to glucose , fructose , and sucrose and was insensitive to 2-DG suggested that the Δcol-26 mutant might have defects in sugar transport and/or metabolism . To test this hypothesis , we measured glucose uptake rates in WT , the Δcre-1 , and the Δcol-26 mutants . Within the first 5 minutes , extracellular glucose was reduced to a similar level in all strains ( Figure 7B ) , suggesting similar glucose transporting capacity . However , over the remaining 55 minutes , glucose uptake rates decreased dramatically in the Δcol-26 mutant ( Figure 7B ) . These data indicate that the Δcol-26 mutant has defects in glucose sensing/metabolism , rather than in glucose transport . Our data supported a role for CRE1 in CCR and a role for COL26 in the regulation of glucose utilization . We therefore tested sensitivity of the Δcre-1; Δvib-1 and the Δcol-26; Δvib-1 mutants to AA . The Δcre-1; Δvib-1 and the Δcre-1 mutants were both sensitive to AA ( Figure 6B ) , indicating the Δcre-1 mutation is epistatic for CCR to Δvib-1 , while the Δcol-26; Δvib-1 mutant was insensitive to AA , consistent with the active CCR phenotype of the col-26 and the Δvib-1 mutants . However , although CCR was impaired in the Δcre-1; Δvib-1 mutant , the double mutant was still unable to produce cellulolytic enzymes and grow on Avicel ( Figure 8A ) . Similar to the Δcol-26 mutant , the Δcol-26; Δvib-1 mutant also showed defects in glucose consumption ( Figure 7B ) . Although the Δcol-26; Δvib-1 mutant was unable to utilize Avicel , it showed slightly higher enzyme levels than that of the Δvib-1 mutant ( Figure 8A ) . We therefore hypothesized that simultaneously preventing CRE1-mediated CCR and reducing glucose sensing/metabolism via inactivation of col-26 would restore cellulase gene expression and enzyme activity in a Δvib-1 mutant . As predicted , a Δcre-1; Δcol-26; Δvib-1 triple mutant utilized Avicel , produced significant cellulase activity and displayed a secretome similar to WT after 5 days of growth on Avicel ( Figure 8A and S5 ) . RT-PCR experiments from the Δcre-1; Δcol-26; Δvib-1 Avicel cultures showed that expression levels of clr-2 and cbh-1 were restored in the triple mutant ( Figure 8B ) . Although simultaneous deletion of cre-1 and col-26 restored utilization of cellulose in the Δvib-1 mutant , a significant lag in growth and enzyme activity in the triple mutant was observed as compared to the WT , Δcre-1 , or Δcol-26 mutants ( Figure 8C ) . To assess whether the Δcre-1; Δcol-26; Δvib-1 mutant was also delayed in transcriptional response upon exposure to cellulose , we measured expression levels of clr-2 , cbh-1 , cre-1 , vib-1 and col-26 in the Δvib-1 , Δcol-26 , Δcre-1 , Δcre-1; Δvib-1 , Δcol-26; Δvib-1 , and Δcre-1; Δcol-26; Δvib-1 mutants as compared to the WT strain at 4 hrs and 24 hrs after cultures were shifted to Avicel conditions . Consistent with the enzyme activity assay and growth phenotype ( Figure 8C ) , induction of clr-2 and cbh-1 was delayed in the Δcre-1; Δcol-26; Δvib-1 mutant ( Figure 9 ) . However , in the Δcol-26 mutant at the 4 hr time point , expression levels of cre-1 were significantly higher than in the Δvib-1 mutant , with the Δcol-26; Δvib-1 mutant showing an additive phenotype of significantly increased cre-1 expression levels . At the 24 hr time point , expression levels of cre-1 were only maintained in the Δvib-1 and Δcol-26; Δvib-1 mutants , but not in the Δcol-26 mutant . These data suggest that COL26 may function to repress cre-1 transcription to promote relief of CCR during the initial response to cellulolytic induction . Surprisingly , although the Δcre-1; Δvib-1 mutant was unable to utilize cellulose , induction of both clr-2 and cbh-1 were near WT levels at the 4 hr time point , unlike the Δvib-1 mutant ( Figure 9A ) . However , at the 24 hr time point , expression levels of clr-2 were low and cbh-1 was undetectable in Δcre-1; Δvib-1 mutant ( Figure 9B ) . These data suggest that although the Δcre-1; Δvib-1 can respond to cellulolytic induction by increasing clr-2 and thus cbh-1 expression levels , induction signaling cannot be maintained , perhaps due to repression by COL26 or by other factors present/absent in a Δvib-1 mutant background . The fact that the Δcre-1; Δcol-26; Δvib-1 mutant does not show WT restoration of initial cellulolytic induction ( Figure 8C; Figure 9A ) supports the hypothesis that additional unknown factors remain to be identified that play a role in nutrient sensing/signaling and the regulation of cellulose utilization in N . crassa .
In this study , we showed that a Δvib-1 mutant displayed severe growth defects on cellulose , which was correlated with a lack of cellulolytic enzyme activity . By using RNA-seq data , we showed that expression of the Avicel regulon was significantly decreased in the Δvib-1 mutant , a phenotype that was rescued by constitutive expression of clr-2 . Induction of clr-2 is dependent upon a signal cascade from cellobiose or derivative and functional CLR1 ( Figure 1 ) [8] . Here we showed that VIB1 is not involved in inducer signal processing or perception because the Δ3βG; Δvib-1 mutant produced cellulolytic enzymes in response to cellobiose . These data indicated that VIB1 functions upstream of regulators that mediate inducer-dependent signal transduction and cellulase gene expression and activity . Our transcriptional profiling revealed that , under Avicel conditions , a deletion of vib-1 led to an increase in transcription of genes in metabolism and energy as well as genes reported to mediate CCR . These results suggested that cellulolytic induction was mis-regulated in the Δvib-1 mutant . In the presence of glucose , N . crassa adjusts its metabolism for a high rate of glycolysis and directs carbon flux to respiration and fermentation for biosynthesis and energy production [58] , while genes involved in utilization of alternative carbon sources are repressed in a CRE1-dependent manner [26] , [27] . When lignocellulose is the only carbon source , CCR is relieved to allow the synthesis of “scouting” enzymes that liberate inducer molecules , such as cellobiose [9] , [44] , [59] . In S . cerevisiae , glucose is sensed through a multifaceted mechanism including direct detection of glucose by glucose receptors/transporters on the plasma membrane and by the sensing of glucose-6-P and other metabolites by metabolic enzymes . The glucose signals are transmitted to CCR mainly through the Snf1 complex and the Mig1 ( CreA/Cre1 homolog ) transcriptional repressor complex [60] , [61] . In A . nidulans , mutations in two hexose kinase genes ( hxkA/glkA4 ) results in inappropriate de-repression of genes under glucose growth conditions , although to a lesser extent than a creA mutant strain [29] . Here we show that simply eliminating CRE1-mediated CCR did not rescue the growth defect of Δvib-1 mutant on Avicel , but that a deletion of col-26 was also required . The Δcol-26 mutant exhibited a growth defect on glucose , fructose and sucrose , which was not associated with a deficiency in glucose transport ( Figure 7B ) . In T . reesei , a strain carrying a mutation in bglR shows reduced expression of β-glucosidase genes , suggesting the BglR plays a positive role in CCR by increasing glucose release from cellobiose [15] . However , our analyses of cellulolytic activity of secreted enzymes in the Δcol-26 mutant showed no difference in glucose versus cellobiose release ( Figure 8A ) , a result that is in contrast to the strongly reduced glucose release from culture supernatants in the Δ3βG mutant ( which lacks extracellular β-glucosidase activity ) ( Figure S2 ) . Although we have not determined how glucose metabolism is changed in the Δcol-26 mutant , the resistance of Δcol-26 to 2-DG inhibition suggests a defect in glucose sensing/metabolism; CRE1-mediated CCR was still functional ( as shown by insensitivity to AA ) . The fact that a deletion of col-26 and cre-1 restored growth of Δvib-1 on Avicel suggests a synergistic effect between glucose sensing/metabolism mediated by COL26 and CRE1-regulated CCR in repressing cellulolytic induction ( Figure 10 ) . However , other unknown factors in addition to CRE1 and COL26 play a role in the Δvib-1 mutant , because the Δcre-1; Δcol-26; Δvib-1 mutant showed a significant lag in gene induction and enzyme secretion under cellulolytic conditions ( Figure 8C ) . Future experiments to identify additional mutations that fully suppress the Δvib-1 cellulolytic phenotype and the identification of direct targets of VIB1 will be most informative for further dissection of glucose sensing and CCR in filamentous fungi . Our data supports the model that the regulatory function of VIB1 on CRE1-mediated CCR and COL26-mediated glucose sensing/metabolism functions during different stages of the cellulolytic response ( Figure 10 ) . At induction stage , both VIB1 and COL26 negatively regulate CRE1-mediated CCR ( Figure 9 ) , thus allowing a relief of CCR and efficient induction of cellulolytic genes in response to cellulose . During the utilization phase , glucose is released from cellulose , and glucose sensing/signaling via COL26 may repress cellulolytic responses , with VIB1 functioning to dampen this inhibition . As many cellulolytic genes are subject to carbon catabolite repression and a requirement for CLR2 for induction , the cellular response to plant biomass may depend on the relative strength of these two antagonizing forces ( Figure 10 ) . Mechanistically , how VIB1 exerts its function on glucose sensing/metabolism via COL26 and CCR via CRE1 remain to be elucidated . In the hyper-secreting T . reesei strain , RUT-C30 , disruption of phosphoglucose isomerase gene ( pgi1 ) blocks formation of fructose-6-P from glucose-6-P and increased cellulase production on glucose . This increase relies on a genetic interaction between the Δpgi1 mutation and the cre1-1-1 mutation in the RUT-C30 background [62] . Interestingly , both the hyper-secreting T . reesei RUT-C30 and PC-3-7 strains have mutations in cre1 and bglR/col-26 [15] , [63] , [64] , but whether a synergy exists between Δcre1 and ΔbglR in T . reesei , as in N . crassa , and its relationship to T . reesei vib1 is unclear . Many cellulolytic enzyme hyper-producers such as T . reesei RUT-C30 and PC-3-7 , and P . decumbens JU-A10-T show relief from CCR , but contain a large number of mutations in additional genes that contribute to the hyper-production phenotype [2] , [15] , [63]–[65] . Identifying and characterizing possible synergistic effects of the different mutations on hyper-production of lignocellulose enzymes , as shown in this study , will be a challenge . The function of VIB1 in regulating glucose sensing/metabolism and CCR plays a role in the utilization of other complex substrates . VIB1 is required for extracellular protease production in response to carbon and nitrogen starvation , a function shared by its homolog in A . nidulans , xprG [66]–[70] . The Δvib-1 mutant also exhibits inappropriate temporal and spatial conidiation and has defects in protoperithecia formation [48] , [70] , two developmental events that are regulated by nitrogen and glucose limitation and signaling [71] . A shotgun proteomic analysis of culture supernatant of the Pvib-1 strains under carbon source depletion showed a higher amount of intracellular proteins relative to WT ( Table S3 ) . These data are in consistent with a role of VIB1 in promoting cell death [47] , [48] , [72] , and in autolysis in A . nidulans [73] , perhaps via perturbed nutritional signaling . Autolysis is frequently observed in submerged batch cultures in industrial bioprocessing , and promotes cryptic growth for survival and protein production under nutrient-depleted conditions [74] . Further manipulations of vib-1 and its homologs in filamentous fungi may yield economic benefits via the regulation of autolysis under industrial settings . In summary , our data show that VIB1 is an essential regulator for cellulase production under inductive conditions and identifies COL26 as an important player in glucose sensing/metabolism . As VIB1 mediates metabolic changes as well as programmed death , two properties shared by mammalian tumor suppressor p53 [75] , [76] , the molecular mechanism in linking the two could be conserved , and further investigation of vib-1 function and its homologs in filamentous fungi may also shed light on cancer research .
FGSC 2489 was used as the WT reference strain and background for mutant strains [46] . FGSC 11308 ( Δvib-1; mat a ) , FGSC 11309 ( Δvib-1; mat A ) , FGSC 11030 ( Δcol-26; mat a ) , FGSC 11031 ( Δcol-26; mat A ) were obtained from the Fungal Genetics Stock Center ( http://www . fgsc . net/ ) [50] . The vib-1 mis-expression strain Pvib-1 ( Pvib-1; Δvib-1 ) was constructed by transforming FGSC 11308 with a DNA fragment containing the promoter of the clock controlled gene 1 ( ccg-1 ) and the open reading frame and 3′ untranslated region ( UTR ) of vib-1 and homologous and flanking regions from the coding sequence of the his-3 gene . Transformants were selected for histidine prototrophy [77] and backcrossed to FGSC 2489 to obtain a his-3::pccg-1-vib-1; Δvib-1 homokaryotic strain . The Tr vib1 mis-expression strain PTr-vib1 ( PTr vib1; Δvib-1 ) was created in the same way except that the open reading frame and 3′UTR of Tr vib1 was used . The Pc clr-2 , the Δcre-1 , the Δ3βG and the Δ3βG Δcre-1 strains were from previous studies [9] , [10] , [26] . The Pc clr-2; Δvib-1 strain , the Δ3βG; Δvib-1 strain , the Δcol-26; Δvib-1 strain , the Δcre-1; Δcol-26 strain , and the Δcre-1; Δcol-26; Δvib-1 strain were created through crosses . N . crassa cultures were grown on Vogel's minimal medium ( VMM ) [78] . Unless noted , 2% ( w/v ) sucrose was used as a carbon source . Strains were pre-grown on 3 mL VMM slants at 30°C in dark for 24 hrs , then at 25°C in constant light for 4–10 days to stimulate conidia production . For flask cultures , conidia were inoculated into 100 mL of liquid media at 106 conidia/mL and grown at 25°C in constant light and shaking ( 200 rpm ) . To test 2-DG and allyl alcohol sensitivity , 3 mL of liquid media containing either 0 . 2% ( w/v ) 2-DG ( Sigma Aldrich , MO ) or 100 mM allyl alcohol were inoculated with 106 conidia/mL and grown in 24-well plates at 25°C in constant light and shaking ( 200 rpm ) . For crosses , one parental strain was grown on synthetic crossing medium [79] as the female for 2 weeks at room temperature for protoperithecial development . The other parental strain was used as the male to fertilize the protoperithecia . Crosses were kept for 3 weeks at room temperature . Ascospores were collected and activated as described [80] , plated on 1% VMM , and incubated at room temperature for 18 hrs . Germinated ascospores were selected and transferred to selective slants for further screen and confirmation . Cultures were grown on sucrose for 16 hrs , centrifuged at 2000 g for 10 min and washed in VMM or MM without a carbon source , followed by 4 hrs growth in 100 mL VMM or MM with 2% carbon source ( sucrose , cellobiose , Avicel PH-101 ( Sigma Aldrich , MO ) ) or with no carbon source added . Mycelia were harvested by filtration and flash frozen in liquid nitrogen . RNA was extracted using the Trizol method ( Invitrogen ) and further purified using RNeasy kits ( QIAGEN ) . Four ng of RNA was used as template in each quantitative RT-PCR ( qRT-PCR ) reaction . qRT-PCR was carried out using EXPRESS One-Step SYBR GreenER kit ( Invitrogen ) and Applied Biosystems Step One Plus Real Time PCR system . qRT-PCR were done in biological duplicates or triplicates with actin as the endogenous control . Relative expression levels were normalized to actin , and fold changes in RNA level were the ratios of the relative expression level on inducing conditions to no carbon conditions . Libraries were prepared according to standard protocols from Illumina Inc ( San Diego , CA ) and sequenced on the HiSeq 2000 platforms at QB3 Vincent J . Coates Genomics Sequencing Laboratory ( CA ) . Sequenced reads were mapped against predicted transcripts from the N . crassa OR74A genome [81] ( Neurospora crassa Sequencing Project , Broad Institute of Harvard and MIT http://www . broadinstitute . org/ ) with Tophat v2 . 0 . 4 [82] . Transcript abundance ( FPKM ) was estimated with Cufflinks v2 . 0 . 2 mapping against reference isoforms and differential gene expression were analyzed with Cuffdiff v2 . 0 . 2 [83] . Biological replicates used for RNA-seq showed high reproducibility . The Pearson correlation of FPKM on log basis ( p-value<2 . 2e-16 ) : rp≥0 . 96 between WT ( Nc ) replicates , rp≥0 . 91 between WT ( Av ) replicates , rp≥0 . 99 between Δvib-1 ( Nc ) replicates , and rp≥0 . 96 between Δvib-1 ( Av ) replicates . For hierarchical clustering analysis , FPKM were log transformed , normalized and centered on a per gene basis with Cluster 3 . 0 [84] so that values from each gene ranged from −1 ( minimum ) to 1 ( maximum ) . Average linkage clustering was performed with Euclidean distance as the similarity metric . Functional category analysis was done as described in [8] . Lists of genes were matched against the MIPS Functional Category Database [85] , and significance of enrichment was calculated . For CMCase and xylanase activity assays , Azo-CM-Cellulose and Azo-xylan ( Beechwood ) from Megazyme ( Wicklow , Ireland ) were used as substrates . Protein concentration was measured with the Bradford assay ( BioRad ) . Cellulase assays were conducted by mixing 500 µL of culture supernatant with 500 µL 0 . 5% ( w/v ) Avicel in 100 mM sodium acetate , pH 5 . 0 , and incubated with shaking at 37°C for 5 hrs . Reactions were stopped by centrifugation at 2000 g for 5 min and by addition of 9 volumes of 0 . 1 M NaOH to the reaction supernatants . Released glucose and cellobiose were separated on a PA-200 HPAEC column and analyzed on Dionex ICS-3000 as described in [45] . Strains were grown in 3 mL VMM with 2% cellobiose as the carbon source in the well of 24-well plates at 25°C in constant light with shaking ( 200 rpm ) for 40 hrs to reach the same mycelial biomass , then glucose was added into each culture such that the culture was grown in MM with 1% ( w/v ) glucose for 1 hr . The cultures were thoroughly washed with MES buffer ( 10 mM 2- ( N-morpholino ) ethanesulfonic acid , 100 mM NaCl ) , and each washed culture was transferred into 4 ml of MES buffer supplemented with 10 mM glucose and grown at room temperature for 1 hr with shaking at 550 rpm . Culture supernatants were sampled at 5 , 20 , and 60 min , and diluted in 50 volumes of 0 . 1 M NaOH . Glucose levels were measured using Dionex ICS-3000 HPAEC-PA 200 and MES buffer instead of VMM was used to avoid precipitation that interferes with downstream analysis . Culture supernatants were mixed with 4× SDS loading buffer and boiled for 10 min before loading onto Criterion 4–15% Tris-HCl Precast Gel ( Bio-Rad ) . GelCode Blue Stain Reagent ( Thermo Scientific ) was used for gel staining . Strains were inoculated in 2% sucrose VMM and grown at 25°C for 12 hrs in eight-chamber Lab-Tek chambered cover glass ( Nalge Nunc International , Naperville , IL ) . Localization of VIB1-GFP was observed using a 100×1 . 4 NA oil immersion objective on a Leica SD6000 spinning disk confocal with 488 nm laser and controlled by Metamorph software . Z-series stacks were collected and maximum intensity projections were created using ImageJ . For medium shift experiment , the cultures in the chamber were washed with VMM without carbon sources and VMM with 0 . 5% Avicel was added , followed by immediate time-lapse recordings with an interval of 15 min . Equal volume of culture supernatants of WT and Pvib-1 strains was subjected to SDS-PAGE and secretome proteins identified as described in [86] . In-gel trypsin-digestion was performed according to manufacture protocol ( Promega , Trypsin Gold ) . Digested peptides were separated using ProtID-Chip-43 ( II ) and analyzed using the Agilent 6510 Q-TOF LC/MS as in [9] . | Many filamentous fungi that grow on plant biomass are capable of producing lignocellulase enzymes to break down plant cell walls into utilizable sugars , thus holding great potential in reducing the cost of the next-generation biofuels . Cellulase production is subject to induction by the presence of plant biomass components and to repression by the availability of easily metabolized sugars , such as glucose . Genes required for repression of cellulase gene expression when preferred carbon sources are present ( carbon catabolite repression ) and those that play a role in mediating glucose sensing/metabolism have been identified in filamentous fungi , but the mechanisms involved in crosstalk between repression versus induction of cellulase gene expression is poorly understood . Here , we report the identification and functional characterization of VIB1 , a transcription factor essential for plant cell wall deconstruction in Neurospora crassa and COL26 , a transcription factor that functions in glucose sensing/metabolism and regulation of CCR . We show that disabling CRE1 repression and modulating the glucose response by deletion of col-26 restored growth of the Δvib-1 mutant on cellulose . Our findings are particularly important in understanding the molecular basis of enzyme production that could allow a further strain improvement for plant biomass deconstruction . | [
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] | 2014 | VIB1, a Link between Glucose Signaling and Carbon Catabolite Repression, Is Essential for Plant Cell Wall Degradation by Neurospora crassa |
Although much effort has been directed at dissecting the mechanisms of central tolerance , the role of thymic stromal cells remains elusive . In order to further characterize this event , we developed a mouse model restricting LacZ to thymic stromal cotransporter ( TSCOT ) -expressing thymic stromal cells ( TDLacZ ) . The thymus of this mouse contains approximately 4 , 300 TSCOT+ cells , each expressing several thousand molecules of the LacZ antigen . TSCOT+ cells express the cortical marker CDR1 , CD40 , CD80 , CD54 , and major histocompatibility complex class II ( MHCII ) . When examining endogenous responses directed against LacZ , we observed significant tolerance . This was evidenced in a diverse T cell repertoire as measured by both a CD4 T cell proliferation assay and an antigen-specific antibody isotype analysis . This tolerance process was at least partially independent of Autoimmune Regulatory Element gene expression . When TDLacZ mice were crossed to a novel CD4 T cell receptor ( TCR ) transgenic reactive against LacZ ( BgII ) , there was a complete deletion of double-positive thymocytes . Fetal thymic reaggregate culture of CD45- and UEA-depleted thymic stromal cells from TDLacZ and sorted TCR-bearing thymocytes excluded the possibility of cross presentation by thymic dendritic cells and medullary epithelial cells for the deletion . Overall , these results demonstrate that the introduction of a neoantigen into TSCOT-expressing cells can efficiently establish complete tolerance and suggest a possible application for the deletion of antigen-specific T cells by antigen introduction into TSCOT+ cells .
T cell tolerance is established mainly in the thymus where the T cell population develops and learns by a process called negative selection to avoid harmful reactivity against self-antigens expressed in that thymus ( reviewed in [1 , 2] ) . In the periphery , organ-specific tolerance can be established by various other mechanisms , including anergy [3] , ignorance [4] , and regulatory T cells [5] . Furthermore , antigen-presenting cells ( APC ) lacking costimulatory molecules in peripheral tissues initiate abortive immune responses [6] . The thymic microenvironment is organized and equipped to achieve efficient self-tolerance by providing stimulatory signals to developing self-reactive thymocytes . For a diverse T cell repertoire , this negative selection process occurs primarily in the thymic medullary compartment ( reviewed in [7 , 8] ) . The major player among the hematopoietic cells is the dendritic cell ( DC ) , which possesses a highly efficient antigen presentation capability . In addition , it is widely accepted that thymic medullary epithelial cells ( mTEC ) that express low levels of tissue-specific peripheral antigens in a promiscuous/ectopic fashion [9 , 10] can also initiate clonal deletion . Discovery of the AIRE gene and its expression in mTEC has led to an understanding of its critical regulatory role in the removal of autoreactive T cells , particularly against tissue-specific antigens expressed in the endocrine system ( reviewed in [11 , 12] ) . However , AIRE is also expressed in non-mTEC , including thymic DC [13 , 14] and in cortical thymic epithelial cells ( cTEC ) from Rag-2–deficient thymus [15] . Furthermore , the cross-presentation pathway can participate in the CD4 and CD8 tolerance for the membrane-bound antigens [16] . Therefore , the natures of cell types responsible for the tolerance induction still remain unsettled . The role of cortical epithelium in tolerance induction has been controversial ( reviewed in [17–20] ) . Several experiments using thymus transplantation have clearly indicated that thymic epithelium exhibits toleragenic function [21–24] . In contrast , experiments using transgenic mice with targeting of major histocompatibility complex class II ( MHCII ) [25] or MHCI [26] molecules to the thymic cortical compartment ( and the skin ) using a fortuitous keratin 14 promoter led to the conclusion that cTEC are not capable of inducing tolerance . Such results have given rise to the idea that the thymic microenvironment is compartmentalized , with positive selection taking place in the cortex and negative selection in the medulla . If this is a true dogma , there will be autoimmune responses to the antigens specifically expressed in the cortical epithelial cells . However , when other antigens were targeted into cTEC using the same promoter , incomplete but significant tolerance to the specific antigens was observed [27 , 28] . In the case of a circulating antigen ( C5 ) , all types of thymic APC , including the cTEC , could effect efficient negative selection in vitro [29] . Finally , the question of the role of circulating peripheral DC in the induction of thymic tolerance has also been raised [30] and tested true [31] . Experiments regarding the ability of cTEC to efficiently present antigens have also been controversial . In early studies , the death of cortical thymocytes upon activation by antibody or peptides was interpreted as resulting from antigen presentation by the cortical stromal cells [32 , 33] . In addition , a study with purified thymic APC suggested that cTEC were able to present antigens to a self-reactive hybridoma , with an efficiency comparable to that of thymic DC [34] . However , later studies indicated that a cell line with cTEC properties was inefficient in processing antigens both in vitro and in vivo [35 , 36] . In contrast , Volkmann and his colleagues , using enriched stromal cell preparations from adult thymus , demonstrated that cTEC are able to present soluble antigens as efficiently as DC or mTEC in reaggregate cultures . In many , if not all , of the above studies , however , difficulties in interpretation still persist , in particular , because of a lack of sufficient understanding about the nature of the defined cTEC subpopulation under study , as well as the purity of the cells expressing the specific antigens that were used in the assays . More recently , Gray and colleagues reported that well-defined , purified cTEC , as well as mTEC , express costimulatory molecules and can stimulate naive T cells as much as thymic dendritic cells do in vitro [37] . Therefore , we felt it was necessary to re-evaluate the role of the cTEC subpopulation in central tolerance induction using a different model system , one perhaps better suited to more directly answering the question of whether subpopulation of cTEC can present endogenous antigens and whether this can lead to deletion of thymocytes . Previously , in an effort to separate thymic epithelial cell ( TEC ) components , we introduced a new marker ( Ly110 ) , designated thymic stromal cotransporter ( TSCOT ) , which is expressed in a specific TEC subpopulation . TSCOT is a putative 12-transmembrane protein , located mainly in the thymic cortex [38] . TSCOT is not expressed in any other tissues , as detected by quantitative reverse-transcription PCR ( RT-PCR ) [39] . It is also not expressed in thymocytes [38] . TSCOT+ thymic stromal cells are all MHCII+ and CDR1+/6C3+ , well-defined cortical epithelial markers [40] , with observable variations in levels during different developmental stages [41] . In this study , we introduce a new mouse model system called TSCOT delta LacZ ( TDLacZ ) that expresses a β-galactosidase ( β-gal ) in the TEC subpopulation . This model system constitutes a new tool for the study of TEC development and function . First , we were able to follow TSCOT-expressing TEC by β-gal activity assays or antibody staining and flow cytometry using an anti-TSCOT monoclonal antibody ( mAb ) [41] . LacZ enzymatic activity could also be assayed for the location of cells with a high degree of sensitivity , in both sections and the whole organism , and expression could be assessed in a quantitative manner . Second , because the protein is generated by an endogenous promoter , this system is designed to express normal doses of neoself-antigen relative to other competing cellular proteins . This is in contrast to some previous systems for the targeting of cortical expression , in which MHC molecules were displayed at unusually low levels [19 , 26] . Third , the absence of the TSCOT promoter activity in peripheral tissues precludes the involvement of recirculating DCs , which might deliver peripheral antigens to the thymus , and present them ectopically . By targeting LacZ protein as a neoantigen within the TSCOT-expressing thymic epithelium , we were able to demonstrate that TSCOT+CDR1+ TEC alone , without any help from the mTEC or DC , is able to establish deletional tolerance in an AIRE-independent manner with a surprisingly high degree of efficiency .
We established a new system by knocking-in the LacZ gene into the TSCOT locus between two BamHI sites ( Figure 1A ) . LacZ was transcribed in the same message with the 5′ portion of the TSCOT message , and translation of LacZ was facilitated by incorporating an internal ribosome entry site ( IRES ) sequence [42] . The targeting was confirmed by Southern blotting ( Figure 1B ) . Northern blotting confirmed that the LacZ message was in a fusion transcript with the 5′ portion of the TSCOT message ( Figure 1C ) . The TDLacZ mice evidenced no distinguishable abnormalities with regard to thymic structure as the result of the deletion in TM5-TM12 portion of the TSCOT protein . In Figure 1E , we show that the similar thymic stromal patterns of the 2-wk-old homozygote and the wild type . The small difference in the fraction of stromal cell populations was within the experimental variations . There was also no difference detected in the profiles between hetero- and homozygote littermates of the various ages ( unpublished data ) . The N-terminal portion including transmembrane spans 1–4 of the protein still remained expressed on the cell surface , as detected by flow cytometry ( unpublished data ) . The only apparent difference was for the total thymocyte yield at 6 wk of age , which was slightly lower in about one-third of the TDLacZ homozygotes ( Figure 1D ) . However , we failed to detect any reproducible differences in the profiles of thymocyte population except the individual variation . In addition , an analysis of 6-mo-old mice also showed no significant differences detected in the recovery of thymocytes and major profiles of CD25 , CD44 , CD4 , and CD8 ( unpublished data ) . When 5CC7 T cell receptor ( TCR ) Tg mouse was bred with TDL , no significant differences for the thymocyte populations were found in selecting or nonselecting background ( F . Flomerfelt , unpublished data ) . When β-gal activity was assessed in TDLacZ mice at embryonic day 11 ( E11 ) , the time at which thymus organogenesis is initiated , LacZ was already expressed in the two separated thymic rudiments , but it was not expressed in the wild-type littermates ( Figure 1F ) . This expression was not detected in any other organs . At E16 , when the thymus harbors mostly developing double-negative ( DN ) and double-positive ( DP ) cells , thymic expression of LacZ also was very clear ( Figure 1G ) . In addition , endogenous β-gal activity appeared in the TDLacZ intestine at E16 , as in the wild-type control ( unpublished data ) . β-gal activity in thymus samples from newborn TDLacZ pups showed a gene dose dependency ( Figure 1H ) . We next located the LacZ-expressing cells in thymic sections . At the newborn stage , anti-LacZ antibody staining revealed the expression mostly in the thymic cortex as expected ( Figure 2A ) . When the thymus had fully matured ( 8 wk of age ) , LacZ activity was also detected in the cortex ( Figure 2B ) . This is consistent with our previous result that TSCOT protein and mRNA expression was located in the cortex [38] . After careful examination , we occasionally found LacZ staining extends to corticomedullary junction ( unpublished data and see later ) . In an attempt to characterize the TSCOT-expressing cells in the mature thymus in greater detail , flow cytometric analysis was conducted using a TSCOT-specific mAb . Previously , we group the thymic stromal cell populations into at least five different subpopulations [43] . Three main population are cTEC as CDR1+UEA-1−MHCIIhiG8 . 8+ , mTEC as CDR1−UEA-1+MHCIIhi or MHCIImedG8 . 8+ , as well as nonepithelial population , nonTEC , CDR1−UEA-1−MHCII−G8 . 8− . As shown in Figure 2C , 35 . 6% of cTEC ( CDR1+MHCIIhi ) population expresses TSCOT , whereas none of the mTEC or nonTEC population expresses detectible levels of TSCOT . Although all of the TSCOT-expressing cells were positive for cortical marker CDR1 [41] , a fraction of TSCOT+CDR1+ cells were found to express UEA-1 ( unpublished data and see Discussion ) . Finally , we examined whether TSCOT mRNA was expressed along with FoxN1 and AIRE mRNAs ( Figure 2D ) . TSCOT and FoxN1 were detectable only in the MHCII+CD45− epithelial compartment . In contrast , the AIRE message was detectable in both the epithelial and CD45+MHCII+ compartments as expected , supporting the previous result on the expression in hematopoietic stromal cells of the thymus [13–15] . Next , in order to measure sensitivity of tolerance induction , we estimated the average quantity of antigen expressed in one adult thymus by measuring the β-gal activity of the LacZ protein in purified thymic stromal cells . We isolated the cells from TDLacZΔ/Δ mice , and stained them with a mAb against TSCOT ( Figure 3A ) . In this preparation using 28 animals , TSCOT+ cells ( 12 . 3% ) corresponded to 1 . 2 × 105 cells . This calculates out to a total of about 4 , 300 TSCOT+ cells per thymus . When this cell preparation was lysed and the β-gal activity was evaluated ( Figure 3B , and unpublished data ) , we were able to determine the LacZ concentration from a standard curve ( 2 × 10−11 M of 50-μl reactions ) . These numbers corresponded to 5 , 017 molecules of LacZ protein per TSCOT+ cell by the simple mathematical calculation of concentration × volume × Avogadro number/cell number; 2× 10−11 M × 50/106 × 6 . 02 × 1023 molecules in 1 . 2 × 105 cells in the experiment shown . In the second experiment , the final number was 6 , 825 molecules per cell . TDLacZ and wild-type animals were immunized with recombinant LacZ protein and examined for a LacZ-specific polyclonal CD4+ T cell response . As shown in Figure 4A , a concentration-dependent LacZ-induced proliferative response was detected in purified CD4+ lymph node T cells from wild-type B6 animals , whereas T cells from TDLacZ mice clearly showed no response to LacZ . Both the heterozygous and homozygous animals showed a large reduction in proliferation ( Figure 4B ) . LacZ-specific antibody responses were then evaluated by ELISA ( Figure 4C ) . When whole LacZ protein was administered in CFA , wild-type mice produced both IgG1 and IgG2b isotypes specific for LacZ . In contrast , heterozygous and homozygous TDLacZ mice did not produce such antibodies ( Figure 4C , top ) . In order to assess the possibility that this represented tolerance at the B cell level , we administered a GST-tagged loop portion of TSCOT ( GST-Loop ) in CFA and screened for specific antibody responses with a His-tagged loop protein ( His-Loop ) in an ELISA . In this case , with help provided by T cells specific for GST , both the heterozygous and homozygous TDLacZ mice made as much anti-loop IgG1 and IgG2b antibodies as the wild-type mice ( Figure 4C , bottom ) . These results clearly show that the presence of LacZ expression in the subpopulation of TSCOT+ TEC was sufficient for the tolerization of LacZ-specific CD4+ T cells , and this tolerance is not due to the absence of whole TSCOT molecules in the animal . Because AIRE is known to play a key role in the establishment of tolerance to antigens promiscuously/ectopically expressed in small amounts by mTEC [44–46] , we investigated the possible role of AIRE in TSCOT+ TEC with regard to the induction of tolerance . We crossed the TDLacZ mouse with an AIRE-deficient mouse , and conducted the same proliferation assay for an anti-LacZ CD4+ T cell response to the LacZ protein . As shown in Figure 5 , the AIRE-deficient mice displayed slightly enhanced anti-LacZ responses compared to the wild type , possibly due to the introduced cross-reactivity or autoreactivity . When one copy of LacZ was expressed by breeding the AIRE knock-out ( KO ) to the TDLacZ mouse , the anti-LacZ-specific proliferative response was clearly reduced . In the specific responses to 1 μg of antigen , the degrees of responses contributed by AIRE were similar ( difference between wild type and AIRE KO vs . that between TDLacZ to TDLacZ AIRE KO ) . These results strongly suggest the presence of another pathway that AIRE does not play a major role in the induction of tolerance to an antigen expressed in TSCOT+ TEC . We further assessed the presence or absence of selected costimulatory and adhesion molecules in the TSCOT-expressing cells . Although there has been reports that cortical epithelium does not express costimulatory molecule by histological analysis , we had reasons to believe that this conclusion may be false based on our observation of disparity between histology and flow cytometry [47] . As shown in Figure 6A–6C , flow cytometry revealed that TSCOT+ cells are all positive for MHCII , CD40 , and CD54 expression . In more detailed analysis , the relative CD40 level of TSCOT+ cells was similar to that of some CD45+ cells ( presumably dendritic cells ) , and higher than TSCOT− cells that contain TSCOT−cTEC and mTEC populations ( Figure 6B , histogram ) . An important costimulatory molecule , CD80 was expressed in some TSCOT+ cells as shown in Figure 6D ( CD45− gate and CD45−MHCIIhi gate where most of the TSCOT+ cells reside ) . In order to compare the relative levels of CD80 between mTEC and TCSOT+ cells , the multiparameter analyses in flow cytometry and in confocal microscopy were applied including LacZ staining with the stromal cells prepared from TDLacZ thymus . As seen in ( Figures 6E and 6F , and S1 ) . In both analyses , mean fluorescence intensity ( MFI ) of the relative levels of CD80 was higher in TSCOT+cTEC ( CDR1+LacZ+ ) than in other cells mTEC ( UEA-1+CDR1− ) and TSCOT−cTEC ( CDR1+LacZ− ) . CD86 was not detected under our conditions , possibly due to the trypsin-sensitive nature of this marker ( unpublished data ) . These results clearly suggest that TSCOT+ cells can function as efficient APC . To determine the specific mechanism for the observed tolerance , we utilized a monospecific TCR Tg mouse , BgII ( D . Palmer , Marc R . Theoret , and N . Restifo , unpublished data; see Materials and Methods ) that carries an anti-LacZ TCR transgene from a CD4+ LacZ-specific T cell clone [48] . This line was crossed with Rag1−/− to establish a monospecific TCR-bearing T cell population . When the BgIITg/Tg Rag1−/− mouse was crossed with the TDLacZΔ/Δ Rag1−/− mouse heterozygote for TCR Tg and TDLacZ , only CD4− CD8− DN cells were detected in the smaller thymus ( Figure 7 ) . The total number of thymocytes recovered was approximately 17 . 5% of what was recovered from a TCR Tg mouse . Most of the cells were arrested at the CD25hi CD44− ( DN3 ) stage , similar to what was observed in a Rag1−/− mouse ( Figure 7B ) . However , massive cell death in the DN as well as CD4 and/or CD8 cells was found only in the BgIITg+ TDLacZΔ/+ mouse ( Figure 7C ) . By contrast , in the BgII Tg alone , the fraction of CD44− CD25− ( DN4 ) cells had the highest DN subpopulation ( Figure 7B ) . Thus , in the presence of LacZ , the TCR Tg thymocytes appeared to be substantially deleted at the post-DN3 stage as soon as they expressed their TCR at the cell surface ( see Figures S2 and S3 for the expression of TCR gene and protein on the surface ) . The pattern of thymic stromal cells ( gated on CD45− cells ) observed in the BgIITg/+ TDLacZΔ/+ Rag1−/− mice was also similar to that of a Rag1−/− mouse ( Figure 7D ) . UEA1+ mTECs , which are prominent in the adult wild-type thymus , were barely detected , and thus the proportion of CDR1+ cTECs was greatly elevated . Therefore , BgIITg/+ TDLacZΔ/+ Rag1−/− mice do not harbor fully developed mTECs , yet they remain able to efficiently delete developing thymocytes . Cross-Presentation by DC or mTEC Is Not Involved in CDR1+cTEC-Mediated Deletion In order to exclude the possibility of cross-presentation by mTEC and DC in the induction of tolerance , we employed a clean reaggregate thymic organ culture system ( RTOC ) [49 , 50] using UEA-1– and CD45-depleted thymic stroma reconstituted with purified anti-LacZ TCR transgenic thymocytes . The stromal cells were prepared from a E14 . 5 fetal thymic organ culture , in the presence of 2-dGuo , which depletes DC , and the population was further depleted of CD45+ and UEA-1+ cells by magnetic bead separation . Using such cells isolated from wild-type or TDLacZ+/Δ thymus samples , anti-LacZ TCR-bearing DN and DP cells ( 1:10 ratio , similar to that of a normal thymus ) from adult BgIITg/Tg animals were reaggregated with them and cultured for 5–6 d . As shown in Figure 7 , the recovery of the thymocytes from the RTOC with TDLacZ cTECs was between 5%–20% of that achieved with the wild-type stroma . In addition , these cultures did not contain a significant number of CD4 single-positive ( SP ) cells ( Figure 8A ) . When the DN and DP transgenic thymocytes were separately reaggregated with TDLacZ cTEC ( Figure 8B ) , both subsets showed reduced cell numbers after culture , indicating that LacZ expression could also deplete the LacZ-responding DP cells in the RTOC . Although these results argue against the idea of impaired differentiation from the DN to the DP stage , they suggest that developing thymocytes are deleted once they react with antigens presented in thymic cortical epithelium .
From the histological analyses , the thymic microenvironment is already known to be complex in nature , and to change during development [41 , 51–53] . In addition , histological analyses alone do not constitute a suitable method for delineating expression profiles for different compartments , because of the poor cortical staining [43] . Using a combination of flow cytometry with compartment-specific markers [43] and LacZ reporter staining , cortical expression of the TSCOT locus was confirmed at the newborn stage ( Figure 2A and [37 , 41 , 51–53] ) . In the mature thymus , β-gal activity was also principally found in the cortex ( Figure 2B ) , and TSCOT surface expression was exclusively detected in CDR1+ TEC populations , not in conventional mTEC or nonepithelial cells ( Figure 2C ) . However , there are cases in which LacZ activity staining extends to the corticomedullary junction of mature TDLacZ thymus and TSCOT marker also stains uncharacterized minor population of cells ( unpublished data ) . A part of these minor populations could be developing transitional TEC , but this possibility requires further detailed study with an improved technology that can handle an extremely small number of cells . Nonetheless , it is clear that TSCOT+ cells are not part of conventional medullary cells . TSCOT/LacZ was never detected in the conventional CDR1−UEA-1+ mTEC population ( Figure 2C ) . Thus , we are able to dismiss the possibility that LacZ is ectopically expressed in the mTEC of the medulla . Furthermore , TSCOT expression was widely located in the Rag1−/− thymus [38] , which lacks mature mTEC ( Figure 7C and [54] ) . In case of BgII mouse on a Rag1−/− background , tolerance at the DN stage was very clear when there was antigen only in the cortical epithelium ( Figure 7 ) . The notion of the exclusion of cortical epithelium in the induction of tolerance was derived from the transgenic expression of MHC molecules exclusively in the cortex of the thymus , using the K14 promoter [25 , 26] . However , the idea of an exclusive tolerance niche has been challenged: incomplete , but significant , tolerance was observed when other antigens were targeted to cTEC using the same promoter [27 , 28] . In addition , it has been clearly demonstrated that the K14-MHCII thymus is in fact autotoleragenic when self-antigens are presented by its own cortical epithelial cells [22] . Our current findings using a specific TSCOT promoter corroborate the notion that cTEC participate in the establishment of CD4 central tolerance in a highly efficient manner . There is no evidence for autoreactivity to specific antigens presented by the TSCOT+ cells . Instead , we found clear tolerance to LacZ antigen . Therefore , the TSCOT+ TEC niche of the cTEC subpopulation is not excluded from the tolerance induction so that it may avoid autoreactivity against its own cell . Taking advantage of sensitive enzymatic activity , our estimate for LacZ under the control of the TSCOT promoter is about 6 , 000 ( the average of two measurements ) molecules per TSCOT+ TEC in homozygote thymus ( Figure 3 ) . This number is surprisingly similar to that of the estimation of mTEC derived from the indirect estimation [55] . However , one half of this amount in heterozygotes was sufficient to induce complete CD4 tolerance in the absence of mTEC ( Figures 7 and 8 ) or DC cross-presentation ( Figure 8 ) . Previous accurate estimates [56] have suggested that recognition of only three to four peptide/MHC complexes by an immature thymocyte was sufficient to generate a negative selection event in transgenic mouse . Therefore , it remains a challenging question as to how such a high efficiency is achieved . The number of cTEC in the adult thymus is far less than that of mTEC [43] . The total number of TSCOT+ TEC , estimated from a large pool of adult thymuses , was only on the order of several thousand per thymus . In order to screen all of the developing thymocytes for potential autoreactivity , the frequency of cell encounters between cTEC presenting the specific antigen and thymocytes would have to be optimized , even considering the average 3-d period in which DP thymocytes remain in the cortex [57 , 58] . This could be accomplished in thymic nurse cells in which multiple thymocytes are found in association with one epithelial cell . Several earlier papers had come to the conclusion that the thymic epithelium induced tolerance by the induction of anergy , rather than deletion [59 , 60] . In contrast , in our anti-LacZ TCR Tg × TDLacZ model , it is evident that deletion is the dominant mechanism ( Figure 7 ) . Deletion has also been observed in a number of other TCR transgenic systems [28 , 61] . Whether this is a normal physiological process or death subsequent to developmental arrest following the premature expression of a transgenic receptor at the DN stage was questioned [62] . However , DP thymocytes included in the RTOC were also deleted , arguing that cTEC-induced tolerance involves the specific deletion rather than the arrest at premature developmental stage ( Figure 8B ) . In the TDLacZ thymus , cortical epithelium expressing a specific antigen was able to tolerize quite thoroughly ( Figures 7 and 8 ) . This was somewhat surprising if thymic epithelial cells are poor presenters of antigen as concluded earlier on the poor expression of costimulatory molecules in cTEC [63] . However , we clearly show that TSCOT+ cTEC express high levels of MHCII , CD54 , CD40 , and CD80 . Surface CD40 and CD80 proteins in particular are expressed at surprising levels that are even higher than those of mTEC ( Figure 6 ) . According to the RT-PCR result from the purified cells , CD40 , CD80 , and CD86 message levels are slightly higher in mTEC than cTEC ( [37] and unpublished data ) . Among cTEC , TSCOT+ cells are the ones expressing more costimulatory molecules ( Figures 6 and S1 ) . Therefore , molecules on the TSCOT+cTEC can provide the environment for the highly efficient deletional tolerance of TCR bearing early thymocytes Figure S2 ) through a unique TSCOT+ cTEC antigen presentation process . As seen in figure 7C , massive apoptosis events in DN TCR transgenic cells in the presence of the LacZ antigen-bearing cTEC are also consistent with the deletional tolerance . In order to determine the molecular mechanism underlying the induction of tolerance , we determined whether or not AIRE was involved . It has been fairly well established that AIRE is involved in mTEC-mediated tolerance induction by facilitating the expression of peripheral antigens in normal and genetically modified animals [12 , 64 , 65] . As a result of the introduction of one copy of the LacZ gene into AIRE-deficient animals , the LacZ-specific CD4 proliferative response was significantly reduced ( Figure 5 ) . Since AIRE expression was , mostly , assumed to be absent in the normal CDR1+ cTEC ( except Rag1−/− ) , it is not surprising that AIRE was not directly involved in TSCOT+ TEC-induced tolerance . Instead , it may suggest that the AIRE-independent tolerance pathway exists in the TSCOT+ TEC . However , there was still the question of whether tolerance was induced by AIRE-expressing DC or mTEC , via a cross-presentation . The results obtained with RTOC using thymocytes from LacZ-specific TCR transgenic mice and purified CD45−UEA-1−CDR1+ cells from 2-dGuo–treated FTOC ( Figure 7 ) show that neither DC nor mTEC are necessary for tolerance induction in vitro . The direct involvement of TSCOT+ TEC in deletional tolerance constitutes strong evidence for the capacity of direct antigen presentation [29 , 32–36] . More detailed studies will be required to identify the specific molecules that are involved in this type of antigen presentation . It is generally accepted that negative selection requires specific conditions of either high-avidity interaction or prolonged signaling [20 , 66 , 67] . The quantitative aspects discussed above seem insufficient to explain negative selection by a simple affinity/avidity model for cTEC . The surface and cytoplasmic levels of MHCII in cTEC are not appreciably lower than in mTEC and MHCII molecules exist on cTEC as aggregates on the surface [43] . Thus , if a self-peptide was presented at sufficiently high concentrations to display multiple complexes in the same aggregate at any one time , these MHCII aggregates could potentially generate high-avidity signaling leading to thymocyte death . If so , then cortical epithelium could function directly in both negative and positive selection . In line with this notion , it has been shown that a single cTEC line can mediate both positive and negative selection [68] . If the amount of any antigen produced by a cTEC is low , then under normal conditions with a random loading mechanism for a diverse set of endogenous peptides [69] , a single MHCII aggregate on the cTEC surface would likely contain only one peptide/MHC complex . This would hinder an avidity-based mechanism from operating as there would be no multimeric presentation . Although such monomeric presentation might be adequate for positive selection , it seems that it would be inadequate for negative selection . This raises the possibility that other mechanisms might exist for increasing the antigen density on cTEC . Such a mechanism might involve intercellular antigen transfer [70] , in addition to sampling of other self-antigen pools [8 , 71] . However , the expression of costimulatory molecules on TSCOT+ cTECs is consistent with the idea that the presence of costimulation/second signals may distinguish negative from positive selection .
All mice were handled according to American Association for Accreditation of Laboratory Animal Care Regulations . In order to generate mice carrying an inserted LacZ allele at the TSCOT locus , a 4 . 2-kb targeting vector was constructed by cloning IRES-LacZ with a neo-selectable marker from p1049 [42] between two BamHI sites in the first exon of the TSCOT gene . A Herpes Simplex Virus thymidine kinase ( HSV-TK ) expression cassette was positioned at the 5′ end of the construct in order to facilitate negative selection for homologous recombination . The targeted allele harbors an IRES-LacZ and PGK-neomycin expression cassette within the first exon , resulting in a small deletion ( 284 bp ) between the two BamHI sites within exon 1 . The mouse 129 embryonic stem cell line ( R1 ) was electroporated with the construct , and the neomycin-resistant clones were screened in the laboratory of Dr . Hua Gu ( National Institute of Allergy and Infectious Diseases [NIAID]/ National Institutes of Health [NIH] ) . Chimeras were generated by blastocyst injection , and one founder mouse was backcrossed to C57BL/6Tac . To study antigen-specific CD4+ T cell responses to β-gal , a transgenic mouse strain on a C57BL/6 background was developed and named BgII . RNA was isolated from an I-Ab–restricted , β-gal–specific CD4+ T cell clone . Total mRNA was isolated using a Qiagen RNeasy kit , and the α and β TCR were amplified by 5′-Rapid Amplification of cDNA Ends ( 5′-RACE , Life Technologies ) using constant region anti-sense primers a1 ( 5′-GGCTACTT TCAGCAGGAGGA-3′ ) and b1 ( 5′-AGGCCTCTGCACTGATGTTC-3′ ) , respectively . The 5′-RACE products were amplified with nested TCR α and β constant region primers a2 ( 5′-GGGAGTCAAAGTCGGTGAAC-3′ ) and b2 ( 5′-CCACGTGGTCAGGGAAGAAG-3′ ) , and cloned into pCR4TOPO TA sequencing vectors ( Invitrogen ) . Genomic cloning PCR primers were designed based upon the method previously described [72] . The genomic variable domains were TA cloned into pCR4TOPO ( Invitrogen ) , validated by sequencing , subcloned into TCR cassette vectors kindly provided by Dr . Diane Mathis ( Harvard ) , and coinjected into fertilized C57BL/6 embryos ( SAIC ) yielding TCR transgenic founder which were then bred . PCR genotyping . Tail or ear samples were employed for genotyping , using the red Extract-N-Amp Tissue PCR kit ( Sigma ) and primers: for the TSCOT locus , Neo primer: ACCGCTATCAGGACATAGCGTTGG , 1C12 F1: TTACTCAAAGTGATGCTGGACTGG , 1C12 B2: CCGAGGGTTCCTTGGTACATTC; for the RAG1 locus , Neo primer: ACCGCTATCAGGACATAGCGTTGG , Rag-1 F: TCGTTTCAAGAGTGACGGGCAC , Rag-1 B: AATCCTGGCAATGAGGTCTGG; and for the AIRE locus , forward primer: GTCATGTTGACGGATCCAGGGTAGAAAGT , reverse primer: AGACTAGGTGTTCCCTCCCAACCTCAG . For the anti-LacZ TCR transgenic allele , BG2 Alpha F1: ACAACCCGGGATTGGACAG , BG2 Alpha R1: GTATAGCGGCCGCCTCCTAGTGCAATGGT , BG2 Beta F1: TATCTCGAGTCCTGCCGTGACCCTACTATG; BG2 Beta R1: CAGCCGCGGAACCCAACACAAAAACTATAC . Antibodies used for flow cytometric analysis were as follows: for stromal cells , FITC-conjugated anti-mouse I-Ab ( Ab ) AF6–120 . 1 ( BD Pharmingen ) , CDR1-PE ( prepared by L . Lantz , NIAID flow cytometric facility ) , CD45 PE-Texas Red conjugate ( Caltag ) , biotinylated Ulex europaeus agglutinin-1 ( Vector Laboratories ) , streptavidin-APC ( BD Pharmingen ) , CLVE1 anti-TSCOT mAb ( prepared by Dr . L . Lanz , NIAID ) , and FITC-conjugated goat anti-Rat IgM ( Jackson Laboratories ) . For thymocytes , FITC-conjugated anti-mouse CD4 ( L3T4 ) ( GK1 . 5 ) ( BD Pharmingen ) , PE-conjugated anti-mouse CD44 ( BD Pharmingen ) , mouse CD8α PE-Texas Red conjugate ( Caltag ) , biotin-conjugated anti-mouse CD25 ( BD Pharmingen ) , streptavidin-APC ( BD Pharmingen ) , annexinV-FITC ( Clontech ) , PE-conjugated anti-mouse CD69 ( BD Pharmingen ) , CD4 PE-Texas Red conjugate ( Caltag ) , biotin anti-mouse αβTCR ( H57–597 ) ( BD Pharmingen ) , FITC-conjugated anti-mouse CD44 ( BD Pharmingen ) , PE-conjugated anti-mouse CD25 ( BD Pharmingen ) , APC-conjugated anti-mouse CD4 ( L3T4 ) ( GK1 . 5 ) ( BD Pharmingen ) , and APC-conjugated anti-mouse CD8α ( BD Pharmingen ) . For LacZ staining , after treating with 30 mM chloroquine diphosphate to block endogenous β-gal activity for 30 min at 37 °C , 33 μM ImaGene Red C12RG substrate ( Molecular Probes , ImaGene Red C12RG lacZ Gene Expression Kit I-2906 ) was used in FACS buffer for 20 min at 4 °C . Either whole embryos or isolated thymuses were washed in PBS and fixed in 1% paraformaldehyde , 0 . 2% glutaraldehyde , 0 . 02% NP-40 , 1 mM MgCl2 in PBS for 1 or 2 h on ice . Staining was conducted using X-gal solution with 100 mM d-galactose in 2 mM MgCl2 , 5 mM potassium ferricyanide , 5 mM potassium ferrocyanide overnight at 37 °C [42] . For the sections , the thymuses were embedded in Tissue Freezing Medium ( Triangle Biomedical Sciences ) . The 4-μm sections were fixed for 2 min in 1% formaldehyde , 0 . 2% glutaraldehyde , 0 . 02% NP-40 1 mM NaCl , then incubated with X-gal solution ( 1 part X-gal 40 mg/ml in dimethyl formamide , in 40 parts 2 mM MgCl2 , 5 mM potassium ferricyanide , 5 mM potassium ferrocyanide in PBS ) at 37 °C for 48 h . For antibody staining , the paraffin sections were stained with DAKO CSA reagent . For the LacZ Lysis assays , thymic cells from 30 TDLacZ mice or control C57BL/6 mice were partially purified via MACS CD45-bead sorting ( Miltenyi Biotech ) . The cell pellets were lysed using Reporter Lysis Buffer from the β-Galactosidase Enzyme Assay System with Reporter Lysis Buffer ( Promega ) . After lysis and centrifugation , the Assay Buffer was added to each supernatant as well as to enzyme aliquots for a standard curve , and then incubated for 30 min at 37 °C . Absorbance was then measured at a wavelength of 405 nm . Isolated TEC ( about 105 ) were washed in cold FACS buffer ( PBS + 1% BSA ) , subsequently stained on ice with 2 . 4G2 , APC-conjugated anti-CDR1 , biotinylated anti-CD80 ( B7–1 , Armenian hamster IgG2κ ) followed by streptabidin-Alexa568 ( Molecular Probes ) . For the detection of LacZ-expressing cells , ImaGene Green C12FDG lacZ Gene Expression kit ( Molecular Probes ) was used . Stained samples were placed on the slides by cytospin at 1 , 200 rpm for 2 min . Images were collected on LSM 510 META ( Zeiss ) , and analyzed with LSM Image Examiner ( Zeiss ) and Photoshop . Three mice per group were immunized with the affinity-purified , LPS-removed recombinant proteins in Complete Freund's Adjuvant in one footpad and the base of the tail . Ten days after immunization , the mice were sacrificed and the inguinal , mesenteric , and para-aortic lymph nodes were collected and crushed in Iscov's Modified Dulbecco's Medium . The CD4+ cells were collected from MACS columns using anti-CD4 antibody ( GK1 . 5 ) ( purity of the cells were usually over 95% ) and incubated at 37 °C with irradiated whole spleen cells and 0 , 1 , 10 , or 100 μg of LacZ protein ( in triplicate ) . The cells were pulsed with 3H-thymidine for the final 24 h of a 72-h incubation . The cells were harvested with a Brandel 96-well harvester , and thymidine incorporation into DNA was measured with a Wallac Trilux 1450 β-scintillation counter . The mice were immunized intraperitoneally with either LacZ or purified recombinant GST-TSCOT-Loop protein in CFA , three times every other week . The mice were bled 3 d after the last injection , and the sera were incubated on His-LacZ protein or His-Loop–coated ( 5 μg/well ) ELISA plates . The bound anti-LacZ Ab was detected with anti-mouse immunoglobulin isotype-specific antibodies conjugated with HRP and assayed with ABTS solution ( Southern Biotechnology Associates ) by following the manufacturer's description . Optical density ( OD ) was measured at 405 nm . The thymic stromal cells were prepared by treating E14 . 5 fetal thymus samples with 2-dGuo for a week . UEA-1− and CD45− cells were then purified using biotinylated reagents and streptavidin-MACS beads . Thymocytes from anti-LacZ TCR transgenic mice were sorted for DP and DN cells and reaggregated with the stromal cells using protocols developed by Anderson and Jenkinson at the University of Birmingham , United Kingdom [49] . Recovered cells were counted and then analyzed by flow cytometry after 4–6 d of culture . | T cells play critical roles in the immune response . While developing in the thymus ( from whence T cells and their precursors , thymocytes , derive their name ) , thymocytes are selected for the ability to recognize harmful antigen ( positive selection ) , while those that respond to antigens present in their own body are eliminated ( negative selection ) . Dogma holds that the thymus is divided into different functional compartments to ensure that these contrasting selection processes occur efficiently: the cortex is thought to be responsible for positive selection and the medulla for negative selection . In this study , we made use of a novel transgenic mouse ( carrying a LacZ marker in a small fraction of cells in the cortex ) to test whether the cortex is really excluded from negative selection . We were able to show that the introduced LacZ “antigen” present only in the cortical cells leads them to eliminate any LacZ-reactive T cells from the immune repertoire and leads to tolerance of the LacZ “antigen” by the body's immune system . This process is highly efficient , such that a relatively tiny number of antigen molecules present in a small fraction of the cells in the thymic cortex can singularly perform proofreading of all developing thymocytes . | [
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] | 2008 | TSCOT + Thymic Epithelial Cell-Mediated Sensitive CD4 Tolerance by Direct Presentation |
The piwi-interacting RNAs ( piRNA ) are small RNAs that target selfish transposable elements ( TEs ) in many animal genomes . Until now , piRNAs’ role in TE population dynamics has only been discussed in the context of their suppression of TE transposition , which alone is not sufficient to account for the skewed frequency spectrum and stable containment of TEs . On the other hand , euchromatic TEs can be epigenetically silenced via piRNA-dependent heterochromatin formation and , similar to the widely known “Position-effect variegation” , heterochromatin induced by TEs can “spread” into nearby genes . We hypothesized that the piRNA-mediated spread of heterochromatin from TEs into adjacent genes has deleterious functional effects and leads to selection against individual TEs . Unlike previously identified deleterious effects of TEs due to the physical disruption of DNA , the functional effect we investigated here is mediated through the epigenetic influences of TEs . We found that the repressive chromatin mark , H3K9me , is elevated in sequences adjacent to euchromatic TEs at multiple developmental stages in Drosophila melanogaster . Furthermore , the heterochromatic states of genes depend not only on the number of and distance from adjacent TEs , but also on the likelihood that their nearest TEs are targeted by piRNAs . These variations in chromatin status probably have functional consequences , causing genes near TEs to have lower expression . Importantly , we found stronger selection against TEs that lead to higher H3K9me enrichment of adjacent genes , demonstrating the pervasive evolutionary consequences of TE-induced epigenetic silencing . Because of the intrinsic biological mechanism of piRNA amplification , spread of TE heterochromatin could result in the theoretically required synergistic deleterious effects of TE insertions for stable containment of TE copy number . The indirect deleterious impact of piRNA-mediated epigenetic silencing of TEs is a previously unexplored , yet important , element for the evolutionary dynamics of TEs .
Transposable elements ( TEs ) are genetic elements that increase their copy number in the host genome by copying themselves to new genomic locations . Despite reported incidences of potentially adaptive TEs [1–6] , the majority of TEs are considered deleterious to their host and widely viewed as “genomic parasites” . TE insertions can disrupt sequence and function of host genetic elements [7] . Additionally , ectopic recombination between nonhomologous TE copies leads to potentially deleterious chromosomal rearrangements [8–12] . Although deleterious impacts of TE insertions are broadly appreciated , our picture for the functional and evolutionary mechanisms of TEs containment in natural populations is still incomplete . Because of the replicative nature of most transposition mechanisms of TEs , it is critical to understand the evolutionary forces counterbalancing the constant increase of TEs . Theoretical models demonstrate that the TE copy number in an outbreeding population reaches an equilibrium when the increase in TE copy number via transposition is counterbalanced by the removal of TEs [13 , 14] . Regulating the transposition rate such that it equals the excision rate of TEs is one possible mechanism of achieving equilibrium . This , however , is not supported by empirical observations ( reviewed in [15] ) . In addition , theoretical studies have shown that transposition rate has only a minimal impact on the predicted frequency spectrum of TEs [13 , 14] . Regulation of transposition rate is unlikely to account for the observed low population frequency of most TEs in outbreeding populations such as Drosophila ( [4 , 16–19] , reviewed in [15] ) . Instead , both theoretical analyses and empirical results support that selection against deleterious impacts of TEs plays a primary role in the evolutionary dynamics of TEs [15 , 20 , 21] . Without regulated TE transposition , selection alone can result in an equilibrium of TE copy number and lead to the skewed frequency spectrum of TEs in natural populations . Most previous work on the deleterious effects of TEs has centered around consequences of TE-mediated physical disruption of genomic DNA , such as TEs’ insertion into functional elements and ectopic recombination between nonhomologous TEs . The exploration of the potential functional and evolutionary consequences of TEs’ epigenetic impacts have been limited [22] . In Drosophila , a class of small interfering RNA , piwi-interacting RNAs ( piRNAs ) , are enriched for TE sequences and post-transcriptionally regulate TE transposition in the germline [23–26] . In addition , piRNAs can suppress TE transposition by inducing heterochromatic formation of euchromatic TEs in the germline [27–31] as well as in larval and adult somatic tissues [32 , 33] . Constitutive pericentric and telomeric heterochromatin “spreads” and usually results in stochastic silencing of the nearby genes , a phenomenon known as “Position-effect variegation” ( PEV [34] , reviewed in [35–37] ) . Similarly , piRNA-mediated heterochromatin of euchromatic TEs was shown to spread into adjacent host genes by using reporter constructs [32] . The resulting perturbation of host gene expression due to the spread of heterochromatin from adjacent TEs probably has deleterious consequences . These TEs are expected to be removed by selection even if they do not generate physical disruptions of the DNA , leading to a skewed frequency spectrum of TEs [20] . Despite detailed functional studies investigating the epigenetic influence of TEs on surrounding sequences , the influence of naturally occurring TEs on the chromatin states and functions of nearby genes has not been explored on a local or genomic scale in Drosophila . The connection between TE-induced epigenetic changes and the evolutionary genomic consequences of TE insertions is also lacking . In this study , we tested our hypothesis that the spread of piRNA-mediated heterochromatin of TEs to adjacent genes is deleterious and represents an important force shaping the population dynamics of TEs ( Fig 1 ) . Throughout our study , we used the distribution of histone modifications as an index for the chromatin state of a region . Within the nucleus , DNA is wrapped around core histones ( H2A , H2B , H3 , and H4 ) to form nucleosomes , the fundamental unit of chromatin . Modifications of histones , such as methylation and acetylation , at different positions of the core histones have been associated with biological consequences , particularly gene expression states ( reviewed in [38] ) . For instance , tri-methylation of histone H3 lysine 4 ( H3-K4me3 ) is enriched at transcription start sites and found to correlate positively with gene expression levels [39 , 40] . The methylation of histone H3 lysine 9 ( H3K9me ) , particularly di- and tri- methylations , is generally regarded as “repressive” mark of the chromatin and is found enriched in the heterochromatic regions of the Drosophila genome [38–41] . We focused on the genomic variation in the level of H3K9me , which allows us to relate TE-composition to the heterochromatic states of sequences .
Previous local studies indicated that the density of heterochromatic marks is elevated in sequences adjacent to TEs [32] , which our hypothesis relies on ( Fig 1B and 1C ) . To confirm this finding with naturally occurring TEs on a genomic scale , we used the genome annotation of the D . melanogaster reference genome ( the Drosophila genome with the best annotated TEs ) , and the modEncode H3K9 tri-methylation ( H3K9me3 ) data of the same strain at nine developmental time stages ( six embryonic stages , two larval stages , and one pupal stage , [40] ) . These experiments were performed using whole animals , which consist of heterogeneous tissues and cell types . Histone modifications could significantly vary across cell types [42] and it is difficult to interpret the modifications of samples consisting of heterogeneous tissues and/or cell types as binary states [43 , 44] . Accordingly , we analyzed H3K9me3 data quantitatively , using the average read density of H3K9me3 for our following analyses ( see Materials and Methods ) . To investigate whether the TE-induced heterochromatin spreads beyond TEs , we first examined the enrichment of heterochromatic marks adjacent to TEs . We did this by estimating the H3K9me3 density at sequences that are 10kb upstream and downstream of euchromatic TEs . We only considered 10kb sequences that are entirely within intergenic regions to avoid the potential influences from functional elements ( genes and regulatory sequences ) , which are generally depleted of “silent” marks ( such as H3K9me ) and enriched with other “active” histone modifications ( [39 , 40] , reviewed in [38] ) . We observed that the H3K9me3 density of 1kb nonoverlapping windows decreases with increasing distance from TEs ( Fig 2A and 2B ) . The windows closest to a TE ( 0-1kb and 1-2kb ) have significantly higher H3K9me3 density than the most distant window analyzed ( 9-10kb , Mann-Whitney U test , p < 0 . 011; S1 Table ) for all developmental stages . For some embryonic stages , the significant difference in H3K9me3 enrichment is still observable for windows that are even farther from TEs ( S1 Table ) . We compared our observations to null genomic expectations by randomly choosing genomic segments that are of the same sizes and the same large-scale chromosome locations as those of TEs and investigating the decay in H3K9me3 enrichment near these “TE-size” sequences ( see Materials and Methods ) . The median ( Fig 2C ) and mean ( Fig 2D ) H3K9me3 of windows adjacent to TEs are indeed higher than those of random sequences for most developmental stages ( see S1 and S2 Figs for all developmental stages ) . Our genomic observations support predictions from functional data [32] that the presence of TEs influences the chromatin status of adjacent sequences . In order to support our hypothesis , it is critical to demonstrate that the spreading of TE-induced heterochromatin also influences the chromatin states of neighboring genes in addition to intergenic sequences ( Fig 1C ) . Using the same H3K9me3 data of the reference strain at nine developmental stages , we contrasted the enrichment of H3K9me3 marks in euchromatic genes that have TEs inside their introns ( in gene ) , in 1kb , 1-2kb , 2-5kb , 5-10kb upstream/downstream of the gene to those of genes that have no TEs within 10kb upstream/downstream . Similar to our observation for intergenic sequences , we found genes with TEs nearby have significantly higher H3K9me3 density than genes without TEs nearby . H3K9me3 enrichment decreases as the windows move further away from TEs ( Fig 3A for 0–4 embryo , see S3 Fig for all developmental stages ) . Consistently , we found significant negative correlations between a gene’s H3K9me3 density and its distance from the nearest TE ( Spearman rank ρ = -0 . 189 ~ -0 . 040 , p < 0 . 05 for L1 and L2 larvae and p < 10–3 for all other developmental stages , S2 Table ) . In addition , the number of TEs in bins further away from genes is less correlated with a gene’s H3K9me3 density ( Fig 3B ) . These observations suggest that genic H3K9me3 enrichment correlates with a gene’s surrounding TE composition , both in terms of TE number and distance between genes and TEs . Our observations are not merely driven by genes that are in highly heterochromatinized genomic regions . Excluding genes in H3K9me3 enriched regions in another strain of the modEncode project ( at embryonic or larval stage of Oregon-R strain ) or in genomic regions that are classified as state 7 ( highly enriched with H3K9me2/3 ) or state 8 ( moderately enriched with H3K9me2/3 ) in either S2 or BG3 cell lines [39] gave similar results ( S4–S6 Figs for comparisons of H3K9me3 density of genes that are of different distance from TEs; S7–S9 Figs for correlations between H3K9me3 density of genes and the nearby number of TEs ) . It is important to examine whether the observed associations between the H3K9me3 enrichment of a gene and its neighborhood TE content can be attributed to other confounding factors . In D . melanogaster , TEs are known to accumulate in genomic regions with low recombination rate [16 , 18 , 45–48] and potentially regions with low gene density ( although see [47] ) . We found that genic H3K9me3 density is negatively correlated with local gene density ( Spearman rank ρ = -0 . 217 ~ -0 . 097 , p < 10–16 for all developmental stages ) and weakly correlated with its local recombination rate ( Spearman rank ρ = -0 . 040 ~ -0 . 021 , p < 0 . 05 for all but one developmental stage ) . However , we still observed significant positive correlations between a gene’s H3K9me3 density and its adjacent TE numbers using partial correlation analyses ( S10 Fig for controlling for gene density and S11 Fig for controlling for local recombination rate ) , suggesting that neither the effects of recombination rate nor gene density can solely account for our observations . Heterochromatin formation of TEs in somatic tissues is observed to be piRNA-dependent [32] . We hypothesized that TEs targeted by large number of piRNAs are likely to be transcriptionally silenced , and , consequently , more likely to influence the chromatin states of nearby genes . We estimated the piRNA density of TEs [the average ( per bp ) number of piRNAs mapped to TEs; see materials and methods] in the reference genome using ovarian piRNA sequence of the reference strain ( generated by Shpiz et al . [49] ) . It is worth noting that the H3K9me3 and piRNA data were collected using different types of tissues [embryos , larvae , and pupae ( H3K9me3 ) vs ovary ( piRNA ) ] . Nevertheless , embryonic piRNAs are known to be maternally deposited [50 , 51] . Consistent with our hypothesis that TEs targeted by piRNAs are more likely to influence the chromatin states of adjacent genes , we observed significant positive correlations between the H3K9me3 density of a gene at an early embryonic stage ( 0–4 hour embryo ) and the ovarian piRNA density of its nearest TE [Spearman rank ρ = 0 . 043 , p = 0 . 016 ( sense piRNA vs H3K9me3 ) ; Spearman rank ρ = 0 . 084 , p < 10–5 ( antisense piRNA vs H3K9me3 ) ] . As expected , these correlations are stronger for gene-TE pairs that are closer to each other than those that are farther apart [sense TE piRNA vs . genic H3K9me3: Spearman rank ρ = 0 . 081 ( p = 0 . 008 ) for short distances between a gene and its nearest TE; antisense TE piRNA vs . genic H3K9me3: Spearman rank ρ = 0 . 131 ( p < 10–4 ) for short , 0 . 063 ( p = 0 . 038 ) for intermediate , and 0 . 057 ( p = 0 . 061 ) for long distances between a gene and its nearest TE; see S12 Fig] . It is worth noting that , although significant , the observed correlations between embryonic H3K9me3 density and ovarian piRNA density of nearest TEs are rather weak . This might be due to the differences between ovarian and embryonic piRNAs . Future studies using embryonic piRNAs could further address this issue . Similar to previous studies [49 , 52 , 53] , we identified a small fraction of piRNAs that target host genes ( S3 Table ) . However , we did not observe similar positive correlations between H3K9me3 density and ovarian piRNA density of a gene ( S3 Table ) , suggesting that our observed higher H3K9me3 of genes with TE nearby is more attributable to the spreading of H3K9me3 from transcriptionally silenced TE instead of by piRNAs’ transcriptional silencing directly at the host genes . H3K9me has been widely associated with the silencing of gene expression ( reviewed in [38] ) . Indeed , we observed significant negative correlations between the H3K9me3 density of a gene and its expression level at the corresponding developmental stage ( Spearman rank ρ = -0 . 248 ~ -0 . 029 , p < 0 . 05 for all genes or only genes having TE within 10kb , S4 Table ) . Given our observation that genes with adjacent TEs have higher H3K9me3 density than genes without , transcriptional output from the former genes should be relatively lower . Indeed , in the majority of developmental stages , genes with TEs nearby have significantly lower expression levels than genes without TEs ( S5 Table ) . The analysis reported above cannot distinguish the causal relationship between a gene’s expression level and its nearby TE content . Our observations are consistent with either TE insertions leading to reduced expression of adjacent genes or TEs preferentially inserting near genes with low expression . To genomically assess these alternative scenarios , we compared the expression of alternative “alleles” ( with or without TEs ) of a gene , using TE polymorphism [19] and adult expression data [54] from a North American population . We compared the average expression rank of alleles with at least one TE within a window ( “with TE” ) to those of “without TE” alleles and performed permutation to assess significance . There is an excess of genes that have significantly ( permutation p-value < 0 . 05 ) lower expression of “with TE” alleles than those of “without TE” alleles for all window sizes chosen and for both adult female and adult male ( Fisher’s Exact , p < 0 . 05 , odds ratio = 1 . 46~2 . 60 , Table 1; see S6 and S7 Tables for results of all genes ) . Because female and male individuals from the same strain have the same type of allele , it is expected that genes having differential expression between “with TE” and “without TE” alleles in one sex should also show the same pattern in another sex . Indeed , we found an excess of genes that are differentially expressed between “with TE” and “without TE” alleles in both female and male ( S8 Table ) . In short , alleles with nearby TEs are more likely to have lower expressions than their homologs without TEs . It is worth noting that the disruption of regulatory sequences by TE insertion could also contribute to this observation [55] . Most TE insertions in the Drosophila population are polymorphic [15 , 18 , 19 , 48] and individual TE insertions present at lower population frequencies are expected to have experienced stronger selection removing them . Our hypothesis predicts that TE insertions inducing higher heterochromatic mark enrichment of neighboring genes should have larger deleterious impacts , are more likely removed from the population by selection , and as a result should segregate at lower population frequencies than other TEs . We classified reference TEs into those that are observed ( high-frequency TEs ) and not observed ( low-frequency TEs ) in a North American population [19] . In the reference genome , genes whose nearest TE occurs at low frequency ( not observed in the North American population ) have higher median H3K9me3 density than those near high-frequency TEs ( Mann-Whitney U test , p < 0 . 01 for all developmental stages except for L1 larvae and pupae , Fig 4 ) . Furthermore , we found significant negative correlations between a gene’s H3K9me3 density and the population frequency of its nearest TE in the North American population ( Spearman rank ρ = -0 . 152 ~ -0 . 083 , p < 0 . 01 for all developmental stages except for L1 larvae and pupae , S9 Table ) . Unless multiple independent insertions at the same site happen frequently , the alternative hypothesis that H3K9me3 enrichment inhibits insertions cannot explain our observed negative associations between genic H3K9me3 enrichment and population frequencies of their nearest TEs . Indeed , even though insertion site preference has been reported for several TE families [56–58] , multiple independent insertions from the same TE family at the same genomic location have not been documented in Drosophila . Another prediction of our model is that TEs inducing heterochromatin spreading at more developmental stages are expected to have larger cumulative deleterious impacts . Indeed , we found a significant negative correlation between the numbers of developmental stages during which a gene is enriched for H3K9me3 ( top 10% genome-wide ) and the population frequency of its nearest TE ( Spearman rank ρ = -0 . 159 , p < 10–5 ) . This association is not merely driven by genes that have low ( below first quantile ) H3K9me3 density in all developmental stages , because a significant negative correlation was still observed after removing these genes ( Spearman rank ρ = -0 . 160 , p < 10–5 ) . Removing genes with high H3K9me3 enrichment in another strain or cell lines ( see Materials and Methods ) and using a different threshold to categorize “high H3K9me3 genes” both gave consistent results [Spearman rank ρ = -0 . 148 , p< 10–3 ( exclude genes with high H3K9me3 in Oregon-R ) , ρ = -0 . 166 , p< 10–4 ( exclude genes in 7 or 8 chromatin state in cell lines ) , and -0 . 112 , p = 0 . 002 ( use top 25% genome-wide threshold ) ] . Compared to young and actively transposing TE families , old TE families were found to have lower piRNA density [59] . According to our model , we expect old TE families to be less likely be epigenetically silenced and influence the chromatin states of their adjacent genes . Members of old TE families are usually observed at higher population frequencies due to the age of their insertion events . This has the potential to confound our observations . We performed ANOVA that jointly considers the effect of the population frequency and family identity of nearest TEs on genic H3K9me3 enrichment . Our analysis still found that whether a gene’s nearest TE is observed in the North American population or not significantly contributes to the variation of genic H3K9me3 density in the majorities of developmental stages ( S10 Table ) , suggesting that variation in TE age across families is unlikely the sole factor driving our observation . Our observed negative associations between H3K9me3 of a gene and the frequency of the nearest TE could be confounded by the longer length of TEs adjacent to genes enriched with H3K9me3 ( Spearman rank ρ between a gene’s H3K9me3 and the length of nearest TE , 0 . 036 ~ 0 . 118 , p < 0 . 05 for all developmental stages ) . Longer TEs are expected to undergo ectopic recombination more frequently , more likely to be selected against , and thus be at lower population frequencies , a theoretical result which has been supported by empirical data [9 , 10 , 48 , 60] . However , given equal piRNA density , longer TEs represent larger piRNA targets and are more likely to be silenced as well as to have deleterious epigenetic impacts on adjacent genes . Furthermore , we found that there is a large positive correlation between piRNA density and TE length [Spearman rank ρ = 0 . 684 ( sense piRNA ) and 0 . 797 ( anti-sense piRNA ) , p < 10–16 for both comparisons] . The positive correlations between H3K9me3 density of a gene and the length of its nearest TE also depend on the distance between genes and TEs [For 0-4hr embryo , Spearman rank ρ = 0 . 151 ( short distance between a gene and its nearest TE ) , 0 . 120 ( intermediate ) , and 0 . 083 ( long ) , p < 0 . 01 for all; see S13 Fig for results of all developmental stages] , suggesting that the high H3K9me3 of genes near long TEs is probably due to the spread of TE heterochromatin . Another potential confounding factor comes from the fact that TEs in low recombination regions of the genome are present at higher population frequencies [16 , 18 , 48 , 61] . This observation is generally attributed to lower probability of ectopic recombination [9 , 10] and/or less effective purifying selection against TEs because of selective interference [62] . Accordingly , we performed logistic regression analyses to investigate the association between genes’ H3K9me3 signature and the population frequencies of nearest TEs while accounting for the effect of recombination rate ( see Materials and Methods ) . We still found that the H3K9me3 density of a gene and the number of developmental stages in which a gene is enriched with H3K9me3 are significant negative predictors for whether the nearest TE is observed in the North American population or not ( S11 Table ) .
Selection removes deleterious TE insertions from the population and plays an important role in the containment of TEs . TE-induced physical disruptions of the genetic elements , such as insertions into functional elements or ectopic recombination between nonhomologous TE insertions , have been viewed as the primary source of the negative fitness impacts of TEs . Empirical investigation and theoretical discussion of TEs’ deleterious impact via epigenetic mechanisms has been limited [22] . In this study , we hypothesized that the piRNA-mediated epigenetic silencing of TEs perturbs transcription of adjacent genes and shapes the population dynamics of TEs ( Fig 1 ) . Using a genomic approach , we discovered an elevated density of repressive chromatin marks , H3K9me3 , in sequences and genes up to several kb away from TEs ( Fig 2 and Fig 3 ) , which supports an important component of our hypothesis ( Fig 1B and 1C ) . The H3K9me3 density of a gene heavily depends on its neighboring TE content ( number and distance ) and the strongest associations were observed for genes that are within 2kb from TEs . These genes account for 10 . 86% of the euchromatic genes , suggesting that the spread of TEs’ heterochromatin could influence the chromatin status and function of an appreciable number of genes in the genome . In accordance with our hypothesis ( Fig 1C ) , the presence of nearby TEs is also associated with reduced gene expression ( Table 1 and S5 Table ) . Our comparisons of with-TE and without-TE alleles of the same gene clearly demonstrated that the transcriptional consequence of TE insertions is not due to other confounding factors and further extend previous observations on the mutational impacts of TEs on gene expression [55] . Importantly , our observations vary with distance between genes and TEs , where larger distance between genes and TEs are associated with weaker effects . This is consistent with an important aspect of our model that the mutagenic effect of TEs is through the spread of heterochromatin from TEs . Future empirical studies investigating the chromatin states of with-TE and without-TE alleles of the same gene will help further distinguish the causal relationship of these observed associations , which could be attributed to either the spread of TE-induced epigenetic silencing or the preferential insertions of TEs near genes with high heterochromatic marks . Supporting our hypothesis that the epigenetic silencing of TEs can have deleterious fitness impacts ( Fig 1D ) , we found a negative association between H3K9me3 density of a gene and the population frequency of its nearest TE . This observation might be confounded by the fact that TEs adjacent to genes enriched with H3K9me3 tend to be longer , which is expected to increase the rate of ectopic recombination . However , ends of chromosomes that are highly heterochromatic are known to have reduced rates of crossing over . If rates of crossing over are also suppressed at TE-induced heterochromatin in the euchromatic regions , TEs with high H3K9me3 enrichment would be less likely to undergo ectopic recombination , and as a result would be removed by selection less frequently . Under this scenario , we would expect an opposite pattern from our observation—a positive correlation between H3K9me3 density of a gene and the population frequency of its nearest TE . In addition , TEs adjacent to genes with high H3K9me3 in multiple tissues also have lower population frequencies . This phenomenon is unlikely to be explained by ectopic recombination removing TEs , but is consistent with the prediction of our hypothesis that TEs epigenetically influencing adjacent genes in more developmental stages have larger overall deleterious impacts . Importantly , the preferential insertion of TEs near genes enriched with heterochromatic marks cannot account for either observation , suggesting that our observed associations between a gene’s H3K9me3 enrichment and its neighborhood TE content should be more attributable to the spread of TE-induced heterochromatin . We observed that the H3K9me3 density of genes is positively correlated with the piRNA density of their nearest TEs , suggesting that our observed patterns depend on the piRNA-pathway . Even though the heterochromatin establishment by the piRNA-pathway occurs primarily in early embryos [63] , the piRNA-dependent heterochromatin formed at this stage was found to have a lasting effect and significantly influence the chromatin states in adults [32 , 63] . Indeed , most of our observations are supported by H3K9me3 data from early embryos through pupae . Accordingly , the epigenetic silencing of TEs during early embryonic development , which depends on maternally deposited piRNAs , can influence the chromatin states of adjacent genes that are expressed at different developmental stages and have large cumulative mutational impacts . Interestingly , the strength and statistical significance of our observed associations between genic H3K9me3 and the properties of their adjacent TEs ( including distance from , number , and population frequency ) varies across developmental stages . Earlier embryonic stages consistently showed the strongest associations , while larval and pupal stages generally showed the weakest or even statistically insignificant patterns . The effect of TE-induced heterochromatin spreading might be suppressed by other mechanisms of heterochromatin regulation differently at different developmental stages . Intriguingly , flies at later developmental stages , which usually showed weaker associations between genic H3K9me3 density and neighboring TE composition , consist of a greater diversity of differentiated tissues and cell types . It will be important in future work to investigate the tissue specificity of the epigenetic impact of TEs . This can elucidate the temporal and spatial variation in TE-induced heterochromatin spreading and will enable precise identification of the functional effects and evolutionary consequences of TE insertions . It is worth mentioning that even though our observed associations between genic H3K9me3 density and the neighboring TE content are significant , they are not particularly strong . The paucity of strong correlations might be an issue of power , however . TEs are generally scarce around functional elements [19 , 64 , 65] and most TEs appear as singletons in natural populations [4 , 15–19] . The majority of genes included in our analyses have few adjacent TEs and their nearest TEs are absent in the North American population , providing us limited variation to estimate correlations . In addition , other biological processes can also influence the chromatin state of genes . By comparing chromatin states across a single genome ( which was the focus of most of our analyses ) , we were unable to distinguish the epigenetic effects of TEs from those of other biological processes . Investigation of variation in chromatin states between “with TE” and “without TE” alleles , which presumably only differ with respect to their neighboring TE composition , can help further address the relative importance of the epigenetic effects of TEs on the chromatin states of genes and the evolutionary dynamics of TEs . It was previously reported that the epigenetic silencing of TEs via methylation could influence the expression of adjacent genes in Arabidopsis thaliana [22] . Epigenetically silenced ( methylated ) TEs , but not unmethylated TEs , were found to be associated with lower expression of nearby genes and are present in lower population frequencies . Even though DNA methylation is rare in the D . melanogaster genome and definite evidence for methylated TEs is still missing [66 , 67] , we observed a similar epigenetic impact of TEs on the expression of neighboring genes with a different mechanism ( histone modifications ) . We provided complementary observations by investigating the associations between the heterochromatic mark enrichment of genes and their neighborhood TE composition . Importantly , combining our observed elevated H3K9me3 density surrounding TEs and the PEV phenomenon that has been known for decades ( [34] , reviewed in [35–37] ) , we are able to provide a mechanistic explanation for the observed deleterious impacts of TE’s epigenetic silencing . There are several properties that make the piRNA-mediated epigenetic silencing of TEs an especially attractive mechanism for containment of TE copy number in natural populations . The generation of piRNAs only depends on the transcription of TE sequences and is thus general to virtually all classes and families of TEs [20] . Importantly , unlike other small RNAs , piRNAs are generated and amplified through a feed-forward ping-pong cycle ( i . e . a positive feedback loop ) [23 , 24] . The TE transcripts are targeted by anti-sense piRNAs and processed into sense-piRNAs , which are involved in generating additional antisense piRNAs . Despite previous suggestions that antisense piRNAs are mostly generated from piRNA clusters in heterochromatic regions [24] , a recent functional study [49] and statistical analysis [59] both showed that euchromatic TE copy number is a major determinant of piRNA amount . It is expected that the probability that the DNA sequence of a TE is targeted by piRNAs increases with piRNA amount and , accordingly , the number of TEs that are epigenetically silenced will depend both on the amount of piRNAs ( which is positively correlated with TE copy number of a family [59] ) and TE copy number . Consequently , the number of epigenetically silenced TEs and the resulting mutational impacts due to TE-heterochromatin spreading might depend quadratically on TE copy number . This can potentially provide the required synergistic epistasis of TEs’ deleterious impacts for stable containment of TE copy number [13] . Our study demonstrates that the spread of piRNA-mediated heterochromatin of TEs is another important , though previously unexplored , mechanism leading to removal of TEs . Given piRNA’s wide phylogenetic distribution in animals [68] , we expect this selective mechanism against TEs also plays an important role in the containment of TEs in other organisms . Further investigations of the relative roles of different selective mechanisms in the containment of TEs , particularly the potential interference between our proposed TE-induced indirect epigenetic effects and the widely empirically supported ectopic recombination between TEs , will help piece together our picture of TE dynamics in natural populations .
Our analyses used D . melanogaster reference genome annotation 5 . 21 for genes , TEs , and other functional sequences . We only included genes and TEs that are in the euchromatic regions of the genome , using the heterochromatin-euchromatin boundaries defined in [69] . Genes and TEs on the 4th chromosome were excluded from the analyses as well . We used H3K9me3 ChIP-seq data generated by modEncode [40] , which used samples from nine developmental stages of the reference D . melanogaster strain ( 0-4hr , 4-8hr , 8-12hr , 12-16hr , 16-20hr , and 20-24hr embryos , and L1 larvae , L2 larvae , and pupae ) . We used the modEncode processed wiggle files , which are background subtracted H3K9me3 read density ( normalized read counts of uniquely mapped reads of H3K9me3 experiments subtracting the re-scaled read counts of uniquely mapped reads of the input experiments , [70] ) . Windows that have negative background-subtracted read density ( no enrichment of H3K9me3 ) were assigned to zero . The average H3K9me3 read density of the upstream/downstream 10kb sequences of TEs was estimated in 1kb nonoverlapping windows . We excluded 10kb sequences that overlap with any annotations other than “intergenic” and analyzed the right side and left side of the sequences separately . To generate a null expectation for the H3K9me3 density decay near TEs , we randomly chose sequences that have the same size and are on the same chromosome as TEs included in the analyses . To account for the observed large-scale variation in histone modifications across D . melanogaster genome [39 , 40] , we divided each chromosomal arm into 4Mb bins ( 5–7 bins per chromosome ) and randomly selected “TE-size” segments within the same bins as the reference TEs . The number of randomly chosen sequences is the same as the number of TEs included in the analysis . For each randomly selected segment , we estimated the H3K9me3 density of adjacent sequences using the same methods as for TEs . This procedure was repeated 1 , 000 times . The H3K9me3 density of genes was estimated as weighted averages across all exons of the longest isoforms . We excluded genes that have TEs inside their coding or noncoding exons . We further removed genes whose 10kb upstream/downstream sequences overlaps with the euchromatin-heterochromatin boundaries , because of how we categorized genes according to their neighboring TE content ( see below ) . Genes are categorized into nonoverlapping groups according to their distance from the nearest TE ( having TEs in introns , in 1kb , in 1-2kb , in 2-5kb , and in 5-10kb upstream/downstream of the gene , or have no TEs 10kb upstream/downstream of the gene ) . These genes account for 3 . 66% ( in gene ) , 4 . 65% ( in 1kb ) , 2 . 55% ( 1-2kb ) , 6 . 83% ( 2-5kb ) , and 9 . 26% ( 5-10kb ) of the analyzed euchromatic genes ( 12 , 204 genes ) . We also counted the number of TEs that are inside introns , less than 1kb , 1-2kb , 2-5kb , and 5-10kb away from each gene . For TEs that span over multiple windows , we classified them to windows that are closest to genes . For analyses considering the properties of nearest TEs , genes that have more than one TEs of equal distance were excluded , resulting in 3 , 193 gene-TE pairs . Gene-TE pairs were further categorized into three equal-size bins according to the distance between them ( short: 0-1451bp , intermediate: 1452-5184bp , and long: > 5184bp ) . Recombination rate were interpolated for the mid-point of genes or TEs using [71] . Gene density was estimated as the number of genes in a 100kb window centered on the focused gene . S12 Table includes estimated H3K9me3 density , TE states , and other genic attributes of analyzed genes . The ovarian piRNA of reference strain [49] were processed and mapped without mismatches ( using BWA [72] ) to reference genome release 5 following methods in [73] . We estimated the density ( average per bp ) of piRNAs mapped to sense ( sense piRNA ) and antisense ( antisense piRNA ) strands of genes/TEs . For piRNAs mapped to multiple genomic locations , each of the n mapped positions in the genome is counted as having 1/n read mapped . Analyses using all mapped piRNAs ( uniquely mapped and multiply mapped ) or only uniquely mapped piRNAs gave consistent results . We presented the results based on all mapped piRNAs because this represents the full probability that a TE is targeted by the piRNA-pathway . To investigate the impact of H3K9me3 on gene expression , we used modEncode developmental stage expression data that were generated also using reference D . melanogaster strain [74] . The normalized and standardized expression level ( RPKM ) was downloaded from FlyBase . No ( zero ) expression of genes could be due to the presence of nearby TE insertions or simply the absence of expression at a particular developmental stage . Because we could not distinguish these alternatives , we excluded genes that have zero expression in the analysis of the particular developmental stage . There are 21 strains of a North American population that have both microarray-based gene expression data [54] and TE calls [19] . We used these strains to investigate the expressional impacts of TEs within population . We downloaded processed gene expression data from the supplementary data of [54] . To avoid the systematic difference in expression level across microarray experiments and/or individuals , we used expressional rank ( from highest to lowest ) within each samples . In [19] , for each strain , each TE insertion site was annotated as “present” ( with TE ) , “absent” ( no TE ) , or “no call” ( missing data due to low sequencing coverage ) . For each gene , the “with TE” alleles are those that have one or more TE insertion sites called as “present” within introns , in 1kb , 2kb , 5kb , or 10kb windows from the gene . An allele is categorized as “without TE” if all the known TE insertion sites near it were called as “absent” . An allele is treated as missing data when any of the known TE insertion sites near it was called as “no call” and none of them were called as “present” ( see S13 Table for TE states of all alleles ) . For genes that have at least two “with TE” and at least two “without TE” alleles , we calculated the difference in mean expression rank between “with TE” alleles and “without TE” alleles . For each gene , we used all possible permutations with respect to TE labels to find the null distribution of rank differences between “with TE” and “without TE” alleles ( “with TE” alleles minus “without TE” alleles ) . Positive differences suggest that the “with TE” alleles have larger expressional rank , or lower expression , than “without TE” alleles . One tail p-values were calculated as the proportion of permuted combinations that have differences greater than or equal to the observed differences . To ensure that a gene could potentially have a significant ( p < 0 . 05 ) p-value , we further restricted our analysis to genes that have more than 20 possible permuted combinations . We also randomly chose the same number of genes that have no TE alleles ( “without TE genes” ) as the number of “genes with TE alleles” . The alleles of these “without TE genes” were randomly partitioned into two groups accordingly to the observed allele frequencies for “genes with TE alleles” , and we used these “without TE genes” to assess the false positive rate of above procedures . This process was repeated 100 times for each window size and separately for each sex . We found that the false positive rate of our approach is slightly smaller than the expected 5% ( S14 Table ) . We used the same TE polymorphism data of the North American population [19] to investigate the evolutionary impacts of heterochromatin spreads from TEs . It is worth noting that this part of the analyses included all 131 genomes whose TEs has been annotated in the North American population [19] , compared to 21 in the above expressional impact analysis . Only TEs observed in the reference genome were included in our analysis . For TEs that are only observed in the reference genome , their population frequencies in the North American population is zero . We performed two-way Analysis of Variance analysis ( ANOVA ) to test whether the nearest TE is observed in the North American population ( binary variable ) , the family of nearest TE ( categorical variable , 52 TE families ) , and the interaction between these two variables contribute to the variation in H3K9me3 density of genes ( model: H3K9me3 density ~ observed/not + TE family + observed/not * TE family ) . The H3K9me3 density of genes has an overall exponential distribution and a large number of genes with zero H3K9me3 density , which made us unable to identify appropriate transformation for the response variable ( H3K9me3 density ) . Accordingly , we restricted our analysis to genes that have positive H3K9me3 density and log-transformed the H3K9me3 density of genes . We also performed mixed linear model analysis to investigate the influence of TE frequency on genic H3K9me3 while treating the influence of TE family as random . Mixed linear model regression was performed using R package nlme version 3 . 1 . We performed logistic regressions , using whether a reference TE is observed ( one ) or not ( zero ) in the North American population as response variable and the H3K9me3 status of its nearest gene and TE’s local recombination rate as predictors . We first performed regression analysis that included only one predictor variable at a time to determine the regression model ( linear , quadratic , or logarithmic ) . We then included all predictor variables and performed backward model selection based on AIC to determine the regression model . Full regression models used are: logitp~H3K9me3density+ ( H3K9me3density ) 2+recombination rate+ ( recombination rate ) 2 , logitp~no . dev . stages+ ( no . dev . stages ) 2+recombination rate+ ( recombination rate ) 2 , where logit p is the log odds of whether a reference TE is observed in the North American population ( one ) or not ( zero ) and “no . dev . stages” is the number of developmental stages a gene has top 10% H3K9me3 density genome-wide . All statistical analyses were performed using R . | The piwi-interacting RNAs ( piRNAs ) are small RNAs that can suppress the expression of selfish transposable elements ( TEs ) in many animal genomes . One mechanism by which piRNAs silence TEs is through the formation of heterochromatin , which is condensed chromatin and generally associated with repressed gene expression . Several functional studies have demonstrated that piRNA-mediated heterochromatin of TEs can spread to adjacent genes . We hypothesized that this spread of TE-induced heterochromatin influences the function of adjacent genes , ultimately resulting in selection against individual TEs . Consistent with our hypothesis , we found that sequences and genes adjacent to TEs are enriched in heterochromatic marks . We determine that this TE-induced variation in epigenetic status is probably piRNA-dependent and that this change in chromatin state influences the expression levels of adjacent genes . Importantly , TEs that lead to higher heterochromatin enrichment of adjacent genes are more strongly selected against , demonstrating the evolutionary consequences of TE-induced epigenetic silencing . In contrast to previously studied deleterious impacts of TEs , which depend on TEs’ physical disruptions of DNAs , our proposed functional effect of TEs is mediated through their epigenetic influence . Our study suggests that the piRNA-dependent epigenetic impact of TEs may play an important role in the evolutionary dynamics of TEs . | [
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] | [] | 2015 | The Role of piRNA-Mediated Epigenetic Silencing in the Population Dynamics of Transposable Elements in Drosophila melanogaster |
Memory T cell inflation is a process in which a subset of cytomegalovirus ( CMV ) specific CD8 T cells continuously expands mainly during latent infection and establishes a large and stable population of effector memory cells in peripheral tissues . Here we set out to identify in vivo parameters that promote and limit CD8 T cell inflation in the context of MCMV infection . We found that the inflationary T cell pool comprised mainly high avidity CD8 T cells , outcompeting lower avidity CD8 T cells . Furthermore , the size of the inflationary T cell pool was not restricted by the availability of specific tissue niches , but it was directly related to the number of virus-specific CD8 T cells that were activated during priming . In particular , the amount of early-primed KLRG1- cells and the number of inflationary cells with a central memory phenotype were a critical determinant for the overall magnitude of the inflationary T cell pool . Inflationary memory CD8 T cells provided protection from a Vaccinia virus challenge and this protection directly correlated with the size of the inflationary memory T cell pool in peripheral tissues . These results highlight the remarkable protective potential of inflationary CD8 T cells that can be harnessed for CMV-based T cell vaccine approaches .
A hallmark of immunological memory is the ability of the adaptive immune system to generate long-lived antigen-specific memory T or B cells . Upon pathogen clearance , most virus-specific T cells undergo apoptosis and few of them form a stable pool of memory T cells , which is maintained lifelong in case of CD8 T cells . Pre-existing memory T cells are beneficial for protection against reinfection with the same pathogens , since they are numerically increased compared to naive antigen-specific T cells , have widened anatomical distribution and respond quickly by vigorous proliferation and acquisition of effector functions , conferring rapid clearance of the infectious agent . Long after resolution of acute viral infection , memory T cells reside primarily in lymphoid tissues as central memory cells [1] until they re-encounter their cognate antigen , with the exception of tissue-resident memory cells that have acquired long-term tissue residence and are largely disconnected from recirculation [2] . In chronic active virus infections , with abundant presence of viral antigens , formation of antigen-experienced memory cells that are long-term maintained in absence of antigen is impaired and virus-specific CD8 T cells exhibit a gradual loss of effector functions , known as T cell exhaustion [3 , 4] . However , during latent reactivating virus infections , such as in the case of herpes virus infection , viruses go into latency with limited/ absent expression of viral proteins . However , sporadic viral reactivation events can occur in response to various external stimuli [5 , 6] , leading to reactivation of the lytic program and hence to expression of viral proteins whose peptides will be presented to CD8 T cells . This leads to sporadic reactivation and stimulation of memory CD8 T cells with specificity for those antigens , resulting in a pool of functional effector-like and not exhausted CD8 T cells [7–10] . One representative of this family of herpesviruses is cytomegalovirus ( CMV ) , a ubiquitous β-herpesvirus . The CD8 T cell response induced by CMV is atypical , as a subset of CMV-specific CD8 T cells shows little decline after initial expansion and continues to increase in size to establish a large pool of effector memory T cells that preferentially localize to peripheral tissues [10 , 11] . This phenomenon of gradual accumulation of some CMV-specific memory CD8 T cells has been termed "memory inflation" [8 , 10–13] . Like in conventional CD8 T cell responses , the majority of inflationary CD8 T cells are primed during acute murine cytomegalovirus ( MCMV ) infection by cross-presenting dendritic cells ( DCs ) , however , they are subsequently reactivated by antigen presentation on latently infected non-hematopoietic cells [14–17] , promoting their accumulation during viral latency . The size of the inflationary T cell pool becomes remarkably large and can reach up to 50% of the whole CD8 T cell pool in an infected individual [18 , 19] . The mechanisms underlying inflation of certain CMV-specific CD8 T cells are still poorly understood . Previous studies have implicated the importance of the location of the epitope within the CMV genome and within the protein context , and the dependence on the constitutive proteasome for antigen processing [20 , 21] . With the emerging interest in the design of T cell-based vaccines , CMV-based vectors have gained a lot of interest due to their ability to induce these large pools of peripheral effector memory CD8 T cells . Accordingly , CMV-based vectors have been very successfully used for the induction of potent CD8 T cell responses that mediate protection against viral challenge infections and even tumors [22–27] . Due to this capacity , it is important to better understand the driving and limiting factors that shape the inflationary CD8 T cell pool in order to optimize memory CD8 T cell responses in the context of CMV-based vaccines . In this study , we addressed the question of how memory inflation is regulated in its composition and size after resolution of lytic MCMV infection . Our data shows that the inflationary CD8 T cell pool consists of high-avidity CD8 T cells . We further demonstrate that the stable size of the inflationary T cell pool in peripheral tissues is not limited by local niches but rather that early primed MCMV-specific cells established during acute infection directly correlate with the size of the inflationary T cell pool during latency . By modulating either the size of early primed cells during CMV infection , or the amount of TCM cells during viral latency , we were able to accordingly modulate the size of the inflationary memory CD8 T cell pool . Moreover , we show that the inflationary T cell pool provides protection from a peripheral virus re-challenge and the ability to control peripheral viral replication was correlated with the size of the inflationary T cell pool .
T cell receptors ( TCR ) exhibit different avidity to a certain MHCI-antigen complex , and usually T cells recognising their cognate antigen with high avidity will be activated and contribute strongly to an effective cytotoxic CD8 T cell response . As low avidity CD8 T cells were reported to contribute to memory inflation during HCMV infection [28] , we addressed the question whether TCR avidity plays a role for memory inflation in MCMV infection . We made use of the TCR beta chain transgenic ( tg ) Mini mouse [15] , in which all T cells express the Vβ10Jβ2 . 1 chain of an M38316-323-specific TCR in combination with endogenous alpha chains , and approximately 10% of these naive CD8 T cells bind the MCMV-specific H-2Kb/M38316-323 tetramer , albeit with a broad range of staining intensity , indicative of variable avidities ( Fig 1A ) . Upon adoptive transfer and MCMV infection ( MCMVΔm157 was used , hereafter referred to as MCMV ) , Mini TCR tg CD8 T cells expanded vigorously , resulting in a large population of M38-tetramer+ cells with apparent high avidity , based on tetramer staining intensity ( Fig 1A ) . To address whether high or low avidity Mini CD8 T cells contributed to the acute and / or inflationary response , we sorted naïve Mini TCR beta transgenic CD8 T cells into a population with low or high tetramer staining , indicative of lower and higher avidity for the M38316-323 peptide H-2Kb complex ( Fig 1B ) . To corroborate that low tetramer binding Mini CD8 T cells can in fact react towards their cognate antigen , we exposed high and low tetramer binding Mini CD8 T cells to a range of different M38316-323 peptide concentrations . Both populations of Mini cells upregulated the early T cell activation markers CD25 ( Fig 1C ) and CD69 ( Fig 1D ) , indicative of TCR stimulation , yet the high tetramer binding Mini cells had increased levels of these activation markers compared to their low tetramer binding counterparts , indicating that the low tetramer binding population has a lower functional avidity as compared to the high tetramer binding population . The low and high avidity subpopulations were adoptively transferred into separate naïve recipients , followed by MCMV infection . We analysed the frequencies of transgenic and endogenous M38-specific CD8 T cell responses longitudinally in the blood ( Fig 1E ) . Mice receiving high avidity Mini CD8 T cells showed a strong contribution of the transgenic Mini CD8 T cells to the overall M38-specific T cell response with a long term contribution of 50% ( Fig 1E , left graph ) . In contrast , low avidity Mini T cells contributed only marginally to the overall M38-specific CD8 T cell response in the blood ( Fig 1E , right graph ) as well as in peripheral organs such as the lungs ( Fig 1F ) . Regardless of their very low frequencies , these low avidity Mini cells exhibited an activated phenotype ( KLRG1+/CD127- ) alike their high avidity counterparts ( Fig 1G ) . Furthermore , despite the obviously reduced numbers of low avidity M38-specific CD8 T cells , we found no significant differences in the mean fluorescence intensities of the M38-tetramer staining ( Fig 1H ) . This indicates that the activated CD8 T cells from the low avidity population might actually be the ones with relative higher avidities , whereas the "really" low avidity cells were outcompeted by endogenous higher avidity M38-specific CD8 T cells . Similar observations were made using OT-III cells [29] . These cells , similar as OT-I T cells , are specific for the SIINFEKL epitope of Ovalbumin; however , OT-III cells express a low avidity TCR as compared to OT-I T cells . Both OT-I ( CD45 . 1+ ) and OT-III ( CD90 . 1+ ) cells were exposed to an MCMV virus expressing the SIINFEKL epitope under the immediate early 2 ( ie2 ) promoter [21] . Of note , this virus expresses the viral protein m157 that binds to the NK cell receptor Ly49H and thereby activates Ly49H+ NK cells early in MCMV infection [30 , 31] . No significant contribution of the OT-III cells to the acute or inflationary response was observed in blood ( S1A Fig , S1B Fig ) or in organs ( S1C Fig ) . Yet , the few recovered OT-III cells exhibited a similar phenotype as their high affinity OT-I counterparts ( S1D Fig ) . These data show in addition that in the presence or absence of the MCMV m157 protein mainly high avidity T cells are recruited into the MCMV-specific response . As OT-I and OT-III T cells are monoclonal T cell populations , the difference in SIINFEKL-H2-Kb tetramer binding , indicative for T cell avidity , were still observed at day 60 post infection ( S1E Fig ) . These findings imply that high avidity CD8 T cells mainly contribute to the inflationary T cell pool—at least within the first 100 days of infection—whereas low avidity CD8 T cells do not seem to be a major contributor to the large pool of inflationary cells during MCMV latency . So far , we have defined a parameter that contributes to fuel memory inflation , which is expression of a high avidity TCR with specificity to an epitope that drives memory inflation . Despite this driving force , inflationary M38-specific CD8 T cells stabilize at about 10% of CD8 T cells in blood and lung tissue [8 , 15 , 33] , raising the question of what limits memory inflation in peripheral tissues . We hypothesized that there might be limited "space" , defined by survival niches for inflationary CD8 T cells in peripheral tissues , such as provision of IL-15 in lung tissue [33] . To test this hypothesis , we used an experimental system in which the CD8 T cell population with inflationary specificity is curtailed by 50% after initial clonal expansion , thereby creating "new space" . If space limitations would limit memory inflation , we reasoned , this vacated space should be re-occupied to regain the level of 10% of M38-specific CD8 T cells within the CD8 T cell population in blood and lungs . For this purpose , sex mismatched adoptive transfer experiments were performed , in which naïve male transgenic CD45 . 1+ Maxi CD8 T cells [15] were adoptively transferred into female recipients , followed by MCMV infection ( Fig 2A ) . Maxi cells are a monoclonal population of TCR transgenic T cells that all express Vα4Jα13 and Vβ10Jβ2 . 1 . In contrast to the Mini cells , where only 10% is specific for the M38316-323 peptide due to the usage of endogenous TCR α-chains , all Maxi CD8 T cells are specific for the M38316-323 peptide of MCMV [15] . In this setting , priming of both endogenous and transferred Maxi cells occurred ( Fig 2B , Fig 2C ) and in the sex-matched hosts , the population of M38-specific CD8 T cells consisted to 50% of endogenous and 50% of Maxi CD8 T cells at day 19 post MCMV infection ( Fig 2B , Fig 2C ) . In the sex-mismatched hosts , the male Maxi cells were rejected between 2 and 3 weeks of infection , thereby halving the total population of M38-specific CD8 T cells ( Fig 2B , Fig 2C ) . If there would be a space limitation , we expected that endogenous M38-specific CD8 T cells would converge to the levels that were observed in sex-matched hosts . Yet , we did not see such a convergence ( Fig 2B ) neither in blood , nor lungs ( Fig 2D ) , indicating that "space" did not limit M38-specific memory CD8 T cell inflation . We performed similar experiments ( S2A Fig ) using TCR beta-chain transgenic Mini cells ( containing roughly 10% CD8 T cells specific for the M38316-323 epitope ) for adoptive transfer , and observed a similar pattern: In female hosts , the rejection of male M38-specific CD8 T cells occurred two to three weeks post infection and the created "space" was not replenished by endogenous M38-specific CD8 T cells ( S2B Fig , S2C Fig ) . To exclude that CD8 T cells with different MCMV-specificities would fill the newly available space , we quantified MCMV-specific CD8 T cells specific for the M45985-993 ( non-inflationary ) , IE3416-423 or m139419-426 ( inflationary ) epitopes , and did not observe any increased frequencies in the lungs of sex-mismatched hosts ( S2D Fig ) . Taken together , these data suggest that "space" does not seem to be a limiting factor for the inflationary T cell pool in blood , spleen or peripheral tissues . During latent MCMV infection , a small fraction of the M38-specific T cell population has a central memory phenotype ( TCM , CD62L+/CD127+/KLRG1- ) and this population is markedly enriched in lymph nodes [15] . One hypothesis is that these TCM cells are able to sense viral antigens derived from viral reactivation events in non-hematopoietic cells and provide newly activated T cells that supply the inflationary T cell pool [15] . To demonstrate that TCM cells have the potential to fuel the pool of inflationary CD8 T cells , we adoptively transferred graded doses of central memory Maxi cells into host mice that were latently infected with MCMV . In addition , these transferred Maxi TCM cells were labelled with a cell proliferation dye in order to track cell division ( Fig 3A ) . The percentage of Maxi cells that had out-diluted the proliferation dye was comparable in all conditions ( Fig 3B ) , indicative of comparable antigen encounter on latently infected cells . Furthermore , cells that had out-diluted the cell proliferation dye were not found in uninfected mice ( Fig 3B ) . Adoptive transfer of increasing numbers of Maxi TCM cells resulted in increasing numbers of Maxi cells having out-diluted the proliferation dye in the spleen , lungs and blood 31 days post transfer ( Fig 3C ) . These data demonstrate that not only TCM cells contribute to the peripheral inflationary T cell pool but also that their number correlates with the size of the inflationary T cell pool . As the number of transferred TCM cells during latent MCMV infection correlated with emerging size of activated T cells , we addressed whether the number of MCMV-specific T cells that is primed during MCMV infection influences the extent of memory T cell inflation . We experimentally increased M38-specific T cells by adoptively transferring different numbers of naïve Maxi CD8 T cells into hosts one day prior to MCMV infection ( Fig 4A ) . Increasing the number of adoptively transferred Maxi CD8 T cells resulted in heightened peak clonal expansion in the acute phase of MCMV infection ( Fig 4B ) . The transfer of different numbers of Maxi cells also translated into corresponding differences in the percentage of Maxi cells during latent MCMV infection ( day 70 ) in the blood ( Fig 4B ) . Furthermore , the total number of Maxi cells in the LN , lungs and spleen was significantly increased when more cells were transferred ( Fig 4C , Fig 4D ) . The amount of TCM cells in the LN ( day 70 ) was also increased when more Maxi cells were transferred ( Fig 4D ) . The same pattern was observed in experiments where different numbers of OT-I CD8 T cells were adoptively transferred and the hosts were infected with MCMV-ie2-SIINFEKL ( an MCMV virus that expresses the viral protein m157 ) ( S3 Fig ) . Taken together , these results show that the precursor frequency of MCMV-specific T cells directly correlates with the size of the inflationary CD8 T cell pool . Our previous results indicated that both the precursor frequency prior to infection , and the number of TCM cells during established latent infection , correlate with the extent of memory inflation . In acute LCMV infection , early primed CD127+/KLRG1- cells have a higher probability to feed into the memory pool [34] . We therefore speculated that the number of KLRG1- M38-specific cells that are established early during MCMV infection might relate to the size of the inflationary pool . We addressed this hypothesis by adoptively transferring M38-specific Mini CD8 T cells at different time points relative to the onset of MCMV infection , thereby creating different ratios of KLRG1+ and KLRG1- cells , with late recruited CD8 T cells preferentially adopting KLRG1- phenotypes [32] ( S4 Fig ) . We transferred Mini CD8 T cells one day prior and one or three days after MCMV infection ( Fig 5A ) . Despite large differences in the size of clonal expansion in the blood , Mini cells reached similar percentages by day 50 post antigen encounter ( Fig 5B ) and equivalent numbers of Mini cells were present in spleen and in the lungs at 130 days post infection ( Fig 5C ) . Thus although there were large differences in the magnitude of clonal expansion , a similar level of memory inflation was achieved , indicating that it is not just the overall number of MCMV-specific T cells in the acute phase of infection that sets the limit for memory inflation . Strikingly , 6 days post antigen encounter the numbers of total Mini and KLRG1- Mini cells in the LNs did not differ between the three groups ( Fig 5D ) . These data imply that the number of primed Mini cells , established early in the lymph node , correlates with the size of memory T cell inflation . As no differences were observed in the number of Mini cells in the lymph nodes when Mini cells were transferred with an interval of 3 days relative to infection , we extended the gap between infection and adoptive transfer of transgenic cells to 7 days ( Fig 5E ) . We quantified Mini cells in the LNs six days post antigen encounter/ adoptive cell transfer , and observed a 7-fold decrease in numbers of total Mini cells and about a 4-fold decreased number of Mini KLRG1- cells in case of 7 days delayed transfer compared to day -1 transfer of Mini cells ( Fig 5F ) . In addition to almost undetectable peak expansion , accumulation of Mini cells was markedly reduced throughout infection in the blood ( Fig 5G ) , with a 5 . 7 to 6 . 3-fold reduction of total numbers of Mini cells in the lungs and spleen 130 days post infection ( Fig 5H ) . Strikingly , this reduction in the number of Mini cells at day 130 of infection was within the range of the reduction of Mini cells established 6 days post antigen encounter . Taken together , these results suggest that the size of the early established pool of KLRG1- MCMV-specific T cells is indicative for the size of memory inflation . To demonstrate that the number of KLRG1- cells early in infection determines the size of the inflationary T cell pool , we adoptively transferred different numbers of KLRG1- and KLRG1+ Maxi cells from day 6 infected mice into infection-matched recipients ( Fig 6A ) , and longitudinally tracked the Maxi cells in the blood . Strikingly , only mice obtaining KLRG1- cells accumulated Maxi T cells in the blood , whereas upon transfer of KLRG1+ cells , a small pool of Maxi cells was observed that did not increase in frequency in time ( Fig 6B ) . Upon transfer of a low number of KLRG1+ cells , hardly any Maxi cells were detected in the circulation after resolution of acute MCMV infection . Moreover , when more KLRG1- Maxi cells were transferred , also a higher number of Maxi cells was found in the spleen and the lungs at day 42 post infection ( Fig 6C ) . The majority of Maxi cells that were derived from a KLRG1- Maxi cell transfer expressed KLRG1 at this time point , indicating that KLRG1- cells gave rise to the KLRG1+ cells ( Fig 6D ) . These data support the notion that the number of KLRG1- cells early in infection correlates to the degree of memory inflation . Inflationary T cells can provide protection from an infection in peripheral tissues [25 , 35] . Next , we addressed the question whether this protective capacity was also linked to the size of the inflationary T cell pool . For this purpose , mice were infected with two different doses of MCMV-ie2-SIINFEKL ( Fig 7A ) , resulting in differences in the number of inflationary T cells that seed peripheral tissues [36] . Mice that were infected with a low dose of MCMV-ie2-SIINFEKL had reduced SIINFEKL-specific CD8 T cells in the blood and the ovaries , the organ of interest for VV challenge , as compared to mice that received a high dose infection ( Fig 7B , Fig 7C , Fig 7D ) . The number of effector memory cells ( CD127-/KLRG1+ ) in the ovaries was also diminished in low dose infected mice ( Fig 7D ) . Similar to SIINFEKL-specific CD8 T cells in the blood , lungs and spleen , the majority of the SIINFEKL-specific cells in the ovaries had an effector-memory phenotype , documented by the expression of KLRG1 ( S5A Fig ) , and did not express CD103 and CD69 ( S5B Fig ) , markers associated with tissue residency . In addition , most of the cells were stained by injection of a fluorescently conjugated anti-CD8 antibody , implying that these cells were within or in close proximity to the vasculature ( S5C Fig ) . These mice were subsequently challenged with VV-OVA and the viral burden was determined three days later . Mice that were infected with a low dose of MCMV-ie2-SIINFEKL only had a minor improved viral control compared to naïve mice ( Fig 7E ) . However , mice that had received a high dose of MCMV-ie2-SIINFEKL had a 2 log-fold lower viral burden in the ovaries compared to low dose infected mice and a 3 log-fold lower viral load compared to naïve mice ( Fig 7E ) . Consistent with the differences in the viral load , mice that had received a high dose of MCMV-ie2-SIINFEKL also had more SIINFEKL-specific CD8 T cells in the ovaries after secondary challenge with VV-OVA ( Fig 7F ) . These results highlight the ability of inflationary CD8 T cells to control a peripheral virus infection , given that they are present in sufficient numbers in the circulation or the target organ prior to infection .
Memory inflation of CD8 T cells represents a process that is driven by low-level persisting antigen that is expressed sporadically and leads to reactivation of memory CD8 T cells , leading to high level accumulation of antigen-specific TEM-like CD8 T cells in the circulation and many peripheral organs [37 , 38] . Murine and human CMV infection establish a condition that promotes such a process , by persistence of viral genomes in latently infected cells that are templates for transcription and translation of viral genes during sporadic viral reactivation events [39 , 40] . In murine CMV infection , such reactivation events engage TCM CD8 T cells with specificity for certain epitopes that are presented on non-hematopoietic cells and these CD8 T cells respond to this trigger by proliferation , effector memory differentiation and dissemination to peripheral organs where they establish a dynamic but stable pool of TEM cells [13 , 15] . The process of memory inflation is not only restricted to CMV infection , as it was also demonstrated during systemic HSV-1 infection , adenovirus infection [41] as well as human parvovirus B19 infection [42–44] . In case of MCMV infection , the fuelling of the inflationary T cell pool was shown to be independent of replication-competent CMV [45] , but involved sensing of CMV antigens during latency by TCM cells , leading to stable maintenance of the inflationary T cell pool in peripheral tissues [33] . However , the localisation of where the viral reactivation events and activation of TCM takes place is still a matter of debate [15 , 46] and cells harbouring ( latent ) MCMV genomes have been described in the lungs , kidney , liver , brain and salivary gland [6 , 47–50] . In addition , a recent study identified CX3CR1int CD8 T cells as possible source of proliferation-competent cells , which might contribute to the inflated T cell pool [51] . In this study , we sought to provide insights into the parameters that promote and limit inflation of CMV-specific CD8 T cells . We addressed the question whether there was a bias for TCR antigen avidity to be recruited into the inflationary pool of CMV-specific CD8 T cells . Our data show that inflationary T cells are almost exclusively consisting of high avidity CD8 T cells , although this selection was already apparent during initial clonal expansion . Most probably all low avidity CD8 T cells were outcompeted during acute and also latent MCMV infection . This is in contrast to human studies where in elderly individuals , low-avidity CD8 T cells are significantly contributing to the inflationary T cell pool during latency [28 , 52] . Recently , low-avidity CMV-specific CD8 T cells were also described to be functional in human individuals with allogeneic stem cell transplantation [53] . These differences might be explained by the different time resolutions , and longer observation periods in mice might also reveal a contribution of low avidity clones [35] . It has been shown that regulatory T cells and IL-10 are restraining memory T cell inflation [54 , 55] . Searching for other determinants that limit the 'size' of memory inflation , we ruled out a possible space ( niche ) limitation in peripheral tissues , for instance provided by IL-15 access in lung tissue [33] . By experimentally implementing a 50% reduction of the overall population of inflationary M38-specific CD8 T cells two to three weeks after priming had occurred , we did not observe a refilling of the vacated niche by the remaining endogenous M38-specific CD8 T cells . Instead , the overall M38-specific CD8 T cell pool remained at two-fold reduced frequencies and numbers for the entire period of more than 5 months of observation ( Fig 2 ) . As there might be competition for antigen at the level of the APC [21 , 56] , we ruled out that inflationary MCMV-specific CD8 T cells with other specificities occupied the newly available space . Thus , we concluded that there must be another critical determinant for the size of memory inflation which is likely to act early during MCMV infection . Indeed , we identified the number of primed cells , and in particular the number of KLRG1- cells , to be indicative for the size of the inflationary T cell pool during MCMV latency . These results are in line with a previous report that showed that the inflationary T cell pool is maintained at least in part by T cells primed early in infection [13] . Transferring distinct numbers of TCM cells with inflationary specificity recapitulated these results . In models of acute infection it has been shown that KLRG1- cells have a higher probability to feed into the memory pool [34] , making it likely that these cells also give rise to the TCM cells in MCMV infection . Thus , the number of central memory T cells that is able to sense viral reactivation events is one feature that drives memory inflation , but more factors promote T cell inflation as well [57] . This for instance includes the amount of latent viral genomes present in non-hematopoietic cells that is also related to the viral inoculum dose and the amount of latent viral genomes in the spleen determined by the route of infection and by immune evasion strategies [15 , 36 , 58 , 59] . Also CD4 T cell help , co-stimulatory signals mediated via 4-1BB , CD27 and OX40 , and IL-2 mediated signals have been shown to promote T cell inflation [38 , 60–66] . The interest of using CMV-based vectors for vaccination purposes is rising and CMV-vectors encoding antigens of heterologous viruses have already shown promising results in experimental studies [26 , 67–71] . The success of these CMV based vaccines is due to the induction of the large pool of effector memory T cells in the circulation and peripheral tissues which requires optimal epitope expression in latently infected , non-hematopoietic cells and its availability for processing by the constitutive proteasome [15 , 72] . Also here we show a prominent role of inflationary CD8 T cells in mediating immediate protection from a viral challenge that needs to be rapidly controlled in peripheral tissues [25 , 35] , and we show that this protective capacity is directly correlated to the size of the inflationary T cell pool . Similar results were shown recently where CMV-based vectors were used in a prophylactic manner to control tumour growth [73] . Although central memory T cells have a superior proliferation capacity as compared to effector memory T cells [74 , 75] , the activation of these TCM cells is rather a slow process as this requires antigen to be transported to secondary lymphoid tissues , reactivation of T cells and local expansion . Furthermore , the expanded cells have to migrate back to the initial site of infection . As inflationary T cells are either within or in close contact with the vasculature [33 , 46 , 76 , 77] , they have the ability to continuously seed non-lymphoid tissues in high numbers and therefore they are already positioned at the site where viral replication needs to be controlled . Related to this it would be interesting to compare the protective capacity of inflationary T cells with memory T cells that are confined to peripheral tissue , such as tissue resident memory cells [37] . Core 2 O-glycan synthesis , which is required to generate functional ligands for E- and P-selectins and therefore promotes trafficking into inflamed tissue in an antigen independent manner , is highly active in TCM cells but limited in TEM cells [78] . Recently it was shown that when MCMV infected mice received a subsequent Vaccinia challenge in the skin , non-inflationary T cells infiltrated the skin much better as compared to inflationary T cells [78] . Although this challenge was performed in an antigen independent manner , on a per-cell basis , TCM cells might be better in entering non-lymphoid tissues as compared to TEM cells . However , inflationary T cells are already at the location in large numbers and it is likely that they will control a peripheral infection quicker due to their numerical advantage . Moreover , we show that if we experimentally diminish the amount of inflationary T cells in a peripheral organ , then also their protective capacity is diminished . Thus , the immediate protective capacity of the inflationary T cell pool is directly correlated to the number of T cells in circulation and / or specific tissues . As the limit for memory inflation is already determined early during infection , changing the balance between early primed KLRG1- and KLRG1+ cells is of interest for the efficacy of CMV-based vaccine vectors as well . Combined , these data emphasise the importance of inflationary T cells in the context of CMV-based vaccine vectors and their ability to protect from a virus challenge in peripheral tissues .
This study was conducted in accordance to the guidelines of the animal experimentation law ( SR 455 . 163; TVV ) of the Swiss Federal Government . The protocol was approved by Cantonal Veterinary Office of the canton Zurich , Switzerland ( Permit number 127/2011 , 146/2014 , 114/2017 ) . Wild-type C57BL/6J were purchased from Janvier Elevage ( Le Genest Saint Isle , France ) . C57BL/6N-Tg ( TcraM38 , TcrbM38 ) 329Biat ( Maxi ) [15] , C57BL/6N-PtprcaTG ( TcrbM38 ) 330Biat ( Mini ) [15] , C57BL/6-Tg ( TcraTcrb ) 1100Mjb/J ( OT-I ) [79] and B6 . ( PL-Thy1a;B6;129P2/OlaHsd ) -Tg ( ( TcrOVA ) 1Zhn/UniL ) ( OT-III ) [29] mice were housed and bred in specific pathogen-free facilities at the Eidgenössische Technische Hochschule ( ETH ) Hönggerberg . Maxi transgenic ( Ly5 . 1+ ) express a TCR ( Vβ10Jβ2 . 1/Vα4Jα13 ) specific for the MCMV peptide M38316-323 [15] . Mini transgenic ( Ly5 . 1+ ) mice express the TCR Vβ10Jβ2 . 1 chain of the Maxi TCR , harbouring M38316-323-specific CD8 T cells at roughly 10% . OT-I transgenic ( Ly5 . 1+ ) mice express a TCR specific for the ovalbumin peptide OVA257-264 ( SIINFEKL ) [79] . OT-III transgenic ( Thy1 . 1+ ) mice express a low avidity TCR specific for the SIINFEKL epitope [29] . Female or male mice were used at 6–12 weeks of age and sex-matched within all experiments . Recombinant MCMV lacking m157 ( MCMVΔm157 ) was previously described and is referred to as MCMV in this study [65] . Recombinant MCMV expressing OVA257-264 SIINFEKL peptide within the ie2 gene was produced as described [21] , contains the m157 gene , and is referred to as MCMV-ie2-SIINFEKL . MCMV strains were propagated on MEFs [80] or M2-10B4 cells [81] as previously described . Virus titres in organs were determined by standard plaque-forming assays on M2-10B4 cells as previously described [81] . Infections were performed intravenously with 1–5 × 106 PFU MCMV , or 2 × 105 ( high dose ) or 2 × 102 ( low dose ) PFU MCMV-ie2-SIINFEKL . Recombinant Vaccinia virus ( Western Reserve ) expressing Ovalbumin protein ( VV-OVA ) inserted into the thymidine kinase gene was grown on BSC40 cells and was provided by Dr . P . Klenerman . MCMV-ie2-SIINFEKL infected female hosts were challenged i . p . with 2 × 107 PFU VV-OVA . VV titres were analysed in the ovaries three days post challenge by standard plaque-forming assay on BSC40 cells as previously described [82] . CD8 T cells from naïve Maxi , Mini , OT-I or OT-III mice were purified from splenocytes using anti-CD8α MACS beads ( Miltenyi Biotech ) according to the manufacturer's instructions . 104 purified Maxi CD8 T cells or 2 × 105 Mini , OT-I or OT-III CD8 T cells were adoptively transferred into recipient mice one day prior to infection , unless otherwise stated . High and low avidity Mini cells were sorted from the spleen of naïve Mini mice according to tetramer staining , and 105 sorted high or low avidity Mini CD8 T cells were transferred into recipient mice . Maxi TCM cells were isolated from the spleen and LNs of infected C57BL/6 mice after at least 60 days of MCMV infection , and sorted according to the expression of CD127 and CD62L . Sorted cells were labelled with cell proliferation dye-eFluor450 ( Life technologies ) according to manufacturer's protocol . Maxi KLRG1- and KLRG1+ cells were isolated from the spleen and LNs of infected C57BL/6 mice at day 6 post MCMV infection . Maxi cells isolated from infected mice were enriched before sorting by depletion of CD4 T cells and B cells using biotinylated CD4 ( GK1 . 5 ) and B220 ( RA3-6B2 ) antibodies and MojoSort Streptavidin nanobeads ( BioLegend ) . For all sorting experiments , a BD FACSAria Sorter was used . Lymphocytes were isolated from spleen and lungs as described before [83] . Cells were isolated from ovaries by mincing the tissue through a 70 μm cell strainer . Before lungs and ovaries were removed , mice were perfused with PBS . Blood samples were obtained from the tail vein . Red blood cells were lysed using ACK lysis buffer for 1 minute at room temperature . Surface staining of cells was performed for 20 min at room temperature in PBS supplemented with 2% FCS . For i . v . labelling of CD8 T cells , 5 μg of a fluorescently conjugated anti-CD8α antibody was injected i . v . 3 minutes prior to euthanasia . For peptide restimulations , cells were incubated with 1 μg/ml peptide in the presence of 20 μM Monensin A ( Sigma Aldrich ) for 6 hours at 37° C . Cell surface staining was performed as described above and cells were fixed with 1% PFA for 20 minutes . Cells were permeabilized using 2x BD lysis buffer ( BD Biosciences ) containing 0 . 05% Tween 20 ( Sigma Aldrich ) for 10 minutes . Intracellular staining was performed at room temperature for 20 minutes . Multiparametric flow cytometric analysis was performed using LSRII flow cytometer ( BD Biosciences ) and FACSDiva software . Data was analysed using FlowJo software ( Tree Star ) . APC- or PE-conjugated MHC class I tetramers were generated as described before [84] . Fluorophore-conjugated antibodies were purchased from BioLegend ( Lucerna Chem ) or eBiosciences ( Thermo Fisher Scientific ) . The following antibodies were used for Flow cytometry: anti-CD8α ( 53–6 . 7 ) , anti-CD8β ( 53–5 . 8 ) , anti-CD45 . 1 ( A20 ) , anti-CD45 . 2 ( 104 ) , anti-CD90 . 1 ( Ox-7 ) , anti-CD90 . 2 ( 30-H12 ) , anti-CD62L ( MEL-14 ) , anti-CD44 ( IM7 ) , anti-KLRG-1 ( 2F1 ) , anti-CD127 ( A7R34 ) , anti-CD69 ( H1 . 2F3 ) , anti-CD103 ( 2E7 ) , anti-CD25 ( 3C7 ) and anti-CD4 ( RM4-5 ) . Live/Dead Fixable near-IR ( Life Technologies ) dead cell stain was used to exclude dead cells . Transgenic Mini and Maxi cells were identified by gating on CD8α+ CD45 . 1+ M38-Tet+ cells . OT-I cells were identified by gating on CD8α+ CD45 . 1+ cells and OT-III were identified by gating on CD8α+ CD90 . 1+ cells . Statistical significance was determined using GraphPad Prism ( La Jolla ) and statistical tests are indicated in each figure . | Cytomegalovirus induces a lifelong infection in the majority of the world's population , due to the ability of the virus to establish latency . Upon CMV infection , large numbers of effector memory T cells are induced in peripheral tissues , a process that is termed memory inflation . As inflationary T cells are highly functional , CMV-based vaccines have gained substantial interest for vaccination purposes . Here we examine factors that promote and limit memory T cell inflation . We found that there were no constraints on the availability of specific niches for inflationary T cells in tissues and that high avidity T cells predominately contribute to the inflationary T cell population in the beginning of infection . Moreover , the number of early primed KLRG1- CMV-specific T cells in the acute phase of infection set the limit for memory T cell inflation . Furthermore , we show that inflationary T cells provided protection from a pathogenic challenge in peripheral tissues such as the ovaries . Thus , inflationary T cells comprise a population of T cells that can protect peripheral tissues from pathogenic infections and their efficacy can be regulated by balancing the number of KLRG1- CMV-specific cells during priming . | [
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] | 2019 | Early primed KLRG1- CMV-specific T cells determine the size of the inflationary T cell pool |
Exit from mitosis in budding yeast is triggered by activation of the key mitotic phosphatase Cdc14 . At anaphase onset , the protease separase and Zds1 promote the downregulation of PP2ACdc55 phosphatase , which facilitates Cdk1-dependent phosphorylation of Net1 and provides the first wave of Cdc14 activity . Once Cdk1 activity starts to decline , the mitotic exit network ( MEN ) is activated to achieve full Cdc14 activation . Here we describe how the PP2ACdc55 phosphatase could act as a functional link between FEAR and MEN due to its action on Bfa1 and Mob1 . We demonstrate that PP2ACdc55 regulates MEN activation by facilitating Cdc5- and Cdk1-dependent phosphorylation of Bfa1 and Mob1 , respectively . Downregulation of PP2ACdc55 initiates MEN activity up to Cdc15 by Bfa1 inactivation . Surprisingly , the premature Bfa1 inactivation observed does not entail premature MEN activation , since an additional Cdk1-Clb2 inhibitory signal acting towards Dbf2-Mob1 activity restrains MEN activity until anaphase . In conclusion , we propose a clear picture of how PP2ACdc55 functions affect the regulation of various MEN components , contributing to mitotic exit .
During most of the cell cycle , Cdc14 is kept inactive and sequestered in the nucleolus through binding to its inhibitor Net1 [1] , [2] . Two pathways , FEAR ( Cdc14 early anaphase release ) and MEN ( mitotic exit network ) , activate Cdc14 , thereby promoting its release from the nucleolus in early and late anaphase , respectively . Both pathways promote Cdc14 activation by phosphorylating Net1 , since the phosphorylated form of Net1 has a low affinity for Cdc14 and loses its ability to inhibit it [3]–[5] . Many proteins , including separase , Cdk1 , PP2ACdc55 ( type 2A protein phosphatase ) , Zds1 , Slk19 , Spo12 and Fob1 , have been implicated in early anaphase Cdc14 release ( reviewed in [6]–[8] ) . Several mutants in the FEAR pathway delay the release of Cdc14 from the nucleolus . At early anaphase , upon APCCdc20 ( anaphase-promoting complex ) activation , securin is degraded by the proteasome and separase is activated , allowing sister chromatid segregation and FEAR-Cdc14 release . The protease separase , the main component of FEAR , allows the Cdk1-dependent phosphorylation of Net1 by downregulating the phosphatase PP2ACdc55 [9] . Zds1 and Zds2 are PP2A-interacting proteins that also participate in the downregulation of PP2ACdc55 [10] , [11] . Once Cdk1 activity starts to decline , cells require the MEN pathway to keep Net1 phosphorylated and Cdc14 fully active . The MEN is a GTPase-driven signaling cascade that is associated with the spindle pole body ( SPB ) [12]–[19] . The main switch of this cascade is the small G protein Tem1 and its regulators: a two-component GAP Bub2–Bfa1 , and the putative exchange factor Lte1 . Upon activation , Tem1 promotes activation of the Cdc15 protein kinase , which in turn activates the Dbf2–Mob1 kinase complex via phosphorylation [20] . It has recently been found to occur in two steps: Cdc15 first creates phospho-docking sites on the MEN scaffold protein Nud1 and Nud1 phosphorylation recruits Dbf2–Mob1 to SPBs followed by Cdc15-dependent activation of Dbf2–Mob1 [21] . An additional function of the Dbf2–Mob1 complex is to phosphorylate Cdc14 at sites adjacent to its nuclear localization sequence , thereby retaining Cdc14 in the cytoplasm [22] . In an unperturbed cell cycle , the Bub2–Bfa1 complex inhibits the MEN until the Cdc5 Polo kinase inactivates it by phosphorylation . Upon activation of the spindle position checkpoint ( SPOC ) , Bfa1 is phosphorylated by the kinase Kin4 [23] . Kin4 inhibits MEN activation by a phosphorylation that protects Bfa1 from the inhibitory phosphorylation of Cdc5 , effectively locking Bub2–Bfa1 in an active state . As a consequence , the MEN pathway is kept inactive until the spindle checkpoint signal is abrogated [24]–[27] . In addition , Cdk1 negatively regulates the function of the MEN components Cdc15 and Mob1 [28] , [29] . The first burst of Cdc14 released induced by FEAR , eventually dephosphorylates Cdc15 , which further activates Cdc14 [17] , [28] , [30] , [31] . Lte1 , in addition to being a putative guanine nucleotide exchange factor for Tem1 , participates in the control of Bfa1 localization and cell polarization [32] . Bub2–Bfa1 localizes asymmetrically to the daughter SPB ( dSPB ) [33] , [34] and this asymmetry is required to recruit MEN components to the dSPB during mitosis [35] . Bfa1 phosphorylation during anaphase induces asymmetric localization onto the dSPB [24] , [36] , [37] . Tem1 is also bound asymmetrically to the dSPB during anaphase and its association with the SPBs is essential for mitotic exit [33] , [38] , [39] . Moreover , in early anaphase , Cdc15 kinase recruits Cdk1 to the mother spindle pole body ( mSPB ) , while conversely , Cdk1 negatively regulates binding of Cdc15 to the mSPB [29] . Here , we propose that PP2ACdc55 phosphatase acts as a functional link between FEAR and MEN , due to its action on Bfa1 and Mob1 . PP2ACdc55 downregulation at anaphase onset facilitates Bfa1 inactivation and thereby initiates the MEN pathway up to the Cdc15 kinase . Premature Bfa1 inactivation observed after PP2ACdc55 inactivation does not entail precocious MEN activation , since the downstream MEN effectors Cdc15 and Dbf2–Mob1 are kept inactive by Cdk1–Clb2 phosphorylation . In this way , Cdk1–Clb2 restrains MEN activity until mid-late anaphase allowing a consecutive order of FEAR- and MEN-Cdc14 functions .
The Bub2–Bfa1 complex keeps MEN inactive by inhibiting Tem1 . Phosphorylation of Bfa1 by Polo kinase , Cdc5 , contributes to MEN activation in an anaphase-specific manner [24] , [30] . However , there is no evidence of upregulation of Polo activity during anaphase , and preliminary results suggested that downregulation of PP2ACdc55 could facilitate anaphase-specific phosphorylation of Bfa1 [9] . To investigate the role of PP2ACdc55 in regulating MEN activity , we studied the dephosphorylation of Bfa1 by PP2ACdc55 . First , we confirmed that Bfa1 was already phosphorylated in metaphase in a cdc55Δ mutant ( Figure 1A and [9] ) . Wild-type and cdc55Δ cells were synchronized at the metaphase-to-anaphase transition by Cdc20 depletion . Bfa1 electrophoretic mobility and Cdc14 release were analyzed after releasing cells into anaphase . In wild-type cells , the electrophoretic mobility of Bfa1 decreases during anaphase . Bfa1 becomes dephosphorylated upon exit from mitosis . In contrast , Bfa1 already has a slower migration form in metaphase in cdc55Δ cells . This result indicates that Bfa1 already has an anaphase-like phosphorylation pattern in metaphase , suggesting that PP2ACdc55 prevents Bfa1 phosphorylation . Secondly , we studied Bfa1 phosphorylation in the absence of PP2ACdc55 phosphatase catalytic activity . We used a strain carrying a deletion of PPH21 , a temperature-sensitive pph22-172 allele that inactivates the two PP2ACdc55 catalytic subunits , and a deletion of PPH3 ( since Pph3 phosphatase can partly compensate for PP2ACdc55 activity in budding yeast [40] ) . Cells were arrested in metaphase with nocodazole and shifted to the restrictive temperature in order to inactivate PP2ACdc55 . Bfa1 phosphorylation increased after shifting to the restrictive temperature in pph3Δpph21Δpph22-172 cells ( Figure 1B ) . Upon treatment at 37°C , Cdc14 was released from the nucleolus , confirming PP2ACdc55 inactivation . This result suggests that PP2ACdc55 catalytic activity is required to keep Bfa1 underphosphorylated in metaphase . However , in this strain the activity of the PP2A complex containing the Rts1 regulatory subunit is also impaired . Therefore , we cannot rule out a contribution of the PP2ARts1 to the Bfa1 phosphorylation in this specific pph3Δpph21Δpph22-172 mutant . However , Bfa1 phosphorylation is not affected in an rts1Δ mutant [41] , indicating that PP2ARts1 complexes do not counteract Bfa1 phosphorylation . Thirdly , we determined Bfa1 phosphorylation status under Zds1 overexpression , which promotes PP2ACdc55 inactivation [10] . Cells were arrested in metaphase by Cdc20 depletion , and Zds1 expression was induced by galactose addition . Elongation of anaphase spindles was not observed as a control of the cells being blocked in metaphase during the experiment . Bfa1 became phosphorylated 40–60 minutes post-induction when Cdc14 was released , representing a marker of PP2ACdc55 phosphatase inactivation ( Figure 1C ) . This indicates that Zds1-dependent inactivation of PP2ACdc55 promotes Bfa1 phosphorylation . Similar results were obtained when we induced inactivation of PP2ACdc55 by separase ectopic expression ( Figure S1 ) . Next , we examined whether forcing Cdc55 expression would specifically remove Cdc5-induced phosphorylation of Bfa1 . Cells containing CDC55 under the GAL1 promoter were arrested in G1 with α-factor and released from the G1 block in the presence of galactose ( Figure 1D ) . Bfa1 became phosphorylated 45 min after the G1 release in the half of the culture that was kept without galactose , as a control . Conversely , Bfa1 was not phosphorylated in cells overexpressing Cdc55 . These results indicate that PP2ACdc55 phosphatase counteracts Bfa1 phosphorylation . Finally , to determine whether Bfa1 is a PP2ACdc55 substrate , we examined whether Cdc55 and Bfa1 physically interact . Co-immunoprecipitation experiments showed that Bfa1 co-purified with Cdc55 at all stages of the cell cycle ( Figure 1E ) . Moreover , the Cdc55 and Bfa1 interaction was impaired in the absence of PP2A catalytic activity ( pph3Δpph21Δpph22-172 mutant at restrictive temperature ) due to the loss of integrity of the PP2A trimeric complex in this mutant . Taken together , these results suggest that Bfa1 is likely to be an in vivo substrate of PP2ACdc55 . Bfa1 phosphorylation in the normal cell cycle depends on Cdc5 Polo kinase activity , so we expected that the premature phosphorylation of Bfa1 observed upon PP2ACdc55 inactivation would depend on Cdc5 . To test this , we compared Bfa1 electrophoretic mobility after Zds1 induction in wild-type and cdc5-14 mutant cells ( Figure S2 ) . Cells were arrested in metaphase by Cdc20 depletion , and shifted to the restrictive temperature . Galactose was then added to induce Zds1 ectopic expression . Bfa1 was already phosphorylated in metaphase in both strains due to the prolonged incubation at 37°C . This temperature-dependent Bfa1 phosphorylation was also observed in the experiment illustrated in Figure 1B in which the cells were incubated at 37°C for at least 120 min . Bfa1 hyperphosphorylation was clearly observed upon Zds1 induction in wild-type cells , but was not induced in cdc5-14 mutant cells during the induction time-course . This result suggests that Bfa1 phosphorylation after PP2ACdc55 inactivation depends on Cdc5 Polo kinase . To determine whether PP2ACdc55 also contributes to Bfa1 phosphorylation upon spindle position checkpoint activation , we compared Bfa1 electrophoretic mobility in wild-type and kin4Δ cells after Zds1 induction . Both strains showed Bfa1 hyperphosphorylation as Zds1 accumulated ( Figure S3 ) . This result indicates that the Bfa1 phosphorylation observed upon PP2ACdc55 inactivation does not depend on kinase Kin4 . We can conclude that PP2ACdc55 counteracts Cdc5-dependent Bfa1 phosphorylation . To screen for new PP2ACdc55 substrates during mitosis , we performed a global study of the PP2ACdc55 phosphoproteome by a quantitative phosphoproteomic analysis based on SILAC labeling . Wild-type cells were labeled using 13C6-lysine and 13C6-arginine ( heavy ) , and cdc55Δ mutant cells were grown in the presence of unmodified arginine and lysine ( light ) . Cells were arrested in metaphase and protein extracts were prepared . Phosphopeptides were enriched by SIMAC-based enrichment . Phosphopeptide analysis of the heavy/light-labelled cells was done by LC-MS/MS . An already known PP2ACdc55 substrate Net1 was identified as being hyperphosphorylated in the cdc55Δ mutant , suggesting that the technique worked properly . The screening revealed that a phosphopeptide corresponding to Mob1 protein was hyperphosphorylated in cdc55Δ mutant cells . The Mob1 peptide contained two S/TP sites: S80 and T85 ( Figure 2A ) . Both sites were detected with the highest confidence ( pRS site probability around 100% and q = 0 . 000168 ) . The T85 is one of the full Cdk1 consensus sites ( S/T-P-x-K/R ) previously reported to be phosphorylated by Cdk1 [29] . This result suggests that Mob1 could be a PP2ACdc55 substrate . To examine this further we studied the Mob1 phosphorylation levels in a synchronous anaphase in cdc55Δ mutant cells . Wild-type and cdc55Δ cells were synchronized at the metaphase-to-anaphase transition by Cdc20 depletion . In wild-type cells , Mob1 is dephosphorylated during anaphase , coincident with spindle elongation and Cdc14 release from the nucleolus . In contrast , Mob1 was hyperphosphorylated in metaphase in cdc55Δ cells and its high electrophoretic mobility forms were still detected during anaphase ( Figure 2B ) . This result indicates that Mob1 is hyperphosphorylated in metaphase and is not correctly dephosphorylated during anaphase in the absence of PP2ACdc55 activity ( despite Cdc14 being already released in metaphase ) , suggesting that PP2ACdc55 is required to dephosphorylate Mob1 properly . Finally , we examined whether Cdc55 and Mob1 physically interact . Co-immunoprecipitation experiments showed that Mob1 co-purified with Cdc55 ( Figure 2C ) . Together , these results suggest that Mob1 is probably an in vivo substrate of PP2ACdc55 . The Bub2–Bfa1 complex inhibits the MEN pathway until Cdc5-dependent phosphorylation of Bfa1 alleviates Tem1 inhibition by Bub2–Bfa1 [24] . Therefore , we would expect the premature phosphorylation of Bfa1 observed in the absence of PP2ACdc55 activity to cause premature MEN activation . If so , activation of the MEN cascade would happen earlier in the cdc55Δ mutant than in the wild-type . However , on the basis of anaphase FACS dynamics ( Figure 1A ) , we found no evidence of accelerated mitotic exit in cdc55Δ mutant cells compared with wild-type cells . Cdc15 is dephosphorylated during exit from mitosis by Cdc14 [14]–[17] , [28] . The Cdc15 dephosphorylation event has often been used as a MEN activation marker , so if MEN is prematurely activated in the absence of PP2ACdc55 activity , we should observe premature dephosphorylation of Cdc15 . Wild-type and cdc55Δ mutant cells were synchronized at the metaphase-to-anaphase transition by Cdc20 depletion , and Cdc15 phosphorylation was analyzed by western blot . In wild-type cells , the electrophoretic mobility of Cdc15 increased , being especially evident between 20 and 40 minutes , when the cells were in anaphase ( Figure 3A ) . At these times , the MEN was active ( as indicated by the anaphase spindle dynamics ) and Cdc15 was dephosphorylated , as previously reported . Interestingly , in the cdc55Δ mutant , we detected a faster migration form of Cdc15 already at metaphase . Note that this could be reminiscent of Cdc14 being released from the nucleolus already in metaphase in the cdc55Δ mutant . However , Cdc15 dephosphorylation was never as efficient as the wild-type cells in anaphase . We conclude that , in the absence of PP2ACdc55 activity , Cdc15 is prematurely dephosphorylated , although MEN is not prematurely activated , since mitotic exit is not accelerated . We did not observe any further increase in Cdc15 mobility during anaphase . One explanation could be that the Cdc14 prematurely released from the nucleolus in metaphase in the cdc55Δ mutant was not active as phosphatase , and therefore , PP2ACdc55 could be required for full activation of Cdc14 during anaphase . To resolve this matter we measured Cdc14 activity in vitro in wild-type and cdc55Δ mutant strains in metaphase and anaphase cells . As can be observed in Figure 3B , Cdc14 activity in the cdc55Δ mutant is equal in metaphase and anaphase cells . More interestingly , the Cdc14 activity in the cdc55Δ mutant is equivalent to anaphase-Cdc14 in wild-type cells . Therefore , this result indicates that Cdc14 activity is not impaired in cdc55Δ mutant cells; indeed , Cdc14 activity has similar levels to the fully active Cdc14 in anaphase . However , although Cdc14 is active in the cdc55Δ mutant , the susceptibility of the Cdc14 substrates to dephosphorylation also depends on kinase levels . The Cdk1/Cdc14 ratio over the course of mitotic exit is read out by Cdk substrates that respond by dephosphorylation at distinct thresholds [42] . Exit from mitosis and cytokinesis require full activation of Cdc14 by the MEN pathway . Cdc14 promotes the inactivation of the Cdk1–Clb2 complex at the end of mitosis by dephosphorylating Cdh1 an activator of the APC complex in late anaphase . Cdh1 is responsible for the degradation of mitotic B-type cyclins , Cdc5 , Swi5 , Sic1 and Cdc20 , among numerous other proteins , including several involved in spindle stability and assembly [43]–[49] . To further characterize how PP2ACdc55 phosphatase activity impinges on MEN activation , we next studied late anaphase events that require MEN activation . Wild-type and cdc55Δ cells were arrested in metaphase by Cdc20 depletion and then released into synchronous anaphase by Cdc20 reintroduction . Cdh1 dephosphorylation , Cdc5 and Clb2 degradation , and Sic1 accumulation were analyzed as markers of late anaphase events by western blot . Wild-type cells showed a decrease of the slower migration forms of Cdh1 at 20–40 minutes when cells were in anaphase ( Figure 3C ) , indicating that Cdh1 becomes dephosphorylated during this period . By contrast , in cdc55Δ mutant cells , phosphorylated forms of Cdh1 persisted during anaphase and only a partial dephosphorylation of Cdh1 was observed . This suggests that Cdh1 , a direct target of MEN , is not correctly activated in the absence of PP2ACdc55 activity . To confirm this result , we also studied the activity of APCCdh1 with respect to its substrates , Cdc5 and Clb2 . In wild-type cells , Cdc5 and Clb2 degradation started when Cdh1 became active in anaphase ( at 30–40 minutes ) . However , Cdc5 protein levels were almost constant in the cdc55Δ mutant cells in anaphase . On the other hand , Clb2 was only partially degraded at 30 minutes in the cdc55Δ mutant cells , indicative of the first Clb2 degradation by APCCdc20 . We conclude that APCCdh1 does not degrade Cdc5 and Clb2 efficiently in cdc55Δ mutant cells . Finally , we studied the accumulation of Sic1 protein levels , the Cdk1 inhibitor . In wild-type cells , Sic1 protein became detectable during anaphase and persisted until the next G1/S phase . Nevertheless , Sic1 was timely accumulated in the cdc55Δ mutant cells , but remained constant at later times since cdc55Δ mutant cells had a longer G1 phase ( see FACS profiles in Figure 1A and [9] ) . This is consistent with the cells not being able to enter the next S phase until the APCCdh1 substrates have been correctly degraded during G1 . Despite the slower kinetics in Cdh1 dephosphorylation and Cdc5 and Clb2 degradation , Sic1 accumulation was probably sufficient to inhibit Cdk1 and to exit from mitosis in the absence of Cdc55 , but not to complete mitosis as efficiently as in the wild-type cells . In fact , Cdc14 resequestration was also delayed in cdc55Δ mutant cells . Taking these results together , we can conclude that PP2ACdc55 activity is required for efficient MEN activation and mitotic exit . In the normal cell cycle , the Bub2–Bfa1 complex localizes asymmetrically at the dSPB in anaphase when Cdc5 Polo kinase phosphorylates Bfa1 and alleviates MEN inactivation [24] , [33] , [34] , [50] . Bfa1 is a phosphoprotein and its asymmetric localization on the dSPB is induced upon Bfa1 phosphorylation [24] , [36] , [37] , [50] . Our results described above indicate that Bfa1 is hyperphosphorylated in the absence of PP2ACdc55 activity , so we next investigated whether Bfa1 also undergoes premature asymmetric localization on the dSPB upon PP2ACdc55 inactivation . First , we examined the Bfa1 localization in wild-type and cdc55Δ metaphase-arrested cells containing Bfa1-eGFP ( Figure 4A ) . In wild-type cells , Bfa1 was preferentially symmetrically located on both SPBs in metaphase-arrested cells . Conversely , in the cdc55Δ mutant Bfa1 was preferentially located on the dSPB in 58% of the cells . Therefore , Bfa1 was mainly asymmetrically localized on the dSPB in the absence of Cdc55 . Next , we induced Bfa1 phosphorylation by Zds1-dependent inactivation of PP2ACdc55 in cells containing Bfa1-mCherry and Spc42-YFP as an SPB marker . Upon Zds1 ectopic expression , Bfa1 localization was asymmetric in 73% of the cells ( Figure 4B and Figure S4 ) . To assess the extent of Bfa1 asymmetric localization we quantified the signal intensity ratio between the two SPBs ( dSPB/mSPB relative intensity; hereafter the SPB ratio ) . The SPB ratio reached a value of 5 . 2 120 min after Zds1 induction . Our results indicate that Bfa1 becomes prematurely asymmetrically localized in the absence of PP2ACdc55 function . Lastly , we studied the changes in Bfa1 localization during the cell cycle in the absence of PP2ACdc55 activity . As a first approach , we synchronized cells at the metaphase-to-anaphase transition by Cdc20 depletion ( Figure 4C ) . In wild-type cells , Bfa1 became asymmetric in anaphase when the cells had a spindle length >6 µm and a mean SPB ratio of 20 . Conversely , in cdc55Δ mutant cells the SPB ratios were already >7 in metaphase . Next , we analyzed Bfa1 localization in cell-cycle progression after synchronous release from G1 in wild-type and cdc55Δ mutant cells bearing the CDC28Y19F allele that is refractory to Cdk1 inhibition ( Figure 4D ) . cdc55Δ cells show a delay in progression through mitosis because their Cdk1 activity is compromised by inhibitory Cdc28–Y19 phosphorylation [51] . However , the cdc55Δ CDC28Y19F mutant shows the premature Cdc14 release from the nucleolus in metaphase that is typical of cdc55Δ deletion mutants [9] . In wild-type cells , the Spc42-YFP signal split around S phase , indicative of the SPB duplication , but the Bfa1-mCherry signal could not be detected ( Figure 4D , S/G2 column ) . Bfa1-mCherry was subsequently visualized transiently in both SPBs . When the cells had a spindle length >6 µm , Bfa1-mCherry was detected only in the dSPB , consistent with other published results [50] . By contrast , in cdc55Δ CDC28Y19F mutant cells , it was detected on the SPBs shortly after the Spc42-YFP signal had been duplicated . As soon as the Bfa1-mCherry signal was visualized , Bfa1 was asymmetrically loaded onto the dSPB ( SPBs ratios of approximately 3 ) . When the cells reached mitosis , the Bfa1-mCherry signal increased and the asymmetry became more evident . These results further confirmed the premature asymmetric Bfa1 localization in the absence of PP2ACdc55 function . Thus , PP2ACdc55 downregulation initiates MEN signalling by allowing Cdc5-dependent phosphorylation of Bfa1 and its asymmetric localization . Given that PP2ACdc55 counteracts Bfa1 phosphorylation in metaphase , and upon downregulation of PP2ACdc55 in anaphase Bfa1 becomes hyperphosphorylated and located asymmetrically to the dSPB , the question arises as to why the exit from mitosis and MEN activation are not accelerated in cdc55Δ cells . It has recently been proposed that increased residence time of Tem1 on SPBs leads to premature Cdc15 loading but not to premature entry into anaphase [39] . To investigate whether something similar occurs in cdc55Δ cells , we examined Cdc15 and Dbf2-Mob1 activation . Cdc15 kinase is dephosphorylated and activated by early released Cdc14 from the nucleolus . Cells were arrested in metaphase by Cdc20 depletion and galactose was added to induce Zds1 ectopic expression ( PP2ACdc55 inactivation ) . Cdc15 phosphorylation status was analyzed by western blot ( Figure 5A ) . Upon Zds1 induction , Cdc15 was dephosphorylated consistently with the result showed shown in Figure 3A , which implies that Cdc15 is dephosphorylated in the absence of PP2ACdc55 activity . To characterize these cells further we checked the localization of Cdc15 on the SPBs upon Zds1-dependent PP2ACdc55 inactivation . After Zds1 induction in metaphase-arrested cells , Cdc15-eGFP asymmetrically located onto the dSPB in 53% of the cells compared with 12% of non-induced cells ( Figure 5B ) . We also studied Cdc15 localization in cdc55Δ cells in a metaphase-to-anaphase transition . Interestingly , in cdc55Δ cells Cdc15-eGFP was prematurely asymmetrically located onto the dSPB at metaphase in 44% of the cells ( Figure 5C ) . In wild-type cells , Cdc15-eGFP was detected on the dSPB when cells entered anaphase , as reported previously [14] , [16] , [17] , [38] , [52] , and as confirmed by the SPBs ratios ( Figure 5D ) . In wild-type cells , the SPB ratio increased in anaphase cells with spindle lengths >6 µm , while in cdc55Δ mutant cells the ratio was already high at metaphase , indicative of asymmetric Cdc15 localization . Similar results were obtained with cells synchronized in G1 by α-factor ( Figure S5 ) . Therefore , upon inactivation of the PP2ACdc55 , Cdc15 was prematurely dephosphorylated and loaded onto the dSPB . These results suggest that in the absence of PP2ACdc55 activity Tem1 is active and the MEN activation signal is transduced up to Cdc15 . Dbf2–Mob1 localizes to both SPBs during anaphase [52] , [53] , which is important , though not sufficient , for Dbf2 kinase activity [52] . Dbf2 phosphorylation levels are inversely associated with its kinase activity [52] . Cdk1 is also known to phosphorylate and inhibit Mob1 [29] . Therefore , Dbf2–Mob1 phosphorylation levels are a good marker of Dbf2–Mob1 activity . For this reason , we next studied the Mob1 phosphorylation levels upon Zds1-dependent PP2ACdc55 inactivation . Cells were arrested in metaphase by Cdc20 depletion and Zds1 ectopic expression was induced ( Figure 6A ) . Strikingly , Mob1 was not dephosphorylated upon Zds1 induced-PP2ACdc55 inactivation . Upon Zds1 overexpression , Cdk1–Clb2 kinase is still active and maintains Mob1 phosphorylated and inactive . Consistent with this experiment , we were not able to detect any Mob1-eGFP signal in the SPBs upon Zds1 induction in metaphase-arrested cells ( Figure 6B ) . Moreover , the Mob1-eGFP signal in SPBs increased in anaphase in a similar way in wild-type and cdc55Δ mutant cells ( Figure 6C ) . Therefore , we can conclude that in the absence of PP2ACdc55 activity , Cdc15 is untimely dephosphorylated and loaded onto the dSPB , but there is no premature mitotic exit because Dbf2-Mob1 remains inactive . Non-phosphorylated Cdc15 is recruited to the mSPB in early anaphase and then helps load Cdk1 and Dbf2–Mob1 onto this SPB . It has been suggested that the close vicinity of the proteins at the mSPB leads to Cdc15 and Mob1 phosphorylation by Cdk1 . Phosphorylated Cdc15 then dissociates from the mSPB and become asymmetrically localized to the dSPB [29] . Since Cdc15 shows premature asymmetric localization at the dSPB , we next investigated whether Cdk1 binds to the mSPB upon PP2ACdc55 inactivation . If MEN activity were inhibited during metaphase by Cdk1–Clb2 in cdc55Δ cells , we would expect to detect Cdk1-eGFP on the mSPB . Indeed , in cdc55Δ cells metaphase-arrested by Cdc20 depletion Cdk1-eGFP was prematurely visualized on the mSPB in 38% of the cells ( Figure 7A ) . However , Cdk1-eGFP could only be detected on the mSPB during anaphase in wild-type cells . It is noted that the Cdk1-eGFP signal always opposes the Bfa1-mCherry signal . This result is consistent with the premise that Dbf2–Mob1 inhibition by Cdk1–Clb2 is predominant in cdc55Δ mutant cells in metaphase and restrains these cells from a premature exit from mitosis . The above results led us to hypothesize that alleviation of the Bfa1–Bub2 inhibitory MEN signal is insufficient to promote premature exit from mitosis in cdc55Δ cells , and that Cdk1–Clb2 inhibitory MEN signal is the additional mechanism that restrains MEN activity until anaphase . We have observed that Mob1 was not dephosphorylated upon Zds1-induced-PP2ACdc55 inactivation despite Cdc14 being released from the nucleolus . Therefore , the Cdk1–Clb2 phosphorylation event towards Mob1 predominates over the action of the phosphatases Cdc14 and PP2ACdc55 . To test this hypothesis , we inactivated PP2ACdc55 by Zds1 induction to promote Cdc14 release , and inhibited Cdk1 by adding 1NM-PP1 in cells bearing the ATP analog-sensitive Cdk allele , cdc28-as1 . We arrested cells in metaphase by Cdc20 depletion and induced Zds1 by galactose addition . After 180 min of Zds1 induction , when 80% of cells had released Cdc14 from the nucleolus ( indicative of PP2ACdc55 inactivation ) , the drug 1NM-PP1 was added to inhibit Cdk1 . Remarkably , upon Cdk1 inactivation Mob1 was rapidly dephosphorylated ( Figure 7B ) . Mob1 phosphorylation status was unchanged in the control cells without the drug . We can conclude that cells can prematurely activate Dbf2–Mob1 after removal of the Cdk1–Clb2 inhibitory signal . This result demonstrates that Cdc14 release from the nucleolus is not enough to promote MEN activation , and additional mechanisms like Cdk1–Clb2 and Bub2–Bfa1 inhibitory signals ensure proper progression through mitosis .
The means by which chromosome segregation is coordinated with sequential Cdk1 inactivation steps during mitosis is a subject of great interest . It is known that separase activation triggers both chromosome segregation and FEAR-Cdc14 release . However , we do not fully understand the separate and specific regulation of the FEAR and MEN components or how these pathways are coordinated during anaphase . Downregulation of PP2ACdc55 phosphatase at anaphase onset facilitates Cdk1-dependent Net1 phosphorylation , which provides the first wave of Cdc14 release [9] . PP2ACdc55 phosphatase downregulation could be involved in other processes , such as facilitating MEN activation in anaphase . In the present study , we determined that PP2ACdc55 regulates Bfa1 phosphorylation: Bfa1 is hyperphosphorylated in cdc55Δ cells , upon inhibition of PP2ACdc55 by Zds1 overexpression or by inactivation of the catalytic PP2A subunits; and overexpression of Cdc55 avoids Bfa1 phosphorylation in G2/M . Moreover , Bfa1 and Cdc55 physically interact , suggesting that Bfa1 is likely to be an in vivo substrate of PP2ACdc55 . In this way , PP2ACdc55 downregulation by separase would unlock mitotic exit , initiating FEAR-Cdc14 release and the MEN pathway . PP2ACdc55 seems to be specific to Bfa1 , since Bfa1 phosphorylation is not affected in rts1Δ deletion mutants [41] . Moreover , upon Zds1 pull-down and mass-spectrometry analysis Cdc55 was identified but Rts1 was not detected [54] , and Bfa1 is properly phosphorylated upon Zds1 overexpression in an rts1Δ mutant ( data not shown ) . Therefore , a contribution of Zds1 to the PP2ARts1 complex is not expected . In the normal cell cycle , Bfa1 inactivation depends on Cdc5 phosphorylation . Although Bfa1 may be a substrate for other kinases that have not yet been described , our results indicate that PP2ACdc55 counteracts Bfa1 Cdc5-dependent phosphorylation . Previous results have already shown that PP2ACdc55 can counteract Cdc5-dependent phosphorylation [55] . Therefore , PP2ACdc55 is not only a Cdk1–Clb2-counteracting phosphatase , but is also able to dephosphorylate Cdc5 targets . Our results indicate that Mob1 phosphorylation is also regulated by the phosphatase PP2ACdc55 . Mob1 is phosphorylated [29] , [56] and inhibited by Cdk1 [29] , and is dephosphorylated by Cdc14 [29] and PP2ACdc55 ( our results ) . Progressive quantitative changes of the Cdc14/Cdk activity ratio during the course of mitotic exit cause dephosphorylation of individual substrates at distinct thresholds [42] . Several observations suggest that Mob1 might be a late Cdc14 substrate that maintains its Cdk1-phosphorylation until late anaphase: ( a ) Dbf2–Mob1 dephosphorylation occurs in mid/late anaphase ( [52] and Figure 6B ) ; ( b ) Mob1 is phosphorylated even upon PP2ACdc55 inactivation in which Cdc14 has already been released from the nucleolus ( Figure 5A ) ; and ( c ) during anaphase , PP2ACdc55 phosphatase activity is reduced [9] helping to keep Mob1 phosphorylated . Later , upon Cdk1 inhibition Cdc14 , and probably also PP2ACdc55 , will dephosphorylate Mob1 , thereby allowing full MEN activation . Premature Bfa1 phosphorylation observed after PP2ACdc55 inactivation does not entail premature MEN activation and mitotic exit . Cdc15 dephosphorylation patterns have often been used as a marker of MEN activity . However , we observed that Cdc15 is prematurely dephosphorylated and loaded onto the dSPB in cdc55Δ mutant cells and after Zds1-induced PP2ACdc55 inactivation . This is consistent with Cdc14 being released from the nucleolus prematurely during metaphase in cdc55Δ mutant cells . Despite Cdc15 being properly recruited to the dSPB , our results suggest that Cdc15 is not totally active as a kinase , since Dbf2-Mob1 is not recruited to the SPBs after PP2ACdc55 inactivation . In accordance with this , Cdc15 dephosphorylation never reaches the levels of wild-type cells in anaphase indicating that it is not fully active . In addition , Mob1 is not dephosphorylated upon PP2ACdc55 inactivation by Zds1 induction ( despite Cdc14 being released ) , further confirming that Dbf2-Mob1 is not active in this condition . The study of late anaphase events confirmed the previous results . Cdh1 , which is a late Cdc14 substrate , is not properly dephosphorylated , resulting in a defective APCCdh1 complex . Thus , APCCdh1 substrates such as Cdc5 Polo kinase and Clb2 are not efficiently degraded . These results led us to propose that there is an inhibitory input into the MEN cascade downstream of Bfa1 that acts as a break when Bfa1 is prematurely phosphorylated , thereby avoiding the untimely full activation of MEN . In fact , Cdk1-dependent Cdc15 inhibition has been postulated before [28] , and previous publications by our group have presented a mathematical model to describe the negative regulation of Cdc15 by Cdk–Clb2 [9] , [57] , [58] . The mutual regulation of Cdk–Clb2 and Cdc15 has been described more recently [29] . These authors also demonstrated that Cdk1 phosphorylates the Mob1 protein to inhibit the activity of Dbf2–Mob1 kinase . Finally , there is genetic evidence of a concerted action by Bfa1 and Clb2 when Tem1 is accumulated in the SPBs , [39] . We found that Cdk1-eGFP was prematurely located on the mSPB in metaphase cdc55Δ cells , as would be expected if MEN activity were inhibited by Cdk1–Clb2 . Moreover , Mob1 was rapidly dephosphorylated upon Cdk1 inactivation after Zds1-dependent Cdc14 release . The above results led us to postulate that alleviation of the Bub2–Bfa1 inhibitory MEN signal is not enough to produce the premature exit from mitosis observed in cdc55Δ cells , and that the Cdk1–Clb2 inhibitory MEN signal is the additional mechanism that restrains MEN activity until anaphase . In this way , cells would initiate FEAR and MEN at anaphase onset upon PP2ACdc55 downregulation , but Cdk1–Clb2 would impose a break for MEN activation , and decreasing Cdk1 activity would be the mechanism that sequentially activates both pathways . APCCdh1 and Bub2–Bfa1 are both required to resequester Cdc14 into the nucleolus after mitotic exit [36] , [52] , [59] . APCCdh1-dependent Cdc5 degradation is important to return Cdc14 to the nucleolus at the correct time , but it is not essential , since Cdc14 is resequestered into the nucleolus , albeit with a delay in cells lacking CDH1 [59] . Moreover , cells lacking both BUB2 and CDH1 eventually resequester Cdc14 into the nucleolus , suggesting that additional mechanisms regulate Cdc14 resequestration . PP2ACdc55 reactivation during late anaphase could be one of these additional mechanisms involved in the resequestration of Cdc14 into the nucleolus . We have demonstrated here that Cdh1 is not fully active since Cdc5 and Clb2 degradation are impaired in cdc55Δ mutant cells . Net1 dephosphorylation is required to reassociate to Cdc14 and resequester it into the nucleolus; Bfa1 dephosphorylation is needed to inactivate MEN and helps to return Cdc14 to the nucleolus . In cells lacking CDC55 , Net1 is hyperphosphorylated for longer [9] , suggesting that PP2ACdc55-dependent dephosphorylation in late anaphase is important for properly resequestering Cdc14 into the nucleolus . Nevertheless , the cdc55Δ mutant cells exit from mitosis , despite their slower kinetics in resequestering Cdc14 to the nucleolus . Sic1 is accumulated at the correct time in the cells lacking CDC55 and its level remains constant for longer periods . Sic1 accumulation is probably sufficient to inhibit Cdk1 and to exit from mitosis in the absence of Cdc55 . This is consistent with GAL-SIC1-db being sufficient to drive exit from mitosis in MEN mutants [53] , [60] . Our model , based on the current findings and those of previous published work , is summarized in Figure 8 . In early anaphase , FEAR-induced inactivation of the PP2ACdc55 promotes the first wave of Cdc14 release from the nucleolus and contributes to the accumulation of the Cdc5-phosphorylated form of Bfa1 . In this way , FEAR promotes the activation of two important MEN components: ( 1 ) PP2ACdc55 downregulation facilitates Bfa1 inactivation , allowing Tem1 activation; and ( 2 ) the FEAR-induced Cdc14 released dephosphorylates Cdc15 . Bfa1 asymmetric localization on the dSPB is induced upon Bfa1 phosphorylation and is important for the proper recruitment of MEN components to the dSPB . In addition , Tem1 binds to the mSPB and recruits Cdc15 . Cdc15 itself recruits Cdk1 and Dbf2-Mob1 to the mSPB . In turn , Cdk1 phosphorylates Cdc15 , which is dissociated from the mSPB and becomes asymmetrically located on the dSPB . At the same time , Cdk1 phosphorylates and inhibits Dbf2–Mob1 . Our results indicate that Mob1 phosphorylation is also regulated by the phosphatase PP2ACdc55 , helping to keep Dbf2–Mob1 inactive in early anaphase . In this way , Mob1 phosphorylation is constant despite the progressive decrease in Cdk1 activity because the counteracting phosphatase PP2ACdc55 is also downregulated . In late anaphase , the increase in Cdc14 activity and the decrease in Cdk1 activity alleviate the Cdk1 inhibitory signal towards Dbf2–Mob1 , and the MEN is fully active . Upon its reactivation , PP2ACdc55 will also help keep Mob1 dephosphorylated . Exit from mitosis is regulated by a conserved signalling pathway in Schizosaccharomyces pombe called the septation initiation network ( SIN ) ( reviewed in [61] , [62] ) . Physical interaction between fission yeast Pab1 ( B-regulatory subunit of PP2A in S . pombe ) and Sid2 ( Mob1 orthologue ) has recently been described [63] . PP2A-Pab1 regulates SIN activity at different levels , suggesting a conserved function for PP2ACdc55 in regulating the MEN and SIN pathways . Although the SIN is analogous to the MEN in budding yeast , its main role is to coordinate cytokinesis . However , in S . cerevisiae , the MEN also goes beyond mitotic exit and regulates cytokinesis . Several MEN components localized to the bud neck and were found to be required for the contraction of the actin-myosin ring [17] , [52] , [53] , [64] , [65] . More recent evidence suggests that Dbf2–Mob1 regulates the cytokinetic components Chs2 , Hof1 and Inn [66]–[68] . The core signalling elements of the MEN/SIN pathway are composed of members that are highly conserved in a range of species from yeast to humans ( reviewed in [69] ) . The MEN , SIN and Hippo pathways in Drosophila and vertebrates share elements of the Cdc15-like kinases ( Sid1 in S . pombe and MTS1/2 in mammals ) and Dbf2-Mob1-like kinases ( Sid2–Mob1 in S . pombe and LATS1/2-MOB1 in mammals ) and the Cdc14 protein phosphatase family ( Clip in S . pombe and Cdc14 in mammals ) . Some studies suggest that the Hippo pathway plays a role in regulating mitotic exit and cytokinesis , although the mechanism is not yet fully understood . Our studies of MEN regulation will contribute to our understanding of MEN-related pathways in other organisms . It will be interesting to discover whether MEN regulation by PP2ACdc55 is conserved among higher eukaryotes .
All yeast strains used in this study were derivatives of W303 . Epitope tagging of endogenous genes and gene deletions were performed by gene targeting using polymerase chain reaction ( PCR ) products . Endogenous CDC55 was N-terminal-tagged as previously described [9] . CDC28Y19F mutant strains were created by integration and loop-out , via 5-FOA ( 5-fluoroorotic acid ) selections . Zds1 in cells that had been arrested in metaphase by Cdc20 depletion was ectopically expressed as previously described [10] . Cell synchronization using α-factor and metaphase arrest by Cdc20 depletion and entry into synchronous anaphase by Cdc20 reinduction were also performed as previously described [70] . For the Zds1-induction experiments cells were fixed in absolute ethanol throughout the experiment and rehydrated in minimum media before visualization . At least 50 cells were used to quantify the SPB ratios . For time-lapse microscopy , cells were incubated in minimum media throughout the experiment in an environmental chamber , and images were acquired every 5 minutes . A Zeiss Axio Observer Z1 inverted epifluorescence microscope with Apotome system equipped with an HXP 120C fluorescent lamp and a Carl Zeiss Plan-Apochromat 63× N . A 1 . 40 oil objective . Filters used were EGFP ( EX BP 470/40 , BS FT 495 , EM BP 525/50 ) and Cy3 ( EX BP 550/25 , BS FT 570 , EM BP 605/70 ) . Z-stacks at 1-µm intervals were taken for each fluorescent channel and projected onto a single image per channel . SPB ratios were quantified as the signal intensity ratio between the two SPBs ( dSPB relative intensity/mSPB relative intensity ) . SPB ratios less than or equal to 2 or greater than 2 were considered to represent symmetric or asymmetric localization , respectively . We used ZEN software for image acquisition and quantitative analysis of fluorescent microscopy ( signal intensity and spindle length ) . Stable isotopes of yeast cells were labeled and protein extracts prepared as previously described [71] . Cells were grown in minimum media containing either 100 mg/L arginine and 100 mg/L lysine or 100 mg/L 13C6-arginine and 100 mg/L 13C6-lysine ( Cambridge Isotope Laboratories Inc . ) . Protein extracts were prepared by mechanical lysis using glass beads . Approximately 250 µg of the mixed heavy/light protein sample were processed for in-solution digestion as previously described [72] . Phosphopeptides were enriched by sequential elution from IMAC ( SIMAC ) as previously described [71] . Peptides were analyzed by LC-MS/MS using an LTQ-Orbitrap Velos mass spectrometer ( Thermo Scientific ) . Peptides were identified using MASCOT software and SILAC quantification was done with ProteomeDiscoverer 1 . 3 Software ( Thermo Scientific ) . The immunoprecipitation assay was performed as described [10] , but using 2×109 cells for Mob1 and 3 . 2×1010 cells for Bfa1 . Protein extracts were prepared by mechanical lysis using glass beads . The clarified extracts were incubated with antibody , and the immunocomplexes were adsorbed onto magnetic protein-A Dynabeads® ( Life Technologies ) . The beads were washed in extraction buffer and the protein-bound fraction eluted with SDS-PAGE loading buffer . The antibody used for immunoprecipitation was the α-Pk clone SV5-Pk1 ( Serotec ) . For the phosphatase assays , Pk epitope-tagged Cdc14 was immunopurified on protein-A Dynabeads as above , and the beads were washed with phosphatase buffer ( 50 mM Tris-HCl pH 7 . 0 , 1% BSA , 150 mM NaCl , 2 mM MnCl2 ) . Reaction mix ( phosphatase buffer containing 34 µM of DiFMUP ) was added to the beads and incubated at 30°C for 2 min . Reactions were terminated by adding 200 mM sodium carbonate and the fluorescence of the samples was determined at 365 nm ( absorption wavelength ) and 455 nm ( emission wavelength ) . Cdc14 recovered on the beads was quantified by western blot using an IRdye 800 coupled secondary antibody and the Odyssey Infrared Imaging System ( Li-COR Biosciences ) . Phosphatase-specific activity was calculated as fluorescence units/protein levels and represented as arbitrary units . Protein extracts for western blots were obtained by NaOH or TCA protein extraction . Bfa1 and Cdc15 western blots where done using 10% protein gel at a 33 . 5∶0 . 3 acrylamide/bisacrylamide ratio . Antibodies used for western blots and immunofluorescence were the α-HA clone 12CA5 ( Roche ) , α-HA clone 16B12 ( Babco ) , Cdc14 ( yE-17 ) sc-12045 ( Santa Cruz Biotechnology ) , α-FLAG clone M2 ( Sigma ) , α-Pk clone SV5-Pk1 ( Serotec ) , Cdc5 ( yC-19 ) sc-6733 ( Santa Cruz Biotechnology ) , Sic1 ( FL-284 ) sc-50441 ( Santa Cruz Biotechnology ) , Clb2 ( y-180 ) sc-907 ( Santa Cruz Biotechnology ) , anti-tubulin clone YOL1/34 ( Serotec ) and anti-phosphoglycerate kinase ( Life Technologies ) . The secondary antibodies were Cy3-labeled α-mouse ( GE Healthcare ) , fluorescein-conjugated α-rat ( Millipore ) and Cy3-labeled α-goat ( Jackson ImmunoResearch ) . | Cell cycle studies over the years have tried to elucidate the molecular mechanisms behind cell division , one of the most highly regulated of all cell processes , which ensures life in all organisms . Protein phosphorylation emerged as a key regulatory mechanism in the cell cycle . The highly conserved family of cyclin-dependent kinases , the Cdks , are considered the main component of the cell cycle control system . However , it has become clear that opposing phosphatases also play a key role in determining the phosphorylation state of the proteins . Cells enter mitosis when mitotic Cdk activity increases , having its pick of activity during metaphase . To exit mitosis , cells must coordinate chromosome segregation with Cdk inactivation processes involving the activation of protein phosphatases . Here we show that the phosphatase PP2A regulates the mitotic exit network ( MEN ) by counteracting the phosphorylation of Bfa1 and Mob1 . Our findings provide new insights into the mechanism by which PP2A-Cdc55 functions affect the regulation of various MEN components that contribute to mitotic exit . The core signalling elements of the MEN , SIN and Hippo pathways are highly conserved . Therefore , studies of MEN regulation will contribute to our understanding of MEN-related pathways in other organisms . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Dual Regulation of the Mitotic Exit Network (MEN) by PP2A-Cdc55 Phosphatase |
Aging and longevity are complex traits influenced by genetic and environmental factors . To identify quantitative trait loci ( QTLs ) that control replicative lifespan , we employed an outbred Saccharomyces cerevisiae model , generated by crossing a vineyard and a laboratory strain . The predominant QTL mapped to the rDNA , with the vineyard rDNA conferring a lifespan increase of 41% . The lifespan extension was independent of Sir2 and Fob1 , but depended on a polymorphism in the rDNA origin of replication from the vineyard strain that reduced origin activation relative to the laboratory origin . Strains carrying vineyard rDNA origins have increased capacity for replication initiation at weak plasmid and genomic origins , suggesting that inability to complete genome replication presents a major impediment to replicative lifespan . Calorie restriction , a conserved mediator of lifespan extension that is also independent of Sir2 and Fob1 , reduces rDNA origin firing in both laboratory and vineyard rDNA . Our results are consistent with the possibility that calorie restriction , similarly to the vineyard rDNA polymorphism , modulates replicative lifespan through control of rDNA origin activation , which in turn affects genome replication dynamics .
The budding yeast Saccharomyces cerevisiae has become a favorite model for studying the genetic and molecular basis for variation in lifespan [1] . In particular , the unequal division of mother and daughter cells in this species makes it especially amenable for analysis of replicative lifespan ( RLS ) , the number of “daughter” cells that a “mother” cell can produce before it senesces , which is typically 20–30 [2] . RLS is thought to be analogous to aging of mitotic cells such as stem cells and epithelia . Genetic screens for RLS alteration have identified many genes whose deletion confers lifespan extension , such as those in growth and metabolism pathways , and even more genes whose absence reduces yeast longevity [3] . The discovery that long-lived mutants redirect the SIR ( Silent Information Regulator ) complex , including the NAD-dependent histone deacetylase Sir2 , to the nucleolus first implicated events at the rDNA as potential determinant of aging [4] . The identification of the rDNA binding protein Fob1 as a regulator of yeast lifespan further supported rDNA's involvement in aging [5] . The rDNA locus is a highly repetitive region of the genome , over 1MB in size , consisting of approximately 150 tandem repeats of a 9 . 1 kb sequence that encode the 35S transcription unit ( 18S , 5 . 8S , 25S RNAs transcribed by RNA polymerase I ) and the 5S gene ( transcribed by RNA polymerase III ) . More than 60% of all transcription in a yeast cell is dedicated to the production of these rRNAs [6] , which can be accomplished only through the concurrent transcription from multiple rDNA repeats [7] . Each repeat also contains non-transcribed regions with features that allow it to maintain its chromosome-like size , such as origins of replication . An RNA polymerase II promoter ( E-pro ) is also found in one of the ‘non-transcribed” regions . It is this promoter whose activity is suppressed by Sir2 . In the absence of Sir2 , RNA polymerase II transcription disrupts rDNA-bound cohesin [8] leading to an increase in intra-chromatid mitotic recombination and the formation of extrachromosomal rDNA circles ( ERCs ) [9] . ERCs have been shown to preferentially segregate to mother cells and their accumulation in old mother cells has been proposed as a cause of aging in yeast , perhaps through sequestration of as yet unknown detrimental factors [10] . An attractive feature of the ERC model is that it could explain how daughters can be rejuvenated , i . e . how daughters can have the same RLS regardless of whether they came from old or young mothers . Accumulation of ERCs in old mothers has been proposed to be responsible for the decreased lifespan seen in sir2Δ mutants [11] and , conversely , the increased lifespan of strains that contain an extra genomic copy of SIR2 . The Fob1 protein binds to the rDNA replication fork barrier ( RFB ) , creating a unidirectional replication block thought to prevent the head-on collision between the rRNA Polymerase I transcription machinery and the DNA replication machinery [12] , [13] . However , Fob1 binding also promotes rDNA recombination by increasing the probability of double strand breaks and hence promoting ERC formation . As expected for a gene whose deletion decreases ERC accumulation , fob1Δ mutants have increased RLS . Calorie restriction is the best-studied intervention that promotes longevity [14] . Restriction of dietary intake to 70% ad libitum was first reported to extend the lifespans of mice and rats [15] , [16] , [17] . Since then , calorie restriction-mediated lifespan extension has also been documented in yeast , worms , flies , and several other organisms [14] , [18] , [19] . Significant progress has been achieved in dissecting the nutrient responsive signaling pathways responsible for this lifespan extension , some of which , like the TOR pathway , are shared between species as distant as mice and yeast . The downstream effectors of these signaling pathways , on the other hand , are unknown . It is known , however , that Sir2 and Fob1 act in a pathway that is genetically distinct from calorie restriction and that calorie restriction is able to extend the lifespan of sir2Δ fob1Δ double mutants [20] , [21] . The mechanism through which calorie extension extends lifespan is therefore not via direct regulation of Sir2 or Fob1 and remains mysterious . To begin to address these gaps in our knowledge we used an outbred yeast model , consisting of strains generated in a cross between the S288c laboratory strain BY4716 ( BY ) and the vineyard strain RM11-1a ( RM ) , to identify naturally occurring polymorphisms that regulate RLS and found that the rDNA locus is the major regulator of lifespan in this cross . Among the rDNA sequences that differ between RM and BY , we identified a polymorphism in the RM rDNA locus that leads to a marked reduction in rDNA origin activity both in a plasmid assay and at its native location in the genome . The less active RM rDNA origin confers a reduced size of the rDNA array and extends lifespan . Critically , this lifespan extension is independent of both Sir2 and Fob1 but the activity of both of these rDNA origins is strikingly affected by calorie restriction , thus suggesting a replication-based mechanism for calorie restriction-mediated extension of RLS .
We measured the RLS of 20 individual cells from each of 88 meiotic segregants previously derived from a cross between a laboratory yeast strain BY4716 ( BY parent ) and a vineyard yeast strain RM11-1a ( RM parent ) [22] . We found continuous variation in lifespan between the segregants , with mean RLS ranging from 12 to more than 40 generations ( Figure 1A ) , suggesting that multiple loci are involved in controlling longevity . This wide variation of RLS among the segregants is derived from parents with similar lifespans ( 26 . 5 and 28 . 6 generations for BY and RM respectively; Figure 1A ) . Such transgressive segregation , in which the progeny have more extreme phenotypes than the parents , suggests the presence of multiple loci that have compensatory effects in the parental strains . To determine the extent to which the observed lifespan variation was genetically determined , we repeated lifespan analysis on 40 of the segregant strains ( data not shown ) . Heritability was 82% , indicating that a large fraction of lifespan variation in this cross is determined genetically . Genome-wide linkage analysis revealed strong linkage to a locus on chromosome XII ( LOD score of 9; Figure 1B , Table S2 ) . Further investigation refined this linkage to the 1 . 2 Mbp region corresponding to the rDNA locus . Only one crossover event between the markers flanking the rDNA region ( potentially a mitotic crossover in the diploid ) was found within segregants , consistent with the previous observation that rDNA is inherited as a single Mendelian locus [23] . We determined that 46% of heritable lifespan variation ( 38% of total variation ) between the segregants is controlled by the rDNA locus , indicating that rDNA is the major regulator of lifespan in this cross . The segregants that inherited the rDNA locus from the BY parent had an average RLS of 23 . 5±4 . 6 ( Figure 1B inset ) , comparable to lifespans previously seen from laboratory strains such as s288c and W303 . In contrast , segregants that inherited this locus from the RM parent had an average RLS of 33 . 1±7 . 0 cell divisions ( Figure 1B inset ) , a 41% increase in longevity ( P = 9 . 5×10−12 , ν = 88 ) . To isolate the effects of the rDNA locus from other genomic polymorphisms , we carried out eight sequential backcrosses to the BY laboratory strain while maintaining the RM vineyard rDNA . This backcrossed strain had a mean lifespan of 38 . 6 , a 45% increase compared to the mean of 26 . 6 for the BY parental strain ( P = 10−3 , ν = 2 , Figure 1C ) . Conversely , we used eight backcrosses to transfer the laboratory rDNA into the RM background . The RLS of the RM parental strain at 28 . 8 was 23% longer than to the RLS of 23 . 4 in the outcrossed RM strain containing the laboratory rDNA , ( P = 5 . 0×10−3 , ν = 2 , Figure 1D ) . Thus , consistent with mapping results , the backcross analysis in both parental backgrounds confirmed that the rDNA has a dramatic effect on lifespan . To investigate whether the lifespan effect of the rDNA is mediated by Sir2 , we deleted SIR2 in 28 randomly selected segregant strains and measured their replicative lifespans . While the average lifespan of all strains dramatically decreased upon SIR2 deletion , sir2Δ segregant strains with RM rDNA had significantly longer RLS ( 17 . 0±2 . 1 ) compared to their BY rDNA counterparts ( 10 . 8±1 . 7 , P = 6 . 1×10−9 , ν = 2 ) ( Figure 2A ) . Sir2 function is therefore not required for RM rDNA-induced increase in RLS . The Sir2-independence of the rDNA's effect on lifespan was recapitulated in our backcrossed strains: BY sir2Δ strains with RM rDNA fared better than the parental strain , with lifespan means of 18 . 0 and 12 . 0 , respectively ( P = 1 . 7×10−3 , ν = 2 , Figure 2B ) . The RM sir2Δ strain also lived longer ( 17 . 0 ) than the BY rDNA sir2Δ strain ( 10 . 1 , Figure 2C , P = 9 . 0×10−4 , ν = 2 ) . Additionally , we generated segregant strains in which an extra copy of SIR2 was inserted in the genome and found that the longevity benefit of the RM rDNA locus was still maintained in the presence of SIR2 overexpression ( 35 . 4±3 . 4 vs . 28 . 1±3 . 0 divisions , Figure 2D , P = 2 . 0×10−2 , ν = 9 ) . These results demonstrate that the effect of the rDNA locus on lifespan extension is independent of Sir2 . To determine whether Fob1 is required for the rDNA's effect on lifespan , we deleted FOB1 both in the parental strains and in the backcrossed strains . In the BY background , FOB1 deletion led to increased lifespan in both the strain with BY rDNA ( 38 . 9 ) and the strain with RM rDNA ( 44 . 3 , P = 6 . 7×10−3 , ν = 2 , Figure 2E ) . Since the RM rDNA locus still conferred lifespan extension in the absence of Fob1 , we conclude that the longevity benefit of the RM rDNA is not dependent on the presence of Fob1 . Unexpectedly , in the RM background , loss of Fob1 reduced lifespan by half , an effect that occurred in both the RM parental strain ( 17 . 2 ) and the strain with BY rDNA ( 10 . 3 , Figure 2F ) . Thus it appears that in the RM strain , unlike most of the laboratory strains , FOB1 deletion decreases lifespan through unknown mechanisms . Nonetheless , the longevity benefit of the RM rDNA locus persisted ( P = 10−4 , ν = 2 ) . Our results thus demonstrate that the effect of the rDNA on life span is independent of Fob1 . Intrachromosomal recombination between the tandem rDNA repeats generates extrachromosomal rDNA circles ( ERCs ) , whose preferential accumulation in mother cells has been proposed as one of the mechanisms that limits replicative potential [10] , [24] . To determine if vineyard and laboratory rDNA loci differ in their ability to generate and/or maintain ERCs , we measured ERC levels in young logarithmically growing cells and in a purified population of old mother cells of both parental strains and their respective backcrossed rDNA replacement strains ( Figure 3A ) . As expected , we observed an increase in ERCs in old cells compared to young cells in both parental backgrounds . In the BY background , old cells with RM rDNA had 33% fewer ERCs compared to cells with the parental BY rDNA . However , in the RM background , the amount of circles was comparable between the strains with RM rDNA and the strain with BY rDNA , demonstrating that the longevity benefit of the RM rDNA cannot be attributed to different propensities of the two rDNAs to accumulate circles in old mothers . Together , these results suggest that the effect of the rDNA on lifespan occurs though a mechanism that is independent of Sir2 , Fob1 , and ERC accumulation . To determine if BY and RM rDNA differ in their capacity to transcribe rRNA and generate ribosomes , and thus to affect global protein translation and growth , we measured doubling time and cell size in logarithmically growing BY strains with RM and BY rDNA . We found that both doubling time and cell size are essentially indistinguishable in the two strains ( Figure S1 ) , indicating that the capacity for new biomass generation , the best measure of global protein synthesis [6] , [25] , is equal for the two rDNA arrays , from which we infer that ribosome biogenesis and rDNA transcription are also equal . To determine if the number of the rDNA repeats differed between the two parental strains , we performed quantitative Southern blot analysis ( Figure 3B ) . The number of rDNA repeats in the vineyard strain was approximately 60% of that in the laboratory strain ( ∼90 vs . ∼150 copies [26] , respectively ) . We confirmed that RM rDNA is shorter than BY rDNA using contour-clamped homogeneous electric field ( CHEF ) gel electrophoresis . Remarkably , the reduced size of the vineyard rDNA locus , estimated from the size of chromosome XII by CHEF gel electrophoresis ( Figure 3C ) , was preserved after 10 successive backcrosses to the laboratory strain ( ∼500 population doublings ) . Conversely , the back crossed strain with the BY rDNA maintained its larger rDNA ( data not shown ) . The copy number of the rDNA array is therefore determined by cis-acting sequences within the repeats themselves . Since our results suggest that the rDNA sequence itself is important for determining longevity as well as rDNA copy number , we compared the sequences of RM and BY rDNAs . We found no sequence polymorphisms in the 37S and 5S regions encoding rRNA transcripts , consistent with the high degree of conservation of these regions . However , we found that the non-transcribed spacer ( NTS ) regions are highly divergent between the two strains ( Figure 4A ) . Within the NTS are several elements that have been found to play an important role in the maintenance of the rDNA locus: NTS2 contains the rDNA origin of replication ( rARS or rDNA autonomously replicating sequence ) [27] and the cohesin associating region ( CAR ) [28]; NTS1 contains a binding site for Fob1 that creates a unidirectional replication fork barrier ( RFB ) [12] and the E-pro RNA Polymerase II promoter which is silenced by Sir2 [8] . We identified sequence changes in the rDNA origin of replication ( rARS ) and in the replication fork barrier ( RFB ) . We focused our attention on these two polymorphisms because they alter known functional elements . Using high-throughput sequencing , we found that the identified RFB and rARS variants are homogenous within a strain , with at most one RM repeat in the BY rDNA and vice versa . The homogeneity within the rDNA array allows us to investigate the effects of an rDNA array as a single sequence rather than a complex mixture of heterogeneous repeats . To assess how the rARS and RFB polymorphisms affect lifespan , we examined rDNA sequences from a diverse collection of wild yeast . The majority of the 37 S . cerevisiae isolates sequenced by the Saccharomyces Genome Resequencing Project ( SGRP ) [29] , [30] possessed the RM rDNA sequence , with only a handful having those polymorphisms seen in the BY strain . Fortuitously , we found several strains that have “hybrid rDNA” sequences: strains whose rDNA contains the BY rARS and the RM RFB or vice versa . To distinguish the effects of the rARS alterations from those at the RFB , we generated outcrossed strains in the BY background with rDNA from three S . cerevisiae isolates with “hybrid rDNA” sequences . The rDNA sequences from the strains DBVPG6765 and L_1374 have the RM ARS and the BY RFB . Backcrossed strains with either of these rDNA sequences had significant lifespan extension ( P = 10−4 , ν = 2 ) on par with that of RM rDNA ( Figure 4B ) , suggesting that the RM rARS polymorphism rather than the RM RFB polymorphism confers increased lifespan . In contrast , when we examined rDNA from the sake strain Y12 , which has the BY rARS and the RM RFB , we found no extension of lifespan ( P = 0 . 1404 , ν = 2 ) . Therefore , we conclude that the RM rARS polymorphism , not the RM RFB polymorphism , is responsible for conferring lifespan extension . Since the rate of rDNA amplification has been found to correlate with rDNA origin activity [31] , we suspected that the reduced array size of the RM rDNA compared to the BY rDNA could be a consequence of reduced origin activity conferred by the RM rARS polymorphism . The RM rDNA origin of replication has a cytosine polymorphism at a highly conserved thymine residue in the A/T-rich rDNA ACS1 sequence ( Figure 5A ) , suggesting that it may impair origin function and DNA replication [27] , [32] , [33] . To compare origin activity of the different rDNA sequences , we cloned the rARS sequences from RM and BY rDNA into an origin-free vector containing a KanMX marker and tested the ability of these sequences to promote autonomous plasmid maintenance upon transformation into a BY host strain . We find high-frequency transformation with the BY rARS confirming robust ARS function; the RM rARS transforms at a greatly reduced frequency and the colonies that arise are variable in size ( Figure 5B ) . Use of the entire 2 . 2 kb rDNA NTSs likewise resulted in greatly disparate ARS activity , eliminating the possibility that different sites within the NTS were serving as origins in the two rDNAs . An origin-free plasmid ( no ARS ) and a plasmid with a highly active yeast origin ( ARS1 ) served as negative and positive controls , respectively . Thus the RM rARS , which is the most common among wild S . cerevisiae , and which confers lifespan extension , is much weaker than the BY ARS . The extreme differences in ARS activity from our plasmid maintenance assay suggest that the replication of the RM rDNA locus could be very different from that of BY . Since the size of the rDNA has been previously shown to affect origin activity [34] , presumably by affecting the fraction of actively transcribed repeats , we deemed it important to compare origin activity between strains containing BY and RM rDNA that have the same number of repeats , and therefore , the same number of actively transcribed repeats . As French et al . [7] observed that in strains with fewer than 30 repeats all of the rDNA subunits were actively transcribed , we used a method described previously by Kobayashi et al . [35] to generated BY strains containing 15 copies of either the RM or the BY rDNA ( Figure 5C ) . To examine origin activity at the endogenous rDNA locus , we used 2D gel electrophoresis to compare the fraction of repeats with an active origin ( bubble-shaped intermediates ) to the fraction of repeats that are passively-replicated ( Y-shaped intermediates ) ( Figure 5D ) . The relative origin activity can then be estimated as the ratio of bubble arc intensity to that of the Y arc . We find that the origin activity of the RM rDNA origin is strikingly reduced ( ∼3-fold ) compared to that of the BY rDNA origin ( Figure 5E ) , consistent with our results from plasmid transformation assays . In the course of our studies of RM rDNA origin usage , we noticed that while the RM rARS performed poorly in the plasmid maintenance assay described above when the BY parent was used as a host , the RM rARS plasmid performed well when the RM parent was used as a host ( Figure S2 ) . We then surmised that there is a host factor that is critical for determining the efficiency of a plasmid DNA replication origin . Further investigation revealed that the host factor responsible was the endogenous rDNA array: the weak RM rDNA rARS performed poorly in the plasmid transformation assay when the backcrossed RM strain with the BY rDNA array was used ( Figure S2 ) but performed well when the backcrossed BY strain with the RM rDNA was used ( Figure 6A ) . To determine whether this effect on replication , like the effect on longevity , is specific to the replication origin polymorphism in the host rDNA array , we performed plasmid transformation assays in our backcrossed strains with “hybrid rDNA” sequences containing either the RM rARS polymorphism with the BY RFB polymorphism or vice versa . The backcrossed BY strain with rDNA from DBVPG6765 , which contains the RM replication origin and the BY RFB , were transformed at high efficiency by plasmids with RM NTS and RM rARS sequences ( Figure S2 ) . In contrast , a backcrossed BY strain with Y12 rDNA , which contains the BY replication origin and the RM RFB , performed poorly in this assay . To address the possibility that our observations were an artifact of the plasmid containing an rDNA sequence , which could possibly direct them to the nucleolus where they would be affected by the same replication forces governing the endogenous rDNA , we tested a plasmid containing a weak non-rDNA origin sequence from Lachancea waltii which previously has been shown to have ARS function in S . cerevisiae [36] . Consistent with our results with rDNA origins , the L . waltii plasmid was more effective in transforming the BY strain that contained the RM rDNA than the complementary strain , demonstrating that these plasmid transformation effects are not specific to plasmids containing rDNA sequences ( Figure 6A ) . We conclude that the replication origins in the rDNA array exert a powerful influence on the ability of weak replication origins , whether from the rDNA or elsewhere , to support plasmid replication . Why would having an impaired rARS in the rDNA array lead to greater plasmid origin activity and extended lifespan ? We hypothesize that having fewer active rDNA origins would reduce competition for replication factors and therefore increase the potential for replication of plasmids with weak origins ( Figure 6B ) . It is important to note that there are ∼300 origins of replication in the yeast genome outside of the rDNA and , in strains with the BY rDNA locus , and an additional 150 rDNA origins . Origins in the rDNA locus can thus comprise one third of the total genomic origins . If the RM rDNA sequence promotes usage of weak origins on a plasmid through increased availability of replication factors , we surmised that the presence of the RM rDNA might also promote origin activity across the genome . We used 2D gel electrophoresis to examine origin activity at two strong origins ( ARS1 and ARS1413; origin efficiencies of 94% ( B . B . , unpublished data ) and 85% [37] , respectively ) and one weak origin ( ARS605; origin efficiency 27% [38] ) in strains with either RM or BY rDNA . Consistent with our model , we found that activity of the inefficient ARS605 origin was increased by the presence of the RM rDNA locus ( Figure 6C ) , while the two strong origins were unaffected . The ARS605 origin was previously noted to have different efficiencies between two different strains , A364A [38] and DKD-5D-HD [39] , possibly reflecting rDNA sequence differences . Our results suggest that the RM rDNA locus increases efficiency at a subset of origins , namely inefficient origins , both on plasmids and throughout the genome . Our results lead us to the hypothesis that the weaker RM rDNA origin is globally compensating for a naturally limiting , unknown replication factor , allowing non-rDNA origins greater access to this limiting factor and promoting replication of the rest of the genome . If this hypothesis is correct , then under conditions where genome replication is specifically compromised by a temperature sensitive mutation in one of the components of replication initiation , we would expect to see the presence of the RM rDNA locus to suppress , at least partially , the growth defect of the temperature sensitive mutation . We created BY strains ( with either the RM or BY rDNA ) in which replication initiation potential would be limited by the presence of the temperature-sensitive orc2-1 mutation [40] , [41] . Orc2 is a subunit of the origin recognition complex ( ORC ) that recruits replication factors to S . cerevisiae origin sequences , and the orc2-1 mutant allele generates an ORC that is unstable at high temperature . An orc2-1 mutant with BY rDNA is unable to grow at the non-permissive temperature of 26°C and also exhibits slower growth at room temperature . An orc2-1 mutant with RM rDNA grows as well as an ORC2 strain at room temperature and significantly improves viability at 26°C ( Figure 6D ) . These results are consistent with the hypothesis that the more efficient rDNA origins in the BY rDNA array can stress global replication . We conclude that having weaker and fewer origins at the rDNA locus can facilitate genome-wide replication . Calorie restriction increases lifespan in organisms ranging from yeast to mammals . Coupled with the fact that these organisms all contain large numbers of rDNA repeats , and presumably replication origins , we speculated that calorie restriction might decrease origin firing at the rDNA , which in turn would alter global patterns of DNA replication in a manner that promotes longevity . Calorie restriction is known to affect rDNA through the reduction of ribosome biogenesis [42] and down-regulation of rRNA transcription [43] , but its effects on rDNA replication have not been examined . To investigate the effect of calorie restriction on rDNA origin efficiency , we examined the rDNA replication intermediates in cells grown in 2% glucose ( normal media ) and 0 . 05% glucose ( calorie-restricted media [20] ) . It is important to note that we are quantifying origin activity as the ratio of the bubble arc to the Y arc , which are both intermediates formed by DNA replication . These measurements therefore come from cells that are in S phase and changes to bubble∶Y ratios are not merely due to changes in number of S phase cells during nutrient deprivation . We found that BY cells with BY rDNA , grown under glucose restriction , had reduced rDNA origin activity by ∼60% when compared to the same cells grown in 2% glucose ( Figure 7A ) . Calorie restriction had a more dramatic effect in BY cells with RM rDNA , where rDNA origin activity was reduced by >80% in 0 . 05% glucose . In fact , directly comparing the rDNA origin activity of the two strains in calorie-restricted medium , in which the promotion of origin activity via rRNA transcription is presumably reduced , underscores the intrinsic difference in rARS activity between the two strains . Because the magnitude of the decrease in origin activity in response to glucose deprivation varied somewhat from experiment to experiment , we decided to assess the effect of dietary restriction on rDNA origin activity using an orthogonal approach , namely to examine rDNA replication in a panel of logarithmically growing , dietary restriction mimetic mutant strains including gpa2Δ , gpr1Δ , hxk2Δ , and rpl6bΔ . GPA2 and GPR1 encode signaling components of a G-protein coupled receptor , HXK2 encodes a major hexokinase responsible for glucose phosphorylation during growth in glucose , and RPL6B encodes a component of the large ribosomal subunit . Loss of any of these genes acts as a calorie restriction mimetic , extending replicative lifespan through effects that are epistatic with calorie restriction [20] , [44] . Compared to the wild type strain , the calorie restriction mimetic mutants that we analyzed all exhibited marked reduction in rDNA origin activity ( ∼45–60% reduction , Figure 7B ) , indicating that replication initiation at rDNA origins is influenced by nutrient signaling . Additionally , we found that effects of calorie restriction on rDNA origin activity are independent of both Sir2 and Fob1 ( Figure 7C ) , as is the effect of calorie restriction on lifespan extension . The magnitude of inhibition of rDNA origin activity by calorie restriction mimetics suggests that calorie restriction may also profoundly improve genome-wide replication . To investigate the possibility that calorie restriction extends life span by alleviating genome-wide replication stress , we examined orc2-1 sensitivity in strains with gpa2Δ , rpl6bΔ , or rpl31aΔ , an additional calorie restriction mimetic mutant . Consistent with our observation that the maximum permissive temperature is raised for orc2-1 mutant in the presence of RM rDNA origins , deletion of each of the three genes also partially suppresses the temperature-sensitivity of orc2-1 mutants ( Figure 7D ) . Taken together , these results suggest that dietary restriction can promote genome-wide replication .
We propose that a reduction in the number of actively replicating origins at the rDNA locus decreases competition for replication factors and therefore increases usage of non-ribosomal genomic origins , helping to ensure complete replication of the rest of the genome . rDNA origins represent a sizeable fraction of total cellular origins , viz . , one third in the case of the laboratory strain . The RM rDNA , with both a weaker ARS and only 60% as many repeats relative to BY , could potentially liberate a significant amount of replication factors for use outside the rDNA . Consistent with this hypothesis , we found that strains with the less active RM rDNA origin sequence had increased activity at weak genomic and plasmid origins . We note , however , that our study examined the effect of RM rDNA on a very limited number of genomic and plasmid origins; assessment of the effect of weakened rDNA origins on activation of a full spectrum of plasmid origins as well as the global replication program will be required to fully evaluate our hypothesis . In further support of the idea that rDNA origins modulate the avilability of replication factors , we also found that the less active origins in the RM rDNA array also partially rescued lethality in orc2-1 mutants . A similar rescue was reported by Ide et al . [34] when they characterized clones that were able to suppress the orc1-4 and orc2-1 temperature-sensitive phenotype . A majority of the rescued strains possessed a dramatically smaller chromosome XII , a result of severe reductions to the rDNA array . This flexibility in rDNA size suggests that it is a dynamic structure that can respond to and compensate for replication constraints . Genome-wide , origins differ in their efficiency and timing of firing . Some origins are very efficient , firing in nearly all cells in the population . Other origins , perhaps less efficient at recruiting all of the necessary components for origin firing , will be passively replicated by forks from adjacent origins in a portion of cells in the population . The stochastic nature of these less-efficient origins can give rise to situations in which adjacent active origins are far enough apart that the converging replication forks will not meet by the time the cell initiates mitosis . Such a “random replication gap” problem [47] would presumably lead to a cell cycle delay or to chromosome and/or nuclear segregation defects . Replication initiation also varies temporally between different genomic origins , with late firing origins important for late S-phase synthesis of remaining stretches of unreplicated DNA . Since rDNA is a later replicating region of the genome ( Alvino , unpublished ) and most likely consists of later-firing origins , having fewer active origins at the rDNA array would allow reallocation of replication resources during a critical period in late S-phase when unreplicated genomic gaps need to be resolved . We hypothesize that this replication problem becomes more acute with age and that a weaker origin in the rDNA repeats can be enough to stave off the random gap problem to allow cells to complete a greater number of cell divisions . This hypothesis raises the question of whether the RM rDNA is relieving replication stress outside of the rDNA at the expense of increasing stress within the rDNA . Due to its repetitive and hyper-recombinogenic nature , however , the rDNA may have options for repair of unreplicated regions that are unavailable for the rest of the genome -for example , single stranded annealing and homologous recombination between direct repeats [48] . Therefore rDNA may be less vulnerable to crises resulting from random replication gap induced double strand breaks . We are proposing that the difference in the genetic potential for origin firing in the laboratory and vineyard strains elucidates a fundamental mechanism by which cells sense and respond to dietary restriction . Specifically , we suggest that nutrient deprivation causes decreased rRNA transcription , resulting in increased nucleosome occupancy at the rDNA , which in turn would reduce firing of rDNA origins of replication . In support of this idea , we found that dietary restriction dramatically reduces rDNA origin firing . Furthermore , it is well established that in situations in which growth is reduced , such as with calorie restriction or TOR pathway inhibition , there is a dramatic down-regulation of genes involved in ribosome biogenesis [49] . rRNA transcription is particularly attuned to growth conditions and the number of repeats transcribed decreases when cells are shifted from nutrient-rich to nutrient-poor media [43] . During such a dietary shift , the rDNA locus undergoes a dramatic increase in nucleosome occupancy , and the rDNA origin itself becomes populated by nucleosomes [50] . Since nucleosome occupancy and local nucleosome positioning exert potent effects on origin selection and function [33] , we propose that rRNA transcription-induced nucleosome alterations couple dietary restriction with regulation of rDNA origin firing . In metazoans , in which DNA replication origins are not specified by DNA sequence and ORC proteins do not exhibit sequence specificity in vitro , chromatin features and transcription have particularly important roles in shaping DNA replication initiation [33] [51] . In human rDNA , transcriptionally active and transcriptionally silent repeats differ in their timing , location , and pattern of DNA replication initiation [52] , suggesting that growth-related stimuli , similarly to what we observed in yeast upon dietary restriction , could alter rDNA replication dynamics with possible consequences for genome-wide replication . In further support of this idea , genome wide trans-effects of the rDNA locus have been observed in Drosophila melanogaster at the transcriptional level [53] , raising the possibility that the rDNA locus may also affect global DNA replication initiation patterns . Highly conserved processes such as the rRNA transcriptional response to nutrient deprivation and resulting alterations in DNA replication dynamics present an appealing mechanism to contribute to the longevity-promoting effects of dietary restriction across a wide range of species [6] . By focusing on rDNA origin activation and its impact on global rDNA replication , our model accounts for a large body of experimental observations . For example , Sir2 is not only a central determinant of RLS but also a potent suppressor of origin activation at the rDNA [54] . It is thus possible that suppression of rARS activation is the mechanism through which Sir2 promotes longevity . Likewise , deleting FOB1 inactivates the unidirectional replication fork barrier and permits replication from rDNA origins to be bidirectional . Replication of the rDNA locus would then require fewer active origins ( Figure 7C ) . We also note that our model can also accommodate a role of ERCs in shortening the lifespan of old mothers since excessive ERCs would also titrate replication factors away from the rest of the genome . In summary we suggest that down-regulation of replication initiation in the rDNA plays a central role in life span extension ( Figure 7E ) . The importance of keeping initiation potential low in the rDNA is suggested by the inherently weak nature of the rDNA ARS [27] and is further supported by the specific rARS polymorphism that we found in RM and other wild strains . We furthermore suggest the beneficial effects of calorie restriction act through the rDNA replication initiation pathway ( Figure 7E ) : ( 1 ) calorie restriction decreases rDNA transcription via nutrient-sensing pathways; ( 2 ) decreased transcription causes decreased firing of rDNA origins; ( 3 ) decreased firing of rDNA origins frees up significant replication factors for the rest of the genome; and ( 4 ) these increased replication factors alleviate the random replication gap problem , a lethal problem that becomes worse with age .
Yeast strains , plasmids , and oligos used are listed in Table S1 . Yeast experiments were carried out using standard YPD medium ( 2% glucose , 1% yeast extract , 2% peptone ) unless otherwise noted ( e . g . , calorie restriction ) . The segregant library has been previously described [55] . Gene deletion mutants were either from yeast ORF deletion collection or were created using standard PCR transformation methods . The Saccharomyces Genome Resequencing Project ( SGRP ) strains were provided by the National Collection of Yeast Cultures ( NCYC ) [30] , [56] . Strains with reduced rDNA copy numbers were generated as described previously [35] using pRDN-hyg1 provided by the Liebman Lab [57] . Briefly , the plasmid pRDN-hyg1 , which contains a single rDNA repeat with a recessive hygromycin resistance mutation in the 18S rRNA sequence , was introduced into strains containing native number of rDNA repeats . The transformants were subjected to hygromycin resistance selection , which selects for mutants that have lost most of the copies of chromosomal rDNA repeats . The size of rDNA in the resulting strains was measured using CHEF gel electrophoresis and quantitative Southern blotting . JRY7459 , the S288c strain containing orc2-1 mutation in the S288c background , was obtained from the Rine lab [41] and crossed with strains of interest to generate tetrads for temperature sensitivity assays . Backcrossed rDNA strains in the BY background were generated by crossing an rDNA-tagged BY parent strain , in which HYG ( HpHMX4 ) was inserted in the rDNA array , to the RM parent strain . We selected multiple spores without resistance to hygromycin B ( have RM rDNA instead of the tagged BY rDNA ) , which were then again crossed with the rDNA-tagged BY parent strain . The rDNA of each strain was sequenced after 8 backcrosses to verify that the desired rDNA sequence was maintained . Replicative lifespan studies were conducted as previously described by Kaeberlein et al . [58] . All lifespan studies were conducted on YPD plates ( 2% glucose ) and 40 individual cells ( virgin mothers ) were analyzed for each strain , except for the segregant strain studies in which 20 cells were analyzed . Statistical significance between strain lifespans was determined using Mantel-Haenszel logrank test . Genome-wide linkage analysis of segregant data was performed using the publicly available R/qtl software . Effects of RM/BY rDNA locus inheritance were examined using R ( box plots ) and Excel ( student's t-test ) . Old cells were harvested using the biotinylation/streptavidin purification technique described previously [10] , [59]: 108 cells from logarithmically growing cultures were biotinylated using EZ-Link sulfo-NHS-LC-biotin ( Pierce ) , cultured in YPD for 10 doublings , labeled with streptavidin magnetic beads ( Miltenyi Biotec ) , and purified using an autoMACs Pro Separator ( Miltenyi Biotec ) . Purification of old mother cells was confirmed by counting bud scars using calcoflour staining . Purified old mother cells exhibited a median of 8 bud scars . Young cells were harvested from logarithmically growing YPD cultures . For ERC Southern blot analysis , 1 µg of undigested genomic DNA was resolved by gel electrophoresis ( 0 . 8% agarose , 1 V/cm for 36 hours ) and blotted to a nylon membrane . Membranes were then hybridized with a 32P-labeled rDNA probe , generated from an rDNA NTS2 template PCR-amplified from genomic DNA using primers 5pNTS2_XhoI/3pNTS2_BamHI ( Table S1 ) , visualized using a Personal Molecular Imager ( Bio-Rad ) , and quantified using Quantity One software . For rDNA copy number quantification by Southern blot , genomic DNA was harvested from saturated 3 mL cultures , digested overnight with BglII , resolved by gel electrophoresis , and blotted to a nylon membrane . Abundance of rDNA and single-copy MCM2 was visualized using 32P-labeled probes from rDNA NTS2 and MCM2 templates amplified from genomic DNA , and quantified using ImageJ . Relative rDNA copy number was determined by normalizing rDNA band intensity to the intensity of single-copy MCM2 [60] . Chromosome XII size was resolved by CHEF ( clamped homogeneous electric field ) gel electrophoresis as previously described by Ganley et al . [31] . For each strain , genomic DNA from saturated cultures from 3 individual colonies was harvested in 1% agarose plugs [61] , each plug containing approximately 108 cells . Chromosomes were visualized by ethidium bromide staining and/or Southern blotting . The S288c ( BY ) rDNA sequence was obtained from the Saccharomyces Genome Database [62] , [63] and the RM11-1a rDNA sequence was obtained from the Broad Institute Saccharomyces cerevisiae RM11-1a Sequencing Project [64] . To verify polymorphisms found in RM and BY rDNA , the 2 . 2 kb NTS sequence ( NTS1/5S/NTS2 ) was amplified from genomic DNA using primers 5pNTS1_XhoI/3pNTS2_BamHI ( Table S1 ) , cloned into pRS416 , and sequenced from multiple clones . Genomic DNA from BY4716 and RM11-1a parental strains and the backcrossed “BY strain with RM rDNA” was further analyzed using Illumina sequencing . rDNA repeat heterogeneity was estimated using read abundance from each . rDNA sequences from the SGRP collection [56] were examined with BLAST searches performed on the SGRP database [29] . S . cerevisiae origin sequences were PCR-amplified from genomic DNA and cloned into the ARS-free pU18-KanMX-LwCEN vector previously used by Di Rienzi et al . [36] . 50 ng of each plasmid was transformed into 108 cells of logarithmically growing culture . Transformation cultures were allowed to recover 24 hours on YPD plates before being replica-plated onto selective YPD+G418 media . Colony formation was visualized after 48 hours at 30°C , 72 hours for plasmids with RM rARS or L . waltii ARSVII-929 . To examine replication intermediates , two dimensional ( 2D ) gel electrophoresis was performed as described previously [65] . Genomic DNA from logarithmically growing cultures was harvested in 0 . 5% agarose plugs or by modified version of the method of Huberman [66] and digested with NheI ( rDNA and ARS14 ) , StyI ( ARS1 ) , or HincII/NcoI ( ARS605 ) . Replication intermediates were visualized on film and analyzed using a Personal Molecular Imager System ( Bio-Rad ) and Quantity One software . Relative origin activity was estimated by normalizing the intensity of bubble-arc ( active origin ) intermediates to the Y arc ( passive replication ) . | Although many aging regulators have been discovered , we are still uncovering how each contributes to the basic biology underlying cell lifespan and how certain longevity-promoting regimens , such as calorie restriction , manipulate the aging process across species . Since many cellular aging processes between human cells and budding yeast are related , we examined a collection of genetically diverse yeast and discovered that a genetic variant in vineyard yeast confers a 41% lifespan increase . The responsible sequence in the vineyard yeast reduces the amount of DNA replication that initiates at the ribosomal DNA ( rDNA ) locus , a chromosome-sized region of the genome that is dedicated to the production of ribosomal RNA required for protein synthesis and growth . Strikingly , we find that calorie restriction conditions also reduce rDNA replication , potentially promoting longevity by the same mechanism . While the rDNA has been previously linked to lifespan control , how this single locus affects global cell function has remained elusive . We find that a weakly replicating rDNA promotes DNA replication across the rest of the cell's genome , perhaps through the re-allocation of replication resources from decreased rDNA demand . Our findings suggest that the cell's inability to complete genome replication is one of the major impediments to yeast longevity . | [
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"molecular",
"cell",
"biology",
"genetics",
"and",
"genomics"
] | 2013 | A Natural Polymorphism in rDNA Replication Origins Links Origin Activation with Calorie Restriction and Lifespan |
Day-length is important for regulating the transition to reproductive development ( flowering ) in plants . In the model plant Arabidopsis thaliana , the transcription factor CONSTANS ( CO ) promotes expression of the florigen FLOWERING LOCUS T ( FT ) , constituting a key flowering pathway under long-day photoperiods . Recent studies have revealed that FT expression is regulated by changes of histone modification marks of the FT chromatin , but the epigenetic regulators that directly interact with the CO protein have not been identified . Here , we show that the Arabidopsis Morf Related Gene ( MRG ) group proteins MRG1 and MRG2 act as H3K4me3/H3K36me3 readers and physically interact with CO to activate FT expression . In vitro binding analyses indicated that the chromodomains of MRG1 and MRG2 preferentially bind H3K4me3/H3K36me3 peptides . The mrg1 mrg2 double mutant exhibits reduced mRNA levels of FT , but not of CO , and shows a late-flowering phenotype under the long-day but not short-day photoperiod growth conditions . MRG2 associates with the chromatin of FT promoter in a way dependent of both CO and H3K4me3/H3K36me3 . Vice versa , loss of MRG1 and MRG2 also impairs CO binding at the FT promoter . Crystal structure analyses of MRG2 bound with H3K4me3/H3K36me3 peptides together with mutagenesis analysis in planta further demonstrated that MRG2 function relies on its H3K4me3/H3K36me3-binding activity . Collectively , our results unravel a novel chromatin regulatory mechanism , linking functions of MRG1 and MRG2 proteins , H3K4/H3K36 methylations , and CO in FT activation in the photoperiodic regulation of flowering time in plants .
The timing of floral transition from vegetative to reproductive development is a critical event in the plant life cycle and is coordinated by internal and environmental cues [1]–[3] . In Arabidopsis , the photoperiodic flowering pathway is regulated by the transcription factor CONSTANS ( CO ) and the florigen FLOWERING LOCUS T ( FT ) [4] . Circadian-clock regulated CO mRNA and light-dependent stabilization of CO protein are crucial for activation of FT expression in leaves under long days ( LDs ) but not short days ( SDs ) ; the FT protein is then translocated to the shoot apical meristem , where it promotes flowering [4] . The CO protein can bind to specific cis-elements in the FT promoter either by itself [5] or in a complex with CCAAT-binding factors [6] . Histone lysine methylation is an important epigenetic mechanism for the regulation of gene expression . Recent studies have revealed that chromatin mechanisms play important roles in flowering time by regulating the expression of key flowering-regulatory genes [7] . For example , FT expression is affected by several factors , including H3K27 methyltransferase CLF [8]–[10] , H3K27 demethylase JMJ12/REF6 [11] , H3K4 demethylases ELF6 and JMJ14/JMJ4 [12]–[14] , and AFR1/2-HDAC histone deacetylase [15] . More recently , other histone methyltransferases ( ATX1 , ATX2 , SDG8 , SDG25 ) and demethylases ( LDL1/2 ) were reported to affect histone methylation status at the FT chromatin [16] . It was proposed that chromatin with methylated histone residues can be specifically recognized by the chromatin effectors that act as readers , which might ultimately direct downstream functions [17] , [18] . The Arabidopsis histone methylation reader proteins ORC1 [19] , AtING [20] , AL [20] , [21] , WDR5a [22] , SDG8/ASHH2 [23] , [24] , LHP1/TFL2 [25] , [26] , PICKLE [27] , and rice protein CHR729 [28] were found to interact with methylated H3K4 or/and H3K27 , and also affect many aspects of plant development from mutant analyses . Although chromatin effectors related to several lysine residues were identified , the proteins recognizing H3K36 methylation remain unknown in plant . In addition , histone mark readers that are directly involved in CO-FT regulatory pathway have not been identified . To identify H3K36 methylation readers , we performed an in vitro peptide pull-down experiment using Arabidopsis nuclear extracts , and identified among proteins detected with mass spectrometry the Arabidopsis Morf Related Gene ( MRG ) group protein MRG2 as a binding protein of the histone H3 N-terminal tail methylated at lysine 36 . MRG1 and MRG2 belong to the MRG protein family , with highly conserved members in fungi , plants , and animals . Several family members , such as Esa1p-associated factor-3 ( EAF3 ) , MRG on chromosomes 15 ( MRG15 ) , and male-specific lethal ( MSL3 ) have been found in yeast and animals as recognition factors of H3K36 methylation [29]–[31] . The yeast eaf3 deletion causes no obvious growth phenotypes and only has a very modest effect on transcription [32] , [33] , while a mutation in the Drosophila MSL3 gene led to male lethality [34] , and overexpression of human MRG15 results in abnormal nuclear morphologies and cell death [35] , making further studies of in vivo MRG functions difficult in animals . We show here using viable Arabidopsis mutants that MRG1/2 promote photoperiodic flowering in Arabidopsis . MRG1/2 proteins act as novel chromatin effectors directly involved in the CO-dependent FT activation in the photoperiodic flowering pathway . The mrg1 mrg2 double mutant exhibits reduced FT mRNA level , with a normal CO mRNA level , and is late-flowering only under LDs . We further demonstrate that MRG2 and CO interact with each other physically and enhance each other's binding to the FT promoter region , thereby activating FT transcription . Furthermore , the co-crystal structures of MRG2 with H3K4me3/36me3 peptides reveal the residues important for peptide binding and a site-specific MRG2 mutation abolishes both histone mark binding activity and flowering time regulation , providing a direct link between the biochemical activity of MRG1/2 proteins and their in vivo biological functions .
An in vitro peptide pull-down assay was used to identify the proteins that bind to histone H3 tail methylated at K36 . Biotinylated histone H3 peptides ( amino acids 21 to 44 ) , with either unmethylated ( control ) or tri-methylated K36 , were immobilized on the streptavidin-coated beads and incubated with Arabidopsis nuclear extracts . Mass spectrometry of proteins bound to the H3 peptides tri-methylated at K36 identified MRG2 , which is a member of the MRG protein family . Members in this group have the same domain organization , with a chromodomain ( chromatin organization modifier domain ) near the N-terminus , and a MRG domain near the C-terminus ( Figure S1 ) . MRG2 has a homolog in Arabidopsis , MRG1 , which shares with MRG2 49% identity and 65% similarity at the amino acid sequence level . To analyze the in vitro binding activity of MRG1 and MRG2 , we tested whether their chromodomains could bind to H3 peptides containing different methylations at K4 , K9 , K27 , or K36 using a pull-down assay . The chromodomains of MRG1 and MRG2 could bind to both the H3K4me2/me3 and H3K36me2/me3 peptides , but not to mono-methylated H3K4 and H3K36 peptides ( Figure 1A ) . The two MRG proteins had a higher affinity to the tri-methylated peptides than to the di-methylated forms in the dot-blot binding assay ( Figure 1A ) . In contrast , H3K9me3 and H3K27me3 were not recognized by either of these two proteins ( Figure 1A ) , indicating a degree of MRG binding specificity . To further verify the specificity of the binding , an isothermal titration calorimetry ( ITC ) assay was performed using free-labeled histone peptides as substrates . H3K4me3 ( residues 1–9 ) and H3K36me3 ( residues 31–41 ) exhibited detectable binding to MRG2 chromodomain ( residues 41–123 ) ( Kd values: 0 . 8 mM for H3K4me3; 0 . 69 mM for H3K36me3 ) ( Figure 1B ) . On the other hand , MRG2 chromodomain bound to H3K4me2 ( residues 1–9 ) , H3K36me2 ( residues 31–41 ) , H3K9me3 ( residues 4–13 ) and H3K27me3 ( residues 23–31 ) at a relatively low affinity ( Kd values: 2 . 4 mM for H3K4me2; 2 . 6 mM for H3K36me2; 1 . 2 mM for H3K9me3; 2 . 5 mM for H3K27me3 ) ( Figure S2 ) . The ITC binding assay results were consistent with those of the in vitro pull-down assay , showing the binding specificity of MRG1/2 with tri-methylated H3K4 and H3K36 in vitro . To investigate the in vivo function of MRG1/2 , the T-DNA insertional mutants mrg1 and mrg2 , obtained from the Arabidopsis Biological Resource Center ( ABRC , http://www . arabidopsis . org ) and the Saskatoon collection [36] ( http://aafc-aac . usask . ca/FST/ ) , were used . The single mutants of mrg1 and mrg2 carry T-DNA insertions in the first intron and the second exon , respectively ( Figure 2A ) . Although MRG1 and MRG2 transcripts were undetectable in both mutants ( Figure 2B ) , their overall phenotypes were normal ( Figure 2C ) . Next , we constructed and analyzed the mrg1 mrg2 double mutant , which showed late-flowering under long-day ( LD ) conditions ( Figure 2C and 2E ) . Both MRG1 and MRG2 were mainly expressed in the vasculature of cotyledons and true leaves , and also in roots and inflorescences , as evidenced by histochemical GUS staining in PMRG1::MRG1-GUS and PMRG2::MRG2-GUS transgenic plants ( Figure 2F ) . The late-flowering phenotype of mrg1 mrg2 double mutant could be fully rescued by introducing either PMRG1::MRG1-GUS or PMRG2::MRG2-GUS into the plants , indicating that MRG1 and MRG2 are functionally redundant in the control of flowering time in Arabidopsis . Additionally , the mrg1 mrg2 late-flowering phenotype was specific for LD conditions . Under short-day ( SD ) conditions , the double mutant showed a flowering time similar to the wild-type ( Figure 2D and 2E ) , suggesting that the mrg1 mrg2 double mutant is defective in the photoperiodic flowering pathway . To investigate the effect of MRG1/2 on flowering time in response to changes of photoperiod , we traced expression of the key genes CO and FT over a 24-h period in wild-type and mrg1 mrg2 double mutant plants . The expression of CO and FT shows different diurnal patterns with their transcript levels [37] . Under LDs , high levels of wild-type CO expression are detected from late afternoon through the first half of the night , while induction of FT transcription occurs at the end of the day ( 16 h after dawn , ZT16 ) ( Figure 3A ) . While the CO expression pattern in mrg1 mrg2 was similar to that of the wild-type , the FT transcript level in mrg1 mrg2 was lower than that of wild-type plants ( Figure 3A ) , suggesting that MRG1 and MRG2 may be involved in activation of FT transcription in the photoperiodic flowering pathway . To further test where in the photoperiodic pathway MRG1/2 act , we introduced 35S::FT and 35S::MYC-CO constructs into the late-flowering mrg1 mrg2 double mutant through introgression of the transgenes . Transgenic plants overexpressing FT or CO in the wild-type flowered significantly earlier ( Figure 3B ) [37] , [38] . Overexpression of FT fully rescued the late-flowering phenotype of the mrg1 mrg2 double mutant ( Figure 3B ) , indicating that MRG1/2 indeed act upstream of FT . In contrast , overexpression of CO failed to induce FT transcription in mrg1 mrg2 background , and the plants still displayed late-flowering phenotype comparing with the wild-type plants , although these 35S::MYC-CO/mrg1 mrg2 plants showed slightly higher FT expression and slightly earlier flowering time comparing with the mrg1 mrg2 double mutant ( Figure 3B ) . It's to be noted that the significantly increased expression levels of CO ( around 100 folds of that in the wild-type ) were similar in the 35S::MYC-CO transgenic plants in wild-type ( 2 copies of 35S::MYC-CO ) , heterozygous ( 1 copy of 35S::MYC-CO ) or homozygous mrg1 mrg2 ( 2 copies of 35S::MYC-CO ) backgrounds . To further verify this phenotype , another transgene 35S::FLAG-CO was also constructed and introduced into the mrg1 mrg2 double mutant . As shown in Figure S3 , transgenic plants overexpressing FLAG-CO in the wild-type exhibited high level of FT transcription and early-flowering phenotype . However , similar to 35S::MYC-CO/mrg1 mrg2 , overexpression of FLAG-CO also failed to induce FT transcription in mrg1 mrg2 background ( with similar expression level of CO as that in the wild-type background , Figure S3 ) and the plants still flowered a little bit later than the wild-type , suggesting that the CO function in promoting flowering requires MRG1/2 proteins . When constructing 35S::MYC-CO/mrg1 mrg2 plants , we found that the 35S::MYC-CO transgenic plants in wild-type , heterozygous ( 1 copy of MRG1 and 1 copy of MRG2 ) or homozygous mrg1 mrg2 backgrounds exhibited different FT transcription levels and flowering time ( Figure 3B ) , noting that the plants with either 1 copy or 2 copies of 35S::MYC-CO showed similar expression levels of CO . The heterozygous 35S::MYC-CO/mrg1 ( +/− ) mrg2 ( +/− ) plants , with about 30% reduction of MRG1 or MRG2 ( Figure S4 ) , showed FT activation and earlier flowering comparing with the wild-type plants , but their flowering time was still later than that of the 35S::MYC-CO plants in wild-type background , indicating that the requirement of MRG1/2 in the effect of CO overexpression is in a dosage-dependent manner . These results imply an important role for MRG1/2 in the photoperiod flowering pathway in Arabidopsis , and MRG1/2 are involved in the FT transcriptional activation . To obtain evidence for the molecular mechanisms of regulation of FT by MRG1/2 proteins , we performed a Chromatin Immuno-Precipitation ( ChIP ) assay to analyze possible in vivo binding of MRG1/2 proteins to the FT locus . The antibody against MRG2 ( Figure 4A ) was used in ChIP assays in wild-type and mrg1 mrg2 plants . The endogenous MRG2 protein was obviously enriched in the FT promoter regions in wild-type comparing with that in mrg1 mrg2 double mutant ( Figure 4B ) , indicating that the MRG2 protein targets directly to the FT promoter . To test whether MRG2 binding to the FT promoter region was dependent on the H3K4 and H3K36 methylation status of the chromatin , we used loss-of-function mutants atx1 and sdg8 , which affect , respectively , the ATX1 gene encoding a H3K4 methyltransferase and the SDG8 gene encoding a H3K36 methyltransferase [39] , [40] . As a control , the atx1 and sdg8 mutations did not affect the expression levels of MRG1/2 ( Figure 4A and 4C ) . ChIP analysis indicated that atx1 and sdg8 mutants showed reduced H3K4me3 and H3K36me3 levels , respectively , in most FT chromatin regions , when compared with wild-type plants ( Figure 4B ) . A detectable reduced H3K4me3 levels in some FT promoter regions were observed in sdg8 , while the H3K36me3 levels of FT chromatin were not affected in atx1 comparing with wild-type . The overall H3K4me3 and H3K36me3 levels of FT in mrg1 mrg2 double mutant were similar as those in the wild-type plants , indicating that the deletions of MRG1 and MRG2 do not change the H3K4me3 and H3K36me3 levels of FT chromatin . Along with reduced H3K4me3 and H3K36me3 levels , MRG2 enrichment at the FT promoter in atx1 and sdg8 mutants was decreased ( Figure 4B ) , suggesting that MRG2 may directly bind to the FT promoter via recognition of methylated H3K4 and H3K36 in planta . The FT promoter contains several canonical CCAAT boxes and one CO-responsive element ( CORE ) , these are essential for CO-mediated FT activation . Given that the CCAAT boxes and CORE sequence are within the MRG2-enriched regions of the FT promoter , we hypothesized that MRG1/2 proteins might be involved in the CO-dependent regulation of FT expression . To test this hypothesis , firstly we introduced a co mutant into the mrg1 mrg2 double mutant and found that the co mrg1 mrg2 triple mutant flowered at nearly the same time as co in LDs ( Figure 5A ) , suggesting that MRG1/2 may act to regulate FT in a CO-dependent genetic pathway . The facts that MRG1/2 and CO are both positive regulators of FT expression , that they both express mainly in the vasculature of leaves , and that they bind to the overlapped regions of FT promoter ( [41] and this study ) drive us to test whether MRG1/2 might interact with CO physically . To test this hypothesis , we examined the protein-protein interaction between MRG2 and CO . In an in vitro pull-down assay , we found that GST-fused CO but not GST alone could pull-down MRG2 tagged with multiple His ( histidine ) residues ( Figure 5B ) . To determine which domain in MRG2 was required for this interaction , we performed a truncation analysis and found that the MRG domain ( MRG2C ) but not the chromodomain ( MRG2N ) of MRG2 contributed to binding to CO ( Figure 5B ) . To verify the interaction observed in the pull-down assay , an in vivo binding assay was performed using bimolecular fluorescence complementation ( BiFC ) analysis . When tobacco leaves were infiltrated with Agrobacterium cells carrying appropriate constructs , MRG2/MRG2C and CO proteins tagged with split YFP interacted in vivo to reconstitute the whole YFP protein with fluorescent signal; therefore only the interaction of MRG2C and CO is shown in Figure 5C . Further support for a physical interaction between MRG2 and CO in planta came from a co-immunoprecipitation ( CoIP ) experiment , in which YFP-MRG2 was detected in the MYC-CO immunoprecipitated fraction from transgenic Arabidopsis expressing both MYC-CO and YFP-MRG2 ( Figure 5D ) . To explore the role of CO and MRG1/2 protein-protein interaction at the FT promoter , we examined MRG2 binding at FT upon loss of CO function by a ChIP assay using the anti-MRG2 antibody in the co-1 mutant . Loss of CO function resulted in declined MRG2 binding to the FT promoter ( Figure 6A ) without changes of the MRG1/2 transcription levels ( Figure S5A ) and H3K4me3/H3K36me3 levels in FT promoter ( Figure S5B ) , indicating that CO is required for normal levels of MRG2 binding to the FT chromatin , and supporting the genetic result that the MRG1/2 function in promoting flowering depends on CO ( Figure 5A ) . Conversely , to test whether MRG1/2 proteins affect the level of CO at the FT promoter , 35S::MYC-CO/mrg1 mrg2 plants were used for a ChIP analysis of the MYC-CO protein with anti-MYC antibody . As observed previously [41] , CO peaks near the FT transcription start site where the CORE sequence is located ( Figure 6B ) . In addition , upstream of this CO peak , there is a relatively low but detectable enrichment of CO at the FT promoter ( Figure 6B ) , where several canonical CCAAT boxes are embedded . Western blot analysis of total protein extracts revealed that CO protein accumulated at similar levels in wild-type and the mrg1 mrg2 double mutant ( Figure 6C ) . Surprisingly , CO enrichment at the FT promoter decreased in mrg1 mrg2 ( Figure 6B ) , implying that MRG1/2 proteins may interact with CO at the FT promoter , and affect the stable binding of CO at the locus . This result is also consistent with that the overexpression of CO failed to rescue the mrg1 mrg2 double mutant late-flowering phenotype ( Figure 3B ) . Taken together , both our biochemical and genetic analyses demonstrate that MRG2 and CO physically interact and depend on each other for their stable association with the FT promoter chromatin to promote flowering . To obtain the information regarding the specific MRG2 residues that are important for recognition of the histone marks H3K4me3 and H3K36me3 , we determined the co-crystal structure of MRG2 and the methylated H3 peptides . To identify the suitable length of MRG2 chromodomain-containing fragments for crystallization , limited proteolysis was performed to remove unstructured flexible regions , because these tend to be difficult to crystalize . The proteolyzed samples were analyzed using mass spectrometry ( MS ) analysis to determine the protein sequence , resulting in the identification of a region containing the chromodomain with residues 53–123 of MRG2 as suitable for crystallization . MRG2 complexes with the H3K4me3 and H3K36me3 peptides were obtained , but the complexes with the H3K27me3 and H3K9me3 peptides could not form crystals under the same conditions , consistent with the ITC results ( Figure S2 ) . The crystals of MRG2 with H3K4me3 or H3K36me3 were diffracted to 1 . 68 and 1 . 65 Å , respectively ( Table S1 ) . The structures were solved by the molecular replacement method using the chromodomain of human MRG15 ( PDB entry 2F5K ) as template . In both structures , the MRG2 chromodomain contains mainly four beta-strands . However , the predicted alpha-helix located at the C-terminus is missing in the electron density map ( Figure S1A and 7A ) . For the complexes of the MRG2 chromodomain with the H3K4me3/H3K36me3 peptides , the recognition modes are almost the same; therefore , only the complex of the MRG2 chromodomain with H3K36me3 peptide is shown in Figure 7A . The tri-methylated lysine projects into the aromatic cage formed by the His62 , Tyr67 , Tyr87 , Trp90 , and Trp94 residues of the MRG2 chromodomain ( Figure 7A ) . The co-crystal structures clearly indicate that the chromodomain of MRG2 can directly bind to tri-methylated H3K4 and H3K36 , and provide strong evidence that the aromatic cage of the MRG2 chromodomain and the associated residues are essential for the binding , the same as other chromodomain proteins [29] . According to the co-crystal structures described above , we specifically replaced Tyr87 with Ala . Consistent with the structure , the Tyr87Ala ( Y87A ) mutation in MRG2 disrupted its association with H3K4me3 and H3K36me3 peptides ( Figure 7B ) , supporting the idea that the hydrophobic pocket formed by the aromatic residues is essential for the binding , as with other chromodomain proteins [29] . To further test whether the Tyr87 residue is important for in vivo MRG2 function , we constructed PMRG2::MRG2-YFP and PMRG2::MRG2 ( Y87A ) -YFP fusions and introduced them into the mrg1 mrg2 double mutant . As expected , the late-flowering phenotype of the double mutant could be fully rescued by the PMRG2::MRG2-YFP transgene ( Figure 7C and 7D ) . On the other hand , the PMRG2::MRG2 ( Y87A ) -YFP transgene could not rescue the mrg1 mrg2 mutant phenotype ( Figure 7C and 7D ) , suggesting that the capability of the MRG2 protein to bind the methylated histones is essential for its biological function in vivo . We then used the PMRG2::MRG2-YFP/mrg1 mrg2 and PMRG2::MRG2 ( Y87A ) -YFP/mrg1 mrg2 transgenic plants with similar mRNA and protein expression levels of MRG2-YFP and MRG2 ( Y87A ) -YFP ( Figure S6 ) for ChIP assays using a specific antibody against GFP . At the FT promoter , the MRG2-YFP proteins in the PMRG2::MRG2-YFP/mrg1 mrg2 plants showed a similar enrichment pattern to the endogenous MRG2 protein ( Figure 7E and 4B ) . However , in the PMRG2::MRG2 ( Y87A ) -YFP/mrg1 mrg2 plants ( Figure 7E ) , in which the mutated MRG2 protein failed to bind to the tri-methylated H3K4 or H3K36 ( Figure 7B ) , the enrichment of MRG2 ( Y87A ) -YFP proteins to FT were obviously decreased , showing that the association of MRG2 with FT promoter depends on its H3K4me3/H3K36me3 binding activity , thus providing a direct link between the biochemical activity of MRG1/2 and their in vivo biological functions .
The photoperiodic regulation of flowering is widely observed among flowering plants . In Arabidopsis , this pathway requires the key regulator CO and its target gene FT . Although some chromatin modifiers have been reported to be involved in FT regulation , little is known regarding direct interaction between chromatin effectors and the CO-FT pathway . In this work , we present Arabidopsis histone mark readers MRG1/2 as novel chromatin effectors that interact with both the CO protein and the FT promoter , thus providing a chromatin regulatory mechanism linking the transcription factor CO and the H3K4/36-methylation readers MRG1/2 in FT activation to promote plant flowering under long-day photoperiods . Firstly , via their chromodomain , MRG1/2 proteins act as readers that recognize methylated H3K4 and H3K36 . Unlike EAF3 , which bind to H3K36me3/2 and only very weakly to H3K4me3/2 [29] , our in vitro binding and crystal structure data revealed that the chromodomain of MRG2 has a preference to interact with tri-methylated forms of both H3K4 and H3K36 . The ChIP assays revealed that MRG2 binds to the FT promoter at positions where the tri-methylated H3K4 is enriched ( Figure 4B ) . Loss of ATX1 function resulted in a decrease of MRG2 enrichment at the FT promoter without change of MRG1/2 expression levels ( Figure 4C ) . Unlike H3K4me3 distribution , which associated with MRG2 binding pattern , we found that H3K36me3 was homogeneously distributed along FT promoter ( Figure 4B ) . The sdg8 mutant exhibited slightly decreased levels of H3K4me3 , and significant reductions of H3K36me3 and MRG2 enrichment in most FT chromatin regions also with no change of MRG1/2 expression ( Figure 4C ) . Given that the slightly reduced H3K4me3 level in sdg8 might not be sufficient for a severe decrease of MRG binding ability , H3K36me3 is very likely to contribute in the MRG binding in vivo . The co mutant again showed declined MRG2 binding to the FT promoter ( Figure 6A ) without changes of the MRG1/2 transcription levels ( Figure S5A ) . Therefore , we propose that H3K4me3/H3K36me3 and transcription factor CO may play together to specify the MRG2 enrichment in FT chromatin in planta . The binding capability of the MRG1/2 proteins with H3K4me3/H3K36me3 is essential for their biological function in planta because the Y87A substitution in the chromodomain of MRG2 , which lost the H3K4me3/H3K36me3 binding capacity , failed to rescue the mrg1 mrg2 double mutant late-flowering phenotype ( Figure 7 ) . However , kinetic analysis in vitro revealed that MRG2 binding affinities with H3K4me3 and H3K36me3 are fairly weak ( Figure 1B ) . Therefore , MRG2 chromodomain itself may have difficulty in remaining associated with methylated histone tails without the assistance of other factors . Here , participation of another domain , namely the MRG domain , provides extra binding affinity and specificity to exert biological function . We demonstrated that MRG2 interacts with CO via the MRG domain , and that this interaction is required for stable binding of MRG2 to H3K4me3 and H3K36me3 at the FT promoter , as loss of CO function results in reduction of MRG2 enrichment at the FT locus . On the other hand , although the effect of mrg1 mrg2 double mutant on flowering time in LDs is comparably mild , MRG binding to FT is important for CO-dependent FT activation because in mrg1 mrg2 background , overexpression of MYC-CO ( Figure 3B ) or FLAG-CO ( Figure S3 ) failed to induce FT activation , and the transgenic plants still displayed late-flowering phenotype comparing with the wild type plants . Furthermore , the requirement of MRG proteins for CO overexpression is in a dosage-dependent manner ( Figure 3B ) . As to the mild late flowering phenotype of mrg1 mrg2 double mutant , we speculate that there might be redundant factors to help CO to induce FT transcription , for in mrg1 mrg2 double mutant , FT expression is down-regulated but not abolished . Overexpression of MRG2 by introducing 35S::MRG2 into the wild-type plants do not affect their flowering time , indicating that the increased MRG2 is not sufficient to induce early flowering and the endogenous MRG proteins are enough . Now we do not know the reason why the deletion of MRGs repressed the effect of CO overexpression , and one possibility may be that without MRG proteins , an more effective combination of CO at FT chromatin probably could not be established to induce a major change in FT activation . CO protein is stabilized towards the end of LDs , and its abundance declines rapidly in the dark [4] , [42] . MRG1/2 do not seem to affect the stability of CO proteins since they accumulate at similar levels in both wild-type and the mrg1 mrg2 double mutant at ZT16 ( Figure 6C ) . Therefore , we propose that MRG1/2 are critical for stabilizing CO recruitment at the FT locus . This reinforcement mechanism may resemble the role of H3K4me3 binding by TAF3 in the TFIID complex , in which TAF3 acts as a transcriptional coactivator of the basal transcription factor TFIID in a PHD finger-dependent manner [43] . In summary , our findings strongly support a model in which MRG1/2 proteins interact with CO to activate FT transcription . CO directly binds to the FT promoter , enhancing the recruitment of MRG1/2 proteins to the FT locus . In addition , MRG1/2 proteins bind to chromatin that contains tri-methylated H3K4 and H3K36 via their chromodomains , the bound MRG1/2 in turn stabilize the binding of CO and ultimately controls the activation of FT transcription . Therefore , MRG1/2 act as a novel type of chromatin modulators , linking H3K4/H3K36 methylations and CO in FT activation in the photoperiodic flowering regulation in plants . How FT expression is modulated after CO and MRG1/2 binding to trigger correct flowering transition remains to be investigated . Evidence from yeast and animals shows that EAF3 and MRG15 are present in both histone acetyltransferase ( HAT ) and deacetylase ( HDAC ) complexes , and are involved in the regulation of chromatin structure [32] , [44] , [45] . It is therefore possible that transcriptional activation of FT might be due to the recruitment of subsequent chromatin effectors , such as the HAT complex . Another important issue is how FT expression is rhythmically regulated . Light signaling regulates CO protein stability and acts to generate daily rhythms in CO abundance [4] , [42] . We propose that the association of CO with MRG1/2 may help its re-association with the FT promoter as MRG1/2 might be more stably bound to FT due to the overall stability of H3K4me3 and H3K36me3 . Gu et al . revealed a periodic histone deacetylation mechanism for dampening FT mRNA expression at dusk , thereby modulating day length-dependent FT expression [15] . Further exploration will help to clarify the chromatin mechanism involved in the photoperiodic regulation of flowering time control .
For the dot-blot binding assay , MRG1N ( 1–321 , AT4G37280 ) and MRG2N ( 1–369 , AT1G02740 ) cDNA were amplified by primers MRG1-1/MRG1-2 and MRG2-1/MRG2-2 , respectively , and cloned into a pET-30a vector ( Novagen , www . novagen . com ) . Site mutation was generated using a Takara MutanBEST Kit ( http://www . takara-bio . com ) with primers MRG2-3/MRG2-4 . The in vitro binding assay of the chromodomain containing proteins with the histone peptides were performed as described previously [46] . Methylated H3K4 ( H3K4me1/2/3 , residues 1–21 ) , H3K9 ( H3K9me1/2/3 , residues 1–21 ) , H3K27 ( H3K27me1/2/3 , residues 21–44 ) , and H3K36 ( H3K36me1/2/3 , residues 21–44 ) peptides were synthesized by Scilight Biotechnology ( http://www . scilight-peptide . com ) . Anti-monomethyl-H3K4 ( 07-436 ) , anti-dimethyl-H3K4 ( 07-030 ) , anti-trimethyl-H3K4 ( 07-473 ) , anti-trimethyl-H3K9 ( 07-442 ) , and anti-trimethyl-H3K27 ( 07-449 ) were purchased from Millipore ( http://www . millipore . com ) , and anti-monomethyl-H3K36 ( ab9048 ) , anti-dimethyl-H3K36 ( ab9049 ) , and anti-trimethyl-H3K36 ( ab9050 ) were purchased from Abcam ( http://www . abcam . com ) . ITC experiments were carried out at 25°C on a MicroCal iTC200 ( GE Healthcare , www . gelifesciences . com ) . Protein and peptide were kept in an identical buffer of 50 mM Tris pH 8 . 0 , 100 mM NaCl . The sample cell was filled with a 0 . 2–0 . 4 mM solution of protein , and peptide ( 2–4 mM ) was added sequentially in 2 µl aliquots ( total of 20 injections ) at 2 . 5 min intervals . Binding isotherms were analyzed by fitting data into the one-site model using the ITC data analysis module Origin 7 . 0 . All Arabidopsis alleles were derived from the Columbia ecotype . mrg1 and mrg2 alleles , corresponding to SALK_057762 and SK28487 T-DNA insertion lines respectively , were obtained from the Arabidopsis Biological Resource Center ( ABRC , http://www . arabidopsis . org ) and the Saskatoon collection [36] ( http://aafc-aac . usask . ca/FST/ ) . The mrg1 mrg2 double mutant was created in our laboratory by genetic crossing . The mutants sdg8-2 , atx1-2 , and co-1 have been previously described [39] , [40] , [47] , as have the 35S::FT and 35S::MYC-CO lines [38] , [48] . Higher order combinations of mutants were produced by genetic crossing . In vitro plant culture was performed on agar-solidified MS medium M0255 ( Duchefa , http://www . duchefa . com ) supplemented with 1% sucrose and 0 . 9% agar . The studied photoperiods were 16 h light and 8 h dark for long-day ( LD ) , and 8 h light and 16 h dark for short-day ( SD ) . For GUS staining , MRG1 and MRG2 genomic sequences were amplified by primers MRG1-3/MRG1-4 and MRG2-5/MRG2-6 , respectively . They were fused to the GUS genomic sequence , and then cloned into pCAMBIA1300 ( CAMBIA , http://www . cambia . org ) . The resulting constructs of PMRG1::MRG1-GUS and PMRG2::MRG2-GUS were used to transform the mrg1 mrg2 plants . GUS activity was assayed by incubating plant tissues in GUS staining buffer [49] for 3–6 hours at 37°C . Plant material was cleared in 70% ethanol , and observed directly under a dissecting microscope ( MZ10F , Leica , Germany ) . Total RNA was prepared from plant tissues using TRI Reagent according to the manufacturer's instructions ( Invitrogen , http://www . invitrogen . com ) . Reverse transcription was performed using standard procedures with Improm-II reverse transcriptase ( Promega , http://www . promega . com ) . PCR amplification from the cDNA template was performed using gene-specific primers ( see Table S2 ) . ACTIN2 was used as a reference gene to normalize the data . MRG2 genomic sequence , amplified by primers MRG2-5/MRG2-6 and fused with EYFP cDNA , was cloned into the pCAMBIA1300 vector . Site mutation was generated using a Takara MutanBEST Kit ( http://www . takara-bio . com ) with primers MRG2-7/MRG2-8 . The resulting constructs PMRG2::MRG2-YFP and PMRG2::MRG2 ( Y87A ) -YFP were used to transform the mrg1 mrg2 plants . MRG2 cDNA , amplified by primers MRG2-1/MRG2-10 and fused with EYFP cDNA , was cloned into the pER8 vector [50] , downstream to an estrogen inducible promoter , and the construct of pER8::YFP-MRG2 was transformed into the wild-type Arabidopsis . ChIP was performed as previously described [39] with minor modifications . After fixation , 14-day-old seedlings were ground crudely in liquid nitrogen . Low and high salt wash buffers were supplemented with 0 . 1% Triton X-100 . Antibodies used in this study were anti-GFP ( A-11122 , Invitrogen , http://www . invitrogen . com ) , anti-MYC ( 11667149001 , Roche , http://www . roche-applied-science . com ) , anti-trimethyl-H3K4 ( 07-473 , Millipore , http://www . millipore . com ) , and anti-trimethyl-H3K36 ( ab9050 , Abcam , http://www . abcam . com ) . The polyclonal antibody against MRG2 was produced by Abmart ( http://www . abmart . com . cn ) . Quantitative real-time PCR was performed with a kit from Takara ( http://www . takara-bio . com ) to determine the enrichment of DNA immunoprecipitated in the ChIP experiments , using gene-specific primers listed in Table S2 . The efficiency values are the ratios determined by taking a fixed aliquot of the DNA extracted from the immunoprecipitated samples and the Input . Error bars show standard deviation from three paralleled biological replicates . MRG2 and MRG2C ( 442–984 ) cDNA were amplified with primers MRG2-1/MRG2-10 and MRG2-9/MRG2-10 , respectively , and then cloned into pET-30a vector ( Novagen , www . novagen . com ) . The resulting constructs and pET-30a-MRG2N were used for purification of His-tagged MRG2 , MRG2C , and MRG2N proteins . CO cDNA was amplified using primers CO-1/CO-2 , and then cloned into pGEX-4T-1 vector for purification of GST-fused CO proteins . Pulldown experiments were performed according to a previously described protocol [51] . BiFC was performed as described by Sun et al . [52] . For BiFC assays , MRG2C cDNA was amplified using primers MRG2-11/MRG2-12 , and then cloned into pXY103 and pXY106 vectors [52] . CO cDNA was amplified by primers CO-3/CO-4 , and then cloned into a pXY104 vector . Leaves of 4- to 8-week-old Nicotiana benthamiana plants were co-infiltrated with Agrobacterium tumefaciens strain GV1301 carrying transgene constructs . Localization of BiFC fluorescence was observed 2–3 days after infiltration using a confocal laser scanning microscope ( LSM 710 , ZEISS , Germany ) . 14-day-old seedlings expressing YFP-MRG2 ( pER8::YFP-MRG2/WT ) , MYC-CO ( 35S::MYC-CO/WT ) , or both YFP-MRG2 and MYC-CO were grown on MS medium with 4 µm estrogen to induce the overexpression of YFP-MRG2 , and a Co-IP assay was performed as described previously [21] . IP was performed using anti-MYC affinity gel ( E6654 , Sigma , http://www . sigmaaldrich . com ) . Immunoprecipitated proteins and the input fractions were separated on a 10% SDS-PAGE and detected by western blotting using anti-GFP antibodies ( A-11122 , Invitrogen , http://www . invitrogen . com ) or HRP-conjugated anti-MYC monoclonal antibodies ( 11814150001 , Roche , http://www . roche-applied-science . com ) . The cDNA encoding the chromodomain of Arabidopsis thaliana MRG2 ( residues 41–123 and 53–123 ) were amplified by PCR , and cloned into a pET28-SMT3 vector [53] . Following purification and removal of the tag , target proteins were concentrated to 20 mg/ml for structural and biochemical studies . To optimize the construct for crystallization , limited proteolysis was performed , and samples treated with endoproteinase Glu-C gave a single band on SDS-PAGE; the digested fragment was identified as residues from 41 to 108 by Mass Spectrometry . A solution of 12 mg/ml MRG2 chromodomain ( residues 41–123 ) was incubated for 2 hours with H3K36me3 peptide and endoproteinase Glu-C at a 200∶400∶1 molar ratio before crystallization . MRG2 chromodomain ( residues 53–123 ) was incubated with H3K4me3/H3K36me3 peptide in the same way , with the exception of the Glu-C treatment . H3K4me3 and H3K36me3 peptides used for crystallization were synthesized , and indicated as residues 1–9 and 31–41 , respectively . Crystallization was performed using the hanging drop vapor diffusion method . Crystals were grown at 16°C by mixing 1 µl of the protein solution with 1 µl precipitant solution . Crystals of MRG2 ( residues 41–108 ) in complex with H3K36me3 peptide were grown under conditions of 0 . 1 M HEPES pH 7 . 5 , 12% PEG 6000 , 5% MPD . Crystals of MRG2 ( residue 53–123 ) in complex with H3K4me3/H3K36me3 were grown under conditions of 0 . 1 M MES pH 6 . 0 , 27% PEG MME 5000 , 0 . 2 M ammonium sulfate . Diffraction data were collected from flash-cooled crystals at 100K at SSRF ( Shanghai Synchrotron Radiation Facility ) . The data was processed using HKL2000 [54] . Molecular-replacement solutions were generated using the Phaser in Phenix program and the crystal structure of MRG15 chromo domain ( PDB entry code 2F5K ) used as a search model . The initial models were built with COOT [55] and refined using Phenix [56] . The final refined structure was represented by Pymol ( The PyMOL Molecular Graphics System , Version 1 . 4 . 1 Schrödinger , LLC ) . Coordinates have been deposited under PDB accession code 4PL6 , 4PLI , and 4PLL . | The photoperiodic flowering in Arabidopsis requires the key regulator CO and its target gene FT . However , how CO regulates FT expression in the context of chromatin remains largely obscure . In this work , we present Arabidopsis MRG1/2 as novel chromatin effectors directly involved in the CO-FT photoperiodic flowering . Firstly , MRG1/2 proteins are identified as recognition factors of H3K4 and H3K36 methylation via their chromodomains . The mrg1 mrg2 double mutant shows a late-flowering phenotype only under long-day conditions through down-regulation of FT but not of CO . MRG2 can directly target in vivo the FT promoter chromatin in a H3K4me3/H3K36me3-level dependent manner . More importantly , MRG2 and CO physically interact and enhance each other's binding to the FT promoter in planta . Determination of co-crystal structures of MRG2 with H3K4me3/H3K36me3 peptides and mutagenesis of a key amino acid residue involved in structural interaction demonstrate that MRG2 reader activity is essential for in planta function . Taken together , our findings uncover a novel mechanism of FT activation in flowering promotion and provide a striking example of mutual interplay between a transcription factor and a histone methylation reader in transcription regulation . | [
"Abstract",
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"Results",
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] | [
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] | 2014 | Regulation of Arabidopsis Flowering by the Histone Mark Readers MRG1/2 via Interaction with CONSTANS to Modulate FT Expression |
The protozoan parasite Giardia intestinalis and the pathogenic bacterium Helicobacter pylori are well known for their high prevalences in human hosts worldwide . The prevalence of both organisms is known to peak in densely populated , low resource settings and children are infected early in life . Different Giardia genotypes/assemblages have been associated with different symptoms and H . pylori with induction of cancer . Despite this , not much data are available from sub-Saharan Africa with regards to the prevalence of different G . intestinalis assemblages and their potential association with H . pylori infections . Fecal samples from 427 apparently healthy children , 0–12 years of age , living in urban Kampala , Uganda were analyzed for the presence of H . pylori and G . intestinalis . G . intestinalis was found in 86 ( 20 . 1% ) out of the children and children age 1<5 years had the highest rates of colonization . H . pylori was found in 189 ( 44 . 3% ) out of the 427 children and there was a 3-fold higher risk of concomitant G . intestinalis and H . pylori infections compared to non-concomitant G . intestinalis infection , OR = 2 . 9 ( 1 . 7–4 . 8 ) . No significant association was found in the studied population with regard to the presence of Giardia and gender , type of toilet , source of drinking water or type of housing . A panel of 45 G . intestinalis positive samples was further analyzed using multi-locus genotyping ( MLG ) on three loci , combined with assemblage-specific analyses . Giardia MLG analysis yielded a total of five assemblage AII , 25 assemblage B , and four mixed assemblage infections . The assemblage B isolates were highly genetically variable but no significant association was found between Giardia assemblage type and H . pylori infection . This study shows that Giardia assemblage B dominates in children in Kampala , Uganda and that the presence of H . pylori is an associated risk factor for G . intestinalis infection .
In low-income countries co-infections involving several different pathogens commonly ocurr [1] . Several recent , cross-sectional studies from different locations , have reported a potential association between G . intestinalis and H . pylori [2] , [3] , [4] . Both organisms colonize the gastrointestinal tract in their human hosts within a close proximity and both organisms are known to infect children at a high rate in low-income countries [5] , [6] , [7] . H . pylori is a gram-negative bacterium that is estimated to infect approximately half of the world population . It colonizes the gastric mucosa of its human host where it may give rise to symptoms such as recurrent peptic ulcers and chronic gastritis , and has also been associated with gastric cancer [8] . The prevalence of H . pylori is high in low-income countries and it was recently shown to colonize 46% of children age 1<3 years in an area of urban Kampala , Uganda [5] . The protozoan parasite G . intestinalis ( syn . G . lamblia , G . duodenalis ) is the causative agent of giardiasis in a wide range of vertebrates , including humans . The parasite is estimated to cause 280 million cases of human giardiasis per year [9] . The disease is characterized by bouts of diarrhea , bloating , flatulence and malnutrition , and is especially troublesome in children living in low-income countries where stunted growth and poor cognitive function have been correlated with the disease [9] , [10] . Asymptomatic Giardia-infections are common [11] , where the host may act as a reservoir for transmission of the disease . Eight different G . intestinalis genotypes or assemblages have been described ( A-H ) [12] , where assemblages A and B infect humans and other mammals and assemblages C through H are more host-specific [13] . Recent data suggest that Giardia assemblage A and B can actually be two different species [14] and several studies have recently shown associations between assemblage type and specific symptoms [15] , [16] , [17] . To date , a large number of human Giardia samples from Europe , Australia , South-America and Asia , have been characterized on the molecular level , mainly on one , but also several genetic loci . Studies dealing with genetic characterization of human-infecting Giardia in the sub-Saharan regions of Africa , are however , much more scarce [18] , [19] . One of these studies was performed in rural western Uganda and an average Giardia prevalence of 40% was detected [19] . Genotyping using the rSSU-rDNA gene showed that the distribution between assemblage A and B was even ( 53% A and 47% B , [19] ) . Although the occurrence of Giardia is assumed to be common in Kampala , Uganda , there are not much data available to confirm this . Also , the prevalence of different Giardia assemblages in infected individuals has not yet been investigated in this area . The aim of this study was to to assess a potential correlation between certain G . intestinalis assemblages and concomitant infection of H . pylori in apparently healthy children aged 0–12 years from Kampala , Uganda using multi-locus genotyping .
This study was part of a survey that was carried out in October/November 2007 in Kampala , Uganda . Details regarding the set up of the study are described in Hestvik et al . [5] , [20] . Sampling of patient fecal material was carried out in Mulago II parish in Kampala , Uganda . Kampala is located just north of the equator and has a tropical , humid climate with two rainy seasons ( mid February – Mid May and September - December ) and two drier periods in between . This is a resource limited area of the town , characterized by informal settlements , congested living , lack of proper sanitation conditions and low education level among adults but it is supported with tap water by Plan International [5] . All samples originated from a completed H . pylori survey on apparently healthy children where 427 fecal samples were analyzed [5] . Children aged 0–12 years were recruited after door-to-door visits; an equal number of children in each age category of 0<1 year , 1<3 years , 3<6 years , 6<9 years and 9<12 years ( around 85 per age group ) . Participants were included in the study if: 1 ) they were apparently healthy , 2 ) aged between 0<12 years , 3 ) had an informed consent from caretaker and 4 ) were able to produce a stool sample within three consecutive days . Ethical approval was obtained from Makerere University , Faculty of Medicine , Research and Ethics Committee in Uganda , the Regional Committee for Medical and Health Research Ethics , West-Norway ( REK-VEST , Ref . Nr . 2007/13898-ANØL ) and the regional ethical committee at Uppsala University , Uppsala Sweden ( Ref . Nr . 2009/025 ) . The data collectors were trained in ethical issues prior to the study . Oral and written information about the study was given to the caretakers ( parents/guardians ) either in English or a preferred local language . Informed consent in writing was obtained from at least one caretaker ( parent/guardian ) of each of the study participants . A stool sample was requested from each participating child and was collected in air-tight containers either at time of the encounter , at the end of the day , or the following morning . Stool samples were transported from the field to the laboratory at ambient temperature twice daily and stored in a +4°C fridge until the same afternoon or the following day when analysis were carried out . All stool samples were investigated by microscopy for protozoa and helminths , a culture was performed to assess for enteropathogens and all samples were tested for Helicobacter pylori . The presence of H . pylori antigen in feces was evaluated using HpSA ImmunoCardSTAT as described in Hestvik et al [5] . This faecal monoclonal antigen test has high sensitivity , specificity , and accuracy in children , 91–96% , 95–96% and 94–96% , respectively [21] , [22] Instructions given by the manufacturer were followed and all positive control tests were positive . The results were reported as positive or negative on the basis of the manufacturer's cut-off values . Furthermore , Giardia cysts and trophozoites were identified on wet fecal smears using light microscopy and subsequently fixed in ethanol as previously described [23] . Human fecal samples containing Giardia cysts ( n = 86 ) were collected . The DNA content of the Giardia cysts was studied using FITC labelled CWP ( cyst-wall protein ) -specific antibodies ( Agua-Glo , Waterborne Inc . , New Orleans , LA , USA ) together with DAPI ( 4′6-diamino-2-phenyl-indole ) prior to the extraction of the DNA . DNA from the 45 samples staining strongly positively with DAPI , indicating intact DNA , was extracted in Sweden as described elsewhere [23] . All PCR primers used in this study can be seen in Supplementary File S1 . Nested PCR was performed to amplify a 511 bp fragment of the ß-giardin gene ( bg ) , a 530 bp fragment of the triose phosphate isomerase gene ( tpi ) , and semi-nested PCR was used to amplify a 430 bp fragment of the glutamate dehydrogenase gene ( gdh ) [24] , [25] , [26] . Samples that were identified as assemblage AII , were further analyzed on loci in chromosome 3 ( ORFs: GL_50803_113553 and GL_50803_3095 ) and chromosome 5 ( ORF: GL_50803_39587 ) according to Cooper et al . [27] , [28] . All PCR products were analyzed using electrophoresis on 1 . 5% agarose gels stained with GelRed ( Biotium , Hayward , CA , USA ) . Positive PCR products amplified with primers from the respective loci were sequenced in both directions using the BIG DYE 3 . 1 sequencing kit ( Applied Biosystems , La Jolla , CA , USA ) . Prior to sequencing , the PCR products were purified using Exo-sap IT™ according to the manufacturer's instructions ( GE Healthcare , Uppsala , Sweden ) . Post sequencing , the labelled products were purified using the Qiagen Dye-ex 2 . 0 spin kit ( Qiagen , Hilden , Germany ) . The sequenced products were analyzed in an Applied Biosystems 9100 Seq ( Applied Biosystems , La Jolla , CA , USA ) . Sequences and chromatogram were analyzed and edited using the BioEdit software , Version 7 . 0 . 5 . BLAST analyses were performed on all sequences ( http://www . ncbi . nlm . nih . gov/blast/ ) , and unique sequences were uploaded in GenBank . Sequence data is found in Supplementary Files S2 , S3 , and S4 . Assemblage A and B specific primers , targeting assemblage-specific regions of the tpi gene [29] , [30] , were utilized to detect mixed infections . The nucleotide sequence datasets for the bg , gdh and tpi genes used in a previous study [16] were used as references in the phylogenetic analyses . Unambiguous gene sequences ( i . e . sequences lacking double peaks ) obtained in this study were added to reference datasets . This yielded datasets of 475 , 393 , and 490 nucleotide positions for bg , gdh and tpi , respectively , suitable for phylogenetic analyses . RAxML version 7 . 0 . 4 [31] was used to perform maximum likelihood analyses with the GTR substitution model and among-site rate variation ( GTRGAMMA ) , together with bootstrap analyses with 500 replicates . The sequences from the chromosome 3 locus listed in Supplementary Table S3 were analysed using the same methodology . The Assemblage B nucleotide sequences obtained in this study , that were unique compared to other Giardia sequences submitted to public databases , have been deposited in Gen Bank under the following accession numbers [GenBankAcc No: JQ303244–JQ303248] . To explore the prevalence of Giardia and factors associated , binary logistic regression as well as multiple logistic regression were performed . Factors with p-values higher than 0 . 1 were not included in the final model .
Baseline characteristics of the whole study population ( n = 427 ) , are presented in the Methods section and in Hestvik et al [5] . Giardia cysts or trophozoites were observed in fresh stool by direct light microscopy in samples from 86 children . In these 86 children the mean age ( ±SD ) was 5 . 0 ( 3 . 1 ) years: for girls 5 . 3 ( 3 . 3 ) years and boys 4 . 5 ( 2 . 8 ) years ( Table 1 ) . The genders were equally represented: 41 ( 47 . 7% ) girls and 45 ( 52 . 3% ) boys . The prevalence of G . intestinalis was 20 . 1% ( 86/427 ) and peaked in the age group including children 3<6 years , 28 . 7% . There was a significantly higher risk of colonization in the older age groups ( 1–12 years ) compared to the youngest ( 0 to 1 years , Table 1 ) . There was no statistically significant association between G . intestinalis and gender , type of toilet , drinking water source and type of housing for the child ( Table 1 ) . Children colonized with G . intestinalis had a statistically significant higher risk for co-colonization with H . pylori ( Table 1 ) . The finding remained significant also after adjustment for age , gender , type of toilet , drinking water source and type of housing for the child OR ( 95%CI ) 2 . 8 ( 1 . 3–6 . 2 ) . The prevalence of H . pylori was already 29% in the youngest age group 0<1 year , peaking in the 6<9 age group with 55% [5] , ( Fig . 1 ) . This should be compared to an 8% Giardia prevalence in the youngest group and a peak in the 3>6 age group ( Fig . 1 ) . Thus , the level of Giardia colonization starts off slower but peaks earlier compared to the H . pylori colonization . Out of the panel of 45 Giardia samples available for molecular analysis , 31 ( 70% ) showed the expected 511 bp bg fragment , 34 ( 76% ) showed the expected 430 bp gdh fragment , and 29 ( 65% ) showed the expected 530 bp tpi fragment when analyzed using agarose gel electrophoresis of the PCR products . Thus , a maximum of 76% of the Giardia positive samples could be verified when analyzed with PCR . In all cases where the PCR gave negative results , the Giardia cysts did not stain as strongly with DAPI , indicating that the DNA had been too degraded , thus explaining the negative PCR result . Products from the 32 positive PCR products at the bg locus yielded five assemblage A , 26 assemblage B and one mixed assemblage infection . Within assemblage A , all samples were of the AII sub-assemblage . Within assemblage B nine sequences were non-heterogeneous , out of these , four were unique and deposited in GenBank ( JQ303244–JQ303247 ) . Three sequences were identical to Sweh095 ( HM165221 ) , one was identical to Sweh003 ( HM165209 ) , and one was identical to Sweh023 ( HM165212 ) . The remaining 15 sequences had one to six heterogeneous substitutions over a total of 19 different positions at the bg locus . At the bg locus , all nucleotide substitutions , with the exception of one isolate , were present at the third coding position , indicating the absence of non-synonymous amino acid changes ( Supplementary File S2 ) . Out of the 34 positive PCR products on the gdh locus , five were assemblage A , 28 were assemblage B and one was a mixed assemblage infection . The samples that indicated assemblage A and mixed infection at the bg locus showed the same results at the gdh locus . All assemblage A samples were of the AII sub-assemblage . Samples that were identified as assemblage B gave rise to 26 sequences with two to 14 heterogeneous substitutions over a total of 37 positions at the gdh locus . Three sequences were without heterogeneous substitutions , where one was identical to RW04 ( AB638286 ) and one was identical to Sweh035 ( HM136889 ) . One sequence was unique and deposited in GenBank [Acc No: JQ303248] . At the gdh locus all nucleotide substitutions , with the exception of one isolate , were present at the third coding position , indicating the absence of non-synonymous amino acid changes ( Supplementary File S3 ) . Sequencing at the tpi locus yielded three assemblage A , 25 assemblage B and one mixed infection . The same samples that indicated assemblage A and mixed infection at the two previously described loci showed the same results at the tpi locus , with the exception that only three out of the five assemblage A samples gave positive PCR results . All assemblage A samples were of the AII sub-assemblage . Sequencing of the assemblage B samples at the tpi locus generated 23 sequences with two to 14 heterogeneous substitutions over a total of 44 positions . Three sequences were without heterogeneous substitutions , where one was identical to Ba7 ( EU272153 ) , and the other two were identical to Sweh136 ( HM140720 . All assemblage B sequences without heterogenous substitutions were included in phylogenetic analysis , performed independently for each genetic locus . At the tpi locus two non-synonymous amino acid substitutions had occurred that resulted in a stop codon in the amino acid sequence ( GU1157 and GU1161 ) , one non-synonymous substitution that had occurred in the majority of the assemblage B isolates when aligned to the GS reference strain was a substitution from a Tyr to a His ( Supplementary File S4 ) . Also several positions , where substitutions in the nucleotide sequences yielded double peaks in the chromatograms , implied potential non-synonymous amino acid substitutions ( n = 16 ) , one of which was present in a position that would lead to a stop codon in one of the isolates ( Supplementary File S4 ) . All 45 samples that were subjected to molecular analyses , were further analyzed using nested PCR of the tpi locus , where the second sets of primers are designed to be A- or B-assemblage specific . Only the 34 samples that previously indicated positive results in the PCR reactions indicated positive results when assayed with the assemblage-specific PCR ( Supplementary Table S1 ) . Out of the 34 samples , a total of four samples indicated positive results with both primer pairs , indicating mixed assemblage infection ( Supplementary Table S2 ) . Thus , four out of the 34 PCR positive samples contained both assemblage A and B Giardia . In summary , five out of 34 ( 14 . 7% ) samples yielded assemblage AII , 25 out of 34 ( 73 . 5% ) yielded assemblage B , and four out of 34 ( 11 . 8% ) yielded mixed assemblage AII and B infection ( Supplementary Table S2 ) . Phylogenetic analyses were performed to examine the within assemblage diversity of the obtained isolates . Sequences lacking double peaks were included in the analyses due to the uncertainty of the origin of the sequence heterogeneity within templates . The diversity of the assemblage B sequences obtained was larger with seven , three and two distinct subtypes for the bg , gdh and tpi , respectively ( Fig . 2 ) . For reference , sequences obtained in previous studies from our laboratory were included [13] , [16] . Unfortunately , none of the isolates yielded unambiguous sequences in all three genes . Therefore , phylogenetic trees based on the individual genes are presented ( Fig . 2 ) . The Ugandan sequences are found in different parts of the assemblage B trees , most clearly in the bg tree ( Fig . 2A ) . This suggests that the genetic diversity of the Giardia assemblage B lineages present in our study area in Uganda is comparable with the total diversity so far observed in humans and animals in different places in the whole world [13] , [16] . Thus , the genetic variability in assemblage B Giardia is extremely high in this small geographical area . It is also obvious that the topologies of the three phylogenetic trees ( Fig . 2A to C ) do not agree . Certain isolates , e . g . UG1083 , show up in different parts of the trees ( Fig . 2 ) , suggesting different evolutionary history of the different genes in one isolate . This could be due to recombination between the different assemblage B isolates . The five assemblage A isolates were sequenced on the bg , gdh and tpi loci and they were all identical to sequences previously classified as MLG AII-2 , earlier detected in 26 Swedish human isolates [16] . In order to increase the resolution of the genotyping of the five assemblage AII isolates we analyzed two additional chromosomal regions: one locus located on chromosome 3 and one on chromosome 5 [28] . Sequences from the assemblage AII isolate JH ( chromosome 3 locus ( EU188624 ) and chromosome 5 locus ( EU188636 ) ) were used as reference for comparative sequence analyses . Sequencing of the five assemblage AII samples at the chromosome 3 locus yielded three subgroups , where one showed a pattern identical to isolate 335 ( GU1119 ) , another one was identical to isolate 303 ( GU1086 ) , and a third one gave rise to a unique pattern ( GU459 , Fig . 3 and Supplementary Table S3 ) . None of the sequences were identical to the JH reference sequence ( Fig . 3 ) . Sequencing of the assemblage AII samples at the chromosome 5 locus yielded two different subgroups; one was identical to the JH reference strain and a second identical to isolate 303 ( Supplementary Table S4 ) . Interestingly , these two subgroups do not show any correspondence to the groups identified in the chromosome 3 locus ( Fig . 3 ) . This suggests again different evolutionary histories of the two loci studied , which could be the result of recombination between sub-assemblage AII isolates . We studied if any of the two human Giardia assemblages ( A and B ) could be specifically associated to H . pylori colonization . Giardia assemblage B infection had a weak association with H . pylori colonization with an Odds Ratio with 95% Confidence interval ( OR 95%CI ) of 5 . 0 ( 1 . 9–16 ) , Table 2 , but more data are needed in order to claim an assemblage-specific association with H . pylori . We also noticed that females mainly harbored assemblage A and males assemblage B ( Supplementary Table S5 ) . Mixed assemblage infections were only seen in the 1<5 age group and assemblage B dominate in the 5<12 age group ( data not shown ) .
This is a multi-locus genotyping study of G . intestinalis isolates from apparently healthy children in an urban setting in Eastern Africa , south of Sahara ( Kampala , Uganda ) . The overall prevalence of G . intestinalis in the study population was 20% , which is lower than in earlier studies in rural districts in Uganda [19] . In the study population , we found that children aged 1<5 years had the highest frequency of giardial infection . These findings correlate with the report from in a survey from rural West-Uganda , where a higher rate of colonization was described in children as compared to adults [19] and this is most likely due to unsanitary conditions in these settings . It is important to note here that even if all children enrolled in this study were apparently healthy they constitute a reservoir for transmission of the disease to other children . As indicated in Table 1 , there was no statistically significant association between G . intestinalis and gender , type of toilet , source of drinking water or type of housing , for the study population , which is indicative of a broad presence of Giardia in the environment where these children reside . Berkman et al , previously reported that Giardia infections may lead to complications such as; stunted growth , poor cognitive function , and sometimes death , in young children [10] , which highlights the significance of our findings and suggests further efforts are needed regarding the prevention of transmission of Giardia and other gastro-intestinal pathogens in young children in low-income countries . The two different Giardia assemblages A and B have been associated with different symptoms [15] , [16] . Here we have shown that children in urban Kampala , Uganda , predominantly carry assemblage B Giardia , which conforms well to reports from several other studied regions of the world [15] , [16] , [23] , [32] , [33] , [34] but it differs compared to an earlier genotyping study in Uganda [19] . This suggests large local differences in the prevalences of different Giardia assemblages . The genetic diversity of the Giardia assemblage B lineages present in our study area in Uganda is very high , comparable to the genetic diversity observed among animals and tourists infected in different places in the world ( Fig . 1 ) [13] , [16] . Genotyping of Giardia isolates is problematic . Several recent reports have shed light on the presence of highly frequent , ambiguous , substitution patterns , which are visualized as double peaks within single nucleotide positions in the sequencing chromatograms upon performing molecular sequence analysis of Giardia isolates [13] , [16] , [23] . It has previously been suggested that these sequence substitutions are due to mixed sub-assemblage infections , allelic sequence heterozygosity ( ASH ) , or a mixture of the two within a patient sample [16] , [23] , [35] , [36] . It should be noted here that Giardia is an unusual eukaryote with two diploid nuclei , thus there are at least four different alleles of each gene . Accumulated data suggest that recombination occur between different Giardia isolates [37] . A high transmission rate , which is common in endemic areas , could potentially lead to a higher rate of exchange of genetic material between different isolates , which in turn could increase the problems with typing and phylogenetic analyses , as seen here . It should , however , be noted that these problems do not rule out the use of molecular epidemiology as a tool during Giardia outbreaks . However , it is clear that better typing methodology is needed . We found a significantly higher frequency ( 3 times ) of giardial infection in cases where infected children also harbored the bacterial pathogen H . pylori . This is comparable with findings from recently published cross-sectional surveys [2] , [3] , [4] . We also found a weak but specific link to Giardia assemblage B ( Table 2 ) . Transmission of H . pylori is not completely clear [38] but it is possible that the two infectious diseases are transmitted via the same route; the fecal-oral route , and that this explains the high level co-infections . It has been suggested that H . pylori transmission in low resource settings is more complex ( transmission via food , water and non-parental caretakers to infant ) than in high-income countries where within-family transmission seem to dominate [38] . It will be interesting to study the genetic variability of H . pylori and G . intestinalis isolates within families in our study area since this can answer questions about transmission and establishment of co-infections . This will be important in the development of measures to reduce transmission of these two important pathogens . The importance of polymicrobial infections has gained tremendous impact in recent years and synergistic infections have been identified [39] . In synergistic polymicrobial infections , one microbe creates a favorable environment in order for another one to more easily colonize a specific niche of their common host [39] . H . pylori has been linked to co-infections earlier , e . g . the fluke Schistosoma japonicum is associated with an alteration in the antibody response to H . pylori during co-infections [40] . Another interesting example is co-infections of H . pylori and Salmonella typhimurium in mice [41] . In this study it was shown that H . pylori represses the Th17 response in the lower gastrointestinal ( GI ) tract via extragastric immunomodulatory factors . Increased IL-10 expression was seen in mesenteric lymph nodes and in the cecum . Regulatory T cells activated by H . pylori in the stomach have been shown to reduce inflammation in the lungs and to prevent induction of allergic asthma [42] . This shows that H . pylori infection in the stomach can induce immunoregulatory responses systemically and also in the intestine . The large number of co-infections in our study is possibly due to an elevated risk of G . intestinalis colonization upon the presence of H . pylori in human patients or , alternatively , H . pylori colonization may be facilitated by a previous establishment of Giardia . A longitudinal study of children from birth to 3 years of age were Giardia and H . pylori infections are diagnosed monthly and during diarrhea episodes could resolve this issue and also show if symptoms are affected by the other infection . The mechanisms behind this potential microbial interplay indeed need to be further investigated . Since both pathogens may be cultured in vitro [43] , [44] as well as they both successfully infect gerbils [45] , [46] , in vitro and in vivo assays may be implemented to gain further understanding of how they interact with the host's immune response but also to determine if H . pylori-induced changes in the pH level of the stomach facilitate Giardia infections [47] . In conclusion , 20% of the 427 healthy children in this region of urban Kampala , Uganda were carriers of Giardia . Molecular sequence analysis of a sub-set of the Giardia positive samples ( n = 45 ) showed 15% assemblage A , 74% assemblage B , and 11% mixed A and B assemblage infections . The genetic variability was very high in the assemblage B isolates , whereas it was low in assemblage A isolates . We found a strong correlation of concomitant G . intestinalis and H . pylori infections in children in Kampala , Uganda and a weak association to Giardia assemblage B . This information will be important in the design of further studies of these pathogens in Uganda and other low-income countries in order to develop new control measures . | G . intestinalis and H . pylori are known to infect the gastrointestinal tract of humans early in life and to be very prevalent in endemic areas throughout life . H . pylori colonizes the gastric mucosa and may give rise to peptic ulcers , chronic gastritis and gastric cancer whereas Giardia causes diarrhea , bloating , flatulence and malnutrition . The genetic variability within G . intestinalis is high with two genotypes or assemblages ( A and B ) infecting humans . These two different genetic types of humans have also been associated with differences in symptoms . Here we have studied these two infections in non-symptomatic children in Kampala , Uganda . H . pylori was found in 44% out of the 427 children and G . intestinalis was found in 20% with children age 3<6 years showing the highest rates of colonization . The children were primarily infected with Giardia assemblage B parasites and Giardia infected children had a 3-fold higher risk of also having H . pylori infection . However , this was independent of Giardia assemblage type . This information will be important in the development of new control measures of these prevalent pathogens in Uganda and other low-income countries . | [
"Abstract",
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] | [
"medicine",
"infectious",
"diseases",
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] | 2012 | Common Coinfections of Giardia intestinalis and Helicobacter pylori in Non-Symptomatic Ugandan Children |
Cell entry by non-enveloped viruses requires translocation into the cytosol of a macromolecular complex—for double-strand RNA viruses , a complete subviral particle . We have used live-cell fluorescence imaging to follow rotavirus entry and penetration into the cytosol of its ∼700 Å inner capsid particle ( “double-layered particle” , DLP ) . We label with distinct fluorescent tags the DLP and each of the two outer-layer proteins and track the fates of each species as the particles bind and enter BSC-1 cells . Virions attach to their glycolipid receptors in the host cell membrane and rapidly become inaccessible to externally added agents; most particles that release their DLP into the cytosol have done so by ∼10 minutes , as detected by rapid diffusional motion of the DLP away from residual outer-layer proteins . Electron microscopy shows images of particles at various stages of engulfment into tightly fitting membrane invaginations , consistent with the interpretation that rotavirus particles drive their own uptake . Electron cryotomography of membrane-bound virions also shows closely wrapped membrane . Combined with high resolution structural information about the viral components , these observations suggest a molecular model for membrane disruption and DLP penetration .
Non-enveloped viruses must breach a membrane to enter and infect a cell . Various groups of viruses have evolved distinct molecular mechanisms to carry out this penetration step , which leads to translocation of the infecting particle from an endocytic vesicle or other intracellular compartment to the surrounding cytosol . For example , picornaviruses and reoviruses release a myristoylated peptide , which forms pores in a lipid bilayer [1] . Structural and mutational evidence suggests that rotaviruses penetrate by disruption of an endocytic membrane , driven by a conformational change in one of its outer-surface proteins; the transition has some similarities to the fusion-promoting conformational change of certain enveloped-virus glycoproteins [2] , [3] . In no case , however , do we yet have a detailed description of conformational changes in viral proteins couple to changes in the membrane being disrupted nor can we confidently place these events in the context of an intracellular compartment . The double-stranded RNA ( dsRNA ) viruses offer particular advantages for analyzing cell entry by individual virions . An infectious rotavirus particle encapsidates eleven distinct double-stranded RNA segments within a three-layer protein coat ( a “triple-layered particle” , or TLP: Fig . 1A ) [4] . The inner two layers , composed of viral proteins 2 and 6 ( VP2 and VP6 ) respectively , remain associated with the RNA as a “double layered particle” ( DLP ) , even after penetration . The outer layer , composed of two proteins , VP4 and VP7 , is the agent that delivers the DLP into the cytosol . VP7 , a Ca2+-stabilized trimer [5] , clamps onto the VP6 trimers [6] , [7] , which form a T = 13 icosahedral array on the DLP surface [8] . The VP7 lattice thus generated holds the sixty trimeric VP4 “spikes” in place [9] . Tryptic cleavage of VP4 into two fragments , VP8* and VP5* , is an activation step that primes the protein for conformational changes linked to penetration ( Fig . 1B ) [10]–[13] . The final , stable conformation of VP5* is probably the folded-back structure shown in the last panel of Fig . 1C , which illustrates a model for membrane disruption generated by conformational changes in VP5* . The model derives from structural studies of VP4 and its fragments [2] , [14]–[16]; the experiments we describe here test some of the model's predictions . Rotaviruses infect enterocytes of the small intestine; in culture , they grow in a variety of epithelial-origin cells [4] . For the best characterized strains , attachment and internalization depend on a surface glycan . Animal rotaviruses generally require sialic acid , either at a terminal or subterminal position ( the corresponding viruses then being neuraminidase sensitive or insensitive , respectively [17] ) , but some human rotaviruses do not [4] . Recent work shows that one such virus interacts with a non-sialylated glycan , the A-type histo-blood group antigen , which contacts VP8* at the position usually occupied by sialic acid [18] . The glycans identified as rotavirus receptors probably mediate uptake when they are headgroups of glycolipids . Knockdown with RNAi of enzymes required for ganglioside biosynthesis in the MA104 epithelial cell line reduced infectivity of several different human and animal rotaviruses , all of which bind sialic acid [19] . The same viruses appeared to attach adequately to the surface of the RNAi-treated cells , but failed to penetrate; knockdown of ganglioside biosynthesis would not have affected the presence of similar glycans on glycoproteins , which therefore could have been sites of the observed non-productive attachment . Conflicting evidence concerning the role of particular routes of uptake in rotavirus infection may come from the potential for more than one productive pathway of entry and from the choice of a particular preferred pathway by any specific viral strain . Thus , one paper reports that of four strains tested in MA104 cells , three showed a dependence on components of clathrin-mediated endocytosis , while the fourth ( RRV , the one we have used for the experiments in this paper ) did not [20] . Previous efforts to detect entering virions directly by optical microscopy have relied on immunofluorescent staining with antibodies specific for specific conformational states of VP4 ( or VP8* and VP5* ) , VP7 , and VP6 [21] . Use of fixed and permeabilized cells , as required for immunofluorescence , precludes tracking of individual virions , and kinetic inferences are therefore indirect . The functional recoating procedure that allows us to add back recombinant VP4 and VP7 to DLPs stripped of their outer layer [22] provides an effective means to label each component with a distinct fluorophore and then to follow entry by live-cell imaging . We report in this paper a series of such experiments , from which we derive the following conclusions . Virions bind tightly upon contact with a cell , becoming relatively immobile on the cell surface in less than a minute . Within about five minutes of attachment , many of the fluorescently tagged rotavirus particles have become sufficiently engulfed that they are inaccessible to antibodies and insensitive to elution with EDTA . Clathrin or the AP-2 clathrin adaptor colocalize only rarely with entering particles , and infectivity in these cells is dynamin independent . Most particles that release a DLP into the cytosol – an event marked by separation of the DLP label from the labels on VP7 and VP4 – have done so by ten minutes post attachment . At later time points , we find virions in Rab5-labeled early endosomes , but these particles rarely penetrate . Moreover , we find that infectivity is insensitive to overexpression of Rab5 mutants . Images from thin-section electron microscopy ( EM ) and electron cryotomography ( cryoET ) are consistent with these live-cell observations and suggest that direct contacts between virion and membrane drive engulfment .
We can monitor individual behaviors and roles of the DLP , VP4 and VP7 during rotavirus infection by combining previously optimized recoating techniques [22] , [23] with amine-specific , fluorescent labeling of these components ( Materials and Methods ) . We produced reconstituted rhesus rotavirus ( RRV ) TLPs with two ( “doubly-labeled” ) or three ( “triply-labeled” ) structural components linked to spectrally distinct fluorescent dyes , which were discernible in confocal microscopy or upon electrophoresis ( Figs . 2A , B ) . The modifications had no effect on viral infectivity , as assessed by fluorescent focus assays ( Fig . 2C ) . RRV requires cell-surface sialic acid for attachment and subsequent infection . We verified that our labeled , assembled particles had the same requirements when infecting the BSC-1 cells used here , by comparing virion binding to neuraminidase treated and untreated cells . The results ( Fig . S1 ) confirmed that removal of sialic acid from surface glycans greatly reduces viral attachment . To visualize rotavirus cell entry in vivo , we infected BSC-1 cells with reconstituted , labeled virus; we then imaged the course of infection by spinning-disk confocal microscopy . BSC-1 cells , which are permissive for various rotaviruses [24] , spread on the coverslip to generate thin , broad periphery , placing much of the non-adherent cell surface within the focal depth of the microscope and allowing us to capture in one image a large area of plasma membrane . We added doubly- or triply- labeled virus to cells and followed over time the fate of the various components . Over a 10–30 min period after addition of labeled virions , we observed gradual accumulation in the cytoplasm of rapidly moving , singly labeled particles , corresponding to dye-linked DLPs – i . e . , virus particles that have lost their outer coat ( Fig . 2D ) . Careful tracking of individual particles revealed a loss in the intensity of the outer-layer proteins preceding an abrupt separation of DLP label from residual outer-layer protein label and rapid diffusional motion of the DLP away from the position of initial attachment ( Fig . 2E , F ) . We interpret this event as release of the DLP into the cytosol . In some cases , the rapidly moving DLP left behind a distinct residue of VP7 and VP4 fluorescence close to the plasma membrane ( Fig . 2E ) ; in the majority of cases , the fluorescence intensity associated with the outer coat of the virus had decreased to the threshold of detection by the time of DLP release . The motion of the released DLP was random and much faster than that of TLP from which it derived ( Fig . 2E , F; Fig . 3C , D ) and consistent with free diffusion in the cytosol for a particle of radius ∼350 Å ( Fig . 3C , D; see caption to Fig . 3C ) . By 30 minutes after addition , the fraction of released particles had reached a plateau of about 20% ( Fig . 3A ) . ( In principle , our observations on DLP release do not exclude the possibility that the DLP has “budded away” from the other components , retaining some surrounding membrane . There is no clear molecular basis for such a mechanism , however: VP5* binds membranes , and DLPs do not . The properties of VP4 mutants make an interpretation other than access to the cytosol very unlikely – see below . ) The distribution of times to DLP release in the experiments we analyzed is shown in Figure 3B . The mean time from binding to penetration is approximately 10 minutes; the shortest times are between 6 and 8 minutes . Upon binding to the cell surface , some particles appeared to move about relatively rapidly on the cell surface ( >4 µm/min: “lateral motion” ) , before becoming nearly stationary ( <1 µm/min: “capture” ) ; the majority ( >70% ) appeared to not to have a lateral-motion phase at all . A correlated , centripetal motion of captured particles in a given region of the cell surface probably arose from retrograde flow of the plasma membrane on which they were bound ( e . g . , capture phase in Fig . 2C ) . For the subset of captured virus particles that ultimately uncoated , the fluorescence intensity of the labeled outer-layer protein ( s ) VP7 and/or VP4 often slowly declined , while the particles remained more or less fixed . The event we designate as “release” is characterized by the sudden onset of rapid motion of the uncoated DLP ( average speed >7 µm/min , with frequent change of direction ) , leaving in place any residual fluorescence from outer-layer proteins . Figs . 3E and 3H show distributions for the durations of the various phases of entry just described . For the majority of viruses that had a lateral-motion phase ( <30% of the total ) , capture occurred within 5 minutes; the mean duration of this phase for all particles was less than a minute ( ∼0 . 7 minutes ) . The mean interval between capture and onset of uncoating ( decline of fluorescence intensity for VP4 and/or VP7 ) was ∼2 . 5 minutes , and the mean duration of the “uncoating” phase ( time between onset of uncoating and decline either to undetectable VP4 or VP7 signal or DLP release , whichever came first ) was ∼4 . 5 minutes . For about half the particles ( ∼50% frequency ) , release coincided with the end of apparent uncoating; for the remaining particles , the DLP appeared to be relatively stationary after the outer-layer fluorescence had declined maximally . For all particles , the mean interval between loss of detectable VP4 or VP7 and release of the DLP was ∼3 . 5 minutes . During this time , there may have been some outer-layer protein continuing to dissociate from these particles , but with a total fluorescence below the detection threshold of our microscope configuration . The VP7-directed neutralizing antibody , m159 , inhibits rotavirus entry by preventing uncoating of trimeric VP7 [25] . We used fluorescently labeled m159 to determine when in the time course of entry just described the virion becomes inaccessible ( Fig . 4A ) . We added antibody 5–7 minutes after adding virus . Some cell-attached virions bound the antibody , but a number failed to do so . Many in the latter group proceeded to uncoat and release , with the characteristic sequence of stages described above ( Fig . 4A ) . We measured the intervals between addition of antibody and onset of uncoating and time DLP release . The mean time between antibody addition and the onset of uncoating of antibody-inaccessible particles was about 2 minutes , with a range between 0 and ∼7 minutes; release followed by about 4 minutes the onset of uncoating ( Fig . 4B ) . Virus particles exposed to the medium on the surface of the cell bound antibody within seconds of its addition and never uncoated ( data not shown ) , in keeping with the known characteristics of m159; controls with particles bound to the coverslip of the imaging chamber confirmed that all viruses that are exposed to the medium bind the antibody quickly and efficiently ( Fig . 3A and data not shown ) . Chelation of Ca2+ , by EDTA or similar agent , dissociates the VP7 trimer and strips the outer layer from TLPs [5] . When cells are pulsed with EDTA-containing medium , any virus not internalized loses both VP7 and VP4 and dissociates from the cell surface ( Fig . 4C , top panel ) . We exposed the cells to EDTA-containing medium for no longer than 10 seconds before reintroduction of medium containing calcium , thus minimizing damage to the cells ( Fig . 4C , middle panel ) . As a control , viruses that were bound directly to the coverslip were monitored to verify that all viruses exposed to medium had lost their outer layer ( Fig . 4C , bottom panel ) . By about 5 minutes , ∼50% of cell-bound viruses were EDTA resistant ( Fig . 4D ) , in good agreement with the time between attachment and the onset of uncoating . Comparison of the overall time to uncoating ( Fig . 3B ) with the kinetics of internalization as determined by EDTA resistance indicates that in our experiments there was on average a ∼1–3 minute lag between time to internalization and time to cytoplasmic release . Thus , sequestered virions spend only a relatively short time in an uptake compartment before release of the DLP into the cytosol . Previously characterized mutations in the hydrophobic loops of VP5* block infection but do not interfere with binding or uptake [3] . These same mutations also prevent trimeric VP5* from associating with membranes , when the trimer is prepared in vitro and in the presence of liposomes , by successive treatment with chymotrypsin and trypsin ( which generates the species designated “VP5CT”: [26] ) . They also fail to release α-sarcin into the cytosol [3] . We followed the uptake of particles recoated with one of these VP4 mutants , V391D , which reduces infectivity by a factor of about 104 [3] . The data in Fig . 5 show that although these particles acquire EDTA resistance with normal kinetics , they fail to release DLPs , even after 45 minutes . Combining this observation with loss of α-sarcin release by the same mutant strengthens our conclusion that DLP release corresponds to cytosolic access . The particles recoated with mutant VP4 also exhibit a somewhat longer lateral motion phase than do wild-type TLPs ( Fig . S2 ) , suggesting that the hydrophobic loops might participate in engulfment as well as in membrane disruption . Disulfide crosslinking of VP7 trimers also blocks infection , by preventing VP7 dissociation [25] . Particles recoated with this modified VP7 have normal uptake kinetics , which we have followed by accessibility to the m159 antibody ( data not shown ) , but like the VP4 V391D mutation , the VP7 disulfide crosslinks prevent DLP release ( Fig . 4 ) . Thus , the effects of these mutations on infectivity correlate closely with their effects on the entry pathway detected by live-cell imaging . Viruses can exploit a variety of cellular uptake mechanisms . We looked for colocalization of entering rotavirus particles with markers for particular intracellular compartments . We followed each particle for long enough that we could confidently classify its entry as “productive” or “non-productive” – the former defined by the release step described above . Less than 20% of the productive events involved particles that had co-localized at any time with the plasma-membrane clathrin adaptor , AP-2 , with dynamin ( required for budding of clathrin-coated vesicles and probably for caveolin-associated membrane remodeling ) , or with Rab5 , a marker for early endosomes ( Fig . 6A ) . Moreover , non-productive events correlated positively with Rab5 ( Fig . 6B ) . We ruled out more directly a requirement for Rab5 by ectopic expression of two Rab5 mutants – a dominant negative , inactive form ( Rab5DN , bearing the mutation S34N ) and a constitutively active form ( Rab5CA , bearing the mutation Q790L ) . We transfected BSC-1 cells , seeded on prepared coverslips , with vectors encoding the Rab5 mutants fused to eGFP . One day later , we added RRV at various MOI , allowed infection to proceed overnight , fixed the cells , and assayed by immunofluorescence for VP7 expression . Each coverslip , corresponding to a particular mutant Rab5 and a particular MOI , had transfected and untransfected cells . We could therefore determine for each MOI the proportion of cells infected , both for those expressing the Rab5 mutant and for those expressing only endogenous , wild-type Rab5 . The data in Fig . S3 show that neither of the two mutant Rab5s influenced the efficiency of infection . We conclude , from the low frequency of escape from Rab5 endosomes and from the negligible effects of Rab5 mutants , that rotavirus entry in BSC-1 cells does not require transport to early endosomes and that particles with a Rab5-endosomal fate may have reached a dead end . This conclusion is consistent with the rapid time course of uncoating and release documented above . We also found that rotavirus infectivity in BSC-1 cells is insensitive to hydroxy-dynasore , a second generation dynamin inhibitor ( Fig . S4 ) . We followed stages of viral entry by conventional thin-section electron microscopy , to complement the live-cell imaging studies with visualization of cellular ultrastructure and membrane morphology . We exposed cells to virus for various times , then chemically fixed the cells and prepared them for EM by standard techniques ( see Methods ) . Fig . 7 shows three apparent stages of viral uptake , which we label “bound” , “engulfing” , and “enclosed” . ( We cannot , of course , rule out a residual membrane neck , oriented away from the plane of the section , connecting an “enclosed” particle with the cell surface . ) We interpret the first and last of these stages as corresponding , respectively , to the “lateral-motion/capture” and “capture/uncoating” phases detected by live-cell imaging , with engulfment as the transition between them; the asynchrony of events at the cell surface and the times required for fixation prevent any direct experimental correlation of the two methods . Heavy-metal staining of virions is somewhat irregular , with a strong concentration of stain on the RNA interior and relatively weak and spotty staining of the protein shells . Weak but perceptible bridges between the virus particles and the cell membrane are consistent features of all the images . The most striking property of the engulfing and enclosed particles is the relatively uniform distance – roughly 550 Å – between the surrounding membrane and the center of the virion . There are no evident specializations on the cytosolic side of the membrane , such as membrane-bound molecules or cytoskeletal assemblies ( Fig . 7 ) , except for some infrequent examples of clathrin-mediated uptake ( Fig . 7B , right-hand panel ) . The most straightforward interpretation is that the particles are driving their own engulfment through direct contacts with the surrounding membrane . We show in the Discussion that the dimensions and structural properties of the VP4 spikes on infectious TLPs are consistent with this interpretation . The fully enclosed particles are generally within 100–200 nm of the plasma membrane , as measured in the plane of the section , consistent with the relative immobility of sequestered virions up to the time of DLP release . The periphery of BSC-1 cells is thin enough in some regions to allow recording of a tomographic tilt series of rapidly frozen cells preserved in a nearly native state . We grew BSC-1 cells on carbon-coated , gold grids ( see Methods ) . At 24 hours after depositing the cells on the grid , we added RRV at high concentration to maximize the likelihood of finding attached virus particles and recorded tilt series from positions at which the edge of a cell was thin enough for transmission EM . The high virus concentration often yielded clusters of particles at the cell surface . We concentrated our analysis of cryo-tomograms on isolated , attached or partially engulfed virions , in order to correspond as closely as possible to the events we followed by live-cell imaging , which always had the fluorescence intensities of single recoated TLPs . We detected various states of attachment and engulfment , as illustrated in Fig . 8 . Unattached particles showed clearly defined VP4 spikes; icosahedral averaging of 18 virus particles ( 1080 repeats ) produced a tomographic 3D reconstruction with ∼4 nm resolution ( Fig . 8D , G ) . Membrane-attached particles appeared to have induced various degrees of membrane wrapping , with the bilayer at a uniform distance from the surface of the particle . We detected two classes of virion-membrane contacts – those for which the separation of the particle surface from the membrane corresponded to the full extent of the VP4 spike ( Fig . 8B ) and those for which the separation was substantially smaller ( Fig . 8C ) . We computed subtomogram averages of just the icosahedral repeats facing the membrane . Contacts in the former class had VP4 spikes similar in appearance to those on free virions , but with some indication of disorder at their tips ( Fig . 8E , H ) ; contacts in the latter class had largely disordered spikes ( Fig . 8F , I ) , which we infer had undergone a conformational change . We do not yet have enough images to describe this apparent spike reorganization in more detail , but we suggest that it could reflect VP8* dissociation from the tips of the spikes and interaction of the VP5* hydrophobic loops with the membrane bilayer . The well-preserved particles and the uniformity of membrane invaginations around them reinforce our interpretation of the images from conventional EM in Fig . 7 .
Functional recoating of rotavirus DLPs with tagged , recombinant VP4 and VP7 enables direct observation , by live-cell imaging , of the stages and kinetics of RRV entry into BSC-1 cells . We have labeled all three components independently with distinct fluorophores without compromising infectivity of the recoated particles and followed them through a reproducible succession of stages leading to DLP release into the cytosol . When added to cells , virions attach and immobilize rapidly , sometimes with an initial short period ( <1 min ) of lateral motion on the cell surface . Within about 5 minutes of attachment , the particles become inaccessible to EDTA ( which releases accessible virions from the cell by dissociating VP7 ) and to VP7-directed antibody . Abrupt release of the DLP follows within a further 3–5 minutes . The sequestered virions lose VP4 and VP7 at variable rates until DLP release , but any residual VP4 and VP7 at the time of release remain at the site of penetration . The sequence of events leading to DLP release does not require association with clathrin adaptors or dynamin , and under the conditions of our experiments , association with Rab5 endosomes appears to be a dead end . Mutations in VP4 and VP7 that block infectivity prevent DLP release without affecting particle sequestration . The particle to infectious unit ratio is high for nearly all viruses that infect animal cells , and the low efficiency of infectious outcome can affect interpretation of experiments on mechanisms of entry . We use the phrase “productive entry” here to mean release of the DLP into the cytosol – the step on which we focus – not synthesis and assembly of progeny virions . Various factors influence whether a particle released into the cytosol will then initiate infection . Indeed , we expect the probability of infectious outcome to be low , even for rotavirus particles containing a perfect assortment of eleven genomic segments that have entered by the principal infectious route , because a sequence of potentially inefficient steps follows DLP release . Evasion of host-cell innate defense mechanisms and transport of the released particle to a location in the cell appropriate for viral RNA and protein synthesis are both likely to reduce the chance that a released particle will produce detectable progeny . For example , if each of only two subsequent events were to have the same relatively high efficiency ( ∼20% ) as does DLP release in the experiments reported here , and if the proportion of genetically competent particles and the efficiency of attachment of incubated virus to cells were each 50% , the overall particle to infectious unit ratio would be about 500∶1 , consistent with published estimates [27] and with our own estimates for fresh RRV preparations . Thus , the route of penetration with the highest frequency is very likely to be the one taken by any particle that ultimately generates an infectious outcome . The release events we describe here have all the properties we expect for functional penetration . They are of much higher frequency than any other mode of productive release we can detect by following individual virions; they deliver DLPs to the cytosol , as monitored using distinguishable fluorophores for DLP and outer-layer proteins; they are sensitive to mutations with known structural and functional consequences . In particular , we expect the VP4 hydrophobic-loop mutant , which has greatly impaired infectivity , to be defective in membrane disruption and particle release , because that mutant also fails to mediate release of α-sarcin into the cytosol [3] . We likewise expect that the disulfide cross-linked VP7 mutant , which also has impaired infectivity , will not release DLPs , because it prevents loss of VP7 from the DLP surface [25] . Conventional thin-section electron microscopy of cells exposed to rotavirus particles shows at least three apparent morphological stages of viral uptake – particles bound at the cell surface , particles partly engulfed in a surface invagination , and particles apparently fully enclosed within a tightly surrounding vesicle . In all three cases , the distance between the densely stained center of the virion and the closest segment of the cell surface appears to be about 500–600 Å . For fully enclosed particles , the surrounding membrane vesicle is generally concentric with the virion . The cryoET analysis yields more accurate dimensions , without distortion from fixation and sectioning , and shows two distinct states of the virion-membrane contact . The distance from the center of the particle to the outer surface of the membrane is 480–500 Å for the “long” contacts – just as expected for sialic-acid binding by spikes in the conformation seen by single-particle analysis . The corresponding distance for the “short” contacts is about 410 Å . The images suggest that the virus drives its own engulfment , by multiple contacts with the target-cell membrane . RRV binds sialic acid in a pocket on the outward-facing surface of VP8* [14] , and the functional receptor on the cell surface is probably a sialylated ganglioside [19] . The distance from the center of a rotavirus particle to the sialic-acid binding sites on the outward facing surfaces of VP8* is about 480 Å , and the sialic acid of a ganglioside such as GD1a , at the outer tip of the glycan , can project 10–20 Å above the mean plane of phospholipid polar headgroups . Thus , the radial position of the receptor-binding site and the length of a typical glycolipid glycan account for the observed distance between the center of the virus particle and the surface of the membrane that contacts or surrounds it , and the micrographs are compatible with the notion that most or all of the VP8* lectin domains on a particle bind a glycosphingolipid sialic-acid group . At normal glycolipid compositions , a spherical shell of membrane 1000 Å in diameter will present several hundred glycans to an attached virus , more than enough to saturate the 120 VP8* domains at the interface . Proposed non-ganglioside receptors for rotaviruses include integrins ( αVβ3 for a site on VP7 and α2β1 for a site on VP4 ) [28]–[30] and Hsc70 [31] . We have explained elsewhere [6] , [7] that the hypothesized attachment sequence on VP7 is buried at the VP7-VP6 contact , so any potential role for αVβ3 integrin would have to be at a post-uncoating step . The DGE sequence on VP5* suggested to be a site for α2β1 [30] is at the base of the protruding spike , near the surface of VP7 , and so oriented that the Asp and Glu of DGE ( the potential MIDAS-site interacting residues ) face away from the surface of the subunit , both on the A and B subunits and on the C subunit [9] . Thus , like the VP7 site , access to α2β1 would probably require a post-uptake conformational change . Hsc70 , which can bind almost any protein , is cytoplasmic; if small amounts of a related Hsp 70 were to appear on the cell surface , it would not be an abundant molecule . The uniformity of wrapping , shown by both cryoET and by conventional EM , and the direct contact of spikes with membrane , shown by cryoET , rule out any irregular , elongated , or low abundance receptor , at least for the engulfment step we have examined . The short class of membrane contacts shown in Fig . 8 requires a conformational change in the spike . The relatively flexible tethering of the VP8* lectin domain to the “foot” of the spike through its extended , N-terminal linker [9] would enable VP8* to move away from the tips of VP5* without dissociating from the foot ( Fig . 1C ) , exposing the VP5* hydrophobic loops . If these loops then insert into outer leaflet of the membrane bilayer , the observed separation agrees well with estimates from the structure . Tomogram sections such as the one in Fig . 8C suggest that this conformational change can occur progressively as the particle engulfs , potentially also explaining why a mutation in a VP5* hydrophobic loop extends the interval of lateral motion on the cell surface following initial attachment . The current observations cannot rule out participation of cellular proteins in the stages of entry we have outlined , but they limit the requirements in the combination of virus strain and host cell we have studied . Electron microscopy shows occasional images of virions captured by a clathrin-coated pit or vesicle ( Fig . 7 ) . Although this route of uptake is clearly rare in BSC-1 cells , as we could not detect colocalization with AP-2 by live-cell imaging , it is reasonable to expect that in other cells – and for other rotavirus strains – a clathrin route might predominate [20] , [32] . The tightness with which VP8* binds its ganglioside receptor , the abundance of the receptor on the cell surface , and the membrane tension ( which affects the free energy of invagination ) could all determine whether invagination needs assistance from the clathrin machinery . Clathrin coats disassemble within 10–15 secs of pinching off [33] , [34] , and the resulting uncoated vesicle resembles closely the virion-generated vesicles we find in our experiments; the diameter of the vesicle itself would also be about the same ( Fig . 7B ) . Thus , release from vesicles derived from clathrin uncoating would have the same mechanism and potentially a similar time course as release from the vesicles detected in the experiments reported here . Our finding , that in BSC-1 cells , RRV does not enter from Rab5 endosomes , appears at first to be at variance with published conclusions that the virus traffics to Rab5 comparments in MDCK cells [35] and MA104 cells [36] . In the former study , overexpressing the same Rab5 mutants used in our experiments gave only a twofold increase for constitutively active Rab5 and a twofold decrease for inactive Rab5; in the latter study , inactive Rab5 had roughly a fourfold effect , while constitutively active Rab5 had no effect at all . These relatively modest differences , compared with those of one or more orders of magnitude produced by mutations in VP5* [3] and VP7 [25] , might reflect variation of principal entry routes in the cell types used , but they might instead reflect indirect effects of perturbations in membrane traffic under the conditions of cell growth in the different experiments . Linkage among pathways of membrane traffic makes inferring mechanism from indirect readout particularly challenging . For example , “knock-down” with siRNA of various endosome-associated proteins affects entry and infectivity ( twofold to fourfold relative to cells transfected with irrelevant siRNA ) [36] , [37] , but the cells have had several generations to adjust to the loss of function . We also detect virus in Rab5 endosomes , but those particles rarely uncoat and penetrate ( Fig . 6 ) . It is certainly possible that in other cells or under different conditions , virions might bud into endosomal membranes and penetrate from that compartment – for example , if the relevant glycan receptor for the strain in question were abundant on the endosomal luminal membrane . Nonetheless , the subsequent mechanism of membrane disruption , rupture of a small vesicle , would be the same as studied here . The kinetics of entry we have analyzed agree well with earlier , less direct observations . Measurements of RRV internalization kinetics in MA104 cells , with protection from neutralization by mAb 159 as a readout , showed roughly 50% protection within 3–5 minutes of warming cells from 4° , at which virus attached but did not internalize , to 37°C [38] . Particles that had not been treated with trypsin attached efficiently , but internalized much more slowly than did trypsinized particles , with a half-time of 30–50 minutes . These observations suggest that entry into MA104 cells proceeds by stages similar to the ones we have followed in BSC-1 cells and that rapid , clathrin-independent internalization requires VP4 cleavage . There is ample trypsin in the part of the gut in which rotavirus propagates , and trypsin treatment of virions harvested from cell culture is probably a good surrogate for a normal , in vivo event . The polyomaviruses , in particular SV40 and murine polyoma virus , have glycolipid receptors [39] and appear to enter at the cell surface by a process that closely resembles the “wrapping” we infer from images such as those in Fig . 7B [40] , [41] . SV40 can induce tight-fitting invaginations even in ganglioside-containing , giant unilamellar vesicles with no cellular proteins , but scission , which presumably occurs during infectious entry , may require cellular factors in addition to the glycoplipids [41] . Dynamin recruitment appears not to be important for scission of the virus-containing invaginations and formation of the “autoendocytic vesicles” we describe here for rotavirus; whether other cellular proteins have any role remains an open question . Even for penetration-incompetent particles ( e . g . , those bearing specific mutations in VP4 or VP7 ) , the overall kinetics of sequestration from EDTA or antibody are the same as for particles recoated with wild-type proteins . Thus , the functions disrupted by the mutations – VP5* membrane interaction and VP7 dissociation – affect only the membrane-disrupting steps . Close inspection of the TLP structure [9] suggests that with VP7 in place , the spike could reorganize to allow the hydrophobic loops of all three VP5* subunits to engage the target membrane ( step 1 in Fig . 1c ) , but that VP7 would hinder the folding back we postulate drives bilayer disruption ( step 2 ) . Loss of Ca2+ from the vesicle that encloses the virion is presumably the event that induces VP7 dissociation . Transient Ca2+ leaks in the membrane should allow the few thousand Ca2+ ions in a vesicle of the observed diameter ( including those bound by VP7 ) to move rapidly down their concentration gradient into the cyotosol . Such leaks might be produced by the inserted VP5* loops , either by perturbations in the membrane bilayer from the loop insertion or by fluctuations of the VP5* trimer toward its folded-back conformation . Our results bear on the molecular details of membrane disruption and DLP release . The critical observation is that release is from a relatively small vesicle that conforms closely to the outer diameter of the virion , not from a much larger endosome . A potential molecular consequence of this observation is illustrated by the right-hand panel in Fig . 1C . We have proposed previously that the conformational change in VP5* leading to the stable , folded-back structure generates the disruptive force that breaks the membrane [2] . The transition couples to the membrane through insertion of the hydrophobic loops at the tip of the β-barrel domain [3] , [23] . A transition from an extended to a folded back structure will inevitably force the membrane to expand in area , because the local “bubble” created by any one trimer must be at the expense of membrane elsewhere . The bursting point for a lipid bilayer undergoing lateral expansion is about 3% . Distortion of the membrane as shown in Fig . 1C , even by a VP5* trimer released from its underlying DLP , will produce an approximately 0 . 5% expansion for each VP5* trimer that attempts to fold back , implying that membrane-coupled conformational reorganization of even a modest fraction of the 60 spikes on a virion will be enough to disrupt a small , tightly fitting vesicle as seen in the experiments reported here . A larger membrane-bound compartment , such as a Rab5 or Rab7 endosome , can withstand many more local impositions of sharp curvature , by compensating elsewhere in its extended and potentially pleated surface . Thus , if folding back of VP5* is indeed the mechanism of membrane disruption , DLP escape is very likely to be from a small , membrane vesicle , closely wrapped around the particle , rather than from a much larger one . The picture we have acquired for uptake and penetration of a non-enveloped virus and its interpretation in molecular terms has depended both on detailed structural information from x-ray crystallography and cryoEM and on tracking of large numbers of individual particles by live-cell imaging . To confirm the proposed sequence of molecular events , we need to determine the stage at which the hydrophobic loops of VP5* engage the cellular membrane and the timing and location of the VP5* fold-back step , relative both to dissociation of VP7 and to release of the DLP . On-going enhancement of imaging sensitivity and use of context-dependent labeling ( e . g . , Ca2+ sensitive fluorophores ) should make it possible to resolve these issues and thus to connect the molecular events sketched in Fig . 1c even more intimately with the cellular steps outlined in Figs . 2 and 7 .
BSC-1 cells , and the derived cell line stably expressing α2-eGFP [42] , were maintained at 37°C and 5% CO2 in DMEM ( Invitrogen Corporation ) , supplemented with 10% fetal bovine serum ( Hyclone Laboratories ) . To obtain cells expressing dynamin2-eGFP or Rab5-eGFP , approximately 60 , 000 BSC-1 cells were seeded into 6-well plates and transfected with 0 . 5 µg of plasmid encoding either rat dynamin2-eGFP ( gift of Dr . Sandra Schmid ) or 0 . 5 µg Rab5-eGFP ( Addgene ) , with the aid of FUGENE 6 , used according to the manufacturer's instructions ( Roche Diagnostics ) . Cells were then trypsinized and re-seeded in T25 mL flasks ( Corning ) in the presence of G418 for at least 48 h , to select for cells that expressed the tagged protein at levels not detrimental to cell growth . TLPs , DLPs , VP7 , VP7 disulfide mutant , and m159 antibody were purified as previously described [7] , [23] , [25] . For TLP and DLP production , MA104 cells were grown in 10-stack cell-culture chambers ( Corning ) , and confluent monolayers were infected with rhesus rotavirus ( RRV; G3 serotype ) , at MOI of 0 . 1 focus-forming unit ( FFU ) /cell in M199 medium supplemented with 1 mg/mL porcine pancreatic trypsin ( Worthington Biochemical ) . We collected the cell culture medium 24–36 h post infection , when cell adherence was <5% , and purified the TLPs and DLPs by freeze-thawing , ultracentrifuge pelleting , Freon-113 extraction , and cesium chloride gradient centrifugation . WT and C-C VP7 were expressed in Sf9 cells infected with a baculovirus vector and purified by successive affinity chromatography with concanavalin A and monoclonal antibody ( mAb ) 159 , which is specific for VP7 trimer ( elution by EDTA ) . DLPs and VP7 were dialyzed into amine-free buffers containing 25 mM Hepes pH 7 . 5 ( VP7: 25 mM Hepes pH 7 . 5 , 100 mM NaCl , 1 mM CaCl2; DLP: 25 mM Hepes pH 7 . 5 , 100 mM NaCl , 0 . 1 mM EDTA ) . We expressed WT and V391D VP4 in baculovirus-infected insect cells [2]; the harvested cells were flash frozen and lysed by thawing; PMSF was added to 1 mM when thawing was complete . The lysate was clarified by centrifugation , and VP4 was precipitated by addition of AmSO4 to 30% saturation . The AmSO4 pellet was resolubilized in a volume of 25 mM Tris pH 8 . 0 , 10 mM NaCl , 1 mM EDTA that gave a conductance matching that of Phenyl HP Start Buffer ( 25 mM Tris pH 8 . 0 , 3 . 5M NaCl , 1 mM EDTA ) and loaded onto a Phenyl HP column ( GE Healthcare ) . Following elution with 25 mM Tris pH 8 . 0 , 10 mM NaCl , 1 mM EDTA , fractions containing VP4 were pooled , dialyzed against the same buffer , loaded onto a HiTrap Q column ( GE Healthcare ) , and eluted in Phenyl HP Start Buffer . Pooled fractions containing VP4 were then concentrated to 1–2 mL with a Centriprep 50 concentrator ( Millipore ) and subjected to a final purification on S200 ( GE Healthcare ) in 25 mM Hepes pH 7 . 5 , 100 mM NaCl , 0 . 1 mM EDTA . DLPs , VP7 , VP4 and m159 antibody in amine-free buffer were conjugated to amine-specific Atto dyes ( ATTO-TEC ) as follows . NaHCO3 ( pH 8 . 3 ) was added separately to each of the components listed to a final concentration of 0 . 1 M , and Atto dyes , suspended in anhydrous DMSO ( Sigma ) to 2 mg/mL , were then added to obtain the following final dye concentrations: for DLP labeling , 20 µg/mL; VP4 , 16 µg/mL; VP7 , 20 µg/mL; m159 , 50 µg/mL . For recoated particles , dye combinations were varied according to the objectives of the experiment ( e . g . , doubly vs . triply labeled particles ) . DLPs . Proteins were incubated with dye for 1 h in the dark at room temperature , and the reactions were quenched by adding Tris pH 8 . 0 to a final concentration of 200 mM . Labeled components were then dialyzed into buffers for ensuing recoating reactions ( see below for recoating methods ) . For recoated particles , dye combinations were varied according to the objectives of the experiment ( e . g . , doubly vs . triply labeled particles ) . Recoating was carried out as previously described [22] , [23] , using recoating components labeled as described above . Briefly , Atto-labeled , recombinant VP4 was added to purified DLPs in at least 5-fold excess in buffer adjusted to pH 5 . 2 with sodium acetate , and the pH of the mix was adjusted to pH 5 . 2 by stepwise addition of sodium acetate and testing by pH paper . After incubation at room temperature for 1 . 5 h , mutant or WT VP7 was added in at least 3-fold molar excess in buffer supplemented with calcium , and the mixture was incubated for a further 30 minutes at room temperature . Recoated particles were separated from excess labeled components by cesium chloride gradient centrifugation and dialyzed into appropriate buffers ( 25 mM Hepes , 100 mM NaCl and 1 mM CaCl2 ) . Titers of recoating reactions were determined by infectious focus assays as described [25] . BSC-1 cells , plated on 25 mm No . 1 . 5 coverslips at a density of 150 , 000 cells/coverslip , were grown overnight at 37°C . The cells were washed twice with HEPES pH 7 . 0 , 140 mM NaCl , 1 mM CaCl2; 300 µl of α-MEM supplemented with 1 mM CaCl2 , with or without 100 mU/ml Vibrio cholera neuraminidase ( Sigma ) , was then added and the plates incubated for 1 hr . at 37°C . Recoated TLPs ( RcTLPs ) , prepared with Atto 565 labeled VP7 , unlabeled VP4 , and Atto 647N labeled DLPs , were activated by 1∶10 dilution in 5 µg/ml trypsin in TNC buffer at 37°C for 30 min and then placed on ice until use . Trypsin-treated RcTLPs ( 30 µl , added to 70 ul of α-MEM and mixed ) were added directly to the medium on the plates ( final MOI∼15 ) . After 15 min of incubation of cells and RcTLPs , confocal z-stack images were collected using transmitted light ( cell ) or laser excitation at 561 nm ( VP7 Atto 565 ) . For focus-forming assays , confluent BSC-1 monolayers were incubated for 1 hr . at 37°C in α-MEM supplemented with 1 mM CaCl2 , with or without 100 mU/ml V . cholera neuraminidase . RcTLPs or native TLPs were then added at an MOI of 15 and allowed to bind at 4°C for 2 hrs . Following incubation , cells were washed three times in PBS , freeze-thawed three times , and the amount of infectious virus bound determined by focus-forming assay on fresh , confluent BSC-1 cells . Approximately 1×105 BSC-1 cells were grown on 25 mm No . 1 . 5 coverslips as described above . Medium was exchanged immediately before imaging with pre-warmed MEM–α , without phenol red , supplemented with 25 mM Hepes ( pH 7 . 4 ) and 2% FBS ( Hyclone ) . Labeled recoated virus particles were added to cells at MOI of 0 . 1–0 . 2 . For experiments in Figure 2 , images were acquired every 1 minute ( Fig . 2D ) or 1 s ( Fig . 2E , F ) using 100 ms ( Fig . 2D ) or 5–30 ms ( Fig . 2E , F ) exposure times ( no binning ) with a previously described laser and confocal microscope configuration [43] . Image and data analysis was performed using Slidebook 4 . 2 ( Intelligent Imaging Innovations , Denver CO ) . For experiments in Figures 3–6 , cells were grown on No . 1 . 5 coverslips as described above and mounted on a Prior Proscan II motorized stage on a Nikon Ti inverted microscope equipped with 100× Plan Apo NA 1 . 4 objective lens and the Perfect Focus System . The microscope was enclosed in a custom built , heated chamber warmed to 37°C; the sample was supplied with 5% CO2 . All images were collected with a Yokagawa CSU-X1 spinning disk confocal with Spectral Applied Research Borealis modification Excitation with solid state lasers was controlled by an AOTF; images , acquired with a Hamamatsu ORCA-AG cooled CCD camera controlled by MetaMorph 7 software , were collected with a 405/491/561/642 band pass dichroic mirror ( Semrock ) at the following wavelengths: 491 nm line with a 525/50 emission filter; 561 nm line with a 620/60 emission filter; 642 nm with a 700/75 emission filter ( Chroma ) . For time-lapse experiments , images were collected every 3–6 s depending on the objectives of the experiment ( described in respective figure legends ) , using an exposure time of 5–30 ms and 2×2 binning , with illumination attenuated by the AOTF between acquisitions . Gamma , brightness , and contrast were adjusted on displayed images ( identically for compared image sets ) using MetaMorph 7 software . Analysis was performed using built-in functions provided by Slidebook 4 . 2 and MetaMorph 7 . EDTA flow-in experiments were performed during imaging experiments without interruption of image collection by quickly pipetting away the MEM-a/FBS medium bathing the cells and replacing the medium by gently layering pre-warmed MEM-a/FBS medium containing 4 mM EDTA onto the culture plate; we were careful not to disturb the plane of focus . Although EDTA treatment caused cells ultimately to detach from the coverslip , they remained in place long enough to determine whether a bound virus particle resisted dissociation . m159 flow-in experiments were performed similarly; the replacement medium contained 25–50 µg/mL fluorescently labeled m159 antibody rather than EDTA . BSC-1 cells , seeded in complete growth medium ( DMEM supplemented with Pen/Strep and 10% FBS ) at 70% confluence on 6-well plates , were transfected with plasmids expressing GFP-Rab5DN ( S34N ) and GFP-Rab5CA ( Q79L ) as described [44] . After 24 h at 37°C , the cells were trypsinized and replated at ∼30% confluency on polylysine-coated glass coverslips placed in 6-well plates . After another 24 h at 37°C , cells were washed twice with warmed phosphate buffered saline ( PBS ) , and RRV TLPs , freshly treated with trypsin in α-MEM , were added to each well at the indicated MOI . Cells were incubated with virus at 37°C for 1 hr , after which the medium was replaced with DMEM supplemented with Pen/Strep and neutralizing mAb m159 at 3 . 6 µg/ml , and the infection was allowed to proceed for 16–18 hours at 37°C . After washing the cells with serum-free α-MEM at 37°C , transferrin labeled with Alexa-Fluor 647 ( Tf-647 ) was added at 50 µg/ml . The cells were incubated with the Tf-647 for 10 min at 37°C , washed with PBS , fixed for 10 min with 3 . 7% formaldehyde in PBS , and prepared for immunofluorescence as described [22] . Infection was assayed by staining with anti-VP7 mAb m60 and counterstaining with Alexa Fluor 568 labeled goat anti-mouse IgG . Images were acquired with a Mariana system ( Intelligent Imaging Innovations , Denver , CO ) based on a Zeiss AxioVert 200M inverted microscope ( Carl Zeiss Microimaging , Inc . , Thornwood , NY ) equipped with a CSU-22 spinning-disk confocal unit ( Yokogawa Electric , Tokyo , Japan ) , a piezo-driven Z-translation , and linear encoded X&Y translations and controlled with SlideBook V5 . 0 ( Intelligent Imaging Inc . , Denver , CO ) . Excitation wavelengths were 491 , 561 , and 660 nm ( lasers from Cobolt , Solna , Sweden ) ; the emission filters ranges were 525–550 nm , 620–660 nm , and 680 nm long-pass ( Semrock , Rochester , NY ) . We collected Z-scans at 10–12 positions in each sample , imaging the entire cell volume in 0 . 5 micron steps , with exposure times per step of 30 ms at 491 nm ( GFP-Rab5 ) and 50 ms at 561 nm ( Alexa-Fluor 568 goat anti-mouse IgG ) and 660 nm ( Tf-647 ) . For each field of view ( 10–12 fields/experimental condition ) , we scored cells for Rab5 expression , RRV infection , and Tf uptake , the last as a control for clathrin-based receptor mediated endocytosis . As each coverslip had transfected and non-transfected cells , we could score both expressing and non-expressing cells for RRV infection in the same fields . Authentic , trypsin-treated TLPs were added at 280 ffu/cell to BSC-1 cells that had been pre-incubated with MEM-a ( without FBS ) for 10 min . The cells were fixed 5–10 min after adding virus by incubating for 1 hr in fixative ( 1 . 25% formaldehyde , 2 . 5% glutaraldehyde , 0 . 03% pictric acid in 0 . 1 M sodium cacodylate , pH 7 . 4 ) . Fixed cells were stained successively with osmium tetroxide ( 1% ) and uranyl acetate ( 1% ) , washed , dehydrated with successive washes in 75% , 90% and 100% ethanol , soaked in propyleneoxide for 1 hr , and infiltrated and embedded in Epon ( polymerized for 24–48 hr at 60°C ) . Sections about 50 nm thick were examined in the FEI Tecnai G2 Spirit BioTWIN electron microscope of the Harvard Medical School Electron Microscopy Core Facility . Freshly glow-discharged EM gold-grids coated with a holey carbon film ( Quantifoil R 2/2 200 mesh , Quantifoil MicroTools GmbH , Germany ) were set on the bottom of a sterile Petri dish and sterilized with 70% ethanol for 10 minutes . The sterilized grids were rinsed twice with 0 . 2 µm filtered water , submerged in 0 . 1% poly-l lysine hydrobromide overnight , and then rinsed once in 0 . 2 µm filtered water and twice in unsupplemented MEMα media . BSC-1 cells were plated over 6 prepared grids at a density of 9×104 cells/ml in a total of 2 ml MEMα supplemented with 10% FBS in a 35 mm diameter glass bottom dish ( MatTek ) . The cells were incubated for 24 hrs . at 37 deg . C and 5% CO2 . They attached to the grids at low density ( about one cell per three grid squares ) , which we verified by DIC light microscopy . Grids with cultured host cells , held by self-closing tweezers at the edge of the grid , were washed with three drops of medium , and TLPs at 9 . 4×109 FFU/ml in 5–10 µl were added to the cells on the grid . The cells were incubated at 37°C for 30 min , after which 1 . 5 µl of 10-nm colloidal gold solution ( Sigma-Aldrich , St . Louis , MO ) was added to create fiducial markers for use during tilt series alignment and tomogram reconstruction . Excess fluid was blotted with filter paper just before rapid freezing by plunging into liquid ethane , using a manual plunge freezing device . The frozen grids were stored in liquid nitrogen . For imaging , we used a Tecnai F30 transmission electron microscope ( FEI , Inc . , Hillsboro , OR ) operating at an accelerating voltage of 300 kV . The microscope was equipped with a field emission gun , a high-tilt stage , a post-column energy filter ( Gatan Inc . , Pleasanton , CA ) and a 2k×2k charge-coupled device camera ( Gatan ) . We recorded low-dose images at −8 µm defocus and at a nominal magnification of 13 , 500× , giving a pixel size of 9 . 86 Å . Single-axis tilt series were recorded by tilting the specimen from −60° to +60° in 1 . 5–2° increments using SerialEM control software [45]; the total electron dose per tilt series was kept below 150 e/Å2 . We used IMOD [46] for fiducial alignment of the tilt series images and for tomogram reconstruction by weighted back-projection . Virus particles were picked from the raw 3D cryo-tomograms . Bound and unbound particles were identified by visual inspection . Subtomogram averaging was performed with PEET [47] , using the published cryo-EM structure ( EMDB 5199 ) [9] , filtered to 25 Å resolution , as an alignment reference . Icosahedral symmetry was applied as described by the Boulder Laboratory for 3D Electron Microscopy of Cells online protocol ( http://www . youtube . com/watch ? v=c9LqABmRd7Q&list=PLGggUwWmzvs_Q8j05yw2B2vVstVZaT9at ) . We averaged 18 unattached particles ( 18×60 = 1080 repeats ) , and 7 membrane-attached particles ( 7×60 = 420 repeats ) . For the class of virons bound to the host cell membrane , we also calculated a “membrane-preserving” average using only those repeats that contained spikes in contact with the membrane ( 78 repeats from 5 particles ) . We used IMOD for 3D visualization and isosurface rendering of the averaged particles . | Non-enveloped viruses ( viruses lacking a lipid-bilayer membrane ) require local disruption of a cellular membrane to gain access to the cell interior and thereby initiate infection . Most double-strand RNA viruses have an outer protein layer that mediates this entry step and an inner-capsid particle that transcribes their segmented dsRNA genomes and extrudes the capped mRNAs into the cytosol . Removing the two rotavirus outer-layer proteins inactivates the virus , but recoating with recombinant outer-layer proteins restores infectivity . We have labeled the recombinant proteins with distinct fluorophores and the stripped inner-capsid particle with a third fluorophore and reconstituted fully infectious particles from the labeled components . We have followed by live-cell imaging the binding and engulfment of the labeled particles and studied the kinetics of inner-capsid particle release . We have interpreted these events in structural terms by examining images of entering particles from conventional electron microscopy and electron cryotomography . When analyzed in view of our previously determined high resolution structures of the virus particle and its constituents , and of information about conformational changes in the outer-layer components , our data lead to a molecular description of the observed entry steps and of the mechanism of membrane disruption . | [
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] | 2014 | Structural Correlates of Rotavirus Cell Entry |
The relationship between mosquito vectors and lymphatic filariasis ( LF ) parasites can result in a range of transmission outcomes . Anophelines are generally characterized as poor vectors due to an inability to support development at low densities . However , it is important to understand the potential for transmission in natural vectors to maximize the success of elimination efforts . Primary vectors in Papua New Guinea ( n = 1209 ) were dissected following exposure to microfilaremic blood ( range 8–233 mf/20 µl ) . We examined density dependent and species-specific parasite prevalence , intensity and yield , barriers to parasite development as well as impacts on mosquito survival . We observed strikingly different parasite prevalence and yield among closely related species . Prevalence of infective stage larvae ( L3s ) ranged from 4 . 2% to 23 . 7% in An . punctulatus , 24 . 5% to 68 . 6% in An . farauti s . s . and 61 . 9% to 100% in An . hinesorum at low and high density exposures , respectively . Injection experiments revealed the greatest barrier to parasite development involved passage from the midgut into the hemocoel . The ratio of L3 to ingested mf at low densities was higher in An . hinesorum ( yield = 1 . 0 ) and An . farauti s . s . ( yield = 0 . 5 ) than has been reported in other anopheline vectors . There was a negative relationship between mosquito survival and bloodmeal mf density . In An . farauti s . s . , increased parasite yield and survival at low densities suggest greater competence at low microfilaremias . In Papua New Guinea the likelihood of transmission will be strongly influenced by vector composition and changes in the mf reservoir as a result of elimination efforts . Global elimination efforts will be strengthened by the knowledge of transmission potential in the context of current control measures .
Human lymphatic filariasis ( LF ) is a mosquito-borne disease that is a leading cause of morbidity worldwide . 1 . 4 billion people in 81 countries are at risk of infection with the nematode parasites Wuchereria bancrofti , Brugia malayi or B . timori . Clinical manifestations , including acute fevers , chronic lymphedema , elephantiasis and hydrocele , result in the loss of 5 . 9 million disability-adjusted life-years [1] . Even individuals with mild manifestations are stigmatized in their societies and suffer psychological impacts [2] . W . bancrofti parasites , which account for 90% of the global disease burden , dwell in the lymphatic system , where the adult female worms release microfilariae ( mf ) into the blood . Mf are taken up in the blood meal of a mosquito , and go through several developmental stages within permissive vector species . Infective-stage larvae ( L3s ) actively escape from the mosquito mouthparts during a bloodfeeding event and enter a new vertebrate host through skin . Infection prevalence and morbidity is on the decline worldwide due to mass drug administration ( MDA ) of anthelminthic drugs coordinated by the Global Program to Eliminate Lymphatic Filariasis ( GPELF ) . These single dose regimens target mf in the bloodstream , and therefore prevent transmission to mosquitoes . However , in order to reach the goal of elimination by the year 2020 , numerous challenges must be overcome . Elimination of LF requires annual MDA with high coverage and compliance for at least 5 years in order to interrupt transmission through the lifespan of adult worms [3] , [4] , [5] , a difficult undertaking in light of logistical and financial constraints . Perhaps most importantly , thresholds for transmission cessation are currently unknown and are site-specific . Therefore , program managers currently lack the necessary tools to make informed decisions about when to stop , scale-up or reinstate MDA . Current transmission cessation thresholds are based on dominant vector genera [6] , due to differences in vector-parasite relationships [7] . Culicine vectors are generally regarded as efficient vectors of LF , with proportionally greater output of L3s as the number of mf ingested decreases ( limitation ) . In contrast , anophelines have been characterized as inefficient vectors , with proportionally lesser output of L3s as fewer mf are ingested ( facilitation ) [8] . For this reason , it has been hypothesized that stopping transmission by reducing the mf reservoir with MDA can be more easily attained in areas where LF is anopheline-transmitted [9] . However this paradigm may not extend to all anophelines . In Papua New Guinea , where the prevalence of LF is among the highest in the world [10] , members of the Anopheles punctulatus group are the primary vectors but MDA has been unsuccessful in stopping transmission [11] , [12] , [13] . This contradiction suggests that critical appraisal of local mosquito vectors is needed to enable better estimations of intervention endpoints and enhance predictive transmission modeling algorithms [14] . As interventions are employed to control LF , whether by MDA or vector-based interventions , it becomes increasingly important to demonstrate the influence of a decreasing mf reservoir on the vector-parasite relationship . The aim of this study was to determine the vector competence of individual species within the An . punctulatus group to W . bancrofti in the context of specific mf densities . This research was performed within a critical timeframe , in an endemic country with ongoing and future plans for MDA-based control of LF [15] as well as a large-scale distribution of insecticide-treated bed nets [16] . Both interventions hold promise to interrupt transmission of LF in PNG , either by reducing human microfilaremia , reducing vector biting rates , or interfering with host-seeking at times of maximum mf density in the host peripheral blood [13] . However , predicting the long-term impact of these campaigns remains difficult without an understanding of the vector-parasite relationships in this highly endemic country .
Anopheles larvae were collected from temporary pools in the Madang , Sumkar and Usino Bundi Districts of Madang Province and the Dreikikir-Ambunti district of East Sepik Province , PNG . Colonized An . farauti s . s . , originating from East New Britain Province , PNG in 1967 , was used for the majority of exposures for this species because comparative studies showed no difference in infection prevalence or mean intensity between colony and wild An . farauti s . s . ( Table 1 ) Mosquitoes were maintained in an insectary with a 12∶12 light cycle that included a 30-minute crepuscular period for dawn and dusk . Natural temperatures in this environment ranged from 27–28°C , in the afternoon , to 23 . 5°C at night . The relative humidity was increased by placing damp towels on top of each cage or carton ( ∼85% RH inside colony cages ) . Larvae were reared in plastic pans with water collected from a local creek , and fed finely ground Tetramin™ in solution . Adult mosquitoes were provided 20% sucrose solution ad libitum via cotton pledgets . Species identification was performed after inclusion in an experiment and prior to dissection for the recovery of parasites . If the adults were reared from wild-caught larvae , then morphological characteristics were observed with a stereomicroscope and used to classify individuals into the three major An . punctulatus morpho groups ( An . punctulatus , An . farauti s . l . and An . koliensis ) prior to dissection . These characteristics include proboscis coloration and presence or absence of the wing sector spot [17] . In addition , the legs of each individual mosquito were collected , coded , and stored for species confirmation by PCR-RFLP of the ITS2 region [18] . A portion of individuals from the An . farauti s . s . colony was also verified . Adults ( ≥18 years of age ) were recruited as study volunteers from suspected LF endemic villages in Madang and East Sepik Provinces . Individuals providing informed consent to participate in the study were initially screened for the presence or absence of W . bancrofti circulating antigen using BinaxNow© Filariasis rapid card tests ( Alere Inc . , Waltham , MA ) . Subsequently , antigen positive volunteers were asked to provide a venous blood sample , which was collected after 22:00 and transported to the laboratory . A compound microscope with phase contrast optics was used to quantify the number of mf per 20 µl blood in a 2% formalin wet mount . Microfilaremia was confirmed in triplicate and the remaining blood was used for feeding mosquitoes via water-jacketed membrane feeders fitted with parafilm or pig intestine membranes [19] . Sucrose-starved , female An . punctulatus ( 2–7 days old ) and An . farauti s . s . ( 3–6 days old ) were allowed to feed on microfilaremic blood from 11–26 hours post collection according to mosquito feeding preferences . Mf motility was observed by wet mount at the time of feeding . Fully engorged mosquitoes were sorted from non-fed and partially fed females and maintained in the insectary for up to 18 DPE . The timing of mosquito dissections was based on average W . bancrofti development times ( Figure 1 ) . Mosquitoes were cold anesthetized and divided into body regions in separate drops of Aedes saline [20] for dissection and parasite recovery . Mosquito tissues were teased apart with 0 . 15-mm insect pin probes , cover-slipped and examined using phase-contrast optics . Additionally , between 3 and 24 hours post engorgement , a portion of mosquitoes' midguts were removed and lysed in distilled water . Slides were dried overnight before methanol fixation and Giemsa staining . They were microscopically examined to quantify the number of mf ingested and the degree of damage , caused by the cibarial armature , following ingestion . Later in parasite development ( 12–18 DPE ) , the body regions were dissected in a drop of saline and worms were observed with a stereomicroscope with dark-field backlighting . Mosquitoes were individually tracked to record morphological identification , molecular species confirmation , parasite prevalence and intensity , mf damage and melanization of worms . To better determine the influence blood feeding and the midgut environment might have on parasite survival , intrathoracic inoculations were used to place mf directly in the hemocoel . Microfilariae were isolated from blood samples using syringe tip filtration devices , fitted with 5 µM membranes ( Millipore Isopore TMTP ) and chilled Aedes saline solution . Mf were rinsed off each filter with 1–2 ml of saline solution in conical vials , and spun at 1 , 000 rpm for 10 min at 4°C to concentrate the parasites and remove most of the fluid . A single drop of saline containing concentrated parasites was transferred to a microscope slide and mf were loaded into finely pulled glass capillary needles for injection into mosquitoes . A dissection microscope was used to observe the loading of approximately 10–20 mf per needle for mosquito injections . Mosquitoes were cold anesthetized for 3 minutes at −20°C immediately prior to the injection procedure . Anesthetized mosquitoes were injected with mf in a minimum volume ( 0 . 5–1 . 0 µl ) of Aedes saline , into a membranous cuticle area on the lateral side of the mesothorax [21] . Mosquitoes that survived for >12 hours post inoculation were dissected and developmental stage of recovered parasites was observed . Microfilaria densities used in this study represent natural infection levels and were categorized as low ( <50 mf/20 uL ) , medium ( 50–100 mf/20 uL ) and high ( >100 mf/20 uL ) for the study communities . Mosquito infection is summarized by the prevalence of infection with 95% CI ( adjusted Wald/Sterne's interval ) and mean intensity ( total number of recovered parasites divided by total number of infected mosquitoes ) with 95% CI ( Bootstrap BCa ) . To compare the prevalence of parasite infection , Fisher's Exact tests were performed for comparisons of 3–6 populations and unconditional exact tests to compare the prevalence of infection between two populations . Bootstrap t-tests were performed to compare mean intensities . All between species comparisons were done on An . punctulatus and An . farauti s . s . Because sample sizes were too low in An . hinesorum for statistical analyses , only intensity and prevalence are presented for illustrative purposes . Quantitative Parasitology 3 . 0 , a freeware program , was used for statistical analysis ( http://www . zoologia . hu/qp/qp . html ) and GraphPad Prism ( version 5 . 0d ) for generating graphs and figures .
To investigate potential differences in the number of parasites ingested by each species following bloodfeeding on a range of microfilaremias , a portion of mosquitoes were dissected immediately ( <18 hours ) following the feeds and mf were counted . Total mf recovery revealed a linear relationship between the number of mf ingested and the density of mf in the bloodmeal at the ranges studied ( Figure 2 ) . There was no significant difference in the mean number of mf ingested between An . punctulatus and An . farauti s . s . ( ANCOVA p = 0 . 6 ) . The prevalence and mean intensity of W . bancrofti in experimentally infected PNG anophelines is presented in Table 2 . Infection prevalence and intensities were calculated for both the number of developing worms ( any stage ) recovered after 1 DPE and infective-stage larvae only . In both An . punctulatus and An . farauti s . s . , the infectious bloodmeal parasitemia had a significant effect on the prevalence of developing and infective- stage larvae ( Fisher's exact p<0 . 001 for each ) . There was also a significant difference in the prevalence of developing worms between the two species within mf densities ( low p<0 . 001 , med p<0 . 003 , high p<0 . 013 ) . The mean intensity of developing worms was significantly higher in An . farauti s . s . as compared with An . punctulatus ( p = 0 . 0015 ) . There was a significant decrease in the mean number of developing worms ( >1DPE ) compared to the mean number of intact mf in the midgut and body ( <1DPE ) recovered from An . punctulatus at all densities and An . farauti s . s . at high density only ( P<0 . 0001 , Figure 3 ) . There was no significant difference between the mean number of worms recovered from 1 . 5 through 13 DPE and the mean number of L3s recovered from 13 . 5 through 18 DPE . The limited data from An . hinesorum suggests attrition through each developmental stage is minimal . The ratio of L3s to the number of mf ingested is presented in Table 2 . This ratio is highest in An . hinesorum , ranging from 1 . 0 to 0 . 94 , and lowest in An . punctulatus , ranging from 0 . 03 to 0 . 07 at low and high densities , respectively . Potential barriers to W . bancrofti development were investigated . A greater proportion of mf were damaged following ingestion of a low density as compared with a high density microfilaremic bloodmeal in both An . punctulatus and An . farauti s . s . ( p<0 . 001 and p = 0 . 03 respectively ) . At low microfilaremias , a greater proportion of damaged mf were observed in An . punctulatus than in An . farauti s . s . and An . hinesorum , but at high microfilaremic bloodmeals the number of damaged mf was comparable between An . punctulatus and An . farauti s . s . ( Figure 3 ) . To test the hypothesis that the high degree of attrition observed in An . punctulatus is attributable to early developmental barriers ( ingestion and/or the mosquito midgut environment ) , mf were introduced directly into the hemocoel , effectively by-passing the midgut . When mf were intrathoracically inoculated , there was no difference in the number of live mf recovered immediately post-injection and developing worms ( Figure 4 ) . Melanization was observed in one An . farauti s . s . exposed to a low density infection . In this individual one L2 was partially covered in melanin . Melanization was observed in one An . punctulatus and one An . farauti s . s . that had received mf via injection . In both cases a single mf was fully melanized . Melanized sheaths were observed in the hemocoel of both species indicating W . bancrofti exsheathment can occur after traversing the midgut . Infection with W . bancrofti had a negative impact on survivorship in An . farauti s . s ( Figure 5 ) and mortality was correlated with the density of infection . No difference was observed in survival between mosquitoes exposed to uninfected blood compared to low density microfilaremia . Survival 14 days post exposure was 60% in mosquitoes exposed to low density microfilaremia and only 20% in mosquitoes exposed to medium and high densities .
In this study , we assessed the vector competence of members of the Anopheles punctulatus group to W . bancrofti . Overall , the prevalence and intensity of parasite infection in mosquitoes , and the proportion of damage to mf upon ingestion , were observed to all be density-dependent . However , not all examined species supported parasite development to the same degree . Some measures of infection , including ( 1 ) overall prevalence and intensity , ( 2 ) prevalence and intensity of infective-stage larvae , and ( 3 ) parasite yield ( i . e . , proportion of mean L3s produced from number of parasites ingested ) , were strikingly different at comparable mf densities between closely related species . An . hinesorum is incriminated as a vector of W . bancrofti here for the first time and our results show that this species is highly competent . Although less abundant than An . punctulatus and An . farauti s . s . in our study sites , this species is ubiquitous in both the inland and coastal regions of PNG , and abundant south of the central range [22] . Current assumptions regarding the inability of anophelines to transmit filariasis at low density microfilaremias may not extend to all vectors , as evidenced by comparing our results to previous studies that employed similar methodology . The An . farauti s . s . parasite yield is five times higher than what has been reported in African anopheline LF vectors at low density parasite exposures , including An . gambiae , An . arabiensis , An . funestus , An . melas and An . merus [8] . In addition , the mean number of L3s produced at medium and high mf density feeds is higher than any other anopheline vector , as reviewed in Snow et al . [7] . Compared to An . farauti s . s . , a greater proportion of An . punctulatus fail to support filarial worm development . The greatest reduction in prevalence occurs at 1 DPE , which corresponds to the time that microfilaria traverse the midgut epithelium . This attrition was not observed when mf were introduced directly into the hemocoel . These results suggest that the reduced vector competence of An . punctulatus is attributable to the midgut barrier . Although the cibarial armature causes some damage to mf , the degree of damage at high densities is comparable to the amount of damage in An . farauti and cannot explain the difference in prevalence between the two species at medium and high densities . Very few mosquitoes harbored melanized W . bancrofti in this study , a result that differs from a previous study that observed nearly 50% of infected An . punctulatus had elicited some degree of melanization response [23] . Differences in the observed melanization phenotypes may be related to differences in midgut microbiota [24] , acquired from the larval environment or differences in reactive oxygen species ( ROS ) . ROS are associated with melanotic encapsulation [25] , [26] and could be elevated due to environmental stress [27] , or inhibited by the anticoagulant and anti-oxidant heparin [28] . The question of whether mosquito survivorship is adversely effected by W . bancrofti infection is paramount in estimating vector competence , yet relatively few studies [29] have addressed this issue . We have shown convincingly that W . bancrofti infection and parasite intensity influence mosquito survivorship . We found increased mortality in An . farauti s . s . that ingested blood with a medium or high density of mf relative to low mf density blood and uninfected controls . In An . punctulatus , previous studies have found that there was no difference in mortality between low and high density feeds [29] . Tissue damage , which may or may not lead to mosquito death , is often observed when development of second-stage larvae ( L2s ) is completed in the thorax and the actively motile L3s relocate to the body and head of the mosquito [30] , [31] . Although greater impacts on survival would be expected in the more competent vector this is not always the case with naturally occurring parasite-mosquito interactions . Co-evolution of parasite-host interactions has likely selected a minimal consequence of infection on host survivorship . This is evidenced by observations of certain mosquito vectors eliciting a minimal immune-related or damage repair response following intracellular filarial worm development , e . g . , Mansonia uniformis and Armigeres subalbatus infected with Brugia malayi and B . phangi respectively [30] , [32] . In vectors such as An . farauti s . s . that are highly susceptible , increased mortality at high density infections will reduce the potential for transmission in the field because these mosquitoes may not survive the extrinsic incubation period ( EIP ) . Alternatively , as microfilaremia decreases in the population , the transmission potential may increase . In An . farauti s . s , the significantly higher survivorship through the EIP at low density coupled with increased parasite yield could result in higher vectorial capacity . This observation challenges the assumption that anophelines are incapable of transmitting LF at a low microfilaremia . This study demonstrates a linear relationship between vertebrate host mf densities and mean number of mf ingested , which corresponds roughly to the number of mf we would expect in 1 µL of blood . However , previous studies [33] have observed a concentrating effect at low host mf densities ( <10 mff/mL ) , which was below the threshold for inclusion in the present study . The effect of mf concentration at low densities warrants further investigation , especially as MDA campaigns continue to decrease the reservoir of mf in endemic communities . Efforts to eliminate lymphatic filariasis through mass drug administration are underway in Papua New Guinea . In addition , the nationwide distribution of long-lasting insecticidal nets is a part of the National Department of Health Malaria Control Program . Both campaigns hold promise for the elimination of W . bancrofti transmission by reducing the prevalence of mf in the human population , reducing vector biting rates , or interfering with mosquito biting at times of peak microfilaremia . The success of such programs hinges on the ability to reach worm breakpoint levels ( the human mf prevalence below which transmission cannot be sustained ) . Our research suggests that estimated thresholds will be different between the two primary vectors and elimination may be more achievable in the inland and lowland regions where An . punctulatus is most abundant . Other studies have also found sympatric species of the An . gambiae complex and M and S molecular forms [34] , [35] to have different competency to transmit W . bancrofti . Further research on the vector competence of primary LF vectors around the world , in the context of a diminishing mf reservoir , is needed in order to maximize the success of the Global Programme to Eliminate Lymphatic Filariasis . Furthermore , models for LF transmission cessation should be catered to geographic region and control efforts must respond accordingly . | Lymphatic filariasis ( LF ) elimination requires interrupting transmission of microfilaria ( mf ) from humans to mosquitoes for 5–7 years , the average life span of adult worms . Current mf prevalence thresholds , below which transmission cannot be sustained , are unknown . Anopheline-transmitted LF is thought to be easily eliminated following community-wide distribution of anthelminthic drugs , based on this genera's poor vectorial ability . We observed up to a 30-fold difference in parasite yield in experimentally infected mosquitoes of the Anopheles punctulatus group , the primary vectors in Papua New Guinea . In two species , An . farauti and An . hinesorum , prevalence and intensity of infection were higher than what has previously been described for anopheline mosquitoes . Differences in vector competence were largely attributable to a failure of microfilaria to survive escape of the midgut . Other barriers to parasite development include damage upon ingestion and , to a lesser degree , melanization . These results challenge the assumption that anophelines are poor vectors and provide further insight as to why mass drug administration alone has been unsuccessful in stopping LF transmission in PNG . Large differences in vector competence among closely related species indicate that transmission thresholds will be site and vector specific , and control efforts should be tailored accordingly . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | Mosquito-Parasite Interactions Can Shape Filariasis Transmission Dynamics and Impact Elimination Programs |
Most mutations that compromise meiotic recombination or synapsis in mouse spermatocytes result in arrest and apoptosis at the pachytene stage of the first meiotic prophase . Two main mechanisms are thought to trigger arrest: one independent of the double-strand breaks ( DSBs ) that initiate meiotic recombination , and another activated by persistent recombination intermediates . Mechanisms underlying the recombination-dependent arrest response are not well understood , so we sought to identify factors involved by examining mutants deficient for TRIP13 , a conserved AAA+ ATPase required for the completion of meiotic DSB repair . We find that spermatocytes with a hypomorphic Trip13 mutation ( Trip13mod/mod ) arrest with features characteristic of early pachynema in wild type , namely , fully synapsed chromosomes without incorporation of the histone variant H1t into chromatin . These cells then undergo apoptosis , possibly in response to the arrest or in response to a defect in sex body formation . However , TRIP13-deficient cells that additionally lack the DSB-responsive kinase ATM progress further , reaching an H1t-positive stage ( i . e . , similar to mid/late pachynema in wild type ) despite the presence of unrepaired DSBs . TRIP13-deficient spermatocytes also progress to an H1t-positive stage if ATM activity is attenuated by hypomorphic mutations in Mre11 or Nbs1 or by elimination of the ATM-effector kinase CHK2 . These mutant backgrounds nonetheless experience an apoptotic block to further spermatogenic progression , most likely caused by failure to form a sex body . DSB numbers are elevated in Mre11 and Nbs1 hypomorphs but not Chk2 mutants , thus delineating genetic requirements for the ATM-dependent negative feedback loop that regulates DSB numbers . The findings demonstrate for the first time that ATM-dependent signaling enforces the normal pachytene response to persistent recombination intermediates . Our work supports the conclusion that recombination defects trigger spermatocyte arrest via pathways than are genetically distinct from sex body failure-promoted apoptosis and confirm that the latter can function even when recombination-dependent arrest is inoperative . Implications of these findings for understanding the complex relationships between spermatocyte arrest and apoptosis are discussed .
Meiosis generates haploid cells from a diploid progenitor by coupling one round of genome replication to two rounds of chromosome segregation . During prophase of the first division , SPO11 protein forms double-strand breaks ( DSBs ) , whose repair enables homologous chromosomes to pair , synapse and recombine [1] . DSB repair failure has deleterious effects , so meiotic recombination is monitored to ensure its completion [2–8] . When recombination intermediates persist , a system often referred to as the pachytene checkpoint is activated to delay or stop meiotic progression or , in some cases , to initiate programmed cell death [9] . In this paper , we use the term “checkpoint” in the well-established sense of a signaling mechanism that creates a relationship ( dependency ) between two otherwise independent meiotic processes [8] . In this context , checkpoint responses to a cellular defect could have any of several non-exclusive downstream consequences including arrested differentiation , cell cycle arrest , and/or programmed cell death . Mouse spermatocytes unable to complete recombination ( e . g . , if they lack the strand-exchange protein DMC1 [10 , 11] ) arrest differentiation without expressing the testis-specific histone variant H1t [2] , which is a marker of mid-pachynema and later stages of spermatogenesis [12 , 13] . In contrast , spermatocytes unable to make DSBs at all ( i . e . , in the absence of SPO11 [14 , 15] ) are able to progress to a state where H1t is expressed [2] ( See S1 Table for a more detailed summary of the phenotypes of these and other mouse mutants used in this study ) . Thus , male mammalian meiocytes respond differently to the inability to complete recombination once it has begun as opposed to the absence of recombination initiation . In both types of mutant , however , spermatocytes undergo apoptosis during pachynema [16] . It is now clear that one DSB-independent trigger of spermatocyte apoptosis involves failure to form a sex body [16] , the transcriptionally repressed , heterochromatic domain encompassing the nonhomologous portions of the X and Y chromosomes . Sex body failure enables expression of sex chromosome genes that for unknown reasons are deleterious for pachytene spermatocytes [17] . This cell elimination mechanism depends on the checkpoint kinase ATR [18] . We will refer to spermatocyte arrest in response to DSB repair defects , measured as a block to H1t expression , as recombination-dependent arrest to distinguish it from the DSB-independent sex body-deficient arrest . It has been suggested that both types of arrest are sufficient to trigger apoptosis [10 , 14 , 15] , although this remains unproven for recombination-dependent arrest . Interestingly , however , even though spermatocytes arrest with physiological states characteristic of different points in prophase depending on the type of recombination defect , they undergo apoptosis at an equivalent stage when evaluated in the context of development of the seminiferous epithelium [2 , 19] . Spermatogenesis occurs within the seminiferous tubules , which can be classified among twelve epithelial stages ( I to XII ) according to the cell types found in each cross-section [20] . Despite showing different arrest points based on H1t staining , Dmc1−/− and Spo11−/− spermatocytes undergo apoptosis at the same epithelial stage ( stage IV ) , which is the equivalent of mid-pachynema for wild-type spermatocytes [2 , 19] , and which is the time when H1t incorporation would normally have occurred [13] ( described in more detail below ) . The inability of epithelial staging alone to discriminate between different physiological arrest points emphasizes the importance of molecular cytological methods for characterizing mutant spermatocyte behavior [2] . A straightforward interpretation is that recombination-dependent arrest blocks spermatogenic differentiation at a stage equivalent to early pachynema , with cells remaining in this arrested state until apoptosis is triggered in epithelial stage IV , equivalent to the time unarrested cells would have transitioned into mid-pachynema . Exploring mechanisms unique to recombinant-dependent arrest is challenging: meiotic recombination drives pairing and synapsis of homologous chromosomes in mouse , so most mutants that fail to complete recombination also show widespread chromosome asynapsis , which indirectly blocks sex body formation [16] . Thus , mutants like Dmc1 that retain numerous unrepaired DSBs at early pachynema also display rampant chromosomal asynapsis , which impedes sex body formation [2 , 16] . Interpreting this arrest phenotype is further complicated if asynapsis per se can trigger a response separate from its effects on sex body formation [21] . Since these mutants have such a complex array of severe defects , it is difficult to use them to explore mechanisms unique to recombination-dependent arrest . Thus , we wished to use mutants that cannot complete DSB repair but that do complete synapsis . Two mutants are known to enter pachynema with unrepaired DSBs yet with substantially normal autosomal synapsis and apparently normal sex body formation , providing an opportunity to investigate factors that specifically regulate recombination-dependent arrest during meiosis . One mutant is Trip13mod/mod , which contains a hypomorphic gene trap allele of Trip13 ( Trip13mod , for “moderately defective”; also termed Trip13Gt ) [22 , 23] . This allele substantially reduces expression of TRIP13 , a AAA+ ATPase required for meiotic recombination . Most Trip13mod/mod spermatocytes arrest at pachynema with unrepaired DSBs and complete autosomal synapsis , then subsequently undergo apoptosis [22 , 23] . We show here that arrest occurs prior to histone H1t incorporation , similar to Dmc1−/− mutants . A small fraction of cells manage to evade arrest and finish meiosis ( “escapers” ) , presumably because they complete meiotic recombination [22 , 23] . Nonetheless , the great majority of TRIP13-deficient spermatocytes undergo what has been inferred to be a recombination-dependent arrest in pachynema since autosomal synapsis is completed and spermatocytes display apparently normal looking sex bodies [22 , 23] . The second mutant is the Spo11+/− Atm−/− mouse [24 , 25] . In somatic cells , the ATM kinase is activated by the MRE11 complex to participate in the DNA damage response by phosphorylating multiple substrates , including the checkpoint mediator kinase CHK2 [26] . In meiosis , ATM regulates SPO11 activity via a negative feedback loop , such that Atm−/− single mutant spermatocytes experience greatly elevated DSB levels [27] . As a result , cells are unable to repair DSBs sufficiently and thus fail to complete synapsis or form sex bodies , arresting at pachynema [28] . However , introduction of Spo11 heterozygosity reduces SPO11 activity , partially decreasing DSB numbers , and so suppresses some of the phenotype of Atm−/− spermatocytes [27] . Although Spo11+/− Atm−/− spermatocytes are unable to repair all DSBs at pachynema , they complete autosome synapsis and sex body formation [24 , 25] . Remarkably , unlike Trip13mod/mod spermatocytes , Spo11+/− Atm−/− spermatocytes do not arrest or undergo apoptosis at pachynema despite the presence of multiple unrepaired DSBs , instead progressing to metaphase I before initiating programmed cell death [24 , 25] . To explain this difference in arrest behavior , we hypothesized that ATM is a critical component of the recombination-dependent arrest mechanism . Consistent with this idea , the canonical ATM-effector kinase CHK2 is required to arrest Trip13mod/mod oocytes [29] . Here we test this hypothesis by performing epistasis experiments combining Trip13mod/mod with mutations that eliminate ATM entirely , attenuate ATM activity , or eliminate a downstream effector kinase . Our findings reveal that recombination-dependent arrest in meiosis prior to the H1t-positive substage of pachynema depends on the MRE11 complex , ATM and CHK2 .
It has been proposed that the arrest and subsequent apoptosis of Trip13mod/mod spermatocytes at pachynema are both consequences of unrepaired DSBs activating a recombination-dependent checkpoint arrest mechanism [16 , 22 , 23] . To test this hypothesis , we asked if Trip13mod/mod cells with unrepaired DSBs were indeed undergoing apoptosis while retaining developmental characteristics of early pachynema . We detected apoptotic cells by performing TUNEL staining on spermatocyte spreads previously immunostained for three markers: γH2AX , a phosphorylated form of histone variant H2AX that is a marker of DSBs and sex body formation [30]; SYCP3 , a component of the synaptonemal complex [31]; and H1t . For Spo11−/− spermatocytes , in which sex body failure triggers arrest and apoptosis after H1t incorporation has begun [2] , all TUNEL-positive cells were also positive for H1t staining ( n = 59 , S1A Fig . ) . By contrast , for Dmc1−/− spermatocytes , all TUNEL-positive cells were H1t-negative ( n = 70 , P≤0 . 0001 , Fisher’s exact test , S1B Fig . ) . These results further validate use of H1t as a molecular marker to distinguish between different arrest points in meiotic prophase . Nearly all Trip13mod/mod TUNEL-positive cells ( 97 . 2% , n = 106 ) were H1t-negative ( Fig . 1A-B ) , implying that most apoptotic cells had arrested in a developmental state with characteristics of early pachynema , as predicted . All Trip13mod/mod TUNEL-positive spermatocytes displayed multiple γH2AX patches ( Fig . 1C ) , confirming the presence of unrepaired DSBs . A grossly normal sex body was also observed , agreeing with prior results [22 , 23] , although further analysis described below revealed subtle sex body defects . If recombination-dependent arrest is activated in Trip13mod/mod spermatocytes , then we would also predict that the subset of cells that progress further to an H1t-positive state are the relatively recombination-proficient escapers documented previously [22 , 23] . If so , these cells should have fewer unrepaired DSBs than the pachytene cells that arrested before incorporating H1t . Indeed , H1t-positive Trip13mod/mod pachytene cells had fewer γH2AX patches than H1t-negative cells ( 8 . 0 ± 12 . 2 vs . 54 . 2 ± 9 . 9 , mean ± SD , respectively; P≤0 . 0001 , t test; Fig . 1D-L ) . Interestingly , there appeared to be two populations of H1t-positive Trip13mod/mod pachytene spermatocytes . The majority ( 77 . 3% ) had very few ( < 8 ) γH2AX patches ( 1 . 7 ± 2 . 2 on average; Fig . 1K-L ) , but still significantly more than wild-type cells ( 0 . 8 ± 1 . 5; P = 0 . 016; Fig . 1D , G-H ) . The remainder ( 22 . 7% ) had more γH2AX patches than the first population ( 29 . 4 ± 6 . 7; P≤0 . 0001 ) , but significantly less than H1t-negative cells ( P≤0 . 0001 ) . For the few Trip13mod/mod cells that reached diplonema , however , the number of γH2AX patches was indistinguishable from wild type ( 0 . 1 ± 0 . 3 vs . 0 . 4 ± 1 . 1 , respectively; P = 0 . 183 , negative binomial regression; Fig . 1D ) . A straightforward interpretation is that stochastic differences between TRIP13-deficient cells in the number of unrepaired DSBs translate into different arrest responses: cells with many unrepaired DSBs experience recombination-dependent arrest at early pachynema ( H1t-negative ) , cells with an intermediate number progress to an H1t-positive stage but then undergo apoptosis ( possibly because of the DSBs , sex body defects , or both; see below ) , and the most repair-proficient subset of cells progress still further to diplonema . Additional implications of these patterns are addressed in Discussion . Spermatocytes from Spo11+/− Atm−/− mice also display γH2AX patches late in prophase I yet progress to the first division; this difference from ATM-proficient Trip13mod/mod mice is consistent with the hypothesis that cells respond differently to persistent DSBs in the absence of ATM . Using H1t staining to more precisely define prophase stages , we observed that H1t-positive Spo11+/− Atm−/− spermatocytes had numerous γH2AX patches , only ∼20% fewer than H1t-negative Spo11+/− Atm−/− spermatocytes ( Fig . 1D , M-P ) . This is in marked contrast to Trip13mod/mod mice , where most cells that progressed to an H1t-positive state had much fewer γH2AX patches than the earlier H1t-negative cells ( see above ) . Moreover , H1t-positive Spo11+/− Atm−/− spermatocytes had significantly more γH2AX patches than total H1t-positive Trip13mod/mod cells ( 35 . 5 ± 15 . 6 , P≤0 . 0001; Fig . 1D ) . Thus , in the absence of ATM , cells with numerous unrepaired DSBs can progress to mid/late pachynema ( and beyond ) . The above results are consistent with our hypothesis that unrepaired DSBs at early pachynema in Trip13mod/mod spermatocytes trigger a recombination-dependent checkpoint arrest that is mediated by ATM . To test this idea , we wished to ask if removing ATM activity allows TRIP13-deficient cells to progress further into prophase . It is uninformative for this purpose to query Trip13mod/mod Atm−/− double mutants because the Atm−/− mutation by itself causes an early block at an H1t-negative stage as a consequence of the enormous ( >10-fold ) increase in DSB numbers ( see Discussion ) [2 , 27] . Instead , since Spo11 heterozygosity ameliorates this catastrophic effect of ATM deficiency , we tested the epistasis relationship between the Trip13mod/mod phenotype ( arrest in an H1t-negative state ) and Spo11+/− Atm−/− phenotype ( no pachytene arrest ) by analyzing Trip13mod/mod Spo11+/− Atm−/− mice ( hereafter referred to as “TSA triple mutant” ) . As detailed below , we indeed find that absence of ATM allows TRIP13-deficient spermatocytes to progress to an H1t-positive state , but that additional defects that cause efficient mid/late-pachytene arrest and apoptosis of TSA triple mutant spermatocytes become apparent . ( Note that DSBs in the Spo11+/− Atm−/− background are still substantially elevated relative to wild type ( ∼6-fold ) [27] , so no aspect of the triple mutant phenotype can be ascribed to a reduction in DSBs below wild-type levels . ) First , we measured testis size as a readout of spermatogenetic progression since mice with spermatogenic failure have small testes [14 , 15] . Sizes of Trip13mod/mod and TSA triple mutant testes were indistinguishable from one another ( P = 0 . 6 , t test ) , both being smaller than in Spo11+/− Atm−/− mice ( P≤0 . 05 , Table 1 ) and approximately one third the size of wild-type testes ( P≤0 . 0001 , Table 1 ) . These results indicate that TSA triple mutant mice experience spermatogenic failure . Second , we assessed the timing of apoptosis by histological staging of seminiferous tubules . Many Trip13mod/mod spermatocytes underwent apoptosis in tubules at epithelial stage IV , corresponding to mid pachynema ( Fig . 2A , B ) , as previously shown [23] . In contrast , and consistent with absence of pachytene arrest , Spo11+/− Atm−/− spermatocytes apoptosed in tubules at epithelial stage XII ( Fig . 2C ) , corresponding to metaphase I . Arrest of Spo11+/− Atm−/− spermatocytes at this point is thought to be caused largely by a spindle checkpoint response to achiasmate ( unconnected ) chromosomes , particularly the X-Y pair [32] . Consistent with the small testis sizes , TSA triple mutant animals showed spermatocyte apoptosis at stage IV ( Fig . 2D ) . Furthermore , whereas few wild-type tubules had >5 TUNEL-positive cells ( 1 . 0% , Table 1 ) , both Trip13mod/mod single mutant and TSA triple mutant testes displayed many such apoptotic tubules ( 21 . 6% and 26 . 0% respectively , P≤0 . 0001 compared to wild type , Fisher’s exact test , Table 1 ) . Surprisingly , no TSA triple mutant cells escaping stage IV apoptosis were observed ( Fig . 2B-D ) , in contrast to Trip13mod/mod or Spo11+/− Atm−/− testes [22–24] . Although these results indicate that ATM deficiency ( in the Spo11+/− Atm−/− background ) does not suppress the pachytene apoptosis of Trip13mod/mod mutant spermatocytes , they do not precisely define the stage of meiotic arrest . To address this question , we examined progression of chromosome synapsis in spermatocyte spreads stained with anti-SYCP3 antibodies . Out of total SYCP3-positive primary spermatocytes , 3 . 3% had progressed beyond pachynema in Trip13mod/mod , but none in TSA triple mutants ( P≤0 . 0001 , Fisher’s exact test , Table 1 ) . This lack of escapers here and in the analysis of tubule sections indicates that overall spermatogenic failure is more penetrant in TSA triple mutants . Moreover , all TSA triple mutant spermatocytes displayed substantial autosome asynapsis ( n = 703 , Fig . 2E , H-I ) , unlike either the Trip13mod/mod or Spo11+/–Atm−/− mutants [23 , 24] . Because failure to complete synapsis has been associated with the inability to properly repair meiotic DSBs , these results could suggest that TRIP13-deficient spermatocytes are unable to cope with the increased DSB numbers produced in the Spo11+/− Atm−/− background [27] , leading to synaptic catastrophe . One consequence of synaptic failure is the inhibition of sex body formation [16] . Accordingly , we did not observe any TSA triple mutant cells with the γH2AX immunostaining pattern diagnostic of sex body formation ( Fig . 2E , G ) . Instead , γH2AX signal was mostly localized as patches on both unsynapsed and synapsed portions of chromosomes and did not accumulate in any particular region of the nucleus . The complete failure to form sex bodies in TSA triple mutants likely accounts for the highly penetrant arrest in pachynema and apoptosis at stage IV . To determine whether ATM imposes a recombination-dependent arrest early in pachynema that is relieved in TSA triple mutant spermatocytes , we examined H1t incorporation . In wild type , 50 . 3% of SYCP3-positive cells were also positive for H1t ( Table 1 ) , whereas only 20 . 8% of SYCP3-positive cells from Trip13mod/mod testes were H1t-positive ( Table 1 ) . Presumably , most of these H1t-positive cells were escapers that would complete meiosis ( see above ) . Notably , 47 . 7% of SYCP3-positive cells from TSA triple mutants were H1t-positive ( see representative image in S2 Fig . ) , a ≥two-fold increase compared to Trip13mod/mod samples but similar to that in wild-type and in Spo11+/− Atm−/− animals ( 45 . 8% , Table 1 ) . Thus , the Spo11+/− Atm−/− combination is epistatic to Trip13mod/mod mutation for the ability of spermatocytes to incorporate H1t . We note that the relatively normal H1t pattern in Spo11+/− Atm−/− animals ( S1 Table ) and the strong block to H1t incorporation in Atm−/− single mutants [2] argue against the possibility that ATM is required to prevent premature H1t expression in early pachynema . To confirm this conclusion , we compared timing of H1t expression in testis sections from wild type and TSA triple mutants . In wild type , H1t was first detected in mid-pachytene spermatocytes in stage IV tubules , and was absent from early pachytene spermatocytes in stage I-III tubules , as previously reported [13] ( see S3A , B Fig . and its legend ) . Staging is less precise for immunofluorescently stained tubules of mutants that lack post-meiotic cell types [19] . Nonetheless , we observed that leptotene , zygotene , and most if not all early pachytene spermatocytes were H1t-negative , and that H1t-positive cells did not appear until approximately mid pachynema in TSA triple mutants ( i . e . , in tubule sections that were likely in stage IV ) ( see S3C Fig . and its legend ) . Thus , there is no evidence for substantially premature expression of H1t in ATM-defective spermatocytes . The increased number of H1t-positive cells implies that the TSA triple mutant spermatocytes are not being restrained by the recombination-dependent arrest mechanism . This further reinforces the conclusion above that the apoptosis occurring in these cells is attributable to the absence of a sex body . If our interpretation is correct , we would expect that absence of the recombination-dependent checkpoint allows spermatocytes to progress to an H1t-positive state even if they have many unrepaired DSBs . Indeed , H1t-positive TSA triple mutant spermatocytes displayed significantly more γH2AX patches than Trip13mod/mod cells ( 61 . 0 ± 18 . 8 , P≤0 . 0001 , t test; Fig . 2J ) . The large number of γH2AX patches also supports the conclusion that the TSA triple mutants do not experience a reduction in DSBs relative to Trip13mod/mod alone . The finding that absence of ATM allows cells to progress to an H1t-positive state even if they contain numerous unrepaired DSBs indicates that ATM is required for implementation of recombination-dependent arrest at early pachynema . The MRE11 complex ( MRE11 , RAD50 and NBS1 ) senses DSBs and promotes their repair , in part by activating ATM [33] . Null mutations of these genes are incompatible with embryonic development , but mice bearing hypomorphic Nbs1 or Mre11 mutations that attenuate ATM activation and/or diminish phosphorylation of subsets of ATM targets are viable ( Nbs1ΔB or Mre11atld ) [34 , 35] . Nbs1ΔB/ΔB and Mre11ATLD/ATLD mice are subfertile , with spermatocytes displaying some synaptic defects and accumulating early recombination markers at pachynema , suggesting that the MRE11 complex plays a role in meiotic recombination in mammals [36] , as in budding yeast and other organisms [37] . Testis samples from both mutants showed a small elevation in the fraction of spermatocytes that were H1t-positive ( Table 1 ) . This could be a consequence of previously documented differences in the overall distribution of meiotic cell types [36] , but the important conclusion for purposes of this study is that the mutant spermatocytes are readily able to progress through pachynema and beyond in roughly normal numbers . To further define the ATM-dependent pathway leading to recombination-dependent arrest , we asked whether these MRE11-complex mutations mimic ATM deficiency with respect to the meiotic progression phenotype of Trip13mod/mod spermatocytes . Indeed , similar to the TSA triple mutant , Trip13mod/mod Nbs1ΔB/ΔB and Trip13mod/mod Mre11ATLD/ATLD mutants had approximately two-fold ( 43 . 4% ) and three-fold ( 73 . 3% ) more H1t-positive spermatocytes , respectively , than Trip13mod/mod single mutants ( Table 1 ) . Importantly , these mutants progressed despite having high numbers of unrepaired DSBs: H1t-positive spermatocytes in Trip13mod/mod Nbs1ΔB/ΔB and Trip13mod/mod Mre11ATLD/ATLD mutants had significantly more γH2AX patches than the Trip13mod/mod mutant ( 59 . 7 ± 12 . 4 and 54 . 1 ± 10 . 8 , respectively; Fig 2J ) , but similar numbers to the TSA triple mutant ( P˃0 . 05 ) . These hypomorphic MRE11 complex mutations are thus epistatic to Trip13mod/mod with respect to the ability of cells to progress to an H1t-positive stage . These results support the hypothesis that the MRE11 complex activates the ATM-mediated recombination-dependent arrest that halts development of most Trip13mod/mod single mutant spermatocytes at early pachynema . Also akin to the TSA triple mutant , these double mutants experienced a more penetrant spermatogenic failure ( i . e . , few or no escapers ) than in the Trip13mod/mod single mutant despite better progression beyond early pachynema . Trip13mod/mod Nbs1ΔB/ΔB and Trip13mod/mod Mre11ATLD/ATLD mice had small testes ( Table 1 ) , and histological analysis revealed many similarities between Trip13mod/mod Nbs1ΔB/ΔB and Trip13mod/mod Mre11ATLD/ATLD double mutants and the TSA triple mutant: apoptosis at epithelial stage IV ( Figs . 3 and S4 ) , a high number of apoptotic cells per tubule ( Table 1 ) , and no evidence of cells escaping and completing meiosis ( Figs 3 . and S4 ) . Likewise , cytological analysis showed that no Trip13mod/mod Mre11ATLD/ATLD spermatocytes and only a negligible fraction of Trip13mod/mod Nbs1ΔB/ΔB spermatocytes ( 0 . 3% ) progressed beyond pachynema , similar to the TSA triple mutant ( P˃0 . 05 , Fisher’s exact test , Table 1 ) but significantly different from the Trip13mod/mod single mutant ( P≤0 . 0001 and P = 0 . 0014 , respectively ) . Also similar to TSA triple mutants , most double mutant spermatocytes had substantial synaptic defects , with only 0 . 5% of Trip13mod/mod Mre11ATLD/ATLD ( n = 348 ) and 5 . 4% of Trip13mod/mod Nbs1ΔB/ΔB ( n = 350 ) SYCP3-positive spermatocytes showing complete autosomal synapsis . These values are much lower than Trip13mod/mod SYCP3-positive spermatocytes ( 40 . 7% , n = 705 , P≤0 . 0001 , Fisher’s exact test ) . As expected from the substantial asynapsis , the double mutants showed no evidence of sex body formation as assessed by γH2AX staining ( Figs . 3 and S4 ) . Taken together , we infer from these data that the more penetrant spermatogenic failure observed in Trip13mod/mod Nbs1ΔB/ΔB and Trip13mod/mod Mre11ATLD/ATLD testes is most probably due to activation of the sex body-deficient arrest , as in TSA triple mutants . Further , our results confirm that the sex body-deficient arrest mechanism can operate even when recombination-dependent arrest is compromised . This was also indicated by the arrest of Spo11−/− mutants as well as repair-proficient mutants with defects in meiotic sex chromosome inactivation ( MSCI ) [2 , 16] . We reasoned that the attenuated ATM signaling in Mre11 and Nbs1 hypomorphic mutants might lead to elevated DSB numbers . To test this , we examined SPO11-oligonucleotide complexes , which provide a measure of whole-testis DSB levels , in Mre11ATLD/ATLD and Nbs1ΔB/ΔB single mutant mice [27] . ( Note that this class of hypomorphic MRE11-complex mutation is unlike the Rad50S type of mutation , which in yeast blocks endonucleolytic release of SPO11 from DSB ends [33] ) . Indeed , we found that Mre11ATLD/ATLD and Nbs1ΔB/ΔB mice displayed an ∼2-fold increase compared to wild-type littermates ( Fig . 4A-B ) . This elevation is less than what is seen in Atm–/– ( ∼12-fold ) or Spo11+/–Atm−/− testes ( ∼6-fold ) , presumably because the Mre11 and Nbs1 hypomorphs attenuate but do not eliminate ATM activity [34 , 35] . ( Note that the overall fertility phenotype of the Mre11 and Nbs1 single mutants is also significantly milder than for mice lacking ATM ( S1 Table ) ) . These results provide strong evidence that ATM-mediated feedback control of DSBs involves an MRE11 complex-dependent ATM activation pathway similar to the response to ionizing radiation . In contrast , Trip13mod/mod animals had roughly normal levels of SPO11-oligonucleotide complexes ( ∼70% of wild type , Fig . 4C ) . Because Trip13mod/mod single mutants have defects in completing DSB repair , we speculate that the autosomal synaptic failure in Trip13mod/mod Mre11ATLD/ATLD or Trip13mod/mod Nbs1ΔB/ΔB mice is a synthetic defect caused by an inability of TRIP13-deficient cells to tolerate increased DSB numbers resulting from ATM activation defects . An alternative but not mutually exclusive possibility is that the MRE11 complex and TRIP13 synergistically promote synapsis separately from their effects on recombination . The CHK2 kinase is an effector of the ATM-signaling cascade in response to ionizing radiation [38] . In meiosis in several species , CHK2 function is required to efficiently activate the recombination-dependent checkpoint [9] . In mouse , CHK2 is required to arrest oocytes with unrepaired DSBs , although it has been suggested based on histological analysis that CHK2 does not play a similar role in spermatocytes [29] . To test for CHK2 involvement in recombination-dependent arrest in mouse spermatocytes , we analyzed Trip13mod/mod Chk2−/− mice . The testicular phenotype of Trip13mod/mod Chk2−/− mice was similar to the Trip13mod/mod single mutant for testis size and histological tubule classification ( apoptosis of spermatocytes at epithelial stage IV , but with presence of some spermatids ) , but also , notably , for the percentage of SYCP3-expressing cells that had progressed beyond pachynema ( 1 . 9% , P = 0 . 2462 , Fisher’s exact test; Table 1 and Fig . 5A-B ) . Thus , CHK2 deficiency does not cause a more penetrant spermatogenic failure in the context of the Trip13mod/mod mutation , unlike MRE11 complex hypomorphs or the absence of ATM itself . Moreover , CHK2-deficient testes displayed similar levels of SPO11-oligonucleotide complexes as wild-type littermates ( Fig . 4D ) . Thus , CHK2 is not required for proper control of meiotic DSB formation , clearly separating CHK2 from both the MRE11 complex and ATM in the regulation of SPO11 activity . These results further support the conclusion above that the synaptic defects and more penetrant block to spermatogenesis observed in TSA triple mutants and in Trip13mod/mod Mre11ATLD/ATLD and Trip13mod/mod Nbs1ΔB/ ΔB double mutants are attributable to the increased DSB numbers . Notably , however , Trip13mod/mod Chk2−/− spermatocytes showed evidence of a defect in recombination-dependent arrest , because two-fold more H1t-positive spermatocytes were observed than with the Trip13mod/mod single mutant ( Table 1 ) . Similarly to TSA triple mutants and the other double mutants described above , these H1t-positive spermatocytes presented more γH2AX patches ( 34 . 1 ± 23 . 3; P≤0 . 0001 , t test; Fig . 5E-G ) . Also , whereas 77 . 3% of the H1t-positive Trip13mod/mod spermatocytes ( i . e . , those inferred to be relatively recombination-proficient escapers ) had very few γH2AX patches ( < 8 per cell ) , only 21 . 4% of cells from Trip13mod/mod Chk2−/− mice were in this category ( P≤0 . 0001 , Fisher’s exact test ) , with the majority of cells having many more γH2AX patches ( Fig . 5G ) . By contrast , H1t-negative cells had similar numbers of γH2AX patches in both Trip13mod/mod single mutant and Trip13mod/mod Chk2−/− double mutant mice ( P = 0 . 18 , t test , Fig . 5G ) . These results suggest that the absence of CHK2 allows spermatocytes with numerous unrepaired DSBs to bypass the recombination-dependent arrest during pachynema , reminiscent of what occurs in oocytes [29] . If this is correct , we would also expect to find evidence of unrepaired DSBs in those spermatocytes that had escaped pachytene arrest entirely . Indeed , γH2AX patches were substantially more prevalent at diplonema in Trip13mod/mod Chk2−/− spermatocytes: only 11 . 1% of diplotene Trip13mod/mod cells had γH2AX patches , but 52 . 9% of the Trip13mod/mod Chk2−/− spermatocytes had one or more of these markers of unrepaired DSBs ( P = 0 . 0045 , Fisher’s exact test ) ( Fig . 5H ) . ( Mean ± SD of 0 . 1 ± 0 . 3 for Trip13mod/mod ( Fig . 1D ) vs . 1 . 2 ± 1 . 8 ( n = 17 ) for Trip13mod/mod Chk2–/–; P = 0 . 0005 , negative binomial regression; S1 Dataset . ) We conclude that CHK2 , like the MRE11 complex and ATM , is required to activate the recombination-dependent arrest occurring in Trip13mod/mod spermatocytes . Although CHK2 deficiency allowed more efficient progression of TRIP13-deficient cells to an H1t-positive stage , the overall spermatogenic failure was not alleviated , such that most Trip13mod/mod Chk2−/− spermatocytes still underwent apoptosis in tubules at epithelial stage IV ( Fig . 5B ) . We therefore hypothesized that the sex bodies of Trip13mod/mod Chk2−/− spermatocytes may have subtle defects that impede their function . Indeed , most Trip13mod/mod Chk2−/− spermatocytes showed an abnormally elongated γsH2AX-positive sex body chromatin domain ( 57 . 8% , N = 45 , Fig . 5E-F ) , unlike the condensed , rounded structure seen in most wild-type H1t-positive spermatocytes ( Fig . 1F , H ) . In addition , the intensity of the sex-body γH2AX signal was reduced to about half the wild-type level on average in H1t-positive pachytene Trip13mod/mod Chk2−/− spermatocytes ( S5 Fig . ; P ≤0 . 0001 , t test ) . Although most H1t-positive pachytene Trip13mod/mod cells had sex bodies with normal morphology , a significant minority also showed the abnormal elongated form ( 11 . 3% , n = 53 , P≤0 . 0001 , Fisher’s exact test ) and sex body γH2AX intensity was reduced on average ( P = 0 . 033; see S5 Fig . ) . ATR is the kinase responsible for the bulk of H2AX phosphorylation in sex bodies [18 , 24 , 25] . In wild type , most pachytene spermatocytes showed ATR staining either as a continuous signal on the sex chromosome axes or spread to the XY chromatin ( 93 . 6% , n = 47; Fig . 6A-B ) , as previously reported [39] . The remainder of the cells displayed stretches of ATR partially covering the X and Y axes . By contrast , only 18 . 0% of Trip13mod/mod Chk2−/− pachytene cells had the axial or chromatin-associated ATR staining most commonly found in wild-type cells , 26 . 2% had short stretches of continuous ATR signal partly covering sex chromosome axes , and most cells had only focal ATR staining ( 55 . 7% , n = 61; Fig . 6C-E ) . Trip13mod/mod spermatocytes had an intermediate phenotype: 38 . 8% displayed ATR completely covering the XY axes or chromatin , 32 . 8% showed ATR stretches over the XY axes , and 28 . 4% had only discrete ATR foci along the X and Y ( n = 67 , Fig . 6E ) . These results suggest that CHK2 deficiency may exacerbate a defect in ATR loading on the sex chromosomes in TRIP13-deficient cells . To further explore ATR-dependent processes , we examined SUMO-1 , which is loaded onto sex chromosomes at pachynema in an ATR-dependent manner [18] . In all wild-type cells ( n = 52 ) and 86 . 0% of Trip13mod/mod cells ( n = 50 ) , we detected SUMO-1 covering the chromatin of the sex body . In contrast , only 72 . 3% of Trip13mod/mod Chk2−/− pachytene spermatocytes contained SUMO-1 in their sex bodies , and of these , SUMO-1 signal had failed to spread over the entire XY chromatin in 60 . 6% of cells ( n = 45 , S6 Fig . ) . Since the analyzed markers suggested that the sex bodies of Trip13mod/mod Chk2–/–spermatocytes may be altered , we studied the functionality of these sex bodies by RNA FISH . As mentioned above , an important function of the sex body is to silence certain X- and Y-linked genes that are toxic for spermatocytes [17] . Thus , we analyzed the early-pachytene expression of two X-linked genes: Zfx , located in an interstitial region; and Scml2 , located near the pseudoautosomal boundary at the centromere-distal end of the chromosome ( Figs . 6F and S7 ) . As expected , only a minority of wild-type cells expressed Zfx or Smcl2 ( 10 . 9% ± 3 . 8 and 17 . 1% ± 1 . 9 , mean ± SD , respectively , Fig . 6F ) , whereas a significantly larger fraction of cells expressed these genes in mutants that fail to form a sex body at pachynema , like Trip13mod/mod Mre11ATLD/ATLD spermatocytes ( for Zfx: 37 . 1% and for Smcl2: 49 . 1% , P≤0 . 0001 and P = 0 . 0002 respectively , Fisher’s exact test ) . As predicted from the altered sex body morphology , we found that Trip13mod/mod and Trip13mod/mod Chk2−/− spermatocytes also showed increased expression of these X-linked genes at early pachynema ( Trip13mod/mod: for Zfx: 27 . 2% ± 3 . 2 and for Smcl2: 27 . 5% ± 0 . 0 , P<0 . 005 , 1 way Anova; Trip13mod/mod Chk2–/–: for Zfx: 28 . 6% and for Smcl2: 30 . 0% , P = 0 . 0217 and P = 0 . 0009 respectively , Fisher’s exact test ) . We conclude that sex body function is altered in Trip13 mutants . The levels of sex chromosome gene misexpression are similar to those reported to be sufficient to arrest spermatocytes at pachynema in other mutants [21] . Indeed , when we assessed whether Trip13mod/mod Chk2−/− pachytene spermatocytes undergo apoptosis at an H1t-positive stage , as occurs in mutants that fail to form a sex body ( S1 Fig . ) [2] , we found that 98 . 5% of TUNEL-positive Trip13mod/mod Chk2−/− spermatocytes analyzed were also H1t-positive , contrasting what occurs in Trip13mod/mod mutants ( N = 67 , 1 mouse , P ≤0 . 0001 , Fisher’s exact test , Fig . 6G-J ) but mimicking what is seen in Spo11−/− mutants ( P = 1 , Fisher’s exact test ) . In summary , the mild sex body defect in Trip13mod/mod testes was more pronounced in Trip13mod/mod Chk2−/− spermatocytes , where it resulted in the failure to silence the X and Y chromosomes at pachynema , suggesting that the stage IV apoptosis observed in these double mutant mice is triggered by the sex body-deficient arrest mechanism .
Two major mechanisms are postulated to mediate arrest of recombination-defective mouse spermatocytes during the pachytene stage [2] . One occurs when sex body formation is impeded by massive synaptic failure , irrespective of the presence of uncompleted recombination events , such as when no DSBs are generated at all [16] . Sex body defects allow expression of X and Y chromosome genes that are sufficient to induce apoptosis [17] . In this scenario , the arrest and later cell death might be caused by misregulation of gene expression , not activation of a checkpoint per se . The other arrest mechanism is proposed to occur when recombination intermediates persist at pachynema [2 , 16] , similar to what is seen in many other organisms [9] . In most species analyzed , ATR ( Mec1 in budding yeast ) activates this checkpoint , most likely due to the presence of single-stranded DNA [6] . However , recent observations in yeast suggest that Tel1 ( ATM ) might also be involved [40] and our findings clearly indicate that the ATM signaling cascade is crucial for recombination-dependent arrest in mammalian spermatocytes . A recent study found that the previously described recombination-dependent arrest of Atm−/− oocytes [4] is mediated by CHK2 [29] . It was further suggested that a protein kinase other than ATM , possibly ATR , is involved in recombination-dependent arrest in oocytes [29] . While our results suggest that ATM is the principal kinase mediating this arrest in spermatocytes , we do not exclude the possibility that ATR also participates in this mechanism , either in wild type or specifically in Atm−/− cells , in the latter case by replacing some ATM functions ( e . g . , H2AX phosphorylation [24 , 25 , 41] ) . In somatic cells , the MRE11 complex participates in ATM activation , and mutations that impair the MRE11 complex lead to an inefficient G2/M checkpoint [26] . NBS1 also specifically promotes ATM phosphorylation of some of its substrates [33] . Our findings show that recombination-dependent arrest at pachynema has similarities to the G2/M checkpoint in somatic cells , as previously speculated [42] . These similarities may be useful for identifying other key members of the meiotic pathway . Involvement of the effector kinase CHK2 in mammalian recombination-dependent arrest , further supported by observations in oocytes [29] , underlines differences between organisms [9] . In budding yeast , for instance , the checkpoint depends on the meiosis-specific CHK2 homolog , Mek1 . In contrast , D . melanogaster uses the non-meiosis-specific CHK2 homolog , Mnk , and in C . elegans CHK1 , but not CHK2 , is required for arrest . This variety emphasizes the importance of studying mouse meiosis to characterize this pathway in mammals . This and previous studies establish H1t expression as a molecular marker of a response to recombination defects in spermatocytes , and our current findings establish that this response occurs via an ATM signaling cascade . Although available data rule out substantially premature H1t expression , it is possible that H1t is expressed slightly earlier than normal in the absence of DSBs or in the absence of the ATM response; such an effect might contribute to the high observed fraction of spermatocytes that are H1t-positive in the relevant mutants . However , if so , we note that this would be consistent with our conclusion that regulation of H1t expression is a checkpoint response since there are many instances where cell cycle regulated events occur prematurely when either the monitored event or the monitoring mechanism are missing ( e . g . , yeast Ndt80 expression when DSBs or Mec1 signaling are absent [43] or anaphase onset when the spindle assembly checkpoint is deactivated [44] ) . It is formally possible that this response has little or no other consequence than the control of H1t expression , and that spermatocytes do not otherwise “care” that they have sensed the presence of unrepaired DSBs . We consider this hypothesis unlikely since it would mean that the male germline in mouse , unlike mouse oocytes and meiotic cells in all other species studied to date , ignores molecular signals that report directly on the progression of one of the most central aspects of meiotic chromosome dynamics ( recombination ) , and ignores signals that have pronounced consequences in other mouse cell types . Instead , we favor the interpretation that the ATM-dependent response described here is an integral part of meiotic quality control surveillance during spermatogenesis . Our analysis exposed a population of Trip13mod/mod spermatocytes that were H1t-positive ( i . e . , had progressed to at least mid pachynema ) even though they displayed as many as 41 γH2AX patches . These findings may suggest an overt defect in the recombination-dependent arrest mechanism or , alternatively , that this checkpoint tolerates a certain level of unrepaired DSBs . We favor the latter interpretation because the sex chromosomes in male meiocytes naturally accumulate DSBs during meiotic prophase , and recombination markers such as RAD51 can be observed on the X chromosome until late pachynema [45 , 46] . Since most DSBs formed on the X and Y chromosomes occur outside the pseudoautosomal region , they are presumably repaired by sister-chromatid recombination , which is thought to be permitted only later in pachynema [30] . Furthermore , in wild-type mice and in humans , many oocytes present multiple unrepaired DSBs at pachynema and even at diplonema [41 , 47] . Thus , we envision that recombination-surveillance mechanisms permit the progression of mammalian spermatocytes and oocytes that have sufficiently few DSBs to be compatible with eventual repair by the end of meiotic prophase and thus with long-term cell viability . As previously stated , homologous chromosome synapsis depends on meiotic recombination [48] . Thus , failure to complete synapsis might be interpreted as a consequence of defective homologous recombination . Unlike the relevant single mutants , spermatocytes from TSA triple mutants and from Trip13mod/mod Mre11ATLD/ATLD and Trip13mod/mod Nbs1ΔB/ΔB double mutants fail to complete synapsis in all , or almost all , cells [22–25 , 36] . The MRE11 complex , ATM and TRIP13 have been reported to promote meiotic recombination [22–25 , 36] . Thus , the fact that double and triple mutants present problems with completing synapsis suggests that the MRE11 complex and ATM could synergize with TRIP13 to promote proper DSB repair during meiotic prophase . However , we provide evidence that proteins required to activate ATM ( MRE11 and NBS1 ) are also involved in the regulation of DSB formation . Thus , since Spo11+/− Atm−/− spermatocytes as well as cells from Mre11ATLD/ATLD and Nbs1ΔB/ΔB single mutants incur more DSBs than wild-type cells and because TRIP13-deficient cells are unable to complete meiotic recombination [22 , 23] , this opens the possibility that TRIP13-defective cells cannot deal with the additional DSBs formed in the absence , or reduction , of ATM activity . Alternatively or in addition , the synapsis phenotype observed in TSA triple mutants or Trip13mod/mod Mre11ATLD/ATLD and Trip13mod/mod Nbs1ΔB/ΔB double mutants might be a manifestation of a function of ATM and/or the MRE11 complex in promoting synapsis more directly and synergistically with TRIP13 . The array of phenotypes in the mutants analyzed here suggests that ATM participates in different aspects of meiotic prophase ( Fig . 7 ) . Our results further demonstrate that , while meiotic progression depends on the MRE11 complex , ATM and CHK2 , the regulation of SPO11 activity requires the MRE11 complex to activate ATM but is independent of CHK2 . This finding may indicate that direct ATM phosphorylation targets exert control of DSB formation , as has been argued in yeast [49] . Interestingly , yeast Mek1 promotes inter-homolog bias in the repair of DSBs [50 , 51] . The fact that homologous chromosome synapsis is not altered in the absence of mouse CHK2 suggests that this protein is not involved in recombination partner choice in mammals , although we cannot exclude that compensation by related kinases ( e . g . , CHK1 ) could be responsible for the absence of an obvious phenotype in Chk2−/− spermatocytes . The apoptosis of spermatocytes in tubules at epithelial stage IV observed in Trip13mod/mod Chk2−/− testis can be explained if TRIP13 also contributes to the formation of a functional sex body ( Fig . 7 ) . In support of this hypothesis , we observed defective loading of ATR onto XY chromatin in pachytene spermatocytes from Trip13mod/mod Chk2−/− mice and in a subset of pachytene cells from Trip13mod/mod single mutants . This defect leads to inappropriate H2AX phosphorylation and SUMO-1 incorporation into the sex body , resulting in inefficient sex chromosome silencing and apoptosis at mid/late pachynema . TRIP13 is required for normal chromosomal association of HORMA ( “Hop1 , Rev7 and Mad2” ) -domain proteins HORMAD1 and HORMAD2 during meiotic prophase [52] . HORMAD1 and HORMAD2 localize to the chromosome axes at leptonema and disappear from synapsed regions during zygonema as synapsis progresses . At pachynema , HORMAD1 and HORMAD2 accumulate at the unsynapsed regions of the X and Y chromosomes where they attract the machinery required to silence these chromosomes [21 , 53] . In Trip13mod/mod spermatocytes , HORMAD1 and HORMAD2 are retained in the synapsed regions of chromosomes and are present on all bivalent axes at pachynema [52] . We speculate that this aberrant localization of HORMA-domain proteins may contribute to inefficient ATR loading on sex chromosomes of Trip13mod/mod Chk2−/− pachytene spermatocytes . It is worth noting that Trip13mod/mod Chk2−/− cells do assemble ATR foci , presumably at resected DSB sites containing single-stranded DNA . This focal ATR localization has been reported previously to be HORMAD2-independent [21] . However , loading of ATR along the entire length of the unsynapsed chromosome axis is HORMAD2-dependent [21 , 53] . Furthermore , Zfx and Scml2 expression levels in early pachytene-stage Trip13 mutant spermatocytes are similar to those reported in Hormad2−/− cells [21] . These findings lead us to propose that proper HORMAD1 and HORMAD2 localization may underlie the function of TRIP13 in sex body formation . The unexpected failure to properly form a sex body found in Trip13 mutants opens the possibility that , although this and other studies have clearly shown that there are at least two distinct arrest mechanisms to respond to recombination or sex body defects [2 , 16] , the apoptosis occurring after the activation of these two arrest mechanisms may be a response only to the common MSCI failure . Nonetheless , we favor the hypothesis that recombination-dependent arrest is sufficient to trigger apoptosis because unrepaired DSBs provoke programmed cell death in oocytes ( where MSCI defects are not an issue ) via the conventional DNA damage response effector molecules , p53 and p63 [4 , 22 , 29]; and because MRE11- and ATM-dependent signaling can directly promote apoptosis in somatic cells [26] . Addressing this issue will require novel meiotic mutants that display recombination defects in the context of an intact ATM signaling cascade and without MSCI defects .
Trip13 , Spo11 , Atm , Dmc1 , Mre11 , Nbs1 and Chk2 mutations were generated previously [10 , 14 , 23 , 28 , 34 , 35 , 54] . All lines were maintained on a C57BL6–129Sv mixed background . Experiments were performed using at least two animals in comparison with littermate controls ( either homozygous or heterozygous for the wild-type alleles ) or , when appropriate littermates were unavailable , control animals obtained from litters of the same matings . Genotyping was performed by PCR analysis of tail-tip DNA as previously described [23] . Testes were harvested from two- to five-month-old animals and processed for histology or cytology , as previously described [23 , 55] . Immunofluorescence was performed using standard methods [47] and antibodies [23] . Additional primary antibodies used were: guinea pig anti-H1t ( kindly donated by M . A . Handel , Jackson Lab ) at 1:500 dilution [12] and a mouse anti-SUMO-1 ( clone 21C7 , Invitrogen ) at 1:100 dilution . TUNEL-staining on IF-stained slides was performed using an in situ cell death detection kit ( Roche Diagnostics ) according to the manufacturer’s instructions , as reported previously [56] . RNA FISH was carried out with digoxigenin-labeled probes as previously described [57] . BAC DNA probes used were: Zfx , bMQ-372M23 ( from Mouse bMQ BAC library ) and Scml2 , RP24-204O18 ( from CHORI BACPAC library ) . Briefly , BAC-containing bacteria were grown in an overnight LB-Chloramphenicol culture at 37°C and BAC DNA was isolated using a standard miniprep method . Approximately 2 μg of BAC DNA was labelled using DIG-Nick Translation Mix ( Roche ) and precipitated with Cot-1 DNA ( Invitrogen ) and salmon sperm DNA ( Stratagene ) . Mouse testes were minced and cells were permeabilized with CSK buffer ( 100 mM NaCl , 300 mM sucrose , 3 mM MgCl2 , 10 mM PIPES , 0 . 5% Triton X-100 , 2 mM vanadyl ribonucleoside ( New England Biolabs ) ) , fixed with 4% paraformaldehyde and dehydrated through an ice-cold ethanol series . DNA-BAC probes were denatured for 10 min at 80°C , pre-hybridized for 30 min at 37°C , and added to the slides for an overnight incubation at 37°C . Stringency washes were performed and digoxigenin was detected using anti-digoxigenin-FITC ( 1:10 , Millipore ) . RNA FISH was then followed by TOPBP1 ( 1:50 , Abcam ) and γH2AX ( 1:100 , Millipore ) immunostaining . Cells were examined on an Olympus IX70 inverted microscope . Images were captured using a computer-assisted ( DeltaVision ) CCD camera ( Photometrics ) , and processed for publication using ImageJ and Photoshop . Early pachytene cells were defined based on a continuous TOPBP1 staining along the X and Y chromosome axes . Testis extract preparation , immunoprecipitation and Western blot analysis were performed essentially as described [27] . SPO11-oligonucleotide complexes and free SPO11 were isolated from testis lysates by two rounds of immunoprecipitation with a SPO11 monoclonal antibody ( Spo11–180 ) on Protein A-agarose beads ( Roche ) . SPO11-oligonucleotide complexes were labeled with [α-32P] dCTP using terminal deoxynucleotidyl transferase ( Fermentas ) , released from the beads by boiling in Laemmli buffer , and fractionated by SDS-PAGE . The electrophoresed products were transferred onto polyvinylidene fluoride ( PVDF ) membrane . Radiolabeled species were detected using Fuji phosphor screens and quantified with ImageGauge software . The same PVDF membrane was then subjected to Western analysis using the SPO11 monoclonal antibody . One-way ANOVA , Student's t tests and Fisher's exact tests were performed using GraphPad Prism software and/or GraphPad QuickCalcs online resource ( http://www . graphpad . com/quickcalcs/ ) . For comparing counts of γH2AX foci , we used t tests for simplicity to compare between genotypes at pachynema because the count data were not highly skewed ( i . e . , were reasonably approximated by a normal distribution ) . However , for diplotene cells we instead used negative binomial regression because the count distributions at this stage were highly skewed for the Trip13mod/mod Chk2−/− sample and contained many zero values for all samples . Regression was carried out using the glm . nb function from the MASS package ( version 7 . 3–33 ) in R ( version 3 . 1 . 1 ) . All experiments performed in this study comply with US and EU regulations and were approved by the MSKCC Institutional Animal Care and Use Committee and the UAB Ethics Committee and the Catalan Government . | Meiosis is the specialized cell division by which haploid cells are produced . As germ cells enter the first meiotic prophase , programmed double-stranded breaks ( DSBs ) are formed throughout the genome . Repair of these DSBs by homologous recombination is crucial for proper segregation of homologous chromosomes at the end of the first meiotic division , and thus , for the production of haploid gametes . Moreover , failure to correctly repair these DSBs can have deleterious effects on the genomic integrity of offspring . To ensure that meiocytes that fail to repair meiotic DSBs do not complete meiosis , recombination is tightly controlled . However , the signaling pathway ( s ) tying meiotic recombination to meiotic progression in mouse spermatocytes is not known . We report here that the ATM-signaling pathway , composed of the MRE11 complex , ATM and CHK2 , is responsible for activation of the recombination-dependent arrest that occurs in Trip13 mutant mouse spermatocytes , which accumulate unrepaired DSBs during meiotic prophase . | [
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] | [] | 2015 | The ATM Signaling Cascade Promotes Recombination-Dependent Pachytene Arrest in Mouse Spermatocytes |
Kupffer cells ( KCs ) are widely considered important contributors to liver injury during viral hepatitis due to their pro-inflammatory activity . Herein we utilized hepatitis B virus ( HBV ) -replication competent transgenic mice and wild-type mice infected with a hepatotropic adenovirus to demonstrate that KCs do not directly induce hepatocellular injury nor do they affect the pathogenic potential of virus-specific CD8 T cells . Instead , KCs limit the severity of liver immunopathology . Mechanistically , our results are most compatible with the hypothesis that KCs contain liver immunopathology by removing apoptotic hepatocytes in a manner largely dependent on scavenger receptors . Apoptotic hepatocytes not readily removed by KCs become secondarily necrotic and release high-mobility group box 1 ( HMGB-1 ) protein , promoting organ infiltration by inflammatory cells , particularly neutrophils . Overall , these results indicate that KCs resolve rather than worsen liver immunopathology .
Kupffer cells ( KCs ) are non-parenchymal cells that account for approximately 15% of the total liver cell population and constitute 80%–90% of the tissue-resident macrophages in the whole body [1] . Due to their intravascular ( sinusoidal ) localization , KCs have long been studied as scavenger cells that physiologically remove particulate material ( e . g . aged blood cells , immune complexes and gut-derived bacterial products ) from the portal circulation [1] . In recent years KCs have been implicated in the pathogenesis of an assortment of inflammatory liver diseases , including viral hepatitis [2] , . Accordingly , the current dogma regarding the role of KCs in hepatitis B virus ( HBV ) or hepatitis C virus ( HCV ) pathogenesis considers these cells as important contributors to liver injury [4] , [5] . As the host tropism of HBV and HCV is limited to humans and human primates [6] , this dogma has been largely inferred from mouse studies in which KCs activated by a variety of stimuli ( including the engulfment of apoptotic hepatocytes ) were shown to i ) promote the intrahepatic accumulation of pathogenic T cells and/or ii ) express/produce inflammatory mediators that are directly toxic for the hepatocyte ( e . g . tumor necrosis factor ( TNF ) -α , FasL , reactive oxygen species , etc ) [2] , [3] , [7] . It is worth noting , however , that most of these studies were performed either by infecting mice with pathogens that preferentially replicate inside KCs ( e . g . cytomegalovirus [8] , [9] , influenza [10] , lymphocytic choriomeningitis virus [11] , listeria [12] or leishmania [13] ) or by injecting mice with lymphocyte mitogens ( such as Staphylococcus enterotoxin B and Concanavalin A [14] , [15] ) that cause massive intrahepatic expansion of activated CD4 T cells . Since HBV and HCV infect almost exclusively the hepatocyte and since liver damage during these infections is primarily a consequence of the virus-specific CD8 T cell response [6] , we decided to assess the role of KCs in more relevant animal models . In one model effector HBV-specific CD8 T cells are adoptively transferred into immunocompetent transgenic mice that replicate HBV at high levels in the hepatocyte [16]–[21] . In a second model wild-type mice previously immunized with a plasmid encoding β-galactosidase ( β-Gal ) are infected with a β-Gal-expressing hepatocyte-tropic adenovirus [21] , [22] . Both models revealed that KCs have no impact on the ability of virus-specific CD8 T cells to home to the liver , recognize antigen and kill hepatocytes , nor do they significantly act as effector cells to destroy the hepatocytes . Instead , KCs reduce the overall severity of T cell-mediated immunopathology by removing apoptotic hepatocytes from the liver .
HBV replication-competent transgenic mice or C57BL/6 mice were injected intravenously with Clo-L . This treatment effectively eliminated F4/80+ KCs within 2 days and for at least 1 week after injection ( Figure S1 and [23] , [24] ) , without reducing the number of liver CD11chigh dendritic cells ( DCs , Figure S1 ) or circulating Gr-1high CD11b+ polymorphonuclear neutrophils ( PMNs ) and Ly-6C+ monocytes ( Figure S1 ) . Intravenously injected fluorescent beads failed to accumulate in the liver of Clo-L-treated mice ( not shown ) , while they were readily up-taken by KCs from control animals ( Figure S1 ) . As the former mice also showed delayed clearance of beads from the circulation ( Figure S1 ) , these results indicate that Clo-L significantly reduced liver phagocytic function . HBV replication-competent transgenic mice were treated with either Clo-L or saline ( NaCl ) 3 days prior to the transfer of effector HBV-specific CD8 T cells . Additional groups of control mice were injected with NaCl , liposomes containing saline ( NaCl-L ) or Clo-L alone . As previously reported [17] , injection of effector HBV-specific CD8 T cells ( derived from immunized syngeneic non transgenic mice , see Methods ) into saline-treated control mice caused a transient liver disease ( monitored biochemically by measuring the serum activity of alanine aminotransferase [sALT] , an enzyme that is released into the circulation by necrotic hepatocytes ) that almost completely resolved 5 days after transfer ( Figure 1A ) . No difference in disease severity ( monitored by sALT activity [Figure S2] and liver histology [not shown] ) was observed when NaCl-L was injected , instead of NaCl , prior to the transfer of effector HBV-specific CD8 T cells . As also previously reported [17] , resolution of disease in this model is due to CD8 T cell-dependent down-regulation of viral antigens ( see below and Discussion ) . Surprisingly Clo-L treatment , which by itself did not cause significant sALT elevation , markedly increased liver disease in CD8 T cell-injected mice at all time points ( Figure 1A ) . This effect occurred independently of the number of effector HBV-specific CD8 T cells that were transferred ( Figure 1 , B and C ) . To exclude the possibility that Clo-L may prolong sALT half-life , we injected liver extracts with a known sALT content in either control or Clo-L-treated mice . sALT half-life in Clo-L-treated mice was not significantly different from that of controls ( 489 versus 538 minutes , respectively , Figure S3 ) , indicating that sALT is a reliable marker of liver disease in Clo-L treated mice . As virus-specific CD8 T cells trigger liver disease in this model , we next asked whether KCs altered the pathogenic potential of these cells in vivo . To this end , we first performed intravital microscopic analysis to monitor the behavior of the transferred T cells . Approximately 30% of visualized intrahepatic HBV-specific CD8 T cells arrested and transiently contacted KCs ( mean interaction time of 5±1 second ) ( Video S1 ) . Adhesion of HBV-specific CD8 T cells to liver sinusoids did not require interaction with KCs as the percentage of CD8 T cells stably adhering ( i . e . >30 s ) to liver sinusoidal endothelial cells was similar between saline controls and Clo-L treated mice ( Figure 1D , Video S2 and S3 ) . The expansion and intrahepatic accumulation of the transferred T cells ( Figure 1 , E and F ) and the relative increase ( fold induction over mice injected with either NaCl or Clo-L alone ) of liver mRNAs encoding for IFN-γ ( a well-established marker of antigen recognition by HBV-specific CD8 T cells in this model [17] , [25] ) ( Figure 1G ) or CXCL9 and CXCL10 ( two IFN-γ inducible chemokines most abundantly expressed by hepatocytes surrounding inflammatory foci [18] ) ( Figure 2 , A and B ) were also unaffected in Clo-L treated mice . The relative increase of liver mRNA encoding for TNF-α - another cytokine known to be produced by activated HBV-specific CD8 T cells in this model [17] , [25] and that has been proposed to promote liver damage [26] - was reduced of about 33% and 55% at days 1 and 2 , respectively , in CD8 T cell-injected mice treated with Clo-L ( Figure 2C ) , indicating that KCs contributed to the production of this pro-inflammatory soluble mediator . As mentioned earlier , liver disease in this model is transient because IFN-γ-dependent mechanisms rapidly eliminate viral gene products from the liver [17] , [25] , [27] . Accordingly , viral DNA , RNA and proteins disappeared from the liver of saline- and Clo-L-treated mice with comparable kinetics ( not shown and Figure S4 ) . Note that 5 days after CD8 T cell injection Clo-L-treated mice showed residual antigen reactivity in hepatocytes that appeared morphologically damaged ( Figure S4 ) , suggesting that these target cells were not readily removed from the liver . Importantly , the number of infiltrating intrahepatic leukocytes ( IHL ) , the vast majority of which are antigen-non-specific inflammatory cells [18] , was significantly increased in CD8 T cell-injected mice that were treated with Clo-L instead of NaCl ( Figure 1H ) , while treatment with Clo-L alone increased neither sALT activity ( Figure 1 , A , B and C ) nor IHL infiltration ( Figure 1H ) . Together , the data indicate that Clo-L treatment exacerbates immunopathology independently of the number or function of intrahepatic HBV-specific CD8 T cells and is associated with a more abundant antigen-non-specific inflammatory cell infiltrate in the liver . Exacerbation of liver immunopathology in face of reduced numbers of KCs ( and reduced levels of the TNF-α they produce also indicates that KCs play little or no role as effector cells in the destruction of hepatocytes in this system . Additionally , the intrahepatic expression levels of potentially anti-inflammatory/hepatoprotective cytokines such as IL-10 , IL-22 or TGF-β [28]–[30] were not reduced in CD8 T cell-injected Clo-L-treated mice when compared to proper controls ( Figure 2 , D , E and F ) , indicating that the increased liver disease observed in the former animals was not associated to decreased production of cytoprotective factors . Notably , intravenous Clo-L treatment is not specific for KCs , but it apparently acts on other cell populations , particularly splenic mononuclear phagocytes [31] , [32] . To rule out a contribution of splenocytes in our system , HBV replication-competent transgenic mice were splenectomized and treated with either Clo-L or saline prior to the transfer of effector HBV-specific CD8 T cells . Splenectomized animals showed a degree of liver disease severity that was virtually identical to that of non-splenectomized controls ( Figure 3A ) , strongly suggesting that the capacity of Clo-L to exacerbate liver disease depends on depletion of KCs rather than depletion of splenic mononuclear phagocytes . Additional evidence supporting the notion that KC depletion is associated with liver disease exacerbation in this model comes from experiments where HBV replication-competent transgenic mice were administered with gadolinium chloride ( GdCl3 ) prior to the transfer of CD8 T cells . GdCl3 is a rare earth metal that - like Clo-L - has been widely used to eliminate KCs in mice [33]–[35] . As shown in Figure S1 and Figure 3B , respectively , GdCl3 treatment reduced KC number by more than 75% ( without reducing the frequency of circulating monocytes and PMNs , not shown ) and caused a significant increase in sALT activity at all time points after CD8 T cell transfer . Next , we extended our study to a previously established infection model [21] in which liver immunopathology is triggered by endogenous β-Gal-specific CD8 T cells that recognize hepatocytes infected by a β-Gal-expressing , replication-deficient adenovirus ( Ad-β-Gal ) . C57BL/6 mice were immunized intramuscularly with a β-Gal-expressing plasmid to generate β-Gal-specific CD8 T cells . Three weeks later the mice were grouped based on the frequency of circulating β-Gal-specific CD8 T cells and then infected with the hepatotropic Ad-β-Gal ( 109 pfu/mouse ) ( see Methods ) . Clo-L treatment was carried out one day after Ad-β-Gal infection , when the percentage of β-Gal-expressing hepatocytes is maximal and viremia is no longer detectable ( [21] and not shown ) . When compared to saline-treated controls , Clo-L-treated mice showed i ) significantly higher sALT activity ( Figure 4A ) , ii ) comparable numbers of intrahepatic β-Gal-specific CD8 T cells ( Figure 4B ) , iii ) comparable amounts of liver IFN-γ mRNA ( Figure 4C ) , and iv ) increased organ infiltration of antigen non-specific leukocytes ( Figure 4D ) . Thus , KC-related pathogenic events that are similar to those observed in HBV replication-competent transgenic mice ( Figure 1 ) were also operative in this infection model of liver immunopathology . We next set out to identify the mechanism by which KCs limit liver immunopathology . When we quantified morphometrically the number of hepatocytes that stained positive for cleaved caspase 3 ( CC3 , a marker of hepatocellular apoptosis ) in either HBV replication-competent transgenic mice killed 1 day after CD8 T cell injection ( Figure 5A ) or C57BL/6 mice killed 3 days after Ad-β-Gal infection ( not shown ) , we observed a ∼3-fold increase of CC3+ hepatocytes in Clo-L-treated mice compared with saline-treated controls , with similar results observed in GdCl3-treated animals ( not shown ) . One day later , the number of apoptotic hepatocytes increased disproportionately in Clo-L- or GdCl3-treated mice , resulting in the formation of large necroinflammatory foci thzat also displayed evident focal hepatocellular necrosis and dropout ( Figure 5B and not shown ) . By day 5 after CD8 T cell injection or Ad-β-Gal-infection , virtually no CC3+ hepatocytes were visible in saline-treated control animals , while these cells remained readily detectable in Clo-L- or GdCl3-treated mice ( Figure 5C and not shown ) . These results indicate that the higher sALT levels observed in these latter animals ( Figures 1 , 3 , 4 and S2 ) probably reflected the secondary necrosis of apoptotic hepatocytes that had not been removed due to the absence of KCs and , therefore , accumulated over time . That these higher sALT levels did not reflect the destruction of greater numbers of hepatocytes by the CD8 T cells was further suggested by the serum levels of albumin and bilirubin ( two indicators of metabolic functions arising from healthy hepatocytes ) , which were virtually identical in the saline- and Clo-L-treated mice ( Figure S5 ) . Cytometric analyses of the liver infiltrate in the mice described in Figures 1 and 4 revealed that the number of intrahepatic PMNs , a prominent intrahepatic inflammatory cell subset , was significantly higher in Clo-L-treated animals both in terms of relative frequency ( Figure 6A and not shown ) and , more importantly , absolute numbers ( Figure 6B and not shown ) . The frequency/number of other abundant intrahepatic subsets such as CD8+ T cells ( both antigen-specific and antigen-non-specific ) and CD11chigh DCs ( peak values of 9%/2 . 3×106 and 4 . 7%/1 . 2×106 respectively ) did not change as a function of Clo-L treatment ( not shown ) . Since HMGB-1 translocation in damaged hepatocytes has been previously linked to liver PMN recruitment in this model [36] , we next monitored HMGB-1 expression in the above-mentioned livers . Large numbers of cytoplasmic HMGB-1+ hepatocytes juxtaposed to or surrounded by PMNs were found in the livers of Clo-L-treated HBV replication-competent transgenic mice injected with CD8 T cells ( Figure 6C ) . Note that nucleo-cytoplasmic translocation of HMGB-1 often reflects its release by necrotic , as opposed to apoptotic , cells [37] . The increased PMN recruitment in Clo-L treated mice occurred in face of intrahepatic CXCL1 and IL-17 ( known PMN chemoattractants [38] , [39] ) expression levels that were comparable to those detected in control mice ( Figure 2 , G and H ) , which showed fewer cytoplasmic HMGB-1+ hepatocytes and fewer infiltrating PMNs ( Figure 6C ) . Similar results were obtained in mice infected with Ad-β-Gal ( not shown ) . Thus , impaired removal of apoptotic hepatocytes by KCs promoted accumulation of cytoplasmic HMGB-1+ hepatocytes and intrahepatic PMN infiltration . Recruited PMNs played a compensatory role in the removal of apoptotic hepatocytes from Clo-L-treated mice , as their depletion by anti-Gr-1 antibodies ( Figure S6 ) was associated with detection of higher numbers of cytoplasmic HMGB-1+ hepatocytes ( not shown ) and higher sALT values ( Figure 6D ) . Higher numbers of cytoplasmic HMGB-1+ hepatocytes ( not shown ) , higher sALT values ( Figure 6E ) and reduced numbers of liver infiltrating PMNs ( Figure S6 ) were also detected in Clo-L-treated HBV replication-competent transgenic mice that were administered with a blocking monoclonal Ab specific for mouse HMGB-1 ( Figure S6 ) - prior to CD8 T cell transfer . Along with the finding that , when compared to controls , serum HMGB-1 levels were increased in Clo-L-treated mice that were injected with CD8 T cells or infected with Ad-β-Gal ( not shown ) , these results reiterate the notion that the release of HMGB-1 from secondarily necrotic hepatocytes most likely contributed to recruit PMNs into the liver . Since PMN depletion and HMGB-1 neutralization prolonged disease severity in these animals only partially , we reasoned that , under the inflammatory conditions generated by CD8 T cells , the depleting effect of a single Clo-L administration ( given 3 days prior to transfer ) probably persisted less than one week ( which is the time frame we and others have observed in un-manipulated mice , not shown and [24] ) . Indeed , F4/80+ KCs ( likely derived from hematogenous monocyte precursors [40] ) re-appeared in the liver 3 days after CD8 T cell transfer ( Figure 7A ) , and a second injection of Clo-L at this time extended the local persistence of apoptotic and cytoplasmic HMGB-1+ hepatocytes ( not shown ) and the relative increase in sALT activity ( Figure 7B ) by about a week when compared to mice receiving a single dose of Clo-L ( Figure 1A ) . Worsening of liver disease severity ( and even mortality in the case of Clo-L treated mice ) ( Figure S7 ) was also observed in HBV replication-competent transgenic mice in which all WBC ( including PMNs and monocytes ) were completely eliminated by whole-body irradiation prior to CD8 T cell transfer ( 7 ) , reiterating the notion that compensatory functions mediated by liver infiltrating phagocytic cells help KCs at containing liver immunopathology . Although the administration of Clo-L- or GdCl3 in CD8 T cell-injected/Ad-β-Gal infected mice did not increase the liver expression of potentially hepatotoxic factors ( e . g . IFN-γ , TNF-α or IL-17 ) , it is formally possible that these treatments might have resulted in the release of undetermined cytotoxic factors exacerbating liver damage . To confirm that KCs contain liver immunopathology independently of Clo-L- or GdCl3 treatments and to provide mechanistic insight on how KCs phagocytose apoptotic hepatocytes , we made use of Polyinosinic acid ( Poly ( I ) ) , a ligand for most macrophage scavenger receptors and a known blocker of KC phagocytosis [41]–[43] . Polyuridylic acid ( Poly ( U ) ) , a non-scavenger receptor ligand , was used as control [41]–[43] . Of note , KCs isolated from the liver of HBV replication-competent or C57BL/6 mice were found to express macrophage scavenger receptor 1 ( MSR-1 ) and scavenger receptor class b1 ( Scarb-1 ) ( Figure S8 and not shown ) , two prototypic class A and class B scavenger receptors , respectively , which can be bound by Poly ( I ) [41]–[43] . Neither Poly ( I ) - nor Poly ( U ) -treatment reduced the number of F4/80+ KCs ( Figure S8 ) or the intrahepatic number of innate immune cells such as CD11chigh DCs , NK1 . 1+ CD3- NK cells and NK1 . 1+ CD3+ NKT cells ( Figure S8 ) . Further , both of these treatments failed to reduce the number of circulating Gr-1high CD11b+ PMNs ( not shown ) . When compared to mice treated with Poly ( U ) , however , mice treated with Poly ( I ) exhibited delayed removal of fluorescent microbeads from the circulation ( Figure S8 ) , providing evidence for the efficacy of this latter compound at inhibiting liver phagocytosis . Importantly , HBV replication-competent transgenic mice and C57BL/6 mice that we treated with Poly ( I ) either 5 minutes before CD8 T cell transfer or 3 days after Ad-β-Gal infection ( more than 2 days following maximal hepatocellular infection ) displayed ( at peak disease severity ) higher sALT values than Poly ( U ) -treated controls ( Figure 8A ) and this was associated with i ) similar numbers of intrahepatic virus-specific CD8 T cells ( Figure 8B ) , ii ) similar amounts of liver IFN-γ mRNA ( Figure 8C ) , iii ) increased accumulation of apoptotic ( Figure 8D ) and necrotic hepatocytes ( not shown ) and iv ) increased infiltration of antigen non-specific leukocytes ( Figure 8E ) . Together , our results , based on mechanistically distinct approaches , indicate that CD8 T cell-induced liver immunopathology can be worsened by depleting KCs or by inhibiting their scavenger receptor-dependent capacity to phagocytose apoptotic hepatocytes .
We show here that KCs play a previously unappreciated role in the pathogenesis of viral hepatitis , i . e . they contribute to the resolution of liver pathology induced by virus-specific effector CD8 T cells . We initially found that the injection of Clo-L ( a treatment widely used to deplete murine KCs [23] , [24] ) was associated with a highly significant 2–3 fold increase in sALT activity ( a commonly used marker of liver cell injury ) at all time points measured . This was an unexpected finding since KCs are currently regarded as contributors to liver damage during viral hepatitis . Also unexpectedly we found that the overall number and function of intrahepatic virus-specific CD8 T cells - two factors directly linked to liver disease severity - were unaffected in these animals . Indeed , none of the various steps leading to hepatocellular destruction by CD8 T cells seemed impacted by the Clo-L treatment . The adhesive behavior of CD8 T cells to the liver microvasculature was shown to be normal , despite the fact that transient CD8 T cell/KC interactions often took place in the sinusoids of control mice . Clo-L treatment did not impinge on the capacity of CD8 T cells to recognize hepatocellular antigens either , as these cells produced IFN-γ ( a cytokine that in our systems is exclusively expressed by in vivo activated virus-specific CD8 T cells [25] ) , divided and accumulated intrahepatically at control levels . Along with the notions that treatment with Clo-L alone did not induce liver inflammation and that the GdCl3-dependent depletion of KCs reproduced the CD8 T cell-dependent disease exacerbation observed after Clo-L treatment , these initial results indicated that KCs were not contributing to liver injury , either as effector cells directly involved in the destruction of hepatocytes , or as promoters of CD8 T cell-induced pathology . The results also raised the question of how sALT levels in KC-depleted animals increased disproportionally when compared to the number or function of virus-specific effector CD8 T cells . Pertinent to this question it is worth mentioning that virus-specific effector CD8 T cells kill hepatocytes by apoptosis [44] , [45] . Apoptotic hepatocytes preserve cell membrane integrity [46] and , as such , they do not release their cytosolic ALT content into the circulation . Accordingly , only necrotic hepatocytes should release ALT . As KCs are known to phagocytose apoptotic cells [47] , we hypothesized that i ) KC depletion resulted in the accumulation of apoptotic hepatocytes in situ , and that ii ) these apoptotic cells secondarily evolved into ALT-releasing necrotic hepatocytes since they were not readily removed by KCs . Evidence supporting these hypotheses emerged from experiments where the administration of an inhibitor of KC phagocytosis that does not deplete macrophages also promoted high sALT levels , control-level intrahepatic numbers of pathogenic virus-specific CD8 T cells and , most relevantly , liver accumulation of apoptotic and necrotic hepatocytes . Notably , these experiments also identified scavenger receptors as mediators of KC-dependent phagocytosis of apoptotic hepatocytes during viral hepatitis . Additional evidence supporting the hypotheses emerged from histological analysis demonstrating progressive accumulation of hepatocellular apoptosis , hepatocellular necrosis and dropout in the liver of animals in which KCs were either reduced in their number or inhibited in their phagocytic function . These pathological signs were also accompanied by the detection of large numbers of hepatocytes displaying nucleo-cytoplasmic translocation of HMGB-1 . As nucleo-cytoplasmic translocation of HMGB-1 frequently reflects the release of this “danger signal” from necrotic cells [48] and as HMGB-1 release is known to trigger PMN liver recruitment [36] , it was not surprising that PMNs were abundantly found in the liver of mice where KCs were numerically reduced or functionally inhibited . The fact that reduced PMN liver infiltration ( mediated by either PMN depletion or HMGB-1 neutralization ) worsened hepatic inflammation even further supports the concept that PMNs performed compensatory phagocytic functions in these livers . This is an interesting observation since PMNs have been previously reported to contribute to CD8 T-cell induced organ damage by facilitating the intrahepatic homing of antigen non-specific mononuclear cells [19] , [20] . Thus , it would appear that HMGB-1-responding PMNs can exert more than just a detrimental role , since - like KCs - they have the potential to remove apoptotic hepatocytes and ameliorate liver immunopathology . It is relevant to point out , however , that this beneficial anti-inflammatory role of PMNs becomes apparent only under conditions of reduced KC function . It is also relevant to point out that the experimental approaches we used to deplete KCs ( intravenous injection of Clo-L or GdCl3 ) or to inhibit their function ( intravenous injection of Poly ( I ) ) are not exclusively specific for KCs ( although they did not alter the frequency of circulating monocytes and PMNs ) . Indeed , these approaches have been shown to moderately impact other macrophage populations , particularly those ( e . g . splenic mononuclear phagocytes ) that - like KCs - are not separated from the bloodstream by an endothelial barrier [31] , [32] , [49] . Experiments utilizing splenectomized mice ruled out the possibility that splenic mononuclear phagocytes are involved in the exacerbation of liver disease severity observed in KC-depleted animals . Of note , hepatic inflammation triggered by virus-specific effector CD8 T cells eventually subsided in mice treated with Clo-L , GdCl3 or Poly ( I ) . This in part relates to the fact that viral antigens were rapidly eliminated from these livers , putting an early end to the CD8 T cell-induced pathology . Indeed , either HBV- or Ad-β-Gal-derived hepatocellular antigens disappeared within 2–3 days after peak disease severity [21] and remained down regulated for at least 4 weeks after transfer ( in the case of HBV replication-competent transgenic mice ) , or they never returned ( in the case of mice infected with the replication-deficient adenovirus ) ( not shown ) . Although significantly prolonged in its severity , hepatic inflammation eventually subsided even in animals subjected to multiple administrations of Clo-L ( and multiple administrations of Poly ( I ) , not shown ) . This reiterates the notion that compensatory phagocytic functions ( likely mediated by PMNs and infiltrating monocytes with KC-precursor capacity ) were operative under these conditions . Experiments utilizing mice in which all circulating WBC ( including PMNs and monocytes ) are permanently eliminated through whole-body irradiation further supported the “compensatory” concept . Predictably , restoration of anatomical integrity was accompanied by hepatocellular regeneration ( evaluated by counting mitotic figures as well as the number of proliferating cell nuclear antigen [PCNA]- or Ki67-positive hepatocytes ) , which remained detectable until liver inflammation was completely resolved ( not shown ) . The results herein described contradict the current dogma that views KCs as solely pro-inflammatory cells during viral hepatitis . Indeed , the results indicate that , while KCs exhibit pro-inflammatory activities ( such as the production of TNF-α ) , their overall net effect is anti-inflammatory . Our experiments were performed in animal models in which - as it occurs during HBV or HCV infection - virus-specific effector CD8 T cells that recognize hepatocellular antigens trigger viral hepatitis . While relevant to study the effector phase of liver immunopathology , our models were not designed to evaluate a possible role for KCs in the priming of virus-specific T cell responses . Future work will address this issue . In conclusion , we found that KCs limit the severity of CD8 T cell-induced liver pathology in mouse models of viral hepatitis . Mechanistically , our data indicate that KCs limit liver immunopathology affecting neither the accumulation nor the function of intrahepatic virus-specific effector CD8 T cells with pathogenic potential . Rather , our results are most compatible with the hypothesis that KCs hasten resolution of liver immunopathology by removing apoptotic hepatocytes that are killed by effector CD8 T cells ( see also the schematic representation depicted in Figure 9 ) . Failure to do so results in the secondary necrosis of hepatocytes and abundant liver inflammation . Similar events may occur in humans during HBV and HCV infections , where CD8 T cell-dependent pathogenic mechanisms similar to those described herein are operative .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . These studies were approved by the Animal Review Board of the San Raffaele Scientific Institute ( Permit Number 390 ) and by the Institutional Animal Committee of The Scripps Research Institute ( Permit Number 09-0124 ) . All surgery was performed with mice kept anesthetized by continuous administration of 2% isoflurane in 2 L/minute oxygen through a nose cone and all efforts were made to minimize suffering . HBV replication-competent transgenic mouse ( lineage 1 . 3 . 32 ) have been previously described [16] . Lineage 1 . 3 . 32 ( inbred C57BL/6 , H-2b ) was crossed with B10 . D2 mice ( H-2d ) to produce H-2bxd F1 hybrids prior to injection of H-2d-restricted hepatitis B surface antigen ( HBsAg ) -specific CD8 T cell lines . C57BL/6 and B6 . PL-Thy1a/CyJ ( Thy-1 . 1 ) mice were purchased from The Scripps Research Institute breeding colony or from Charles River Laboratories ( Calco , Italy ) . Bone marrow chimeric phosphoglycerate kinase ( PGK ) -GFP mice replicating HBV were created by transplanting BM cells derived from PGK-GFP ( H-2bxd F1 hybrids , a kind gift of Michele De Palma , San Raffaele Scientific Institute , Milan , Italy ) into irradiated 1 . 3 . 32 HBV mice . Thy-1 . 1 mice were crossed once with B10 . D2 mice prior to immunization with plasmid DNA- and vaccinia virus-encoding HBsAg as previously described [21] . In some experiments , mice were subjected to whole-body irradiation or splenectomy , as described [50] , [51] . In all experiments mice were matched for age ( 8 weeks ) , sex ( males ) and , in case of lineage 1 . 3 . 32 , for serum hepatitis B e antigen ( HBeAg ) levels before experimental manipulation . All animals were housed in pathogen-free rooms under strict barrier conditions . HBV-specific CD8 T cell lines were derived from spleen cells of immunized nontransgenic Thy-1 . 1 x B10 . D2 male mice as described [21] . After 3 weeks of in vitro stimulation , the cells were tested for antigen specificity by flow cytometry as described [18] , [21] . CD8+ cells that were over 95% specific for the immunodominant peptide epitope Env 28–39 of HBsAg [44] were injected intravenously at different doses ( 0 . 5×107 cells/mouse , 1×107 cells/mouse or 5×107 cells/mouse ) into 1 . 3 . 32 mice . One , 2 , 3 , 5 , 7 or 14 days later mice were killed and their livers were perfused and harvested for histological and flow cytometry analyses , or they were snap frozen in liquid nitrogen and stored at -80°C for subsequent molecular analyses ( see below ) . Fifty micrograms of a plasmid expressing β-Gal under the control of the human CMV enhancer/promoter were injected into regenerating tibialis anterior muscles of C57BL/6 mice 5 days after injection of cardiotoxin as described [21] . Three weeks later mice were grouped based on the frequency of circulating CD8+/β-Gal96+ T cells ( between 0 . 2% and 0 . 3% of the total white blood cells ) and infected with a single intravenous dose ( 1×109 pfu/mouse ) of a β-Gal-expressing adenovirus vector ( Ad-β-Gal ) as described [21] . Mice were killed 1 , 3 , 4 , 5 and 7 days after infection and their livers were processed as described above . The immunization strategy abovementioned allowed us to focus our attention on CD8 T cell effector functions that are independent of priming , and to quantitatively measure the β-Gal-specific CD8 T cell response . Indeed , under these conditions mice develop a severe liver injury that is entirely mediated by an effector memory CD8 T cell response specific for a single H2b-restricted immunodominant epitope ( β-Gal96 ) contained within β-Gal . The response precedes any other adenovirus-specific CTL response [21] . KC depletion was achieved by intravenous injection of 200 µl of clodronate-containing liposomes ( Clo-L , a gift of Roche Diagnostics GmbH , Mannheim , Germany ) 3 days before CD8 T cell transfer or one day after Ad-β-Gal infection . In some experiments saline-containing liposomes ( NaCl-L ) were used as control . In other experiments KCs were depleted by the intravenous injection of 50 µg of gadolinium chloride ( GdCl3 ) 24 hours and 30 minutes before CD8 T cell transfer . In selected experiments a second Clo-L injection was administered into 1 . 3 . 32 mice 3 days after CD8 T cell transfer . PMN depletion was achieved by intravenous injection of 100 µg of a rat IgG2b monoclonal antibody specific for mouse Gr-1 ( Ly-6G/Ly-6C , clone RB6-8C5; BD PharMingen ) on days 1 , 2 and 3 after CD8 T cell transfer as described [19] . Control mice received an equal volume of a rat IgG2b irrelevant ( Irr ) Abs ( clone A95-1; BD PharMingen ) at the same time points . Although clone RB6-8C5 has been shown to deplete subsets of dendritic cells and monocytes [52] , [53] , it has no effect on KCs . Indeed , we detected comparable numbers of liver F4-80+ cells in mice injected with either RB6-8C5 or the IgG2b control ( not shown ) . The mouse monoclonal IgG1 DPH1 . 1 Ab specific for mouse HMGB-1 was generated by injecting C57BL/6 mice at two-week intervals with four doses ( 50 mg/mouse ) of the 17-mer peptide P1 ( KGKPDAAKKGVVKAEKS ) derived from HMGB-1 . Hybridomas were generated from splenocytes by standard techniques and tested by ELISA against the immunogen . Specificity of DPH1 . 1 Ab was monitored by both Western blot and immunofluorescence as shown in Figure S4 . Briefly , 500 ng or 100 ng of recombinant HMGB-1 and the ( negative control ) recombinant Box-A fragment of HMGB-1 ( HMGBiotech , Milan , Italy ) were separated by gel electrophoresis and transferred onto membranes as described [48] . DPH1 . 1 Ab , anti-Box-A Ab ( HMGBiotech , Milan , Italy ) and goat anti-mouse IgG1 Ab ( BD PharMingen ) were applied at 1 µg/ml dilution . Immunofluorescence was performed as described [48] on mouse embryonic fibroblasts ( MEFs ) derived from either wild type mice or HMGB-1−/− mice . DPH1 . 1 Ab and AlexaFluor 633-labelled goat anti-mouse IgG1 Ab ( BD PharMingen ) were applied at a 50-µg/ml dilution . The in vitro activity of DPH1 . 1 Ab was monitored in trans-well migration assays as basically described [54] and shown in Figure S4 . Briefly , 3T3 cells were assessed for their migration ability by a modified Boyden chamber assay . Recombinant HMGB-1 was added to the lower chamber at the concentration of 30 ng/ml . Increasing concentrations of DPH1 . 1 . Ab were added to fifty thousand 3T3 cells seeded in the upper chamber . Boyden chambers were incubated at 37°C in 5% CO2 for 3 hours . Cells remaining on the upper section of the filters were removed mechanically . Cells that migrated to the lower section of the filters were fixed with ethanol , stained with Giemsa ( Sigma-Aldrich ) , and counted in 10 random fields/filter . Each assay was performed in triplicate and repeated at least three times , independently . In vivo , DPH1 . 1 . Ab was administered intravenously ( 220 µg/mouse ) 3 hours before CD8 T cell transfer . Control mice received an equal amount of a mouse IgG1 control Abs ( BD PharMingen ) at the same time point . Clo-L- or Poly ( I ) -treated mice were intravenously injected with 5×108 rhodamine beads of 4 µm in diameter ( provided by Z . M . Ruggeri , The Scripps Research Institute , La Jolla , CA ) . Immediately after injection and 2 , 4 , 6 , 10 , 15 , 20 , 25 and 30 minutes later mice were bled and the number of beads in blood samples was assessed by flow cytometry . Livers from selected mice were processed by confocal microscopy as described below . PGK-GFP-1 . 3 . 32 BM chimeras and 1 . 3 . 32 mice treated or not with Clo-L were kept anesthetized by continuous administration of 2% isoflurane ( Abbott S . r . l , Aprilia , Italy ) in 2 L/minute oxygen through a nose cone . After the insertion of a tail vein polyethylene catheter attached to a syringe-pump able to deliver continuous infusion of a 37°C saline solution ( 0 . 25 ml/hr ) , mice underwent surgery . After opening the skin with a midline incision and detaching peritoneal adherences , midline and left subcostal incisions were made in the peritoneum through a high-temperature cautery . The left liver lobe was exteriorized and placed within a U-shaped , water-holding , silicon chamber placed on an adjustable thin base . The chamber was then covered with a cover slip at the bottom of which the left liver lobe gently flattened . The stage was then moved to a heated microscope stage of an up-right Axiotech Vario microscope equipped with a Colibri system of high-performance Light Emitting Diodes ( LEDs ) that are fully integrated/automated by AxioVision system software ( Carl Zeiss , Göttingen , Germany ) , allowing high contrast images with simultaneous 3 color-imaging in real time . HBV-specific effector CD8 T cells were fluorescently labeled with either CFSE ( 20 µM for 7 minutes at room temperature ) or Hoechst 33342 ( 2 µg/ml for 15 minutes at 37°C; Invitrogen , Carlsbad , CA ) . Importantly , CFSE- or Hoechst-labeled HBV-specific CD8 T cells caused the same sALT elevation as unlabeled cells upon in vivo transfer ( not shown ) . Control effector CD8 T cells ( specific for the lymphocytic choriomeningitis virus , LCMV ) were derived from the spleen of mice ( C57BL/6 × B10 D2 F1 ) that resolved an acute LCMV infection and were in vitro stimulated as described [55] . Labeled CD8 T cells ( 1 or 5×07 cells/mouse ) were transferred into mice through the tail vein catheter and parameters of cell motility and adhesion to liver vasculature were recorded with a AxioCam HSC color videocamera ( Carl Zeiss , Göttingen , Germany ) at an acquisition rate of 15 frames/second . The sticking fraction of HBV-specific CD8 T cells was defined as the percentage of total cells that became firmly adherent for ≥30 s while passing a liver sinusoid within a 30 minutes observation period , as described [56] . Liver extracts containing ∼6000 U of ALT were prepared as previously described [17] and injected into 1 . 3 . 32 mice . Poly ( I ) and its control Poly ( U ) were injected intravenously ( 200 µg/mouse , Sigma ) into 1 . 3 . 32 mice or C57BL/6 mice 5 minutes before CD8 T cell transfer or 3 days after Ad-β-Gal infection , respectively . Intrahepatic leukocyte ( IHL ) isolation was performed as described [20] , [21] . Cells were surface-stained with phycoerythrin ( PE ) -conjugated anti-CD4 ( clone RM4-5; BD Pharmingen ) and anti-CD11c ( clone HL3; BD Pharmingen ) ; Pacific Blue-conjugated anti-CD8 ( clone 53-6 . 7; BD Pharmingen ) and anti-CD3 ( clone 145-2c11; BD Pharmingen ) ; PE-Cy7-conjugated anti-CD11b ( clone M1/70; BD Pharmingen ) ; allophycocyanin ( APC ) -conjugated anti-TCR ( clone H57-597; BD Pharmingen ) and anti-Ly6G ( clone 1A8; BD Pharmingen ) ; fluorescein isothiocyanate ( FITC ) -conjugated anti-Gr-1 ( clone RB6-8C5; BD Pharmingen ) . HBV-specific CD8 T cells were quantified by staining IHL with PE-conjugated anti-Thy1 . 1 ( clone OX7; BD Pharmingen ) and APC-conjugated anti-TCR ( clone H57-597; BD Pharmingen ) as described [20] , [21] . Ad-β-Gal-specific CD8 T cells were quanftified from PBMC or IHL by intracellular IFN-γ staining using a recombinant soluble dimeric H-2Kb:Ig Fusion Protein ( BD Pharmingen ) complexed with the β-Gal96 immunodominant peptide as described [21] . Liver non-parenchymal cells enriched of KCs were isolated as previously described [57] . Total DNA and RNA were isolated from blood or frozen livers ( left lobe ) for analyses by Southern blot , Northern blot , RNAse protection ( RPA ) and real-time PCR as previously described [17] , [55] . Nylon membranes were analyzed for HBV DNA , β-Gal DNA and RNA , and glyceraldehydes-3-phosphate dehydrogenase ( GAPDH ) RNA as previously described [25] . Analysis of cytokine , chemokine and scavenger receptors mRNAs was performed by RPA or TaqMan Gene expression Assay ( Applied Biosystems ) as previously described [17] , [19] , [20] . Real-time PCR for Ad-β-Gal was performed as described [21] . The extent of hepatocellular injury was monitored by histological analysis and by measuring sALT activity as described [17] . Quantitative morphometry was carried out by calculating the mean size of necroinflammatory foci contained in 100 high power fields , corresponding to about 4 mm2 of liver tissue , as described [21] . Immunohistochemical staining for hepatitis B core antigen ( HBcAg ) , HMGB-1 , CC3 , PCNA and Ki67 was performed as described [16] , [17] , [36] , [58] . The number of CC3+ hepatocytes was calculated in 100 high power fields , corresponding to about 4 mm2 of liver tissue . Immunofluorescence staining for F4/80 was performed as described [50] . Confocal microscopy was carried out with an Axioskop 2 plus direct microscope ( Zeiss ) equipped with a Radiance 2100 three-laser confocal device ( Bio-Rad ) . Images were analyzed with Paint Shop Pro X ( Corel ) . The serum concentration of HMGB-1 was measured by the use of the mouse ELISA kit II ( Shino-Test Corporation , Japan ) as previously described [59] . In all studies , values are expressed as mean ± SD . All statistical analyses were performed in Prism ( GraphPad Software ) . Means between two groups were compared by using a two-tailed t-test . Means between three or more groups were compared by using a one-way or two-way analysis of variance with Bonferroni's post-test . Kaplan–Meier survival curves were compared by using the log-rank ( Mantel–Cox ) test . Differences were considered statistically significant at p<0 . 05 . The GeneBank or NCBI Reference Sequence ( RefSec ) numbers for the genes and proteins cited in the text are: IFN-γ K00083 . 1 ( GeneBank ) ; CXCL9 , NC_000071 . 5 ( RefSec ) ; CXCL10 , NC_000071 . 5 ( RefSec ) ; TNF-α , M11731 . 1 ( GeneBank ) ; IL-10 , M37897 . 1 ( GeneBank ) ; TGF-β M13177 . 1 ( GeneBank ) ; CXCL1 , NC_000071 . 5 ( RefSec ) ; HMGB-1 , NC_000013 . 10 ( RefSec ) ; MSR-1 NM_031195 . 2 ( RefSeq ) ; Scarb-1 NM_016741 . 1 ( RefSeq ) . | Kupffer cells ( KCs ) , the resident macrophages of the liver , are considered important contributors to liver injury during viral hepatitis due to their pro-inflammatory activity . Herein we utilized two different mouse models of viral hepatitis ( where liver damage is triggered , as during viral hepatitis in humans , by virus-specific CD8 T cells ) to show that KCs do not directly induce liver injury nor do they affect the pathogenic potential of virus-specific CD8 T cells . Instead , KCs limit the severity of liver immunopathology . Mechanistically , our results are most compatible with the hypothesis that KCs contain liver immunopathology by removing dying hepatocytes . Dying hepatocytes not readily removed by KCs release high-mobility group box 1 ( HMGB-1 ) protein , promoting organ infiltration by inflammatory cells , particularly neutrophils . These results indicate that KCs resolve rather than worsen liver disease . | [
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] | 2011 | Kupffer Cells Hasten Resolution of Liver Immunopathology in Mouse Models of Viral Hepatitis |
Mosquito-borne diseases continue to remain major threats to human and animal health and impediments to socioeconomic development . Increasing mosquito resistance to chemical insecticides is a great public health concern , and new strategies/technologies are necessary to develop the next-generation of vector control tools . We propose to develop a novel method for mosquito control that employs nanoparticles ( NPs ) as a platform for delivery of mosquitocidal dsRNA molecules to silence mosquito genes and cause vector lethality . Identifying optimal NP chemistry and morphology is imperative for efficient mosquitocide delivery . Toward this end , fluorescently labeled polyethylene glycol NPs of specific sizes , shapes ( 80 nm x 320 nm , 80 nm x 5000 nm , 200 nm x 200 nm , and 1000 nm x 1000 nm ) and charges ( negative and positive ) were fabricated by Particle Replication in Non-Wetting Templates ( PRINT ) technology . Biodistribution , persistence , and toxicity of PRINT NPs were evaluated in vitro in mosquito cell culture and in vivo in Anopheles gambiae larvae following parenteral and oral challenge . Following parenteral challenge , the biodistribution of the positively and negatively charged NPs of each size and shape was similar; intense fluorescence was observed in thoracic and abdominal regions of the larval body . Positively charged NPs were more associated with the gastric caeca in the gastrointestinal tract . Negatively charged NPs persisted through metamorphosis and were observed in head , body and ovaries of adults . Following oral challenge , NPs were detected in the larval mid- and hindgut . Positively charged NPs were more efficiently internalized in vitro than negatively charged NPs . Positively charged NPs trafficked to the cytosol , but negatively charged NPs co-localized with lysosomes . Following in vitro and in vivo challenge , none of the NPs tested induced any cytotoxic effects .
Vector-borne diseases continue to be major causes of morbidity and mortality worldwide [1] . Malaria remains a major burden on humankind; more than 3 billion people are at risk for infection and more than 200 million people per year are infected , resulting in more than 600 , 000 reported deaths annually [2] . There has been a dramatic reduction in malaria mortality in the last decade , especially in sub-Saharan Africa and principally attributable to the use of long lasting insecticide treated bed nets and indoor residual spraying ( IRS ) [2] . Unfortunately these dramatic gains in public health are threatened by the emergence of insecticide resistance , particularly pyrethroid resistance , in mosquito vector populations [3] . Insecticide resistance has emerged to most classes of chemical insecticides , and no new insecticides have been licensed or approved for large-scale application in decades [4] . There is a compelling need to develop new insecticidal interventions and approaches for control of mosquito vectors and the pathogens they transmit . RNA interference ( RNAi ) -based technologies are a promising means to induce lethality in pest insect populations by down-regulating physiologically essential genes [5 , 6] . For example , oral administration of dsRNA in the western corn rootworm , Diabrotica virgifera LeConte , induces larval stunting and mortality [7] . However , major challenges need to be overcome to make this technology broadly applicable for molecular insecticide delivery . First , there is wide variability in the successful application of RNAi for gene silencing in insects , in part as a function of whether exposure to an RNAi trigger induces a localized ( i . e . , tissue-specific ) or systemic effect [6 , 8 , 9] . Second , dsRNA must persist in the harsh conditions that exist both in the environment and in vivo where dsRNA is subject to degradation by enzymes in the body of the insect [10 , 11] . An efficient delivery vehicle that can enhance dsRNA environmental stability , delivery ( orally or contact ) , internalization , and RNAi efficiency of would be of enormous value for control of insect pests including vectors . All of these efficiencies could result into dose-sparing of dsRNA , an important component of the cost of goods for successful development of intervention strategies . Chitosan NPs have been used to deliver dsRNAs and siRNAs to silence host genes in An . gambiae and Ae . aegypti mosquitoes [12 , 13] , but there is much to be learned regarding physicochemical characteristics of NP for optimized stability and delivery to control pest insects including vector species . NPs have been explored extensively to deliver associated/encapsulated biomolecules such as siRNA , DNA and antigens for a broad spectrum of medical applications [14–16] . Initiatives , such as encapsulation of insecticides , are already underway to prolong their lifespan during indoor residual spraying ( IRS ) [17] . For therapeutics , NPs fabricated by Particle Replication in Nonwetting Templates ( PRINT ) technology show superior promise as delivery agents due to their controlled size , shape , and composition [18–23] . Furthermore , as a proof-of-principle for this application , PRINT particles have proven efficacy in delivery of nucleic acid [24 , 25] and small molecule cargoes [26–29] . Therefore , we reasoned that PRINT NP technology could be combined with RNAi to develop a new generation of effective , safe , and target-specific insecticides for control of both juvenile and adult An . gambiae . To determine optimal NP physicochemical characteristics for delivery of mosquitocidal dsRNA molecules for control of juvenile An . gambiae mosquitoes , larvae and mosquito cells were exposed to PRINT polyethylene glycol-based hydrogel NPs of defined shape , size , and surface charge . Particle surface charge was varied by utilizing either hydroxyl or amine functionalized monomers to yield negative or positive particles respectively . The biodistribution of the NPs in orally challenged Anopheles gambiae larvae and internalization efficiencies and pathways in target mosquito cells were determined as a first step toward NP delivery of mosquitocides to juvenile An . gambiae .
Stock particle concentrations were determined by thermogravimetric analysis ( TGA ) using a TA Instruments Q5000 TGA . TGA analysis was conducted by pipetting 20 μL of the stock NP solution into a tared aluminum sample pan . Samples suspended in water were heated at 30 °C/min to 130 °C , followed by a 10 minute isotherm at 130 °C , then cooled at 30 °C/min to 30 °C , followed by a 2 minute isotherm at 30 °C . TGA was also performed on a 20 μL aliquot of supernatant from a centrifuged sample of the stock solution to account for the mass of any stabilizer remaining in each sample . The concentration of stabilizer was subtracted from the concentration of stock particle solution to determine the actual particle concentration . Anopheles gambiae G3 strain mosquitoes were reared at 27°C and 70% humidity . Eggs were collected from ovipositional dishes 3 days after bloodfeeding and transferred to a pan of deionized water for hatching the following day . Larvae were distributed to a density of 300 per pan and fed ground TetraMin Tropical Flakes Fish Food ( Tetra , Blacksburg , VA ) at all instars in the following amounts: 1st instars—5 drops plus a pinch sprinkled on water surface; 2nd instars—10 drops daily; 3rd instars—15 drops daily; 4th instars—20 drops daily until peak pupation was reached . To evaluate the internalization of NPs , C6/36 cells ( Aedes albopictus larval cells ) were plated in Liebovitz’s L-15 media ( fetal bovine serum ( FBS , 10% ) , penicillin-streptomycin ( 1% ) and L-glutamine ( 1% ) ) at density of 0 . 5 x106 cells/mL in a 24-well plate with coverslips and incubated overnight at 28°C . The following day , cells were incubated with DyLight 488 labeled NPs ( 15 μg/mL ) for 24 h . Cells were then washed with phosphate buffered saline ( PBS , pH 7 . 4 ) to remove non-adherent or loosely adherent NPs and fixed in 4% paraformaldehyde ( methanol free ) . Cells were permeabilized with 0 . 1% Triton X-100 in PBS for 3 min and washed with PBS . Actin staining was performed by incubating cells with Alexa Fluor 546 Phalloidin ( Life Technologies , NY ) for 20 min in PBS at room temperature [32] . Coverslips containing stained cells were washed and mounted on glass slides using ProLong with DAPI ( Life Technologies , NY ) . Confocal microscopy was performed using an inverted Olympus Fluoview 1000 laser scanning microscope . Final images were prepared using Image J v1 . 47m software ( NIH , Bethesda , MD ) . 4a-3b cells ( Anopheles gambiae larval cells ) were plated in Schneider’s media with fetal bovine serum ( FBS , 10% ) , at density of 0 . 25 x106 cells/mL in a 24-well plate with coverslips and incubated overnight at 28°C . The following day , cells were incubated with DyLight 488 labeled NPs ( 15 μg/mL ) for 12 h . Cells were then washed with phosphate buffered saline ( PBS , pH 7 . 4 ) to remove non-adherent or loosely adherent NPs and incubated with 100 nM LysoTracker Red DND-99 ( Molecular Probes , Invitrogen ) containing media for 3 h . Following the incubation , cells were fixed with 4% paraformaldehyde , and mounted on glass slides using ProLong Gold with DAPI ( Life Technologies , NY ) . Confocal microscopy was performed using an inverted Olympus Fluoview 1000 laser scanning microscope . Images were prepared using Image J v1 . 47m software ( NIH , Bethesda , MD ) . C6/36 cells were seeded in a 96-well microtiter plate at a density of 1 . 6x 105 cells/well in Leibovitz-15 media ( 10% fetal bovine serum , 1% Penicillin/Streptomycin and 1% L-glutamine ) for 16 h at 28°C . Hydrogel NPs were added to cells at 250 μg/mL and incubated for 2 h and 48 h at 28°C . Following the incubation , 15 μL of dye solution [CellTiter 96 Non-Radioactive cell proliferation assay MTT ( 3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , diphenyltetrazoliumbromide ) ( Promega ) ] was added and cells were further incubated for 3 . 5 h at 28°C . After the incubation , 100 μL of solubilization solution was added and cells were incubated for 1 h at room temperature . The optical density ( OD ) was measured at 570 nm with a background subtraction at 650 nm . Cells not incubated with NPs were used as controls . Cell viability is presented as a percentage of OD ( 570–650 ) Experimental/ OD ( 570–650 ) Control . Larvae were transferred individually to filter paper as means to immobilize them for injections . Individuals were viewed with a stereo-microscope and , using a Nanoject Microneedle ID Injection ( Drummond Scientific Company , Broomall , PA ) and micromanipulator to position the capillary needle , larvae were injected with 45 nL of nuclease-free water or NP ( 5 mg/mL ) solution through the dorsal arthrodial membrane ( mid-sagittal axis ) joining the head and thorax . Injection needles were changed for each treatment group . Larvae were dissected into head , thorax , abdomen , fore- and mid- and hindgut regions . Images were captured at 0 , 24 , 48 , 72 h post- injection . Images of adults injected during their larval stage were also collected post-pupation . Epifluorescence microscopy was performed using a Nikon microscope equipped with red , green and blue filter sets and a cooled CCD camera . Final images were prepared using Image J v1 . 47m software ( NIH ) . For mortality studies , fourth instars were injected with NPs as mentioned above . Survivorship was recorded every day through adulthood ( 6 post-injection ) . A mixture of NPs , ground Tetramin ( Tetra , Blacksburg , VA ) fish food and agarose was prepared by mixing 25 μL ( 5 mg/mL ) NPs with 25 μL Tetramin slurry that then was mixed with 100 μL molten agarose ( 1% ) . Blocks of agarose were introduced into cartons containing larvae . 3–5 larvae were randomly selected at each time point for dissection and subsequent microscopic analysis . Fourth instar An . gambiae were injected with NPs . Larvae were placed in a drop of PBS on a glass slide under light microscope . Heads were removed using a forceps . The thorax was then held in place with one forceps as the gut was removed from the body by gently pulling and removing the last abdominal segment . Dissected tissues were placed on an imaging tray . The tissues were imaged with an In vivo Multispectral FX Pro imaging system ( Carestream , Rochester , NY , USA ) using 480 nm excitation and 535 nm emission wavelengths . Fluorescence measurements were performed at the same exposure setting to compare all data sets to each other . A white light image was also captured to define the tissue boundaries and regions of interest . Image Analysis NIH Image Jv1 . 47m was utilized to quantify the mean fluorescent intensities ( MFI ) values . A region of interest ( ROI ) was drawn around each tissue utilizing the white light image . The same ROI was then applied to the fluorescent image and MF quantified utilizing the Analyzex→Measure function . To account for the fact that the different particle groups have different inherent fluorescence , a correction factor was applied to the raw MFI . The correction factor was calculated as the ratio of fluorescent intensity of the particle group to the intensity value of the brightest particle group ( 80 nm x 320 nm negative ) . The resulting number was then divided by the raw MFI of each individual to obtain a corrected MFI value ( cMFI ) . Data for in vivo imaging were log transformed . Statistical analysis was performed using JMP software ( SAS Institute , Cary , NC ) . Comparisons between treatments were made by Tukey’s HSD ( honest significant difference ) . Differences were considered significant for p < 0 . 05 .
In order to be effective , the delivery vehicle of choice should be efficiently internalized by target cells . Size , shape and charge play a crucial role in the internalization of NPs [33] . To test whether the PRINT hydrogel NPs are internalized by insect cells and if the physicochemical properties play a role , Aedes albopictus C6/36 cells were incubated with fluorescently labeled NPs and evaluated for internalization by fluorescence microscopy . NPs of all sizes and charge were taken up efficiently ( Fig 1 ) . Positively charged NPs were more efficiently internalized than negatively charged NPs ( Fig 1 ) . The differences could be dramatic , for example , when cells were challenged with 80 nm x 5000 nm positively charged NPs , intense fluorescence was detected in the cells; in contrast there was only low-level fluorescence detected in cells challenged with the negatively charged NPs ( Fig 1 ) . Similar results were observed when NPs were incubated with An . gambiae cell lines 4a3a and 4a-3B ( S2 and S3 Figs ) . To activate the RNAi response efficiently , we reasoned that the NPs should deliver the dsRNA cargo to cytosol where it can be incorporated into the RISC complex . The trafficking of NPs to lysosomal compartments may result in degradation of dsRNA . To identify the trafficking patterns of PRINT NPs , 4a-3B cells were exposed to the respective NPs , fixed , stained , and visualized using confocal microscopy ( Fig 2 ) . The cellular biodistribution of the positively and negatively charged NPs were strikingly different . The majority of 80 nm x 320 nm and 200 nm x 200 nm negatively charged NPs co-localized with acidic organelles , e . g . , lysosomes ( Fig 2A ) . In contrast , positively charge NPs were detected principally in the cytosol , indicating that they either avoided or escaped lysosomal compartments of cells ( Fig 2B ) . Interestingly , the 80 nm x 5000 nm negative NPs and were not co-localized with the lysosomes , similar to positively charged NPs ( Fig 2A ) . To determine the biodistribution of NPs in vivo , An . gambiae larvae were parenterally challenged with the respective NPs , dissected into head , thorax , and abdomen regions , and further to fore- , mid- and hindgut sections of the gastrointestinal tract at day 0 , 1 , 2 and 3 post-injection ( p . i . ) , and examined by fluorescence microscopy to determine the biodistribution of the respective NPs . A striking difference was observed in the biodistribution of negatively and positively charged NPs; the former showed an intense punctate staining while the latter exhibited diffuse fluorescence ( Fig 3A and 3B ) . This biodistribution of positively and negatively charged particles was also seen in parenterally challenged adult mosquitoes ( see Figs 10 and 13 in companion paper ) [34] . In the head , NPs of all sizes and both charges were observed through day 3 p . i . , although the fluorescence signal with negatively charged NPs was greater than that with positively charged NPs ( Figs 3 and S1 ) . The most intense fluorescence for both positively and negatively charged NPs was observed in thorax and abdominal segments of the larval body . Here too , negatively charged NPs showed punctate fluorescence while positively charged NPs presented as more diffuse ( Fig 3A and 3B ) . nterestingly , positively charged NPs of all sizes were more often associated with gastric caeca in the foregut than the negatively charged NPs ( Fig 3B ) . Adults emerging from larvae injected with NPs were also evaluated for the persistence of NPs during metamorphosis . Negatively charged NPs persisted through metamorphosis and were present in the head , thorax , abdomen and gut of adult female mosquitoes at day 1 post-emergence ( Fig 3C ) . Ovaries were also imaged to determine the possibility of vertical transmission of NPs . Negatively charged NPs of all sizes were observed in follicles and tracheae of the ovaries ( Fig 3C ) . 80 nm x 5000 nm negatively charged NPs were also present in the maxillary palps and proboscis and other tissues in the head ( Fig 3C ) . Negatively charged particles were much more likely to be associated with these tissues than positively charged particles in adult mosquitoes ( see Figs 7 and 11 in the companion paper ) [34] . Field-applicable control of larvae necessitates gut or transcuticular delivery of NPs loaded with dsRNA . In a proof-of-concept experiment , An . gambiae were exposed to PRINT 80 nm x 5000 nm NPs mixed in larval food and agarose . The larvae readily ingested the formulation containing NPs ( Fig 4 ) . NPs were observed in fore- , mid- and hindgut . However , no fluorescence was observed outside of the lumen of the digestive tract ( Fig 4 ) . For efficient , systemic RNAi , dsRNA should reach and persist in the hemolymph where it will be distributed throughout the body by virtue of circulation through the open circulatory system [35] . Studies have shown that dsRNA is enzymatically degraded in hemolymph of some insects e . g . , Manduca sexta [11] . Thus , to increase hemolymph delivery , the NP vehicle should protect the dsRNA when trafficking in the larval body . In this study , in vivo imaging was employed to test the persistence of hydrogel NPs in larval thoraces and abdomens . All of the particle groups tested persisted through 3 days p . i . ( Fig 5 ) . Interestingly , the 80 nm x 320 nm and 200 nm x 200 nm negatively charged NPs were more abundantly detected at each time point than positively charged NPs . This difference was not as great with the 80 nm x 5000 nm NPs . This may be attributable to more efficient internalization of the positively charged NPs in mosquito cells in vivo as was shown in vitro ( Fig 1 ) . To determine if the NPs themselves have inherent cytotoxicity in mosquito cells , C6/36 cells were incubated with NPs and cell viability was tested at 2 and 72 h post-incubation by MTT assay . None of the particle groups exhibited any cytotoxic effects on C6/36 cells at any of the indicated time points ( Fig 6A and 6B ) . Interestingly , 80 nm x 5000 nm NPs , which were internalized most efficiently , also demonstrated an excellent cell viability profile . Complementing the in vitro cell viability studies , in vivo studies were conducted to determine if the NPs caused untoward effects in An . gambiae larvae . Fourth instar larvae were intrathoracically injected with NPs and their survival monitored for 6 days p . i . , Larvae injected with water were used as controls . No significant mortality was observed by any of the NP groups studied compared to controls ( Fig 7 ) . The low levels of mortality observed in larvae injected with water are likely caused by the stress of injection . Together , these data show that PRINT NPs themselves do not induce any untoward effects in An . gambiae larvae .
Novel vector control strategies are critically needed to combat emerging and re-emerging vector-borne diseases . Alternative approaches such as dsRNA induced RNAi mediated lethality have great potential for vector control . Critical steps in RNAi-based approaches are 1 ) environmental stability and delivery of the RNAi trigger , and 2 ) uptake of the dsRNA by target cells . Our data show that NPs of all sizes and charges are efficiently internalized by mosquito cells both in culture and in vivo ( Fig 1 ) . Previous work has shown that positively charged NPs are internalized more efficiently than negatively charged NPs in mammalian cells because of their electrostatic interactions with negatively charged plasma membrane [36–38] . Similar results were obtained with mosquito cells , in which positively charged PRINT NPs are internalized better than the negatively charged NPs ( Fig 1 ) . In addition to charge , size and shape of NPs also can play an important role in uptake . Indeed , positive NPs of 80 nm x 320 nm , 200 nm x 200 nm and 80 nm x 5000 nm were internalized more readily than 1000 nm x 1000 nm positive NPs ( Fig 1 ) . Once the delivery vehicle is internalized , it is crucial that the payload ( e . g . , dsRNA ) is delivered to the cytosol where the RNAi machinery is located and not to the lysosomes where it could get degraded . Lysosomal trafficking studies revealed that size and charge of the NPs is important in the trafficking events post-internalization by cells . A majority of negatively charged 80 nm x 320 nm and 200 nm x 200 nm NPs trafficked to lysosomes whereas positively charged NPs of all sizes were not co-localized with lysosomes ( Fig 2 ) . Positively charged NPs also showed a diffuse fluorescence pattern inside cells that suggests presence in cytosol . These data suggest that positively charged NPs might be better candidate ( s ) than negatively charged NPs to deliver dsRNA triggers . To the best of our knowledge , this is the first study showing the effect of particle charge , shape and size on internalization and trafficking in insect cells . A critical step during in vivo dsRNA-mediated gene silencing is association of the RNAi trigger with specific tissues in the host body . Biodistribution studies of PRINT NPs were performed to study the effect of size and charge on spatiotemporal distribution . Negatively charged NPs of all sizes exhibited intense punctate fluorescence ( Fig 3A ) whereas positively charged NPs had a diffuse fluorescence pattern ( Fig 3B ) . This could be due to more efficient internalization of positively charged NPs leading to loss of fluorescence in vivo . Because both negatively and positively charged NPs were present in head , thorax and abdomen , NPs are likely distributed throughout the body via hemolymph ( Fig 3 ) . Interestingly , positively charged NPs exhibit a strong localization affinity toward the gastric caeca ( Fig 3B ) . A possible explanation could be the faster attachment and/or internalization of positively charged NPs on the above-mentioned tissues . Thus , positively charged NPs might function better to deliver dsRNA to silence genes expressed specifically in gut tissue . Malpighian tubules did not contain detectable NPs indicating that these NPs may not be the suitable delivery candidates to target this organ system . In contrast , NPs were detected abundantly in malpighian tubules of parenterally but not orally challenged adult mosquitoes [34] . Very interestingly , NPs ( principally negatively charged ) persisted through metamorphosis to the adult stage in mosquitoes that matured from larvae that were injected with NPs ( Fig 3C and 3D ) . Negatively charged NPs were detected in to the thorax , abdomen , ovaries , proboscis and other head tissues ( Fig 3C and 3D ) . Presence of NPs in the latter tissues suggests exciting potential for the vertical transmission to subsequent generation as well as via contact spread . The physiologic mechanism ( s ) that facilitate NP persistence through metamorphosis remain to be determined . Oral feeding is the simplest and most field applicable way of environmentally delivering dsRNA to insects; however , the efficiency of inducing RNAi orally is poor [39] . The proof-of-concept per os delivery experiments indicated that larva could feed on PRINT NPs mixed in food slurry and agarose . Twenty four hours after exposure , NPs were observed in fore , mid-and hindgut regions of the larval digestive tract ( Fig 4 ) . It is possible that NPs tested in this study did not traffic into the body because the agarose matrix in which the NP were delivered did not release particles in the gut , or because the larval peritrophic matrix ( PMII ) presents an impermeable barrier . NP fluorescence was quantified in thorax and abdomen of parenterally challenged larvae . The fluorescence signal of negatively charged 80 nm x 320 nm and 200 nm x 200 nm NPs was greater than that with positively charged NPs , most notably at day 0 and day 1 p . i . ( Fig 5A and 5B ) . The fluorescence signal of the negatively charged NPs declined with time from day 0 to day 3 p . i . ( Fig 5 ) . The fluorescent signal with 80 nm x 320 nm positively charged NPs was lower but more stable through day 3 p . i . ( Fig 5A ) . The 80 nm x 5000 nm NPs differed from other NPs; the signal from positively charged NPs was greater than negatively charged NPs at day 0 and day 1 p . i . ( Fig 5B and 5C ) . It is likely that the differences observed in fluorescence intensity over time are a function of cellular uptake and trafficking of the particles as revealed in three separate mosquito cell lines . It is noteworthy that the positively and negatively charged particles exhibited exactly the same phenotype following parenteral challenge of adult mosquitoes ( see Fig 4 in the companion paper ) [34] . In order for an insecticide delivery system to be viable , it is key that it not be inherently cytotoxic . Previous studies showed that these particular particles demonstrate high levels of cellular internalization in mammalian cells , with minimal cytotoxicity [40] . PRINT particles are non-toxic in vitro in multiple human cancer cell lines [24 , 25 , 29 , 40 , 41] . We have also evaluated PRINT particles in vivo in mouse models and shown that they are non-toxic and do not induce an inflammatory response [42] . In keeping with these data , mosquito cell viability assays showed that PRINT NPs did not induce any undesired toxic effects in mosquito cells after 2 and 72 hour incubation ( Fig 6 ) . To further validate the in vitro results , 4th instar larvae were injected with PRINT NPs and their survival monitored through day 6 p . i . ( Fig 7 ) . None of the particle groups studied caused difference in survivorship from the water injected controls , and thus had no adverse effect on larval development , pupation , and emergence to the adult stage . In conclusion , we report the detailed biodistribution and viability evaluations of PRINT NPs in mosquito cells and in An . gambiae larva as the first step in rational design of molecular mosquitocides . The excellent , low cell and larval toxicity profiles , efficient internalization , and widespread biodistribution make these NPs attractive candidates for dsRNA delivery in mosquitoes . The presence of NPs in head and ovaries may be indicators of contact uptake and vertical transmission capabilities , respectively . These attributes could be exploited to control adult as well as larval mosquitoes . Nanotechnology mediated delivery of mosquitocides offers a new paradigm for designing next-generation vector control tools . | There is an urgent need for new interventions and novel insecticides to control mosquito vectors of human disease agents . Nanoparticle-based strategies have been explored extensively as means to deliver a molecule of interest for medical applications ( e . g . , antigens , drugs , nucleic acids , etc . ) to particular tissues and cells in mammalian systems . Our particular application of interest is to use nanoparticles as a platform to deliver nucleic-acid based mosquitocidal molecules . As a first step to developing nanoparticle delivery for molecular vector control , we investigated the physicochemical properties of PRINT hydrogel nanoparticles with respect to their biodistribution , persistence and safety in Anopheles gambiae mosquito larvae and in cell culture . This technology could be applied to studies of pathobiology and physiology in any vector species , with the benefit that the particles can be tailored to move to and target specific delivery of a cargo to a particular organ system and even sub-cellular location . Therefore , this technology and specific information about where the particles go and with what affinity sets the stage of describing a platform that has tremendous potential use in vector control and disease transmission intervention . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Biodistribution and Toxicity Studies of PRINT Hydrogel Nanoparticles in Mosquito Larvae and Cells |
Both aging and chronic inflammation produce complex structural and biochemical alterations to the lung known to impact work of breathing . Mice deficient in surfactant protein D ( Sftpd ) develop progressive age-related lung pathology characterized by tissue destruction/remodeling , accumulation of foamy macrophages and alteration in surfactant composition . This study proposes to relate changes in tissue structure seen in normal aging and in chronic inflammation to altered lung mechanics using a computational model . Alterations in lung function in aging and Sftpd -/- mice have been inferred from fitting simple mechanical models to respiratory impedance data ( Zrs ) , however interpretation has been confounded by the simultaneous presence of multiple coexisting pathophysiologic processes . In contrast to the inverse modeling approach , this study uses simulation from experimental measurements to recapitulate how aging and inflammation alter Zrs . Histologic and mechanical measurements were made in C57BL6/J mice and congenic Sftpd-/- mice at 8 , 27 and 80 weeks of age ( n = 8/group ) . An anatomic computational model based on published airway morphometry was developed and Zrs was simulated between 0 . 5 and 20 Hz . End expiratory pressure dependent changes in airway caliber and recruitment were estimated from mechanical measurements . Tissue elements were simulated using the constant phase model of viscoelasticity . Baseline elastance distribution was estimated in 8-week-old wild type mice , and stochastically varied for each condition based on experimentally measured alteration in elastic fiber composition , alveolar geometry and surfactant composition . Weighing reduction in model error against increasing model complexity allowed for identification of essential features underlying mechanical pathology and their contribution to Zrs . Using a maximum likelihood approach , alteration in lung recruitment and diminished elastic fiber density were shown predictive of mechanical alteration at airway opening , to a greater extent than overt acinar wall destruction . Model-predicted deficits in PEEP-dependent lung recruitment correlate with altered lung lining fluid composition independent of age or genotype .
The lung has a stylized architecture that is crucial for the efficient exchange of gases between the vasculature and the airways . Organ level mechanical function and the distribution of ventilation during health and disease are dependent upon the complex interactions between heterogeneous airway and parenchymal elements . Alterations in histologic structure and mechanical function of the lung are established consequences of aging [1 , 2] . With increasing age or advancing , decreases in tissue elastin content and destruction of alveolar septae contribute to a loss of parenchymal elasticity , with resulting increases in lung volume [2] . In addition , these changes may alter parenchymal tethering forces , thus reducing airway caliber , particularly during expiration . These processes occur gradually with age and , due to robustness in the complex architecture of the tissue , structural alteration may progress without any apparent impact on gas exchange or quality of life [3] . In the setting of chronic lung inflammation , structural and mechanical alterations must be interpreted with respect to changes that occur with normal aging [4–6] . Mice deficient in Surfactant protein-D ( Sftpd ) develop chronic lung inflammation characterized by the persistence of enlarged activated macrophages , destruction of acinar walls with airspace dilation and altered composition of the lung lining fluid [7 , 8] . These changes are minor , but observable in young mice , with the phenotype becoming progressively more severe with age [9] . Earlier work has demonstrated that mechanical function in Sftpd deficient mice mimics that observed in wild-type C57BL6/J mice at 8 [10]and 27 weeks of age , but diverges at 80 weeks [9] . At 80 weeks , respiratory system elastance is elevated relative to C57BL6/J , a paradoxical observation given the emphysema-like histological change . Each of the aforementioned factors characterizing the complex pathology of Sftpd-/- mice is individually expected to contribute to respiratory system dysfunction , however the extent to which each contributes to the observed mechanical defect is unknown . Respiratory impedance , as measured at airway opening is widely regarded as a sensitive indicator of pulmonary pathology in lung disease [4 , 11–15] . Impedance measurements are typically analyzed using inverse modeling , where spectral data are fit to simple mechanical models with a small number of free parameters . Due to the complex and multiscale nature of the physical forces that influence lung mechanics , discrete biochemical and histological alterations that occur in pathology rarely relate directly to obvious changes in the impedance spectra or to changes in the model parameters . An alternative strategy for studying these concurrent processes is forward modeling [16–18] , whereby a simulation of pathology is constructed based on perturbations to a control condition based on empiric data . In order to examine how specific pathologic observations may influence organ level function , the present study employs an anatomic computational model of the murine lung for forward simulation of pulmonary mechanics . These simulations utilize measured histological changes in acinar wall number and elastic fiber thickness as well as changes to lung recruitment in order to simulate respiratory impedance as a function of positive end expiratory pressure ( PEEP ) . Incorporation of experimentally determined alterations into the model allows prediction of the magnitude of each effect when compared to experimental Zrs . In this way , the study is designed not to identify the mechanism by which pathology occurs , but to address the extent to which proposed mechanisms may contribute to altered mechanical function . Simulations were designed to test the hypothesis that destruction of alveolar septae and reduction in parenchymal elastic fiber thickness underlie the changes in lung resistance and elastance seen with age . Additionally , simulations were used to test the proposal that accelerated loss of tissue architecture , infiltration of the airspaces by cellular and crystalline material and loss of surfactant homeostasis secondary to chronic inflammation in Sftpd-/- mice are essential components of the complex mechanical phenotype seen in these mice .
Experiments were performed in accordance to Rutgers University IACUC approved protocols conforming to the NIH guidelines for the care and use of laboratory animals . Male wild-type C57BL6/J and congenic Sftpd -/- mice were bred at Rutgers University under the care of Laboratory Animal Services . All mice were housed in micro-isolation cages under sterile conditions with food and water provided ad libitum . Mice were examined at 8 , 27 and 80 weeks of age . General anesthesia was induced via single intraperitoneal injection of ketamine and xylazine . At 6 minutes post injection withdrawal from footpad pinch was used to determine depth of anesthesia . Surgical tracheosteomy was performed under aseptic technique . Following lung function measurement , animals were euthanized while on the ventilator by exsanguination via aortic incision with concurrent en-bloc perfusion of the lungs via instillation of heparinized saline through the right ventricle . Lung mechanical function was assessed as previously described [9] . Briefly , anesthetized mice ( N = 8 per group ) were ventilated using the Flexivent Small Animal Ventilator ( Scireq , Montreal QC ) via tracheostomy , at 120 breaths/minute and a tidal volume of 10mL/kg body weight . Mechanics were assessed at 5 positive end expiratory pressures ( PEEPs ) of 0 , 1 , 3 , 6 and 9 cm H2O . Following equilibration at each PEEP forced oscillation measurements and quasi-static pressure volume loops were generated in triplicate , with each measurement perturbation separated by 15 s of normal tidal ventilation . Forced oscillation measurements were made by transducing pressure during a flow-controlled broad-band waveform composed of 17 sinusoidal waveforms with mutually prime non-integer frequencies between 0 . 5 and 20 Hz , lasting 8 s . Respiratory system impedance , Zrs , was calculated as the ratio of the Fourier transformed pressure to flow signals as a function of frequency . From this , respiratory resistance , reactance and elastance were determined from the real and imaginary portions of Zrs . Constant phase parameters were estimated off-line using a Nelder-Mead simplex method in Matlab ( Mathworks , Natick , MA ) . Sub-maximal quasi-static pressure-volume ( PV ) loops were generated as previously described [19] . Briefly , the lung underwent stepwise inflation to a peak pressure 10 cm H2O above PEEP in 8 equal-magnitude pressure-regulated steps over 16 s . PV hysteresis was calculated as the area between the inspiratory and expiratory limbs of the loop . Elastance was calculated as the ΔV/ΔP across the entire PV loop as well as for each incremental pressure step . BAL was performed using 4 x 1mL washes with saline as previously described [9] . Cells were removed by centrifugation at 300x g for 10 min . Supernatant was frozen at -80°C . Samples were defrosted and centrifuged at 20 , 000x g for 1 hr to separate BAL into large and small aggregate fractions . Large aggregate phospholipid content was determined by the method of Dyer and Bligh with each sample run in triplicate [20] . Relative SP-B content in the large aggregate fraction was determined by western blot with each lane loaded for constant phospholipid content . Gels were run under reducing conditions and imaged using horseradish peroxidase catalyzed chemiluminesence ( ECL prime , GE Life Sciences , Pittsburgh , PA ) and quantified by Bio-rad gel imager . Phospholipid content and SP-B intensity were normalized to control ( 8-week-old C57BL6/J mice ) , analyzed by 2-way ANOVA on genotype and age followed by Welch’s post-hoc test ( p<0 . 05 for significance ) and reported as mean ± standard error . Following BAL collection , the lung was inflated to TLC using 25 cm H2O with 2% Paraformaldehyde , 3% sucrose . The trachea was tied to maintain inflation , incubated overnight and then transferred to 70% ethanol . Tissues were paraffin embedded and sectioned at 5 μm thickness . Tissue sections were imaged at 100x and 400x magnification using the Olympus VS-120 microscope . Hematoxylin and eosin staining was used for assessment of radial alveolar counts ( RAC ) and measurement of obstruction of the parenchymal space by inflammatory cells and/or crystalline and hyaline debris . RAC was determined by counting the number of alveolar tissue septae traversed by a chord drawn perpendicular from a terminal bronchiole to the lung edge [9 , 21] . The RAC is thus not a measure of acinar volume , but is proportional to the number of intact tissue septa and will decrease as acinar walls undergo destruction , but will not change in the case of airspace dilatation . All terminal bronchioles within a 10 x view from a pleural surface or a connective tissue septum were used for counting . Counts were made by two blinded observers on 3–4 mice per condition , with 15–20 counts made per slide to generate a robust distribution . The fraction of tissue obstructed was measured as the percentage of the parenchymal airspaces filled with either enlarged immune cells or acellular debris in each section . Tissue collagen and elastic fiber content was examined using a Modified Verhoeff-VanGeisson stain technique ( Sigma-Aldrich St . Louis , MO ) . For each mouse at least 10 high powered fields were analyzed , with the thickness of each parenchymal elastic fiber measured using ImageJ . Fiber thickness was measured in triplicate and averaged , with most slides having 15–20 parenchymal fibers per field; fibers lining airways and vasculature rather than airspace were excluded from analysis . An anatomic model of the airway tree was developed by hybridizing CT-based structural information from the C57Bl6/J mouse upper airway tree with measurements made from silicone casts of the micromus lung . For the upper portion of the tree , airways greater than 1 mm in diameter were assigned a mean radius and length based on micro CT imaging [22] . Below this threshold radius , the subtending airway network was modeled based on the recursive branching model structure of the micromus lung [23] . At the transition from CT-based to cast based-model the generation of the two daughter branches was determined based on matching the length and radii of the parent airway to the nearest order within the cast model . Branching pattern beneath this juncture was dichotomous , with each order n , being subtended by orders n-1 and n-1-Δn . The branching nature of the upper airway tree is illustrated in Fig 1 . The volume of each airway was calculated as Vseg = πr2l . Total volume of the modeled tree was compared to the estimated airway volume at TLC using a recursive algorithm to traverse the entire structure beginning at the trachea: Vn=Vseg ( n ) +Vn−1+Vn−1−Δn . The simulation was run repeatedly to determine a mean and standard deviation of the total volume of the airway tree at TLC and found to agree within 2% of the volume estimate for the mouse lung at 25 cmH2O ( 0 . 152 vs 0 . 155 mL ) [23] . Impedance modeling was performed as previously described and are described briefly , with detailed equations given in the included supplement ( S1 Appendix ) . Mechanical properties for each airway were based on the length , l , and radius r , of each segment , i , which are determined stochastically based on airway order . Impedance of each airway segment contained a resistance given by Poiseuille’s law , an inertial component from gas acceleration , as well as parallel contributions from the distension of airway walls and gas compression [16] . Airway wall distension and gas compression may contribute significantly to lung impedance during pathology and were simulated as viscoelastic based on airway geometry [24 , 25] . Parallel impedances were modeled as acting at the airway midpoint , bisecting the longitudinal impedance into components acting proximal and distal to compression/distension processes . The impedance into a given branch of the tree can be determined by combining the impedances of the subtending branches in parallel , adding this in series to the distal longitudinal impedance [17] . This sum is combined in parallel with gas compression and wall distension impedances , and then the total is added in series to the proximal longitudinal impedance [16] . A recursive algorithm was employed to generate the branching nature of the tree . As the cast-based tree branches dichotomously , the impedance was readily calculated using standard approaches for bifurcating networks [16] at each transition point where the anatomical definition changes from CT based to plaster-cast based . As no recursive relationship exists for the CT-based upper airway tree , each cast-based network was simulated individually and added as the downstream impedances to ends of the CT based tree . The impedance network of the upper airway tree was then constructed by appropriate combination of downstream impedances in parallel and series until reaching the trachea , at which point the impedance of the lungs , ZL , is fully computed . Tissue contributions to ZL were simulated by adding viscoelastic constant phase tissue elements to the terminal branches of the tree structure described above as previously described [26] . Impedance of the chest wall is considered negligible in mice , and was omitted [27] . All simulations were performed at the 17 frequencies below 20Hz to match experimental measurements . A hierarchical modeling approach was undertaken to evaluate the impact of alveolar wall destruction , elastic fiber thinning , and airway derecruitment on lung resistance and elastance . ( Fig 1 ) . First , the effects of aging and loss of Sftpd on airway caliber were examined . Airway radii were scaled by constant value minimizing the error in airway resistance between the model tree and experimental data . Using the scaled airway tree , the control condition ( 8-week old C57Bl6/J recruited to PEEP 6 cmH2O ) was simulated with tissue elastance distributions generated from the radial alveolar count and elastic fiber thickness measurements . The contribution of each of the above factors on error between simulated and measured Zrs spectra was determined by replacing control tissue properties with those from the appropriate age-genotype distribution . In order to determine the role of PEEP-dependent acinar recruitment , probabilistic collapse of terminal tissue elements was incorporated into the model based on estimates of minimum tissue elastance from the Pressure-Volume curve [28] . As the geometry of each airway generation was determined from measurements made at TLC , it was necessary to scale the airway radii in order to match the measured resistance spectra for each experimental condition as a function of PEEP . Simulation of Zrs was performed at each PEEP for all conditions by setting the tissue mechanical properties equal to estimated values of H and η from the constant phase model , and performing a one dimensional optimization on a constant multiplier applied to all radii . This scalar factor , r/r0 , constrained between 0 and 1 was estimated by bisection method to reduce the sum of squared residuals between model and experimental Zrs data . As radii in the tree are stochastic , simulations were repeated 100 times per condition and reported as a mean and standard deviation . All subsequent simulations were run with r/r0 set as the mean of the radial scale factor distribution at each PEEP . Heterogeneity in tissue mechanics is modeled using extracellular matrix composition or variation in alveolar wall number within the acinus . Simulations were performed using measurements of these factors from tissue histology . As these properties naturally vary within the tissue , measurements were treated as a discrete random variable and pooled across all samples within a condition to generate a probability distribution function . A total of 8 models for generating elastance distributions are proposed– homogenous lung , plus three ways of incorporating RAC , each with or without an elastic fiber contribution . In order to generate the tissue elastance distribution a baseline elastance , H0 , unique to each of the proposed models was estimated . This was accomplished by minimizing the error between the simulated anatomic model impedance and the measured Zrs spectra from maximally recruited control mice ( 8-week-old C57Bl6/J mice at PEEP of 6 cm H2O ) by least squares optimization on the single variable H0 . The value of the H0 parameter can be considered the intrinsic stiffness of a reference alveolar unit upon which the distribution of model stiffness is based . Each terminal tissue unit is assigned an individual elastance , Hi , value drawn from a distribution influenced by 4 possible factors: H0 ( model ) , elastic fiber thickness , λ , radial alveolar count , Γi , and PEEP-dependent probability of derecruitment , pcollapse Hi={H0 ( model ) × ( λ ) × ( Γi ) Randi≥pcollapseInfRandi<pcollapse} ( 1 ) Incorporation of elastic fiber thickness into the model began with pooling the thickness measurements across subjects for each condition . A thickness measurement , λi , was drawn at random for each of the terminal elements in the tree . Tissue elastance was scaled based on the assumption that fiber thickness linearly related to the stiffness , so that the elastance of a given unit was scaled based on λi so that Hi ∝ ( λi/λ0 ) where λ0 is the average thickness of the control distribution . Acinar destruction with alveolar wall loss was incorporated into the simulation using the radial alveolar count . For each terminal tissue unit an individual radial alveolar count measurement ( RACi ) was randomly selected from the experimental RAC distribution and value for Γi determined by comparing it to a value RACcrit . All sampling was done with replacement . Three methods of relating radial alveolar count to tissue elastance distributions were examined and are detailed in the supplemental material ( Appendix ) . Briefly , the model variants include: 1 ) comparing RACi to a fixed threshold ( RACcrit = 12 ) , if [RACi > RACcrit Γi = 1; RACi < RACcrit Γi = 0 . 5] . 2 ) : comparing RACi to a random RACcrit drawn from the control distribution , if [RACi > RACcrit Γi = 1; RACi < RACcrit Γi = 0 . 5] . 3 ) comparing RACi to a fixed threshold ( RACcrit = 12 ) , and scaling elastance hyperbolically—if [RACi > RACcrit Γi = 1; RACi < RACcrit Γi = ( RACi / RACcrit ) ] . The probability of a terminal element being nonventilated , pcollapse , was modeled as a PEEP dependent phenomenon based on elastance changes within the PV loops , estimates of the constant phase parameter H and the % tissue opacification from tissue histology . The quasi-static nature of the PV loop permits computation of effective elastance at each step in the maneuver . Elastance of the fully recruited lung was estimated as the nadir of the step elastance curve during progressive recruitment , Emin , as previously published [19 , 28] and is detailed in the supplemental methods . This elastance minimum was incorporated into a simple model of 1000 parallel elastance units allowed to be recruited or derecruited such that total lung elastance is hyperbolically related to the number of open pathways . The fraction of recruitable lung which is open was thus estimated as fopen=Htissue Ntotal xHCP ( PEEP ) where the ratio Htissue/Ntotal is the elastance of the fully recruited lung—Emin above—and HCP ( PEEP ) is the constant phase elastance value at a given level of PEEP . Each condition was run as a monte-carlo simulation with 100 replicates; the mean and variation of all simulated spectra and estimated parameters quantified . Coefficient of variation of impedance spectra for all simulated experimental conditions was < 10% under every model of tissue mechanical behavior presented . For each simulated model spectrum goodness of fit to the experimental data was given by the total error , calculated as: ϕM=∑K=117[ ( RL ( fK ) −RM ( fK ) ) 2+ ( XL ( fK ) −XM ( fK ) ) 2] ( 3 ) where RL and XL are the resistance and reactance spectra of the experimental data and RL and XL are the simulated spectra under model M . In order to compare the ability of each modeled factor on their contribution to the recapitulation of mechanical phenotype , each model goodness-of-fit must be compared between simulations . As these simulations are not fit to the data using optimization , conventional approaches to comparing models are not appropriate . Although such statistical hypothesis testing and information criteria based approaches cannot be employed , model likelihood ratios can be computed as LM=−2log ( ∑[RL ( fK ) −RM ( fK ) ]2+∑[XL ( fK ) −XM ( fK ) ]2 ) +2log ( ∑[RL ( fK ) −R0 ( fK ) ]2+∑[XL ( fK ) −X0 ( fK ) ]2 ) where R0 and X0 are the simulated resistance and reactance spectra of the null model ( 8wk WT distributions ) . Though this strategy gives direct quantitative comparison , it is limited in that increasing model complexity is not explicitly penalized .
Respiratory mechanical properties were assessed by forced oscillation and by quasi-static pressure volume loop . These data are complementary examining both static and dynamic respiratory properties as a function of PEEP ranging from 0 to 9 cm H2O . Respiratory resistance and elastance spectra are presented for a PEEP of 6 cm H2O ( Fig 2 , panels A and B ) , chosen as a PEEP that balances maximal recruitment with minimal overdistension across the conditions . Zrs spectra were mostly PEEP independent , with the inertial effects on high frequency EL spectra being the most affected by increasing end expiratory pressure ( S1 Fig ) . At the PEEP of 6 cm H2O , EL spectra at 8 and 27 weeks are indistinguishable from each other irrespective of genotype ( Similar results were observed at a PEEP of 3 cm H2O ) . At 80 weeks of age C57BL6/J mice have a significantly lower EL spectrum than Sftpd ( -/- ) mice . There is a progressive fall in the RL spectra with increasing age independent of genotype ( Fig 2A ) . At 80 weeks of age there is a divergence of the high frequency spectra in the Sftpd ( -/- ) mice , which was observable at all levels of PEEP . Constant phase model parameters are useful in anatomically compartmentalizing these changes , placing them in a physiologic context and providing a quantification of lung properties for the basis of modeling the pathology . Parameters were examined using 3-way ANOVA on age , genotype and PEEP , followed by Dunnett’s post hoc test ( p<0 . 05 ) All PEEP trends in constant phase parameter values are presented in S2 Fig , with salient features shown in Fig 2 , panels C-F . The frequency invariant airway resistance , RN , demonstrates significant effects in genotype , age and their interaction . At 8 weeks , RN is significantly elevated with the loss of Sftpd ( Fig 2C ) and increases as a function of PEEP . At 27 weeks , RN is lower than at 8 weeks but there is no longer a dependence on genotype . At 80 weeks , the values for RN have significantly diverged , with C57BL6/J mice demonstrating a resistance close to that of 8-week-old mice and Sftpd ( -/- ) mice being dramatically reduced . Tissue resistance , G , demonstrates age and age-genotype interactions , with a trend similar to that seen in elastance ( Fig 2D ) . Estimated elastance , H , becomes significantly lower with age and demonstrates dependence on genotype , but not PEEP . There is a significant interaction between Age and genotype , showing that loss of Sftpd reduces the magnitude by which H falls at 80 weeks compared to C57BL6/J mice ( Fig 2E ) . Tissue hysteresivity , η defined as the ratio of G to H , is significantly effected by age and PEEP , with increasing age tending to decrease η , and PEEP effect being non-monotonic , with a minimum value at either PEEP of 1 or 3 cm H2O ( Fig 2F ) . Pressure volume curves were analyzed to detect changes in lung recruitment throughout the process of quasi-static inflation ( Fig 3 ) . In all mice , increasing PEEP reduces the peak volume reached for the PV loop , as expected as the lung approaches TLC . Peak lung volume during the PV maneuver is unchanged at 27 weeks of age , but is significantly increased in 80 week old mice . With loss of Sftpd age-related volume increase is significant but is reduced relative to that in C57BL6/J mice . For 8 and 27 week old mice the PV area monotonically decreases with increasing PEEP . This relationship is maintained in 80 week Sftpd ( -/- ) mice , however , in C57Bl6/J mice there is non-monotonic dependency ( Table 1 ) . This indicates an increased pressure dependence for recruitment with age , and shows that the Sftpd ( -/- ) lung is harder to recruit by increasing PEEP . Elastance over the entire PV loop gradually increases with PEEP in both 8-week-old C57Bl6/J and Sftpd ( -/- ) mice . However , at a PEEP of 9 cm H2O there is a marked increase in stiffness , as is expected with strain stiffening behavior ( Table 2A ) . At 27 weeks loop elastance closely mimics that seen in 8-week-old mice , however , the strain stiffening behavior is blunted in both genotypes . There is a considerable loss of elastance over the PV loop at 80 weeks of age in both genotypes . However , there are significant differences between the genotypes . 80-week-old Sftpd ( -/- ) mice have elastance measurements higher than those of old C57Bl6/J mice , however , there is minimal PEEP dependence with no significant strain stiffening at a PEEP of 9 cm H2O . These observations are consistent with a loss of fiber integrity in both genotypes , but with increased recruitment in the Sftpd ( -/- ) allowing for maintenance of lung elastance at low PEEP despite the surfactant abnormalities seen in these mice . A similar trend is observed in the minimum step elastance values reached during PV maneuvers ( Table 2B ) . As a means to assess alterations in lung structure , tissue sections stained with Verhoeff’s stain were examined by light microscopy following full inflation with paraformaldehyde and are shown at 100x and 400x magnification ( Fig 4A–4F ) . At low magnification ( inset ) , age related airspace enlargement is evident , with some apparent loss of septae , particularly in the subpleural acini . These changes are exacerbated by the loss of Sftpd , with more pronounced evidence of septal destruction at higher magnification . Quantification of septal loss using radial alveolar counts ( RAC ) was expressed as a cumulative probability distribution for each condition , where a leftward shift in the distribution reflects a loss of septae ( Fig 4G ) . In C57Bl6/J mice , RAC is reduced to an equivalent extent in both 27 and 80-week-old mice . In 8-week-old Sftpd ( -/- ) mice there is a similar leftward shift in RAC distribution to that seen in 27 and 80-week-old C57Bl6/J mice . The loss of septae that occurs within Sftpd ( -/- ) mice is progressive , with the greatest loss of acinar walls being observed in 80-week-old mice . Thinning of the elastic fibers within the parenchyma is also evident with age ( Fig 4H ) , accompanied by a decrease in the fraction of walls staining for elastin fibers . Within C57Bl6/J mice there is no change in fiber thickness at 27 weeks of age , but at 80 weeks there is a dramatic reduction . Relative to C57BL6/J , increased fiber thickness is observed in 8-week-old Sftpd ( -/- ) mice . However , this difference is not observed at 27 weeks of age . No significant obstruction of parenchymal space by hyaline , cellular or crystalline material was observed in the C57BL6/J mouse with age , however opacification of the airspaces was progressive with age in the Sftpd ( -/- ) mouse ( 8week = 3 . 4 ± 1 . 7% , 27 week = 6 . 8 ± 2 . 3% , 80 week = 13 . 8 ± 4 . 2% , ) . Initial estimation of model parameters is required in order to prepare the model for simulation of specific experimental data . From Fig 1 one can see that our modeling strategy requires three parameters r/r0 ( a radial scaling factor as a fraction of radius at TLC ) , a measure of lung recruitability , and a baseline estimate of H0 for the 8-week-old C57Bl6/J mouse . These parameters can be combined with our experimental measurements of fiber thickness and RAC to generate a simulation ( Fig 1 ) . A r/r0 was estimated in order to map the TLC airway tree to the appropriate geometry at each PEEP . As parenchymal destruction may alter tethering forces , a value for r/r0 was estimated at each PEEP for each experimental condition ( Table 3 ) . Airway radial scale factors follow the trend in PEEP , age and genotype predicted by RN estimates . Notably , r/r0 are significantly divergent at 80 weeks of age , falling in the C57Bl6/J mouse but increasing significantly in the Sftpd ( -/- ) . Estimation of the fraction of recruitable lung , which is ventilated at a given PEEP was estimated from pressure-volume data and constant phase estimates ( Table 4 ) . The approach undertaken matches a-priori predictions with PEEP levels from 1 to 6 cm H2O , but fits less well at extreme levels of PEEP ( 0 and 9 cm H2O ) , where tissue hysteresivity changes due to extensive collapse or strain stiffening . In the physiological PEEP range , the fraction of recruited lung increases linearly with PEEP , though the extent of collapse is age and genotype dependent . At 8 weeks , the Sftpd ( -/- ) has greater recruitment than WT at low PEEP , but peaks at the same % opening with recruitment . By 27 weeks , the fraction of recruitable lung was lower in the Sftpd ( -/- ) at PEEP of 1 cm H2O , but overlapped at all higher levels of PEEP . The recruited fraction continues to increase up to a PEEP of 9 cm H2O , in contrast to 8-week-old mice . At 80 weeks of age , C57Bl6/J mice follow a similar recruitment trend to that seen at 27 weeks , with evidence of maximal recruitment and strain stiffening at 9 cm H2O . In 80-week-old Sftpd ( -/- ) mice the fraction of recruitable lung was significantly elevated over all other conditions , and is near 100% at 9 cm H2O . For each model , the value of H0 was estimated at PEEP 6 in the 8-week-old C57Bl6/J mouse , and remained fixed for simulation of all other conditions . All models were fit on the single parameter H0 by reduction of the sum of squared residuals between model and Zrs data . Error and H0 values are reported as the mean and standard deviation of 100 optimizations per model ( Table 5 ) . All 8 models tested converged reliably and produced parameter estimates in the physiologic range . Residual error was nearly equivalent for 6 of the 8 models proposed . Notably , the model with the poorest fit when optimized is the sole model to assume homogenous parenchymal mechanics . The error between each condition and the 8-week C57Bl6/J mouse was calculated at a PEEP of 6 cm H2O for the H0 model . There was no significant difference in goodness of fit for the 8-week-old Sftpd ( -/- ) ( ε = . 9844 times the control error ) . At 27 weeks , both the C57Bl6/J and Sftpd ( -/- ) mice resulted in an increase in error by 61 . 2 and 62 . 6% respectively . At 80 weeks , this error increased dramatically , 5 . 2 fold for the C57Bl6/J , and 3 . 8 fold for the Sftpd ( -/- ) . With the exception of 27-week-old C57Bl6/J mice , incorporation of elastic fibers was the single most effective perturbation to the model in terms of reduction of error . RAC alone was successful in reducing the error in 27-week C57Bl6/J mice and 27 and 80-week Sftpd ( -/- ) mice . PEEP dependent derecruitment potentiated the reduction in model error produced by RAC based models in 27-week-old C57Bl6/J and Sftpd ( -/- ) mice and 80-week-old Sftpd ( -/- ) mice . When all factors were incorporated the model error was significantly improved in the 80-week Sftpd ( -/- ) mouse , otherwise only modest benefit was observed for the incorporation of additional complexity . The relative contributions of each change are further quantified using the likelihood ratios ( Fig 5 ) in the paragraph below and PEEP dependent error ( S3 Supporting Material ) . As the purpose of these simulations was to discern the relative significance of each factor , we used likelihood ratios without hypothesis testing to compare model fitness ( Fig 5 ) . In C57Bl6/J mice at 27 weeks RAC incorporation , but not fiber thickness results in increased model likelihood . Incorporation of differential collapse is critical for improving model likelihood , increasing the log-likelihood ratio of each model roughly 2 . 5 fold . The 80-week C57Bl6/J mice are poorly characterized by RAC based models and depend critically on elastic fiber thickness distributions . In all Sftpd ( -/- ) mice elastic fiber distributions drive an improvement in model fit . While the goodness of fit in the 8-week Sftpd ( -/- ) mouse is not affected by incorporation of lung collapse , the benefit of fiber thickness is diminished by the addition of RAC . In the 27-week-old Sftpd ( -/- ) elastic fibers are the most important single factor for improvement of model fit , with PEEP dependent collapse but not RAC potentiating the improvement in model likelihood . At 80 weeks , both RAC and fiber thickness improve model likelihood , however their combination results in a dramatic reduction in error that synergizes with PEEP dependent collapse . In these mice , a model incorporating all factors has greater than a 10 fold log-likelihood vs control , corroborating the importance of derecruitment , fiber thickness and alveolar wall destruction in this pathology . Simulated spectra from each maximum likelihood model is shown in the supplemental data ( S5 Fig ) Although model comparison was performed at PEEP of 6 , observed Zrs spectra demonstrate some PEEP dependence ( S1 Fig ) . To determine the extent by which these changes result from altered recruitment , additional simulations were run for each model at every PEEP with and without appropriate changes in the recruited fraction of terminal units , as estimated from the experimental P-V loops . As a measure of PEEP dependence , the variance of the model error was calculated across the 5 levels of PEEP under both simulated recruitment conditions ( S3 Fig ) . In each experimental condition and for every model the simple addition of estimated recruitment eliminates virtually all of the PEEP dependence in the error between data and the model . The extent to which derecruitment explains the PEEP variance was expressed as the ratio of the variances measured across levels of PEEP with PV-based derecruitment to those without . Error variance with PEEP was effectively reduced to less than 10% of original model variance in 30 out of 48 simulations , with the 80-week C57Bl6/J mouse demonstrating the least PEEP responsiveness ( S4 Fig ) . No particular model was demonstrably less responsive to PEEP across experimental conditions , though models incorporating only fiber thickness showed generally less error reduction with derecruitment . Maintenance of an appropriate surfactant protein to phospholipid ratio is essential for trafficking of surface-active material between the hypophase and air-liquid interface . Hence phospholipid and protein content is near optimized for maintaining small airway patency and excursions either above or below the physiologic level are deleterious . Dramatic accumulation of phospholipid occurs within the lungs of Sftpd ( -/- ) mice . We examined this change as a potential mechanism for the observed increased propensity for collapse . Mechanistic data relating BAL composition directly to surface-active function and recruitment are not available for incorporation into the model; however , we examined the relationship between alteration of these quantities and estimated recruitment ( Fig 6 ) . These data show a relationship between altered SP-B to phospholipid ratio and reduced recruitable fraction of terminal lung units .
This study uses structural and mechanical measurements to simulate age and inflammation related lung dysfunction in a murine model of emphysema . There is disagreement in the literature as to collagen and elastin content , organization and crosslinking in human aging and chronic lung disease [35–39] . Such disparities may become even greater when considering differences between rodents and humans [40] . The C57BL6/J mouse at 80 weeks has previously been reported to have reduced elastin without changes in collagen content [1] . Histology in this study is consistent with these data , however collagen was not specifically quantified . As fibrillar collagens principally contribute to mechanics at higher lung volumes , their impact on low amplitude forced oscillation measurements may be minimal , however they are expected to contribute to apparent strain stiffening of the PV curves . In considering the nonlinear dependence of tissue rheology on specific collagen isoform content , fiber size distribution and the extent of intra-microfibrillar and inter-microfibrillar crosslinking recapitulating such phenomena would require extensive characterization of the tissue ultrastructure . Stereologic quantification , particularly in conjunction with electron microscopy , though technically demanding , would be a uniquely robust strategy for assessing these changes in tissue structure . Were such detailed tissue investigation to be performed , particularly in an experimental model of fibrosis , these alterations could be incorporated into the described computational strategy with ease . The inability to mechanistically link surfactant composition to organ level lung mechanics is another limitation of this work . Though disruption of PEEP-dependent changes in PV loop are correlated with surfactant alteration , this is presented solely as an empirical observation . Ideally , the SP-B and phospholipid data would be incorporated into a model that predicts the pressure dependence of recruitment and derecruitment . To date , no physical or empirical relationship has been proposed to relate these measurements with either organ level or ex-vivo surfactant function . An alternative approach to such modeling could incorporate direct measurements of surfactant function using ex-vivo surfactometry , such as the Langmuir-Blodgett trough . Such a study that quantitatively relates protein and lipid content to both surface active function and in vivo mechanics would represent a major advance in the study of lung mechanics . This , too would further increase this potential applicability of this study’s strategy to acute lung injury and the neonatal respiratory distress syndrome . The multi-faceted approach presented here which included detailed histological analysis in conjunction with functional assessment allows for accurate forward modeling of lung function . This is a novel approach that allows for direct examination of how changes in lung structure affect respiratory function and the relative importance of these changes . Such integrated measurement and modeling approaches may in fact be required to relate heterogeneous structural and functional measurements across multiple length scales as a result of the inherent complexity in biological systems . We propose that the use of this modeling approach may allow for assessment of the mechanisms involved in the loss of lung function as a consequence of inflammatory disease . | Aging and chronic inflammation produce complex changes to the structure of the lung including accumulation of cells and debris , thinning and destruction of air sacs , altered airway size and increased tendency for airway collapse . As these structural changes are observed concurrently , their individual contributions to altered lung function cannot readily be determined by conventional measurement of lung function . Our study employs a novel approach to identifying the age progression of these effects in mice with and without chronic lung inflammation . Histologic changes in lung tissue were incorporated into a computational model of the mouse lung and used to simulate measured changes in lung function . By incorporating experimentally measured factors into the model in a stepwise fashion , the contribution of destructive and remodeling processes to alterations in lung function can be assessed . This modeling approach provides a framework for determining the significance of structural changes to the altered function observed in complex lung pathologies such as emphysema and chronic obstructive pulmonary disease . Such an approach could be utilized to assess mechanisms by which compounds alter lung function and the capacity of specific therapies to produce improvements in lung function at the organ level . | [
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] | 2017 | Histologic and biochemical alterations predict pulmonary mechanical dysfunction in aging mice with chronic lung inflammation |
The aim of this study was to compare the safety and immunogenicity between purified vero cell rabies vaccine ( PVRV ) and purified chick embryo cell vaccine ( PCECV ) in patients with WHO category II animal exposure , especially in different age groups . In one-year clinical observation after vaccination with PVRV or PCECV under Zagreb ( 2-1-1 ) or Essen ( 1-1-1-1-1 ) regimens , information collection for the demographic and adverse events ( AEs ) and rabies virus laboratory examination of neutralizing antibody ( RVNA ) titers were performed for all patients with WHO category II animal exposure in Wuhan city . The results showed no significant differences of safety and immunogenicity between PVRV and PCECV both in Zagreb and Essen regimens . However , when compared with other age groups , most systemic AEs ( 36/61 ) occurred in <5-year-old patients , and <5-year-old patients have significant lower RVNA titer and seroconversion rate ( RVNA ≥0 . 5 IU/ml ) at day 7 both in Zagreb and Essen regimens or PVRV and PCECV groups . Our data showed that vaccination with PVRV is as safe and immunogenic as PCECV in patients of all age groups , but might be more popular for clinical use . When performing a vaccination with rabies vaccine in young children , the most optimal vaccine regimen should be selected .
Rabies , caused by rabies virus infection , remains a global health threat , and became the leading cause of infectious disease mortality in May 2006 in China [1] . In the world , Rabies is estimated to cause more than 55000 deaths every year , and is considered to be endemic in more than 150 countries and territories [2] , [3] . Nowadays , China is in the midst of its third epidemic that begun in 1996 and peaked in 2007 ( 3300 cases ) , Wuhan , the largest city in the middle of China with about 10 million residents , has a medium incidence of rabies [4] . Although deadly , rabies can be prevented by timely initiation of post-exposure prophylaxis ( PEP ) which includes proper local treatment of bite wounds , administration of rabies vaccines either by intramuscular ( IM ) or intradermal ( ID ) route and local infiltration of rabies immunoglobulins ( RIG ) [5] . Due to high number of animal bites , there is a huge demand for rabies vaccines in developing countries of Asia and Africa [6] . Nowadays , purified chick embryo cell vaccine ( PCECV ) and purified vero cell rabies vaccine ( PVRV ) are currently recommended by WHO for PEP , and are being widely used in many countries in the world . In addition , compared to chick embryo cell , vero cell is a more practical manufacturing platform for vaccine production , which should be considered as an advantage of PVRV over PCECV . From 2001 , PVRV has been successfully manufactured in China . ChengDa rabies vaccine ( PVRV ) was licensed by the Health Ministry of China and the State Food and Drug Administration of China ( SFDA ) in 2002 and has been marketed throughout the country since that time [7] . Although ChengDa PVRV under 2-1-1 regimen has been proved to be equally safe and immunogenic as the PCECV for PEP vaccination in adult volunteer [7] , and has been marketed for more than 10 countries in the world , however , to our knowledge , there has been little reported about the safety and immunogenicity of PVRV or PCECV in different age groups , especially for young children . Thus we performed this study to compare the safety and immunogenicity of PVRV and PCECV under Zagreb and Essen regimens , especially in different age group patients with WHO category II animal exposure .
From August 2010 to February 2013 , the patients who visited the clinic of Wuhan Centers for Disease Prevention and Control ( WHCDC ) , and were professionally evaluated as WHO category II exposure to suspected rabid animals according to WHO criteria for animal exposure ( Nibbling of uncovered skin , minor scratches or abrasions without bleeding ) , were enrolled , and were divided single-blind and equally into two groups ( Zagreb 2-1-1 and Essen 1-1-1-1-1 ) ( Fig . 1 ) . All patients lived in Wuhan for more than 6 months , and visited the clinic within 24 hours after exposure . The patients , who had chronic infectious diseases , or known hypersensitivity to any vaccine component , or received of rabies vaccine previously , were excluded . The protocol of this study was approved by the Institutional Review Board of WHCDC , and written informed consent was obtained from all participants , or their legal guardians in the case of children up to 18 years of age . The sample size estimation was conducted according to the “Practical Manual of Sample Size Determination in Health studies” as described previously [8] , a minimal of 75 cases in each group was required . The detailed study flow was shown in Fig . 1 . For the patients in Zagreb and Essen groups , immunization with PVRV ( Liaoning ChengDa Co . , Ltd . , Shenyang , China , 7 . 0 IU/0 . 5 ml/dose ) or the imported PCECV ( Rabipur , Novartis Vaccines and Diagnostics , 6 . 4 IU/1 . 0 ml/dose ) was performed at day 0 , 7 , 21 or day 0 , 3 , 7 , 14 , 28 respectively . Safety monitoring was conducted by face-to-face observation after each immunization or by telephone during the study . In order to analyze the efficacy of vaccination , rabies virus neutralizing antibody ( RVNA ) titers in the serum were measured using a rapid fluorescent focus inhibition test ( RFFIT ) as described by Yu et al . [9] . Briefly , a constant dose of previously titrated , cell culture adapted , challenge virus ( CVS-11 ) is incubated with serial dilution ( three-fold serial dilution , from 1/3 to 1/6561 ) of the sera to be titrated . A reference serum ( NIBSC , UK . The 2nd International Standard for Anti-Rabies ) of known titer was included in each test . After one hour of incubation at 37°C , BSR cells ( clone BHK21 ) were added into each well . After 24 h incubation , the estimation of the percentage of infected cells for each dilution of the sera allows determination of the titer of the unknown sera by comparing with the reference serum . Meanwhile , one of reference sera that we bought was sent to Chinese Center for Disease Control and Prevention for testing the antibody level to avoid deviation . Our data showed good reliability with assay variation of <15% . RVNA titers in sera were expressed as International Units per millilitre ( IU/ml ) . Serum with titers ≥0 . 5 IU/ml , the WHO recommended protective level , was considered as a protective titer . GraphPad Instat statistical software ( GraphPad Software ) was used for statistical analysis , and a P value of <0 . 05 was considered statistically significant . Where appropriate , data were expressed as mean ± standard deviation ( SD ) if not defined . Categorical variables were tested with chi-square of the Fisher exact test , and comparison between two groups was tested with the Student t test .
During the study period , 496 patients with WHO category II animal exposure were enrolled in this study . Finally , 387 patients have completed data sheet and blood collection , and a complete study flow was showed in Fig . 1 . There are no significant differences between PVRV and PCECV groups on mean ages ( p = 0 . 103 or 0 . 432 for Zagreb and Essen respectively ) , sex , and RVNA titers before immunization ( Day 0 in Fig . 2 and Fig . 3 ) . During the study period no patient was injected with RIG according to WHO post-exposure prophylaxis ( PEP ) measures for WHO category II animal exposure , and no patient developed clinical rabies . In order to evaluate the safety of PVRV and PCECV in different age groups , both local adverse events ( AEs ) and systemic AEs were recorded during the study process . Table 1 showed the most common AEs in four age groups , of which no significant difference was found in the patients with AEs between PVRV and PCECV , even compared in different age groups or different administration regimens ( Zagreb or Essen ) . However , most systemic AEs ( 36/61 ) occurred in <5-year old patients , and when analyzing the number of patients with the severity of fever ( defined according to the “Preventive vaccine clinical trials , adverse events grading guidelines” issued by the China Food and Drug Administration ) , PCECV seemed to have more patients with medium fever ( 37 . 6∼39 . 0°C ) than PVRV ( P = 0 . 039 , Table 1 ) for Zagreb , but no significant difference for Essen ( P = 0 . 494 , Table 1 ) . The same to our previous data [8] , in this study all patients have low RVNA titers of <0 . 5 IU/ml when enrolled , and reach a highest RVNA titers at day 45 for all vaccination methods ( PVRV and PCECV ) and regimens ( Zagreb and Essen ) ( Fig . 2 and Fig . 3 ) , of which all patients developed a protective RVNA titers of ≥0 . 5 IU/ml at day 14 and day 45 . However , based on the data of RVNA titers on day 7 , <5-year old patients seem to have significant lower seroconversion rates compared to other three age groups , especially for the patients with PCECV vaccination , and PVRV vaccination in Essen regimen ( p<0 . 001 , Table 2 ) . In contrast , <5-year old patients immunized with PVRV have no significant difference ( p = 0 . 114 , Table 2 ) to>5-year old patients immunized with PVRV , but did have a similar low seroconversion rate compared to PCECV administered under Zagreb regimen ( p = 0 . 957 , Table 2 ) . In addition , RVNA titers in patients aged>60 years also showed a significant difference to that of children and adults ( aged 5∼60 years ) only when injected with PVRV under Essen regimens at day 7 ( p<0 . 05 ) . When compared RVNA titers between PVRV and PCECV , both Zagreb and Essen groups have no significant differences in different age groups , only for 5-18-year old patients at day 45 and 19-59-year old patients at day 365 under Essen regimen ( Fig . 2 and 3 ) .
Nowadays , many approved vaccines with different components ( such as PVRV , PCECV , and Human diploid cell vaccine ( HDCV ) [2] ) and many regimens with different vaccination schedules ( Zagreb , Essen [7] , [8] ) are being used in the world . However , which kinds of vaccines or regimens is the best choice for different age group patients remains unclear . In current study , we compared the safety and immunogenicity of PVRV and PCECV , especially in different age groups . Although only small number of patients was analyzed , our results indicated that PVRV had no significant difference of both safety and immunogenicity to PCECV , even in young children or elderly . However , because of availability , PVRV may be preferred by patients in developing country [7] . For the safety analysis , pain and fever were the most common AEs in local and systemic AEs respectively ( Table 1 ) , which was in agreement to our previous study [8] , but different to the report by Madhusudana et al . [10] . The possible reason for the difference might be induced by different regimen , or patients with different exposure grade . In this study , PVRV showed non-inferiority to PCECV on safety both in Zagreb and Essen regimens , even in different age groups . However , although no difference of local AEs was found among different age group patients , most systemic AEs ( 59 . 0% , 36/61 ) , especially for fever ( 67 . 5% , 27/40 ) , occurred in young children ( <5 years ) ( Table 1 ) . When compared the severity of fever , more patients immunized with PCECV under Zagreb regimen had medium fever ( 37 . 6∼39 . 0°C ) than the patients with PVRV ( p = 0 . 039 ) , which may be associated with the different volumes of vaccines ( 2 ml and 1ml at first time immunization with PCECV and PVRV , respectively ) , because the difference was not significant ( p = 0 . 494 , Table 1 ) when vaccinating with Essen regimen , a program with only one dose at first time immunization . As pointed out by Gozdas et al . [11] , it is not enough only to evaluate the safety of rabies vaccine after vaccination . Thus , we also analyzed the immunogenic profile over a one-year period . The mean RVNA titers based on different age groups and different immunization regimens were compared between PVRV and PCECV . No significant differences in RVNA titers between PVRV and PCECV in most of the age groups , neither Essen nor Zagreb regimen , were observed ( Fig . 2 and Fig . 3 ) . These results are in agreement with previous studies in India [10] . Interestingly , when comparing the seroconversion rate and RVNA titers of different age groups at day 7 , the patients aged <5 years have significantly lower seroconversion rate ( Table 2 ) and significantly lower antibody titers ( Table 3 ) than that of ≥5-year old patients , which may be caused by the immature immune system of young children . Further , at day 7 the patients of <5-year immunized with Zagreb showed higher seroconversion rate than the patients of <5-year immunized with Essen regimen ( 43 . 8% vs 16 . 7% and 42 . 9% vs 15 . 4% for PVRV and PCECV respectively , Table 2 ) , and no difference of seroconversion rate ( p = 0 . 114 ) was observed within the Zagreb immunized group between <5-year versus ≥5-year patient groups immunized with PVRV under Zagreb ( Table 2 ) . Thus , because of no significant difference on the safety between Zagreb and Essen regimen , vaccination under Zagreb regimen in <5-year patients might be preferred . In conclusion , our data showed that , under either Zagreb or Essen regimen , PVRV is equally safe and immunogenic as PCECV immunized in all age groups , and Zagreb regimen might be more suitable for young children to develop protective antibody as soon as possible . However , only small number of children , a population with more than 50% of human rabies deaths [12] , was analyzed in this study , more data on the safety and immunogenicity for choosing a suitable vaccine and vaccination schedule for young children will be needed in the future . | Nowadays , many approved vaccines with different components ( such as purified vero cell rabies vaccine [PVRV] , purified chick embryo cell vaccine [PCECV] , and Human diploid cell vaccine [HDCV] ) and many regimens with different vaccination schedules ( Zagreb , Essen ) are being used in the world . Thus , we compared the safety and immunogenicity between purified vero cell rabies vaccine ( PVRV ) and purified chick embryo cell vaccine ( PCECV ) in patients with WHO category II animal exposure , especially in different age groups . Our data showed no significant differences of safety and immunogenicity between PVRV and PCECV with Zagreb or Essen regimen in four age groups . However , compared with the other three age groups , young children aged less than 5 years have more systemic adverse events ( AEs ) , and lower rabies virus neutralizing antibody ( RVNA ) titer and seroconversion rate ( RVNA ≥0 . 5 IU/ml ) at day 7 post-immunization . These findings highlight that it is important for young children , a population with more than 50% of human rabies deaths , to find the most optimal vaccine and vaccination schedule in the future . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"biology",
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] | 2014 | Comparison of Safety and Immunogenicity of PVRV and PCECV Immunized in Patients with WHO Category II Animal Exposure: A Study Based on Different Age Groups |
ClinicalTrials . gov Clinical Trial NCT00594880
Infection with Human Immunodeficiency Virus ( HIV ) results in widespread inflammation in infected individuals with key changes in the immune system that have important clinical implications . Untreated HIV disease is characterized by high levels of systemic and tissue-localized proinflammatory cytokines such as an increase in type I interferon activity and interferon-gamma ( IFNγ ) , each of which is thought to contribute to tissue damage [1 , 2] . Importantly , these hallmarks of infection result in immune dysfunction that can often persist despite the initiation of antiretroviral therapy ( ART ) . Immune dysfunction , as measured by type I IFN activity for example , is predictive of non-AIDS related morbidity and mortality in ART-treated individuals [3–6] and residual immune activation may account for the overall shorter life expectancy of HIV-infected subjects , particularly in those with incomplete immunologic restoration after ART initiation [7–12] . HIV infection also causes significant disruption of the mucosal immune system , a site of progressive CD4+ T cell loss and a tissue that harbors a substantial reservoir of latent HIV [13 , 14] . In particular , progressive SIV/HIV disease is associated with the preferential depletion of CD4+ T helper type 17 ( Th17 ) cells producing the cytokine interleukin 17 ( IL-17 ) , a key epithelial support cytokine whose depletion during viremia is proposed to be the mechanistic basis for epithelial barrier dysfunction and pathogenesis [15–19] . Intestinal epithelial cells ( IECs ) play both physical and physiological roles in maintaining gut homeostasis [20] by acting as a spatial separation between luminal contents and underlying immune cells and by expressing and secreting various molecules that promote barrier integrity . HIV-infected individuals exhibit a defect in the epithelial barrier , as characterized by increased epithelial permeability , decreased expression of tight junction proteins [21–23] , and elevated cell death [24] during viremia . ART can reverse these changes , albeit to variable degrees [21 , 24 , 25] , and it is thought that residual immune alterations during ART may be due to continued permeability of the epithelial barrier , driving a chronic inflammatory response against luminal microbial contents [26] . While epithelial dynamics have been studied at the intestinal level , specific investigation of the epithelial compartment in HIV-infected patient cohorts has not been conducted in a systematic manner . Furthermore , specific mediators of IEC dysfunction and restoration during HIV disease and treatment have yet to be identified . Given the lack of evidence for direct infection of IECs with HIV in vivo [27 , 28] , we hypothesized that the changes in epithelial dynamics during infection are effected indirectly by HIV via virus-induced changes in cytokine secretion , resulting in barrier dysfunction and disease progression . Reciprocally , we speculated that these pathways may also determine the extent of disease resolution after the initiation of ART . To address these hypotheses , we analyzed intestinal epithelial cell isolates and lymphocytes from primary human intestinal tissue obtained from HIV-infected individuals at varying stages of disease and treatment . Here , we show that the induction of the ubiquitin-modifying enzyme , A20 , is associated with markers of epithelial function after the initiation of ART . We also demonstrate that deletion of A20 in a mouse intestinal organoid model is associated with hallmarks of epithelial dysfunction , particularly in the presence of IFNγ . On the basis of these findings , we speculate that changes in the expression of A20 may play a pivotal role in the dynamics of the intestinal barrier during untreated and treated HIV disease .
We studied 34 participants from the SCOPE cohort at Zuckerberg San Francisco General Hospital , which included risk-matched uninfected controls ( n = 9 ) , untreated HIV viremic participants ( n = 6 ) , and those effectively suppressed on ART ( n = 19 ) ( Table 1 ) . Intestinal biopsies were obtained by flexible sigmoidoscopy and analyzed . Alterations in immunological measurements were consistent with prior observations [11 , 15 , 23] . Compared to HIV-uninfected participants , viremic individuals had a significantly lower proportion of gut CD4+ T cells and a commensurately increased proportion of CD8+ T cells ( S1A Fig ) . The frequencies of these cell subpopulations were partially but incompletely restored towards normal in the ART-treated subgroup . The frequencies of gut-associated IL-17A- and IL-22-producing CD4+ T cells were significantly lower in untreated subjects and largely restored to normal levels amongst ART-suppressed participants ( S1B–S1D Fig ) . Importantly , the production of IL-17A by all lymphocytes ( gated as all CD45+ cells ) paralleled that found with CD4+ T cells , indicating broad IL-17A depletion in the gut during viremic HIV ( S1E Fig ) . Despite full restoration of the percent of T cells expressing IL-17A and IL-22 , signs of peripheral immune activation ( as reflected by CD8+ T cells co-expressing CD38 and HLA-DR ) remained elevated in ART-treated patients relative to uninfected controls , suggesting persistent activation of the immune system despite viral suppression ( S1F Fig ) . To determine which IEC-specific pathways are involved in the observed defects in epithelial function during HIV disease , we utilized a transcriptomics approach to discern gene expression patterns that might be correlated with HIV infection and/or treatment . Whole RNA was obtained from EDTA-isolated IECs from all subgroups and interrogated using RNA sequencing ( RNAseq ) . Pairwise comparisons between IECs from viremic and ART-treated subgroups revealed an expression pattern consistent with upregulated A20 activity during ART ( Fig 1A ) . TNFAIP3 , the gene encoding A20 ( and herein referred to as A20 ) , was upregulated in IECs from ART-treated subjects relative to viremic individuals . This was not due to an increased presence of epithelial cells in isolates from ART-treated individuals as the percent of epithelial cells ( defined as CD45-EpCAM+ ) by flow cytometry was similar across subgroups and there was no correlation between transcript levels of A20 and EPCAM ( S2A and S2B Fig ) . A20 is a known negative regulator of NFκB signaling and plays a key role in restricting inflammatory responses via ubiquitin-editing mechanisms [29–31] . NFKBIA , the gene encoding the inhibitor IκBα that directly binds and sequesters NFκB [32] , was also upregulated in ART-treated participants relative to viremic individuals and was highly correlated to A20 ( p = 0 . 00001 ) ( Fig 1B ) . Transcript levels of NFKBIA were positively associated with those for CTNNB1 , the gene for proliferation-associated β-catenin ( p = 0 . 0009 ) , suggesting a relationship with epithelial cell survival ( Fig 1C ) . Importantly , β-catenin has also been shown to interact with and inhibit NFκB signaling in a manner similar to IκBα [33] . A20 and IκBα ( NFKBIA ) expression also positively correlated with the expression of the tight junction genes CLDN4 and TJP1 ( p = 0 . 001 and p = 0 . 01 , respectively ) ( Fig 1D and 1E ) , consistent with previous work implicating a role for A20 in maintaining gut barrier integrity [34] . A20 transcript levels were also enriched in ART-treated relative to uninfected individuals ( S2C Fig ) . Concurrent to A20 upregulation , IL1R2 , an antagonist of IL-1β signaling [35] , was downregulated in ART-treated subjects relative to uninfected individuals . Since IL-1β signaling has been shown to drive A20 expression through NFκB [36] , the finding of reduced IL1R2 is consistent both with previous studies showing elevated IL-1β signaling in SIV-infected monkeys [37] and with the observed upregulation of A20 in ART-treated subjects . RNAseq expression values were validated by targeted qPCR on epithelial isolates ( S2D Fig ) . There was no association between A20 levels in treated individuals and ART regimens that did or did not contain the non-nucleoside reverse transcriptase inhibitor , efavirenz , the nucleotide analog reverse transcriptase inhibitor , tenofovir , or the nucleoside reverse transcriptase inhibitor , abacavir ( S2E Fig ) , consistent with the hypothesis that the observed changes in A20 expression are due to mechanisms distinct from drug effect alone . Despite the fact that A20 expression was upregulated in ART-treated versus viremic individuals ( Fig 1A ) , there was no difference in A20 expression between uninfected and viremic subjects ( S2F Fig ) . This was the case even though cytokines such as IL-1β and TNF are known to induce A20 [29 , 36 , 38] and also known to be elevated in untreated HIV disease [37 , 39] . Since IFNα has been shown to suppress A20 in dendritic cells in the context of HCV infection [40] , we hypothesized that the lack of A20 upregulation during untreated disease might be due to the high levels of type I interferon in viremic participants . Consistent with prior literature [1 , 17 , 41] , elevated type I interferon activity was observed during untreated HIV , including an increase in the plasma ratio of kynurenine-to-tryptophan ( K/T ) [3] ( S3A Fig ) and elevated levels of IFN-stimulated genes ( e . g . , OAS2 in a statistically significant manner and a trend for MX1 ) in whole gut biopsies ( S3B Fig ) . To determine whether such elevated levels of type I IFN might have a direct effect on A20 expression in IECs , we generated organoid cultures from murine small intestine . Derived from primary tissue , comprised solely of epithelial cells , and exhibiting a crypt/villus architecture , intestinal organoids have been previously shown to serve as a relevant and experimentally tractable model of the intestinal epithelium [42–44] . Organoids were treated with recombinant IFNα for 48 hours and then harvested to measure A20 protein levels by western blot . After incubation with IFNα , A20 levels were found to decrease in a dose-dependent manner ( Fig 2A and 2B ) . These observations are consistent with the possibility that A20 is downregulated in viremic individuals by high levels of type I IFN activity . We hypothesized that type I IFN-mediated downregulation of A20 in viremic individuals may render the epithelium more vulnerable to the damaging effects of other proinflammatory cytokines , e . g . , IFNγ , that are abundant in untreated disease . As shown in prior studies [45] , elevated levels of IFNγ were found in viremic participants at the protein level in terms of frequency of CD8+ T cells producing the cytokine ( S4A Fig ) and at the transcript level in whole rectosigmoid biopsies ( S4B Fig ) . Additionally , RNAseq analysis on IECs revealed that IFNGR1 , which encodes the primary signaling receptor for IFNγ , was upregulated in viremic subjects relative to ART-treated individuals ( Fig 1A ) . Taken together , these data suggest that IECs experience higher levels of IFNγ signaling in the viremic state . To test the effect of high IFNγ levels on the expression of genes important to epithelial cell function in the presence or absence of A20 , we utilized murine epithelial organoid cultures derived from mice genetically modified to conditionally delete A20 . A20FL/FL villin-ERCre+ mice are engineered such that , in an epithelial-specific manner ( driven by the villin promoter ) , ligands that signal through the estrogen receptor ( ER ) induce deletion of A20 . 4-hydroxytamoxifen ( 4OHT ) , the active metabolite of tamoxifen , is used in culture to quickly and potently induce ER-signaling [46] . As such , A20 deletion in organoids is induced upon 4OHT administration in vitro , whereas organoids derived from A20FL/FL villin-ERCre- mice continue to express A20 even after 4OHT administration . Organoids were exposed to 4OHT for 24 hours to allow for A20 deletion , as confirmed by western blot analysis ( Fig 3A ) , and then stimulated with rIFNγ for 12 hours , after which transcript levels of genes for inflammatory chemokines , mucins , tight junction proteins , and antimicrobial peptides were assessed ( Fig 3B–3E and S1 Table ) . In organoids from A20FL/FL villin-ERCre+ mice , A20 deletion alone in organoids , in the absence of exogenous stimuli , had effects on gene expression of a small number of genes , with statistically significant reductions in the transcript levels of the mucin genes , Muc2 and Muc4 ( Fig 3C ) , and the tight junction gene Tjp1 ( Fig 3D ) . Separately , IFNγ treatment alone led to more widespread changes in gene expression , with significant decreases in transcript levels of mucin genes ( Muc2 , Muc4 , Muc13 ) ( Fig 3C ) and tight junction genes ( Tjp1 , Ocln ) ( Fig 3D ) . Importantly , when A20 deletion was combined with IFNγ , we observed a robust increase in the expression of the inflammatory chemokines , Cxcl1 and Cxcl2 ( 15- and 300-fold , respectively ) ( Fig 3B ) and further reductions in the expression of Muc2 transcripts ( Fig 3C ) , suggesting an A20-specific role in protecting organoids from IFNγ-mediated inhibition of mucin expression . Similarly , downward trends in the transcript levels for Muc4 , Muc13 , Tjp1 , and Ocln , ( Fig 3C and 3D ) indicate that A20 deletion further potentiates the effect of IFNγ , although those changes did not reach statistical significance . These data , in sum , suggest that A20 expression has the capacity to protect IECs from the detrimental effects of IFNγ . Interestingly , and in contrast to the expression of other measured mucin genes , Muc1 transcripts increased with IFNγ but were not affected further by deletion of A20 ( Fig 3C ) . Previous literature has linked Muc1 to intestinal inflammation with a specific role in promoting cytokine-mediated immune responses [47] , suggesting this might be a mechanism through which IFNγ is exerting its negative effects on the epithelium . In a similar manner , the expression levels of Cldn4 increased upon IFNγ stimulation and were even further increased upon deletion of A20 ( Fig 3D ) . Since Cldn4 expression has been shown to be directly induced by NFκB [48] , the observed increase of this particular gene could be a by-product of ongoing NFκB-related inflammation in the absence of A20 . Intriguingly , antimicrobial peptide Defb1 was markedly inhibited by IFNγ and was not further affected by A20 deletion ( Fig 3E ) , suggesting that A20 is not directly involved in regulation of Defb1 expression but that IFNγ alone can modulate the antibacterial defense of the epithelium . Given the observation that IL-17A levels are restored to normal in ART-treated subjects ( S1B and S1C Fig ) and prior work in stromal cell lines showed that IL-17 signaling is restricted by A20 [49] , we next tested whether A20 deletion altered the effects of IL-17A stimulation in organoid IECs . Intestinal organoids were treated with 4OHT to delete A20 and were then stimulated with rIL-17A for 12 hours . IL-17A stimulation in the setting of A20 deletion led selectively to dramatic increases in the expression of the proinflammatory genes Lcn2 [50] ( by 30-fold ) , Cxcl1 ( by 15-fold ) , and Cxcl2 ( by 60-fold ) ( Fig 4A–4C and S2 Table ) . A20-sufficient Cre- organoids showed no such increase in gene expression , suggesting that these changes occur specifically in the absence of A20 . Similar to the effect seen with IFNγ , the expression of the mucin gene Muc1 also increased with IL-17A independent of A20 function ( since this effect was also observed in Cre- organoids ) , with the magnitude of the effect being much more modest than that observed with the inflammatory genes ( Fig 4D ) . Taken together , these results indicate that A20 expression in IECs restricts the ability of IL-17 to upregulate pro-inflammatory genes while not affecting the expression of epithelial integrity-related genes ( e . g . , Cldn4 , Tjp1 , Ocln , Muc2 , Muc 3 , Muc4 , and Muc13 ) ( S3 Table ) . In addition to the effects described above , both IFNα and IFNγ were found to have significant cytotoxic effects on organoid cultures ( Fig 5A and 5B ) . Cell death was defined by organoid morphology , propidium iodide staining , and a quantitative ATP-based luminescence assay . Compared with A20-sufficient organoids in media alone , A20-deficient ( 4OHT-treated ) organoids demonstrated a small but statistically significant increase in cell death ( Fig 5A ) . When A20-sufficient organoids were treated with the indicated cytokines ( IFNγ , IFNα , or IL-17A ) ( black line in Fig 5A ) , only treatment with IFNγ caused elevated cell death . Treatment of A20-deficient organoids with either IFNα or IFNγ revealed increased levels of cytotoxicity relative to A20-sufficient organoids , an effect that was enhanced upon exposure to increasing concentrations of cytokine ( red line in Fig 5A ) . Furthermore , confocal microscopy revealed that A20 deletion combined with IFNγ treatment resulted in a loss of crypt structure in organoids ( Fig 5B ) . This is consistent with prior work demonstrating IFNγ induction of epithelial cell death such that IFNγ-knockout animals were protected from epithelial damage in vivo in a DSS-colitis model [51 , 52] and , in epithelial cell lines , IFNγ inhibited epithelial proliferation by suppressing β-catenin activity [53] . In contrast to the phenomena observed with IFNα and IFNγ , IL-17A showed no such effect on cell viability ( Fig 5A and 5B ) . These results , together with the observed IFNγ-mediated suppression of epithelial genes ( Fig 3 ) , suggest that IFNγ may be a primary driver of IEC dysfunction in HIV , an effect exacerbated by type I IFN-mediated downregulation of A20 expression . To extend our findings in the organoid system to more clinically relevant scenarios , we assessed the effect of pegylated IFNα2a ( Peg-IFNα2a ) immunotherapy in ART-suppressed chronically infected subjects on A20 gene expression in samples obtained from a previously published study [54] . Briefly , HIV-infected individuals on ART were treated weekly with Peg-IFNα2a for five weeks , with blood samples taken at baseline and after the immunotherapy period ( Fig 6A ) . RNA was isolated from peripheral blood mononuclear cells ( PBMCs ) , and probed for the transcript levels of A20 and a key interferon-stimulated gene , ISG15 [55] . After five weeks of Peg-IFNα2a , the expression of ISG15 was highly elevated in PBMCs , at an average of 21-fold increased from baseline levels ( range of 7 to 44-fold ) ( P≤0 . 0001 ) ( Fig 6B ) . Concurrently , there was a significant decrease ( a mean of 1 . 56-fold ) in the mRNA expression of A20 ( P<0 . 05 ) ( Fig 6C ) , an effect that appeared most dramatic in those with the highest A20 levels at baseline . These observations provide clear evidence that high levels of type I IFN signaling can regulate A20 expression in the context of HIV infection .
Our study is the first to identify a role for A20 in modulating inflammatory signals and epithelial function in the context of HIV infection and treatment . The work outlined here advances our understanding of the molecular underpinnings of epithelial barrier function as well as its relationship to inflammation during HIV infection . It has long been observed that disruption in epithelial homeostasis is a hallmark of HIV infection [21 , 22 , 24 , 25 , 56] . Prior work examined whole intestinal tissue , while our approach of isolating IECs allowed us to identify A20 as a key regulator of epithelial cell survival and function . These observations deepen our understanding of HIV pathogenesis and further expand our knowledge about the capacity of A20 to regulate epithelial barrier function during viral infections . Given our findings , we propose that the intestinal epithelial dysfunction found in untreated HIV disease is caused by high levels of type I and type II IFN activity likely mediated in part by IFNα production by plasmacytoid dendritic cells [41 , 57–59] , IFNβ production by TLR4-stimulated macrophages [60] , and IFNγ production by CD8 T cells recognizing viral particles respectively [45] , that exist concurrently with low levels of IL-17A ( Fig 7 ) . We show that IFNα is capable of suppressing A20 expression in IECs . Since A20 limits induction of inflammatory genes and cell death by IFNγ , and preserves expression of epithelial function genes ( e . g . , for some tight junctions proteins and some mucins ) , high type I IFN levels during untreated HIV disease may render epithelial cells more susceptible to IFNγ-induced damage by preventing A20 upregulation . We speculate that , in the setting of ART , reduction of type-I IFN-mediated signaling [61] results in higher levels of A20 , leading to the re-establishment of epithelial homeostasis , as indicated by the positive association between A20 transcript levels and tight junction gene expression in ART-treated individuals . While prior studies defined a role for A20 in maintaining the proper localization of tight junction proteins [34] , these results show that A20 can modulate cytokine-mediated inflammatory signaling to promote the production of components associated with epithelial barrier function . In addition to the impact of the virus itself on the cytokine milieu of the intestine , it is likely that HIV also causes additional shifts in the intestinal immune system that persist in the relative absence of virus , e . g . , after the initiation of ART . The intestinal immune system is considered a “trialogue” between epithelial cells , lamina propria immune cells , and the commensal bacteria [62] . Recent work from our lab and others has shown that there is a shift in the composition of microbial communities during HIV infection , the extent of which was associated with inflammatory biomarkers [63 , 64] . Of note , we found that IFNγ inhibited the expression of a key antimicrobial peptide , Defb1 ( Fig 3E ) . As such , epithelial barrier dysfunction could initially be induced by the combination of proinflammatory cytokines secreted by virus-infected cells , altering the profile of antimicrobial peptides and thus leading to microbial dysbiosis . As productively-infected cells are depleted due to progressive disease and viral particles are no longer the primary driver of inflammation , this dysbiosis appears to result in the translocation of microbial products past the epithelium , resulting in a chronic inflammatory response [26] and pushing forward the cycle of cytokine release and disrupted barrier integrity . The specific stages in which this crosstalk between the epithelium , immune cells , and microbial species occurs remains to be clearly identified , and will be important in understanding the establishment and maintenance of disrupted barrier integrity during HIV infection and treatment . IL-17A is thought to be important for the maintenance of intestinal mucosal integrity in the case of HIV infection [15–18] , yet , it is also capable of driving tissue inflammation [19] . Our data on A20 expression in intestinal organoids suggest these dual effects might be dependent , at least in part , on the presence or absence of A20 . Thus , we observe that deletion of A20 clearly enhances the proinflammatory effects of IL-17A signaling ( e . g . , with increased expression of Lcn2 , Cxcl1 , and Cxcl2 ) . In the setting of ART , where IL-17A levels rise in the context of ongoing inflammation , this could be particularly important in protecting the epithelium from inflammation-mediated damage . Interestingly , the A20 gene is a susceptibility locus for inflammatory bowel disease ( IBD ) [65] . IBD is characterized by high levels of IL-17A cytokine [66] , yet inhibitors targeting IL-17 pathways have been shown to be ineffective at curbing inflammation [67 , 68] . Our data suggest that it is the presence or absence of IL-17A coupled with modulation by A20 that is important for disease progression . IL-17A levels did not directly associate with markers of epithelial function in our cohort , as would have been predicted by prior studies [15]; instead , A20 and related genes ( e . g . , IκBα ) demonstrated a positive association with epithelial function . This observation suggests that A20 modulation of cytokine signaling plays an important , and perhaps primary , role in barrier dynamics during HIV disease and treatment . A caveat attending our studies in intestinal organoids is that the concentrations of IFNα used in vitro ( 0 . 25–25 ng/ml ) are considerably higher than those found in the plasma of acutely SIV-infected rhesus macaques [16 , 69] or chronically-infected humans [70] , in which maximum levels of 0 . 05 ng/ml have been observed . Nevertheless , a plethora of studies indicate that HIV disease progression is associated with persistent type I IFN signaling [59 , 71] . Likely , such signaling occurs upon secretion of IFNα and IFNβ ( e . g . , by plasmacytoid dendritic cells or myeloid cells ) within lymphoid or mucosal organs , microenvironments in which type I IFNs levels may be higher than those found in the circulation and also not possible to measure . It will be of interest in future studies to more precisely measure these intra-organ cytokine concentrations and to determine their relevance to the in vitro studies presented here . It is important to recognize that the ART-treated individuals studied here exhibited substantial inter-individual variability in levels of IL-17A , IFNα , IFNγ , and A20 . We postulate that such variation , and the balance amongst each of these , might , in turn , reflect differences in the physiologic status of the gut . In addition , though our experiments parsed out the individual effects of IFNα , IFNγ , and IL-17A on epithelial function , these cytokines are expressed concurrently in vivo , and their potential interactions were not studied here . In future studies , it will be important to ascertain whether these cytokines potentiate each other ( e . g . , in the case of IFNγ and IL-17A ) or , conversely , if they synergize ( e . g . , in the case of IFNα and IFNγ ) to establish a setpoint of epithelial function . Treatment of primary organoids with HIV-associated cytokines IFNα and IFNγ , especially when combined with A20 deletion , recapitulated the epithelial biology observed in HIV-infected individuals , specifically in the case of elevating epithelial cell death and inhibiting expression of the tight junction genes , Ocln and Tjp1 . While these data provide clear evidence for A20 downregulation and high proinflammatory cytokine levels as being involved in the establishment of intestinal epithelial dysfunction during HIV , more thorough analyses of tight junction dynamics during the modulation of these pathways will need to be done . Future work should seek mechanistic insight into how A20 is modulated by IFNα and , downstream of this , how IFNα and IFNγ regulate barrier-related genes . Contrary to prior literature that identified a reduction of tight junction expression in viremic subjects [21 , 22 , 56] and our findings in organoids demonstrating an impact upon many epithelial function genes , our RNAseq analysis did not reveal differences in tight junctions , mucins , or antimicrobial peptides across participant subgroups . One potential explanation for this discrepancy could be a difference in methodology . While previous studies assessed epithelial function by measuring either transcripts or protein at the whole biopsy level [21 , 22 , 56] , we specifically isolated and profiled IECs . Thus , observations made in the context of whole biopsies may be more inclusive of events occurring in hematopoietic cells and non-hematopoietic stromal cells . A more robust analysis of genes mentioned above ( e . g . , tight junctions , mucins , antimicrobial peptides ) is warranted , perhaps at the protein level with epithelial specificity , to fully understand the impact of A20 upon epithelial function in vivo during HIV infection . It should also be considered that the demographics of the cohort from which we sampled , the SCOPE cohort at UCSF , are such that the vast majority of clinical samples were obtained from male participants [4 , 17 , 63] . It is thus important that these analyses be carried out in a cohort more balanced for gender type in future work . Despite these caveats , we were able to reproduce the epithelial barrier dysfunction ( e . g . , reduced levels of transcripts for tight junction proteins and elevated cell death ) observed in prior studies during HIV infection using our organoid model , and expanded upon these findings by identifying key cytokines ( IFNα , IFNγ ) that are likely to be causing these changes in vivo , as well as establishing the importance of A20 levels in mediating these changes . Given prior associations made between A20 and epithelial function [34] , the work outlined here indicates potential utility for A20 as a modulator and biomarker of intestinal health in HIV-infected individuals . In sum , our identification of A20 involvement in epithelial survival and function during HIV infection and treatment is a novel finding that adds to our understanding of intestinal epithelial dysfunction during HIV disease . Further studies are warranted to quantify A20 levels and function in treated HIV-infected individuals , investigating further how A20 modulates epithelial barrier function to affect immune activation and HIV persistence .
The study from which human tissues were obtained was approved by the institutional review board at UCSF and all participants provided written informed consent ( IRB #: 10–01218 ) . All recruited human participants were of adult age and provided consent themselves . Cryopreserved PBMCs were obtained from Clinical Trial NCT00594880 . All mice used in this study were housed and bred in a specific pathogen-free facility and euthanized by CO2 inhalation according to the guidelines of the UCSF Animal Care and Use Program ( IACUC Protocol #: AN144847 ) . The UCSF Animal Care and Use Program adheres to guidelines established by the Office of Laboratory Animal Welfare ( Domestic Assurance #: A3400-01 ) , and is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) International . The goal of this study was to isolate intestinal epithelial cells in an effort to identify , in a specific manner , how their function changes during HIV infection and treatment . Upon identification of key pathways , we utilized a murine organoid model to determine how manipulation of these pathways altered epithelial function . Detailed information of individual experiments is provided in the sections below . HIV-infected individuals and controls were recruited and consented from the SCOPE ( Observational Study of the Consequences of the Protease Era ) cohort at UCSF for sigmoidoscopy and collecting relevant gastrointestinal biopsy samples for research purposes . HIV-negative controls , HIV viremic untreated , and individuals suppressed with ART were recruited from this cohort . The ART-treated group included HIV-infected individuals maintaining undetectable viral loads ( <40 copies/mL ) on stable ART for at least one year and spanned a full spectrum of peripheral blood CD4+ T cell recovery , ranging from 220 to 1107 CD4+ T cells per ml of blood . Prior to their procedure , study participants underwent a blood draw and received a Fleets enema . Consenting participants underwent flexible sigmoidoscopy with rectal biopsies obtained at 10–20 cm above the anus using jumbo forceps . Twenty biopsies were placed immediately in 15 ml of RPMI 1640 with 10% fetal calf serum , with piperacillin–tazobactam ( 500 μg/ml ) , and amphotericin B ( 1 . 25 μg/ml ) , and transported within one hour to the Division of Experimental Medicine at UCSF , where they were processed the same day . Freshly drawn blood was also transferred to the AIDS Specimen Bank , where plasma was isolated and stored . The study was approved by the institutional review board at UCSF and all participants provided written informed consent ( IRB #: 10–01218 ) . Cells of interest were isolated from rectosigmoid biopsies in two sequential ethylenediaminetetraacetic acid ( EDTA ) ( Corning , Fremont , CA ) and collagenase ( Type II from Clostridium histolyticum , Sigma-Aldrich , St . Louis , MO ) treatment steps . Briefly , biopsies were washed with phosphate buffered saline ( PBS ) ( Corning ) and then incubated in 8 mM EDTA ( in Hank’s Buffered Saline Solution ) ( HyClone , Logan , UT ) on a shaker for one hour at 37°C . After vortexing , supernatants were filtered using a 70 μM mesh filter and centrifuged to obtain the IEC fraction . EDTA isolates ( estimated to be in the range of 80–90% enriched for IECs , as assessed by flow cytometry ) were lysed in Trizol ( Thermofisher Scientific , South San Francisco , CA ) and RNA was extracted using a phenol-chloroform extraction [72] . RNA Clean & Concentrator 5 ( IC ) columns ( Zymo Research , Tustin , CA ) were used to clean RNA preps . Whole tissue RNA was isolated using an AllPrep RNA Isolation Kit ( Qiagen , Hilden , Germany ) . The RNA concentration of all samples was measured on a Nanodrop 1000 Spectrophotometer ( ThermoFisher Scientific ) . Remaining tissue was washed with PBS with 2% FBS and subsequently incubated in 1 mg/ml collagenase in Complete RPMI ( RPMI-10 media supplemented with penicillin-streptomycin , L-glutamine , and FBS ) for one hour at 37°C on a shaker . Peripheral blood lymphocytes were obtained from whole blood by density centrifugation using Histopaque-1077 ( Sigma-Aldrich ) and washed in Complete RPMI . Freshly isolated lymphocytes were analyzed by flow cytometry . Cells were surface stained with Aqua amine reactive dye for viability ( Thermofisher Scientific ) , and antibodies against CD45 ( Clone HI30 , Alexa 700-Conjugated , Thermofisher Scientific ) , CD3 ( Clone UCHT1 , V450-Conjugated , BD Biosciences , San Jose , CA ) , CD4 ( Clone S3 . 5 , PE Texas Red-Conjugated , Thermofisher Scientific ) , CD8 ( Clone 3B5 , Qdot 605-Conjugated , Thermofisher Scientific ) , CD38 ( Clone HB7 , PE-Conjugated , BD Biosciences ) , and HLA-DR ( Clone L243 , FITC-Conjugated , BD Biosciences ) for 30 minutes at 4°C , and washed in PBS with 2% FBS . Separately , 5 x 105 cells were plated and stimulated with 10 ng/ml phorbol 12-myristate 13-acetate ( PMA ) ( Sigma-Aldrich ) and 1 μg/ml ionomycin ( Sigma-Aldrich ) at 37°C for five hours in the presence of BD GolgiPlug Protein Transport Inhibitor ( BD Biosciences , San Jose , CA ) . Stimulated cells were surface stained with antibodies against CD45 ( Clone H130 , PerCPCy5 . 5-Conjugated , Biolegend , San Diego , CA ) , CD3 ( Clone SP34-2 , Pacific Blue-Conjugated , BD Biosciences ) , CD4 ( Clone L200 , Brilliant Violet 605-Conjugated , BD Biosciences ) , and CD8 ( Clone 3B5 , Qdot 705-Conjugated , Thermofisher Scientific ) for 30 minutes at 4°C , fixed in 4% PFA , and then permeabilized in Perm/Wash Buffer ( BD Biosciences ) according to manufacturer’s instructions . Permeabilized cells were stained intracellularly with antibodies against IL-17A ( Clone eBio64DEC17 , FITC-Conjugated , eBioscience , San Diego , CA ) , IL-22 ( Clone 142928 , PE-Conjugated , R&D Systems , Minneapolis , MN ) and IFNγ ( Clone B27 , Alexa 700-Conjugated , BD Biosciences ) and washed in PBS with 2% FBS . Freshly-isolated EDTA-isolates were surface stained with Aqua amine reactive dye for viability ( Thermofisher Scientific ) and antibodies against CD45 ( Clone H130 , PerCPCy5 . 5-Conjugated , Biolegend ) and EpCAM ( Clone 9C4 , BV421-Conjugated , Biolegend ) . All samples were acquired on a BD LSRII flow cytometer ( BD Biosciences ) and analyzed using FlowJo 9 software ( FlowJo , LLC , Ashland , OR ) . Samples utilized for RNAseq included epithelial RNA from all participants as well as whole biopsy RNA from a subset of patients , representing each disease subgroup . The whole biopsy samples included five uninfected individuals , all six viremic untreated participants , and five ART-treated individuals selected to span the full range of CD4 count . Total RNA ( 2 ng ) from IEC isolates and from whole rectosigmoid biopsies was converted to pre-amplified cDNA using template-switching reverse transcription via the SMARTer Ultra-Low RNA Input Kit ( Clontech , Mountain View , CA ) , with modified procedures for low input ( Fluidigm , South San Francisco , CA ) [73 , 74] . Pre-amplified cDNA libraries were quantified by Quanti-IT PicoGreen dsDNA assay ( ThermoFisher Scientific ) and normalized to 0 . 15 ng/ml for input into fragmentation reactions . Fragmentation was performed enzymatically using a Nextera XT DNA kit with indexing primers ( Illumina , San Diego , CA ) . All 50 samples were normalized and multiplexed into a single library . The compiled library was purified by a double-sided bead-based size-selection method using Agencourt AMPure XP beads ( Beckman Coulter Genomics , Danvers , MA ) . A fragment size distribution of 200–500 base pairs for the library was confirmed by running a High Sensitivity dsDNA assay on a Bioanalyzer 2100 ( Agilent Technologies , Santa Clara , CA ) . A quality-controlled library was sequenced as 50-base single end reads on a HiSeq 4000 in rapid-output mode at the UCSF Center for Advanced Technology ( San Francisco , CA ) . Sequence reads were aligned to the human genome ( GRCh37 ) using STAR [75] , and gene expression estimation was performed with RSEM [76] . Differential expression analysis was performed on counts data using DESeq2 [77] , and candidate genes were false discovery rate corrected prior to determining lists of significantly altered genes . Data were visualized as transcripts per million ( TPM ) . All data are available in the NCBI Gene Expression Omnibus under accession GSE81198 . To validate RNAseq results , original RNA isolates from each sample were used to generate cDNA by reverse transcriptase using the Omniscript RT kit ( Qiagen ) . Amplification was performed on a StepOnePlus ( Applied Biosystems , Foster City , CA ) with PrimePCR SYBR Green Assays ( Bio-Rad Laboratories , Hercules , CA ) using the assay-specified cycling protocol . After normalization to housekeeping genes B2M and ACTB , the fold-change in expression of target genes ( TNFAIP3 , NFKBIA , IL1R2 ) was calculated relative to the uninfected subgroup and correlated against fold-change calculations of RNAseq data from matched participants . Concentrations of kynurenine and tryptophan in the plasma were measured by high-performance liquid chromatography–tandem mass spectrometry , as previously described [17] . To measure transcript levels of cytokines in the gut , qPCR was conducted on whole rectosigmoid biopsy RNA , isolated as above . IFNγ mRNA expression was assessed using a Taqman Gene Expression Assay ( Thermofisher Scientific ) . Expression was normalized to the housekeeping gene ACTB and fold-change calculated for each participant relative to the average of all uninfected controls . A20 flox mice were generated in the Ma lab as described previously [78] . Transgenic mice harboring a tamoxifen-inducible Cre recombinase under the control of the villin-promoter ( villin-ER-Cre ) were a kind gift from S . Robine ( Institut Curie-CNRS , Paris , France ) These two strains of mice were intercrossed to generate A20FL/FL villin-ERCre+ mice , used for the establishment of organoids . All mice used in this study were housed and bred in a specific pathogen-free facility according to the IACUC guidelines of UCSF . Murine small intestinal organoids were established as previously described [42] , with modifications . Intestinal crypts were isolated from the small intestine and cultured as outlined , with substitution of 10% R-spondin1 conditioned medium for recombinant R-spondin1 , and the addition of Normocin ( 100 mg/ml , Invivogen , San Diego , CA ) . R-spondin1-expressing 293T cells were a kind gift from Dr . Noah Shroyer ( Baylor College of Medicine ) . Cultures were passaged every three to four days . Three to four days after last passage , 250 nM 4-hydroxytamoxifen ( 4OHT ) was added to organoid cultures for 24 hours to allow A20 deletion . Mouse recombinant IL-17A , IFNγ ( R&D Systems ) , or IFNα ( PBL Assay Science , Piscataway , NJ ) were added to cultures and incubated for 12 hours . Cells were harvested by adding PBS and pooling wells of the same condition into a single conical tube . Cultures were centrifuged , resuspended in Cell Recovery Solution ( Corning ) , and incubated on a rotator at 4°C for 15 minutes . Cells were centrifuged , pellets were resuspended in Trizol and stored at -80°C , if for qPCR analysis , or snap frozen in a dry ice-ethanol bath and stored at -80°C , if for western blots . RNA was isolated using a phenol-chloroform extraction as above and cDNA generated by reverse transcriptase using the Omniscript RT kit ( Qiagen ) . Transcripts of interest were probed for by qPCR using Taqman Gene Expression Assays ( Thermofisher Scientific ) . All genes probed for are listed in S3 Table . Amplification reactions were performed on a StepOnePlus ( Applied Biosystems ) . Cycle threshold ( Ct ) values were normalized to three housekeeping genes ( Rplp0 , Gapdh , Actb ) . The fold-change was calculated by normalizing to values for media alone , resulting in the control condition in each run to appear as 1 . Cell lysates were made from snap frozen pellets of organoids using ice-cold 1% NP-40 ( Calbiochem , San Diego , CA ) containing 50mM Tris HCl pH 7 . 4 , 150mM NaCl , and 10% glycerol and a protease inhibitor cocktail ( cOmplete mini EDTA-free , Roche , Indianapolis , IN ) . Protein was quantified using a Pierce BCA Protein Assay Kit ( Thermofisher Scientific ) according to manufacturer’s instructions , with absorbance measured on a Spectramax M5 ( Molecular Devices , Sunnyvale , CA ) and analyzed using SoftmaxPro software ( Molecular Devices ) . Lysates were combined with NuPAGE LDS Sample Buffer ( Invitrogen , Carlsbad , CA ) and NuPAGE Sample Reducing Agent ( Invitrogen ) , and resolved on a NuPage precast Novex 4–12% Bis-Tris Protein Gel ( Invitrogen ) in volumes normalized to protein content alongside PageRuler Plus Prestained Ladder ( Thermofisher Scientific ) . Protein was transferred onto a PVDF membrane . Nonspecific binding was blocked by incubation in 5% nonfat dry milk dissolved in Tris-Buffered Saline with Tween-20 ( TBST ) for one hour . Membrane was incubated in 5% Bovine Serum Albumin ( BSA ) TBST with 0 . 1% azide containing primary antibody at 4°C overnight on a shaker . Primary antibody for GAPDH was purchased from EMD Millipore ( Billerica , MA ) , while all other primary antibodies used were purchased from Cell Signaling Technology ( Danvers , MA ) . After washing with TBST , membrane was incubated with HRP-conjugated secondary polyclonal antibodies to mouse or rabbit IgGs ( Cell Signaling Technology ) in 5% milk TBST for one hour at room temperature . Signals were developed using SuperSignal West Pico , Dura , or Femto Chemiluminescent Substrate ( Thermofisher Scientific ) . Blots were imaged on a ChemiDoc Touch Imaging System ( Bio-Rad Laboratories ) . For reprobing , blots were stripped of existing antibodies by incubation in Restore Western Blot Stripping Buffer ( Thermofisher Scientific ) at 56°C with gentle rocking for 20–30 mins and incubated with primary and secondary antibodies as above . Band intensity was quantified using ImageLab Software ( Bio-Rad Laboratories ) . Cultures were treated with 250 nM 4OHT for 24 hours prior to addition of cytokine . Organoids were stimulated with 10 ng/ml of IFNα ( PBL Assay Sciences ) , IFNγ , or IL-17A ( R&D Systems ) for 24 hours and then stained with 1 μg/ml propidium iodide ( PI ) ( Biolegend ) . Confocal imaging of intestinal organoids was performed on a Leica SP5 laser scanning confocal system ( Leica Microsystems , Wetzlar , Germany ) using a 10X dry objective . Images were acquired in a format of 512x512 , with a line average of at least 3 , scan speed of 400 Hz , and pinhole airy unit 1 . Excitation for both PI and bright field was done with the 488nm laser line at 30% power with a detection band of 550-732nm . Image analysis was performed on the Leica Application Suite ( Leica Microsystems ) . Cell death assays of intestinal organoids were performed by resuspending in Matrigel ( Corning ) and plating 25 μl per well in 96-well round bottom plates ( Corning ) . After 24 hours , organoids were stimulated as indicated . Viability was measured using the CellTiter Glo 3D assay ( Promega Corporation , Sunnyvale , CA ) according to the manufacturer’s specifications . Luminescence was read on a SpectraMax M5 ( Molecular Devices ) and analyzed using SoftMax Pro ( Molecular Devices ) . HIV-infected ART-treated participants of Clinical Trial NCT00594880 were enrolled based on pre-determined criteria , including viral load ( <50 copies per ml as an indicator of effective suppression ) and CD4 count ( >450 cells per ul ) . Eligible individuals were treated with pegylated-IFNα2a ( Roche ) , as published previously [54] . Cryopreserved PBMC samples taken from each participant at baseline and at five weeks post-initiation of IFNα administration were obtained for use in this study , providing paired samples from before and after cytokine treatment . 250 ng of RNA isolated from whole PBMCs were transcribed into cDNA using the SuperScript VILO cDNA Synthesis Kit ( Invitrogen ) . Quantitative real-time PCR measuring TNFAIP3 and ISG15 using TaqMan real-time PCR was performed using the QuantStudio 6 Flex Real-Time PCR System ( Applied Biosystems ) . Raw cycle threshold ( Ct ) numbers of amplified gene products were normalized to the housekeeping gene , GAPDH , to control for cDNA input amounts . Fold induction was determined using the comparative Ct method [79] . For data obtained from clinical samples , Kruskal-Wallis tests were performed in Prism Version 6 ( GraphPad , La Jolla , CA ) ( www . graphpad . com/scientific-software/prism/ ) . A p-value of less than 0 . 05 ( normal-based 95% Confidence Interval ) was considered significant . Spearman correlations and associations were generated using R software ( www . r-project . org ) with general data analysis ( https://cran . r-project . org/package=Hmisc ) and statistical calculation ( stats ) packages . For data generated by murine organoid experiments , data were determined to be normally distributed and analyzed for significance by ANOVA in Stata ( StataCrop LLC , College Station , Texas ) ( www . stata . com ) ; post-hoc pairwise comparisons were made using t-tests with Scheffe adjustment for multiple comparisons . Analysis of qPCR measurements from PBMC samples taken from participants in the IFNα-treatment clinical trial was completed by paired t-test in Prism Version 6 . | Though the advent of antiretroviral therapy ( ART ) has significantly improved the lives of individuals infected with the Human Immunodeficiency Virus ( HIV ) , those on therapy still suffer from an enhanced risk of morbidities and mortalities that is caused , at least in part by , overactivation of the immune system . Disruption in the intestinal epithelial barrier , which when intact cordons off the immune system from components in the gut that can drive an inflammatory response , is thought to contribute significantly to this process . How HIV causes this disruption has not been identified . In this study , we analyzed the expression profile of intestinal epithelial cells from HIV-infected individuals , on and off therapy , compared to uninfected controls and observed an increase in expression of an anti-inflammatory regulator , A20 , in treated participants but not in those without treatment . In a culture system that parallels features of the intact gut barrier , we saw that a cytokine induced by HIV suppressed the expression of A20 . In addition , treatment of ART-suppressed HIV-infected individuals with this same cytokine led to reductions in A20 expression . Furthermore , experimental deletion of A20 in our culture system and treatment with HIV-associated cytokines led to changes consistent with a defective intestinal barrier . As such , we suggest A20 is central to epithelial dynamics during HIV disease and could protect the epithelium from damage associated with HIV-induced cytokine changes . | [
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] | 2018 | A20 upregulation during treated HIV disease is associated with intestinal epithelial cell recovery and function |
The distribution of transposable elements ( TEs ) in a genome reflects a balance between insertion rate and selection against new insertions . Understanding the distribution of TEs therefore provides insights into the forces shaping the organization of genomes . Past research has shown that TEs tend to accumulate in genomic regions with low gene density and low recombination rate . However , little is known about the factors modulating insertion rates across the genome and their evolutionary significance . One candidate factor is gene expression , which has been suggested to increase local insertion rate by rendering DNA more accessible . We test this hypothesis by comparing the TE density around germline- and soma-expressed genes in the euchromatin of Drosophila melanogaster . Because only insertions that occur in the germline are transmitted to the next generation , we predicted a higher density of TEs around germline-expressed genes than soma-expressed genes . We show that the rate of TE insertions is greater near germline- than soma-expressed genes . However , this effect is partly offset by stronger selection for genome compactness ( against excess noncoding DNA ) on germline-expressed genes . We also demonstrate that the local genome organization in clusters of coexpressed genes plays a fundamental role in the genomic distribution of TEs . Our analysis shows that—in addition to recombination rate—the distribution of TEs is shaped by the interaction of gene expression and genome organization . The important role of selection for compactness sheds a new light on the role of TEs in genome evolution . Instead of making genomes grow passively , TEs are controlled by the forces shaping genome compactness , most likely linked to the efficiency of gene expression or its complexity and possibly their interaction with mechanisms of TE silencing .
Transposable elements ( TEs ) are selfish genomic elements on the order of one to several kilobases in length . They spread by replication and insertion across the host's genome , either with the help of enzymes they encode or by parasitizing the transposition machinery provided by other elements . TEs occur in virtually all sexually reproducing species and can contribute significantly to genome size . While TEs account for only about 3% of the yeast genome , their share of the genome is roughly one half in humans and 80% in frogs [1] . Besides their abundance in genomes , TEs are of biological importance because they can affect gene and chromosome evolution in numerous ways , including insertional mutation and retrotransposition , as well as gene duplication and chromosomal rearrangements . Further , TEs have been shown to be involved in the evolution of gene expression [2–4] . The density of TEs not only differs between species , it is also very heterogeneous within genomes . Studies on the distribution of TEs in the genomes of D . melanogaster , Caenorhabditis elegans , Arabidopsis thaliana , and humans have shown that elements tend to be enriched in regions of low gene density [5–8] , with the notable exception of human SINE elements [7] . In D . melanogaster , TEs account for 50%–60% of the heterochromatic regions and for only 4%–6% of the euchromatin [5 , 9] . Moreover , only 28% of euchromatic TEs occur within genes , although genes make up over half of the euchromatic DNA [10 , 11] . Similar to gene density , local recombination rate has been shown to correlate negatively with TE density ( [12] , but see [6 , 13] ) . Again in D . melanogaster , TE density is more than 6-fold higher in genomic regions with little or no recombination than those with high recombination [14] . The genomic distribution of TEs has been interpreted as the result of selection against the deleterious effects of insertions . Negative selection is thought to result either from the insertion of TEs into functional regions or from ectopic recombination , events of crossing-over between identical elements at different chromosomal positions , which generate deleterious chromosome rearrangements [15] . Under both mechanisms TE density is predicted to increase with low recombination rate , either because Hill-Robertson interference reduces the efficiency of selection against deleterious insertions or because the probability of ectopic recombination declines [16] . This general picture appears to suggest that the distribution of TEs is determined primarily by the removal of new insertions because of the action of selection , while variations in insertion rate across the genome contribute comparatively little to the distribution of TEs . This impression might be biased by the fact that studies assessing the variation in TE density have been performed at a very large genomic scale , thus putting factors potentially modulating insertion rate beyond their scope . Studies of insertion rates , in contrast , have mostly been restricted to identifying the features of insertion sites of a few specific TE families [17–20] . Because of this dichotomy in approaches , little—if anything—is known about general factors modulating the insertion rates and their relative contribution in generating variation in the local density of TE insertions across genomes . One candidate factor able to modulate insertion rates is gene expression . Bownes [21] was the first to propose the idea that transcriptional activity could favor insertion , based on the empirical observation of P element insertion patterns in fruitflies . Insertion bias towards expressed genes can be explained mechanistically: transcription is associated with a decondensation of the chromatin , which renders the DNA accessible to the transcriptional machinery but potentially also to the enzymes involved in transposition [22 , 23] . The effect of gene expression on insertion rate can be assessed relatively easily , because it will lead—over successive generations—to an accumulation of element insertions in and around germline-expressed genes relative to soma-expressed genes . This differential accumulation arises from the fact that only those transposition events taking place in the germline are transmitted to future generations , whereas all somatic insertions are lost . So far , differential accumulation has only been studied in the P element and over the short term ( over one generation ) , by identifying new insertions after the artificial mobilization of elements [21 , 23] . While these studies indicate the existence of an expression-related insertion bias , they cannot inform us about the generality of such a bias or its relative importance compared to forces of counterselection . Addressing this question requires an analysis at a genomic scale that is able to detect the effects of both insertion bias and counterselection for all element types and over many generations . In this paper , we present such an analysis of the fine-scale distribution of TEs in the D . melanogaster euchromatic genome . We focus on the question of whether gene expression favors TE insertion but also take into account other parameters of genome organization that have been shown or can be expected to influence TE distribution . Our results show that insertion bias towards germline-expressed genes has a detectable effect on the distribution of TEs in the D . melanogaster genome . However , the effect is confounded and overridden by the fact that germline-expressed genes are under strong selection for compactness ( against excess noncoding DNA ) , compared to soma-expressed genes . We show that , along with recombination rate , selection for local genome compactness is the major determinant of local TE density in the fruitfly . Furthermore , both of these factors are related to the organization of the genome into coexpressed gene clusters . As a consequence , the fine-scale distribution of TEs is strongly shaped by genome architecture .
We analyzed the distribution of 5 , 062 TE insertions annotated in the genome sequence of the D . melanogaster reference strain [24] ( see Materials and Methods for details ) . The genome sequence contains annotated insertions for a total of 151 TE families including the interspersed element 1 ( INE-1 ) , which accounts for ∼40% of euchromatic insertions . No other TE family exceeds 5% of the total number of insertions , but two-thirds of the families are represented by at least five copies ( Tables S1–S3 ) . We located all TEs based on the annotations and classified them as mapping to UTRs , exons , introns , or intergenic regions . Three expressed sequence tag ( EST ) libraries ( head , testis , and ovary ) allowed us to classify 1 , 829 genes as exclusively expressed in somatic tissue ( head ) and 2 , 388 genes as exclusively expressed in germline cells ( testes or ovaries ) . These two classes of genes ( exclusively germline- or soma-expressed ) are expected to show contrasted effects of gene expression on TE distribution and hence to maximize the statistical power of our analysis . We have , in addition , performed an alternative analysis that does not rely on a strict classification of genes . Instead , this approach takes advantage of a recently published Affymetrix microarray dataset ( FlyAtlas , http://www . flyatlas . org/ , [25] ) that contains the expression levels of 13 , 046 genes measured in male and female germline as well as eight somatic tissues . The results of this alternative approach are in complete agreement with conclusions of the EST approach ( see Text S1 ) . We used the statistical framework of Generalized Linear Models ( GLMs ) to analyze the distribution of TEs in the genome . Specifically , we modeled the number of TEs mapping to genes ( transcripts: exons + introns + UTRs ) or intergenic regions as a function of several parameters describing the properties of these genomic entities . Two parameters captured aspects of gene expression . One designated the expression of the element itself as germline- or soma-expressed , whereby intergenic regions were classified as “germline-expressed” if at least one of the adjacent genes was expressed in the germline . A second variable captured the broader expression context as the proportion of germline-expressed genes among the ten closest neighbors of a gene/intergenic region . This parameter allowed us to assess whether germline-expression can affect TE insertion in more distant genes . The window size of 10 was chosen on the basis of pilot analyses assessing the effect of germline expression among 20 neighbors on TE number in a focal gene/intergenic region ( Figure S1 ) . In addition to the variables describing gene expression , we entered four measures of genomic context . The first was recombination rate , which has been shown to have a profound impact on TE distribution [12 , 14 , 26] . Recombination rates are not distributed randomly with respect to gene expression; they are greater around soma-expressed genes than germline-expressed genes ( medians: 2 . 75 versus 2 . 58 cM/Mb; Wilcoxon rank test , p < 0 . 01 ) . The second genomic feature used was the amount of noncoding DNA , excluding TE lengths . This is of importance because virtually all TE insertions in the D . melanogaster genome reside in noncoding DNA [10] , and because noncoding length is also correlated with gene expression . Indeed , germline-expressed genes have shorter noncoding sequences ( median 567 bp , introns + UTRs ) than soma-expressed genes ( 1 , 212 bp , Wilcoxon rank test , p < 0 . 001 ) , and germline intergenic regions are shorter than soma intergenic regions ( 577 bp versus 813 bp , p < 0 . 001 ) . Third , we included the proportion of noncoding sequence that is evolutionarily conserved [sensu 27] . Sequence conservation is important because it is thought to be associated with a functionality [28–30] . Since insertions in functional elements are likely to be deleterious , the degree of sequence conservation reflects the portion of the noncoding sequence in which new insertions will be rapidly eliminated by selection . Furthermore , sequence conservation is a highly relevant covariable in our particular analysis because it is also correlated with gene expression; the proportion of conserved sequences is smaller in germline-expressed as compared to soma-expressed genes ( 12 . 9% and 14 . 7%; Wilcoxon rank test , p < 0 . 001 ) and smaller in germline intergenic regions than in soma intergenic regions ( 12 . 5% and 13 . 1% , respectively; p < 0 . 001 ) . Finally , our statistical model includes a factor describing the chromosomal position of a gene or intergenic region as X-linked or autosomal . This distinction is important because male hemizygosity for the X chromosome entails a difference in the intensity of selection between the X and autosomes [16 , 31] . The GLM was able to account for more than 35% of the variance in TE number between genes and intergenic regions ( Table 1 ) . The analysis showed that TE number is affected both by genomic features and by gene expression . We will first briefly summarize the results before discussing the main new points in more detail . As indicated by previous studies , local recombination rate has a negative effect on TE density . In our GLM analysis , the effect of recombination rate accounts for almost a fifth of the variance in TE number between genes/intergenic regions , and it is highly significant . However , the analysis also revealed important effects of genomic context that had not previously been described . Notably , noncoding length—a measure of genome compactness—has a highly significant and positive effect on TE density , indicating that TEs accumulate in regions of the genome that are less compact . This factor explains a portion of the variance that is comparable to that accounted for by recombination rate . The remaining two genomic features , proportion of sequence conservation and chromosomal location , also have significant effects but each explains only a small part of the variance in TE number . Finally , gene expression affects TE density in a significant way . However , as indicated by several significant interaction terms , the effect of gene expression is dependent on genomic context . As shown in Table 1 ( row c ) , the degree of sequence conservation has a significant effect on TE density ( F1 , 15663 = 139 . 5 , p < 0 . 001 ) and explains 1 . 9% of the total variance in TE number between genes/intergenic regions . The factor has a significant negative coefficient , indicating that the number of insertions decreases with the portion of noncoding sequence that is conserved between Drosophila species . This result is interesting because sequence conservation is an indication of purifying selection on the sequence and hence reflects functionality . In the case of the noncoding sequences considered here , conservation most likely arises because part of the sequence is composed of regulatory elements . Our result therefore indicates that , as expected , insertions of TEs into regulatory elements causes a deleterious fitness effect , just as insertions into coding sequences do . Our GLM analysis shows that TE distribution is shaped to a large degree by local variations in genome compactness . For both genes and intergenic regions , the number of insertions increases with the length of noncoding sequences ( Table 1 , row b ) . This correlation is not unexpected . Indeed , if TEs insert randomly and insertions are selectively neutral , long intergenic regions and genes with more noncoding DNA ( introns + UTRs ) should have more TEs because they represent a larger target for insertion . However , several pieces of evidence suggest that insertions in noncoding sequences are not selectively neutral and that they are not only affected by the presence of regulatory elements but also by local selection for genome compactness . First , according to the GLM analysis , intergenic regions accumulate about 75% more TEs than introns and UTRs . A difference in TE density between genes and intergenic regions had been found earlier [11] . However , by correcting for both noncoding length and sequence conservation , our analysis demonstrates that a difference in TE density exists even between noncoding DNA within and outside genes . Such a difference would not be expected if target size alone determined the number of TE insertions . Second , the canonical length of TEs ( the length of the functional element at the moment of insertion ) tends to be positively correlated with the noncoding length of the genes ( Spearman rank correlation , rho = 0 . 11 , p = 0 . 075 ) and intergenic regions ( rho = 0 . 24 , p < 0 . 001 ) that they are inserted in . Thus , long functional TEs are less likely to be retained in short intergenic regions and short genes than in less compact intergenic regions and genes . In addition to this difference in retention , compact genes also seem to eliminate their TE insertions quickly . Accordingly , the amount of TE degradation ( the difference between canonical and present length ) is negatively correlated with the noncoding length of genes they are inserted in ( rho= −0 . 21 , p = 0 . 001; intergenic regions rho= −0 . 11 , p = 0 . 15 ) . Because of the combination of these two effects , we observe a significant positive relationship between the present length of TEs and the noncoding length of the genomic entities they are inserted in ( genes: rho = 0 . 22 , p < 0 . 001; intergenic regions: rho = 0 . 11 , p < 0 . 01; Figure 1A and 1B ) . Assuming that insertion rates are independent of the length of both the TE and the targeted intron or intergenic region , the above data suggest that short intergenic regions and in particular short genes are under selection for compactness . Accordingly , TE insertions elongating these compact regions are deleterious and will be eliminated either immediately or degraded by deletions more quickly . Long genes and intergenic regions , on the other hand , seem to be more tolerant to insertions . This relationship between compactness and selection against insertions could explain the nonlinear correlation between the proportion of genes and intergenic regions containing TE insertions and the lengths of noncoding sequences ( Figure 1C and 1D ) . On the other hand , a nonlinear relationship could be also generated by an insertional rate depending on the size of the target . Regardless of the relative contribution of these two effects , selection for compactness or insertional rates , the nonlinear relationship has an important implication: TE insertions are restricted to a limited number of genes with very large noncoding content , whereas genes with short noncoding sequences are virtually free of TEs ( Figure 1C ) . Thus , 99% of the insertions are concentrated in the 61% of genes in which the noncoding length is greater than 500 bp . Selection for genome compactness could arise for a variety of reasons . Within genes , compactness has been suggested to be selectively advantageous because it reduces the cost of transcription [32 , 33] . It has also been shown both in vitro and in vivo that long introns increase the rate of exon-skipping , the erroneous splicing of an exon [34] . From this perspective , the elongation of an intron will cause a greater increase in deleterious alternative splicing in genes that use to have short introns compared to less compact genes . Why compactness per se should be selected for in intergenic regions is less clear . Recent work has provided evidence for the existence of groups of coexpressed genes in eukaryotes , similar to bacterial operons [35] . In this context , short intergenic regions might be selectively advantageous in that they facilitate the coordinated expression of adjacent genes . According to our GLM analysis ( Table 1 , row d ) , the density of TEs is significantly higher on the X chromosome than on autosomes . A higher density of TEs on the X chromosome could be expected for two reasons . First , selection against TEs due to deleterious effects of insertions can be less efficient on the X , since theoretically the X chromosome has a lower effective population size than autosomes . Whether this is actually the case is currently unclear . Based solely on the fact of male hemizygosity for X-linked genes one would predict a reduced effective population size for X-linked genes compared to autosomal genes [31] . However , this difference can be reduced or even reversed if males vary more in their reproductive success than females [36] . Genetic data from D . melanogaster are inconsistent , showing that genetic polymorphism is higher on the X compared to autosomes in African populations , whereas the contrary is true for populations sampled outside Africa [37–39] . Thus , it is difficult to judge whether an increased TE density on the X could be the result of inefficient selection against deleterious insertions . Alternatively or in addition to increased drift , the increased density of TE insertions on the X could be explained by an effect of dosage compensation . In Drosophila , male hemizygosity for the X chromosome is compensated by doubling expression of all X-linked genes , which adjusts mRNA levels in males to those of females bearing two X chromosomes . This effect has been shown to be associated with an alteration of the chromatin structure that spreads along the chromosome [40] . Given this large-scale change in the accessibility of X-chromosomal DNA , dosage compensation has the potential to increase TE insertion rate along the entire X chromosome , in a manner equivalent to the localized effect of gene expression . Consistent with this hypothesis , Pasyokova and Nuzhdin [41] found that new copia insertions occur more frequently than expected on the X chromosome . However , the same is not true for another element family ( Doc ) . The GLM analysis supports the hypothesis that gene expression increases the probability of TE insertions . However , the effect depends on the genomic context . Figure 2 is a graphical representation of the GLM analysis ( Table 1 ) and shows the effects of the expression context ( the proportion of germline expressed genes in the neighborhood of a focal gene ) on genic or intergenic TE density . As illustrated by this figure , the effect of expression in intergenic regions is straightforward: regions adjacent to germline-expressed genes tend to have more TEs than those next to soma-expressed genes . In addition , the density of TEs increases in a highly significant manner with the proportion of neighbors that are germline expressed . Patterns of insertion are more complex for genes: while soma-expressed genes , like intergenic regions , accumulate more TEs when they are surrounded by more germline-expressed neighbors , the trend is reversed among germline-expressed genes . Here , TE number is higher when the proportion of germline-expressed neighbors is lower . The difference in the neighborhood effect between the two types of genes can be understood as the result of a combination of two factors previously described: the differences in the compactness of genomic regions and the virtual absence of TE insertions in genes with noncoding content < 500 bp . As also already mentioned , the genome of D . melanogaster is very compact around germline-expressed genes . Their noncoding sequences ( introns and UTRs ) are less than half as long as those in soma-expressed genes . In other words , only 53% of germline-expressed genes have a length of noncoding sequence > 500 bp , whereas the value is about 71% in soma-expressed genes . This difference in compactness translates immediately into the frequency of TE insertions in the two types of genes: while TE insertions are present in only 4 . 2% of germline-expressed genes ( 100 out of 2 , 388 ) , they occur in 8 . 7% of soma-expressed genes ( 159 out of 1 , 829 ) . This difference is highly significant ( Chi2= 205 . 7 , p < 0 . 001 ) . On the other hand , although flanking intergenic regions are 30% shorter around germline-expressed genes than around soma-expressed genes , the prevalence of TE insertions is not significantly different ( Chi2= 0 . 9 , p = 0 . 33 ) : insertions occur in 10 . 2% and 10 . 9% of germline and soma intergenic regions , respectively ( see Table S2 ) . Genome compactness around a gene not only depends on its tissue of expression but also on the gene expression of its neighbors . As shown in Figure 3 , in germline-expressed genes , compactness increases even further if the neighboring genes are also germline expressed . On average , 53% of germline-expressed genes have a length of noncoding sequence > 500 bp . However , this value climbs as high as 58% in a germline-free environment whereas it drops to 38% in an environment containing 50% of germline-expressed neighbors . Consequently , stretches of germline-expressed genes have a low probability of accumulating TEs since the average length of their noncoding sequences is below the threshold of TE insertion . Soma-expressed genes , in contrast , are not only generally less compact but also increase in noncoding content when they are surrounded by more germline-expressed genes . Our additional analyses based on germline-expression level suggest that the expression in ovaries affects the distribution of TEs more strongly than the expression in testes ( see Text S1 ) . Thus , ovary-expression level has a stronger effect on TE density in neighboring genes than testis-expression level . Furthermore , TE density is also higher within genes with higher levels of ovary-expression , although it is not clear whether this effect is due to their expression or due to the fact that ovary-expressed genes are less compact than testis-expressed genes . Figures 2 and 3 show how the distribution of germline- and soma-expressed genes within the D . melanogaster genome affects genome compactness , and hence the TE density . For this analysis , we assessed the genome organization in terms of tissue of expression ( more precisely , the proportion of germline-expressed genes among ten neighbors ) . However , other studies have evaluated genome organization in terms of level of gene expression , i . e . , the amount of mRNA . In a wide range of eukaryotes , these studies have shown that coexpressed genes are not randomly distributed in the genome but are clustered into contiguous regions [42–46] . These transcriptional territories are often interpreted as chromosomal domains whose expression is regulated through a higher-order control of chromatin packaging [47] . In D . melanogaster , Spellman and Rubin [48] described 211 transcriptional territories containing between four and 45 genes ( total: 3 , 325 genes; Tables S1–S4 ) . Based on our classification by tissue of expression , these clusters are not tissue specific in their expression ( Table S4 ) but are enriched in clusters of germline- and soma-expressed genes compared to the rest of the genome ( Table 2; Figures S2 and S3 ) . This therefore suggests that gene expression is a noisy process , as opening chromatin to express one gene might incidentally allow leaky expression of neighboring genes [49] . In addition , transcriptional territories differ in lengths of noncoding content ( Figure S4 ) , recombination rates ( Figure S5 ) , and proportion of conserved sequences ( Figure S6 ) . Transcriptional territories are interesting for understanding the factors affecting TE density for two reasons: they represent regions of the genome that have an unusual gene clustering , and they represent regions of the genome where TE insertion rate could be modulated homogeneously over long stretches of DNA through the action of a unique mechanism of gene expression regulation . Indeed , if chromatin packaging controls the accessibility of several genes at the same time , we expect to observe a correlation between TE density and the proportion of germline-expressed genes in transcriptional territories , whereas no difference in TE accumulation should be found between soma- and germline-expressed genes within territories . To test this hypothesis , we set up a second GLM that analyzed the total number of TE insertions per transcriptional territory . The model explains a remarkable 63% of the total variance ( Table 3 ) . It confirms that TEs accumulate preferentially in transcriptional territories that have a low recombination rate , a high noncoding DNA content , a low proportion of conserved sequences , and are situated on the X chromosome ( Table 3 , rows a–d ) . Furthermore , as expected , transcriptional territories enriched in germline-expressed genes contain a higher density of TEs: the proportion of germline-expressed genes per transcriptional territory accounts for 4 . 5% of the total variance in the data ( Table 3 , row e ) . Importantly , this effect is specific to clusters and cannot be found to a comparable degree when analyzing random groups of adjacent genes ( Table S5; see Materials and Methods ) . To separate the effects related to genomic organization from those of gene expression , we also included in the model the probability of observing stretches of three genes expressed in the same tissue . Although this variable is not completely independent of the proportion of germline-expressed genes , the correlation is relatively low ( Spearman rho = 0 . 35 , p < 0 . 001 ) . In the GLM ( Table 3 , row f ) , this variable is significant and explains a small part ( 1 . 8% ) of the variance . Contrary to our prediction , TE density is also not homogeneous within transcriptional territories: germline-expressed genes and their intergenic regions accumulate more TEs than soma-expressed genes and their intergenic regions ( Figure 4; Table S6 ) . In summary , these results suggest that the accessibility of DNA in transcriptional territories is affected by properties of the transcriptional territory as a whole ( i . e . , the proportion of germline expression ) . However , the factors associated with individual genes and their neighbors , which are important outside transcriptional territories , still have some effect on TE density . It therefore appears that the variations in TE insertion rates affecting a transcriptional territory as a whole are partially overridden by differential selection on individual genes . In this paper , we analyzed the fine-scale distribution of TEs in the D . melanogaster euchromatic genome . Transposition is a highly stochastic process . Accordingly , the distribution of TEs is affected by randomness in both the location of new insertion and their retention through successive generations in the face of genetic drift , as well as by the idiosyncrasies of individual element families in their modes of transposition and target site preferences . Despite the inherent randomness of the data and potential measurement error in the predictor variables , our analysis has revealed a number of highly significant effects that are strong enough to be detected in a global analysis such as the one presented here . Thus , in agreement with earlier studies [12 , 14 , 26 , 50] , we found that recombination rate is a major determinant of TE density across the genome; regions that recombine more frequently have fewer TE insertions . In addition , our study identified factors modulating TE density whose importance had hitherto not been recognized: genome compactness ( the length of noncoding DNA ) and gene expression . Regarding TE distribution , genome compactness encapsulates two effects: the size of the target and the tolerance to insertions . From a probabilistic point of view , long stretches of noncoding DNA are more likely to be the target of TE insertion . However , our analysis also shows that regions of the genome also vary in their tolerance to TE insertions . First , intergenic regions are typically more tolerant to TE insertions and accumulate more TEs than introns or UTRs . Second , compact regions of the genome are less tolerant to insertions than regions with large amounts of noncoding DNA , resulting in the rapid elimination of insertions by natural selection . Selection against new insertions into compact genes could arise for several reasons . First , selection against insertions could be due to the reduction in the efficiency of splicing caused by the elongation of introns . It has been shown that long introns are associated with increased levels of alternative splicing , both in vitro and in vivo ( Drosophila and humans ) [34] . Accordingly , the elongation of an intron through the insertion of a TE will lead to a larger increase in the level of accidental alternative splicing in compact genes with short introns than in genes in which introns were initially large . Second , insertions into compact genomic regions could be deleterious because they reduce the efficiency of gene expression . The amount of noncoding DNA within genes has been shown to decrease with both the level and the breadth ( number of different tissues ) of expression in a variety of species , consistent with a cost of transcribing noncoding sequence [32 , 33] . Following this interpretation , TE insertions into compact genes would be deleterious by increasing the cost of transcription . Third , we could also speculate that TE insertions into compact regions are counterselected because of the epigenetic silencing they induce in their vicinity . In D . melanogaster TEs can be silenced by chromatin modifications ( formation of localized heterochromatin , see [51 , 52] for reviews ) . Chromatin-based silencing might be deleterious in compact genomic regions , because the heterochromatic structure can spread into the regulatory sequences of adjacent genes [53] . Fourth and finally , selection against TE insertions into compact genes could arise from the association between compactness and germline expression described above , in conjunction with germline-specific TE silencing . D . melanogaster germline cells ( but not somatic cells ) suppress TE activity with the help of repeat-associated short interfering RNAs ( rasiRNAs ) , which target and degrade transcripts containing repeat sequences [54] . As a consequence of this silencing , TE insertions within germline-expressed genes are extremely deleterious . Indeed , the presence of insertions not only causes the degradation of TE transcripts but also the degradation of mRNA produced by genes bearing insertion . The strong selective pressure exerted by this post-transcriptional silencing mechanism could explain the quasi-absence of TEs within germline-expressed genes and may help to maintain the compactness of these genes . Despite the deficit of TE insertions in germline-expressed genes , our study demonstrates that germline expression increases the local rate of TE insertion . Thus , TE insertions are denser around germline-expressed genes than elsewhere in the genome , unless these regions are under selection for increased compactness . This result is consistent with the positive effect of germline expression on insertion rate observed in experimental studies of novel P element insertions [21 , 23] . Our study shows that this effect is general , rather than specific to the P element , and significantly shapes the genomic distribution of TEs . Furthermore , the fact that the signal of expression-related insertion bias can be detected across coexpressed gene clusters provides evidence that higher TE accumulation is associated with chromatin decondensation , as speculated earlier [21 , 23] . Taken together , these results make a strong case for an expression-associated increase in TE insertion rates . It is not impossible that other factors contribute to the association between germline expression and TE density . For example , germline-specific TE silencing could reduce the deleterious effects of TEs inserted close to germline-expressed genes and thus decrease the strength of counterselection in these positions relative to selection in the vicinity of soma-expressed genes . While conceivable as a mechanism , the molecular basis of such an effect remains speculative in the absence of empirical evidence . The factors discussed above—recombination rate , noncoding length , gene expression , transcriptional territories , and TE silencing—can be shown to have ( sometimes strong ) individual effects on local TE density . However , our analysis has also made clear that all of these genomic variables interact to create a complex genomic landscape that in turn shapes TE distribution ( Figure 5 ) . Thus , germline-expressed genes are compact , with short noncoding sequences , and are even more so when organized in groups of adjacent germline-expressed genes . As a consequence of their compactness and the selective forces driving it , germline-expressed genes accumulate few TEs . Inversely , soma-expressed genes have a higher noncoding content . Accordingly , they accumulate many TEs when located among germline-expressed neighbors . Transcriptional territories thus accumulate fewer TEs than nontranscriptional territories ( Figure 4 ) since they are enriched in gene clusters: germline-expressed genes are extremely compact , and the soma-expressed genes are not strongly exposed to the transpositional effects of germline-expressed neighbors . Because of the inter-relationships between noncoding content and tissue of expression revealed by our study , genome organization emerges as one of the major determinants of TE distribution , on a par with recombination rate . The direct link between genome compactness and TE density identified here is not only important to understand the distribution of TEs in the D . melanogaster genome , but also sheds a completely new light on the role of TEs in genome evolution . The current view is that TEs are responsible for the growth in the noncoding part of genomes [55] and are thus a driving force in the evolution of genome size . However , our work shows that TE insertions can be deleterious just by elongating the noncoding part of otherwise compact genes , and TEs are restricted to regions of the genome where noncoding DNA is tolerated . Thus , rather than making genomes grow passively , TEs are controlled by the forces shaping genome compactness , most likely linked to the efficiency of gene expression [32 , 56] or its complexity [29 , 57] . It is possible that selection against noncoding DNA affects TEs more strongly in D . melanogaster with its compact genome [58 , 59] compared to species with larger genomes . However , variations in noncoding content between genes are a general phenomenon [60 , 61] and , as a consequence , TEs will be subject to selection against noncoding DNA across organisms . Finally , our results on insertion bias towards germline-expressed genes add an interesting perspective to recent work on duplicate genes arising by TE-mediated retrotransposition ( retrogenes ) . Recent studies have shown that new retrogenes are generally expressed in germlines , in particular in testis [62 , 63] . So far , this expression bias has been attributed either to the fact that gene expression is generally incremented in testes or to the fact that retroposition from the X to the autosomes can provide a way for genes to escape the inactivation during spermatogenesis that affects X-linked genes . The insertion bias documented in our study suggests that testis expression is to be expected for retrogenes , because they preferentially insert into or close to germline-expressed genes . Since gene expression is a leaky process [49] , a new retroposed duplicate gene is then likely to be transcribed in germ cells , merely because it is surrounded by germline-expressed neighbors . We also observed a greater transposition activity in the X chromosome than in autosomes , and we could envisage more retrogenes on the X chromosome . However , this prediction seems to be in contradiction with the observed excess of functional retrogenes that originate from the X chromosome and retropose to autosomes in D . melanogaster [64] . Based on this discrepancy , it appears that the recruitment of duplicate genes , relative to their chromosomal location , is more immediately affected by natural selection rather than insertion bias . This interpretation is in agreement with data from human and mouse , suggesting that movements of genes by retroposition are affected by selection more than random processes [65] .
The 13667 annotated genes in D . melanogaster genome ( release 4 . 2 . 1 ) were downloaded from FlyBase ( ftp://ftp . flybase . net/genomes/Drosophila_melanogaster/current/fasta/ ) . We discarded 621 overlapping genes . We extracted the lengths of intergenic regions , as well as the coding sequence ( CDS ) and the noncoding sequence ( introns and UTRs ) of each gene after excluding the TE lengths . Recombination rates were obtained from Hey and Kliman [66] ( http://lifesci . rutgers . edu/~heylab/HeylabData . htm ) . We used the R estimate , which is based on a comparison of the genetic and physical map locations of 493 X-linked and autosomal genes . Conserved sequences were obtained from genome-wide multiple alignments of eight insect species ( http://genome . ucsc . edu/ ) [27] . The 50 , 000 top-scoring elements were used , accounting for approximately 14% of the Drosophila euchromatin . The conserved sequences overlapping TEs were excluded . To classify the genes as germline- and soma-expressed , we used three published EST libraries ( Berkeley Drosophila Genome Project ) . The AT ( adult testes ) library was made from RNA extracted from D . melanogaster adult male ( 0–3-d-old nonisogenic Oregon-R strain ) testes and seminal vesicles . The AT library contains 23 , 505 EST from 3 , 921 genes present in the FlyBase release 4 . 2 . 1 . The GM library was made from RNA extracted from ovaries , at stage 1–6 of oogenesis ( nonisogenic Oregon-R strain ) . The GM library contains 11 , 151 EST from 3 , 152 genes . Finally , the RH ( Riken head ) library was made from RNA extracted from adult heads ( isogenic y; cn bw sp strain ) . The RH library is normalized and contains 51 , 487 EST from 4 , 361 genes . Each library includes tissue-exclusive genes and genes shared with the two other libraries . The 1 , 829 genes present exclusively in the RH ( head ) library have been called soma-expressed genes . The 1 , 567 and 821 genes present exclusively in the testis ( AT ) and ovary ( GM ) libraries , respectively , have been called germline-expressed genes . We categorized intergenic regions by looking at the tissue of expression of the two flanking genes: an intergenic region surrounded by one or two germline-expressed genes is called germline intergenic . Conversely , a soma intergenic region has no germline-expressed genes among its close neighbors . Spellman and Rubin [48] described 211 large groups ( transcriptional territories ) of adjacent genes that are similarly expressed ( Table S4 ) . Their analysis was carried out on the basis of 267 GeneChip Drosophila Genomes Arrays ( Affymetrix ) from 88 different experimental conditions studying diverse biological processes ( aging , immune response , etc . ) . Consequently , these transcriptional territories are neither tissue specific , nor sex specific ( see [67] for testis or ovary clusters ) , nor are they function specific ( see [68] for dosage compensation clusters ) . Rather , they are believed to represent a higher order of function-independent expression regulation that takes place at the level of the chromatin structure ( but see [35] ) . We calculated descriptive statistics for each territory: the number and the mean length of genes , the total length covered by genes , the total length of intergenic sequence , and the proportion of germline-expressed genes . Each cluster has between four and 45 genes ( total: 3 , 325 genes ) and covers between 23 and 553 kbp . They contain on average 19 . 7% ( range: 0%–60% ) and 14 . 0% ( 0%–50% ) of germline- and soma-expressed genes , respectively . We generated 500 random datasets of chromosomal territories by sampling contiguous gene clusters after excluding transcriptional territories . Each dataset has the same structure as the dataset of transcriptional territories ( 211 clusters containing between four and 45 genes [total: 3 , 325 genes] ) . We downloaded all annotated natural TE insertions from FlyBase ( release 4 . 2 . 1; ftp://flybase . net/genomes/Drosophila_melanogaster/current/fasta/ ) [24] . The complete dataset contains 6 , 006 insertions from 159 TE families , but only 5 , 060 of these insertions are within genes and intergenic regions ( Table S2 ) . One TE ( interspersed element 1 , INE-1 ) is predominant , accounting for ∼40% of insertions , and the others never exceed 5% each . The DNA transposons ( class II ) and the retrotransposons ( class I ) account for ∼20% and ∼40% of the insertions , respectively ( Table S3 ) . Using their chromosomal position , we localized each insertion within genes ( within introns , UTRs , or exons ) and intergenic regions . If more than 50% of an insertion length is outside the gene sequence , the insertion was recorded as intergenic insertion and if an insertion is within two overlapping genes , it was excluded . Lengths of TEs were calculated after excluding nested elements [5] . The canonical TE lengths were obtained from FlyBase ( http://flybase . bio . indiana . edu/transposons/ ) . We analyzed the number of TE insertions with a GLM ( function glm ( ) in R , R Development Core Team , 2005 ) using a quasipoisson error distribution and log link . We used a backward procedure to refine models . We first included all predictor variables and their interactions . The main terms were entered in the order of decreasing deviance explained in separate analyses using only single terms ( parsimony ) . We then removed those terms from a model that were not significant ( unless they were main terms involved in a significant interaction term ) and re-ran the model . We repeated this procedure until no more terms could be removed from a model . Significance of terms was tested with F tests . | Transposable elements ( TEs ) are parasitic DNA segments that can move within a host genome . These selfish mobile elements are present in virtually all eukaryote species and can contribute significantly to their DNA . TEs multiply by copying themselves within the genome . Depending on where they land , new copies can alter the organism's phenotype , often negatively but sometimes positively . Although TEs have some preferences , they have few opportunities to choose their landing places . It has been proposed that new copies arise in places that are easily accessible to their insertion . Increased accessibility can occur close to genes that are actively transcribed , because the DNA is uncoiled and laid bare . We have tested whether this effect has a detectable influence on the distribution of TEs in the genome of the fruitfly , D . melanogaster . Our analysis shows that this is indeed the case . Thus , TE insertions are denser around genes expressed in the cells that give rise to sperm and eggs ( the germline ) . This is expected because only those new copies arising in these cells are transmitted to future generations . In addition , we found that genomic regions vary in their tolerance to insertions . Thus , TEs are rare wherever a considerable increase in noncoding DNA is deleterious . | [
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] | 2007 | Genome Organization and Gene Expression Shape the Transposable Element Distribution in the Drosophila melanogaster Euchromatin |
Consideration of previous successes and failures is essential to mastering a motor skill . Much of what we know about how humans and animals learn from such reinforcement feedback comes from experiments that involve sampling from a small number of discrete actions . Yet , it is less understood how we learn through reinforcement feedback when sampling from a continuous set of possible actions . Navigating a continuous set of possible actions likely requires using gradient information to maximize success . Here we addressed how humans adapt the aim of their hand when experiencing reinforcement feedback that was associated with a continuous set of possible actions . Specifically , we manipulated the change in the probability of reward given a change in motor action—the reinforcement gradient—to study its influence on learning . We found that participants learned faster when exposed to a steep gradient compared to a shallow gradient . Further , when initially positioned between a steep and a shallow gradient that rose in opposite directions , participants were more likely to ascend the steep gradient . We introduce a model that captures our results and several features of motor learning . Taken together , our work suggests that the sensorimotor system relies on temporally recent and spatially local gradient information to drive learning .
Whether a previous action is successful or unsuccessful is an important contributor to sensorimotor learning . Indeed , binary reinforcement feedback ( e . g . , reward ) is sufficient to cause adaptation of hand aim during a reaching task , independent from error feedback [1 , 2 , 3 , 4 , 5 , 6 , 7] . It has been proposed that updating aim of the hand based on reinforcement feedback is model-free and occurs by sampling a continuous set of possible motor actions until one or more actions are found that improve task success [8 , 9] . Sampling motor actions presumably allows the sensorimotor system to use information from the reinforcement landscape to drive adaptation . Here we broadly define the reinforcement landscape as the mapping between all possible motor actions and the expected reward of those actions . In this context , the sensorimotor system can maximize expected reward by ascending the reinforcement landscape [10] . However , for a meaningful change in behaviour to occur there has to be an underlying process that either evaluates or accounts for whether one action is better than another . More specifically for learning to occur the sensorimotor system must account for the gradient of the reinforcement landscape , which defines the rate of change in the expected reward with respect to a change in motor action . Intuitively , the evaluation of different actions may be easier with a steeper gradient , as there would be a more salient change in the expected reward for a change in action . The form of the reinforcement feedback influences the shape of the reinforcement landscape . Reinforcement feedback can be binary or graded , and can be provided deterministically [1 , 11] or probabilistically [2 , 5] . Binary reinforcement feedback signifies only whether the action was successful or unsuccessful [1 , 2 , 5] . Graded feedback varies the magnitude of positive feedback ( reward ) or negative feedback ( punishment ) as a function of motor action [11 , 12] . Thus , the reinforcement landscape gradient can be influenced by the magnitude and or the probability of feedback . Another consideration when using graded reinforcement feedback is that humans form a nonlinear relationship between different reward ( or punishment ) magnitudes and their perceived value [13] . This nonlinear relationship could potentially influence how the sensorimotor system evaluates perceived changes in expected reward . Movement variability is also thought to influence the gradient of the reinforcement landscape by creating uncertainty between intended actions and actual actions . That is , the expected reward can change depending on whether it is a function of the intended action or the actual action [10] . Further , greater movement variability has been linked to faster learning in reinforcement-based tasks as it promotes exploration of the reinforcement landscape [14 , 15] . Here we designed two experiments to examine how humans adapt the aim of their hand when receiving binary reinforcement feedback . Specifically , we tested the hypothesis that the gradient of the reinforcement landscape influences sensorimotor adaptation . We manipulated the reinforcement landscape gradient by altering the expected reward ( the probability of receiving reward ) given the angular distance between the hand location and target . To maximize reward , participants had to update the aim of their unseen hand to a location that was not aligned with the visually displayed target . Importantly , we normalized the reinforcement landscapes to baseline movement variability on an individual basis . This normalization allowed us to assess the influence of the reinforcement landscape gradient on learning while accounting for individual differences in movement variability . We used binary reinforcement feedback to eliminate the potentially confounding nonlinear relationship between different magnitudes of reward and their perceived value . We tested the prediction that a steep reinforcement landscape would lead to faster learning than a shallow landscape ( Experiment 1 ) . Building on these results , in Experiment 2 we used a complex reinforcement landscape where each participant’s initial action was positioned in the ‘valley’ between two slopes that had different gradients ( steep and shallow ) and rose in the opposite direction ( clockwise or counterclockwise ) . We predicted that participants would ascend the steeper portion of the complex reinforcement landscape . Finally , we introduce a model that relies on binary reinforcement feedback to update the aim of the hand during a reaching task .
In Experiments 1 and 2 , 120 participants performed 450 forward reaching movements ( Fig 1A ) . For each trial they began at a starting position and attempted to pass their hand ( unseen ) through a virtually displayed target . We recorded reach angle , which was calculated relative to the line that intersected the visually displayed target and starting position , the moment their hand was 20 cm away from the starting position . Participants began by completing 50 baseline trials , where no feedback was received on whether reaches were successful or unsuccessful . During the next 350 experimental trials participants received binary reinforcement feedback according to their randomly assigned reinforcement landscape ( see Experiment 1 and Experiment 2 ) . Like baseline , the final 50 washout trials were also performed without feedback . We instructed participants to “hit the target” . We informed participants that no feedback would be received if they missed the target , and for each target hit 1 ) the target would expand , 2 ) they would hear a pleasant noise , and 3 ) they would receive monetary reward , such that they could earn up to $5 . 00 CAD . To test the idea that the gradient of the reinforcement landscape influences sensorimotor learning , we manipulated the probability of receiving positive reinforcement feedback ( i . e . , reward ) as a function of reach angle . In Experiment 1 we tested the idea that the gradient of the reinforcement landscape would influence the rate of learning . In Experiment 2 we tested the notion that the sensorimotor system would use gradient information from a complex reinforcement landscape to find the best of multiple solutions that improved performance . We tested the idea that the gradient of the reinforcement landscape influences the rate of learning . We predicted that a steeper reinforcement landscape would lead to a faster learning rate . Participants either experienced a steep reinforcement landscape ( n = 40 ) or a shallow reinforcement landscape ( n = 40 ) . To control for direction , the probability of positive reinforcement ( reward ) rose either in the clockwise ( Fig 1B; Eq 2 ) or counterclockwise direction ( Eq 3 ) . We created these landscapes by manipulating the probability of reward as a function of reach angle . The width of each reinforcement landscape , that is the probability of reward given reach angle , was normalized to baseline movement variability on an individual basis . This normalization ensured that participants in an experimental group ( steep or shallow ) experienced the same gradient for a particular landscape , irrespective of movement variability . This also allowed us to calculate the change in reward probability for a change in intended aim ( Fig 1C , Eqs 7–9 ) across participants , as well as the optimal intended reach aim ( θ o p t a i m ) that maximized success ( Eq 10 ) . Reach angles were normalized by baseline movement variability on an individual basis and expressed as a z-score . Further , to allow for visual and statistical comparison irrespective of the direction that the reinforcement landscape rose ( clockwise or counterclockwise ) , we multiplied the normalized reach angles by −1 . 0 for all participants that experienced a reinforcement landscape that increased in the counterclockwise direction [5 , 16] . Similar to others [17 , 18] , we found two subpopulations of participants in Experiment 1: learner and non-learners . When examining the histogram of final reach position ( average normalized reach angle of the last 100 experimental trials ) , we found a bimodal distribution ( S1 Data , S1 Fig ) . Based on this analysis , we found that a cutoff z-score of 1 . 0 did well to partition the bimodal distribution and separate the learners from the non-learners . Fig 2A and 2B shows individual data from two participants . The participant experiencing a steep reinforcement landscape quickly changed their behaviour towards a reach angle that maximized reward ( z-score between 3 and 6 ) . The participant experiencing a shallow reinforcement landscape took comparatively longer to change their reaching behaviour . The difference in learning rates between these two participants is most evident during the first 50 experimental trials . Fig 2C shows the average reach angle over trials for participants ( learners ) that experienced either a steep or shallow reinforcement landscape . To compare the rate of learning between these two groups of participants , we fit an exponential function ( Eq 6 ) over the experimental trials via bootstrapping ( see Methods for details ) . We were interested in the time constant of the exponential function , λ , which defines the rate of learning . The exponential bootstrap fit analysis was performed separately first with the data from the learners alone , and then again with all participants ( learners and non-learners together ) . As hypothesized , we found that the participants experiencing the steep landscape had faster learning ( i . e . , a lower exponential function time constant , λ ) than those experiencing a shallow reinforcement landscape ( p = 0 . 012 learners only , p = 0 . 021 for combined learners and non-learners , one-tailed ) . Fig 2D shows the posterior probability distribution and cumulative distribution of the time constant λ given the reach angles of participants experiencing either a steep or shallow reinforcement landscape . The inset of Fig 2D shows the posterior probability distribution of the time constant difference between the two experimental groups , from which we calculated the p-values reported directly above . The direction of the reinforcement landscape , clockwise or counterclockwise , did not influence the rate of learning ( p = 0 . 540 , two-tailed ) . We also found that participants who experienced a steep landscape were more likely to be classified as learners than those who experienced a shallow reinforcement landscape ( p = 0 . 036 , two-tailed; Table 1 ) . In this experiment we tested the notion that the sensorimotor system uses gradient information from a complex reinforcement landscape to find the solution that maximizes reward . The probability of reward was at a minimum for reaches toward mean baseline behaviour but increased at different gradients ( steep or shallow ) for reaches in either direction ( clockwise or counterclockwise ) away from the target . We predicted that a significantly greater number of participants would adapt their reach aim in the direction of the steeper gradient . Two different reinforcement landscapes were used in this experiment: one landscape had a steep slope that rose in the clockwise direction and a shallow slope that rose in the counterclockwise direction ( steep clockwise; n = 20; Fig 3A; Eq 4 ) , and the other landscape had a steep slope that rose in the counterclockwise direction and a shallow slope that rose in the counterclockwise direction ( steep counterclockwise; n = 20; Fig 3C; Eq 5 ) . As in Experiment 1 , for both reinforcement landscapes we calculated the probability of reward given intended aim ( Fig 3B and 3D; Eqs 7–9 ) , as well as the optimal intended reach aim ( θ o p t a i m ) to maximize reward ( Eq 10 ) . Here we were interested in the frequency of participants that changed their reach behaviour in the clockwise or counterclockwise direction , depending on whether they experienced the steep clockwise or steep counterclockwise reinforcement landscape . We used the average of the last 100 experimental trials to classify the direction of their final reach behaviour . Final reach direction was classified to be counterclockwise ( z-score ≤ −1 . 0 ) , center ( −1 . 0 < z-score < +1 . 0 ) or clockwise ( z-score ≥ +1 . 0 ) . This classification was done separately for those experiencing a steep clockwise or steep counterclockwise reinforcement landscape . Fig 4A and 4B show the average reach angle of steep learners , shallow learners and non-learners for participants experiencing the steep clockwise or steep counterclockwise reinforcement landscapes , respectively . The steep and shallower learners in Fig 4A respectively look qualitatively similar to the steep and shallow learners in Fig 4B when reflecting either of these figures about its x-axis . The behaviour of the non-learners was less consistent based on whether they experienced the clockwise or counterclockwise landscapes , but there was a limited frequency of non-learners ( n = 2 and n = 3 , respectively ) . As an additional classification , participants that had a final reach position corresponding to the direction of the steep slope , shallow slope or a central location , were deemed steep learners , shallow learners and non-learners , respectively . This was done separately for participants that experienced either the steep clockwise or steep counterclockwise reinforcement landscape . For this experiment we predicted that participants would ascend the steeper gradient of their assigned reinforcement landscape . Specifically , we expected more participants who experienced the steep clockwise reinforcement landscape to have their final average reach angle to be classified as clockwise . Similarly , we expected participants who experienced the steep counterclockwise reinforcement landscape to have their final average reach angle to be classified as counterclockwise . Using z-score cutoffs of −1 . 0 and +1 . 0 , we found that there were significant differences in the final average reach classification between participants who experienced a steep clockwise or steep counterclockwise reinforcement landscape ( p = 0 . 010 , two-tailed , Fig 4C ) . These results were robust to whether we used z-score cutoffs of ±0 . 5 ( p = 0 . 016 , two-tailed ) or ±1 . 5 ( p = 0 . 020 , two-tailed ) to classify final reach direction . Further , we found that the direction ( clockwise or counterclockwise ) did not influence behaviour in terms of whether a participant was classified as a steep learner , shallow learner or non-learner ( p = 0 . 810 , two-tailed ) . Thus , the direction of the reinforcement landscape had an effect on their final reach direction , but it did not impact the frequency of steep learners , shallow learners , and non-learners . Here we introduce a learning model that predicts reach angle ( θn ) on a trial-by-trial basis ( Eq 1 ) . This model takes the form θn=N ( θ¯naim , σn2 ) ( 1a ) , θ¯n+1aim , σn+12={ θ¯naim+α ( θn−θ¯naim ) , σm2r=1θ¯naim , σm2+σe2r=0 ( 1b ) , ( 1c ) , where n and n + 1 represent the current and next trial , respectively . The model considers whether the current reach angle was successful ( r = 1 ) or unsuccessful ( r = 0 ) . The model explores small regions of the workspace in a natural way via movement variability . Here , the variance of movement variability on the current trial ( σ n 2 ) is a function of motor ( execution ) variance ( σ m 2 ) after a successful reach , and the addition of both motor variance and exploratory variance ( σ e 2 ) after an unsuccessful reach [2] . It was assumed that the variance of movement variability follows a Normal distribution N ( θ ¯ n a i m , σ n 2 ) [19 , 20 , 21] , where θ ¯ n a i m represents the intended reach aim on the nth trial . Inspired by Haith and Krakauer ( 2014 ) [22] , the only action cached in memory is related to the location of the last successful reach . That is , an update in the intended reach aim ( θ ¯ n a i m ) occurs only after a successful reach . Specifically , this update is some proportion ( α ) of the difference between the current intended aim ( θ ¯ n a i m ) and the location of the last successful reach ( θn ) . After an unsuccessful reach , the intended aim remains the same ( i . e . , θ ¯ n a i m is still stored based on the last successful reach ) but the subsequent movement has greater variance ( σm + σe ) . This results in a similar formulation to the equation just recently published by Therrien and colleagues ( 2018 ) [23] . There are some slight differences between the present model and the Therrien et al . ( 2015 , 2018 ) model in terms of how they update the intended aim following a successful reach [23 , 24] ( see Discussion ) . Nevertheless , in the following we show the utility of this class of model in terms of replicating several features of sensorimotor adaptation . As previously suggested by van Beers ( 2009 ) [25] and Zhang et al . ( 2015 ) [26] , our model assumes that the nervous system has some knowledge of movement variability when updating intended reach aim . This allows for an estimated difference between intended aim and actual reach angle , despite the participants have no vision of their hand during trials . Our model has three free parameters: α = 0 . 40 ( unitless ) , σm = 0 . 81 ( z-score ) , and σe = 0 . 90 ( z-score ) . The initial guesses of σm and σe for the fitting procedure were made with a trial-by-trial difference analysis ( S2 Data , S2 Fig ) that we modified from Pekny et al . ( 2015 ) . It is expected that σm is slightly lower than a z-score of 1 , or baseline movement variability , since here we were interested in the movement variability on a single-trial and not the additive variance that results from repeatedly subtracting two successive trials ( see S2 Data , S2 Fig for further details ) . We found the best-fit parameters using a bootstrap optimization fitting procedure using only the data from Experiment 1 ( S3 Data ) . With our learning model , we simulated 40 individuals experiencing the steep reinforcement landscape of Experiment 1 , and then simulated another 40 individuals experiencing the shallow landscape . We found that simulated individuals displayed similar trial-by-trial variance and rates of learning compared to the behavioural data ( compare Fig 5A and 5B to Fig 3A and 3B ) . We averaged across the 40 simulated individuals in each condition ( steep or shallow reinforcement landscape ) . The model did well to capture between-subject variance . Similar to the behavioural data , we also found the emergence of exponential learning curves ( Fig 5C ) . We then simulated 100 , 000 individuals experiencing the steep landscape and 100 , 000 individuals experiencing the shallow landscapes . Simulating a large number of individuals allowed us to numerically converge on the theoretical exponential learning curves produced by the model . We then averaged across simulated individuals in each group and fit an exponential function . The best-fit time constant , λ , of the exponential function for the steep and shallow reinforcement landscapes were 28 . 0 and 49 . 6 , respectively . Both values fall within the 95th percentile confidence intervals of the corresponding behavioural data . ( steep [10 . 7 , 36 . 2] , shallow [27 . 4 , 102 . 1]; Fig 2D ) . In S2 Data , S2 Fig we present a trial-by-trial analysis , as a function of reinforcement history , of both the model simulations and behavioural data . We show in S4 Data with model simulations that changing the initial reward probability of the shallow landscape has a marginal influence on learning rates . Here we simulated Experiment 2 using our learning model ( n = 100 , 000 simulated individuals ) by using the best-fit parameters obtained from the behavioural data in Experiment 1 . To compare the model to the behavioural results , we combined the data from all participants in Experiment 2 . This was accomplished by multiplying the normalized reach angles by −1 . 0 for participants that experienced the steep counterclockwise reinforcement landscape . Fig 6A shows a histogram of the final reach angle of both the behavioural data and model simulations . We then used the same final reach direction classification for the model simulations that we used for the behavioural data . Based on these classifications , we found that the model produced a similar frequency of steep learners , shallow learners and , to some extent , non-learners as the behavioural data ( Fig 6A and 6B ) . Further , we found that the model did well to explain reach angle over trials for these three different groups ( R2 = 0 . 85; Fig 6B ) . We also performed an analysis to explore the influence of reinforcement feedback during the initial periods of experimental trials . To this end , we calculated how a participant’s Nth success predicted their final reach classification . This was done separately for successful reaches made on the shallow ( Fig 6C ) and steep ( Fig 6D ) slopes of the complex reinforcement landscape . We found that if a participant had their 1st success on the steep slope that they would likely be classified as a steep learner ( Fig 6D ) . Conversely , a 1st success on the shallow slope was not a good predictor of final reach classification ( Fig 6D ) . However , a participant was likely to be classified as a shallow learner if their 15th success and beyond was on the shallow slope . As shown , the model and data were highly correlated with each other ( R2 = 0 . 933 and R2 = 0 . 995 , respectively ) . This analysis shows that the participants and model simulations were both heavily influenced by early exploration and gradient information when they experienced a complex reinforcement landscape . Using the same set of best-fit parameters found from the data of Experiment 1 , we replicated the results of Izawa and Shadmehr ( 2011 ) and our previous work [5] ( see Fig 7A and 7B , respectively ) . In the study by Izawa and Shadmehr ( 2011 ) , participants were only provided binary feedback if they hit a target region that was gradually rotated from a visual displayed target . In our previous work [5] , cursor position was laterally shifted according to a skewed probability distribution and participants received binary feedback on whether the laterally shifted cursor hit the visually displayed target . In both these studies , participants had no vision of their hand or arm . We had our model experience the same reported conditions from both these studies . Our model did very well to capture average reach behaviour , between-subject variance , trial-by-trial movement variability as a function of reinforcement history ( see [2]; S2 Data , S2 Fig ) , and suboptimality . Here , we define suboptimality as approaching but not quite reaching the optimal behaviour that maximizes reward ( i . e . , x o p t m a x ( h i t s ) in Fig 7B ) . Suboptimality is often a feature of ‘greedy’ algorithms that place greater emphasis on locally optimal information rather than globally optimal information [27] . Our learning model would be considered a greedy algorithm since it samples from spatially local motor actions and updates its aim based on the last recent success . A greedy algorithm can lead to suboptimal performance in non-symmetrical landscapes ( e . g . , [5] , Fig 1B and 1C ) and complex landscapes with local maximums ( e . g . , Fig 2 ) . Behaviourally , this was particularly evident in Experiment 2 where a relatively high proportion of participants ( 22 . 5% ) performed suboptimally by ascending the shallow slope and having a final reach direction aligned with a local maximum . Further motivated by the model of Haith and Krakauer ( 2014 ) [22] , we ran simulations to examine how movement variability influences the rate of learning and whether our model could capture random-walk behaviour . There is some debate to whether movement variability is beneficial [14 , 22] or detrimental [15 , 28 , 29 , 30] when learning from error feedback , which to some extent may be explained by the consistency ( entropy ) of the environment [31] . Recent work has suggested that movement variability is important when learning from reinforcement feedback and can influence the rate of learning [14] . Here we manipulated both motor ( σm ) and exploratory ( σe ) contributions to movement variability when simulating the experimental conditions of Experiment 1 . We found that increasing the variance of movement variability , either σm or σe , led to increased rates of learning for both the steep ( Fig 8A ) and shallow ( Fig 8B ) reinforcement landscapes . However , it should be noted that with different amounts of movement variability there may exist a trade-off between the rate of learning and the probability of reward . In previous literature , random-walk behaviour along task-irrelevant dimensions has been attributed solely to error-based learning [32 , 33 , 34 , 35] . In the study by van Beers and colleagues ( 2013 ) , participants received error ( visual ) feedback when reaching to large targets ( Fig 9D ) . They displayed random-walk behaviour ( i . e . , trial-by-trial correlations ) along the task-irrelevant dimensions that had no bearing on task success . Here we tested whether reinforcement feedback can also lead to random-walk behaviour . To test this idea , we used our model to simulate the experiment van Beers et al . ( 2013 ) . Critically however , we did not use error feedback as in the original study—instead we only provided binary reinforcement feedback to the model based on whether it had hit or missed the target . Interestingly , we found that random-walk behaviour along task-irrelevant dimensions also emerged from our model ( Fig 9A , 9B and 9C ) . Thus , our simulations suggest that random-walk behaviour , at least in part , may be attributed to reinforcement-based processes . Our model relies on updating intended reach aim by using only the recent success ( temporally current information ) based on sampling the reinforcement landscape via movement variability ( spatially local information ) . Given the strong relationship between our model and the behavioural data throughout the simulations above , our results suggest that the sensorimotor system largely depends on temporally recent and spatially local information to update where to aim the hand during our reinforcement-based sensorimotor learning task .
We found that manipulating the gradient of the reinforcement landscape influenced sensorimotor learning . First , we found that a steep reinforcement landscape led to faster learning . Second , participants were more likely to adjust their aim in the direction of the steepest portion of a complex reinforcement landscape . Our learning model that relies on reinforcement feedback to update aim of the hand was able to replicate the results in Experiment 1 and predict the results found in Experiment 2 . Taken together , our data and model suggest that the sensorimotor behaviour observed in our experiments does not necessitate a full representation of the entire reinforcement landscape ( storing the expected reward for all possible actions ) . Rather , the majority of learning behaviour can be captured using temporally recent and spatially local information about actions and rewards . Participants learned faster when they experienced a steep reinforcement landscape , compared to those experiencing a shallow landscape . To our knowledge this is the first work showing that the gradient of the reinforcement landscape influences the rate of learning . The present study may be distinguished from previous work showing that a graded reinforcement landscape can augment error-based learning [11 , 12] . Here we show that the gradient of a binary , positive reinforcement landscape influences learning in the absence of error feedback . Using a visuomotor rotation task , Nikooyan and Ahmed ( 2014 ) used both graded reinforcement feedback and error feedback to study their effects on learning . Participants moved a cursor which was rotated from the unseen hand as it moved away from a start position towards a virtual target . Participants performed the task either with or without error ( cursor ) feedback . They experienced a graded reinforcement landscape , such that the magnitude of reward changed with the angular distance of the hand from the target , according to either a linear or cubic function . The maximum reward magnitude occurred when the rotated cursor hit the target . Relative to learning using only error feedback , linearly and cubically graded reinforcement landscapes combined with error feedback accelerated learning . They also found differences in the amount of adaptation between participants who experienced only graded reinforcement feedback ( without any visual error feedback ) based on either a linear or cubic reinforcement landscape . However , these differences reversed in direction during the course of the experiment and , in some instances , opposed theoretical predictions from a temporal-difference ( TD ) reinforcement algorithm [11 , 36] . These inconsistent findings may have been caused by not controlling for individual differences in movement variability [14] or the nonlinear relationship between different reward magnitudes and their perceived value [13] . In our experiments , we used binary feedback that always had the same magnitude of reward . This eliminated the nonlinear relationship between different reward magnitudes and their perceived value [13] . Further , we controlled for individual differences in movement variability , which can influence exploration and the rate of learning in reinforcement-based tasks [14 , 15 , 37] . Thus , our work is the first to our knowledge that has isolated how the gradient of the reinforcement landscape influences the rate of sensorimotor learning . In our second experiment , each participant’s initial action was positioned in the ‘valley’ between two slopes that had different gradients ( steep or shallow ) and rose in opposite directions . As predicted , we found participants were more likely to ascend the steepest portion of a complex reinforcement landscape . While the majority of participants ascended the steep slope , several participants ascended the shallow slope . The probability of whether they would be classified as a steep learner or shallow learner seemed related to initial success on either the steep or shallow portion of the landscape . In particular , participants were very likely to be classified as a steep learner if their first successful reach was on the steep slope of the complex landscape . Our learning model did well to capture trial-by-trial behaviour , between subject variability and exponential learning curves in Experiment 1 . Using the same set of best-fit parameters found using Experiment 1 data , we then simulated Experiment 2 . The model produced similar distributions of steep-learners , shallow-learners and , to some extent , non-learners . The model was also able to capture several aspects of learning reported in previous work [1 , 2 , 5] . As mentioned , the behavioural findings of Experiment 2 were well predicted by our learning model . Critically , our model does not build up a full representation of the reinforcement landscape . Rather , it relies on using movement variability for spatially local exploration and temporally recent reinforcement feedback to update intended reach aim . Considering that the model does not build up a representation of the reinforcement landscape and that it was highly correlated with the behavioural results , suggests that whether participants ascended up the shallow portion or the steep portion of the complex reinforcement landscape was largely due to movement variability and the probability of reward . As an example , a participant’s initial reach angle had an equal probability of being aligned with either the steep or shallow slope due to movement variability . However , a participant’s initial reach was more likely to be rewarded on the steep slope because of its higher rate of reward . Moreover , the further a participant ascended either the steep or shallow slope it became increasingly unlikely that future successes would promote them from descending a slope . In particular , the steep slope had a stronger effect of promoting participants to ascend since its reward rate was double that of the shallow slope . This is evident in Fig 6D , where both the participants and model simulations were very likely to be classified as a steep learner when they had their 1st success on the steep slope . Conversely , final reach classification for both the participants and model simulations only became reliable after approximately the 15th success on the shallow slope ( Fig 6C ) . Thus , participants and the model were more likely to be initially rewarded on the steep slope and also more likely to ascend the steep slope . Taken together , our behavioural results and model simulations support the idea that the nervous system does not build up a representation of the reinforcement landscape . Rather , the nervous system seems to rely on spatially local movement variability for exploration and temporally recent reinforcement feedback to update hand aim . Importantly , our findings also suggest that early exploration is highly influential when attempting to avoid local maximums and discover a global maximum . Several hallmarks of motor learning simply emerged from our phenomenological learning model . Specifically , we found that the model produces exponential learning curves , between-and within-subject movement variability , suboptimal performance , increased learning rates with greater movement variability , trial-by-trial variance given a successful or unsuccessful reach ( S2 Data , S2 Fig ) , reduced variability when hand aim approaches the optimal solution to maximize success , and random-walk behaviour in task-irrelevant dimensions . To our knowledge , random-walk behaviour has only been previously associated with error-based learning [32 , 33 , 34 , 35] . Future work should examine whether random-walk behaviour can be replicated with experiments involving only reinforcement feedback . The model of Haith and Krakauer ( 2014 ) [22] and the recently published model of Therrien and colleagues ( 2018 ) [23] would also be able to reproduce the rich set of behavioural phenomena mentioned in the above paragraph . These two models also rely on movement variability for exploration and caching a single aim direction that can be updated based on recent feedback . The Haith and Krakauer model stems from a Markov chain Monte Carlo ( MCMC ) algorithm and relies on sampling different motor actions . Actions are drawn from a probability distribution with a previously cached action acting as the distribution mean . If a recently experienced action is deemed less costly and or more rewarding than the previously cached action , this recent action becomes the newly cached action . Although this model was demonstrated with error-based tasks ( i . e . , visuomotor rotation and force-field adaptation ) , it could be extended to update hand aim using reinforcement feedback . As mentioned above , the work of Haith and Krakauer ( 2014 ) [22] and Pekny et al . ( 2015 ) [2] provided the motivation for our model . This resulted in a similar set of equations as recently proposed by Therrien and colleagues ( 2018 ) [23] , albeit with some slight differences in terms of how the model updates hand aim . In their model , updating hand aim relies on the assumption that the sensorimotor system has perfect knowledge of additional exploratory movement variability following an unsuccessful reach and partial knowledge of the motor ( execution ) variability following a successful reach . Conversely , our model assumes that the same proportion of motor and exploratory movement variability are known by the sensorimotor system when updating hand aim . While some studies have explored the idea that the sensorimotor system has some awareness of movement variability [25 , 26] , to our knowledge no study has explored what proportion of movement variability is known by the sensorimotor system following a successful or unsuccessful reach . Nevertheless , our present work highlights the utility of this class of models , which rely on movement variability for exploration and caching a single action , to predict sensorimotor adaptation . Emergent behaviour and simplicity are perhaps the most attractive features of our learning model . The model uses movement variability to sample the reinforcement landscape locally , and temporally recent information to update where to aim the hand . These features distinguish our model from several mainstream reinforcement algorithms in the motor literature that rely on building a full representation of the reinforcement landscape [1 , 11 , 37 , 38] . The explicit goal of these algorithms is to maximize reward . For many of these reinforcement learning models , exploration and maximizing reward is accomplished by selecting actions using a soft-max function that considers the expected value of all possible actions . In general , such models rely on a large number free parameters and assumptions . Depending on the task and the discretization of considered actions and states , storing a representation of the reinforcement landscape in real-world situations could require vast amounts of memory and may be implausible . In comparison , our model ( similarly , [22 , 23] ) has a small number of free parameters , makes few assumptions , implicitly maximizes reward , and uses minimal memory . Our learning model does well to capture several aspects of behaviour during learning . For the model to adapt however , there has to be a non-zero gradient within the range of naturally occurring movement variability . Thus , the model is limited to small areas of the workspace . It has been shown in previous studies that participants are unaware of a change in aim when operating over small areas of the workspace [1 , 39] . In our task , the average change in behaviour was ∼ 7 . 0 degrees , suggesting that the participants in our experiments were also likely unaware of the small shifts in reach angle [40] . Learning beyond these small areas of the workspace would likely also require active ( cognitive ) exploration strategies [41] and explicit awareness of the reinforcement landscape [17] . Nonetheless , our model did well to capture many features of sensorimotor adaptation over small areas of the workspace . Behaviourally , we found that a steeper reinforcement landscape leads to faster learning . We also found that humans are more likely to ascend the steepest portion of a complex landscape . Our model was able to replicate our findings without the need to build up a representation of the reinforcement landscape . Further , several hallmarks of human learning simply emerged from this model . Taken together , our data and our model suggest that the sensorimotor system may not rely on building a representation of the reinforcement landscape . Rather , over small areas of the workspace , sensorimotor adaptation in reinforcement tasks may occur by using movement variability to locally explore the reinforcement landscape and recent successes to update where to aim the hand .
80 individuals participated in Experiment 1 ( 20 . 1 years ± 2 . 8 SD ) and 40 individuals participated in Experiment 2 ( 20 . 5 years ± 2 . 8 SD ) . Participants reported they were healthy , right-handed and provided informed consent to procedures approved by Western University’s Ethics Board . In both experiments , participants held the handle of a robotic arm ( InMotion2 , Interactive Motion Technologies , Cambridge , MA; Fig 1A ) and made right-handed reaching movements in a horizontal plane . An air-sled supported each participant’s right arm while providing minimal friction with the desk surface during the reaching movements . A semi-silvered mirror blocked vision of both the participant’s upper-limb and the robotic arm , and projected images from an LCD screen onto a horizontal plane passing through the participant’s shoulder . An algorithm controlled the robot’s torque motors and compensated for the dynamical properties of the robotic arm . The position of the robotic handle was recorded at 600 Hz and the data were stored for offline analysis . During both experiments , participants were exposed to one of several different reinforcement landscapes . We manipulated the gradient of the reinforcement landscapes by controlling the probability of positive reinforcement ( reward ) as a function of reach angle . These landscapes were constructed such that participants had to learn to change their reach angle , relative to baseline performance , to maximize the probability of reward . The width of the reinforcement landscape experienced by a participant was normalized to the variability of their baseline reach angles . Reach angle was measured at the position where the robot handle first became 20 cm away from the center of the starting position , and was calculated relative to the line that intersected the starting position and the displayed target . The last 25 baseline trials were used to calculate their average baseline reach angle and the standard deviation of their angular movement variability . All reach angles were converted into z-scores . Specifically , reach angles were expressed relative to the average baseline reach angle and then normalized by the participant’s average standard deviation recorded during baseline . Thus , a z-score of 0 . 0 corresponded with their average baseline reach angle . A z-score of 1 . 0 or −1 . 0 indicated that a reach angle was ± 1 SD away from their average baseline reach angle in the clockwise or counterclockwise direction , respectively . Defining the reinforcement landscape in terms of a z-score served two purposes . First , we controlled for slight differences in individual aiming bias by positioning all participants on the same location of the reinforcement landscape during the start of the experimental trials . Second , we normalized the width of the reinforcement landscape for each participant based on baseline movement variability , allowing us to isolate how the reinforcement landscape gradient influenced learning . We performed data analysis using custom Python 2 . 7 . 11 scripts . For all participants in both Experiments , we recorded their endpoint reach angle for each of the 450 trials . Reach angles were normalized based on baseline reach behaviour , as described above , and expressed as a z-score . Tests between means were performed using bootstrapped hypothesis tests with 1 , 000 , 000 resamples ( Python 2 . 7 . 11 ) [5 , 45 , 46 , 47] . Fisher’s exact test was used to test frequency tables ( R 3 . 2 . 4 ) . Coefficient of Determination ( R2 ) was used to compare model simulations to behavioural data ( Python 2 . 7 . 11 ) . One-sided tests were used for planned comparisons based on theory-driven predictions . For all other comparisons we used two-tailed tests . Multiple comparisons were corrected for Type-I error using the Holm-Bonferroni procedure [48] . Statistical tests were considered significant at p < 0 . 05 . | In recent years it has been shown that reinforcement feedback may also subserve our ability to acquire new motor skills . Here we address how the reinforcement gradient influences motor learning . We found that a steeper gradient increased both the rate and likelihood of learning . Moreover , while many mainstream theories posit that we build a full representation of the reinforcement landscape , both our data and model suggest that the sensorimotor system relies primarily on temporally recent and spatially local gradient information to drive learning . Our work provides new insights into how we sample from a continuous action-reward landscape to maximize success . | [
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] | 2019 | The gradient of the reinforcement landscape influences sensorimotor learning |
Rates of evolution span orders of magnitude among RNA viruses with important implications for viral transmission and emergence . Although the tempo of viral evolution is often ascribed to viral features such as mutation rates and transmission mode , these factors alone cannot explain variation among closely related viruses , where host biology might operate more strongly on viral evolution . Here , we analyzed sequence data from hundreds of rabies viruses collected from bats throughout the Americas to describe dramatic variation in the speed of rabies virus evolution when circulating in ecologically distinct reservoir species . Integration of ecological and genetic data through a comparative Bayesian analysis revealed that viral evolutionary rates were labile following historical jumps between bat species and nearly four times faster in tropical and subtropical bats compared to temperate species . The association between geography and viral evolution could not be explained by host metabolism , phylogeny or variable selection pressures , and instead appeared to be a consequence of reduced seasonality in bat activity and virus transmission associated with climate . Our results demonstrate a key role for host ecology in shaping the tempo of evolution in multi-host viruses and highlight the power of comparative phylogenetic methods to identify the host and environmental features that influence transmission dynamics .
RNA viruses display exceptionally variable rates of molecular evolution , with up to 6 orders of magnitude in nucleotide substitution rates observed among viral species [1] . Because of the importance of genetic and phenotypic evolution for infecting new host species , evading immune responses and obstructing successful pharmaceutical development , understanding the factors that govern the speed of viral evolution is critical for mitigating viral emergence [2] . To date , explanations for evolutionary rate heterogeneity have been predominately virus-oriented . These have focused on features of genomic architecture that determine underlying mutation rates , aspects of the virus life cycle such as latency and transmission mode that can influence the replication rate within hosts and generation times between hosts , and diversification through positive selection [2]–[5] . Less well understood are the determinants of evolutionary rate variation among closely related viruses ( e . g . , species within genera ) or among lineages of the same viral species circulating in different geographic regions or host species [6] , [7] . Because viral genomic features and replication mechanisms are minimally variable at such shallow taxonomic levels , aspects of host biology that influence rates of transmission and replication may be more likely to control the tempo of viral evolution . Consistent with this hypothesis , several human viruses ( e . g . , HTLV , HIV and Chikungunya virus ) that exploit multiple modes of transmission or experience variable immunological pressures within-hosts demonstrate accelerated molecular evolution in conditions associated with enhanced transmission and replication [6] , [8] , [9] . The propensity of many RNA viruses to ‘jump’ between host species presents an intriguing natural experiment to test whether viral evolutionary rates change according to traits of host species that influence viral replication and transmission or remain evolutionarily conserved along the ancestral history of the virus , reflecting intrinsic biological features of viruses [10] , [11] . Moreover , knowledge of accelerated viral evolution in certain reservoir hosts might be useful for predicting the geographic or species origins of future host jumps if faster evolution enhances the genetic diversity on which natural selection may operate . Despite these implications for viral emergence and evolution , the relationship between host biology and viral evolution remains largely unexplored . In two recent analyses , influenza A viruses infecting wild and domesticated birds and infectious haematopoietic viruses of wild and farmed fish each showed some intra-specific variation in viral evolutionary rates . However , host species identity failed to explain these differences , perhaps because high rates of transmission between host groups in each system diluted the effects of any single species on virus evolution [7] , [12] . Elucidating the influence of host biology on viral evolution therefore requires large datasets of closely related viruses from multiple ecologically distinct host species that are largely capable of independent viral maintenance . Rabies virus ( Lyssavirus , Rhaboviridae ) is a globally distributed and lethal zoonotic agent that causes more than 50 , 000 human deaths annually [13] . Although most human rabies is attributed to dog bites in developing countries , rabies virus also naturally infects over 80 bat species from 4 chiropteran families , and bats represent an increasing source of human and domesticated animal rabies in the Americas [14] . The phylogeny of bat rabies virus reveals viral compartmentalization into many largely species-specific transmission cycles , which have arisen from repeated host shifts within the bat community [15] , [16] . Coupled with the diverse behavioral and life history strategies of bats , rabies virus therefore provides a unique opportunity to explore the effects of host biology on virus evolution while explicitly accounting for the effects of the ancestral history of the virus on evolutionary rate by using the rabies virus phylogeny as a guide to past host shifts . Moreover , because many American bat species and genera are broadly distributed with distinct viral lineages in different parts of their geographic range , ecological effects that reflect geographic variation in host behavior can be distinguished from taxonomic effects that arise from the physiological similarity of closely related host species . Bats represent an especially pertinent taxonomic group for exploring the effects of host biology on viral evolution because of growing interest in how bat ecology influences zoonotic agents such as SARS virus , Nipah virus and Ebola virus [17] . If the behavioral and ecological traits of bats that are hypothesized to influence the maintenance and emergence of pathogens affect either virus replication within hosts or the rate of transmission between hosts , they might also have consequences for viral evolution . For example , overwintering of temperate bats through hibernation or extended bouts of torpor might cause a seasonal pause in transmission and/or decelerated disease progression within hosts , perhaps due to metabolic down regulation of cellular processes or reduced contact rates while bats are inactive [18] , [19] . These climate-mediated mechanisms might slow evolution in viruses associated with temperate bats compared to tropical species , where year-round food availability and milder temperatures extend bat and virus activity through all seasons . Next , high contact rates in colonial bats may promote infections with greater virulence and reduced incubation periods , increasing the number of viral generations per unit time and speeding viral evolution [19] , [20] . Finally , long distance migration , a relatively common strategy in bats , may slow viral evolution by homogenizing viral populations or by reducing transmission if the physiological stress from migration removes infected hosts from the population , i . e . , ‘migratory culling’ [21] . Here , we compile large datasets of bat rabies virus sequences to quantify variation in the evolutionary rate of rabies virus when associated with ecologically and behaviorally distinct reservoir species found in different geographic regions of the Americas . Further , we test whether the tempo of evolution undergoes episodic shifts when rabies virus establishes in new species , implicating host biology as a key driver of viral evolution , or evolves gradually along the ancestral history of the virus , reflecting the greater importance of conserved viral features in controlling evolutionary rates . Finally , we integrate ecological and genetic data through newly developed Bayesian hierarchical phylogenetic models to identify the traits of hosts and the environment that influence rates of viral evolution .
We employed maximum likelihood ( ML ) and Bayesian phylogenetic analyses to define 21 subspecies , species or genus specific lineages of rabies virus for comparative analyses of evolutionary rates ( see Materials and Methods for analytical details and operational definitions of lineages ) . A relaxed molecular clock analysis indicated that viral lineages were relatively young , ranging in age from 83–305 years , with a most recent common ancestor of all bat lineages dating back to 1585 ( 95% Highest Posterior Density , HPD: 1493–1663; Table S1 ) . To describe the evolutionary rate variation among viral lineages , we focused on substitution rates in the third codon position ( CP3 ) , as these predominately synonymous substitutions can indicate more clearly how viruses respond to processes affecting their replication rate and generation time between infections [22] . Average substitution rates estimated for each lineage independently ( Independent Lineage Models , ILM ) and by a hierarchical phylogenetic model ( HPM ) each spanned approximately one order of magnitude among viral lineages compartmentalized to different host species ( ILM range: 8 . 31×10−5–2 . 08×10−3; HPM range: 2 . 16×10−4–1 . 07×10−3 substitutions/site/year ) . This indicates that select lineages exhibit approximately 5–22 fold acceleration of evolution relative to the slowest evolving viruses . The HPM substantially improved the precision of parameter estimates relative to the ILMs , with only negligible differences in point estimates for most lineages , as previously described in other host-virus systems [8] , [23] ( Figure 1 ) . Notably , the more extreme values estimated by the ILMs , typically from viral lineages with less informative datasets , were drawn closer to the population mean , suggesting less susceptibility of the HPM to stochastic noise introduced by sampling error and potentially more accurate estimates . If evolutionary rate is a relatively static trait of viruses , it should be conserved in novel environments and would be expected to reflect the ancestral history of the virus , with closely related viral lineages having similar rates , regardless of their contemporary host environment . We tested the degree of phylogenetic signal in the evolutionary rates of bat rabies virus lineages by quantifying values of Blomberg's K ( a common statistic for diagnosing phylogenetic non-independence in comparative analysis ) [24] , [25] . In the context of viral host shifts , significant values of K would indicate that the evolutionary rate tended to remain similar after establishment in the recipient species , whereas weaker values of K would indicate greater shifts in evolutionary rates than expected between the donor and recipient host species . We detected very low and non-significant values of K for both rates estimated under the ILMs ( K = 0 . 36 , P = 0 . 58 ) and the HPM ( K = 0 . 39 , P = 0 . 40 ) and these estimates of K did not differ significantly from expected values given our phylogenetic tree and the observed rates under a null model with randomly distributed rates ( Figure 2B ) . Indeed , lineages that shared a most recent common ancestor sometimes had disparate rates of evolution ( e . g . , LcV and LxV and PhV and MyV2 in Figure 2A ) , although other closely related virus pairs showed minimal differences in evolutionary rate ( e . g . , LnV and PsV ) . Notably , viral lineages maintained by bat species in the temperate zone frequently had slower rates of evolution than lineages from tropical or subtropical bats ( Figure 2A ) . The plasticity of evolutionary rate was corroborated by our Bayesian phylogenetic analysis , which , accounting for uncertainty in the evolutionary history of bat rabies lineages , found no correlation in the evolutionary rates along consecutive branches in the bat rabies virus phylogeny ( covariance = 0 . 005 , 95% HPD: −0 . 051–0 . 058 ) . These analyses demonstrated that rates of viral evolution may be altered or conserved following establishment in new host species and point to host biology rather than the ancestral history of the virus as the most likely candidate for controlling rabies virus evolution . Using a phylogeny of bat hosts from mitochondrial sequence data , we found that viral evolutionary rates were similarly unconstrained by host evolutionary relatedness ( ILMs: K = 0 . 07 , P = 0 . 18; HPM: K = 0 . 21 , P = 0 . 08 ) , such that viruses associated with closely related bat species or sub-species often had dissimilar evolutionary rates ( Figure 1 , Figure S1 ) . Because the evolutionary rates of rabies virus lineages could not be explained by the reservoir host or virus phylogeny alone , we tested whether physiological , ecological or environmental traits of hosts ( Table S2 ) could instead determine viral evolutionary rates using a generalized linear model ( GLM ) comparison approach . The factors that we tested included both evolutionary conserved traits of bats ( i . e . , basal metabolic rate , coloniality , long-distance migration ) and descriptors of sampling effort and climatic region of viral lineages that were largely independent of the bat phylogeny ( Table S3 ) . The GLM identified the climatic region of bat taxa as the single strongly supported predictor of viral evolution ( Akaike importance weight = 1 . 0 ) , alone explaining 66% of the variance in viral evolutionary rates ( F1 , 19 = 37 . 2 , R2 = 0 . 66 , P<0 . 0001; Table S4 ) . A phylogenetic generalized least squares regression approach , designed to control for any residual effects of the virus phylogeny on rates of viral evolution , yielded similar results ( Table S5 ) . A weakness of the statistical methods described above was that they could not account for the often-substantial uncertainty in point estimates of evolutionary rate from the ILMs ( Figure 1 ) . We therefore incorporated the same categorical and continuous terms directly into a Bayesian HPM that allowed us to simultaneously quantify the posterior distribution of the rate of evolution for each viral lineage from the molecular sequence data , estimate regression model parameters and compare candidate models using Bayes factors ( BF ) while accounting for phylogenetic uncertainty . The Bayesian model echoed the strong support for accelerated viral evolution in the tropics and subtropics relative to viruses restricted to the temperate zone ( log effect size , β = 1 . 24 [95% highest posterior density = 0 . 72–1 . 76]; BF = 466 . 54 ) with negligible support for all other predictors ( BF≤1; Figure 3A ) . On average , rabies viruses found in tropical or subtropical bat species accumulated 9 . 44×10−4 ( ILM: 1 . 15×10−3 ) substitutions per site per year ( subs/site/year ) , while viruses in temperate bats accumulated only 2 . 53×10−4 ( ILM: 2 . 92×10−4 ) subs/site/year ( Figure 3B ) – a nearly fourfold deceleration of viral evolution in temperate bats .
By applying an integrated ecological and genetic comparative approach to a unique dataset spanning hundreds of viruses isolated from many host species , our study demonstrated strong effects of host biology on the tempo of molecular evolution in an RNA virus . We observed approximately an order of magnitude of variation in rates of evolution among rabies viruses , indicating that certain lineages evolve up to 22 times faster than others , depending on the reservoir species ( Figure 1 ) . Such rate heterogeneity within a single virus species is exceptional given that similar variation is more commonly observed among different viral families and comparable to the variation in mitochondrial DNA divergence rates between vertebrate taxa genetically isolated for millions of years ( e . g . , whales versus rodents ) [26] . Moreover , the young age of viral lineages in our analysis indicated that evolutionary rate could be altered within decades , consistent with rapid rate adjustment after shifts to new host species ( Table S1 ) . Since the genomic structure , transmission route and replication mechanisms are not known to vary among rabies virus lineages , the plasticity in evolutionary rate that we observed could only have arisen from ecological differences among reservoir bat species that influence transmission and/or replication . Because the tempo of evolution shifted freely through the ancestral history of rabies virus , we sought to identify the traits of bat species that influenced viral evolution . Strikingly , the viral molecular clock ticked nearly four times slower in rabies viruses in temperate bat species compared to tropical and subtropical species ( Figure 2A , Figure 3B ) . This pattern could not be explained by geographic structuring of bat diversity or evolutionary conserved aspects of bat physiology or behavior because several widely distributed bat species and genera supported disparate rates of evolution in viral lineages circulating in different climatic regions ( Figure 1 ) . This resulted in a weak phylogenetic signal of viral evolution in the bat phylogeny ( Figure S1 ) . Similarly , the geographic clustering of viral evolutionary rates in our analysis was unlikely to reflect contrasting patterns of natural selection among climatic regions because evolutionary rates were estimated exclusively from the third codon position of sequences , where most nucleotide substitutions are synonymous , and therefore most likely to be neutral . Alternative explanations for the relationship between climatic region and the rate of viral evolution parallel previous work on latitudinal gradients of molecular evolution in free-living plants and animals . This work has suggested that accelerated evolution in the tropics might arise from shorter generation times and higher metabolic rates ( potentially increasing mutations through greater production of free radicals ) associated with warmer environmental temperatures [26] , [27] . The latter effect of temperature on host metabolism is unlikely to influence the evolutionary rates of viruses found in heterothermic species such as bats , which also lack a strong relationship between latitude and basal metabolic rate [28] . In our study , the independence of the evolutionary rate of rabies virus from bat metabolic rates further argued against a metabolism-mediated relationship between environmental temperature and viral replication ( Figure 3 ) . In contrast , generation times between viral infections likely differed among climatic regions in ways that could produce the patterns of viral evolution that we observed . Specifically , year-round transmission and replication may increase the annual number of viral generations in tropical and subtropical bats relative to seasonal pulses of transmission in temperate species , thereby speeding evolution . Indeed , when we conditioned our Bayesian analysis on 231 iterations of the HPM that lacked the climatic region term , seasonal inactivity was the only predictor that gained strong statistical support ( BF = 36 ) with significantly faster viral evolution in bat species that remain active year-round relative to species that hibernate or use prolonged torpor during winter ( β = 1 . 01 [95% highest posterior density = 0 . 39–1 . 52] ) . In our statistical models , the selection of climatic region ( a surrogate of seasonal activity ) rather than records of activity collected from the literature may be explained by poor understanding of the occurrence and duration of seasonal inactivity and torpor for many bat species [29] . Because assignment of overwintering records often required generalization of a few observations to an entire species range , climatic region may have been a more accurate descriptor of seasonal activity , especially for species that demonstrate geographically variable overwintering behaviors [30] . Still , the effects of variable transmission dynamics on viral evolution that we suggest should be confirmed in other host-virus systems with natural variation in seasonality or through experimental manipulation of virus transmission . Rapid evolution can enable the cross-species emergence of RNA viruses by increasing the genetic and phenotypic variation available to natural selection [2] . However , whether accelerated viral evolution increases the likelihood of emergence depends on the underlying forces of selection in the reservoir host , whether faster evolution increases variability in the genomic regions that are key to adaptation , and the strength of other ecological and physiological barriers to infecting new host species . In the case of bat rabies , faster viral evolution seemed to arise through an epidemiological mechanism: a greater number of viral generations per year . Enhanced transmission could therefore increase the likelihood of viral emergence in the tropics/subtropics by allowing more ecological opportunities for cross-species transmission . However , whether escaping seasonal transmission bottlenecks also provides an evolutionary advantage for host shifting requires understanding how the evolutionary rates that we estimated relate to standing diversity in the genomic regions that mediate viral adaptation to new host species . Although previous work in plant RNA viruses has demonstrated effects of host species on viral genetic diversity , the role of evolutionary rate in generating these effects and their impacts on cross-species emergence remain unknown [31] . Therefore , identifying the genomic regions that enable rabies virus host shifts and the ecological and evolutionary factors that may contribute to their diversity should be a key goal for predicting future rabies virus emergence . Beyond bat rabies , accelerated molecular evolution in tropical environments is a topic of general interest for understanding the maintenance and emergence of viral infections that occur across geographic regions or experience altered transmission dynamics as a result of anthropogenic environmental change . For example , lineages of Chikungunya virus evolve more slowly in seasonal African environments where mosquito populations and transmission dynamics are more variable relative to urban transmission cycles in Asia , where consistently large human and mosquito populations may shorten times between infections and support epidemic maintenance over multiple years [9] . Similarly , viruses such as influenza show reduced seasonality in the tropics relative to temperate zones [32] . Our results would predict that this sustained transmission might accelerate evolution in tropical viral lineages relative to their temperate counterparts if each is maintained independently . We therefore emphasize the need to consider not only functional traits of viruses , but also the seasonality and epidemiological dynamics of the host-virus interaction for a more complete understanding of the tempo of viral evolution . In conclusion , our study demonstrated a relationship between climate and the speed of viral evolution , which reinforces similar geographic structuring of molecular evolution as observed in free-living plants and animals [33] . This speeding up of evolution appeared to be driven by changes in the generation time between infected hosts , but not host genetic relatedness , indirect effects of temperature on host metabolism or differences in selective pressures . The broad geographic and host range of many rapidly evolving viruses , together with the increasing availability of molecular sequence data , makes them an ideal , real-time system to examine the epidemiology and evolution of host-pathogen ensembles . Our study revealed the complex interplay between ecological and evolutionary dynamics in multi-host viruses and highlighted an equally integrated framework for dissecting those interactions by combining ecological and genetic data .
Viral sequences were generated from tissue samples from naturally infected bats that were collected by state public health laboratories following human or domesticated animal exposures . Total RNA was extracted directly from bat brains without passage using Trizol ( Invitrogen , Carlsbad , CA ) according to the manufacturer's instructions . A 903 bp fragment comprising the last 687 bp of the N gene , a non-coding region following the 3′ end of N and a small fragment of the phosphoprotein gene was amplified by reverse transcription-polymerase chain reaction and sequenced using oligonucleotide primers 550F and 304R , as described previously [16] . To enable comparison with existing sequences in GenBank , only the coding region of N was used in subsequent analyses . Sequences generated herein have been deposited into GenBank under accession numbers JN594500–JN594503 and DQ445318–DQ445330 , DQ445352 ( updated sequences ) . An additional 650 complete or partial rabies virus nucleoprotein gene sequences that were associated with bats from North and South America and contained information on sampling date to year were downloaded from GenBank . We collected information on the overwintering activity patterns , migratory behavior , roosting behavior and metabolic rates ( basal and during seasonal torpor ) of the bat species that served as reservoir hosts for the rabies viruses included here from the primary literature and existing databases ( Table S2 ) . Long distance migration was defined as seasonal movement of individual bats of at least 1000 km [34] . For 4 species for which basal metabolic rate ( BMR ) data were unavailable , we borrowed values from species within the same genus or family that had similar body mass . Notably , body mass explains >92% of variation in BMR in bats and phylogeny explains much of the residual variation [29] . When torpid metabolic rate ( TMR ) estimates spanned a range of temperatures or spatial locations , rates were selected to match the conditions that bats are likely to experience in northern latitudes of their range where hibernation/torpor is most important . To calculate TMR for species that lacked values in the literature , we estimated the relationship between BMR and TMR for the 9 species in our dataset for which both values were available . This relationship was remarkably consistent across species ( TMR = 2 . 2–3 . 2% of BMR , mean = 2 . 8% ) , with the exception of Tadarida brasiliensis , for which TMR was 12 . 6% of BMR . Because the reported estimate of TMR for that species ( and for the other subtropical and tropical species in our study ) likely represented a daily torpor rather than longer-duration , seasonal torpor , it was excluded from the calculation of the average mentioned above [35] . Overwintering activity was challenging to classify because the frequency and duration of bat activity during winter are poorly understood for many temperate bat species and can vary substantially throughout their geographic range [30] , [36] . Therefore , we classified species as inactive during winter if extended bouts of seasonal torpor or hibernation were reported in any part of their geographic range , recognizing that this classification may have been overly conservative . As a potentially more geographically sensitive proxy of year-round activity , the climatic region ( tropical , subtropical , temperate ) of the center of the geographic range of each viral lineage was also recorded . North American lineages circulating between 35° and 23 . 5° latitude and South American lineages circulating south of −23 . 5° latitude that support mean winter temperatures of ≥10°C were considered subtropical , and lineages found towards and away from the equator relative to these latitudes were classified as tropical and temperate , respectively [37] . To define phylogenetic lineages to be included in subsequent analyses , ML and Bayesian phylogenetic analyses were performed using Garli v . 0 . 96b and BEAST v . 1 . 6 . 1 , respectively [38] , [39] . The ML analysis used the General Time Reversible ( GTR ) model of nucleotide substitution with invariant sites ( I ) and Γ distributed rate variation among sites as suggested by Akaike's information criterion corrected for small samples size ( AICc ) in jModeltest [40] . The ML tree was estimated by 5 independent searches with random starting trees , followed by 5 additional searches using the best tree from the previous set of searches as the starting tree . For the Bayesian analysis , we linked substitution rates for the first and second codon positions ( CP12 ) and allowed independent rates in CP3 . Separate substitution models were selected for CP12 and CP3 in jModeltest using AICc after partitioning aligned sequences by codon position . The BEAST analysis therefore applied the TIM1ef+I+Γ substitution model to CP12 and the TVM+Γ substitution model to CP3 . We used the Bayesian skyride model as a flexible demographic prior for viral effective population size and an uncorrelated lognormal relaxed molecular clock to accommodate rate variation among lineages . Five independent Markov Chain Monte Carlo ( MCMC ) analyses were run for 50 million generations each , with samples from the posterior drawn every 50 , 000 generations following variable burn-in periods based on convergence of likelihood values and model parameters . The results from the five runs were combined to generate a maximum clade credibility tree and divergence time summaries . Lineages for subsequent analyses of substitution rates included those that ( i ) contained at least 8 sequences ( mean = 30 . 9 sequences ) , ( ii ) were supported by Bayesian posterior probabilities of >0 . 9 and ( iii ) were sampled over a minimum time span of 4 years ( mean = 19 . 4 years ) . These conditions aimed to achieve a compromise between precision in estimates and hypothesis testing ability . To ensure that sparsely sampled viral lineages did not bias our central findings , statistical analyses were conducted with covariates designed to identify effects of sampling heterogeneity or using hierarchical phylogenetic modeling to incorporate uncertainty in rate estimates . Of the 28 viral lineages identified in the initial phylogenetic analyses , 21 fit our criteria for inclusion in subsequent analyses , amounting to a final dataset of 648 sequences collected between 1972 and 2009 from 21 bat species or sub-species . Sequence alignments were constructed for each viral lineage and nucleotide substitution models were selected for CP12 and CP3 as described above . For each lineage , the substitution rate was estimated in BEAST assuming an uncorrelated lognormal relaxed molecular clock to accommodate rate variation along branches and the Bayesian skyline model as a flexible demographic prior that could be applied to all viral lineages . The evolutionary rate in CP3 was calculated by multiplying the mean substitution rate by the relative rate parameter for that partition . Each simulation was run for at least 100 million generations , with parameters sampled every 5 , 000–10 , 000 generations . The first 10% of each run was discarded prior to the construction of the posterior probability distributions of parameters . Each analysis was run sufficiently long that effective sample sizes for parameters were >200 and results of several independent runs were combined . Analyses of evolutionary rates focused on substitution rates in CP3 since these largely synonymous substitutions reflect differences in evolution associated with generation time [22] . However , rates in CP12 were closely correlated with CP3 rates ( r = 0 . 87 , P<0 . 0001 ) . Rate estimates for all codon partitions are shown in Table S6 and Table S7 . The separate estimation of substitution rates for each rabies virus lineage assumes complete independence of parameters across viral lineages , but this is unlikely the case given the close evolutionary relationships among lineages and biological similarities of the processes of infection and replication among lineages . Because the quantity of data varied among lineages ( the number and temporal range of sequences ) , independent estimation in sparsely sampled lineages may lack power , causing imprecise estimates . HPMs have been proposed to improve the precision of parameter estimates for partially independent datasets such as these ( e . g . , populations of HIV within different patients ) by assuming that individual lineage parameters vary around a shared unknown , but estimable population mean [23] . More recently , tools have been developed within BEAST to incorporate fixed effects into HPMs and to select among candidate models via Bayes factors [8] . These models take the general form of: ( 1 ) where θ is the evolutionary response variable of interest ( here , the rate of molecular evolution in CP3 ) , β0 is an unknown grand mean , δ is a binary indicator that tracks the posterior probability of the inclusion of predictor , P , in the model and β is the estimated effect size of predictor P . The use of binary indicator variables ( δ ) within the MCMC search allows for a Bayesian stochastic search variable selection approach that simultaneously estimates the posterior probabilities of parameters for all possible combinations of predictors and allows for calculation of the Bayes factor support for individual predictors as the ratio of the posterior odds to the prior odds of each predictor in the model . We constructed a HPM for the 21 bat rabies virus lineages that assigned separate strict molecular clocks to CP12 and CP3 of each viral lineage and included fixed effect predictors of the evolutionary rate in CP3 . For each CP , the evolutionary rate parameter of the molecular clock , the parameters of the GTR substitution model and the shape parameter of the discrete Γ distribution were modeled hierarchically across lineages , with all other parameters varying independently across data partitions . Results were robust to simpler substitution models lacking Γ heterogeneity within each codon partition and to statistical analyses using the rate of evolution averaged across all three codon positions . The fixed effects in the full model included climatic region ( temperate vs . tropics/subtropics ) , mass-independent BMR , mass-independent TMR , coloniality ( solitary vs . colonial ) , seasonal activity ( non seasonal vs . hibernation/periodic seasonal torpor ) and long-distance migration ( migrants vs . non-migrants ) ( Table S2 ) . Climatic region was condensed to two categories in the HPM based on exploratory analyses that demonstrated no difference in evolutionary rate between tropical and subtropical viral lineages . All continuous variables were log transformed . The HPM was implemented in BEAST using four independent MCMC searches of 150 million generations each , with the posterior sampled every 5 , 000 generations . Results from the four runs were combined after discarding the first 10% of each . Effect sizes of predictors reported from the HPM were calculated conditionally on the portion of the posterior distribution for which the respective effects were included in the model ( i . e . , βi|δEffect i = 1 ) . Similarly , evolutionary rate estimates from the HPM were calculated conditionally on samples of the posterior in which the statistically supported predictor was included in the model ( 107 , 773/108 , 004 samples ) . Source files for the BEAST analysis and R scripts for conditional effect size and parameter estimation are available from the corresponding author upon request . In addition to the Bayesian hierarchical hypothesis testing framework described above , we conducted a more traditional GLM analysis of host predictors of mean viral evolutionary rates and a phylogenetic least squares ( PGLS ) regression analysis . These analyses contained the factors included in the HPM above , but because these methods do not account for uncertainty in the estimation of evolutionary rates ( Figure 1 ) , we also included several factors to identify the influence of estimation error: the number of years spanned and the number of sequences that comprised each dataset . For the GLM analysis , an initial model containing all terms was simplified using an exhaustive search of possible models using AICc in the glmulti package of R [41] , [42] . Models with Akaike weights within 10% of the highest were retained in the confidence set shown in Table S4 . The PGLS regression was conducted in the caper package of R , using the Pagel's λ statistic to account for phylogenetic non-independence of viral evolutionary rates [43] . Because λ for climatic zone was low in the virus phylogeny ( λ = 4 . 5e-5 ) , we had little power to reconstruct ancestral climatic states . This precluded testing whether viral jumps between climatic zones were correlated with directional changes in viral evolution . Blomberg's K measures the degree of phylogenetic non-independence of species traits , with values ranging from 0 to infinity [24] , [25] . Values of K<1 indicate less phylogenetic signal ( more trait lability ) than expected under a Brownian motion model of evolution and K>1 indicate more correlation with phylogeny than expected . The K statistic was calculated for the ILM and HPM sets of rate estimates using the topology of the rabies virus phylogeny in Figure 2A in the picante package of R [42] , [44] . The statistical significance of estimates of K was tested by comparing the expected distribution of values on our phylogenetic tree to 5 , 000 randomizations of observed rates along the tips of the tree . A similar analysis using the ML phylogeny of bats estimated from published mitochondrial cytochrome oxidase I sequences assessed whether the ecological and physiological similarity of closely related host species promotes evolution towards similar rates of viral evolution ( Table S3 , Figure S1 ) . | Rapid evolution of RNA viruses is intimately linked to their success in overcoming the defenses of their hosts . Several studies have shown that rates of viral evolution can vary dramatically among distantly related viral families . Variability in the speed of evolution among closely related viruses has received less attention , but could be an important determinant of the geographic or host species origins of viral emergence if certain species or regions promote especially rapid evolution . Here , using a dataset of rabies virus sequences collected from bat species throughout the Americas , we test the role of inter-specific differences in reservoir host biology on the tempo of viral evolution . We show the annual rate of molecular evolution to be a malleable trait of viruses that is accelerated in subtropical and tropical bats compared to temperate species . The association between geography and the speed of evolution appears to reflect differences in the seasonality of rabies virus transmission in different climatic zones . Our results illustrate that the viral mechanisms that are commonly invoked to explain heterogeneous rates of evolution among viral families may be insufficient to explain evolution in multi-host viruses and indicate a role for host biology in shaping the speed of viral evolution . | [
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] | 2012 | Rates of Viral Evolution Are Linked to Host Geography in Bat Rabies |
Elite suppressors ( ES ) are a rare subset of HIV-1–infected individuals who are able to maintain HIV-1 viral loads below the limit of detection by ultra-sensitive clinical assays in the absence of antiretroviral therapy . Mechanism ( s ) responsible for this elite control are poorly understood but likely involve both host and viral factors . This study assesses ES plasma-derived envelope glycoprotein ( env ) fitness as a function of entry efficiency as a possible contributor to viral suppression . Fitness of virus entry was first evaluated using a novel inducible cell line with controlled surface expression levels of CD4 ( receptor ) and CCR5 ( co-receptor ) . In the context of physiologic CCR5 and CD4 surface densities , ES envs exhibited significantly decreased entry efficiency relative to chronically infected viremic progressors . ES envs also demonstrated slow entry kinetics indicating the presence of virus with reduced entry fitness . Overall , ES env clones were less efficient at mediating entry than chronic progressor envs . Interestingly , acute infection envs exhibited an intermediate phenotypic pattern not distinctly different from ES or chronic progressor envs . These results imply that lower env fitness may be established early and may directly contribute to viral suppression in ES individuals .
A minor subset of HIV-1–infected individuals maintains stable CD4+ T cell counts in the absence of antiretroviral therapy . A small proportion of these long-term nonprogressors ( LTNPs ) , termed elite suppressors ( ES ) , control plasma viral loads to <50 copies/ml [1] . Mechanism ( s ) responsible for this elite control are poorly understood but likely involve host and viral factors . Studies have explored the contributions of the innate and adaptive immune responses , host genetic polymorphisms , and viral dynamics ( reviewed in [2] ) . For example , the major histocompatibility complex class ( MHC ) I group B alleles HLA-B27 , -B51 , and –B57 have been strongly associated with slower rates of HIV-1-associated disease progression [3]–[6] . Although these HLA-B alleles are overrepresented in ES and LTNPs , they are only expressed in a subset of these individuals indicating that the presence of these alleles is not necessary to suppress viremia and that other factors are likely involved [4] , [7] . Although much previous work on ES has focused on host factors , less is known about viral fitness in these individuals . The impact of viral attenuation on disease progression was first described in a cohort of LTNPs infected by a common donor with virus containing a deletion in the nef gene [8] , [9] . Investigation of other LTNP cohorts has shown both the presence [10] , [11] and absence [12] , [13] of defective nef genes . In other cohorts , the presence of viruses with reduced replication capacity has been associated with slower disease progression [14]–[19] . This viral attenuation could be the result of divergent evolution as a result of direct selective pressure by the host immune response [16]–[19] . However , recent work has shown that replication-competent viruses can be recovered from ES individuals indicating that ES harbor functional virus [20] . Furthermore , large scale sequencing of ES viruses yielded no identifiable common genetic defects [21] . Investigating the relative fitness of viral quasispecies in ES will help determine whether viral fitness is influencing disease outcome in these individuals . Low HIV-1 genetic diversity in ES may be indicative of the presence of lower fitness variants [22] . Sequence analysis of functional envelope glycoprotein ES clones showed significantly decreased env diversity compared to individuals with chronic viremia suggesting that viruses in these patients experience minimal viral replication and diversification [23] . Lack of env diversification suggests that ES envs may be closely related in genotype and phenotype to the founder virus establishing infection . In this study we have performed rigorous phenotypic analysis on subtype B env clones from ES plasma virus to determine whether env fitness may be contributing to viral suppression in ES . A novel cell line was utilized to show that ES env clones exhibit low CD4 receptor and CCR5 co-receptor usage and slow fusion kinetics compared to chronic infection envs . Analysis of control viruses indicated that these characteristics directly correlated to reduced replication capacity in vitro . Acute infections envs were intermediate in their entry efficiency and not significantly different from either chronic or ES envs . This study provides the first direct evidence that decreased env function is a property of ES and that this may contribute to viral suppression .
This study was conducted according to the principles expressed in the Declaration of Helsinki . The study was approved by the Institutional Review Board of Johns Hopkins School of Medicine and Rockefeller University hospitals . All patients provided written informed consent for the collection of samples and subsequent analysis . The elite suppressor and chronic progressor patients have been previously described [23] ( Table 1 ) . Patients identified with acute/early HIV-1 infection have been previously described [24] . The estimated duration of infection was calculated 2 weeks prior to the onset of acute retroviral illness unless the patient could identify a precise high risk event . Table 1 contains relevant enrollment data for all acute/early infection patients . Elite suppressors were defined as individuals who maintained viral load to below 50 copies of RNA/ml plasma in the absence of retroviral therapy yet were Western blot positive for infection . Informed consent was obtained prior to phlebotomy . The protocol for ES/CP or acute/early infection was approved by an institutional review board of the Johns Hopkins University School of Medicine and the Aaron Diamond AIDS Research Center , respectively . Envelope expression vectors were generated as previously described [23] . Envelope pseudotypes were generated by cotransfection of 293T cells with the 1 µg of the luciferase-encoding pseudotyping vector pNLLuc . AM and 1 µg of envelope expression vector . Cells were washed after 24 h , and pseudoviruses were collected after a subsequent 48 h . Relative particle numbers were determined by limiting dilution reverse transcriptase assay . Viruses were characterized as exclusively CCR5-utilizing by comparison of infectivity of U87-CD4/CCR5 and U87-CD4/CXCR4 cells , as previously described [25] . Affinofile cells were generated by selection of 4 vector stable cells ( Johnston et al . , submitted ) . CCR5 expression is controlled by a two vector ecdysone-inducible promoter . pVgRXR encodes the VgEcR fusion protein under control of the CMV promoter , and the RXR open reading frame under control of the RSV 5′ long terminal repeat . pIND-CCR5 encodes CCR5 under control of the minimal heat shock promoter with inducible control provided by five repeats of the glucocorticoid receptor DNA binding domains ( 5×E/GRE ) . Addition of the ecdysone derivative ponasterone A ( the inducer ) results in recruitment of a transcriptional coactivator to the 5×E/GRE element and activation of transcription of the CCR5 ORF . CD4 expression is inducibly regulated by the TREx expression system ( Invitrogen ) . Cells contain pcDNA5-TO-CD4 and transcription of the CD4 ORF is controlled by the addition of the tetracycline analog minocycline . Single cell clones were isolated to generate cell populations with consistent levels of induction upon stimulation of CD4 and CCR5 expression . Affinofile cells were plated at a density of 10 , 000 cells per well in a 96-well plate and allowed to adhere for 48 hours . Cells were induced in a matrix pattern to express CD4 and CCR5 . Minocycline was added to cells in 2-fold dilutions over 6 separate dilutions ( 5 ng/ml–0 ng/ml ) to induce CD4 expression . Ponasterone A was added in 2-fold dilutions over 6 separate dilutions from a final concentration of 4 µM to 0 µM to induce CCR5 expression . This matrix results in 36 unique CD4 and CCR5 induction surface concentrations . Each drug concentration was induced in triplicate . Cells were induced for 24 hours prior to infection . Cells were then exposed to pseudovirus for 48 h , washed with PBS , and lysed with Glo lysis buffer ( Promega , Inc . ) . Maximal infection was considered luciferase activity generated by infection at the highest CD4 and highest CCR5 concentration . To control for effects caused directly by minocycline and/or ponasterone A on viral infectivity , U87-CD4/CCR5 cells were treated with a similar matrix of both drugs . CCR5 and CD4 expression levels were unchanged by flow cytometry , and no changes in infectivity of Yu-2 and SF162 envelope pseudoviruses were noted , thus variation in infectivity was assumed to be due to variations in receptor expression levels ( Johnston et al , submitted ) . For the kinetic fusion assay , HIV-1 pseudoviruses bearing either ES or chronic envelopes were spinonculated onto U87-CD4/CCR5 cells . 2 . 5×106 cells were spin-infected with pseudovirus-containing supernatant for 90 min at 1 , 200×g at 4°C . The cells were washed twice with cold phosphate buffered saline ( PBS ) to remove unbound virions . Cells were resuspended in cold medium and split into 96-well plates ( 50 µl/well ) . Virus-cell mixes were synchronized for entry by addition of 130 µl of 37°C medium , and then ENF at 10 µM was added to each well in 20 µl of medium at fixed-time intervals after addition of warm medium , which is defined as tim = 0 for synchronization of viral replication . Cells were incubated for 48 h and then treated with lysis buffer and luciferase activity was determined . For the kinetic fusion assay 6 hours was used as 100% or maximal luciferase activity . For the reverse transcription assay , Affinofile cells were induced with 5 ng/ml minocycline 24 hours prior to infection . ES or chronic pseudoviruses were synchronously added to cells . Efavirenz ( EFV ) was added at a concentration of 1 µM to each well at fixed time intervals after the addition of virus . For reverse transcription assay , 12 hours was used as 100% or maximal luciferase activity . Affinofile cells were induced 24 hours prior to infection with 5 ng/ml minocycline . Cells were incubated with serial 10-fold dilutions of either chemokine ( CCL5 [50 nM to 0 . 1 nM] ) or drug ( ENF ( T-20 ) [1 µM to 0 . 1 nM] , TAK-779 [1 µM to 0 . 1 nM] ) for 1 h prior to the addition of virus . Cells were incubated for 48 h , washed with PBS , lysed , and luciferase activity determined . Plots of luciferase activity versus drug concentration were used to determine IC50 values for each pseudovirus . Luciferase activity without drug was used as maximal or 100% infection value . Peripheral blood mononuclear cells ( PBMCs ) were isolated from heparin-treated venous whole blood from HIV–seronegative donors by Ficoll-paque density gradient centrifugation ( GE Healthcare , Piscataway , NJ ) . Isolated PBMCs were washed twice in wash buffer [phosphate-buffered saline ( PBS ) supplemented with 2% fetal bovine serum ( FBS ) , 0 . 1% glucose , 12 mM HEPES , and penicillin ( 100 U/m ) and streptomycin ( 100 µg/ml ) ] and activated in RPMI 1640 ( Mediatech , Inc . , Manassas , VA ) supplemented with 10% FBS , penicillin/streptomycin , and 2 µg/ml phytohemmaglutinin ( PHA , Sigma-Aldrich , St . Louis , MO ) and 100 U/ml interleukin 2 ( IL-2 , Invitrogen , Carlsbad , CA ) for 3 days at 37°C and 5% CO2 . Total PBMCs were subsequently maintained in RPMI 1640 supplemented with 10% FBS , pen/strep , and 100 U/ml IL-2 . For flow cytometry experiments , a total of 10° PBMCs were collected 4 days post-stimulation by centrifugation at 2500 rpm×10 minutes and washed once in FACS staining buffer ( PBS with 2% FBS , 0 . 5% bovine serum albumin , and 0 . 02 sodium azide ) . The cells were incubated in either an anti-CD4 antibody [Fluorescein isothiocyanate ( FITC ) -conjugated anti-CD4 , BD Biosciences Pharmingen , San Jose , CA] or FITC-conjugated IgG1 isotype control ( BD Biosciences Pharmingen ) , or an anti-CCR5 antibody [Phycoerythrin ( PE ) -conjugated anti-CCR5 clone CTC5 , R&D Systems , Minneapolis , MN] or PE-conjugated IgG2B isotype control ( R&D Systems ) . All antibodies were incubated at a final concentration of 12 . 8 µg/ml for 30 minutes at room temperature in FACS staining buffer . Stained PBMCs were washed again in FACS staining buffer and samples were analyzed on a FACSCalibur flow cytometer ( Becton Dickinson , Franklin Lakes , NJ ) with Cellquest software . For assessment of Affinofile cell receptor expression levels relative to PBMCs , 5 . 0×105 cells were added to a 6-well plate and allowed to adhere for 48 hours in Dulbecco's Modified Eagle's Medium ( DMEM , Mediatech , Inc . ) supplemented with 10% FBS , pen/strep , and 50 µg/ml blasticidin ( Sigma ) . Cells were stimulated with 20 ng/ml minocycline ( Sigma ) for 24 hours and subsequently recovered from plates with 3 mM EDTA in PBS . Cells were washed , stained , and analyzed by flow cytometry as reported above for PBMCs . Flow cytometry of PBMCs and Affinofile cells was performed in the same experiment . Results were analyzed by Flow-Jo software and receptor expression levels reported as events relative to mean fluorescence intensity . Envelope-pseudotyped viruses prepared with ES or CP envelopes by transfection of 293T cells were quantified by limiting dilution reverse transcriptase activity . Equivalent virion numbers were pelleted by centrifugation at 38 , 000×g for 2 hours at 4°C . Supernatant was removed and virion pellets were lysed in SDS lysis buffer [40 mM Tris-HCl ( pH 6 . 8 ) , 10% glycerol , 10% ß-mercaptoethanol , 1% SDS] . Virus lysates were separated on SDS-10% polyacrylamide gels and transferred to nitrocellulose . Membranes were blocked with gelatin and proteins were detected either with a mouse monoclonal anti-gp120 antibody that recognizes a conserved C2 region linear epitope ( B13 , courtesy of Dr . Bruce Chesebro , NIAID ) or HIV-Ig ( courtesy AIDS Research and Reference Reagent Program ) . Primary antibodies were detected with horseradish peroxidase-conjugated goat-anti-mouse or goat-anti-human secondary antibodies , respectively ( Pierce Biotechnology , Rockford , IL ) , revealed with the ECL Plus Western Detection kit ( Pierce Biotechnology ) and exposed to X-ray film . Data were analyzed by the UCLA Statistical/Biomathematical Consulting Clinic using repeated measures ANOVA , simultaneously taking into account effects due to disease group ( ES vs CP ) , CD4 level , and CCR5 level . Within each group , the intrapatient variability in relative infection was small and did not differ significantly between the ES and CP groups . Therefore , results are reported with interpatient variance . For evaluation of surface plots between groups ES and CP , P values are given using the average of clones for a given patient as a single value or using each individual clone as a single value . For groups of 3 or more ( ES , CP , and acute ) we evaluated independent means by One-way ANOVA using the Kruskal-Wallis test and Dunns post-test for data that did not pass a normalcy test . For drug sensitivity and kinetic analysis statistics were performed using each individual clone as a single value . We considered a P value of <0 . 05 as statistically significant .
The ability of HIV-1 to infect a cell is largely influenced by surface expression of CD4 and CCR5 [26]–[32] . This study evaluates a previously described cohort of 38 independent full-length plasma env clones derived from 7 ES individuals [23] . The env clones from this cohort expressed similar levels of protein by Western blot ( Figure S1 ) and readily infected the indicator cell line TZM-bl demonstrating their functionality [23] . As described below , observed differences in entry efficiency could not be explained by any minor variations in Env levels on the virus . As with most cell lines , TZM-bls express CD4 at levels comparable to primary activated CD4+ T cells ( approximately 65 , 000–100 , 000 molecules/cell ) , however CCR5 expression is significantly higher than on primary T lymphoctyes ( approximately 500 to 7000 molecules/cell ) [26] , [33]–[37] . CCR5 expression also varies widely from patient to patient not only in absolute number of cells expressing CCR5 , but also in CCR5 density/cell [26] , [27] , [33]–[35] . This study utilizes the Affinofile system , a novel cell line with independent dual-inducible surface expression of CD4 and CCR5 ( Figure 1 , Figure S1 ) ( Johnston et al . , submitted ) . The ability to modulate receptor and co-receptor expression on the Affinofile cells provides a more physiologic measure of HIV-1 entry efficiency . Since the ability of HIV-1 to infect a cell is largely influenced by cell surface levels of CD4 and CCR5 , it is important to consider expression levels when evaluating infectivity [26]–[32] . Receptor usage as measured by the Affinofile system was validated as a surrogate marker of entry fitness . Yu-2 and a V3 crown mutant of Yu-2 [Yu-2 ( Y318R ) ] known to affect CCR5 usage were evaluated for infectivity using Affinofile cells induced at each pairwise combination of [Minocycline] and [Ponasterone A] ( 42 unique combinations ) ( Figure S1 ) . Three-dimensional surface plots were generated from luciferase activity expressed as a function of virus infectivity at each combination of CCR5 and CD4 , which was confirmed by flow cytometry ( Figure 1A ) . CD4 and CCR5 surface levels at each drug combination are given as an average level calculated from a pool of cells expressing a range of CD4 and CCR5 molecules ( Figure S4 ) . Reduced infectivity of the Yu-2 ( Y318R ) variant over the wild type was observed over a range of CCR5 and CD4 ( Figure 1A ) . This decreased ability of Yu-2 ( Y318R ) to infect cells expressing low CCR5 is consistent with a 90% reduction in replicative fitness measured by competitive replication assays in peripheral blood mononuclear cells ( PBMCs ) ( Figure 1B , p<0 . 01 , unpaired student's t-test ) . The direct relationship between entry efficiency using the Affinofile system and replicative fitness in human PBMCs has been validated for multiple primary HIV-1 isolates . Generally , viruses of increased replicative fitness display increased infectivity of cells expressing low CCR5 , CD4 , or both CCR5 and CD4 in the Affinofile system ( Johnston et al , submitted ) . To assess relative infectivity of chronic and ES env clones , pseudotyped viruses carrying a non-LTR driven luciferase were generated for each clone . Pseudotyped viruses generated from 38 independent plasma virus env glycoprotein clones from 7 ES and 32 independent plasma virus clones from 7 chronic progressors ( CP ) were evaluated for infectivity at each pairwise drug combination described above and surface plots were generated ( Figure S2 and Figure S3 ) . The percent infection defines the infection at each surface CCR5/CD4 level relative to a 100% infection at the highest CD4 and CCR5 surface density . This method permits the direct comparison of CD4 and CCR5 usage by each env clone and provides a rapid and efficient way to measure viral env replicative fitness . Relative infectivity of 38 independent ES env clones and 32 independent CP env clones was ascertained at multiple combinations of CCR5 and CD4 density ( Figure 2A–2F and Figure 3A–3F ) . Values for each of the env clones tested are shown as well as for Yu-2 and SF162 . Varying CD4 levels with constant CCR5 ( Figure 2A–2F ) or varying CCR5 levels with constant CD4 ( Figure 3A–3F ) consistently demonstrated that ES env clones supported lower levels of infection than the CP clones . At the highest CD4 and lowest CCR5 expression level , ES clones averaged 36 . 7% while CP clones were reliably higher averaging 53 . 3% ( Figure 2A–2C ) . Infectivity differences were significant for each CD4 concentration ( P values ranged from 0 . 01 to <0 . 0001 , repeated measures ANOVA ) at a fixed high or low CCR5 level regardless if individual env clones were evaluated ( Figure 2C and 2F ) or if the env clones were averaged for a given individual and compared as patient averages ( Figure 2B and 2E ) . Additionally , consistent with previous data , the neurotropic envs SF162 andYU-2 readily infected cells expressing sub-threshold levels of CD4 while the primary isolates could not ( Figure 3A–3F ) [38] . Taken together , these results reveal that ES clones inefficiently infect cells expressing low CCR5 in the presence of threshold or higher levels of CD4 compared to CP clones . Additionally , the discrepancy in infectivity between ES and chronic clones at fixed , high CCR5 levels indicates that ES clones also require higher levels of CD4 to achieve similar infection as chronic clones . To further evaluate CCR5 usage independent of CD4 expression , infection was determined at minimal and maximal CD4 levels as CCR5 expression was varied . At each concentration of CCR5 below maximal examined , ES clones infected significantly less efficiently than chronic clones ( Figure 3D–3F ) . Therefore , even in the presence of optimal CD4 concentrations , ES clones inefficiently utilize CCR5 for entry . Differences in receptor and co-receptor utilization of ES and chronic progressor envs were most significant when infection was performed in the context of ( 1 ) low CCR5 and varying CD4 levels or ( 2 ) low CD4 and varying CCR5 levels . Thus , these conditions were repeated to examine entry efficiency of 23 pseudotyped env plasma clones from 20 acutely infected individuals ( Figure 4A and 4B ) . At low CCR5 expression , acute envs averaged an intermediate pattern of infectivity compared to ES and chronic envs , but these differences were not significant ( Figure 4A ) . Similar results were obtained when infections were performed at minimal surface CD4 levels ( Figure 4B ) . Consistent with previous reports using similar systems , these results indicate that acute envs show a broad pattern of infectivity which is not significantly different from chronic or ES envs [39] . Previous studies suggest that major differences in entry efficiency may impact on susceptibility to various entry inhibitors [25] , [40]–[42] . Thus ES , CP , and acute clones were tested for their susceptibility to the natural CCR5 ligand CCL5 ( RANTES ) , the small molecule CCR5 antagonist TAK-779 , and the fusion inhibitor enfuvirtide ( ENF ) in Affinofile cells induced to express CD4 and CCR5 to levels that closely mimic primary CD4+ T cells ( approximately 125 , 000 molecules CD4/cell and 1274 molecules CCR5/cell ) ( Figure S4 ) . Susceptibility to CCL5 did not differ significantly between chronic and ES env clones ( Figure 5A ) . Interestingly , acute clone IC50 values ranged from 0 . 05 to 50 nM ( 1 , 000-fold ) with an average of 8 . 25 nM . The range of IC50 values for acute clones was much larger relative to ES clones whose values ranged from 0 . 4 to 10 nM ( 25-fold ) , with an average of 3 . 05 nM . Although similar trends were observed with the small molecule CCR5 antagonist TAK-779 , clones were not significantly different with average IC50 values of 48 . 8 nM for acute , 21 . 0 nM for chronic , and 14 . 7 nM for ES ( Figure 5B ) . These results show that ES , CP , and acute envs have no remarkable differential susceptibility to CCL5 or TAK-779 however the broad range of IC50 values for acute envs highlights the variability in env phenotypes associated with acute infection . Finally , susceptibility of clones to ENF was evaluated ( Figure 5C ) . IC50 values for acute clones again showed a broad range from 0 . 10 to 623 nM ( >5000 fold range ) with an average of 58 . 13 nM . Again , ES ( 33-fold range ) clones exhibited a significantly narrower range of IC50 values compared to acute clones . The average IC50 value for acute envs was significantly greater than for ES ( P<0 . 05 , ANOVA , Kruskal-Wallis test ) . Overall , ES clones showed trends towards increased susceptibility to entry inhibitors consistent with decreased entry efficiency . These susceptibility profiles suggest that ES clones have a high degree of phenotypic similarity indicated by the narrow range of susceptibility to entry inhibitors . Conversely , chronic and especially acute envs showed broad ranges of susceptibility indicative of their diverse entry phenotypes . Infection data of cells expressing sub-maximal concentrations of CD4 and CCR5 indicates that ES-derived env clones require higher levels of receptor and co-receptor for efficient entry . This requirement for higher receptor levels could suggest that these envs also exhibit differences in the rates of HIV-1 entry into host cells [42] , [43] . To assess host cell entry kinetics , U87-CD4/CCR5 cells were first spinoculated with virus at a temperature non-permissive for viral fusion with the host cell . Enfuviritide ( ENF ) was added once to each well at a concentration of 10 µM at various times after the cells were shifted to temperatures permissive for viral fusion ( Figure 6A ) . Viruses that have completed the final step in HIV-1 entry ( six helix bundle formation ) are ENF insensitive and will continue the viral replication cycle regardless of the addition of ENF . Thus , this assay permits determination of the entry kinetics of each env clone . Fusion kinetics were measured for each ES , CP , and acute clone . ES clones fused with an average T1/2 of 92 . 1 minutes while acute clones averaged a T1/2 of 67 . 5 minutes and chronic clones a T1/2 of 58 . 3 minutes ( Figure 6B ) . This delay in ES fusion kinetics was significant when compared with acute and CP clones ( P< . 0001 and P< . 0001 respectively , One-way ANOVA ) . Therefore , even in the presence of saturating levels of CD4 and CCR5 , ES clones do not complete entry processes as efficiently and exhibit slower kinetics than both CP and acute env clones . This delay in entry kinetics was maintained during subsequent steps of the replication cycle as indicated by a kinetic reverse transcription assay . For these analyses , Efavirenz ( a non-nucleoside reverse transcriptase inhibitor ) was added at various times post-infection to arrest infection events which have not completed reverse transcription . As expected , ES derived env clones completed reverse transcription slower ( mean T1/2 of 8 . 89 hours ) than acute and CP clones [mean T1/2 values of 7 . 74 ( P< . 0001 ) and 7 . 95 ( P< . 0001 ) respectively with One-way ANOVA] ( Figure 6C ) . Due to the isogenic background of the pseudotyping virus these results suggest that delays in reverse transcription are the result of delays in entry processes . ES env clones exhibit a kinetic lag in entry processes which are maintained during downstream events in the viral life cycle .
This study represents an evaluation of intrinsic phenotypic characteristics of full-length functional subtype B env quasispecies derived from ES plasma . Envelope glycoproteins from ES clearly exhibited reduced capacity to support HIV-1 entry into host cells compared to CP envs . Given the wide range in entry efficiencies observed with envs derived from acute infections it is possible that relatively lower fitness env variants are selected early in infection in ES . The impact of this observed entry deficiency with ES clones is still not fully understood but decreased replicative fitness and lack of diversification in ES viruses suggests these individuals may have contracted a less fit HIV-1 variant or these low fitness variants are selected for early in infection[7] , [44] , [45] . To date , phenotypic studies of ES viruses have been difficult to perform due to the low amount of virus in these individuals . Analysis of minor differences in env function has been confounded by the use of cell lines expressing non-physiologic amounts of co-receptor ( CCR5 ) . Given the high degree of variability in expression of CCR5 among patients it is important to evaluate env function over a wide range of CCR5 levels [26] , [27] , [34] , [35] . Detailed analyses of env function in the presence of physiologic levels of CD4 and CCR5 was possible in this study through the use of the novel Affinofile system . Although ES and chronic individuals each harbored quasispecies with different receptor utilization phenotypes , ES clones from each individual showed an average decreased entry efficiency compared to chronic clones over almost all CD4 and CCR5 expression levels . These differences between chronic and ES clones were most dramatic at low CCR5 surface levels . Thus , this low fitness phenotype could be further accentuated in vivo in an individual expressing low CCR5 levels or possibly higher levels of CCR5 ligands which have been associated with viral control [46]–[48] . Several reports have suggested a correlation between susceptibility to entry inhibitors and relative env fitness [25] , [40] . ES , CP , and acute clones exhibited a diverse range of IC50 values consistent with previous data [40] , [41] , [43] , [49] , [50] . However , acute clones showed consistently the most variation in susceptibility indicating the diverse phenotypes associated with early infection [40] , [43] , [49] . Conversely , the low range of IC50 values for ES clones underscores their phenotypic homogeneity . Due to reported differences between primary cells and cell lines entry inhibitor susceptibility assays were performed in the Affinofile cells induced to express CD4 levels and CCR5 levels that closely mimic primary CD4+ T cells ( approximately 125 , 000 molecules CD4/cell and approximately 1274 molecules CCR5/cell ) [51] . Additionally , it would be expected that variations in CD4 utilization would result in variations in susceptibility to soluble CD4 ( sCD4 ) [52] . However , consistent with previous reports showing the relative resistance of primary isolate viruses to sCD4 [53] , meaningful inhibition of ES , CP , and acute infection envelopes was not observed at maximal achievable concentrations of sCD4 ( 25 µg/ml ) . Previous studies have also highlighted an association between receptor utilization profiles and susceptibility to neutralizing antibodies . It is possible that ES envs display altered susceptibility to broadly neutralizing antibodies given their observed entry phenotype . Previous studies suggest that antibody binding and neutralization have a kinetic component [54] . It may potentially be generalized that slow fusing viruses , independent of the mechanism , may be more susceptible to neutralizing antibodies that act with a kinetic dependence . However , it has been shown that ES individuals generate low titers of neutralizing antibodies against autologous virus and thus the role of neutralizing antibodies in maintenance of low level viremia in ES is nominal [23] . The host entry process is thought to be a rate limiting step in HIV-1 replication . It was thus important to determine if poor entry efficiency by ES clones leads to a reduced rate of entry kinetics . ES env clones were found to fuse on average over 1 . 5 times slower than chronic or acute clones in the presence of saturating levels of both CD4 and CCR5 . As result of poor entry efficiency , this kinetic delay was maintained during subsequent steps of the retroviral lifecycle . Compounded effects of inefficient CD4 and CCR5 usage by ES clones likely contributes to kinetic delays in entry processes and thus overall decreased replicative fitness . The delayed entry kinetics may be even more important in vivo if there is a limited time frame over which entry can occur due to competing inhibitory processes such as the binding of neutralizing antibodies or the presence of CCR5 ligands . Despite genotypic differences in both the virus and the ES host , viral quasispecies in different ES individuals are remarkably similar phenotypically . This implies that poor envelope function is a common feature in ES individuals . This result is in sharp contrast to data from acute infection envs where clones exhibited much phenotypic diversity . Potentially , HIV-1 infection in ES may be established by lower fitness env ( s ) which are present in a subset of acutely infected individuals . Alternatively , HIV-1 infection in ES may be established by phenotypically diverse envs and early pressure from the immune results in the outgrowth of lower fitness escape variants . At present , no data exists on the natural history of acute infection of ES . It remains unclear whether these individuals experience typical high level viremia that is subsequently reduced to an undetectable setpoint or control their viral load from the onset of infection . It would be of great interest to be able to address this significant gap in our understanding of viral dynamics in elite control of viremia . Lower env fitness is likely not sufficient to mediate absolute viral suppression . Viral control could be achieved in those individuals who are also able to mount a potent immune response and/or are genetically predisposed to better control HIV-1 viremia . In these individuals , viral replication and diversification of early infection viruses required to achieve efficient receptor utilization by env quasispecies may never be attained . Lower fitness of other viral factors may also be contributing to reduced replication and lower viral load . Full understanding of the in vivo impact of lower env fitness in ES will require further study however this data underscores the important contribution of viral factors in elite HIV-1 suppression . | The majority of HIV-1–infected individuals experience high plasma viral loads and CD4+ T cells loss in the absence of antiretroviral therapy . However , a very rare and important subset of individuals termed elite suppressors is able to maintain HIV-1 plasma viral loads below the limit of viral detection in the absence of treatment . The reasons behind this ability to control the virus are poorly understood , but they likely involve both an effective host immune response against HIV-1 and factors related to the virus itself . Here , we analyze the function of the HIV-1 coat protein or envelope glycoprotein from a group of elite suppressors . HIV-1 envelope mediates entry into the host cell via interaction with the cellular receptors CD4 and CCR5 . Envelopes from elite controllers interacted with these receptors inefficiently compared to those from individuals with detectable viral loads . These inefficient interactions by elite suppressor envelopes led to slow rates of entry into host cells . Envelopes from acutely infected individuals were not significantly different from elite suppressors or chronically infected individuals . These findings suggest that the decreased envelope efficiency may contribute to viral control in elite suppressors . | [
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] | 2009 | Elite Suppressor–Derived HIV-1 Envelope Glycoproteins Exhibit Reduced Entry Efficiency and Kinetics |
Toxoplasma gondii infects up to one third of the world's population . A key to the success of T . gondii as a parasite is its ability to persist for the life of its host as bradyzoites within tissue cysts . The glycosylated cyst wall is the key structural feature that facilitates persistence and oral transmission of this parasite . Because most of the antibodies and reagents that recognize the cyst wall recognize carbohydrates , identification of the components of the cyst wall has been technically challenging . We have identified CST1 ( TGME49_064660 ) as a 250 kDa SRS ( SAG1 related sequence ) domain protein with a large mucin-like domain . CST1 is responsible for the Dolichos biflorus Agglutinin ( DBA ) lectin binding characteristic of T . gondii cysts . Deletion of CST1 results in reduced cyst number and a fragile brain cyst phenotype characterized by a thinning and disruption of the underlying region of the cyst wall . These defects are reversed by complementation of CST1 . Additional complementation experiments demonstrate that the CST1-mucin domain is necessary for the formation of a normal cyst wall structure , the ability of the cyst to resist mechanical stress , and binding of DBA to the cyst wall . RNA-seq transcriptome analysis demonstrated dysregulation of bradyzoite genes within the various cst1 mutants . These results indicate that CST1 functions as a key structural component that confers essential sturdiness to the T . gondii tissue cyst critical for persistence of bradyzoite forms .
Toxoplasma gondii , an Apicomplexan , is an obligate intracellular protozoan parasite that can cause severe human disease . It is estimated that a third of the human population is chronically infected with T . gondii [1] , with prevalence rates ranging from a few percent to nearly 80% depending on the population [2] . This parasite can cause lethal encephalitis in immune compromised individuals such as those with AIDS or organ transplant recipients on immune suppressive medications . It is also the cause of a devastating congenital disease , which may result in blindness and mental retardation if infection occurs in a T . gondii seronegative pregnant woman . During acute infection , the parasites proliferate as the fast-growing tachyzoite life cycle form , which causes a disseminated systemic infection . This disseminated acute infection is controlled by interferon-γ and T cell responses . In response to stress signals during acute infection , such as the immune response or programmed spontaneous differentiation responses , tachyzoites differentiate into the slow-growing bradyzoite life cycle stage that remains latent in the host . Bradyzoites can form tissue cysts in brain , muscles and visceral organs and when tissue cysts are orally ingested the released bradyzoites differentiate into tachyzoites , causing an acute infection in a new host . Bradyzoite differentiation processes and the development and maintenance of tissue cysts are critical for transmission of T . gondii infection . Evidence suggests that the latent tissue cysts evade the immune response [3] and can persist for the host life span [4] . It is likely tissue cysts occasionally rupture and any released parasites [5] are cleared by immune system . In the absence of an effective immune response these released organisms can differentiate into tachyzoites causing an acute infection . Thus , tissue cysts serve as reservoir for the reactivation of the toxoplasmosis when the host becomes immune compromised with conditions such as AIDS or organ transplantation . Tissue cysts can range from 5 to 100 µm in size containing just a few to thousands of encysted bradyzoites . Tissue cysts can be found in any organ , but are especially prevalent in the central nervous system . The bradyzoites within the tissue cyst are covered by a prominent translucent 0 . 25 to 0 . 75 µm thick cyst wall structure ( cyst wall ) which can be visualized using electron microscopy [6] . The cyst wall forms beneath a modified parasitophorous vacuole membrane containing bradyzoites . The cyst wall is highly glycosylated and stains easily with periodic acid-Schiff , Dolichos biflorus lectin ( DBA ) , and succinylated wheat germ agglutinin [6]–[8] . These carbohydrate modifications of the cyst wall are hypothesized to mask cyst wall proteins from host immune responses and to provide structural and chemical resistance against environmental stress , facilitating transmission of this pathogen [9] . The biogenesis , composition , and functions of the cyst wall are not yet well defined . A cyst wall glycoprotein CST1 was discovered more than a decade ago [10] . This protein , CST1 , binds to DBA lectin , suggesting that it is a glycoprotein that contains N-acetyl-galactosamine . CST1 localized to the in vivo and in vitro cyst wall , but was not found associated with the tachyzoite parasitophorous vacuole . The corresponding gene , CST1 , has not previously been identified , as the available monoclonal antibody 73 . 18 [11] to CST1 recognizes a glycoepitope and attempts to identify the glycoprotein recognized by this monoclonal antibody were unsuccessful ( Weiss LM , unpublished ) . While some progress has occurred , cyst wall biology is still poorly understood despite the clinical and biological importance of this structure for transmission and latency in this important protozoan infection [12] . We produced a new monoclonal antibody library to T . gondii tissue cysts and used a combination of microscopic , genetic and proteomic approaches to identify cyst wall components . Using this approach we identified CST1 , the gene corresponding to CST1 , and characterized the effect of a knockout of this gene on T . gondii .
To identify cyst wall proteins , a hybridoma library was created from mice immunized with a lysate of T . gondii ME49 cysts purified from the brains of mice with chronic T . gondii infection . From this library , we screened monoclonal antibodies by immunofluorescence against ME49 T . gondii in vitro cysts ( bradyzoite- ) and tachyzoite-containing vacuoles . Among the 189 cyst-wall positive hybridomas , we identified an mAb clone SalmonE that reacted with bradyzoite-containing parasitophorous vacuoles and uniformly stained the limiting parasitophorous vacuole membrane of BAG1-positive parasites ( bradyzoites ) but did not stain vacuoles containing BAG1 negative parasites ( Figure 1A ) . BAG1 negative vacuoles were positive for SAG1 , a tachyzoite specific marker ( data not shown ) . This candidate cyst wall reactive monoclonal antibody was used to further characterize the cyst wall . Since in vitro cysts do not completely differentiate , we examined the localization of SalmonE in cysts isolated from mice with chronic T . gondii infection . Within these more mature cysts there is a more organized cyst wall structure , and the bradyzoites within these cysts enter Go and arrest in the cell cycle [12] , [13] . T . gondii ( ME49 strain ) in vivo brain cysts harvested from infected mice were labeled with SalmonE and analyzed using immuno-electron microscopy ( Figure 1B ) . SalmonE recognizes the diffuse thick layer of the cyst wall beneath the limiting membrane of the cyst wall in a distribution similar to the reactivity of the mAb specific for CST1 as well as the DBA lectin [10] . To determine the target of monoclonal antibody SalmonE , antigens from ME49 in vitro derived bradyzoite lysates were immunoprecipitated with mAb SalmonE , separated with SDS-PAGE and the two major candidate protein bands ( a low signal intensity 150 kDa band and a high signal intensity high molecular weight band in the stacking gel ) were excised and analyzed by MALDI-TOF mass spectrometry . The high molecular band had two peptides that matched the predicted gene product of TGME49_064660 ( peptides: RGGGFLTTYTLNVPRL and KEFLRPLADLVPGASLKL , MASCOT , p<10−7 , Figure 2A ) , which had been annotated as SRS44 in a published analysis of SRS domain-containing proteins [14] . The low molecular weight band had two peptides that matched SRS13 ( peptides: KLPEKPAAAVAR and LTLDAGPPQATTLCYK ) , a glycoprotein we also subsequently characterized ( Tomita and Weiss , in preparation ) . Polyclonal murine antiserum raised to the first 200 amino acids of SRS13 did not react with the cyst wall ( data not shown ) . There was no other protein identified from SalmonE immunoprecipitated bands . To verify that the TGME49_064660 gene product is responsible for the cyst wall staining of monoclonal antibody SalmonE , mouse antiserum was raised against recombinant TGME49_064660 protein consisting of the first 200 amino acid of the predicted gene ( Figure 2A , rTGME49_064660 ) . Probing in vitro cysts with the anti-rTGME49_064660 serum revealed a similar pattern of staining as seen with the monoclonal antibody SalmonE ( Figure 1A and 2B ) . This verified that TGME49_064660 is indeed a cyst wall gene . After completion of molecular verification ( see below for details ) TGME49_064660 was identified as CST1 , the gene corresponding to the previously identified protein CST1 [10] . The cyst wall localization as well as its identification as an SRS protein suggested that CST1 should be a secreted protein; however , the current annotated TGME49_064660 gene product in ToxoDB . org does not contain a potential signal peptide sequence ( SignalP 4 . 0 prediction [15] ) . Examination of the upstream sequences ( Figure S1A ) , suggested that the gene model was incorrect . Use of 5′ rapid amplification of cDNA ends ( RACE ) demonstrated additional coding sequence , a revised 5′ UTR ( Figure S1A ) and also confirmed that CST1 ( TGME49_064660 ) does not extend into the predicted upstream gene TGME49_064670 . Sequence analysis of the 5′ RACE product revealed an in-frame methionine codon located 43 residues upstream of the annotated predicted start site ( Figure S1A ) . The protein predicted using this upstream methionine codon encodes a high probability signal peptide with a cleavage site; therefore , it is likely that CST1 translation begins at this methionine , 43 residues upstream of the annotated initiator methionine codon . CST1 contains thirteen SRS domains ( Figure 2A ) and is unique among the SRS family proteins in having such a large number of SRS domains . Another striking feature of the predicted protein is the presence of a 263 amino acid stretch with multiple threonine-rich tandem repeats of T5–11[R/I]K2P; this region has homology to the mucin-like domains in a major glycoprotein of Cryptosporidium parvum ( GP900 , CMU_014140 ) ( Figure 2A ) . Since mucin domains are typically extensively O-glycosylated on Ser or Thr residues , the probability of O-glycosylation at this mucin domain was assessed using neural network model NetOGlyc 3 . 1 [16] . Of the 157 threonines in the mucin domain , 95% were predicted to be O-glycosylated using NetOGlyc 3 . 1 ( Figure 3A ) . To investigate whether the CST1 is O-glycosylated , SalmonE-immunoprecipitates were probed with Dolichos biflorus lectin ( DBA ) , a marker for the cyst wall that recognizes GalNAc [17] ( Figure 3B ) . DBA lectin overlays verified that CST1 , the TGME49_064660 gene product , is a glycoprotein . CST1 mRNA is expressed in type I ( RH ) , type II ( P/ME49 ) and type III ( CTG ) strains as evidenced by expression data from www . ToxoDB . org and our RNA-seq data ( Figure S1 ) . To understand the function of CST1 we deleted the entire CST1 gene ( Figure 4A ) in the PruΔku80 background [18] . This strain has a high frequency of homologous recombination that facilitates the development of knock-outs and also contains GFP under the control of bradyzoite specific LDH2 promoter so that the brain cysts containing bradyzoites can readily be identified by fluorescence microscopy [18] . The deletion of the CST1 ( Δcst1 T . gondii strain ) was verified by PCR ( Figure S1B ) as well as by RNA-seq ( Figure S1C ) . T . gondii lysates of parasites grown in vitro at pH 8 . 1 ( bradyzoites ) and pH 7 ( tachyzoites ) were probed with mAb SalmonE by immunoblot ( Figure 4B ) . The mAb SalmonE reactive band at pH 8 . 1 is seen in the stacking gel , suggesting that this is a high molecular mass antigen with extensive post translational glycosylation that may prevent entry into the resolving gel . While the mAb SalmonE reactivity is virtually absent in parasites grown at pH 7 , a strong signal was observed in lysates from wild-type parasites grown at pH 8 . 1 . These CST1 bands also bind to DBA lectin consistent with the presence of glycosylation in this protein ( Figure S2 ) . Since CST1 was originally defined by reactivity to DBA and recognition by mAb 73 . 18 [10] , [11] , the CST1 deficient strain ( Δcst1 ) should not be recognized by mAb 73 . 18 . Immunopurified CST1 , and cell lysates from pH 8 . 1 treated T . gondii Pru wild type or Δcst1 cultures were probed with the previously described CST1 specific mAb 73 . 18 and mAb SalmonE ( Figure 5A ) . Monoclonal antibody SalmonE and mAb 73 . 18 had similar patterns of reactivity on immunoblot , and , as expected , the Δcst1 did not have the major immunoreactive band and had lost the characteristic cyst wall labeling seen with DBA , mAb SalmonE ( Figure 4C ) , or mAb 73 . 18 ( Figure 5B ) . These cysts that did not stain with DBA , mAb SalmonE or 73 . 18 were still positive with BAG1 antibody ( Figure 6 ) . Examination of brains of mice infected with Δcst1 T . gondii demonstrated that cyst formation could still occur in this knockout strain ( Fig . 7 ) . Collectively , these results indicate CST1 is not required for bradyzoite or cyst formation and that CST1 is the cyst wall protein recognized by mAb SalmonE , DBA , and mAb 73 . 18 . Both DBA and mAb 73 . 18 recognize glycoepitopes , and we noted that while cyst wall reactivity was lost in Δcst1 , there was some residual reactivity seen within the parasites by mAb SalmonE , DBA and mAb 73 . 18 , suggesting that other less abundant glycoepitopes that react with mAb SalmonE , DBA , and mAb 73 . 18 are present in bradyzoites . To examine the role of glycosylation in CST1 function , we complemented the Δcst1 strain with two variants of CST1 ( Figure 4A ) . One Δcst1 line was complemented with a full-length cDNA ( Δcst1::cst1 ) and the other with a CST1 lacking the 789 bp region coding for the mucin domain ( Δcst1::cst1Δmuc ) . Following transfection and selection , the presence of those complemented genes were verified by PCR ( Figure S1B ) and RNA-seq ( Figure S1C ) . Stage specific expression of CST1 was equivalent to that seen in the wild type parasite in the complemented Δcst1::cst1 T . gondii strain as verified by immunoblot with mAb SalmonE ( Figure 4B ) . IFA of pH 8 . 1 treated Δcst1::cst1 in vitro cysts demonstrated that the strain complemented with full-length cDNA of CST1 has the correct localization of CST1 to the cyst wall , as well as the restoration of DBA staining ( Figure 4C ) and mAb 73 . 18 staining ( Figure 5B ) of the cyst wall . These cysts remained IFA positive for BAG1 ( Figure 6 ) . In contrast , the Δcst1::cst1Δmuc parasites lack the major mAb SalmonE and DBA reactive band in immunoblot ( Figure 4B and S2 ) , and lack cyst wall staining with DBA , mAb SalmonE , and mAb 73 . 18 in IFA ( Figure 4C and 5B ) . The expression and localization of CST1Δmuc protein was verified with a polyclonal antibody produced against recombinant CST1 ( rTGME49_064660 AA1-200 ) ( Figure S3 ) . To determine the effect of CST1 deletion or mucin domain deletion in vivo , C57BL/6 mice were infected with wild type , Δcst1 , Δcst1::cst1 , and Δcst1::cst1Δmuc parasites at 200 parasites per mouse . The mouse survival rates ( Figure 7A ) during acute infection with each parasite line were not statistically different ( n = 20 , Log-rank test ) . The number of brain cysts per mouse at 4 weeks after infection ( Figure 7B ) was reduced by 41% in the Δcst1 strain ( p<0 . 05 , Mann-Whitney U test ) . Complementation with full length ( Δcst1::cst1 ) , but not mucin-null ( Δcst1::cst1Δmuc ) , restored the cyst number level back to the wild type levels . Histological analysis of the brains suggested that inflammation was less severe in Δ cst1 than the wild type ( Figure S4 ) ; however this did not achieve statistical significance . Cysts were produced in vivo by all mutants ( Figure 7C ) and there was no difference in the size of cysts produced by these mutants . Brains from mice infected with the wild type , Δ cst1 , Δ cst1::cst1Δmuc , and Δcst1::cst1 parasites were fed to Balb/cDM1 and all were capable of transmitting infection . During the brain cyst isolation procedure , cysts are subjected to mechanical stress to release them from brain tissue to purify them by isopycnic centrifugation [19] . The wild type cysts stayed intact during this procedure , but Δcst1 brain cysts were much more fragile and broke apart when homogenized using a pestle tissue homogenizer to purify cysts from brain samples [19] ( Figure 8A ) . Despite many attempts , we were unable to develop a reliable procedure to purify intact Δcst1 cysts from mouse brains or a method to standardize cyst inocula to compare the transmissibility of cysts from our mutant strains . To further investigate the fragile phenotype , we examined the ultrastructure of the brain cysts by electron microscopy . Figure 8B demonstrates a wild type brain cyst , which has the classic organized cyst wall with an underlying amorphous granular layer . In contrast , the Δcst1 brain cysts lack this organization and displayed a disrupted layer . Independently isolated Δcst1 clones had the same fragile phenotype . Full length cDNA complementation of Δcst1 ( Δcst1::cst1 ) rescued the fragile brain cyst phenotype as well as restoring the cyst wall layer as seen by TEM ( Figure 8 ) . In contrast , complementation of Δcst1 parasite with cst1Δmuc gene did not rescue the fragile cyst wall phenotype or correct the disruption of cyst wall layer seen by TEM ( Figure 8 ) . Measurements of the cyst wall confirmed a significant decrease in the cyst wall thickness with disruption of cst1: WT 153±28 nm , Δcst1 24±10 nm , Δcst1::cst1 284±65 nm , and Δ cst1::cst1Δmuc , 34±14 nm ( p<0 . 05 WT vs Δcst1 , WT vs Δ cst1::cst1Δmuc ) as well as an increase in cyst wall thickness in the Δcst1::cst1 strain ( p<0 . 05 WT vs Δ cst1::cst1 ) . To determine if cyst wall thickness affected cyst fragility , we compared cyst fragility in individual brains using a relatively vigorous disruption method with a small sintered glass pestle tissue homogenizer ( size A: 0 . 1–0 . 15 mm clearance , frosted inner glass surface ) that disrupts a significant fraction of wild-type cysts in brain homogenate . For this experiment , brains ( n = 4 per group ) were cut in half , the cysts in the right half were homogenized unfixed , and the left half was fixed with 4% paraformaldehyde overnight at 4°C prior to homogenization with the pestle tissue homogenizer . Using this procedure 100±0% of Δcst1 and 100±0% Δcst1::cst1Δmuc cysts were broken . Interestingly , fewer Δcst1::cst1 ( 15±3% ) cysts were broken than wild type cysts ( 61±8% ) , suggesting that the increased thickness of the cyst wall seen on TEM ( Figure 8 ) with Δcst1::cst1 parasites does protect the cysts/bradyzoites from mechanical stress . To examine any growth defects of the Δcst1 parasite , parasite growth was measured with incorporation of 3H-uracil in pH 7 . 1 ( tachyzoite stage ) and pH 8 . 1 ( bradyzoite differentiation ) medium . The deletion of CST1 resulted in a reduction in the growth rate at pH 8 . 1 of the Δcst1 parasite compared to the growth rate of the wild type ( WT ) parasites ( Figure 9A , p<0 . 005 ) , this reduction in growth was not seen at pH 7 ( Figure S5 ) . This slower growth phenotype seen with bradyzoite inducing condition in Δcst1 parasites was rescued by full length CST1 complementation , but only partially by the mucin-null CST1 ( Figure 9B ) . Since there was reduction in growth rate at pH 8 . 1 in vitro for the Δcst1 parasite and fewer brain cysts in vivo , we investigated whether the cst1 mutants might have global changes in gene expression under the bradyzoite-inducing conditions . The transcriptome of parasites cultured at pH 7 ( tachyzoite ) was compared with the transcriptome of parasites cultured at pH 8 . 1 ( bradyzoite ) for 3 days using RNA-seq . Figure 10A shows the heat map of top 50 upregulated genes at bradyzoite conditions in wild type ( WT ) T . gondii ( see Table S1 for a complete list of these genes ) . Expression of 49 of 50 genes was less efficiently induced by pH shock in the Δcst1 strain compared to control WT parasites . The complementation of full-length CST1 ( Δcst1::cst1 ) restored the majority of these genes back to their wild type level . However , the mucin-null complement ( Δcst1::cst1Δmuc ) was not able to restore expression of these genes to wild type levels . Figure 10B shows the fold change in gene expression for several known bradyzoite specific genes . These bradyzoite genes also follow a similar pattern of reduced gene upregulation with Δcst1 , restoration with Δcst1::cst1 and only a partial restoration with Δcst1::cst1Δmuc . The altered gene expression pattern was not evident for housekeeping or tachyzoite specific genes . This RNA-seq data suggests that lack of CST1 disrupts bradyzoite differentiation , but not enough to prevent in vitro and in vivo cyst formation .
This study identifies the gene encoding the major cyst wall DBA-binding protein CST1 . CST1 is a SRS containing protein with an extended mucin domain . The mucin domain of CST1 is necessary for DBA binding and is a major domain for glycosylation of this protein . Δcst1 parasites can differentiate and form mouse brain cysts without CST1; however , CST1 and CST1 glycosylation is required for formation of an organized cyst wall layer that confers structural rigidity to the cyst wall . This is the first cyst wall protein that has been shown to be essential to establish the physical integrity of in vivo brain cysts . Complementation demonstrates that the mucin domain of CST1 is necessary for the cyst wall organization and rigidity . In addition to its role in structural stability of the cyst wall , a lack of CST1 also reduces in vitro growth rate , mouse brain cyst number , and pH 8-induced bradyzoite specific gene upregulation in T . gondii . These results suggest that expression of CST1 or glycosylation of CST1 in early cyst development influences the expression pattern of genes during bradyzoite differentiation . Previous work has suggested that the cyst wall contains several glycoproteins including CST1 [10] , a proteophosphoglycan [11] and other unknown glycoproteins reacting with s-WGA [8] . CST1 is highly unusual SRS protein in that it has a large mucin domain and thirteen SRS domains . The glycoprotein gp900 , from another Apicomplexan parasite Cryptosporidium parvum , has a large mucin-like domain that has 68% sequence similarity with the mucin domain of CST1 . Other than the presence of mucin-like domain , there is no sequence similarity between these two glycoproteins . The C . parvum gp900 has a transmembrane domain , is expressed on the plasma membrane , and is shed into the environment [20] . The gp900 protein is localized to the tethers on the inner surface of oocyst walls [20] , [21] . This suggests that gp900 , a large mucin-like protein , is important for making a structurally rigid enclosure for this parasite . Other smaller glycoproteins ( gp40 and gp15 ) are present in the oocyst wall tethers of C . parvum [20] , [21] . Our studies with CST1 in T . gondii tissue cysts suggest phylogenetic conservation of the functions of these secreted structural glycoproteins in the Apicomplexa . The protozoan parasite Trypanosoma cruzi has up to 850 highly glycosylated and GPI-anchored surface mucin genes that form a stage specific mosaic coat on their cell surface . It is suggested that these glycoproteins have a protective role against the proteases in the intestine of the insect vector [22] , function in attachment to the host cell , and as an immune evasion mechanism [23] . CST1 , which has large mucin domain and a predicted GPI-anchor ( as with other SRS domain containing proteins ) , may have comparable functions in terms of protecting bradyzoites in the cyst from the proteases present in the gastrointestinal tract during the oral infection or in the surrounding necrotic tissues when the host dies [22] . The ability of mucins to retain large amount of water probably protects parasites by preventing dehydration , facilitating parasite transmission to the next host . Of the 189 cyst-wall positive hybridomas we identified , 34 had an identical immunoblot pattern as mAb SalmonE . This suggests that CST1 may be highly immunogenic toward the Th2 pathway . Cysts are present not only in the central nervous system , but also in the visceral organs and muscles . CST1 from ruptured cysts can induce a strong antibody response and this may facilitate the clearance of cysts by the immune system when their host cells are dead . Cloned hybridomas specific to CST1 included other classes of antibodies ( e . g . IgG2b and IgM ) , therefore , CST1 does not only elicit the production of IgE antibody . In a previous study , CST1 was detected by its reactivity to the mAb 73 . 18 in the R5 strain of T . gondii , an atovoquone resistant mutant that spontaneously formed cysts more readily [24] . In this strain CST1 was reported as a 116 kDa band on 2D SDS-PAGE [10] . In our current study , both mAb SalmonE and mAb 73 . 18 detected two distinct bands ( Figures 5A , S6 ) , one migrating at 150 kDa ( SRS13 , Weiss , unpublished data ) and one band ( CST1 ) in the stacking gel ( >210 kDa ) . Similar immunoblot patterns were seen using Pru , ME49 or the ME49 mutant R5 ( Figure S6 ) . Both bands also react with DBA lectin indicating that the SRS13 has similar glycoepitope as CST1 . This SRS13 reactivity is still present in the Δcst1 strain and does not localize to cyst wall ( Tomita and Weiss , unpublished data ) and mAb SalmonE staining of the 150 kDa band is absent in a Δsrs13 strain ( Figure S6 ) . Monoclonal antibody SalmonE , mAb 73 . 18 , and DBA all display no cyst wall staining in the Δcst1 strain , consistent with SRS44 being CST1 , a major cyst wall glycoprotein identified by our laboratory over 15 years ago [10] , [11] . During previous studies we were not able to detect the band in the stacking gel since the stacking gel was separated from the resolving gel before the transfer to a membrane . The discrepancy in the molecular-weight of 116 kDa and 150 kDa may be due to the difference in gel conditions or molecular markers . CST1 is one of several cyst wall proteins that are induced during bradyzoite development . Recently , a screening of insertional mutants for a reduction of in vitro cyst development led to the identification of another T . gondii cyst wall protein , proteophosphoglycan ( TgPPG ) [25] . TgPPG is expressed in the cyst wall and is probably highly glycosylated , as evidenced by its retention in the stacking gel on SDS-PAGE . Disruption of TgPPG gene results in the delay in cyst wall formation and bradyzoite conversion; however , complementation only rescued cyst wall formation measured by the DBA staining , but not bradyzoite differentiation as measured by BAG1 expression . In another recent study , transcriptomic analysis of brain cysts yielded two distinct cyst wall proteins Bradyzoite Pseudokinase 1 ( BPK1 ) and Microneme Adhesive Repeat domain-containing protein 4 ( MCP4 ) [13] . Subsequent study demonstrated that BPK1 plays a role in effective oral transmission [26] . Finally , several GRA proteins have been demonstrated to localize the cyst wall in bradyzoite parasitophorous vacuoles , as well as to the dense granules in both tachyzoites and bradyzoties [27] . Deletion of cyst wall associated GRA6 was shown to dramatically decrease tissue cyst burdens in mice [18] . The biological functions of these cyst wall proteins await further study . Toxoplasma gondii is one of the most successful parasites partly because it forms persistent latent cysts that last for the life of its hosts , and the cyst wall is a critical biological structure for this persistence . CST1 functions as a key structural component reinforcing the cyst wall structure and conferring resistance to physical stress to the T . gondii cyst . The fragile cyst phenotype in the Δcst1 strain suggests that transmission and persistence could be affected by this gene deletion , but this hypothesis could not be fully tested , as intact viable cysts cannot be purified from mouse brains to perform quantitative oral challenge experiments [10] , [11] . There are 5 putative UDP:GalNAc:polypeptide N-acetylgalactosaminyltransferases ( ppGalNAc-T ) in the T . gondii genome ( Toxo DB ) . Two of these , T1 and T3 , were expressed in insect cells and their glycosyl transferase activity was confirmed [28] . Both transferases turned out to be “follow-up” transferases , only transfering to the pre-O-glycosylated peptide . At this point , it is not known which glycosyl transferasase is responsible for the heavy glycosylation of CST1 mucin domain . A recent study showed that the nucleotide sugar transporter ( TgNST1 ) is necessary for the glycosylation of cyst wall proteins such as CST1 [29] . Deletion of TgNST1 resulted in fewer brain cysts and the authors concluded that glycosylation of the cyst wall is required for persistence of bradyzoites , but did not identify which glycoproteins were most critical . Our studies show a similar phenotype for parasites lacking CST1 , consistent with CST1 being the major cyst wall glycoprotein involved in the maintenance of brain cyst burden . Furthermore , parasites expressing a mutant CST1 lacking the mucin domain have a similar defect , demonstrating that post translational glycosylation of the CST1 mucin domain is critical for CST1 biological function . Therefore enzymes involved in glycosylation of cyst wall proteins including candidate enzymes such as T . gondii ppGalNAc-Ts have potential as therapeutic agents to prevent T . gondii bradyzoite persistence .
Human foreskin fibroblasts ( HFF ) were maintained in 10% fetal bovine serum pH 7 DMEM with penicillin-streptomycin at 5% CO2 . Confluent monolayers were infected with Type II strains ME49 , the reference genome strain , or PruΔku80 strain of T . gondii , which is widely used for genetic studies [18] . For in vitro bradyzoite differentiation , parasite strains were grown in differentiation medium ( DMEM medium adjusted to pH 8 . 1 with 10 mM HEPES and 1% fetal bovine serum with penicillin-streptomycin ) for 3 days at 0 . 5% CO2 . All animal experiments were conducted according to the U . S . A . Public Health Service Policy on Humane Care and Use of Laboratory Animals . Animals were maintained in an AAALAC-approved facility and all protocols were approved by the Institutional Care Committee of the Albert Einstein College of Medicine , Bronx , New York ( Animal Protocols 20121104 , 20121109 and 20121110; Animal Welfare Assurance number A3312-01 ) . No human samples were used in these experiments . Human foreskin fibroblasts were obtained from ATCC . BALB/cdm1 mice , which have a deletion in the Ld gene at the HLA-2L locus and produce more brain cysts than wild type BALB/c [30] , were infected with ME49 strain of T . gondii and treated with sulfamerazine at 30 mg/L in drinking water to minimize death from the acute infection . Four weeks after the infection , brain cysts were isolated using previously described isopycnic centrifugation [19] . Briefly , brains were isolated and homogenized in PBS with a pestle tissue homogenizer with clearance of 0 . 15–0 . 23 mm ( Thomas Scientific ) for 10 times . Percoll was added to 40% of the total volume and centrifuged at 27 , 000×g for 20 minutes . The middle layer was recovered and centrifuged with equal volume of PBS at 100×g for 10 minutes . Cysts were then subjected to ten freeze-thaw cycles , ( 3 minutes each: 100% ethanol-dry ice bath followed by room temperature water bath ) and emulsified with an equal volume of Freund's complete adjuvant . The emulsion was injected into BALB/c mice subcutaneously . Two months later , spleens were isolated from the immunized mice and fused with myeloma cell line to create hybridoma libraries . Using the IFA , ELISA and immunoblot , those hybridoma supernatants were screened against parasites that were cultured in pH 8 . 1 medium . IFA patterns were similar for two Type II strains ME49 and Pru . Subcloned hybridoma cells were cultured in CELLine bioreactor ( Integra ) for large-scale production of monoclonal antibodies . The BALB/cdm1 mice were infected with Pru strain of T . gondii for 4 weeks in the presence of sulfamerazine at 30 mg/L in drinking water . After they were harvested , whole brains were fixed in 4% paraformaldehyde in PBS for overnight , followed by homogenization and isopycnic centrifugation as described above . While wild-type cysts did not require fixation prior to processing , fixation of Δcst1 infected brains was necessary in order to prevent breakage of fragile brain cysts . The isolated cysts were fixed with 2 . 5% glutaraldehyde , 2% paraformaldehyde in 0 . 1M sodium cacodylate buffer , postfixed with 1% osmium tetroxide followed by 2% uranyl acetate , dehydrated through a graded series of ethanol and embedded in LX112 resin ( LADD Research Industries , Burlington VT ) . Ultrathin sections were cut on a Reichert Ultracut UCT , stained with uranyl acetate followed by lead citrate and viewed on a JOEL 1200EX transmission electron microscope at 80 kv . For immunoelectron microscopy , the cysts were fixed with 4% paraformaldehyde 0 . 05% glutaraldehyde in 0 . 1M sodium cacodylate buffer , dehydrated through a graded series of ethanol , with a progressive lowering of the temperature to −50°C in a Leica EMAFS , embedded in Lowicryl HM-20 monostep resin ( Electron Microscopy Sciences ) , and polymerized using UV light . Ultrathin sections were cut on a Reichert Ultracut E , immunolabeled with SalmonE , and then stained with uranyl acetate followed by lead citrate . Stained sections were viewed on a JOEL 1200EX transmission electron microscope at 80 kv . SalmonE was crosslinked to the Protein L agarose beads with disuccinimidyl suberate following the manufacturer's protocol ( Thermo Scientific ) . Human foreskin fibroblasts were infected with ME49 strain of T . gondii and incubated for 3 days in pH 8 . 1 medium with 10% FBS at 0 . 5% CO2 . Cells were lysed with 1% TritonX-100 in PBS with proteinase inhibitor cocktail and incubated with SalmonE-beads for 2 hours at 4°C . The beads were extensively washed with 1% TritonX-100 PBS and eluted with 0 . 1M glycine at pH 2 . 5 . The eluate was neutralized and separated on SDS-PAGE . The gel was stained with Coomassie Brilliant Blue and a visible high molecular-weight band was excised . The protein in the band was reduced and alkylated using TCEP and iodoacetamide then digested with trypsin in 25 mM ammonium bicarbonate/0 . 01% ProteaseMax at 50°C for 1 hour . The resulting digest was cleaned with C18 ziptip and the peptides eluted onto a MALDI plate with a saturated solution of α-cyanohydroxycinnamic acid in 70% acetonitrile/0 . 1% trifluoroacetic acid . MS/MS analysis of the digested sample was carried out using the AB Sciex 4800 MALDI-TOF-TOF ( Applied Biosystems ) , operated at 20 kV accelerating voltage in the reflector positive ion mode . The MS/MS data generated were converted to mgf files and searched against EPICDB [31] using the in-house Mascot Protein Search engine ( Matrix Science ) for protein identification . RNA was isolated from ME49 strain of T . gondii and a cDNA library was created using SuperScript III First Strand Kit ( Invitrogen ) . The first 200 peptides of the CST1 ( TGME49_064660 ) , as predicted in ToxoDB ( i . e . this does not include the probable N-terminal extension of CST1 gene predicted using SignalP 4 . 0 [15] ) , was amplified by PCR , cloned into the pET32 vector and used to transform BL21 competent E . coli . Recombinant CST1 protein was expressed using Overnight Express Autoinduction System 1 ( Novagen ) , purified with nickel column , and separated on SDS-PAGE . The band was cut out and emulsified with Freund's complete adjuvant and immunized into BALB/c mice intraperitoneally . Three months after the immunization , antisera were collected and probed against in vitro cysts using IFA . Type II Prugniaud strain with deletion of the Ku80 gene ( PruΔku80 ) [18] was used as the background strain for the creation of Δcst1 strain . The construct for the knockout was built as previously described [18] . Briefly , 1 kb upstream and downstream genomic DNA sequence of TGME_064660 gene were amplified from the parental PruΔku80 strain . These fragments were concatenated into pRS416 yeast shuttle vector ( ATCC ) flanking the selectable marker hypoxanthine-xanthine-guanine phosphoribosyltransferase [HXGPRT] cassette using the yeast strain ATCC#90845 . This construct deletes whole CST1 gene as well as 205 nucleotides 5′ upstream region from the predicted start site . This 5′ region includes the probable start site and signal peptides . All the primers used in the plasmid construct are listed in the Supplemental material ( Table S2 ) . The parasites were transfected with the linearized Δcst1 vector and subcloned in the presence of 25 µg/ml mycophenolic acid and 50 µg/ml xanthine . Integration of Δcst1 vector at the TGME49_064660 locus was verified by PCR . Lack of CST1 protein expression was confirmed with IFA and immunoblot . For CST1 complementation , DNA fragments were concatenated into pSMART-BAC plasmid ( Lucigen ) using In-Fusion system ( Clontech ) following the manufacture's protocol . The homologous sequences 1 kb upstream and downstream of UPRT coding region for the UPRT locus were isolated from genomic DNA . Flanking sequences 1 . 3 kb 5′ and 3′ of CST1 were isolated from Pru genomic DNA . TGME49_064660 cDNA was generated from RNA harvested from Pru strain cultured at pH 8 . 1 . The fragments were concatenated in the following order , 5′ UPRT recombination sequence , TGME49_064660 upstream element , TGME49_064660 cDNA , TGME49_064660 3′ UTR , and 3′ UPRT recombination sequence . For the mucin domain null mutant complementation , two fragments ( base 1–6006 and 6766–7035 with appropriate adapters ) were used instead of whole cDNA . This mucin domain null vector replaces the mucin domain ( nt 6007–6765 ) with a 1xFLAG sequence . The parasites were transfected with complementation vectors and subcloned in the presence of 5 µM 5-fluorodeoxyuridine ( FUDR ) . Integration of complementing vectors at the UPRT locus was verified with PCR . Our attempts to use the HXGPRT selectable marker present in the Δcst1 parasite to complement the endogenous cst1 locus were not successful . This was probably due to the low level expression of HXGPRT at the cst1 locus that was not sufficient for negative selection using 6-thioxanthine , but was sufficient for positive selection in constructing the knockout using mycophenolic acid ( MPA ) . We have experienced similar problems using the Δku80 system with the HGXPRT selectable marker and have found that removal of this marker by negative selection is not feasible at all loci and needs to be evaluated for each knock-out . Therefore complementation was performed at the UPRT locus for the cst1 gene since this target provides a direct selection and the loss of UPRT does not influence cyst development or cyst burdens in mice [18] . RNA was isolated using RNeasy mini with DNase treatment from HFF infected with ME49 strain of T . gondii culture in pH 8 . 1 medium for 3 days . 5′ UTR was amplified with FirstChoice RLM-RACE kit ( Ambion ) with gene specific primer GGGCGGGTCGAAAATGTTG following the manufacturer's protocol . The DNA was sequenced with ABI 3730 DNA analyzer . Cells were fixed with 4% paraformaldehyde in PBS for 30 minutes on ice and then permeablized with 0 . 2% TritonX100/0 . 1% glycine/0 . 2% bovine serum albumin ( BSA ) in PBS on ice for 20 minutes . The cells were washed with 0 . 2% BSA in PBS three times and blocked with 1% BSA in PBS at 4°C overnight . All incubation with primary and secondary antibodies was done at 37°C for 90 minutes in a moist chamber . Concentration of the antibodies and probes used were 20 µg/ml for FITC-conjugated DBA ( Vector Lab ) , 1∶50 for antibody 73 . 18 , 1∶100 for SalmonE , 1∶25 for mouse polyclonal anti-CST1 ( 1–200 ) , 1∶50 for rabbit anti-BAG , 1∶200 anti-FLAG M2 ( Sigma ) all in 1% BSA/PBS . Cells were washed three times with 0 . 2% BSA/PBS then incubated with appropriate secondary antibody at 1∶500 . After incubation , the cells were washed three times with 0 . 2% BSA/PBS and mounted with ProLong Gold antifade ( Invitrogen ) . Photomicrographs were taken either with a SP5 confocal microscope ( Leica ) or Microphoto-FXA epifluorescence microscope ( Nikon ) . HFF cells were infected with T . gondii in normal DMEM medium or differentiation medium and kept in 5% or 0 . 5% CO2 respectively . Cells were harvested at 3 dpi with cell scraper , centrifuged at 3000×g for 15 minutes and lysed with 1% TritonX100 , 1% SDS , protease inhibitor cocktail in PBS . The samples were resolved in 10% SDS-PAGE at 100 V and transferred at 250 mA for 90 minutes on the PVDF membrane Immobilon-FL ( Millipore ) . The membrane was blocked overnight with 5% non-fat dry milk ( NFDM ) in PBST , then probed with SalmonE at 1∶250 and anti-GRA1 at 1∶500 in 1% BSA/PBS at 4°C on shaker overnight . Following antibody incubation the membrane was washed three times with PBST and then incubated with donkey anti-mouse antibody conjugated with IRDye800 ( Licor ) at 1∶40 , 000 in 5% NFDM/PBST at room temperature for 90 minutes . The membrane was then scanned using an Odyssey ( Licor ) imaging system . For the Immunoprecipitation-Immunoblot experiments , SDS was omitted from the lysis buffer to minimize the disruption of antigen-antibody interaction . Immunoprecipitation was performed as described in the mass spectrometry method section . The gel was transferred to nitrocellulose membrane and probed with DBA conjugated with alkaline phosphatase ( EY Laboratories ) at 1∶100 in PBST for 90 minutes at room temperature . The membrane was then washed three times with PBST and developed with BCIP/NBT color development substrate ( Promega ) . The image of the membrane was scanned using a desktop scanner . For double labeling with SalmonE and 73 . 18 ( CST1 reactive; [10] , [11] , samples were run in 6% SDS-PAGE gel with 3% stacking gel . The membrane was probed with 73 . 18 ( 1∶20 ) then with anti-mouse IRDye700 ( 1∶20000 ) . After the membrane was scanned , it was re-probed with SalmonE ( 1∶100 ) then rat anti-mouse IgE antibody ( 1∶2000 ) then anti-rat IRDye800 ( 1∶20000 ) . For WT and Δcst1 parasites , a growth assay was performed using a previously described 3H-uracil incorporation method [32]–[34] . Since mammalian host cells are not capable of uracil uptake but T . gondii is [33] , [34] , the incorporation of radio-labeled uracil is used to measure the growth of parasites . Briefly , HFF monolayers in 12-well tissue culture plates were infected ( 100 or 10 , 000 parasites/well ) in either pH 7 . 1 or pH 8 . 1 medium and incubated in 5% or 0 . 5% CO2 respectively . Cells were incubated with 1 ml of 2 µCi/ml 3H-uracil per well for 24 hours before the each harvest time point . At 24 , 48 , 72 , or 96 hours after infection , medium was removed and cells were lysed with 1% SDS and 100 µg/ml of unlabeled uracil in PBS on ice . Nucleic acids were precipitated with 10% trichloroacetic acid on ice for 2 hours . The contents of each well were filtered with the glass fiber filters . The radioactivity of 3H on the filter was measured with a scintillation counter . For assessment of Δcst1::cst1 and Δcst1::cst1Δmuc parasite growth , microscopic observation of the growth of intracellular parasites was used , as the disruption of the UPRT gene locus results in parasites that no longer incorporate uracil . Parasites were grown as described above then fixed in 4% paraformaldehyde , permeabilized with 0 . 2% TritonX100 and probed with anti-GRA1 antibody to visualize the parasites . For pH 8 ( bradyzoite permissive condition ) a total of 600 host cells were examined for each time point/condition and the number of intracellular parasites per 600 cells determined . Bradyzoite vacuoles have been reported to be resistant to lysis so direct counting of vacuoles was used to evaluate growth . For pH 7 ( tachyzoite permissive condition ) parasites were grown as described above ( 10 , 000 parasite initial inoculum ) , cells were scraped from each well of a 24 well plate , centrifuged and then re-suspended in 100 µl of 0 . 5% saponin/PBS , pipetted 20 times to ensure host cell lysis and then counted in a Neubauer hemocytometer in triplicate . Female 4 to 8 week old C57/BL6 mice ( Jackson Laboratory ) were infected with 200 tachyzoites intraperitoneally . Any observed mortality was recorded until 28 days after infection when the mice were sacrificed and brains harvested . Brains were fixed in 4% PFA in PBS overnight . Right brain halves were partially homogenized with a syringe with PBS into 600 µl suspension then 120 µl were counted under fluorescent microscope . Left halves of each brain were fixed in 10% neutral buffered formalin for additional 72 hours and processed for paraffin embedding . Samples for histopathology were sectioned to a thickness of 5 µm and stained using hematoxylin and eosin ( H&E ) stains . Slides were analyzed for the presence of tissue cysts , inflammation , and gliosis by light microscopy and graded on a scale of 0–5 ( where 0 = no lesions; 1 = minimal lesions; 2 = mild lesions; 3 = moderate lesions; 4 = marked lesions; and 5 = severe lesions ) . Sections were graded by the pathologist in a blinded fashion to avoid confirmation bias . HFF cells grown in seven 150 mm tissue culture plates were infected with parasites for each strain WT Pru , Δcst1 , Δcst1::cst1 and Δcst1::cst1Δmuc in regular medium ( pH 7 DMEM with 10% fetal bovine serum , incubated in 5% CO2 ) . Eight hours later , free parasites were removed by washing with PBS and replaced with regular medium ( pH 7 , 5% CO2 ) or differentiation medium ( pH 8 . 1 DMEM with 1% fetal bovine serum , 10 mM HEPES , incubated in 0 . 5% CO2 ) . Three days after the infection , cells were harvested , passed through 27G needle twice to lyse HFF cells and filtered through 3 µm pore polycarbonate membrane to remove HFF cells . This separation process was performed on ice . Purified parasites were pelleted at 1000×g for 15 minutes 4°C . RNA was extracted using TRIzol Reagent ( Invitrogen ) , followed by genomic DNA removal and cleaning using RNase-Free DNase Set kit and Mini RNease kit ( Qiagen ) . Integrity of the RNA samples was assessed using the Aligent 2100 Bioanalyzer . RNA samples having RNA Integrity Number between 9 and 10 were used in this work . MicroPoly ( A ) Purist Kit ( Ambion ) was used for enrichment of transcripts . The SOLiD Total RNA-Seq Kit was used to construct template cDNA for RNA-Seq following the protocol recommended by Applied Biosystems . Briefly , mRNA was fragmented using chemical hydrolysis followed by ligation with strand specific adapters and reverse transcription was used to generate cDNA . The cDNA fragments , 150 to 250 bp in size , were subsequently isolated by electrophoresis in 6% Urea-TBE acrylamide gel . The isolated cDNA was amplified through 15 amplification cycles to produce the required number of templates for the SOLiD EZ Bead system , which was used to generate the template bead library for ligation base sequencing by the either SOLiD4 or 5500xl SOLiD instrument ( LifeTechnologies ) . The 50-base short read sequences produced by the SOLiD sequencer were mapped in color space using the Whole Transcriptome analysis pipeline in Life Technologies LifeScope software version 2 . 5 against the genome of T . gondii strain ME49 using the default mapping setting . Both Fasta and GFF files were obtained from ToxoDB website ( www . toxoDB . org; Release 6 . 1 ) . The output of the Whole Transcriptome analysis generated ( 1 ) a gene counts file , with the base counts summed to a single value across the entire gene length , and with a RPKM value also given for each gene; ( 2 ) a BAM file containing the sequence of every mapped read and its mapped location; ( 3 ) two pairs of * . wig files ( one pair for the two strands on each chromosome ) giving the mapped counts at each base position; and ( 4 ) a statistics summary on alignment and filtering report . Fold change in gene upregulation at bradyzoite induction was calculated by dividing the RPKM values of pH 8 . 1 by that of pH 7 . The genes with low level expression ( RPKM<5 ) in pH 8 . 1 WT parasites were removed . The top 50 genes that were upregulated in WT parasites were plotted with gplot heatmap . 2 in R . | Toxoplasma gondii causes severe encephalitis in immune compromised hosts after reactivation of brain cysts that persist for the life span of the host . The biological mechanisms of bradyzoite persistence within cysts are not fully understood . The glycosylated cyst wall is thought to play a crucial role in survival of bradyzoites during chronic infection as well as successful oral transmission of infection . Here we have identified the gene encoding cyst wall glycoprotein CST1 . When we delete the CST1 gene , parasites form dramatically fragile brain cysts . Parasites lacking CST1 develop fewer brain cysts , show dysregulation of bradyzoite-specific gene expression and are less able to grow under stressed conditions . The rescue of these phenotypes requires the heavily glycosylated mucin domain of CST1 . These studies demonstrate that the glycosylation of CST1 plays a significant role in the structural integrity and persistence of brain cysts . Agents that perturb CST1 glycosylation have the potential to disrupt formation of latent brain cysts , preventing chronic Toxoplasma infection . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | The Toxoplasma gondii Cyst Wall Protein CST1 Is Critical for Cyst Wall Integrity and Promotes Bradyzoite Persistence |
Human respiratory syncytial virus ( HRSV ) and , to a lesser extent , human metapneumovirus ( HMPV ) and human parainfluenza virus type 3 ( HPIV3 ) , can re-infect symptomatically throughout life without significant antigenic change , suggestive of incomplete or short-lived immunity . In contrast , re-infection by influenza A virus ( IAV ) largely depends on antigenic change , suggestive of more complete immunity . Antigen presentation by dendritic cells ( DC ) is critical in initiating the adaptive immune response . Antigen uptake by DC induces maturational changes that include decreased expression of the chemokine receptors CCR1 , CCR2 , and CCR5 that maintain DC residence in peripheral tissues , and increased expression of CCR7 that mediates the migration of antigen-bearing DC to lymphatic tissue . We stimulated human monocyte-derived DC ( MDDC ) with virus and found that , in contrast to HPIV3 and IAV , HMPV and HRSV did not efficiently decrease CCR1 , 2 , and 5 expression , and did not efficiently increase CCR7 expression . Consistent with the differences in CCR7 mRNA and protein expression , MDDC stimulated with HRSV or HMPV migrated less efficiently to the CCR7 ligand CCL19 than did IAV-stimulated MDDC . Using GFP-expressing recombinant virus , we showed that the subpopulation of MDDC that was robustly infected with HRSV was particularly inefficient in chemokine receptor modulation . HMPV- or HRSV-stimulated MDDC responded to secondary stimulation with bacterial lipopolysaccharide or with a cocktail of proinflammatory cytokines by increasing CCR7 and decreasing CCR1 , 2 and 5 expression , and by more efficient migration to CCL19 , suggesting that HMPV and HRSV suboptimally stimulate rather than irreversibly inhibit MDDC migration . This also suggests that the low concentration of proinflammatory cytokines released from HRSV- and HMPV-stimulated MDDC is partly responsible for the low CCR7-mediated migration . We propose that inefficient migration of HRSV- and HMPV-stimulated DC to lymphatic tissue contributes to reduced adaptive responses to these viruses .
The paramyxoviruses human respiratory syncytial virus ( HRSV ) , human metapneumovirus ( HMPV ) and human parainfluenza virus type 3 ( HPIV3 ) are common respiratory pathogens . HRSV is the most important viral agent of severe pediatric respiratory tract disease worldwide [1] , [2] , followed by HPIV3 [3] , [4] and HMPV [5] , [6] , [7] , [8] . The orthomyxovirus influenza virus type A ( IAV ) infects and causes respiratory tract disease in all age groups [9] , [10] , [11] . These human respiratory viruses share a tropism for the respiratory epithelium and have overlapping spectra of disease , ranging from rhinitis to bronchiolitis and pneumonia [12] , [13] . IAV usually induces long-term immunity following infection , such that re-infection depends on significant antigenic change [14] , [15] . In contrast , HMPV , HRSV and HPIV3 are able to symptomatically re-infect humans throughout life without significant antigenic change . This is particularly common with HRSV . Glezen and colleagues followed children from birth , and found that more than two-thirds were infected with HRSV during the first year of life , and almost half of these individuals were re-infected during each of the next two years [16] . In experimental infections of adults , typically 50–80% of subjects are re-infected with HRSV , and the majority has acute illness [17] . In another study , adults were challenged at intervals of 2–6 months over a period of 26 months with the same HRSV isolate , with the result that 73% were infected at least twice and 43% at least three times , and more than half of these infections were symptomatic [18] . These observations have been widely interpreted to indicate that HRSV in particular blunts or skews the immune response , resulting in suboptimal protection . Antigen-presenting dendritic cells ( DC ) are critical for a functional adaptive immune response . During a lower respiratory tract infection , the number of DC in the bronchi and lung increases by chemotactic influx of precursors that originate primarily from circulating monocytes [19] , [20] , [21] , [22] . Migration to non-lymphoid peripheral tissues such as the lung is mediated by so called “inflammatory” chemokine receptor-ligand pairs , including CCR1-CCL3/MIP-1α , CCR2-CCL2/MCP-1 or CCR5-CCL5/RANTES . Exposure of DC to antigen in peripheral tissue initiates DC maturation . During maturation , DC increase the surface expression of co-stimulatory molecules such as CD38 , CD40 , CD80 CD86 , and CD83 [23] , [24] . DC also change their expression of cell surface chemokine receptors: expression of CCR1 , CCR2 , and CCR5 is reduced , reducing responsiveness to inflammatory chemokines , and expression of CCR7 is increased [25] , [26] . CCR7 has two specific ligands , CCL19 and CCL21 , which are expressed by endothelial cells in lymphatic venules , in high endothelial venules ( HEV ) in lymph nodes , and in the T cell zone of lymphoid organs [27] , [28] , [29] . CCL19 and CCL21 direct migration of maturing , CCR7-expressing DC through the afferent lymphatics to the draining lymph nodes , and control DC positioning within defined functional lymphoid compartments [25] , [26] , [30] , [31] for efficient interaction with naïve and/or antigen-specific memory T lymphocytes . DC have a key role in determining the magnitude and quality of the adaptive immune response . We previously reported that HMPV , HRSV , and HPIV3 induce low-to moderate levels of human monocyte-derived dendritic cell ( MDDC ) maturation , cytokine/chemokine expression , and CD4 T cell proliferation , with the magnitude increasing slightly in the order HRSV , HMPV , and HPIV3 [32] , [33] . MDDC generated in vitro from primary human monocytes by treatment with IL-4 and GM-CSF represent an appropriate model for lung DC because monocytes give rise to myeloid DC in the resting lung [34] and mucosa [35] , and are phenotypically and functionally similar to DC located at sites of inflammation in vivo [36] . In the present study , we expanded our previous findings by screening MDDC for expression of genes related to maturation in response to HMPV , HPIV3 , HRSV and , for comparison , IAV . We found that CCR7 mRNA and protein expression were substantially increased in response to HPIV3 and IAV , but minimally increased in response to HMPV and HRSV . These differences detected by qRT-PCR and flow cytometry were functionally relevant , since MDDC stimulated with HMPV or HRSV were less efficient in their migration along a CCR7 concentration gradient than IAV- and HPIV3 stimulated MDDC . Secondary stimulation of HRSV- or HMPV-exposed MDDC with the strong DC activator LPS enhanced CCR7 expression and in vitro migration , suggesting that suboptimal stimulation , rather than inhibition , is responsible for this poor-migration phenotype . Finally , we provide evidence that low CCR7 expression by MDDC in response to HRSV and HMPV is at least partly due to the low level of expression of pro-inflammatory cytokines ( TNF-α , IL-1α and IL-6 ) .
Elutriated monocytes were obtained from healthy donors at the Department of Transfusion Medicine of the National Institutes of Health , under a protocol ( 99-CC-0168 ) approved by the IRB of the Clinical Center , NIH . Written informed consent was obtained from all donors . Recombinant ( r ) HMPV ( strain CAN97-83 ) , rHRSV ( strain A2 ) and rHPIV3 ( strain JS ) with or without the GFP gene were described previously [12] , [37] , [38] . The present study employed a genetically “stabilized” version of rHMPV , in which the SH gene was modified to silently remove tracts of A and T residues that had been sites of spontaneous mutations during passage in vitro [39] . Human Influenza/A/Udorn/72 , a wildtype virus of subtype H3N2 , was used as control . All viruses were grown on Vero cells and purified by centrifugation through sucrose step gradients as described previously [32] . Sucrose purified viruses were pelleted by centrifugation to remove sucrose . Virus pellets were resuspended in Advanced RMPI 1640 ( Invitrogen , Carlsbad , CA ) supplemented with 2 mM L-glutamine ( aRPMI ) , and aliquots were snap frozen and stored at −80°C until use . Virus titers were determined by immuno-plaque assay on Vero cells under methylcellulose overlay ( containing trypsin for titration of rHMPV and IAV ) as described previously [37] . In some experiments , UV-inactivated viruses were included as controls which were prepared using a Stratalinker UV cross-linker ( Agilent , Santa Clara , CA ) at 0 . 5 J/cm2 . Complete inactivation was monitored by plaque assay ( limit of detection: 5 plaque forming units per mL ) . Elutriated monocytes were obtained from healthy donors at the Department of Transfusion Medicine of the National Institutes of Health , under a protocol ( 99-CC-0168 ) approved by the IRB of the Clinical Center , NIH . As previously described [32] , monocytes were subjected to CD14+ sorting on an Automacs separator ( Miltenyi Biotec , Auburn CA ) , and cultured in presence of recombinant human IL-4 ( R&D Systems , Minneapolis , MN ) and recombinant human granulocyte-macrophage colony-stimulating factor ( GM-CSF , Bayer Healthcare , Wayne , NJ ) for 7 days to generate immature MDDC . These were confirmed by flow cytometry to be CD14− , CD38− , CD80low , CD86low , CD40low , CD54low . Immature MDDC were seeded in 12-well plates at 6×105 cells per well and were infected with live virus at an MOI of 3 PFU/cell , or with an equivalent amount of UV-inactivated virus , stimulated with 1 µg/ml of the superantigen Staphylococcus enterotoxin B ( SEB; Sigma , St Louis , MO ) or with 1 µg/ml of lipopolysaccharide ( LPS ) from Escherichia Coli O55:B5 ( Sigma ) . The infectivity of rgHMPV , rgHRSV and rgHPIV3 for MDDC was similar ( approximately 3–5% of GFP+ MDDC at 24 or 48 h post infection , no significant differences at the p≤0 . 05 confidence level for any of the data sets of this study ) . In some experiments , immature MDDC infected with rgHMPV , rgHRSV , rgHPIV3 at an input MOI of 3 PFU/cell were further stimulated 4 to 6 h later with 1 µg/ml of LPS or 150 IU of Interferon ( IFN ) -β ( PBL Interferon source , Piscataway , NJ ) or with a cocktail of pro-inflammatory cytokines of 6 ng/ml TNF-α , 10 ng/ml IL-6 and 0 . 36 ng/ml IL-1α ( R&D systems ) . All inoculations or stimulations were performed in advanced RMPI 1640 ( Invitrogen ) supplemented with 10% heat-inactivated FBS ( Hyclone , Logan , UT ) , 2 mM L-glutamine ( Invitrogen ) , 200 U/ml penicillin , and 200 µg/ml streptomycin ( Invitrogen ) at 37°C in 5% CO2 . Cell-associated RNA was isolated using the RNeasy mini kit ( Qiagen ) as recommended by the manufacturer and treated with DNAse I to remove residual genomic DNA . Analysis was done in two ways . The first involved a custom made low-density Taqman gene array containing 62 genes . Here , 1 µg of isolated RNA was reverse transcribed using SuperScript II ( Invitrogen ) in a 50 µl mix using random primers , and each cDNA mix was loaded onto an array in triplicate . The second method involved individual RT-PCR reactions . Here , 600 ng of isolated RNA was reverse transcribed using superscript II ( Invitrogen ) in a 25 µl mix using random primers . The reverse transcription product was diluted three-fold , and two µl of the diluted cDNA mix were used in each quantitative TaqMan PCR ( Applied Biosystems , CA ) for quantification of the targets of interest , namely CCR1 ( Hs00174298_m1 ) , CCR5 ( Hs00152917_m1 ) and CCR7 ( Hs00171054_m1 ) . qPCR results were analyzed using the comparative threshold cycle ( ΔΔCT ) method , normalized to 18S rRNA and expressed as fold change over mock . To determine the surface expression level of chemokine receptors , cells were stained with allophycocyanin ( APC ) -conjugated anti-human mAbs [anti-CCR1 ( CD191 , clone 53504 ) , anti-CCR2 ( CD192 , clone 48607 ) , anti-CCR5 ( CD195 , clone 2D7 ) , anti-CCR7 ( CD197 , clone 2H4 ) ( BD Biosciences , San Jose , CA ) ] . Isotype-matched mAbs were included as controls . Propidium iodide staining was used to exclude dead cells from further analysis . At 48 h post infection , the median viability of MDDC from six independent experiments was 85% for HMPV- , 86% for HRSV- , and 82% for HPIV3-exposed MDDC , reflecting the anti-apoptotic effects of virus-induced DC maturation [32] . In order to avoid interference , CCR1 , 2 , 5 and 7 expression was analyzed individually . At least 20 , 000 events were acquired using a FACSCalibur flow cytometer ( BD Biosciences ) and analyzed using FlowJo version 8 . 8 . 6 software ( Tree Star , Inc . , Ashland , OR ) . After 48 h stimulation , migration of the virus-stimulated MDDC in response to a CCL19 concentration gradient was evaluated using polycarbonate 5-µm diameter pore size transwells ( Corning , Lowell , MA ) . 1×105 live MDDC were seeded in the upper chamber , and incubated in presence or absence of CCL19 ( 1 µg/ml , ( R&D Systems , Minneapolis , MN ) in the lower chamber . Duplicate wells were used for each condition . After 3 h incubation , MDDC from the lower chamber were harvested , and the cell density of live cells was determined using a FACS Calibur flow cytometer ( BD Biosciences ) . For each sample , data acquisition was performed for 1 min at constant flow using 200 µl final volume . Forward scatter , side scatter , live/dead staining , and GFP expression were analyzed using FlowJo version 8 . 8 . 6 software ( Tree Star , Inc . , Ashland , OR ) . The average number of MDDC specifically migrating in response to CCL19 was calculated as follows: ( Average number of stimulated MDDC migrated in the presence of CCL19 ) – ( Average number of stimulated MDDC migrated in the absence of CCL19 ) . Data sets were assessed for significance using parametric one-way repeated measures ANOVA with the Tukey post hoc tests for normally distributed data sets or the non-parametric Friedman test with Dunns post hoc test . A log10 transformation was applied to data sets when necessary to obtain equal standard deviation among groups , a necessary requirement of both tests . Statistics were performed using Prism 5 ( GraphPad Software , Inc , San Diego , CA ) . Data were only considered significant at P<0 . 05 . Analysis of CCR5 and CCR7 expression: To account for the smaller data set of the IAV control ( n = 8 donors , except for IAV , n = 6 donors ) , data were analyzed using an unbalanced repeated measures ANOVA ( JMP version 8 . 0 . 2; SAS , Cary , NC ) .
We used RT-qPCR to survey maturation-related gene expression in MDDCs from 3 donors 24 h after exposure to either the superantigen SEB or to purified live or UV-inactivated rHRSV , rHMPV , rHPIV3 , or IAV ( Fig . S1A and B ) . In general , all four live viruses induced the up-regulation of the same array of genes but differed in the intensity of up-regulation increasing in the order rHRSV<rHMPV<IAV<rHPIV3 . The donors also had substantial responses to UV-inactivated IAV , but weak responses to UV-inactivated rHMPV , rHRSV or rHPIV3 . Donors 1 and 2 were refractory to stimulation by rHMPV and rHRSV , respectively . Among the genes surveyed , expression of CCR7 mRNA was substantially increased in response to IAV and rHPIV3 , but not in response to rHMPV and rHRSV ( Fig . S1A ) . Based on these preliminary results , we analyzed CCR7 mRNA expression by qPCR in additional donors ( total n = 9 , Fig . 1 ) , and found that , while IAV and HPIV3 induced a strong increase of CCR7 mRNA ( median increases of 23-fold and 7 . 2 fold , respectively ) , HRSV and HMPV only induced a 2 . 2- and 2 . 5-fold increase compared to mock treated cells . The effects of HMPV and HRSV on CCR7 expression were significantly smaller compared to HPIV3 and IAV ( Fig . 1 ) . By contrast , expression of CCR1 and CCR5 mRNA was increased in response to all viruses , but without any statistical difference between the viruses , except that the CCR5 mRNA expression was significantly different between rHPIV3 and IAV ( Fig . 1 ) . Because CCR7 has a unique role in DC migration towards lymph nodes and the subsequent adaptive response [26] , we explored the effect of these viruses on MDDC chemokine receptor expression and migration . We next used flow cytometry to measure surface expression of CCR1 , 2 , 5 , and 7 on MDDC 48 h after exposure to the different viruses ( Fig . 2 ) . We included CCR2 in this analysis since , like CCR1 and CCR5 , it directs monocytes and DC to inflamed tissue and is down-regulated during DC maturation . LPS was used as positive control because it strongly activates DC [40] , [41] . Fig . 2A shows primary data for a representative donor , and Fig . 2B–C show the compiled results for six to eight donors . In this and all subsequent experiments , we used versions of rHMPV , rHRSV , and rHPIV3 that express GFP from an added gene ( rgHMPV , rgHRSV , and rgHPIV3 , respectively ) . In mock-treated MDDC , substantial subpopulations of cells expressed CCR1 ( median 91% of total ) , CCR2 ( 34% ) , and CCR5 ( 75% ) , and were CCR7-negative or low ( Fig . 2A , B , C ) . High CCR1/2/5 and low CCR7 values would be typical for immature DC residing in peripheral tissue . As expected , LPS treatment induced a significant down-regulation of CCR1 ( 32% ) , CCR2 ( 9% ) , and CCR5 ( 24% ) , and up-regulation of CCR7 ( 48% positive cells ) ( Fig . 2A , B , C ) . Stimulation of MDDC with rgHPIV3 or IAV also induced a significant decrease in frequency of cells expressing the inflammatory chemokine receptors CCR1 , 2 , and 5 compared to mock-treated cells ( Fig . 2B ) . However , only IAV significantly decreased all median fluorescence intensities ( MFIs ) ( Fig . 2C ) . In contrast , stimulation with rgHMPV or rgHRSV had only moderate effects on chemokine receptor surface expression . Cell surface expression of CCR1 and 2 decreased after stimulation with rgHRSV and rgHMPV , but the difference compared to mock-treated MDDC was not significant ( Fig . 2B and C ) , except for the MFI of CCR1 ( Fig . 2C ) . Stimulation with rgHMPV or rgHRSV reduced the percentage of CCR5+ MDDC significantly compared to mock-treated MDDC , but treatment with IAV reduced CCR5 expression significantly more than rgHMPV or rgHRSV treatment ( Fig . 2B ) . CCR5 expression of rgHPIV3 stimulated MDDC was intermediate between HMPV and HRSV on the one hand , and IAV on the other hand , with no significant differences to any of the viruses ( Fig . 2C ) . The limited down-regulation of CCR1 , 2 , and 5 in response to rgHMPV and rgHRSV was coupled with a weak increase of CCR7 expression occurring on only a small subpopulation of cells ( Fig . 2B , median 7% CCR7+ cells for for rgHMPV , and 6% for rgHRSV , with no statistical difference to mock ) . Stimulation with rgHPIV3 or IAV was associated with a significantly stronger up-regulation of CCR7 than mock , rgHMPV or rgHRSV stimulation , resulting in 13% and 37% CCR7+ cells , respectively . Taken together , these results showed that compared to LPS , IAV , and rgHPIV3 , stimulation with rgHMPV and rgHRSV induced a smaller down-regulation of surface expression of CCR1 , CCR2 , and CCR5 , and a smaller up-regulation of CCR7 surface expression occurring on a smaller fraction of cells . We used flow cytometry to compare chemokine receptor surface expression on virus-exposed cells that were GFP-positive versus GFP-negative ( Fig . 4 ) . We previously showed that , following infection with rgHMPV , rgHRSV , or rgHPIV3 at an MOI of 3 , approximately 3–5 % of MDDC were GFP+ at 24 or 48 h post-infection [32] . This was indicative of robust viral gene expression , which was confirmed by RT-qPCR . In the GFP– population , we detected a low level of viral gene expression , suggestive of abortive virus replication [32] . Thus , comparing host gene expression in GFP+ and GFP– cells provides an indication of the effects of a robust versus abortive infection . Fig . 4A shows primary data for GFP expression and CCR7 surface expression for a single donor , and Fig . 4B summarizes data for the expression of CCR1 , 2 , 5 , and 7 for six donors . After treatment of MDDC with rgHMPV or rgHPIV3 , the extent of down-regulation of CCR1 , 2 , and 5 was similar between the GFP+ and GFP− MDDC ( Fig . 4 ) . In contrast , after rgHRSV treatment , these receptors were decreased only in the GFP− cells; indeed , in the GFP+ cells , CCR2 and CCR5 expression was slightly increased compared to mock treated cells . Thus , robust rgHRSV gene expression did not induce the down-regulation of the inflammatory chemokine receptors CCR1 , 2 , and 5 that normally occurs as part of DC maturation . CCR7 was expressed at higher levels in the GFP+ cells than in the GFP− cells after treatment with rgHMPV or rgHPIV3 , indicating that robust infection by these viruses stimulated rather than inhibited expression ( Fig . 4A and B ) . In contrast , CCR7 expression was not increased in either the GFP+ or the GFP− subpopulations of cells treated with rgHRSV . One possible explanation for the weak chemokine receptor modulation and migration by rgHMPV- and rgHRSV-treated MDDC was direct virus-mediated inhibition . Alternatively , it was possible that these viruses were insufficiently stimulatory , perhaps due to the low production of cytokines by virus-treated MDDC as described in our previous study [32] . We therefore investigated whether exposure of virus-stimulated MDDC to secondary stimulation with LPS or to higher concentrations of cytokines would result in more efficient chemokine receptor modulation and migration . We tested possible cytokine and IFN candidates based on the gene expression analysis described above ( Fig . S1 ) and previously published data by ourselves and others [32] , [42] , [43] , [44] , [45] , [46] . The individual additions of IFN-β , IL-28 , IL-29 , TNF-α , IL-1α , IL-6 and prostaglandin E2 to virus-treated MDDC had little or no effect on CCR7 mRNA levels or on the ability of MDDC to migrate to a CCL19 concentration gradient ( data not shown ) . These preliminary results confirmed previously published data showing that CCR7 is not an IFN-regulated gene in human or mouse DC [47] , [48] , [49] . Thus , the poor up-regulation of CCR7 by rgHMPV and rgHRSV is unlikely to be the result of a more stringent IFN antagonism by these viruses . We next tested the effect of a cocktail of pro-inflammatory cytokines containing TNF-α , IL-1α and IL-6 on chemokine receptor expression , with each cytokine in concentrations similar to those induced by LPS under our experimental conditions [32] . MDDC ( n = 4 donors ) were treated with rgHMPV or rgHRSV , and , 4–6 h later , received a secondary stimulation with the cocktail of pro-inflammatory cytokines or with LPS . The expression levels of CCR7 mRNA were quantified 24 h post-infection ( Fig . 5A ) . The secondary treatment with LPS induced a significant ( p<0 . 05 ) increase of CCR7 mRNA expression in rgHMPV- and rgHRSV-stimulated MDDC . Thus , the relatively low level of expression of CCR7 in MDDC exposed to rgHMPV or rgHRSV was not due to an irreversible block . Following treatment with the cocktail of pro-inflammatory cytokines , there was an increase of CCR7 mRNA in mock- , rgHMPV- or rgHRSV-stimulated MDDC , although there was substantial individual variation and this increase did not reach statistical significance . This suggests that the low level of expression of CCR7 mRNA in MDDC stimulated with rgHMPV or rgHRSV might be partly a consequence of the low levels of TNF-α , IL-1α and IL-6 produced after exposure to rgHMPV or rgHRSV . To measure cell surface protein expression , MDDC that were treated with rgHMPV or rgHRSV and given a secondary stimulation with the pro-inflammatory cytokine cocktail or LPS , as described above , were analyzed by flow cytometry at 48 h post-infection . Consistent with the results at the mRNA level , stimulation with the proinflammatory cytokine cocktail induced a partial decrease in CCR1 , 2 and 5 as well as a partial increase in CCR7 surface expression ( Fig . 5B and C; B: 1 representative donor , and C: n = 6 donors ) . Secondary stimulation with LPS had stronger effects in all cases . We also evaluated replicate samples to investigate if the profile of CCR7 mRNA and protein expression correlated with the ability of MDDC to migrate to a CCL19 concentration gradient , measured 48 h post-infection ( Fig . 5D , n = 5 donors ) . Indeed , secondary stimulation with LPS induced a strong and significant ( p≤0 . 05 ) increase of migration of rgHMPV- and rgHRSV-stimulated MDDC as compared to virus-treated cells given a mock secondary treatment . Following secondary stimulation of virus-treated cells with the cocktail of pro-inflammatory cytokines , there was an increase in migration of mock- , rgHMPV- and rgHRSV-stimulated MDDC , although this did not reach statistical significance , and did not reach the level of increase induced by LPS . Taken together , these results suggest that the low concentration of TNF-α , IL-1α and IL-6 induced by rgHMPV and rgHRSV is partly responsible for the low CCR7 mediated migration . We next investigated possible effects of robust viral infection ( indicated by intracellular GFP expression ) on chemokine receptor expression following treatment with the pro-inflammatory cytokine cocktail or LPS . This was done by infecting MDDC ( n = 6 donors ) with rgHMPV , rgHRSV , or rgHPIV3 , subjecting them to a secondary stimulation with the pro-inflammatory cytokine cocktail or LPS at 4 h post-infection , and using flow cytometry to analyze the cell surface expression of CCR1 , 2 , 5 , and 7 in the GFP-positive versus the GFP-negative populations at 48 h post-infection ( Fig . 6 ) . Secondary stimulation of rgHMPV- , rgHRSV- , or rgHPIV3-stimulated MDDC with LPS decreased cell surface expression of CCR1 , 2 , and 5 on both GFP+ and GFP− cells . Secondary stimulation with the cocktail of pro-inflammatory cytokines also induced a decrease in surface expression of CCR1 , 2 , and 5 . However , the magnitude of the effect usually was less than that observed with LPS . Secondary stimulation of rgHMPV- , rHRSV- , or rgHPIV3-exposed MDDC with LPS induced an equally strong increase of CCR7 surface expression on GFP+ and GFP− cells , compared to cells that did not receive the secondary treatment ( Fig . 6 ) . Secondary stimulation of virus-infected cells with the pro-inflammatory cocktail also induced increases in CCR7 expression on both GFP− and GFP+ cells , although only in the case of rgHRSV GFP−+ and GFP− cells and rgHPIV3 GFP− cells was this difference statistically significant compared to cells receiving a mock secondary treatment . This provided further evidence that the poor expression of CCR7 in MDDC exposed to rgHRSV or rgHMPV could be overcome by secondary stimulation with LPS , and substantially overcome by secondary stimulation with the cocktail of pro-inflammatory cytokines . These increases were observed both in GFP+ and GFP− cells , indicating that robust viral infection did not irreversibly block CCR7 expression .
Compared to HPIV3 or IAV , stimulation of human MDDC with HRSV or HMPV in vitro resulted in inefficient maturational changes in chemokine receptor usage – namely down-regulation of CCR1 , CCR2 , and CCR5 and up-regulation of CCR7 – that are necessary for DC migration in vivo following antigen uptake . MDDC stimulated with HRSV or HMPV did not migrate efficiently towards a CCL19 gradient in an in vitro assay , compared to HPIV3 or IAV , confirming that the poor surface expression of CCR7 had functional consequences . The weak chemokine receptor modulation and migration by MDDC exposed to HMPV and HRSV , viruses that are thought to induce incomplete immunity , was particularly evident compared to MDDC exposed to IAV , a virus that induces effective immunity . In vivo , maturing , antigen-bearing DC migrate from peripheral tissue to secondary lymphatic tissue and localize in defined lymphoid compartments , where they present antigens to CD4+ and CD8+ T lymphocytes , initiating and polarizing the T cell response [26] , [50] . DC migration to and positioning within lymphatic tissue are critical towards mounting an effective adaptive immune response [50] . While there are multiple chemokine receptors that direct immature DCs towards peripheral sites , CCR7 is the only receptor that mediates migration toward and positioning within lymphatic compartments for interaction with T lymphocytes [30] , [51] , [52] , [53] . Thus , differential effects of pathogens on CCR7 expression in particular could be functionally relevant for differences in the immune response to these pathogens . Accordingly , the reduced migration observed in our in vitro assay for HMPV- and HRSV-treated MDDC following stimulation with HRSV and HMPV suggests that , during an HMPV or HRSV infection in vivo , maturing DC migrate with reduced efficiency from the infected mucosa towards secondary lymphatic tissues . This might lead to reduced adaptive immune responses that could explain the greater ability of HMPV and HRSV to reinfect humans throughout life without need for significant antigenic change . The present study was done with primary human cells from multiple donors . While the use of cells from an outbred population provides data with substantial individualistic differences and reduced statistical significance compared to convenient , uniform hosts like inbred mice , it is important to note that the natural host of the viruses in the present study is the human and not the mouse . Direct in vivo studies of virus-specific effects on DC migration during respiratory infections of humans are difficult , especially in children . Gill et al [54] noted that DC persisted in the lungs of children hospitalized for HRSV infection for as long as 8 weeks following the resolution of infection [55] . Resorting to data from mice , sustained increases in pulmonary DC have also been observed following HRSV infection [56] . Lucken et al [57] tracked the migration of mouse DC following HRSV infection and showed that the increase in DC numbers in the mouse mediastinal lymph node was slower compared to IAV or Sendai virus infection [58] , [59] , [60] . These observations would be consistent with inefficient migration from the lung to lymphoid tissue . Our in vitro studies now provide a mechanism for these previous in vivo observations . In addition , we provided data that MDDC maturation also was reduced with HMPV compared to HPIV3 and IAV . We previously provided data indicating that the level of MDDC maturation in response to exposure to HMPV and HRSV is lower compared to HPIV3 [32] and IAV ( not shown ) . In vivo , the combination of these two factors , namely reduced overall maturation and inefficient CCR7-CCL19 driven migration , might result in additive net effects that could affect both the magnitude and the quality of the adaptive immune response . Compared to infection with IAV , HRSV and HMPV infections may yield lower overall numbers of virus-stimulated mature DC in the afferent lymphatics . Reduced expression of co-stimulatory surface molecules and reduced cytokine expression could affect the quality of the response as well as its magnitude . In addition , the inefficient migration of maturing DCs may also play a role in viral pathogenesis: specifically , the sustained presence of mature DC in the mouse lung has been suggested to contribute to airway inflammation [56] . Another paramyxovirus , measles virus ( MeV ) , was recently shown to inhibit CCR7-driven DC migration . Interference with DC maturation and function is considered to be central to MeV-induced immunosuppression . Compared to LPS , MeV infection failed to promote the switch from CCR5 to CCR7 expression , and MeV-matured DC exhibited chemotactic responses to CCL3 rather than to CCL19 [61] . Inhibition of CCR7-driven migration was also described for vaccinia virus and for herpes simplex virus type 1 [45] , [62] , [63] . However , the effects of reduced DC maturation and migration on long-term protection might be particularly significant for respiratory viruses such as HMPV and HRSV . Both of these viruses are restricted in tropism to the superficial cell layer of the respiratory tract , and protection against re-infection has reduced effectiveness ( compared to viremic viruses , for example ) due to the short-lived nature of local IgA antibodies , the inefficiency with which serum antibodies access the respiratory lumen , and the down-regulation of virus-specific CD8+ T cell functionality in the respiratory tract [64] . Thus , even modest decreases in the magnitude of the adaptive response could result in decreases in viral clearance and protection against re-infection . We used recombinant GFP-expressing viruses to distinguish between effects in robustly infected ( GFP-positive ) and uninfected/abortively-infected ( GFP-negative cells ) MDDC . This revealed additional differences between the viruses . For MDDC infected with HMPV or HPIV3 , the GFP-positive population expressed significantly more surface CCR7 than the GFP-negative population . In contrast , for MDDC infected with HRSV , the GFP-positive subpopulation resembled the GFP-negative population in having very low CCR7 surface expression . Thus , whereas robust infection with HMPV and HPIV3 stimulated expression of CCR7 , robust infection with HRSV did not . Furthermore , GFP-positive cells infected with HRSV showed no down-regulation of CCR1 , 2 , and 5 surface expression . Thus , compared to HMPV or HPIV3 , even the subpopulation of DC that is robustly infected with HRSV and contains abundant intracellular antigen would not be mobilized for migration . This would impede the delivery of HRSV antigen from the periphery to lymphoid tissue . Furthermore , DC that are robustly infected with a virus can readily process newly synthesized viral antigens for display on MHC class I molecules and presentation to CD8+ T cells . Reduced migration of DC that are robustly infected with HRSV to lymphoid tissue would reduce this activity . This would make activation of CD8+ T cells more dependent on cross-presentation by non-infected DC , and could reduce the efficiency of CD8+ T cell activation during HRSV infection , reducing viral clearance and the disease-sparing regulatory effects of HRSV-specific CD8+ T cells [65] . Secondary stimulation of HRSV- or HMPV-stimulated MDDC with LPS , a strong DC activator , resulted in up-regulation of CCR7 expression on both GFP-negative and GFP-positive cells and increased in vitro migration . In contrast , with vaccinia virus or human cytomegalovirus , a secondary stimulation of the infected DC with LPS failed to up-regulate the CCR7 chemokine receptor [45] , [62] . LPS is a strong NFκ-B and AP-1 dependent DC activator [66] , [67] . Secondary stimulation of HRSV- and HMPV-infected MDDC with the NFκ-B/AP-1-dependent pro-inflammatory cytokines TNF-α , IL-1α and IL-6 , at concentrations comparable to those induced by LPS treatment , up-regulated CCR7 expression and was pro-migratory . This suggests that , in contrast to MeV , vaccinia virus , or herpes simplex virus , suboptimal stimulation , rather than inhibition , is responsible for the poor-migration phenotype of pneumovirus-exposed MDDC . In summary , compared to HPIV3 and , in particular , IAV , the pneumoviruses HMPV and HRSV were inefficient in inducing the maturation-related changes in cell surface chemokine receptor expression in MDDC that are necessary in vivo to re-direct DC from the periphery to lymphoid tissue . Consistent with this , both HRSV and HMPV were poor inducers of MDDC maturation and migration in vitro . These effects could be contributing factors in the incomplete nature of protection induced by HRSV infection in humans . | The respiratory viruses human respiratory syncytial virus ( HRSV ) and , to a lesser extent , human metapneumovirus virus ( HMPV ) and human parainfluenza virus ( HPIV3 ) , can re-infect humans throughout life without significant antigenic change , suggesting that immunity to these viruses is incomplete . In contrast , re-infection by influenza A virus ( IAV ) depends on antigenic change , suggestive of more complete immunity . Dendritic cells ( DC ) take up virus antigen at the site of infection , undergo maturation , and migrate to the lymphatic tissue to present antigen to T lymphocytes , orchestrating the immune response . In response to antigen uptake , DC switch chemokine receptors on their surface , decreasing expression of receptors for inflammatory chemokines in the infected tissue , and increasing expression of CCR7 , the sole chemokine receptor that directs DC to migrate to lymphatic tissue . By stimulating human DC in vitro , we found that , in contrast to HPIV3 and IAV , HMPV and HRSV did not efficiently induce the switching of these surface receptors . In cell migration assays , we showed that , compared with IAV-treated DC , HRSV- or HMPV-treated DC migrated less efficiently to CCL19 , a chemokine that directs T cell migration to lymphatic tissue . Thus , during infection with HRSV and HMPV , inefficient migration of DC to the lymphatics could reduce the adaptive immune response to these viruses . | [
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] | 2011 | Low CCR7-Mediated Migration of Human Monocyte Derived Dendritic Cells in Response to Human Respiratory Syncytial Virus and Human Metapneumovirus |
Trypanosoma cruzi , the etiological agent of Chagas' disease , is an early divergent eukaryote in which control of gene expression relies mainly in post-transcriptional mechanisms . Transcription levels are globally up and down regulated during the transition between proliferating and non-proliferating life-cycle stages . In this work we characterized a nuclear adenylate kinase isoform ( TcADKn ) that is involved in ribosome biogenesis . Nuclear adenylate kinases have been recently described in a few organisms , being all related to RNA metabolism . Depending on active transcription and translation , TcADKn localizes in the nucleolus or the cytoplasm . A non-canonical nuclear localization signal was mapped towards the N-terminal of the protein , being the phosphate-binding loop essential for its localization . In addition , TcADKn nuclear exportation depends on the nuclear exportation adapter CRM1 . TcADKn nuclear shuttling is governed by nutrient availability , oxidative stress and by the equivalent in T . cruzi of the mammalian TOR ( Target of Rapamycin ) pathway . One of the biological functions of TcADKn is ribosomal 18S RNA processing by direct interaction with ribosomal protein TcRps14 . Finally , TcADKn expression is regulated by its 3′ UTR mRNA . Depending on extracellular conditions , cells modulate protein translation rates regulating ribosome biogenesis and nuclear adenylate kinases are probably key components in these processes .
Trypanosoma cruzi , the causative agent of Chagas' disease , is a protozoan parasite with a complex life cycle which involves two intermediary hosts , triatomine insects and mammals and three main parasite stages , epimastigotes and amastigotes which replicate in the insect vector and mammalian host respectively; and trypomastigotes the non-replicative form [1] . The complexity of its life cycle involves multiple morphological and metabolic changes that are possible due to a strict control of gene expression [2] . Early eukaryotes from the order Kinetoplastida , transcribe their genes as large polycistronic arrays and therefore rely on post-transcriptional mechanisms for gene expression regulation [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] . Furthermore trypanosomatids present unique characteristics regarding ribosome structure [11] , [12] and ribosomal locus organization [13] . Instead of having the typical ribosomal locus organization which consists of ribosomal promoter , ETS1 ( external transcribed spacer 1 ) , 18S rDNA , ITS1 ( internal transcribed spacer 1 ) , 5 , 8S rDNA , ITS2 ( internal transcribed spacer 2 ) , 28S rDNA , ETS2 ( external transcribed spacer 2 ) , ribosomal terminator and the 5S rDNA , they present the 28S rDNA fragmented in 7 small rDNAs [13] . There is almost no information about ribosome biogenesis in trypanosomatids , but their extremely divergent ribosomal locus suggests that they might present unique characteristics in ribosome biogenesis and assembly . For example in T . brucei ribosomal 5S rRNA biogenesis involves proteins which are exclusively found in trypanosomatids [14] , [15] , [16] . In the last few years , an atypical nuclear adenylate kinase ( ADK , ATP∶AMP phosphotransferase , EC: 2 . 7 . 4 . 3 ) isoform has been characterized in several organisms , such as Drosophila melanogaster [17] , Saccharomyces cereviciae ( FAP7 ) [18] , Caenorhabditis elegans [19] and Homo sapiens ( hCINAP ) [20] , [21] . ADKs are mainly involved in maintaining the adenine nucleotide pool , which includes ATP synthesis from ADP [22] . They are distributed in all kind of organisms , from bacteria to vertebrates , presenting conserved motifs , structures and functions . However , nuclear ADKs present unique characteristics and differ enormously in terms of primary structure and function from other previously characterized ADKs . It has been shown that all nuclear ADKs , present phosphotransferase activity in vitro , furthermore the human and yeast variants also present ATPase activity [17] , [19] , [23] , [24] . In S . cereviciae , FAP7 has shown several diverse functions; first of all it has been related to oxidative stress response by the activation of the transcription factor POS9 [18] , secondly overexpression of FAP7 confers , resistance to arsenite exposure , a powerful oxidant [25] , [26] . Finally FAP7 has been related to ribosome biogenesis; being involved in the final step of maturation of the 20S pre-rRNA , which corresponds to the cleavage at “site D” by direct interaction with Rps14 , a ribosomal protein that is found near the 3′ end of the 18S rRNA [23] , [25] . Interestingly , conserved residues predicted to be required for nucleoside triphosphate ( NTP ) hydrolysis are essential for FAP7 function in vivo [18] , [23] . Furthermore the human isoform ( hCINAP ) has also been vastly characterized and is involved in Cajal body organization [27]; transcription process and cell cycle progression [28] . In trypanosomatids ADKs have been identified in Leishmania [29] , Trypanosoma [30] , [31] and Phytomonas [32] . In T . brucei [31] and T . cruzi [30] several isoforms have been characterized with different subcellular localization including , flagella , glycosomes , mitochondria , and cytoplasm [30] , [31] , [33] , [34] , mainly related to energy balance maintenance . In the following work we characterized T . cruzi nuclear ADK isoform , showing that it is involved in ribosome processing and presents unique characteristics being completely different from the other isoforms found in these parasites .
Stock cultures of T . cruzi epimastigotes of the Y strain were maintained in axenic conditions at 28°C in BHT ( Brain Heart Triptose ) media ( pH 7 ) supplemented with 10% fetal calf serum , 100 U . mL−1 penicillin , and 100 mg . L−1 streptomycin [35] . Transfected parasites were maintained in the same media containing 500 µg . mL−1 of G418 and 10% fetal calf serum . Parasites were counted in a Neubauer hemocytometer chamber . T . cruzi TcADKn gene ( Systematic ID: Tc00 . 1047053507023 . 280 ) was amplified from genomic DNA of epimastigotes from the Y strain and cloned in the pRSET-A vector ( Invitrogen ) by digesting with HindIII/XhoI and TcRps14 ( Systematic ID: Tc00 . 1047053506945 . 230 ) was amplified from genomic DNA of epimastigotes from the Y strain and cloned in the pGEX vector ( GE Healthcare ) digested with BamHI/XhoI . The sequence coding for full-length T . cruzi TcADKn was cloned in the pTEX-eGFP expression vector by digesting with HindIII/SalI . The pTEX-eGFP plasmid was constructed by cloning the eGFP into the pTEX-TAP vector , kindly provided by Dr . Esteban Serra ( IBR , Rosario ) . A total of 108 parasites of the Y strain , were grown in BHT medium at 28°C , harvested by centrifugation , washed with PBS , and resuspended in 0 . 35 mL of electroporation buffer ( PBS containing 0 . 5 mM MgCl2 , 0 . 1 mM CaCl2 ) . The cell suspension was mixed with 50 µg of plasmid DNA in 0 . 2 cm gap cuvettes ( Bio-Rad ) . Parasites were electroporated with a single pulse of 400 V , 500 µF with a time constant of about 5 ms . Stable cell lines were achieved after 30 days of treatment with 500 µg . mL−1 G418 ( Sigma ) [36] . For deletion analyses TcADKn segments were amplified by PCR from the pTEX-ADKn-eGFP plasmid , cloned into pGEM T-easy vector ( Promega ) and subcloned in the pTEX-OMNI-eGFP vector . The pTEX-OMNI vector derives from the pTEX-GFP vector [37] , by the addition of the 3-FLAG ( Sigma ) , HA ( influenza virus hemagglutinin ) , and aT ( C-terminal alpha tubulin ) epitopes present in the pDIY cloning vector ( GI:374430409 ) . TcADKn locus was amplified from genomic DNA , the 3′ UTR was cloned in the pTREX-OMNI-eGFP vector which contains the same epitopes as the pTEX-OMNI vector but in a pTREX backbone . The pTEX-Dhh1-eGFP plasmid was kindly provided by Dr . Alejandro Cassola ( IIB-UNSAM ) . For oligonucleotide sequence refer to Table S2 . Freshly grown trypanosome samples were washed twice in PBS . After letting the cells settle for 30 min at room temperature in poly-L-lysine coated coverslips , parasites were fixed at room temperature for 20 min with 2% formaldehyde in PBS , followed by a cold methanol treatment for 5 min . Afterwards , all the samples were treated with anti-TcADKn ( 1∶200 ) , anti-GFP antibody ( 1∶500 ) ( Invitrogen ) or anti-TcPABP1 ( 1∶500 ) for 1 h followed by 1 h incubation with anti-mouse ( daylight 488 , Jackson Immuno Research ) or anti-rabbit ( daylight 594 , Jackson Immuno Research ) secondary antibody . Slides were mounted using Vectashield with DAPI ( Vector Laboratories ) . Cells were observed in an Olympus BX51 fluorescence microscope . Images were recorded with an Olympus XM10 camera . Images were analyzed with MBF ImageJ for microscopy bundle . Fusion proteins were expressed in Escherichia coli BL21 ( DE3 ) or DH5α . Cells were grown in Luria broth medium ( LB ) at 37°C with ampicillin to an optical density of 0 . 4 to 0 . 5 measured at 600 nm ( OD600 ) . Protein expression was induced with 1 mM of isopropyl-β-D-thiogalactoside ( IPTG ) for 16 to 20 h at 37°C . Cells were harvested by centrifugation , and pellets were resuspended in 5 to 8 volumes of breaking buffer ( 350 mM NaCl , 50 mM Tris-HCl , pH 7 . 4 , 0 . 5 mM EDTA ) containing a protease inhibitor PMSF ( 10 µg/ml ) and disrupted by sonication . Extracts were clarified by centrifugation at 12 , 000 rpm for 20 minutes . To purify recombinant 6x-His-TcADKn protein , clarified extracts were incubated with Ni-nitrilotriacetic acid beads ( 1 mL beads for 5 g of cell pellet; QIAGEN ) for 12 h at 4°C . Proteins were eluted with 5 bead volumes of breaking buffer containing 200 mM imidazole . Eluates containing nearly homogenous recombinant protein were pooled and dialyzed overnight in breaking buffer containing 20% glycerol and stored at −80°C . This procedure yielded 90%-pure recombinant protein , as judged by SDS-polyacrylamide gel electrophoresis ( SDS-PAGE ) and Coomassie brilliant blue staining . To purify glutathione S-transferase ( GST ) fusion proteins , GST-TcRps14 and the GST epitope , clarified extracts were incubated with 2 ml of glutathione-Sepharose beads ( Amersham ) for 4 h at 4°C . The beads were washed extensively with breaking buffer , and proteins were eluted with the same buffer containing 20 mM of reduced glutathione ( Amersham ) . Eluates were dialyzed overnight in breaking buffer containing 20% glycerol and stored at −80°C . For ADK activity , 50 µg of purified recombinant protein fraction were added to the reaction mixture ( 100 mM Tris-HCl buffer pH 7 . 5 , 20 mM glucose , 5 mM MgCl2 , 100 mM KCl , 2 mM dithiothreitol , 1 mM NADP+ , 5 U . mL−1 and 2 U . mL−1 glucose-6-phosphate dehydrogenase ) in a cuvette in a final volume of 0 . 5 mL . After 5 min incubation at 35°C the reaction was started by the addition of a small volume of ADP to a final concentration of 10 mM . ADK activity was calculated by measuring the increase in absorbance at 340 nm that accompanied the reduction of NADP+ [30] . For ATPase activity a sample of 50 µg of protein was added to the reaction mixture ( 100 mM Tris-HCl , pH 7 . 5 , 60 mM KCl , 5 mM MgCl2 , 5 U . mL−1 of polynucleotide kinase , 5 U . mL−1 of lactate dehydrogenase , 20 mM phosphoenolpyruvate , 1 mM NADH ) . After 5 min incubation at 35°C the reaction was started by the addition of a small volume of ATP . ATPase activity was calculated by measuring the decrease in absorbance at 430 nm that accompanied the oxidation of NADH . The results were plotted and the slope was used to calculate specific activities . 1×108 epimastigotes , were harvested by centrifugation , washed with PBS , resuspended in 1 mL of Tri-Reagent ( Sigma ) , mixed by inversion and 200 µL of chloroform were added followed by centrifugation at 12 , 000×g at 4°C . The supernatant was transferred to a clean test tube with 500 µL of isopropanol , after 10 min incubation at room temperature; they were centrifuged at 12 , 000×g for 15 min at 4°C . The pellet was washed with ethanol 75% , left to dry and resuspended in 20 µL of RNase-free water . RNA concentrations were determined spectrophotometrically , purity was confirmed by gel electrophoresis . 3 µg of RNA were used for retrotranscription , which were previously treated with DNaseI ( Sigma ) in order to eliminate any DNA contamination . TcADKn mRNAs were isolated by RT-PCR cloned in pGEM T-easy vector ( Promega ) and sequenced . TcADKn differential mRNA expression along the epimastigote's growth curve was quantified by SYBR green-based real-time PCR in a Real-Time PCR system ( Bio-Rad ) using default protocols . Data were relativized to 18S expression . Three independent experiments were carried out . eGFP expression along the growth curve of parasites expressing the pTREX-OMNI-eGFP-LAN or pTREX-OMNI-eGFP constructions was quantified by SYBR green-based real-time PCR in a Real-Time PCR system ( Bio-Rad ) using default protocols . GFP expression was relativized to neomycin ( Neo ) expression ( present in the pTREX-OMNI-eGFP vector ) . Three independent experiments were carried out . 1 . 25×108 epimastigotes from day 2 of culture were harvested washed with PBS , resuspended in buffer A ( 20 mM Tris-HCl , pH 7 . 6 , 2 mM MgCl2 , glycerol 10% , Nonidet P-40 0 . 5% , 1 mM EDTA , 1 mM DTT , 0 . 25 M sacarose , 50 mM KCl , 1 mM E64 and RNAse inhibitor from Sigma ) and incubated for 30 min in ice . They were harvested at 10 , 000×g for 15 min at 4°C , the supernatant was transferred to a clean tube containing 20 mg of protein G-agarose ( Sigma ) and 10 µl of preimmune serum the mixture was left in agitation for 1 h at 4°C . This fraction corresponded to the clarified extract . In parallel 20 mg of protein G-agarose were blocked with 100 µg of BSA in buffer A for 2 h at room temperature . After the pre-blocking 10 µl of anti-TcADKn serum were added and incubated for 2 h at 4°C , washed three times with 500 µL of buffer A and 400 µL of clarified epimastigotes extract was added and left in agitation for 1 h at 4°C . After incubation beads were washed three times with buffer A and five times with PBS 1× . The pellet was resuspended in 800 µL of TriReagent ( Sigma ) for RNA extraction . A small fraction was separated for protein analysis . 10 µg of recombinant 6x-His-tagged T . cruzi arginine kinase ( TcAK , Tc00 . 1047053507241 . 30 ) and 6x-His-tagged TcADKn were incubated with 10 µg of GST or 5 µg of GST-TcRps14 , in buffer K ( 150 mM NaCl , 50 mM Tris-HCl pH 7 . 4 , 0 . 5 mM EDTA ) containing protease inhibitor PMSF ( 10 µg . mL−1 ) in a final volume of 30 µL . After 1 h of incubation in ice , 190 µL of buffer K were added to the mixtures , 20 µL were removed for analysis ( 10% of the input ) , and the remainder was incubated with 10 µL of glutathione-Sepharose beads ( Amersham ) for 1 h in ice with regular agitation . Beads were spinned down by centrifugation and 20 µL of the supernatant were subsequently removed for analysis . The beads were washed three times with 1 mL of ice-cold buffer K . Bound proteins were extracted by boiling the beads in SDS-PAGE loading buffer ( output ) and resolved in 12% polyacrylamide denaturing gels . Proteins were identified by Western Blot analysis . Western Blots were performed using total T . cruzi extracts fractioned by electrophoresis in polyacrylamide denaturing gels and transferred to polyvinylidene fluoride ( PVDF ) membranes . The PVDF membranes were treated for 1 h with 5% non-fat dry milk in PBS and then incubated with the primary antibody ON , using anti-TcADKn diluted 1∶5000 , anti-His 1∶3000 ( Sigma ) or anti-GST diluted 1∶2000 ( Invitrogen ) , anti-GFP diluted 1∶2500 and anti-α-tubulin diluted 1∶2000 ( Abcam ) . Membranes were washed and incubated with the corresponding secondary antibody for two hours ( anti-mouse HRP 1∶2500 , anti-rabbit HRP 1∶2500 , Vector Labs ) . Detection was done by chemiluminescence ( Pierce ) . The 3HA-FAP 7 strain ( mat α ura3-52 lys2-80 ade2-101 trp163 his3-200 leu2-1 ) was kindly provided by Dr . Baserga . Strains were grown in YPG ( 1% yeast extract , 2% peptone , 2% galactose ) until transformation . Yeasts were transformed as explained in http://home . cc . umanitoba . ca/~gietz/ . The genes of TcADKn , TbADKn , FAP7 , E . coli ADK , TcADK6 , TbADKF were amplified and cloned in the p416 vector [38] , kindly provided by Dr . Cecilia D'Alessio , Fundacion Instituto Leloir . Transformed yeast were grown in minimum medium with galactose for selection and afterwards shifted to minimum medium with glucose for complementation assays [23] . Exponentially growing T . cruzi epimastigotes were treated with different drugs: actinomycin D ( Sigma ) 10 µg . mL−1 for 4 h , cicloheximide ( Sigma ) 50 µg . mL−1 for 4 h , puromycin 200 µg . mL−1 4 h , starvation in PBS 24 h , leptomycin B ( Sigma ) 0 . 1 µg . mL−1 for 5 h , rapamycin ( Sigma ) 100 µM for 6–8 h , phleomycin 150 µg . mL−1 for 4 h , hydrogen peroxide 200 µM for 1 h . After leptomycin and rapamycin treatment fluorescence was quantified for forty treated and untreated parasites . In each parasite the fluorescence from the green ( GFP ) channel was quantified in an area selected according to blue signal ( DAPI ) fluorescence ( nucleus ) using the RGB plugin in ImageJ ( http://rsb . info . nih . gov/ij/ ) . Cytoplasmic fluorescence was quantified in the same way selecting the brightest perinuclear areas in the green ( GFP ) channel . Selection criterion was the same for all transfected parasites . Cytoplasmic fluorescence determinations included the previously selected nuclear areas and fluorescence values , which were afterward subtracted , resulting in the area and values corresponding to the cytoplasm . For each parasite , the relationship between fluorescence/area was obtained for the nucleus and cytoplasm and then the ratio between the nuclear and cytoplasmic values was calculated . For media supplementation experiments BHT medium of parasites in day 10 of culture was supplemented with glucose or proline 2% and were incubated for 24 h . Results were monitored by immunofluorescence . For RNAseI treatment , epimastigotes in day 2 of culture , were harvested , washed with PBS , resuspended in buffer ( 50 mM Tris-HCl pH 7 . 8 ) and broken with liquid nitrogen , samples were treated for 2 h at 37°C with 20 mg . mL−1 of RNaseI . Samples were boiled in SDS-PAGE loading dye and analyzed by Western Blot . For native gel analysis , loading buffer ( Tris-HCl 100 mM pH 8 , sucrose 2% , BPB 0 , 05% ) was added to protein samples and analyzed by Western Blot . 18S rRNA precursors were isolated using a simple adapter ligation protocol . A standard oligonucleotide blocked in its 5′ end was adenylated using the New England Biolabs ( NEB ) adenylation kit following manufacturer's indications . Epimastigotes RNA was extracted as explained above , for each ligation reaction three RNA samples were pooled . The 3′ adenylated adapter was ligated in the absence of ATP using the T4 RNA ligase 2 truncated ( NEB ) using manufacturer's indications . The final ligation products were reversely transcribed into cDNA using a complementary oligonucleotide to the adapter . PCRs were performed using specific primers for the 18S and ITS region . PCR products were cloned in the pGEM T-easy vector ( Promega ) and submitted for sequencing . The same strategy was used for RNA extracted from immunoprecipitates against TcADKn . RT PCR were done using oligonucleotides , for the ITS region , TcH2B ( Systematic ID: Tc00 . 1047053511635 . 20 ) and TcNDPK3 ( Systematic ID: Tc00 . 1047053510879 . 210 ) . A standard PCR protocol was used , 5 minute denaturation at 95°C and 30 cycles: 1 minute denaturation at 95°C , 1 minute at the corresponding annealing temperature , and 1 minute at 72°C; finally 10 minutes at 72°C . Controls without retrotranscriptase were done to eliminate any DNA contamination possibilities . Sequences were obtained from the TriTrypDB ( http://tritrypdb . org/tritrypdb/ ) . Assembly and sequence data analysis , including ORFs prediction , were carried out using the software package Vector NTI 10 . 3 . 0 ( Invitrogen ) and the online version of BLAST at the NCBI ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) . Sequence analysis and nuclear localization signals and nuclear exportation signal were carried out using the online predictors http://www . psort . org/ [39] , http://www . cbs . dtu . dk/services/NetNES/ , and http://psort . hgc . jp/ , respectively [40] .
Adenylate kinases have been mainly related to nucleotide interconversion and energy management . [74] . In 2005 the first nuclear ADK isoform was found [21] , later they were characterized in several organisms [17] , [18] , [19] . This “atypical” isoform in terms of primary structure was associated to ribosomes biogenesis in yeast [23] and to Cajal bodies organization in humans [20] , [27] , [71] . In these enzymes the P-loop domain , responsible of nucleotide binding in phosphotransferases , could be involved in other functions such as protein interactions , endonucleolytic RNA cleavage , RNA-protein interaction or RNA metabolism [17] , [18] , [23] , [28] . Even though the function of nuclear adenylate kinases is not completely understood it must be extremely important as they are essential for cell viability [17] , [19] , [23] , [28] . In this paper we report the existence of the 7th ADK variant in T . cruzi , which corresponds to the nuclear isoform . We studied its nuclear shuttling and characterized its non-canonical nuclear localization signal , being one of the few atypical NLS that involves the catalytic site of the protein ( Walker domain or P- loop ) . Probably its enzymatic activity is not essential for its nuclear importation as fusion proteins did not yield a higher-than-expected-band in western blotting suggesting that they might be inactive . We postulate that TcADKn enters the nucleus in an unfolded conformation , being the nuclear localization signal within the P-loop , once it enters the nucleus it folds correctly regarding the active site inside the protein . The available data does not allow us to conclude how the importation process takes place; TcADKn could be forming a complex with other proteins , which are recognized by the importin and then enter the nucleus or it could be recognized directly by the importing complex . Further experiments should be carried out in order to understand the nuclear importation mechanism . We could also relate its nuclear exportation to the CRM1 exportin adapter [49] , being one of the few proteins in T . cruzi which has been reported to use this transporter . T . cruzi ribosomes have been studied for a long time because they exhibit unique characteristics which are absent in higher eukaryotes and that could be capitalized for therapeutic drug design [12] . Scientific studies have focused on ribosomal structure rather than in its biogenesis . In trypanosomatids there is almost no evidence about ribosome processing sites or the proteins involved in each step . Proteomic data has revealed that many ribosomal proteins and accessory non-ribosomal proteins are conserved in T . cruzi [11] , however their function has not been determined [12] . TcADKn homolog in yeast has been related to ribosome processing , being associated to the final cytoplasmic step of maturation of the 18S rRNA by direct interaction with TcRps14 . [23] . By yeast complementation assays we could postulate that TcADKn could be involved in ribosome 18S rRNA processing . This idea is reinforced with the fact that we detected in vitro interaction between TcADKn and TcRps14 and moreover we detected ribosomal precursors in TcADKn immunoprecipitates . These data suggest that ribosome biogenesis in T . cruzi presents conserved characteristics with yeast . However it also presents similar characteristics to mammals . In yeast the ribosomal precursors of the 18S rRNA subunit presents only one intermediary after A2 cleavage within the ITS1 , while mammals present two intermediaries [75] . In our experiments we could detect two 18S pre-rRNA precursors indicating that the 18S rRNA processing presents both mammalian and yeast characteristics . So in T . cruzi 18S rRNA biogenesis would be unique as it combines characteristics of both mammals and yeast . Finally , TcADKn nuclear shuttling is regulated by nutrient availability , ribosome biogenesis , DNA integrity , oxidative stress and probably by the equivalent of the mammalian TOR pathway in T . cruzi . Furthermore the results obtained after puromycin and cicloheximide treatment suggest that ribosome assembly might be necessary for TcADKn nucleolar localization as this one is lost when ribosomes cannot reassemble after cicloheximide treatment . On the contrary in puromycin treatment in which protein synthesis is blocked but ribosomes can re-assembly , nucleolar localization is not disrupted [56] , [57] , [58] , [59] . Similar regulation mechanisms have been observed for other ribosomal proteins [76] . The existence of tight regulation mechanisms gives us an idea of the complexity of ribosome biogenesis and the susceptibility of this process to environmental changes and unfavorable conditions . Figure 7 summarizes the role of TcADKn in the formation of the 18S ribosomal subunit and its regulation mechanisms . We hope that the information presented encourages the study of ribosome biogenesis in these divergent organisms . | Infection with Trypanosoma cruzi produces a condition known as Chagas disease which affects at least 17 million people . Adenylate kinases , so called myokinases , are involved in a wide variety of processes , mainly related to their role in nucleotide interconversion and energy management . Recently , nuclear isoforms have been described in several organisms . This “atypical” isoform in terms of primary structure was associated to ribosomes biogenesis in yeast and to Cajal body organization in humans . Moreover nuclear adenylate kinases are essential for maintaining cellular homeostasis . In this manuscript we characterized T . cruzi nuclear adenylate kinase ( TcADKn ) . TcADKn localizes in the nucleolus or cell cytoplasm . Nuclear shuttling mechanisms were also studied for the first time , being dependent on nutrient availability , oxidative stress and by the equivalent of the mammalian TOR pathway in T . cruzi . Furthermore we characterized the signals involved in nuclear importation and exportation processes . In addition , TcADKn expression levels are regulated at an mRNA level , being its 3′UTR involved in this process . These findings are the first steps in the understanding of ribosome processing in trypanosomatids . | [
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] | 2013 | Molecular and Functional Characterization of a Trypanosoma cruzi Nuclear Adenylate Kinase Isoform |
Functional effects of different mutations are known to combine to the total effect in highly nontrivial ways . For the trait under evolutionary selection ( ‘fitness’ ) , measured values over all possible combinations of a set of mutations yield a fitness landscape that determines which mutational states can be reached from a given initial genotype . Understanding the accessibility properties of fitness landscapes is conceptually important in answering questions about the predictability and repeatability of evolutionary adaptation . Here we theoretically investigate accessibility of the globally optimal state on a wide variety of model landscapes , including landscapes with tunable ruggedness as well as neutral ‘holey’ landscapes . We define a mutational pathway to be accessible if it contains the minimal number of mutations required to reach the target genotype , and if fitness increases in each mutational step . Under this definition accessibility is high , in the sense that at least one accessible pathway exists with a substantial probability that approaches unity as the dimensionality of the fitness landscape ( set by the number of mutational loci ) becomes large . At the same time the number of alternative accessible pathways grows without bounds . We test the model predictions against an empirical 8-locus fitness landscape obtained for the filamentous fungus Aspergillus niger . By analyzing subgraphs of the full landscape containing different subsets of mutations , we are able to probe the mutational distance scale in the empirical data . The predicted effect of high accessibility is supported by the empirical data and is very robust , which we argue reflects the generic topology of sequence spaces . Together with the restrictive assumptions that lie in our definition of accessibility , this implies that the globally optimal configuration should be accessible to genome wide evolution , but the repeatability of evolutionary trajectories is limited owing to the presence of a large number of alternative mutational pathways .
The dynamics of adaptation of a haploid asexual population on a given fitness landscape is governed by population size , selection strength and mutation rate , and different regimes for these parameters have been identified [22]–[24] . Here we assume a ‘strong-selection/weak mutation’ ( SSWM ) regime [25] , [26] , which implies that mutations are selected one by one and prohibits the populations from crossing valleys of fitness . In natural populations of sufficient size , a number of double mutants is present at all times , and the crossing of fitness valleys can be relatively facile [27] , [28]; the SSWM assumption may therefore seem overly restrictive . However , we will see that even under these conditions , the landscapes considered are typically very accessible . In the remainder of the paper , the genetic configuration of the organism will be represented as a binary sequence of total length , where ( ) stands for the presence ( absence ) of a given mutation in the landscape of interest . The SSWM assumption together with the fact that we only consider binary sequences gives the configuration space the topological structure of a hypercube of dimension . Accessibility can then be quantified by studying the accessible mutational paths [2] , [3] , [29] . A mutational path is a collection of point mutations connecting an initial state with a final state . If these two states differ at sites , there are shortest paths connecting them , corresponding to the different orders in which the mutations can be introduced into the population [30] . The assumed weak mutation rate implies that paths longer than the shortest possible path have a much lower probability of occurrence and hence are not considered here , adding to the constraints already imposed on accessibility . A mutational path is considered selectively accessible ( or accessible for short ) if the fitness values encountered along it are monotonically increasing; thus along such a path , the population never encounters a decline in fitness . If two states are separated by a fitness valley , the path is inaccessible . Neutral mutations are generally not detected in the empirical fitness data sets of interest here , though they may be present at a finer scale of resolution [31] . In our modeling we therefore assume that the fitness values of neighboring genotypes can always be distinguished ( but see the discussion of the holey landscape model below ) . Unlike Ref . [3] we only consider whether a given path is at all accessible or not , independent of the probability of the path actually being found by the population . Our reason for focusing on this restricted notion of accessibility is that it can be formulated solely with reference to the underlying fitness landscape , without the need to specify the adaptive dynamics of the population ( see also Discussion ) . The endpoint of the paths considered here , much like in the experimental studies [3] , [4] , [10] , is the global fitness maximum , and the starting point is the ‘antipodal’ sequence which differs from the optimal sequence at all loci . Because it is at the opposite end of the configuration space , these are the longest direct paths . As such , they are a priori the least likely to be accessible and thus give a lower limit on the accessibility of typical paths ( note that the mean length of the path from a randomly chosen genotype to the global maximum is ) . For a fitness landscape comprised of up to mutations , there are a total of paths connecting the antipodal sequence to the global maximum . How many of them are selectively accessible in the sense described above ? Given that natural selection is expected to act genome-wide , we are interested in the behavior of accessibility properties when the number of loci becomes very large . Two questions are of particular interest: What is the probability of finding at least one accessible path , and what number of accessible paths can one expect to find on average ? The first question addresses the overall accessibility of the global fitness maximum [32] , while the second question is relevant for the repeatability of evolution: If there are many possible mutational pathways connecting the initial genotype to the global maximum , depending on population dynamics different pathways can be chosen in replicate experiments and repeatability will be low . To address these questions in a quantitative way , consider a sample of fitness landscapes , obtained e . g . as random realizations of a landscape model or by choosing subsets of mutations from a large empirical data set ( see Figure 1 ) . The fraction of these that have exactly accessible paths is denoted by , and gives an estimate of the probability that a given fitness landscape has accessible paths ( cf . Figure 2 ) . The expected number of paths is given by the mean of this probability distribution , ( 1 ) and is the probability to find at least one accessible path . The behavior of these two quantities will be investigated in the following , both for model landscapes and on the basis of empirical data .
Consider a model where fitness values are uncorrelated and a single mutation may change fitness completely [21] , [24] , [29]; following Kingman [33] we refer to this as the ‘House of Cards’ model . In real organisms one expects fitnesses of closely related genotypes to be at least somewhat correlated , and in this sense the HoC model serves as a null model . The expected number of accessible paths can be computed exactly by a simple order statistics argument [34] . Each of the shortest paths contains genotypes . Out of the fitness values encountered along a path , all but the last one ( which is known to be the global maximum ) are arranged in any order with equal probability . One of the possible orderings is monotonic in fitness , hence for the HoC model ( 2 ) for all . The probability of not finding any path is more difficult to compute and was so far only analyzed by numerical simulations . We find that for sequence lengths up to , appears to approach unity , see inset of Figure 2 and Figure S1 . Whether this is asymptotically true remains to be established , but the scaling plot in the inset of Figure 3 suggests that is indeed monotonically increasing for all finite . This behavior changes drastically when the antipodal state is required to be the global fitness minimum . This case was considered previously by Carneiro and Hartl [32] , who postulated that saturates to an asymptotic value around for large . However by continuing the simulations to , one sees a clear decline ( inset of Figure 2 ) , indicating that accessibility increases with increasing . We will see in the following that this is in fact the generic situation . Next we ask what happens if some fitness correlations are introduced . The Rough Mount Fuji ( RMF ) model [35] accomplishes just that: Denoting the number of mutations separating a given genotype from the global optimum by , the RMF model assigns fitness values according to ( 3 ) where is a constant and the are independent normal random variables with zero mean and unit variance . When the RMF reduces to the HoC case , and thus it can serve as starting point for approximate calculations to first order in . For the expected number of accessible paths one obtains [34] ( 4 ) where and terms of higher order have been neglected ( see also Eq . ( 7 ) ) . In this limit grows like for large and constant . Compared to the HoC case , this shows that the large -behavior of a landscape with even the slightest correlation between fitness values is substantially different from the case without correlations . The probability of finding no accessible paths was again obtained by numerical simulation , and is shown in Figure 3 ( A ) . In striking contrast to the unconstrained HoC model , the probability of finding at least one accessible path is seen to increase for large . Motivated by the result ( 4 ) , in the inset of Figure 3 ( A ) the simulation results are plotted as a function of , which leads to an approximate collapse of the different data sets . On the basis of these results we conjecture that , for any , the probability decreases for large , and most likely approaches zero asymptotically for . Better known as the NK-model [21] , [36] , this classical model explicitly takes into account epistatic interactions among different loci . Each of the sites in the genome is assigned a certain number of other sites with which it interacts , and for each of the possible states of this set of interacting loci the site under consideration contributes to the fitness by a random amount . Thus the parameter defines the size of the epistatically interacting parts of the sequence and provides a measure for the amount of epistasis . Like the RMF model , the model reduces in one limit to the HoC case , which is realized for . Due to the construction of the model , even local properties such as the number of local fitness optima [37] , [38] are generally very difficult to compute . Figure 3 ( B ) shows the variation of with obtained from numerical simulations of the model . In this figure two different relations between ( the number of interacting loci ) and ( the total number of loci ) were employed . In the main plot the fraction of interacting loci was kept constant . Under this scenario , the curves show a non-monotonic behavior of similar to that of the RMF model at constant epistasis parameter . In the inset , the number of interacting loci is kept fixed , which results in a monotonic decrease of . A third possibility is to fix the difference ( the number of non-interacting loci ) , see Figure S2 . In this case one can argue that for , the difference in behavior between and , say , should not be substantial , and indeed the curves for seem to be monotonically increasing with , showing qualitatively the same behavior as the curve for , which is equivalent to the HoC model . Finally , in Figure S3 we show the expected number of accessible paths for different values of and . The data are seen to interpolate smoothly between the known limits for and for . The neutral theory of evolution [39] implies a very simple , flat fitness landscape without maxima or minima . When strongly deleterious mutations are included , the resulting fitness landscape has plateaus of viable states and stretches of lethal states [40] . Such ‘holey’ landscapes can be mapped [41] to the problem of percolation , a paradigm of statistical physics [42] . In percolation , each configuration is either viable ( fitness ) with probability or lethal ( fitness ) with probability , independent of the others . Our definition of accessibility must be adapted in this case , as there is no notion of increasing fitness and no global fitness optimum . However , one can still ask the question whether it is possible to get from one end of configuration space to the other on a shortest path of length without encountering a ‘hole’ , i . e . a non-viable state . Apart from the restriction to shortest paths , the probability of finding at least one connecting path then corresponds to the percolation probability . The percolation problem on the hypercube differs from the standard case of percolation on finite-dimensional lattices [42] in that the parameter represents both the dimensionality and the diameter of the configuration space . Percolation properties are therefore described by statements that hold asymptotically for large under some suitable scaling of the viability probability [43] , [44] . Specifically , when for some constant , it is known that for a giant connected set of viable genotypes emerges for . Conversely , taking at fixed one expects that two antipodal genotypes are connected by a path with a probability approaching unity . Indeed , the simulation results shown in Figure S4 support the conjecture that the quantity corresponding to vanishes for large and any . The equivalent of computing is straightforward: The probability that consecutive states are viable factorizes by independence of the fitness values to the product of the individual probabilities of viability , to simply yield , which , as , decays exponentially . We already know that there are possible paths in the sequence space , thus we find ( 5 ) Since grows faster than declines , grows without bounds for large . Next we compare the predictions of the models described so far to the results of the analysis of a large empirical data set obtained from fitness measurements for the asexual filamentous fungus A . niger . As described in more detail in Materials and Methods , we analyzed the accessibility properties of ensembles of subgraphs containing subsets of out of a total of 8 mutations which are individually deleterious but display significant epistatic interactions [17] . The full data set contains fitness values for 186 out of the possible strains , and statistical analysis shows that the 70 missing combinations can be treated as non-viable genotypes with zero fitness . The distribution of the non-viable genotypes in the subgraph ensemble is well described by a simple two-parameter model which reveals that the lysine deficiency mutation lysD25 is about 25 times more likely to cause lethality than the other seven mutations ( see Materials and Methods ) . Results of the subgraph analysis are displayed in Table 1 and in Figure 4 . The data in Figure 4 ( A ) show a systematic increase of the average number of accessible paths with the mutational distance in the empirical data , which rules out the null hypothesis of uncorrelated fitness values and is quantitatively consistent with the RMF model with ( inset ) . The data for even subgraph sizes are equally well described by the model with and ( main figure ) . Alternatively , the empirical data can be compared to the results of a subgraph analysis of a fitness landscape with fixed and ( Figure S5 ) . While the fit between model and data is less satisfactory than that shown in Figure 4 ( A ) , the comparison is consistent with a value of between 4 and 5 , which again indicates that each locus interacts with roughly half of the other loci . Further analysis of statistical properties of the A . niger landscape confirms this conclusion . As an example , in Figure 4 ( B ) we display the cumulative distribution of the number of accessible paths ( 6 ) obtained from the analysis of the largest subgraph ensemble with . The main figure shows that good quantitative agreement is achieved with the model . The inset displays a similar comparison to the RMF-model , which leads to the estimate for the roughness parameter , in close agreement with the estimate obtained from . For the subgraph ensemble , the probability of finding no accessible path is approximately 0 . 5 . Corresponding estimates for other values of can be found in the last column of Table 1 . Up to , the probability is found to increase with , which implies that the ultimate increase of accessibility ( decrease in ) predicted by the models cannot yet be seen on the scale of the empirical data . This is consistent with the estimates of the epistasis parameters and mentioned above , for which the maximum in is reached at or beyond six loci ( compare to Figure 3 ) .
The models considered here represent a wide variety of intuitions about fitness landscapes , from the null hypothesis of uncorrelated fitness values through explicitly epistatic models to the holey fitness landscapes derived from neutral theory , thus covering all classes of fitness landscapes that are expected to be relevant for real organisms . With the exception of the extreme case of uncorrelated fitness values , which is ruled out by comparison to the empirical data , all models show that fitness landscapes become highly accessible in the biologically relevant limit of large : The probability of finding at least one accessible path is an increasing function of which we conjecture to reach unity for , and the expected number of paths grows with without bounds . The latter feature limits the repeatability of evolutionary trajectories . In view of the robustness of these properties , we believe that their origin lies in the topological structure of the configuration space: The probability of accessibility of a given path ( and thus the relative fraction of accessible paths ) decreases exponentially with , but this is overwhelmed by the combinatorial proliferation of possible paths ( ) , see Eq . ( 5 ) for the neutral model and Eq . ( 8 ) for the RMF model . As we have imposed severe constraints on the adaptive process by prohibiting the crossing of fitness valleys by double mutations and by only considering shortest paths , our estimate of accessibility is rather conservative . We therefore expect that naturally occurring , genome-wide fitness landscapes should show a very high degree of accessibility as well . A second general conclusion of our study is that pathway accessibility in epistatic fitness landscapes is subject to large fluctuations , as evidenced by the typical form of the probability distribution in Figure 2 and Figures S6 , S7 . For landscape dimensionalities in the range relevant for the available empirical studies , a substantial fraction of landscapes , given by , does not possess a single accessible pathway . On the other hand , for all models except the HoC model , the average number of accessible pathways exceeds unity and increases rapidly with increasing . This implies that in those landscapes in which the maximum is accessible at all , it is typically accessible through a large number of pathways . For example , among the 70 subgraphs of the A . niger landscape , half do not contain a single accessible path , but the average number of paths among the graphs with is 4 , and two subgraphs display as many as 10 accessible paths . This observation becomes relevant when applying similar analyses to empirical fitness landscapes based on mutations that are collectively beneficial , such as the examples described in [4] , [15] , [16] . In these cases the adapted multiple mutant could not have been formed easily by natural selection ( alone ) unless at least one selectively accessible pathway from the wildtype to the mutant existed . The statistics of such landscapes is therefore biased towards larger accessibility , and a comparison with random models should then be based on the probability distribution conditioned on . The general question as to whether landscapes formed by combinations of beneficial or deleterious mutations have similar topographical properties can only be answered by further empirical studies . The analysis of accessible mutational pathways in the empirical A . niger data set has allowed us to quantify the amount of sign epistasis in this landscape in terms of model parameters like the roughness scale in the RMF model or the number of interacting loci in the model . Similar to a recent experimental study of viral adaptation [45] , we ruled out the null model of a completely uncorrelated fitness landscape . Nevertheless our results suggest that the epistatic interactions in this system are remarkably strong . To put our estimate of into perspective , we carried out a subgraph analysis of the TEM -lactamase antibiotic resistance landscape obtained in [3] ( Figure S8 ) . In this case the number of loci is , and the comparison of the mean number of accessible paths in subgraphs of sizes with simulation results for the model suggests that , significantly smaller than the estimate obtained for the A . niger landscape . A low value of was also found in the analysis of a DNA-protein affinity landscape for the set of all possible 10 base oligomers [46] . Our finding of a high level of intergenic sign epistasis , compared to the examples of intragenic epistasis considered in [3] and [46] , contradicts the general expectation that epistatic interactions should be stronger within genes than between genes [15] , [16] , [47] . Note , however , that the comparisons among the available epistasis data are confounded by differences in the combined fitness of the mutations involved: while the A . niger mutations were chosen without a priori knowledge of their ( combined ) fitness effects , the mutations considered in most studies were known to be collectively beneficial [3] , [4] , [9] , [13] , [15] , [16] , and hence biased against negative epistatic combinations . In the present paper we have focused on the existence of accessible mutational pathways , without explicitly addressing the probability that a given pathway will actually be found under a specific evolutionary scenario . This probability is expected to depend on population parameters , primarily on the mutation supply rate , in a complex way . In the SSWM regime characterized by it is straightforward in principle to assign probabilistic weights to mutational pathways in terms of the known transition probabilities of the individual steps [3] , [26] . For larger populations additional effects come into play , whose bearing on accessibility and predictability is difficult to assess . On the one hand , an increase in the mutation supply rate may bias adaptation towards the use of mutations of large beneficial effects , which makes the evolutionary process more deterministic [24] but also more prone to trapping at local fitness maxima [48] . While this reduces the accessibility of the global optimum , at the same time the crossing of fitness valleys becomes more likely due to the fixation of multiple mutations at once [28] , which tests mutants for their short-term evolvability [49] and enlarges the set of possible mutational pathways . We plan to address the interplay between landscape structure and population parameters in their effect on pathway accessibility in a future publication .
For the numerical simulations of random landscapes , fitness values were assigned to each of the genotypes according to the ensemble to be sampled from ( HoC , RMF or model ) . The number of paths was then found by a depth-first backtracking algorithm implemented as an iterative subroutine starting at the antipodal genotype and either moving forward , i . e . towards the global fitness maximum , or , if a local maximum is reached , going back to the last genotype encountered before the local maximum . For finding the probability of no accessible paths , the search was ended upon finding the first path , making this search much faster than that for the full distribution of paths and thus enabling us to consider much larger genotype spaces . Results were typically averaged over realizations of the random landscape . In analyzing the empirical A . niger data , the same routines were used but with the measured fitness values as input instead of fitness values sampled from one of the models . It was argued above that both the expected number of accessible paths and the probability of no accessible path behave fundamentally different for ( HoC-model ) and the RMF model with strictly positive , even if . Here we provide additional information on the relation ( 4 ) and lend support to the statement that typically , the probability of a given path being accessible , decays exponentially in . Since by linearity of the expected value , it is sufficient to consider to compute . It was shown in [34] that ( 7 ) for , where is the probability density of the random fitness contribution . From this form it is clear that the HoC case is quite different from the general case . Note that according to ( 7 ) , still decays factorially as . This changes , however , when higher order terms in are taken into account . For the special case when the random fitness contributions are drawn from the Gumbel distribution , the probability can be computed explicitly for any [34] . One obtains the expression ( 8 ) with . For large , the denominator approaches a constant given by ( 9 ) and thus decays exponentially , . We expect this behavior to be generic for most choices of . The fitness values constituting the 8-locus empirical data set are presented in Table S1 . Here we briefly describe how these values were obtained . A detailed description of the construction and fitness measurement of the A . niger strains is given elsewhere [10] , [17] . Briefly , A . niger is an asexual filamentous fungus with a predominantly haploid life cycle . However , at a low rate haploid nuclei fuse and become diploid; these diploid nuclei are often unstable and generate haploid nuclei by random chromosome segregation . This alternation of ploidy levels resembles the sexual life cycle of haploid organisms and is termed parasexual cycle , since it does not involve two sexes . We exploited the parasexual cycle of A . niger to isolate haploid segregants from a diploid strain that originated from a heterokaryon between two strains that were isogenic , except for the presence of eight phenotypic marker mutations in one strain , one on each of its eight chromosomes . These mutations include , in increasing chromosomal order , fwnA1 ( fawn-colored conidiospores ) , argH12 ( arginine deficiency ) , pyrA5 ( pyrimidine deficiency ) , leuA1 ( leucine deficiency ) , pheA1 ( phenyl-alanine deficiency ) , lysD25 ( lysine deficiency ) , oliC2 ( oligomycin resistance ) , and crnB12 ( chlorate resistance ) . The wild-type strain only carried a spore-color marker ( olvA1 , causing olive-colored conidiospores ) on its first chromosome to allow haploid segregants to be distinguished from the diploid mycelium with black-colored conidiospores . Because these mutations were individually induced with a low dose of UV and combined using the parasexual cycle it was unlikely that the two strains differed at loci other than those of the eight markers . From the possible haploid segregants , 186 were isolated after forced haploidization of the heterozygous diploid strain on benomyl medium from among 2 , 500 strains tested . Fitness of all strains was measured with two-fold replication by measuring the linear mycelium growth rate in two perpendicular directions during radial colony growth on supplemented medium that allowed the growth of all strains , and was expressed relative to the mycelium growth rate of the olvA1 strain with the highest growth rate ( see Table S1 ) . As will be explained in the next section , missing genotypes are assigned zero fitness . To analyze the data set , first one has to address the problem of missing strains . In the experiments , out of possible strains were found in approximately segregants . Assume first that all genotypes are equally likely to be found in the sample . Denoting the number of segregants by , the probability for a given strain to be missed by chance is . The probability for at most genotypes to have been missed is then given by a Poisson distribution with mean . This gives the estimates and . For a more conservative estimate , one may assume that different genotypes have different likelihoods to be found , which are uniformly distributed in the interval with . Choosing which corresponds to the lowest relative fitness that was observed among the viable genotypes , simulations of this scenario yield and . We conclude that it is unlikely that more than one viable genotype has been missed by chance . This justifies the assignment of zero fitness to the missing 70 genotypes . Next we need to verify that accessibility in the empirical fitness landscape is predominantly determined by sign epistasis among viable genotypes , rather than by the presence of lethals . As described in the main text , we consider subgraphs of the A . niger data set containing all combinations of of the eight mutations in total . The set of subgraphs of size is composed of distinct -locus landscapes , each of which spans a region in genotype space ranging from the wild type genotype shared by all subgraphs to one particular -fold mutant . We focus here on the ensembles with . Key properties of the subgraph ensembles are summarized in Table 1 . The first column shows the total number of subgraphs , and the second column shows the number of viable subgraphs ( VSG's ) , defined as subgraphs which contain no non-viable strains . Two of the four VSG's with were previously analyzed in [10] , and three of the 19 VSG's with are shown in Figure 1 . To assess the impact of lethal genotypes on accessibility , let denote the average number of accessible paths per subgraph ( averaged across all subgraphs of fixed ) that would be present if only lethal states were allowed to block a path and the actual fitness values of viable genotypes were ignored . Similarly , denotes the average number of accessible paths per subgraph for fixed if both mechanisms for blocking are taken into account . Comparison between the two numbers , displayed in the fourth and fifth column of Table 1 , shows that the contribution of the lethal mutants to reducing pathway accessibility is relatively minor . For example , for lethals reduce the number of accessible paths from to , by a factor of , whereas the epistasis among viable genotypes leads to a much more substantial further reduction from to , by a factor of ; for the corresponding factors are and . We conclude that pathway accessibility is determined primarily by epistasis among viable genotypes . Inspection of the VSG's shows that the role of different mutations in causing lethality is strikingly inhomogeneous . In particular , we find that the lysine deficiency mutation lysD25 is not present in any of the VSG's , whereas the distribution of the other mutations across the VSG's is roughly homogeneous . The lys mutation is also strongly overrepresented in the non-viable strains , being present in out of cases . The main features of the set of lethal mutations can be captured in a simple model in which the presence of a mutation leads to a non-viable strain with probability , and different mutations interact multiplicatively , such that a strain containing two mutations and is viable with probability . The data for the number of VSG's for different cannot be described assuming the to be the same for all mutations , but a two-parameter model assigning probability to the mutation and a common value to all others suffices . Simple analysis show that under this model the expected total number of viable strains is , while the total number of viable strains in the subset of strains excluding lys is . With and we obtain the estimates and . Given that the VSG's do not contain the lys mutation , the expected number of VSG's depends only on , and is given by ( 10 ) The prediction for the expected number of viable subgraphs is shown in brackets in the third column of Table 1 , and is seen to match the data very well . Similarly , the expected number of paths that do not contain any lethal genotypes can be computed analytically , resulting in the expression ( 11 ) which is shown in brackets in the fourth column of Table 1 . The accessibility of mutational pathways in the A . niger data set was analyzed using two different approaches . The first approach is based on a single set of fitness values obtained by averaging the two replicate fitness measurements for each strain; these average fitness values are shown in Table S1 . In this approach the fitness assigned to each viable genotype is a normally distributed random variable with the mean given by the average of the two fitness measurements and a common standard deviation estimated from the mean squared differences between replicate fitness values in the entire data set; the fitness of genotypes identified as non-viable remains zero . Statistical properties of accessible pathways are then computed by averaging over realizations of this resampled landscape ensemble . Empirical data points and error bars shown in Figure 4 represent the mean and standard deviations obtained from the second approach . Results obtained by directly analyzing the mean fitness landscape ( first approach ) do not differ significantly from those presented here . | Fitness landscapes describe the fitness of related genotypes in a given environment , and can be used to identify which mutational steps lead towards higher fitness under particular evolutionary scenarios . The structure of a fitness landscape results from the way mutations interact in determining fitness , and can be smooth when mutations have multiplicative effect or rugged when interactions are strong and of opposite sign . Little is known about the structure of real fitness landscapes . Here , we study the evolutionary accessibility of fitness landscapes by using various landscape models with tunable ruggedness , and compare the results with an empirical fitness landscape involving eight marker mutations in the fungus Aspergillus niger . We ask how many mutational pathways from a low-fitness to the globally optimal genotype are accessible by natural selection in the sense that each step increases fitness . We find that for all landscapes with lower than maximal ruggedness the number of accessible pathways increases with increases of the number of loci involved , despite decreases in the accessibility for each pathway individually . We also find that models with intermediate ruggedness describe the A . niger data best . | [
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] | 2011 | Evolutionary Accessibility of Mutational Pathways |
Gastrointestinal side effects are among the most common classes of adverse reactions associated with orally absorbed drugs . These effects decrease patient compliance with the treatment and induce undesirable physiological effects . The prediction of drug action on the gut wall based on in vitro data solely can improve the safety of marketed drugs and first-in-human trials of new chemical entities . We used publicly available data of drug-induced gene expression changes to build drug-specific small intestine epithelial cell metabolic models . The combination of measured in vitro gene expression and in silico predicted metabolic rates in the gut wall was used as features for a multilabel support vector machine to predict the occurrence of side effects . We showed that combining local gut wall-specific metabolism with gene expression performs better than gene expression alone , which indicates the role of small intestine metabolism in the development of adverse reactions . Furthermore , we reclassified FDA-labeled drugs with respect to their genetic and metabolic profiles to show hidden similarities between seemingly different drugs . The linkage of xenobiotics to their transcriptomic and metabolic profiles could take pharmacology far beyond the usual indication-based classifications .
Side effects are unintended effects of administered drugs that lead to a decrease in the efficacy of treatment , lower patient compliance , and eventually the cessation of treatment with the development of adverse physiological consequences . Additionally , up to 25% of drug development programs fail because of a lack of safety in first-in-human trials [1] . In particular , since the oral administration of drugs is the most common route of disposition , the gastrointestinal side effects are among the most common class by occurrence [2 , 3] , particularly in geriatrics [4] . Therefore , identifying compounds that can cause serious gastrointestinal adverse reactions from the ones that have benign effects solely using in vitro data could help optimizing drugs in the preclinical phase before first-in-human trials and ultimately , decrease the failure rates of new chemical entities . The prediction of side effects have been addressed mainly through a target-based approach wherein the inhibition of a specific target induces the desired effect and also suppresses all physiological processes involving the target protein [5] . Recently , with the availability of genome-wide transcriptome profiles of more than 20 , 000 compounds in the connectivity map [6] , new approaches have considered linking off-target effects rather than target effects to adverse reactions . Specifically , the interaction of the compound with nontarget genes has been hypothesized to drive the emergence of side effects [7] . Recent efforts have combined drug-induced gene expression with chemical structures and Gene Ontology ( GO ) processes as features to predict side effects accurately [8] . Notably , metabolic genes are among the most predictive features for the classification [8] . Additionally , context-specific drug metabolic models have been built using a generic genome-scale reconstruction of human metabolism [9] and the connectivity map of gene expression [6] to identify metabolic dysregulation underlying the emergence of side effects [10] . In this study , we first considered a metabolic model of the small intestine epithelial cells ( sIECs ) , where drug-induced gene expression data constrained the set of predicted metabolic phenotypes ( Fig 1 ) . The assessment of possible reaction rates , while constrained by gene expression data , was used to derive differential scores between the drug-specific model and the unperturbed model . Second , we used the corresponding metabolic reactions and the drug-induced gene expression taken as features to build and cross-validate a multilabel support vector machine in order to predict the occurrence of gastrointestinal side effects . Such a classifier could be applied to drugs in preclinical development based on in vitro parameters solely to predict the likelihood of side effect occurrence in first-in-human trials . Finally , the transcriptomic and metabolic profiles of drugs were used to cluster compounds by their signatures , enabling a new classification that goes beyond the usual chemical class , thereby offering new insights into drug repurposing . The combination of local sIEC metabolism [11] with drug transcriptomic profile allowed to contextualize gene expression data thereby increasing the predictive capability of side effect classifiers . Extending the classification to a more comprehensive set of side effect and tissue-specific models could provide useful information at the preclinical phase of drug development thus reducing costs and attrition rates .
A manually curated metabolic model of sIECs has been previously constructed to study the effect of inborn errors of metabolism ( IEMs ) on human physiology [11] . The sIEC model consists of 1282 reactions and 844 metabolites . The exchange reactions for the sIEC model has been set for a standard European diet , as described previously [11] , over an interval of 24 h . Consequently , we prioritized drug-induced gene expression measured after 24 h on intestinal cell lines , namely , HT115 , MDST8 , SW-948 , NCI-H716 , HT-29 , SW620 , HCT 116 , and LoVo obtained from the LINCS database [6 , 12] ( see ‘Data generation’ ) . For each drug , an sIEC-tailored metabolic model was generated in the form of a linear program ( LP ) as follows: max:cTvsubjectto:Sv=0vmin≤v≤vmax ( 1 ) where cT . v is the objective function , v is the flux vector of metabolic reactions , c is the vector of objective coefficients , S ( m , n ) is the stoichiometric matrix linking m metabolites and n reactions , vmin is the reaction lower bound vector , and vmax is the reaction upper bound vector . The system assumes a steady state such that S . v = 0 , which is referred to as flux balance analysis ( FBA ) [13] . Differential gene expression zi of gene i encoding reaction j modifies the allowable range of each reaction obtained by flux variability analysis ( FVA ) [14] , which determines the minimal and maximal values feasible by each reaction , through maximizing and minimizing each reaction as an objective function , consistent with the applied constraints . The constraints were updated as follows: v m i n , j = m i n F V A , j + z i * s t d ( v j ) v m a x , j = m a x F V A , j + z i * s t d ( v j ) where vmin and vmax are the new lower and upper bounds of the sIEC drug model respectively; minFVA and maxFVA are the lower and upper bounds of the drug-free sIEC determined by FVA , respectively; and std ( vj ) is the standard deviation in reaction j assuming a normal distribution of the fluxes between minFVA and maxFVA . This formulation of reaction constraints is similar to E-Flux [15 , 16] and retains the original structure of the model while changing the reaction bounds according to the gene expression . This formulation was chosen because transcript levels cannot be used as conclusive evidence of the enzymatic activity of proteins [17–19] and metabolic fluxes but are rather used to constrain the capacity and space of possible flux values of the corresponding reaction . Because FVA-calculated minimal and maximal bounds determine the solution space , scaling FVA bounds by gene expression constrains a new space of predicted phenotypes . Other recent formulations have considered protein concentrations to constrain flux capacities [20] . An infeasible sIEC-drug model may occur because of conflicting constraints , particularly with the exchange reactions . If problem 1 was infeasible , then we minimally relaxed the constraints in both the amplitude of relaxation and the cardinal of relaxed reactions [21] , by solving the following problem: min:| | p | |1 , | | q | |1subjectto:Sv=0vmin−p≤v≤vmax+q where p is the relaxation vector of the lower bound and q is the relaxation vector of the upper bound . Minimizing the 1-norm of p and q ensures sparsity ( a minimal cardinal of reactions to be relaxed ) and a minimal total sum of relaxation amplitudes [21] . Under the drug constraints , we calculated the possible flux values for each of the 1282 reactions through the uniform sampling of the LP solution space using Artificially Centered Hit-and-Run ( ACHR ) implemented in the COBRA Toolbox [21] . Sampling is an unbiased method because it does not assume any objective function . We generated 100 , 000 points for each model using 1000 iteration steps per point , starting from 10 , 000 warmup points . The sampling of metabolic models has been used to determine a set of phenotypes of the modeled condition-specific cells and the distribution of reaction rates under a set of applied constraints [22–24] . For each metabolic reaction of a specific drug-constrained sIEC , the sampled flux distribution was compared to the drug-free sIEC model , and z-scores were derived for each reaction . We used the Side Effect Resource ( SIDER ) [5 , 25] side effect database to extract the intestinal side effects described as preferred terms ( PTs ) . The compounds corresponding to a side effect were then queried in the L1000 LINCS dataset of compound gene expression [6 , 12] through the iLINCS API [26] . The level four data reported differential expression z-scores of the 978 measured genes [6] . On average , 50 drug gene expression signature genes overlapped with the genes present in the sIEC model [11] , and only the genes that were differentially expressed with a p-value lower than 0 . 05 were retained for further analyses . These genes were used for setting the constraints , as aforementioned . We created a feature matrix consisting of gene expression and metabolic flux samples in columns and drugs in rows representing the observations . The matrix had 605 drugs and 2260 features ( 978 genes plus 1282 metabolic reactions ) . Standardized predictors were directly used as z-scores for learning and cross-validation . We calculated the minimal and maximal flux capacity for each reaction using FVA . The resulting flux values served as features in the classification , as previously suggested [27] . This second feature matrix had 605 drugs and 1282*2 columns . We also considered the gene expression data alone , yielding a third feature matrix consisting of 605 drugs and 978 genes . The support vector machine multilabel learning was converted to 43 binary single-label problems using binary relevance in a one-versus-all scheme , where each classifier corresponded to an intestinal side effect as reported by SIDER PTs . The side effects occurring for only one drug were discarded , resulting the final set to 36 side effects . The dataset used in classification was standardized in the SVM call , where the mean is subtracted from each entry , followed by a division by the standard deviation of the training set . The support vector machine classifier [28] was compared to random forest [29] , logistic regression [30] , and Naïve Bayes [31] with their defaults parameters ( S1 Fig ) . The performance was assessed using the following metrics [32]: accuracy , area under the ROC curve ( AUROC ) , area under the precision-recall curve ( AUPR ) , weighted accuracy , and weighted recall . The weighted recall and weighted accuracy were calculated using the average of the accuracy and recall of each label and weighted by the label size . The significance of the difference between the AUROC of the classifiers was determined using the Hanley and McNeil test [33] , which is a nonparametric method that corrects for the correlation between ROC curves derived from the same cases i . g . , drugs . The genes and metabolic reactions were then ranked by importance and used as input for the SVM multilabel model .
Drug-induced gene expression can provide strong features for side effect prediction , especially with side effect labels that have links to metabolism [8] . We used this observation to further improve the prediction of intestinal side effects by subjecting gene expression as constraints in the metabolic model of the sIEC , thereby contextualizing sIEC metabolism to derive the intestinal metabolic fingerprints of every drug . This approach combines features linked to side effects in both the gene expression space and the metabolism of the entercoyte . Consistent with a previous study [8] , the gene expression data alone was the support of most predictive features ( Fig 2-A ) . Metabolic reaction fluxes alone were less predictive than gene expression as they only consider genes with defined metabolic activity in the sIEC model ( Fig 2-A ) . Particularly , sampling the metabolic model improved the classification because it provided information about the distribution of metabolic fluxes for each reaction rather than only having the minimal and maximal flux values when using FVA ( Fig 2-A ) . Combining gene expression and predicted metabolism gave the highest predictive rates in the multilabel SVM classifier as the average of individual labels ( Fig 2-A ) , and in comparison to other classifiers ( S1 Fig ) . The merged multilabel intestinal side effect classifier using the individual binary classifiers of each side effect better predicted ( AUROC = 0 . 94 ) intestinal side effects based on the combination of gene expression and metabolic flux distributions compared to 1 . gene expression alone ( AUROC = 0 . 935 , p = 0 . 02 ) , 2 . FVA predictions alone ( AUROC = 0 . 92 , p = 0 . 00005 ) , or 3 . sampling results alone ( AUROC = 0 . 931 , p = 0 . 006 ) as shown by the microaveraged ROC curve ( Fig 2-B ) . The most predictive metabolic reactions were enriched in the subsystems of sIEC , and the most predictive genetic features were enriched in the GO biological processes database . The ten most represented subsystems ( p <0 . 001 ) mainly involved transport reactions ( extracellular , exchange , mitochondrial , and endoplasmic reticulum ) as well as catabolic and anabolic functionalities ( Fig 2-C ) . The GO biological processes-enriched groups ( p <0 . 001 ) involved mainly the regulation of transcription and apoptotic processes ( Fig 2-D ) . Unspecific or likely nonmetabolic side effects , such as gastrointestinal obstruction , were among the least predictable with an AUROC of 0 . 67 using combined gene expression and sampled reaction fluxes . Side effects involving the gut wall metabolism were highly predictable using combined features ( Fig 3 , S3 Table ) , such as intestinal carcinoma ( 0 . 96 ) , ulcer ( 0 . 97 ) , and toxicity ( 0 . 92 ) . These results motivated us to employed in the following the matrix of combined drug transcriptomic and metabolic features to predict the labels of gastrointestinal side effects . Therefore , we used this matrix to perform a drug-centric analysis in order to cluster drugs with respect to their metabolic and transcriptomic signatures and investigate common genome-scale similarities of drug action that can provide new drug repurposing strategies . The construction of the drug feature matrix consisting of gene and metabolic reaction vectors per drug could facilitate the use of clustering techniques to classify drugs in the gene and metabolism space . In particular , drugs that have similar gene expression and metabolic profiles could be suggested for repurposing in novel indications . Using Jaccard-Louvain [48] the community detection algorithm , we identified eight drug clusters based on their genetic and metabolic signatures ( Fig 4-A ) . Each cluster had a stability and purity index greater than 0 . 75 ( S10-D Fig ) , thereby validating the obtained clusters . In particular , transcriptional and intestinal metabolic activities were aligned with the identified clusters ( Fig 4-B ) , such as each drug cluster could have either high or low metabolic and transcriptomic activity . Interestingly , the identified clusters did not map to the FDA NDCD’s Established Pharmacological Class ( EPC ) ( S10-A Fig ) suggesting that classical indication-based classification may overlook the genetic and molecular aspects of small molecule pharmacodynamics . Most small molecules had a low genetic and metabolic fingerprint and mainly targeted the various transport subsystems ( S10-C Fig ) of the enterocytes , which is consistent with transport being a chief function of the gut wall . Clusters one and eight involved a high number of genes and metabolic reactions mainly due to cytotoxic drugs , which were also reflected by the presence of terms linked to inflammation and immunity in the bipartite graph linking the drugs to the FDA NDCD’s Physiological Effects ( PEs ) ( Fig 5-A ) . Additionally , clusters eight and one were linked to malignant side effects ( S10-A Fig ) . Cluster seven had a high number of active fluxes in the small intestine epithelial cells . Interestingly , a number of terms linked to the central nervous system were found ( Fig 5-A ) , which hints to potential gut-brain shared molecular processes , probably linked to the similar composition of the blood-brain barrier and the gut wall transporters [58] . In addition to cluster seven , cluster two had a low transcriptomic and a high metabolic profile . The EPCs linked to these clusters were mostly compounds whose action is mediated through metabolic functions , such as xanthine oxidase , or signaling , such as PPARα molecule binding in the bipartite graph linking the drugs to their EPCs ( Fig 5-B ) . Ubiquitous targets , including cyclooxygenase and histamine receptors , could consequentially induce pronounced metabolic effects . In the high transcription and high metabolism profiles represented by cluster one and eight , the presence of molecules acting on the central nervous system by their FDA NDCD’s Mechanism of Action ( MoA ) ( Fig 5-C ) , confirmed links between the gut and brain metabolisms . Additionally , since clusters one and eight encompassed anticancer drugs , as we previously observed , this finding further supports ongoing repurposing trials of antidepressants in cancer therapy [59] . Moreover , neurokinin-1 antagonists , a class of drugs prescribed for the suppression of cytotoxic drug-induced emesis , and 5-lipoxygenase inhibitors , indicated for inflammatory bowel disease , had a high genetic and metabolic profile indicating potential links between gut symptomatology and genome-wide transcriptional and metabolic modulation . We further enriched the top differentially enriched genes in each cluster in the KEGG database [54 , 56] ( Fig 5-C ) and selected the terms pertaining to gastrointestinal physiology . Epithelial cell signalling in Helicobacter pylori infection was linked to cluster seven , which had a low transcriptomic and high metabolic profile suggesting metabolism-modulated signaling through kinases following the infection . The Escherichia coli infection term belonged to this cluster , which suggests that both pathogens might involve the same kinase but also that similar treatment strategies may be able to combat their infections . Phenotypes involving signaling mechanisms rather than metabolism , e . g . , Vibrio cholerae infection , belonged to cluster six that had a low intestinal metabolic fingerprint . Taken together , the multilayer biology of drug effects could accurately predict iatrogenic gastrointestinal effects using an SVM classifier . The clustering of drugs based on their metabolic and genetic signature has the potential to unravel potential similarities in the model of action of compounds in relation to their physiological effects .
The connectivity map [6] has provided a large-scale resource of small-molecule transcriptomic signatures and enabled the genome-wide assessment of drug off-target effects , thereby expanding pharmacology beyond the study of the drug primary target alone . The integration of drug-induced gene expression with generic metabolic models of human metabolism could be used to identify key disrupted metabolic functions [10] resulting from adverse reactions . Similarly , the integration of known target effects of drugs as identified from DrugBank [60] and flux bounds obtained by FVA as features [27] has been used to predict accurately several labels of side effects . Although , this approach remains limited to drugs with inhibitory effects on metabolic targets and a fortiori of known targets . In our approach , the integration of drug-induced gene expression with metabolic networks allowed to circumvent the inhibitory target limitation [61] and extend to other classes of drugs . Additionally , the integration of gene expression allowed the modeling of drug off-target effects , which were suggested to be the main driver of side effects . We showed that informing the classifier with the distribution of metabolic fluxes per reaction using sampling rather than by providing the bounds of the reaction using FVA increased the predictive power of the classifier ( Fig 2-A and 2-B ) . Additionally , restricting the predictions to a set of organ-specific side effects using a manually curated tissue-specific metabolic model captured the local metabolism in relation to the emergence of organ-specific adverse reactions . This finding was in accordance with a recent review [62] that demonstrated a link between organ-specific functions of drug targets and the likelihood of organ-specific side effects . Improving the prediction of side effects relies greatly on the quality and completeness of the dataset used . Weighing the variables by the side effect frequencies per drug likely improves the predictions and can leverage the prediction of rare side effects . Nevertheless , only 46% of side effects had associated frequency information whose inclusion did not improve the prediction accuracy ( S7 Fig ) . The missing information could be potentially filled by either manual expert curation or crossing databases . Moreover , the PEs and mechanisms of action in the FDA NDCD were missing for many drugs as well . Additionally , the chronopharmacology of drug action has been found to be also of importance in detecting the emergence of side effects [63] . The connectivity map provides many experiments at several time intervals that we did not exploit in our analysis because not all drug-induced gene expression have been measured at different time points . Such data could transform predictions from snapshots of transcription and metabolism to dynamical models linking the emergence of side effects to time-dependent processes . Conceptually , drug-induced gene expression could play a significant role in the genesis of adverse reactions and have been predictive towards side effects classification , especially when combined with other drug features , such as chemical structure and cell morphology after treatment [8] . The combination of local gastrointestinal metabolism constrained by metabolic gene expression and the differential expression of nonmetabolic genes achieved the most accurate prediction of gastrointestinal side effects blackas assessed by the micro-average ( Fig 2-A ) where each class is treated equally , and the micro-average ( Fig 2-B ) with a correction for the class imbalance . The improvement in the multilabel classification was related to an increase in the AUROC of a specific set of side effects ( Fig 3 ) but not all side effects had an improved prediction using combined gene expression and metabolism ( S3 Table ) . This set consisted of nine side effects , including gastrointestinal pain , disorder , obstruction , and fistula . The reasons for such a decrease are related to our parameter optimization procedure . In fact , we performed a class-wide optimization of the classifier parameters such as the number of features and the feature selection algorithm . A class-specific optimization procedure could be tested in future applications but would i ) require larger computational resrouces , ii ) increase the complexity of the classifier , and iii ) reduce the interpretation of common factors related to all the gastrointetsinal side effects as our approach optimizes for all the side effects simulatenously . The combination of multiple layers of biology consisting of transcriptomic and predicted metabolic reaction fluxes as features was pivotal to capturing drug-induced perturbations related to side effects . Furthermore , the approach could be scaled to several tissues to include all the labels of side effects using manually curated models of human metabolism [64] . Remarkably , sampling metabolic models alone achieved good accuracy taking into account that only the metabolic subset of genes from the connectivity map was modeled . In particular , the AUROC equaled 0 . 931 and was not statistically different ( p = 0 . 29 ) from classifiers using gene expression as features ( AUROC = 0 . 935 ) . Therefore , we suggest that a reduced set of in vitro experiments to measure the differential expression of metabolic genes would give an invaluable insight into the emergence of adverse reactions of a new chemical entity in the preclinical phase , which could guide the rational design of first-in-human trials . Furthermore , the emergence of whole-cell models [65 , 66] , which integrate metabolism alongside with several physiological functions , could be used to map nonmetabolic genes onto computational models of the cell to capture the cell-wide disruption of physiological processes leading to the emergence of side effects . With the generated combined gene expression and sIEC metabolic reactions matrix in hand , we classified the small molecules with respect to their signatures to highlight their shared features . Drugs are often classified based on their pharmacological indications and their chemical family . The many examples of marketed drugs repurposed for new indications [67] show that a small molecule can indeed have many , diverse effects . Drug repurposing has gained great interest in recent years because it can significantly accelerate the drug development process using compounds with well-documented safety . Additionally , transcriptome-based clustering could be used to recommend new activities for investigational molecules [68] . To find shared properties of drugs , we identified clusters of compounds that share similar genetic and metabolic signatures in the gut wall ( Fig 4-A and 4-B ) . Interestingly , compounds that involved a high number of metabolic reactions with a high amplitude of variation included CNS drugs , such as serotonin antagonists , which were indicated primarily for psychotic episodes and were later suggested to treat chemotherapy-induced emesis ( Fig 4-C ) . Moreover , these compounds belonged to the same cluster as neurokinin inhibitors , which are indicated for the prevention of emesis ( Fig 4-E ) . Serotonin antagonists are also indicated to treat inflammatory bowel syndrome , which further showed a similarity between the blood-brain barrier and the gut wall metabolism and gene expression ( Fig 4-E ) . Furthermore , anticancer drugs and the drugs that treat their side effects , the antiemesis drugs , clustered together in the high transcriptomic , high metabolic activity cluster , further supporting the idea that reversing the molecular fingerprint of a compound could reverse its effects . Particularly , reversing the fingerprint of the compound locally in the gut wall would be a potential strategy to reverse gastrointestinal side effects of drugs through the administration of codrugs , while preserving its primary activity in the target tissue . Interestingly , the clusters of drugs that we identified in our analysis did not match FDA marketing date ( S10-B Fig ) . Despite the emergence of the key-lock paradigm [69] in drug development using molecular dynamics and docking experiments in the early 1990s that decreased the number of drugs interacting with a high number of targets , colloquially called ‘dirty drugs’ , there seems to remain opportunities to further enhance the design of precise therapies . In conclusion , we developed and employed a multilabel support vector machine on the genetic and metabolic fingerprints of marketed small-molecule compounds to accurately predict the occurrence of gastrointestinal side effects . The drug features could be used to classify drugs based on their metabolic and genetic profiles , which is a promising avenue for drug repurposing to reverse side effects and unravel new indications . The development of large-scale , publicly available compound resources combined with complex mathematical models of cellular biology may represent a new method of providing patients with safer and more efficient therapies . | The gut wall is the first barrier that encounters orally absorbed drugs , and it substantially modulates the bioavailability of drugs and supports several classes of side effects . We developed context-specific metabolic models of the enterocyte constrained by drug-induced gene expression and trained a machine learning classifier using metabolic reaction rates as features to predict the occurrence of side effects . Additionally , we clustered the compounds based on their metabolic and transcriptomic features to find similarities between their physiological effects . Our work provides a better understanding of the compound physiological effects solely using in vitro data , which can further improve the translation of new chemical entities to clinical trials . | [
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] | 2019 | Predicting gastrointestinal drug effects using contextualized metabolic models |
Japan has been free from rabies since the 1950s . However , during the early 1900s several large-scale epidemics spread throughout the country . Here we investigate the dynamics of these epidemics between 1914 and 1933 in Osaka Prefecture , using archival data including newspapers . The association between dog rabies cases and human population density was investigated using Mixed-effects models and epidemiological parameters such as the basic reproduction number ( R0 ) , the incubation and infectious period and the serial interval were estimated . A total of 4 , 632 animal rabies cases were reported , mainly in dogs ( 99 . 0% , 4 , 584 cases ) during two epidemics from 1914 to 1921 , and 1922 to 1933 respectively . The second epidemic was larger ( 3 , 705 cases ) than the first ( 879 cases ) , but had a lower R0 ( 1 . 50 versus 2 . 42 ) . The first epidemic was controlled through capture of stray dogs and tethering of pet dogs . Dog mass vaccination began in 1923 , with campaigns to capture stray dogs . Rabies in Osaka Prefecture was finally eliminated in 1933 . A total of 3 , 805 rabid dog-bite injuries , and 75 human deaths were reported . The relatively low incidence of human rabies , high ratio of post-exposure vaccines ( PEP ) and bite injuries by rabid dogs ( minimum 6 . 2 to maximum 73 . 6 , between 1924 and 1928 ) , and a decline in the proportion of bite victims that developed hydrophobia over time ( slope = -0 . 29 , se = 3 , p < 0 . 001 ) , indicated that increased awareness and use of PEP might have prevented disease . Although significantly more dog rabies cases were detected at higher human population densities ( slope = 0 . 66 , se = 0 . 03 , p < 0 . 01 ) , there were fewer dog rabies cases detected per capita ( slope = -0 . 34 , se = 0 . 03 , p < 0 . 01 ) . We suggest that the combination of mass vaccination and restriction of dog movement enabled by strong legislation was key to eliminate rabies . Moreover , the prominent role of the media in both reporting rabies cases and efforts to control the disease likely contributed to promoting the successful participation required to achieve rabies elimination .
Rabies has been one of the most feared diseases throughout human history and has the highest known case-fatality rate [1] . This zoonosis is mainly maintained in domestic dog populations , and continues to cause approximately 59 , 000 human deaths every year globally [2] . More than 99% of these deaths occur in low- and middle-income countries , where rabies is endemic in domestic dog populations [3] . However , rabies can be controlled and even eliminated from domestic dog populations using existing tools , as seen from mass vaccination programmes in Western Europe and North America [4] . Japan was one of the first countries to eliminate rabies from domestic dog populations , and has maintained freedom from the disease ever since [5] . The history of rabies in Japan had not been well documented . Until recently , only a few articles were published in English or Japanese . The first case of dog rabies in Japan was reported in 1732 in Nagasaki , which includes Dejima , the only port allowing access from abroad during the Edo period ( the era governed by Tokugawa Shogun ) from 1639 to 1854 ( Fig 1 ) . This dog rabies outbreak caused many human deaths . Dog rabies rapidly spread to Hiroshima in that year , and arrived in Edo ( now Tokyo ) in 1736 . It took 29 years for rabies to reach the north end of Honshu Island , Shimokita Peninsula in 1761 , and by that time , the disease had spread to wide areas of the country [6] . Japan opened her ports at the beginning of the Meiji era ( from 1868 to 1912 ) . Although the governance systems were modernized , there was no reporting system for rabies at the beginning of this era . In 1873 , a long lasting epidemic started in Tokyo , which continued until its elimination . Rabies spread again to several parts of Japan , and in 1892 and 1893 , epidemics were recorded in Oita and Nagasaki in Kyushu Island [7] . Post-exposure prophylaxis ( PEP ) was first administered in 1895 in Nagasaki , using attenuated vaccine produced in rabbits , in which the brain tissue of a rabid dog had been inoculated , and was successful for all the 25 treated individuals [7] . The animal infectious disease prevention law took effect in 1897 , and dog rabies cases were recorded in prefectural reports since then [8] . Aggregated cases at the national level were also available since 1897 [9–10] . Although annual cases had decreased by 1906 , a major epidemic that spread nation-wide began in 1907 ( Fig 2 ) . In this year , rabies entered Hokkaido Island for the first time ( Fig 1 ) . The world’s first rabies vaccine for dogs was developed using rabbit nerve tissue in 1915 , and the quantity of vaccine production and its safety were improved in 1920 by using dog nerve tissue vaccine [11–12] . The most dog rabies cases ( 3 , 205 ) and human deaths due to rabies ( 235 ) in Japan were recorded in 1924 [10] . Shortly thereafter , vaccine began to be used for mass vaccination , and in total 254 , 067 dogs were vaccinated in 1925 [13] . Dog vaccination was maintained at a similar scale over the following years; 210 , 515 , 227 , 607 , and 243 , 582 dogs were vaccinated in 1926 , 1927 , and 1928 , respectively . This was accompanied by large-scale capture of stray dogs with 156 , 844 , 297 , 396 , 200 , 129 , and 201 , 959 captured in 1925 , 1926 , 1927 , and 1928 , respectively [13] . Japanese people were not accustomed to chaining and leashing of dogs , or the use of muzzles before the regulation [14] . However , as seen in the Osaka Prefecture Rule for regulation of pet dogs , registration and attachment of collars became the responsibility of dog owners , and prefectures could order chaining of dogs during rabies epidemics [15] , and thus most of the captured dogs were unowned . Dog rabies cases were still high with 1 , 799 recorded in 1926 , but continued to decline thereafter [10] ( Fig 2 ) . Although dog rabies was covered in the animal infectious disease prevention law , the responsibility of dog rabies control was transferred from the Ministry of Agriculture and Commerce to the Ministry of Home Affairs under the cabinet decision in 1928 [16] , because of its public health importance . Dog cases declined further , and there were fewer than ten cases annually from 1936 to 1940 . A single rabies case was detected in 1943 and in the chaos of World War II , a smaller epidemic followed ( Fig 2 ) . After the war ended in 1945 , public records regarding rabies control including vaccination become available from 1949 [17] . In 1950 , when numbers of dog rabies cases peaked ( 867 ) , the rabies prevention law was established [10] . By law , full-time veterinary officers were placed in health centers , and authorized to pursue their responsibilities to control rabies . Before this , the police were in charge of rabies control in most prefectures , since the establishment of the animal infectious disease prevention law in 1897 [18–19] . Annual registration of dogs over 91 days old , and vaccination twice a year became the duty of dog owners , including payment of a registration fee , tax , and vaccination fee . Catching stray dogs , previously done by private companies , was placed under the direct supervision of the government , and dog catchers were given more authority to impound dogs . Free roaming dogs , even though registered , were caught in the same way as unregistered stray dogs . Use of a phenol inactivated vaccine , which was more effective than dog nerve tissue vaccine , also began in 1952 . These coordinated activities were successful , and canine rabies was eliminated in 1956 [17] . The final animal rabies case detected was a rabid cat in 1957 [20] . Although rabies was eliminated in that year , vaccination of dogs is still mandatory in Japan . There are many lessons that can be learned from the success of countries such as Japan , where rabies has been eliminated . The purpose of this study was to understand the epidemiology of rabies in Osaka Prefecture during the nationwide epidemics in the first half of the 20th century using artifacts compiled from prefectural libraries and the media . Osaka Prefecture had the second largest population ( 2 , 593 , 701 ) after Tokyo Prefecture , with 2 cities , 31 towns and 263 villages in 1920 [21] . These historical records provide an unusual window into the spatiotemporal dynamics of rabies , and offer valuable lessons of relevance to the elimination of rabies from endemic countries today .
Large-scale epidemics of rabies were recorded in Osaka Prefecture between 1914 and 1933 , and this study investigates dog and human rabies cases in this period . A database which included the annual dog rabies cases in all the 47 prefectures in Japan between 1897 and 1956 , was constructed . Prefectural reports between 1914 and 1933 from libraries of Osaka and surrounding prefectures ( Hyogo , Kyoto , Nara , Shiga , and Wakayama Prefectures ) were investigated for animal rabies cases that occurred in Osaka Prefecture . According to a detailed description in the newspaper in 1914 , although the animal species and exact method used were not indicated , confirmatory diagnosis of suspected dogs that died or were killed was carried out using experimental animals . In Kyoto Prefecture , inoculation of brain and spinal nerve tissue emulsion of a rabid dog to the brain of rabbits was a confirmatory diagnostics test [22] , and Osaka Prefecture most likely used this method . For animal rabies cases , the species , date of disease onset and location found , and date and cause of death ( died or killed ) were recorded in these prefectural reports . Microfilms of two newspapers ( Osaka-Jiji-Shinpou and Osaka-Asahi ) were used to cross-validate these reports , and to supplement missing information . Annual dog rabies cases of Japan and Tokyo Prefecture between 1897 and 1937 were collected from Metropolitan Police reports [9] , for comparison with the epidemic in Osaka Prefecture . Annual dog rabies cases in Japan between 1938 and 1956 were collected from a published document [10] , and those of Tokyo Prefecture were collected from the public reports of Tokyo and peripheral prefectures . The purpose of the data from Tokyo Prefecture was the presentation of temporal patterns of dog rabies from the start of reporting until its elimination . The data from Japan were collected to describe the dog rabies situation in a nationwide context . The annual number of human rabies cases ( hydrophobia ) in Osaka Prefecture in 1920 , and between 1924 and 1928 were extracted from 1920 population census data which included infectious disease information [21] , and Tokyo Metropolitan Police reports , respectively . About 50–80% of rabies patients develop hydrophobia , which is a characteristic and the most specific manifestation of the disease [23] . Rabies patients who develop hydrophobia may initially experience pain in the throat or difficulty swallowing , and particularly later in the course , hydrophobic spasms of the inspiratory muscles occur , associated with terror in attempting to swallow water [23] . In the archival reports in Japan , only ‘hydrophobia’ was recorded , and the physicians that diagnosed them , under the infectious disease prevention law , reported cases to prefectures . At that time , Tokyo Metropolitan Police received all reports of human rabies cases from throughout Japan . For the other years ( 1914 to 1919 , 1921 to 1923 , and 1929 to 1933 ) , human cases recorded in newspapers were collected and the age and sex of victims , and the dates of exposure , symptom onset , and death were extracted . Administrative unit shapefiles at village ( son ) level in 1919 were obtained from the National Land Numerical Information Download Service , Geospatial Information Authority , the Ministry of Land , Information , Transport and Tourism of Japan ( 2015 ) [24] . In these shapefiles , cities and towns were divided into smaller areas equivalent to the village level , and in total , there were 720 administrative units in Osaka Prefecture . The areas of administrative units were calculated , and one- kilometer buffer zones from unit boundaries were generated ( see Analysis ) using ArcMap version 10 . 1 ( ESRI , Redland , USA ) . Demographic data were obtained from the 1920 population census in Japan [21] . The numbers of dogs vaccinated , stray dogs captured , injuries to humans caused by bites by rabid dogs , and PEP given between 1924 and 1928 in Osaka Prefecture were collected from a report of the Veterinary Investigation Center [13] . The newspapers reported the number of dog-bite injuries in Osaka Prefecture from time to time , and the information between 1914 and 1918 , and 1922 and 1923 was extracted from the reports . There was no report on dog-bite injuries in newspapers for the other years . All archival data used were publicly available at the time of the epidemics , and no sensitive data were used . For the correlation between dog rabies cases in Osaka and Tokyo Prefectures , Spearman’s correlation test was performed using the records between 1897 and 1956 . We investigated how the occurrence of dog rabies cases was affected by human population density using Mixed-effects models [25] . In the first model , the logged number of dog rabies cases per km2 in each administrative unit was the response variable , with logged human population per km2 in each administrative unit a fixed effect , and year and administrative units random effects . In the second model , the logged number of dog rabies cases per capita was the response variable , with the same fixed and random effects as the first model . No dog population records were available for these administrative units . Several dates relating to animal rabies cases were recorded in the prefectural reports including the dates of disease onset , the bite , the diagnosis , and when the animal was killed or died . The earliest date reported was used as the date of notification for analysis in this study . For available data mainly from prefectural reports , supplemented by newspaper reports , the distributions for the periods between notification and death , due to disease or being killed , were estimated by fitting gamma distributions to these intervals . Distributions of the incubation period , and the period between symptom onset and death in humans were also estimated by fitting a gamma distribution to these data extracted from newspapers using the fitdist function in R version 3 . 0 . 2 [26] . Serial intervals ( Tss ) , the periods between the date of disease onset in an index case and the disease onset in the subsequent secondary case [27] , were estimated . Records of direct transmission between dogs were not available . We therefore defined an index case as the first case observed in an administrative unit where no previous rabies cases had been recorded for the preceding six months in the focal unit and surrounding units that lie within or overlap with areas of one-kilometer distance from the boundary of the focal administrative unit . Secondary cases were defined as the second case recorded in the area defined above . We considered a six-month interval without detected cases and a 1km radius , sufficient to isolate an index case as very few incubation periods are thought to exceed this interval [28] and because most distances moved by rabid dogs are thought to be less than 1 km [29] . A gamma distribution was fitted to these serial intervals . As the number of serial intervals calculated was limited , and there was no obvious temporal trend by observation , single Ts distribution was fitted using data between 1914 and 1933 . R0 , the average number of secondary dog rabies cases caused by a primary case in a totally susceptible population [27] , was estimated from Eq 1 [30]: R0=1/∑t=0∞Ts⋅tλ−t ( Eq 1 ) where λ is the growth rate of the epidemic curve and t is time . A regression with negative binomial errors was fitted to the weekly occurrence of dog rabies cases to estimate λ , for the epidemics from 1914 to 1921 and from 1922 to 1933 , respectively . The start and end points used for the estimation of λ for these time series were from the week when the first case was reported after 4 weeks without any detected cases until the week with the most cases . The 4 weeks gap was used to avoid data from the last epidemic . For a partially immunized population , an effective reproductive number R , defined as the number of secondary infections that arise from a typical primary case when control measures are in place , could also be calculated . However when these outbreaks started , vaccination was thought to be very rare ( see discussion ) . R0 was therefore estimated using Eq 1 , sampling 5 , 000 times from the values between the 90% CIs of λ under equal probability , and from the gamma distribution of Ts . The relationship between annual rabies cases in dogs and dog-bite injuries was tested using negative binomial regression , with annual cases of dog-bite injuries as the outcome variable . Negative binomial regression was chosen because the residual deviance in a Generalized Linear Model ( GLM ) with Poisson errors showed overdispersion ( residual deviance: degree of freedom ratio was 2 . 2 > 1 . 5 [25] ) . The relationship between one year lagged annual dog rabies cases and dog-bite injuries was also examined , as was the relationship between annual cases of hydrophobia and dog rabies cases . We tested for temporal trends in the probability of a bite victim developing hydrophobia using GLMs with binomial errors . The sex ratio of bite victims was compared using the chi-square test based on the one-sample proportion test with continuity correction . All calculations were performed in R version 3 . 0 . 2 [31] .
According to the authors’ database , in 1911 , dog rabies was occurring in Tokyo Prefecture and surrounding areas , East-North region , and Kyushu Island that includes Nagasaki ( Fig 1 ) . In 1912 , dog rabies entered in Hyogo Prefecture , and consequently , the epidemic in Osaka and neighboring prefectures started . The proportion of prefectures with dog rabies cases was 42 . 6% ( 20/47 prefectures ) in 1914 , and decreased to 29 . 8% ( 14/47 ) in 1921 . In 1922 , dog rabies spread further again , and in 1923 dog rabies was present throughout most of Japan ( 38/47 , 80 . 9% ) . Dog rabies cases in Osaka and Tokyo Prefectures were significantly correlated throughout the period when dog rabies cases were recorded ( rho = 0 . 516 , p < 0 . 01 ) , and regardless of the locality of epidemics , the two cities tended to have dog rabies during the same years . Osaka was the second largest city after Tokyo at that time , but in some years , dog rabies cases in Osaka Prefecture exceeded that of Tokyo Prefecture ( Fig 2 ) . Two dog rabies epidemics occurred in Osaka Prefecture between 1914 and 1933: the first between 1914 and 1921 ( 879 dog rabies cases ) and the second much larger epidemic lasted from 1922 to 1933 ( 3 , 705 cases , Fig 2 ) . In total , 4 , 632 cases of animal rabies were reported between 1914 and 1933 . Dogs accounted for 99 . 0% of cases ( 4 , 584 ) , followed by 14 cattle , 14 cats , two horses , and one pig ( species for 17 rabid animals were not provided ) . Of 4 , 584 dog rabies cases , owned dogs and unowned stray dogs accounted for 52 . 9% ( 2 , 425 ) and 34 . 5% ( 1 , 582 ) , respectively . Information on ownership was not available for the remaining dogs ( 577 , 12 . 6% ) . The very first dog rabies case documented in Osaka Prefecture , was when a rabid dog bit a victim in mid June 1914 in his residence in West Ward of Osaka City . According to the newspaper report , the rabid dog died around this time , but it is not written whether the dog died of disease or was killed . The victim began showing symptoms one month later , and died of rabies three days later on July 23rd , 1914 . Osaka Prefecture and the newspaper announced the case on July 25th , and control measures started thereafter . People in Osaka Prefecture were not aware that rabies was spread from rabid dogs , otherwise the police could have acted earlier potentially mitigating this epidemic . Although this first epidemic grew rapidly , cases had sharply declined by 1916 . The second larger epidemic in Osaka Prefecture escalated in 1922 , and peaked in 1923 ( 1338 cases ) , while across Japan the epidemic peaked in 1924 ( 3205 cases ) [10] , before declining to the final case in 1933 . After that , Osaka Prefecture was free from rabies until 1948 . The geographical spread of rabies between 1914 and 1915 ( Fig 3A and 3B ) was limited; however , during the second larger epidemic between 1922 and 1923 ( Fig 3C and 3D ) , rabies spread across the entire prefecture . Approximately half of the dog rabies cases in the prefecture occurred in Osaka City ( 2 , 344/4 , 541 with the locations known; 51 . 6% , highlighted area with a bold line in Fig 3 ) . In 1913 , the year before the first outbreak , dog rabies cases were reported only in Hyogo Prefecture ( Fig 1 ) among neighboring prefectures , according to the authors’ database . In 1922 , dog rabies cases were reported in Hyogo and Kyoto Prefectures , and in 1923 , all the neighboring prefectures had dog rabies cases . Less than one in the legend means that there were cases with less than one case per km2 . The boundary of Osaka City is highlighted with a bold line . The south half of left hand boundary is the coast , while the other boundaries are share borders with the other prefectures . More cases in animals were found in areas with higher human population densities: logged dog rabies cases per km2 increased with logged human population density ( slope = 0 . 66 , se = 0 . 03 , p < 0 . 01 , Table 1 ) . While the higher the ( logged ) human population density , the fewer ( logged ) animal rabies cases were detected per capita ( slope = -0 . 34 , se = 0 . 03 , p < 0 . 01 , Table 2 ) . The mean infectious period for dogs that died of rabies was 3 . 3 days ( 90%CI: 0 . 06–11 . 4 , Fig 4A ) , whereas for those that were killed it was 2 . 4 days ( 90%CI: 0 . 3–6 . 3 , Fig 4B ) . The mean serial interval between identified primary and secondary cases was 45 . 0 days ( 90%CI: 4 . 7–118 . 8 , Fig 4C ) . The growth rate , λ , of the first epidemic was 1 . 03 ( 90%CI: 1 . 018–1 . 037 ) , and was 1 . 01 ( 90%CI: 1 . 010–1 . 011 ) for the second epidemic ( Table 3 ) . Given the observed serial intervals , R0 of dog rabies was estimated as 2 . 42 ( 90%CI: 1 . 94–2 . 91 ) for the first epidemic , and 1 . 50 ( 90%CI: 1 . 48–1 . 52 ) for the second ( Table 3 ) . A total of 3 , 805 injuries due to bites by rabid dogs were reported between 1914 and 1933 in Osaka Prefecture . Seventy-five human rabies deaths were reported , corresponding to 2 . 0% ( 75/3 , 805 ) of bite victims . As mentioned above , the public became aware of rabies after the announcement of the first human rabies death on July 25th , 1914 . In this first year , 13 people died of rabies . Time series of dog rabies cases , dog-bite injuries , and hydrophobia in Osaka Prefecture ( Fig 5 ) showed similar temporal patterns of increase and decrease , but dog-bite injuries and hydrophobia were lagged during the second epidemic . Annual cases of dog-bite injuries had significant positive relationships with dog rabies cases that year ( slope = 0 . 003 , se = 0 . 001 , p = 0 . 01 ) , and the previous year ( slope = 0 . 004 , se = 0 . 001 , p = 0 . 01 ) . Annual cases of hydrophobia also had a significant positive relationship with dog rabies cases ( slope = 0 . 003 , se = 0 . 0002 , p < 0 . 01 ) . There were less human rabies deaths during the second epidemic , with no reports of hydrophobia between 1916 and 1919 . The mean number of deaths during years when deaths occurred was 8 . 1 ( range: 3–14 ) , and the mean incidence was 0 . 31 deaths per 100 , 000 persons ( range: 0 . 11–0 . 54 ) . The most deaths , 14 , were recorded in 1924 . The proportion of dog-bite victims that developed hydrophobia each year significantly declined over time ( slope of logit = -0 . 29 , se = 0 . 03 , p < 0 . 01 ) . There were no reports of hydrophobia or the number of dog-bite injuries in newspapers between 1929 and 1933 . The mean incubation period and infectious period in humans was 76 . 4 days ( 90%CI: 2 . 5–242 . 9 , estimated from 13 cases , Fig 6A ) and 2 . 7 days ( 90%CI: 1 . 0–5 . 1 , estimated from nine cases , Fig 6B ) , respectively ( Table 3 ) . The sex of dog bite victims was rarely recorded ( 73 cases , 1 . 9% ) , but of these , significantly more men ( 49 , 67 . 1% ) were bitten than women ( 24 , 32 . 9% , x2 = 7 . 9 , df = 1 , p < 0 . 01 ) . The age of dog-bite victims was recorded in just 49 cases ( taken only from newspaper records ) . The median age of these bite victims was 13 ( mean 19 . 4 ) , with 49% in children 5–14 years old . Most bites were in the 10–14 year age group , followed by the 5–9 and 15–19 year age groups ( Fig 7 ) . Table 4 shows rabies control measures taken in Osaka Prefecture between 1914 and 1933 . On the day of the announcement of the first human death due to rabies in Osaka Prefecture , July 25th 1914 , police started culling stray dogs , and 150 stray dogs were culled by July 31st . Police organized health checks of dogs by veterinarians examining for rabies based on clinical signs , and marking checked dogs with a red color . Victims of dog-bite injuries by suspected rabid dogs were encouraged to receive PEP for free . On January 4th 1915 , Osaka Prefecture Rule for regulation of pet dogs was revised , and registration of dogs over three months old , attachment of dog collars with a name and address , and chaining or application of muzzles to dogs with biting tendencies became the duty of dog owners . The rule also stated that dogs must be chained when the authority orders during dog rabies epidemics , and otherwise free roaming dogs would be regarded as unowned stray dogs [15] . As rabies vaccine for dogs was not available in this epidemic , rabies control was based on keeping pet dogs chained , and on mass culling of stray dogs . According to Osaka Prefecture Notices and newspaper articles , Osaka Prefecture government announced dog chaining orders several times during the epidemics . In 1919 , when dog rabies cases began increasing , a ban of dog movement from prefectures with rabies cases was applied . In 1922 , the Act on domestic animal infectious disease control ( a current act with the same name was established in 1951 ) was established , and dog vaccination became compulsory for inter-prefectural dog movement . Mass vaccination started in 1923 , and a certificate of vaccination effective for one year was provided to owners . In 1923 , the order of chaining dogs , and leashing for walking was announced again . If not , dogs were regarded as strays , and were killed . In 1925 and 1926 , a reward for capturing stray dogs was announced . Until 1933 , a combination of dog vaccination and restriction of free movement of dogs continued . Table 5 shows the numbers of dogs vaccinated , captured stray dogs , dog-bite injuries by rabid dogs to humans , and PEP administered in Osaka Prefecture between 1924 and 1928 . The number of PEP and the rate of PEP delivered to dog-bite injuries was high , particularly in 1928 . No records of PEP use were available for other years . If the ratio of human deaths to dog rabies cases in 1914 ( 13 deaths: 236 dog rabies cases = 1: 18 . 2 ) had continued , the epidemic between 1914 and 1933 could have caused around 252 deaths ( 252: 4 , 584 dog rabies cases ) , compared with 75 recorded deaths .
Here we describe the epidemiology of rabies in Osaka Prefecture , during a period when nationwide epidemics were occurring in Japan . For the analyses , the most reliable data were collected , as far as possible . However , there is a limitation in data accuracy . The biggest challenge of data collection was the lack of human rabies records in the years without special attention to rabies . By law , human cases with hydrophobia were reported to Osaka Prefecture , but unfortunately , prefectural annual hygiene reports during the period were lost , according to our archive survey . The records were not stored in the Ministry of Health , Labour and Welfare as well . Moreover , as mentioned in the materials and methods , hydrophobia is seen in 50–80% of rabies patients , and other types of illness such as paralysis ( about 20% of the patients ) [23] may not have been reported . Also , a report of hydrophobia was based on the notification by a physician , and it may not have involved biological confirmation . Nevertheless , newspapers not only reported each single case , but also presented summary reports from time to time , which contributed greatly in filling data gaps . On the other hand , although there might be underreporting , dog rabies cases were confirmed by the inoculation of brain tissue emulsion to rabbits [22] . During the times of special attention to rabies , dog rabies reports to Osaka and neighboring prefectures were thought to be accurate even in rural areas , considering strong enforcement of dog registration and mandatory reporting of suspected rabies cases accompanied by penalties in case of failure , and information sharing between neighboring prefectures . However , dog rabies cases without clear furious symptoms may not have been reported , and unowned rabid dogs may have been died in remote areas unnoticed . This potential underreporting may have caused underestimation of R0 . Of the two major epidemics in Osaka Prefecture , the second was more severe than the first , in terms of both geographic spread and numbers of dog rabies cases . However , R0 was significantly larger in the first epidemic ( 2 . 42 ) than the second ( 1 . 50 , Table 1 ) and larger than previously recorded elsewhere in the world [29] . Our estimate of R0 is potentially conservative , since we used the serial interval from Osaka , which was longer than that reported from Tanzania [29] ( 45 . 0 , 90%CI: 4 . 7–118 . 8 vs 24 . 9 , 95%CI: 23 . 7–26 . 2 , and our method of estimating the serial interval may have been biased towards longer intervals because direct transmission was not observed ) . The first case of dog rabies in Osaka Prefecture in 1914 occurred in the West Ward of Osaka City , which shares a boundary with Hyogo Prefecture , and dog rabies was considered to be introduced from this neighboring prefecture . Since this was the first large-scale rabies epidemic in the history of Osaka Prefecture , lack of awareness about rabies may have resulted in such a large number of cases . The high growth rate in the first epidemic in 1914 might be also due to fewer case reports during the initial phase because of a lack of awareness . There was a one-month period between the dog-bite injury and the onset of hydrophobia in the first victim in Osaka Prefecture . During this time considerable undetected transmission likely occurred leading to rapid spread in this entirely susceptible population . Furthermore , as shown in Table 4 , an estimated population of 50 , 000 unconstrained stray dogs at that time likely exacerbated spread . As shown in Fig 4B , sometimes it was difficult to kill free roaming rabid dogs . The longest time between illness onset until a rabid dog was killed was 8 days . However , the mean infectious period of dogs that were killed was short ( 2 . 4 days ) . This rapid response may explain the long serial interval and moderate R0 calculated . Moreover , once rabies control started , stray dogs were rapidly captured , and most of these were captured in 1914 . Dog owners seemed not to have had a habit of keeping dogs chained before the epidemic , because Osaka Prefecture repeatedly communicated this message to the public through prefecture notices and media . Strict enforcement with a punishment , in case pet dogs were not kept chained , in 1915 may have been effective . The number of dog rabies cases sharply declined in 1916 , which suggests that human responses to encounters with rabid dogs may have reduced epidemic growth , as has been previously noted [32] . A ban of inter-prefectural dog movement in limited areas in 1919 seemed ineffective for rabies control , and the nation-wide epidemic grew from 1921 . The second epidemic was widespread and appeared to involve neighboring prefectures . In 1922 , the animal infectious disease prevention law was established [10] , and dog vaccination became available for inter-prefectural movement of dogs . However , this still did not slow the epidemic , probably because it was already widespread , there were large numbers of stray dogs with high rates of turn-over , and low compliance with registration and tethering dogs . A newspaper on 1922 June 29th estimated that there were 10 , 000 unregistered and 20 , 000 stray dogs in Osaka Prefecture . Comparing descriptions in newspapers between 1914 and 1916 , with those between 1919 and 1922 , mass culling of stray dogs appeared more frequently in the former years , but not in the latter . This may have reflected the expectation towards efficacy of vaccine for dogs . Mass vaccination in Osaka Prefecture started in 1923 , at the peak of the epidemic , and annual dog rabies cases declined sharply as a result . However , dog rabies cases increased again in 1926 . With the restart of capturing stray dogs , rabies was finally eliminated from Osaka in 1933 , suggesting mass vaccination alone as applied during this epidemic may not have been sufficient to eliminate dog rabies . Information on the dog population in 1920s’ is unfortunately not available , and vaccination coverage may not have exceeded the immunity threshold . The number of dog rabies cases per unit area was positively correlated with human population density . However , more information on whether transmission rates ( and R0 ) scale with dog population density is needed to determine whether transmission is density or frequency-dependent . Unfortunately , no data were available on the size of the dog population at administrative levels . The negative relationship between human population density and dog rabies cases per capita found in this study implies that the dog: human ratio may be lower in densely populated areas i . e . more dog ownership or more dogs per human in less densely populated ( or more rural ) areas . Although large numbers of dog rabies cases were reported , the incidence of human rabies deaths remained low . The range of annual human rabies incidence ( 0 . 11 to 0 . 54 deaths per 100 , 000 ) was far lower than in Tanzania ( 4 . 9 deaths per 100 , 000 ) estimated using active surveillance [33] , but is comparable with the situation in Bali in 2009 . Between December 2008 and November 2009 , the incidence of human rabies deaths was 0 . 51 per 100 , 000 persons ( 20/3 , 890 , 757 ) in Bali , Indonesia , and in the following 12 months , incidence increased to 2 . 13 deaths per 100 , 000 ( 83/3 , 890 , 757 ) [34–35] . Moreover , the probability of developing hydrophobia after a dog bite decreased over time . The PEP used at that time already seemed to have high efficacy: 10–15% of unvaccinated patients bitten by rabid dogs died , whereas only 0 . 25–1% of patients vaccinated with the Pasteur dry toxin vaccine method died in Tokyo Prefecture [36] . As shown in Table 5 , large numbers of PEP were administered to dog bite victims , and the number increased during the last part of the epidemic . The big differences between the number of dog-bite injuries by rabid dogs and PEP suggested that the victims of dog-bite injuries may have sought PEP immediately regardless of the status of infection of the dog . In the record of discussion at the transition of responsibility of rabies control from the Ministry of Agriculture to the Ministry of Home Affairs in 1929 , it is written that human rabies was almost eliminated from Japan in 1928 [37] . The increased PEP use in 1928 ( Table 5 ) may reflect this transition of responsibility of rabies control , though a year earlier . Based on the ratio of hydrophobia cases and dog rabies cases in 1914 , if PEP use had remained at initial levels , 252 humans rabies deaths would have occurred during the two epidemics in Osaka Prefecture . However , these data suggest that over 170 lives may have been saved by the increased PEP use . The time lag between the incidence of dog rabies cases and dog bite injuries might also reflect increased awareness of rabies over time , and consequently , improved reporting of dog bites . In fact , reported dog-bite injuries could include those by healthy as well as by rabid dogs . Although there is limited information on dog bite victims , more men were bitten than women and children were more at risk than adults . Previous studies have also revealed that being male or a child is a risk factor for bite injuries from rabid dogs and rabies infection [3 , 34 , 38] . In conclusion , this paper provides a useful example of the elimination of canine rabies . Authorities raised awareness of rabies and encouraged victims of dog bite injuries to receive PEP , saving many lives . Mass vaccination of dogs undoubtedly played a critical role in controlling and ultimately eliminating rabies . Authorities also aimed to reduce the number of stray dogs using a reward scheme , and enforced the tethering of pet dogs , even while walking , which was not common in Japan at that time . Strict legislation was important in dramatically changing the behavior of dog owners . A newspaper described the first case on July 26 , 1914 vividly; ‘his wife dropped a coin on the floor and it slipped off through mats and floor woods . When he raised up the mat and was trying to pulling the coin with a bamboo stick , suddenly a dog rushed from the floor space , and bit his left finger strongly . The stray dog hid in the floor space during the day time , and strayed and bit anything in front of him during nights . ’ And the article ends with this; ‘we strongly request police to make efforts in killing stray dogs , and advise people to assume any dogs to be rabid’ . Such views on strict stray dog population control may not be accepted , in terms of culture and animal welfare , in current rabies endemic countries . However , the effective control options recommended nowadays–vaccination and improvements in dog husbandry—have not been changed since almost a century ago , especially after dog vaccine came in to use . Although socioeconomic conditions may be challenging in some rabies endemic countries and regions , and the findings in past Osaka Prefecture may not be directly comparable with these places , the elimination of rabies is surely possible , by well-coordinated implementation of control programs with participation of all stakeholders from local communities to global partners . Finally , our experience of rabies in Japan suggests that the media can play an important role in reporting the dangers of rabies and promoting participation in rabies control activities to ensure progress towards elimination . | Rabies is a lethal zoonosis mostly transmitted through bites by dogs infected with rabies virus . Japan has been free from rabies since the 1950s . However , there were several large-scale epidemics before its elimination . These past epidemics occurred both before and after dog rabies vaccine became available . Moreover , Japanese dog owners used to allow free roaming of dogs at that time . Therefore , studying how Japan controlled the disease will help discussion on current rabies control in endemic countries . In this study , we investigated archival data of Osaka Prefecture including newspapers between 1914 and 1933 . There were two epidemics from 1914 to 1921 , and 1922 to 1933 . During the first epidemic , dog vaccine was not available , and the epidemic was controlled through capture of stray dogs and tethering of pet dogs . Dog mass vaccination began during the second epidemic in 1923 . After these vaccination campaigns and capturing of stray dogs were conducted , rabies in Osaka Prefecture was finally eliminated in 1933 . The probability of human dying from rabies declined over time , due to increased use of post-exposure vaccines . We suggest that the combination of mass vaccination and restriction of dog movement enabled by strong legislation was key to eliminate rabies . | [
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] | 2017 | The rise and fall of rabies in Japan: A quantitative history of rabies epidemics in Osaka Prefecture, 1914–1933 |
Human cytomegalovirus ( HCMV ) infections of healthy individuals are mostly unnoticed and result in viral latency . However , HCMV can also cause devastating disease , e . g . , upon reactivation in immunocompromised patients . Yet , little is known about human immune cell sensing of DNA-encoded HCMV . Recent studies indicated that during viral infection the cyclic GMP/AMP synthase ( cGAS ) senses cytosolic DNA and catalyzes formation of the cyclic di-nucleotide cGAMP , which triggers stimulator of interferon genes ( STING ) and thus induces antiviral type I interferon ( IFN-I ) responses . We found that plasmacytoid dendritic cells ( pDC ) as well as monocyte-derived DC and macrophages constitutively expressed cGAS and STING . HCMV infection further induced cGAS , whereas STING expression was only moderately affected . Although pDC expressed particularly high levels of cGAS , and the cGAS/STING axis was functional down-stream of STING , as indicated by IFN-I induction upon synthetic cGAMP treatment , pDC were not susceptible to HCMV infection and mounted IFN-I responses in a TLR9-dependent manner . Conversely , HCMV infected monocyte-derived cells synthesized abundant cGAMP levels that preceded IFN-I production and that correlated with the extent of infection . CRISPR/Cas9- or siRNA-mediated cGAS ablation in monocytic THP-1 cells and primary monocyte-derived cells , respectively , impeded induction of IFN-I responses following HCMV infection . Thus , cGAS is a key sensor of HCMV for IFN-I induction in primary human monocyte-derived DC and macrophages .
Human cytomegalovirus ( HCMV ) is a highly host-adapted , opportunistic β-herpesvirus that copes amazingly well with the host’s immune response due to a plethora of different evasion mechanisms [1 , 2] . These strategies allow HCMV to establish latency and to silently spread to naïve individuals . Currently 60–100% of the world population is latently infected with HCMV [3] . In immunocompromised hosts , e . g . , transplant recipients , HCMV reactivation may cause serious disease , while congenital infection can lead to abortion or dramatic disabilities in the infant , such as deafness and mental retardation [4 , 5] . As a first line of antiviral defense , innate immune cells express cytokines that activate and recruit innate as well as adaptive immune cells . One of the earliest and most prominent families of antiviral cytokines are the type I interferons ( IFN-I ) . IFN-I in humans comprise 13 functional IFN-α genes that encode intronless mRNAs , two of which ( IFN-α1 and IFN-α13 ) presumably were derived from gene duplication and encode identical protein sequences , and one single IFN-β [6–8] . IFN-I facilitate immune responses against viruses by inducing an antiviral state in host cells and orchestrating innate as well as adaptive immune responses [9 , 10] . They are induced upon sensing of pathogen associated molecular patterns by pattern recognition receptors ( PRR ) such as Toll-like receptors ( TLR ) , RIG-I like helicases ( RLH ) , and more recently identified intracellular DNA sensors [11 , 12] . In CMV infection IFN-I play a key role as indicated by IFN-I receptor ( IFNAR ) deficient mice that show highly increased sensitivity to infection with murine CMV ( MCMV ) [13 , 14] . The evolution of multiple CMV evasion mechanisms targeting IFN-I induction and IFNAR signaling further underscores the significance of the IFN-I system [15–19] . In MCMV infection , typically two waves of IFN-I expression are detected [20] . While the cellular source and the underlying recognition platform of the first IFN-I wave are still poorly defined , it was shown previously that the second IFN-I wave is primarily contributed by plasmacytoid dendritic cells ( pDC ) [21 , 22] , which are known for their extraordinary IFN-I production capacity [23 , 24] . This IFN-I response is highly dependent on endosomally-located TLR9 [25] , which recognizes double-stranded hypomethylated CpG-rich DNA and signals via the adaptor molecule MyD88 [26] . Mice deficient for components of the TLR9 axis showed increased mortality upon MCMV infection [27] . Reminiscent of conditions in mice , also HCMV-stimulated human pDC mount substantial IFN-α responses , while pDC are largely resistant to HCMV infection when compared with either CD11c+ DC or freshly isolated monocytes [28 , 29] . Treatment of human pDC with TLR7/9 inhibitory CpG oligodinucleotides ( ODN ) abrogated HCMV induced IFN-α responses [30] indicating that also in human pDC TLR play a central role in HCMV recognition . However , patients suffering from primary MyD88 deficiency did not show increased susceptibility to infection with herpesviruses [31 , 32] questioning the central function of pDC in the pathogenesis of HCMV in humans and suggesting the involvement of other cells and recognition platforms in the production of protective IFN-I . In human fibroblasts several intracellular dsDNA receptors , such as DNA-dependent activator of interferon regulatory factors ( DAI , also known as ZBP1 ) [33] and interferon-γ inducible protein 16 ( IFI16 ) [34] , have been identified to recognize HCMV [35–37] . Yet , fibroblasts mount only weak IFN-I responses , whereas in addition to pDC other myeloid cells such as monocytes and monocyte-derived macrophages ( moMΦ ) have been reported to produce abundant IFN-I following stimulation with MCMV or HCMV [38–40] . As myeloid cells are also sites of HCMV latency [41–43] and may contribute to HCMV dissemination [44] , their role as sentinels of HCMV infection is of particular interest . Studies with in vitro polarized macrophages reported that pro-inflammatory macrophages are less susceptible to HCMV infection than anti-inflammatory macrophages , whereas both cell types can be productively infected [39 , 45] . Murine macrophages stimulated with HCMV mount IFN-I responses that are dependent on the adaptor protein stimulator of interferon genes ( STING ) [46 , 47] . Furthermore , in human macrophages the dsDNA receptor IFI16 that was shown to associate with STING [34] seems to play a role in HCMV sensing [48] . Recently , the cytosolic dsDNA receptor cyclic GMP/AMP synthase ( cGAS ) was identified to be activated upon DNA binding and to produce the second messenger cyclic 2´-5´/3´-5´ GMP/AMP ( cGAMP ) [49–52] . cGAMP , that may also spread via gap junctions to bystander cells [53] , directly activates STING that then translocates from the endoplasmic reticulum to perinuclear punctate structures in order to mediate IFN-I induction via TANK-binding kinase 1 ( TBK1 ) and interferon regulatory factor 3 ( IRF3 ) [46 , 47 , 50 , 52] . In human foreskin fibroblasts cGAS has been shown not only to be a direct sensor of cytosolic DNA , but also to stabilize IFI16 against proteasomal degradation [54] . Nevertheless , to date in human antigen presenting cell subsets the involvement of cGAS in HCMV recognition and subsequent IFN-I induction remains to be elucidated . Here we investigated the role of the cGAS/STING axis in HCMV induced IFN-I responses of primary human pDC as well as of monocyte-derived DC ( moDC ) , GM-CSF MΦ , and M-CSF MΦ . We found that cGAS and STING were expressed by these cell subsets to varying degrees . Furthermore , HCMV induced intracellular cGAMP formation was detected in monocyte-derived DC and MΦ , but not in pDC . Finally , an essential role for cGAS in HCMV recognition and subsequent IFN-I induction was confirmed by siRNA mediated cGAS knock-down in primary human monocyte-derived cells .
To address the role of the cGAS/STING pathway in HCMV infected primary human immune cells , in a first step we studied cGAS and STING expression of pDC , moDC , GM-CSF MΦ , and M-CSF MΦ . To this end , we isolated primary human pDC and monocytes from PBMC and in vitro differentiated moDC , GM-CSF MΦ , and M-CSF MΦ from monocytes ( S1 Fig ) . Freshly isolated as well as 24 h cultivated pDC showed enhanced levels of cGAS mRNA expression , whereas monocyte-derived cells expressed lower amounts of basal cGAS mRNA ( Fig 1A and 1B and S2 Fig ) . On the contrary , STING mRNA expression was abundant in GM-CSF MΦ , while pDC and M-CSF MΦ showed intermediate , and moDC rather low level expression ( Fig 1A and 1C ) . Upon stimulation for 6 h with recombinant IFN-α2b , cGAS mRNA expression as well as the classical interferon stimulated gene ( ISG ) MxA were significantly induced in all cell subsets analyzed , whereas STING mRNA expression remained overall stable and was only moderately enhanced in M-CSF MΦ ( Fig 1A ) . Similarly , HCMV ( TB40/E ) treatment for 24 h induced cGAS mRNA in all cell types analyzed to comparable levels as observed upon IFN-α2b stimulation ( compare Fig 1A and 1B ) . In contrast , upon HCMV treatment STING mRNA expression was moderately enhanced only in pDC , whereas it stayed overall unchanged in moDC and M-CSF MΦ and showed the tendency of even reduced expression in GM-CSF MΦ ( Fig 1C ) . Western blot analysis confirmed cGAS induction upon IFN-α2b or HCMV stimulation , although minor donor-dependent variabilities were observed ( Fig 1D ) . In contrast , moderate STING induction was detected only upon IFN-α2b stimulation for 24 h in moDC and M-CSF MΦ , but not upon HCMV treatment ( Fig 1D ) . Thus , pDC , moDC , GM-CSF MΦ , and M-CSF MΦ expressed the main components of the cGAS/STING axis , although expression levels differed between the myeloid cell subsets . While pDC expressed particularly high levels of inducible cGAS , moDC expressed very low levels of STING . To study IFN-I expression by pDC , moDC , GM-CSF MΦ , and M-CSF MΦ , cells were infected with HCMV at MOI 3 for 24 h . qPCR analysis revealed significant IFN-α and IFN-β induction in all HCMV treated cell subsets , which was particularly strong in pDC ( Fig 2A and 2B ) . Quantification of IFN-α expression on the single cell level indicated that HCMV treated pDC and M-CSF MΦ comprised higher percentages of IFN-α positive cells ( 1 . 2 and 1 . 5% , respectively ) than moDC and GM-CSF MΦ ( 0 . 9 and 0 . 7% , respectively ) ( Fig 2C and 2D ) . As expected , also cell-free culture supernatants contained substantial amounts of IFN-α that were approximately 10 to 50-fold higher in supernatants from pDC compared with the other cell types . However , low to robust IFN-α levels were detected in supernatants of moDC , GM-CSF MΦ , and M-CSF MΦ cultures ( Fig 2E ) . Furthermore , we found that upon HCMV stimulation the amount of IFN-α secreted per IFN-α+ cell was particularly high in pDC , while GM-CSF MΦ as well as M-CSF MΦ produced intermediate and moDC particularly small quantities ( Fig 2F ) . Thus , upon HCMV infection pDC mounted the most abundant IFN-I responses , whereas the two analyzed MΦ subsets also showed high IFN-I expression upon HCMV infection . Surprisingly , HCMV infected moDC showed especially low IFN-α production when compared with the other cell subsets , as determined by the absolute quantities detected in the cell-free supernatant as well as the amount of IFN-α produced per IFN-α+ cell . To next address whether cGAS played a role in the recognition of HCMV by pDC , moDC , GM-CSF MΦ , and M-CSF MΦ , we aimed to determine the product of cGAS activation , cGAMP . This was done by a HPLC-MS/MS method that on the basis of different retention times allowed discrimination of cGAS-derived 2´-5´/3´-5´ cGAMP and of other cyclic di-nucleotides such as the bacterial-derived 3´-5´/3´-5´ cGAMP ( Fig 3A ) . Interestingly , despite pDC expressed abundant levels of cGAS and STING ( see Fig 1 ) , in lysates of HCMV treated cells no cGAMP was detected ( Fig 3B and 3C ) . Analysis of lysates of HCMV stimulated monocyte-derived cells revealed that M-CSF MΦ contained particularly high cGAMP levels ( Fig 3B and 3C ) , whereas in moDC and GM-CSF MΦ intermediate to low cGAMP levels were detected ( Fig 3B and 3C ) . For the determination of the kinetics of cGAMP formation , monocyte-derived cells were analyzed at different time points after HCMV infection . These experiments revealed that already 2–4 hpi intracellular cGAMP was detected that increased until 24 hpi ( Fig 3D ) . Thus , HCMV induced cGAMP responses of monocyte-derived cells preceded IFN-α secretion , which was first detectable in cell culture supernatants at 12 hpi ( Fig 3D ) . Notably , although at 24 hpi moDC showed higher cGAMP responses than GM-CSF MΦ , moDC secreted significantly less IFN-α than GM-CSF MΦ ( Figs 3D and 2E ) . Furthermore , cells were stimulated with modified vaccinia virus Ankara ( MVA ) , a highly attenuated vaccinia virus strain , which is encoded by a DNA genome and in murine conventional DC was shown to be sensed in a cGAS-dependent manner [55] . MVA stimulation did not induce cGAMP synthesis in pDC ( Fig 3E ) , whereas IFN-α was detectable in the supernatant ( Fig 3F ) . In contrast , MVA treatment induced cGAMP responses in all monocyte-derived cell subsets tested ( Fig 3E ) . Although overall similar cGAMP levels were detected , the IFN-α levels produced by GM-CSF MΦ and M-CSF MΦ were higher than those of moDC upon MVA stimulation ( Fig 3F ) . As another control , monocyte-derived cells were also treated with RNA encoded VSV-M2 ( a vesicular stomatitis virus strain carrying a mutation in the M protein ) , which is sensed in murine macrophages and conventional DC in a RLH-dependent manner [56 , 57] . In these experiments cGAMP was not detected in any of the tested cell subsets ( Fig 3G ) , while all three monocyte-derived cell subsets mounted abundant IFN-α responses of similar magnitude ( Fig 3H ) . Thus monocyte-derived cells infected with RNA encoded VSV-M2 did not show cGAMP formation , whereas treatment with DNA encoded HCMV or MVA indeed activated cGAS to synthesize cGAMP . To further verify that in monocyte-derived cells cGAS can be activated by HCMV-derived viral DNA , we analyzed cGAMP synthesis by recombinant human cGAS in a cell-free system . As shown previously , cGAMP was synthesized from ATP and GTP upon activation of recombinant cGAS by a 50 bp control dsDNA ( Fig 4A ) [58] . While in HCMV preparations no cGAMP was detected ( Fig 4B and S3 Fig ) , incubation of cGAS in the presence of ATP and GTP together with HCMV resulted in cGAMP formation at moderate but significant levels ( Fig 4B ) . However , if HCMV preparations were subjected to DNA digestion no cGAMP formation was detected ( S3 Fig ) . In contrast , viral particles that were disrupted by heat treatment induced significantly enhanced cGAMP formation , whereas DNA digestion following heat treatment again inhibited cGAMP synthesis ( Fig 4B and S3 Fig ) . These experiments indicated that in heat treated HCMV preparations DNA was the cGAS activating component . To next address whether the viral DNA genome was able to trigger cGAS , we purified the viral genome from HCMV preparations , quantified the viral DNA by qPCR and incubated recombinant cGAS with 10 pM of this DNA . Indeed , under such conditions significant cGAMP formation was detected , whereas DNA digestion again inhibited the effect ( Fig 4C ) . In experiments with decreasing amounts of viral DNA , 0 . 3 pM of viral DNA still activated cGAS ( Fig 4D ) . Thus , the data obtained in a cell-free system indicated that minute quantities of viral DNA indeed were able to activate human cGAS . As pDC showed no activation of cGAS-dependent cGAMP synthesis upon HCMV stimulation and previous studies suggested that endosomal TLR mediated HCMV recognition in pDC [30] , we aimed at specifying the involvement of TLR9 in HCMV sensing . Indeed , HCMV induced IFN-α responses of pDC were significantly impaired by the addition of the TLR9 specific antagonistic oligonucleotide IRS869 , whereas IFN responses induced by the TLR7/8 agonist R848 were not inhibited ( Fig 5A ) . This indicated that pDC sensed HCMV primarily in a TLR9-dependent manner to mount IFN-I responses . However , since pDC expressed abundant levels of cGAS and STING ( see Fig 1 ) , we next addressed whether the cGAS/STING axis was functional . Notably , transfection of pDC with synthetic cGAMP induced robust IFN-α production ( Fig 5B ) implying an effective signaling transduction cascade downstream of STING . We next addressed the role of cGAS in HCMV mediated IFN-I induction in myeloid cells . As the DNA sensor IFI16 has been reported to be involved in HCMV sensing and IFN-I production of macrophages [48] and to cooperate with cGAS for DNA sensing in fibroblasts [54] , we also studied the role of IFI16 in HCMV induced IFN-I responses . We analyzed monocytic THP-1 cells in which cGAS , IFI16 , or STING was deleted by CRISPR/Cas9 technology ( S4 Fig ) [59] . These cells were used either undifferentiated ( S5 Fig ) or upon differentiation by incubation with PMA ( Fig 6 ) . Unlike WT THP-1 cells , HCMV treated cGAS or STING deficient THP-1 cells showed significantly impaired IFN-β production ( Fig 6A and S5A Fig ) . In contrast , HCMV treated IFI16 deficient THP-1 cells mounted abundant IFN-β responses that were even moderately enhanced when compared with WT THP-1 cells ( Fig 6A and S5A Fig ) . Western blot analysis of phosphorylated IRF3 ( P-IRF3 ) , which is an indicator of downstream signaling of STING , revealed the presence of P-IRF3 only in HCMV treated WT and IFI16 deficient THP-1 cells , whereas it was not detected in cGAS and STING deficient THP-1 cells ( Fig 6A and S5A Fig ) as well as in unstimulated cells ( S5D and S5E Fig ) . Similarly , treatment with DNA encoded MVA triggered IFN-β responses only in WT and IFI16 deficient THP-1 cells , whereas upon cGAS or STING ablation no responses were detected ( Fig 6B and S5B Fig ) . Again only WT and IFI16 deficient THP-1 cells , but not cGAS or STING deficient THP-1 cells , showed P-IRF3 induction upon MVA treatment ( Fig 6B and S5B Fig ) . In contrast , infection with RNA encoded VSV induced IFN-β mRNA expression as well as P-IRF3 in all tested THP-1 variants ( Fig 6C and S5C Fig ) . Interestingly , IFN-β mRNA expression and IRF3 phosphorylation of one cGAS deficient clone were slightly decreased compared with WT THP-1 cells , whereas one IFI16 deficient clone showed enhanced IFN-β responses ( Fig 6C ) , which might be explained by off-target effects . Since here we stimulated THP-1 cells with a VSV variant that carried a functional M protein , instead of VSV-M2 , as done in experiments shown in Fig 3H , no IFN-β protein secretion was detected due to M protein mediated inhibition [60] . To next address , whether HCMV sensing of primary human monocyte-derived cells was similarly dependent on the cGAS/STING axis as detected in THP-1 cells , we applied a Viromer-based transfection method to induce siRNA-mediated knock-down of cGAS in moDC , GM-CSF MΦ , and M-CSF MΦ . Indeed , western blot analysis confirmed efficient and specific cGAS knock-down in siRNA treated monocyte-derived cells , whereas IFI16 expression was not affected ( Fig 7A ) . Upon infection with a HCMV variant expressing GFP under the control of the major immediate early promotor ( HCMV-GFP ) siRNA-treated monocyte-derived DC and MΦ mounted significantly reduced IFN-α responses ( Fig 7B and 7C ) , while irrespective of siRNA-treatment they showed overall similar percentages of HCMV-GFP+ cells ( Fig 7C ) . To ensure that cGAS knock-down would not affect down-stream signaling of STING or stimulation with TLR ligands , we next stimulated cells subjected to siRNA mediated cGAS knock-down with synthetic cGAMP or LPS . cGAS siRNA-treated moDC , GM-CSF MΦ , and M-CSF MΦ that mounted reduced IFN-α responses upon HCMV-GFP treatment were still induced to express IFN-β or TNF-α upon transfection with synthetic cGAMP and stimulation with LPS , respectively ( Fig 7D and S6 Fig ) . Notably , synthetic cGAMP induced IFN-β responses were more abundant in moMΦ than in moDC . Thus , cGAS was not only activated upon HCMV stimulation in monocyte-derived cells , but it was essential to mount abundant IFN-I responses . Previous studies with murine and human immune cells implied that the vulnerability to CMV infection and the support of viral gene expression varied between myeloid cell subsets and that pDC were particularly resistant to infection [28 , 29 , 39 , 40 , 45] . This led us to hypothesize that the susceptibility to CMV infection was one prerequisite for the engagement of the cytoplasmic cGAS/STING axis in CMV sensing . Indeed , upon HCMV-GFP infection at MOI 3 , pDC exhibited the least GFP expression as indicated by 0 . 3% GFP+ cells ( Fig 8A and 8B ) . Analysis of monocyte-derived cells revealed that M-CSF MΦ contained the highest percentages of HCMV-GFP+ cells ( 46 . 9% ) , whereas moDC exhibited intermediate and GM-CSF MΦ low amounts of GFP+ cells ( 11 . 7 and 2 . 0% , respectively ) ( Fig 8A and 8B ) . Notably , cell subsets that showed higher percentages of HCMV-GFP+ cells also synthesized enhanced cGAMP levels ( compare Figs 8B and 3C ) . Indeed , upon combined analysis of pDC , moDC , GM-CSF MΦ , and M-CSF MΦ we verified that the percentage of HCMV-GFP+ cells correlated with the amount of cGAMP that was formed in myeloid cells ( Fig 8C ) . To analyze the correlation between infection with HCMV and IFN-α production , we stimulated pDC and M-CSF MΦ with gradually increasing MOI ( MOI 0 . 1–30 ) of HCMV-GFP . In this setting , the percentage of IFN-α+ pDC and the amount of IFN-α secreted into the supernatant increased up to levels of 15% and 130 ng/ml , respectively , whereas the percentage of GFP+ cells stayed comparably low ( Fig 8D ) . In contrast , in M-CSF MΦ cultures highly increased percentages of HCMV-GFP+ cells were detected ( Fig 8D ) . These correlated with increased percentages of IFN-α+ cells as well as enhanced amounts of secreted IFN-α up to approximately 40% GFP+ cells , whereas at further enhanced percentages of GFP+ cells IFN-α responses waned ( Fig 8D ) . These data indicated that in monocyte-derived M-CSF MΦ , but not pDC , IFN-α responses were the higher the more HCMV-GFP+ cells were detected until reaching a certain infection threshold . In conclusion , our data showed that the susceptibility of the different myeloid cell subsets to HCMV infection correlated with the amount of synthesized cGAMP . Abundantly HCMV infected monocyte-derived DC and MΦ showed enhanced cGAMP formation , whereas in pDC , which were highly resistant to infection , cGAMP synthesis was not detected .
The knowledge of diverse mechanisms applying for primary human immune cell sensing of human-specific viruses , such as HCMV , is of major importance to better understand complex virus/host interactions . Here we report that in primary human monocyte-derived DC and MΦ cGAS was essential for HCMV sensing and subsequent IFN-I induction . The degree of cGAS-dependent cGAMP formation correlated with the susceptibility of different monocyte-derived cell subset to HCMV infection . In line with this observation , pDC that were not readily HCMV infected mounted IFN-I responses in a TLR9-dependent manner , although they expressed abundant amounts of cGAS and STING . Our observation that pDC are triggered by HCMV in a TLR9-dependent manner was based on selective TLR9 inhibition using the IRS869 oligonucleotide [61] and confirmed an earlier study in which a TLR7/9 inhibitory CpG ODN was used [30] . Interestingly , pDC expressed enhanced constitutive cGAS levels compared with monocyte-derived cells , which were significantly induced upon IFN-α2b or HCMV stimulation . A previous study showed that upon in vitro cultivation pDC spontaneously expressed low levels of IFN-I [62] , which might have accounted for the enhanced basal expression of cGAS in pDC . However , this was not the case , because also freshly isolated pDC expressed high cGAS levels . Therefore , we conclude that already naïve pDC show an inherent interferon pre-activation status that facilitates swift responses to pathogens [63] . It is possible that in human pDC enhanced constitutive cGAS levels further supported this status as suggested by experiments with murine macrophages in which cGAS expression was a prerequisite for normal levels of constitutive ISG expression [64] . Furthermore , pDC constitutively expressed abundant levels of STING that slightly increased upon HCMV stimulation . Transfection of pDC with synthetic cGAMP induced IFN-α responses , indicating that in pDC the cGAS/STING axis downstream of STING was fully functional . Nevertheless , a contribution of the cGAS/STING axis in HCMV sensing and IFN-I induction of pDC was not observed , because upon HCMV stimulation of pDC no intracellular cGAMP was detected . This raises the question why in pDC the abundantly expressed cGAS was not activated upon HCMV stimulation to synthesize cGAMP . One possible explanation is that cGAS activity is reduced in pDC by mechanisms like cGAS glutamylation and/or beclin-1 interaction , which have been reported to reduce cGAS DNA binding and enzymatic activity [65 , 66] . However , our observations that the degree of HCMV-GFP infection and the extent of cGAMP production correlated in myeloid cells , and that pDC were not efficiently infected , suggested that in pDC the viral genome did not reach the cytoplasm to trigger cGAS , and instead directly entered the endosomal/lysosomal pathway to trigger TLR9 . A previous study showed that naïve pDC were triggered by the yellow fever live vaccine YF-17D in a RIG-I-dependent manner , whereas upon contact with YF-17D infected cells IFN-I induction was dependent on endosomally located TLR7 [67] . Thus , it is conceivable that depending on how pDC encounter nucleic acids they might also be triggered in a cGAS-dependent manner . Furthermore , in pDC constitutive expression of cGAS might also be associated with not yet defined functions other than virus sensing . In contrast , constitutive STING expression and the resulting sensitivity to cGAMP might be a mechanism of pDC to directly respond to viruses that carry cGAMP in their virions such as HIV-1 , MVA , and MCMV [68 , 69] . Although we did not detect cGAMP in HCMV preparations , we cannot exclude the presence of cGAMP levels in the virus at concentrations below the detection limit of the HPLC-MS/MS method we used , which still might suffice to further boost pDC stimulation . To address the role of the cGAS/STING axis in monocyte-derived cells we analyzed cGAS and STING expression in moDC and moMΦ . Interestingly , stimulation with recombinant IFN-α2b and HCMV increased cGAS mRNA expression to a similar extent , although upon HCMV infection IFN-I production of single myeloid cell subsets differed significantly . In contrast , treatment with recombinant IFN-α2b moderately induced STING expression in moDC and M-CSF MΦ , whereas HCMV infection did not induce STING , although under such conditions the cells produced IFN-I . As STING rapidly degrades upon activation [70] , it is possible that virus induced IFN-I responses compensated virus induced STING activation and degradation . Thus , these results were compatible with the conclusion that upon HCMV infection of monocyte-derived cells STING was activated , as similarly observed in a previous study with murine bone marrow-derived macrophages that also sensed HCMV in a STING-dependent manner [47] . Furthermore , the detection of cGAMP formation in HCMV stimulated monocyte-derived cells proved the activation of cGAS . Additionally , we confirmed that stimulation with DNA-encoded MVA , which was shown to induce cGAS-dependent responses in murine conventional DC [55] , also led to cGAMP formation in primary human monocyte-derived DC and MΦ . Because stimulation of monocyte-derived cells with the RNA-encoded VSV-M2 did not induce cGAMP responses and in vitro incubation of recombinant cGAS with HCMV DNA resulted in efficient cGAMP synthesis , we conclude that cGAS was activated by binding to the viral DNA genome . Previous studies showed that macrophages evolved a mechanism to degrade the capsid of HSV-1 to release viral DNA into the cytoplasm and that also HCMV DNA co-localized with the cellular protein IFI16 in the cytoplasm [48] . These data support the hypothesis that in HCMV infected human macrophages the viral DNA genome is released into the cytosol , where it activates cGAS . Additionally , HSV-1 was reported to induce cellular stress and subsequent release of mitochondrial DNA into the cytosol , which enhances cGAS activation [71] . Such a mechanism could similarly augment sensing of HCMV infections in monocyte-derived cells by cGAS . Furthermore , impaired IFR3 phosphorylation and IFN-β induction in cGAS or STING deficient monocytic THP-1 cells demonstrated that the cGAS/STING axis was required for efficient IFN-I expression upon HCMV infection . Interestingly , IFI16 deficiency did not inhibit HCMV induced IFN-I expression in THP-1 cells . This was in contrast to an earlier study , which showed reduced IFN-I responses in HCMV stimulated THP-1 cells upon shRNA mediated cGAS knock-down [48] . This discrepancy might be explained by the different methods used to ablate IFI16 , i . e . , CRISPR/Cas9 mediated complete IFI16 knock-out vs . shRNA mediated IFI16 knock-down . Additionally , in human fibroblasts cGAS knock-down was reported to increase IFI16 proteasomal degradation [54] . Nevertheless , in cGAS knock-out THP-1 cells we detected normal IFI16 levels by western blot analysis . Thus , in our experiments IFI16 did not compensate for cGAS deficiency upon HCMV stimulation . In previous studies , IFI16 was identified as an important immune sensor of HCMV in human fibroblasts , however , its activity is inhibited by the HCMV protein pUL83 ( also known as pp65 ) [37] . Since pUL83 is the most abundant tegument protein of HCMV with more than 2000 molecules being integrated into the mature virion [72] , inhibition of IFI16 may occur right after HCMV infection without the need of viral gene expression . Thus , IFI16 might be inhibited in HCMV infected macrophages and therefore is unable to compensate cGAS deficiency . To furthermore analyze the requirement of cGAS for IFN-I expression in primary human immune cells we applied a Viromer-based siRNA delivery approach that efficiently inhibited cGAS translation . We demonstrated that also in primary human monocyte-derived cells IFI16 was still present after siRNA mediated cGAS knock-down . HCMV stimulated monocyte-derived cells showed dramatically reduced IFN-α responses upon cGAS knock-down , confirming that IFI16 did not compensate cGAS deficiency and that cGAS was essential for efficient IFN-I expression in primary human monocyte-derived cells . Of note , HCMV stimulated moDC and moMΦ produced different levels of cGAMP , which correlated with the percentage of cells supporting HCMV gene expression . Thus , M-CSF MΦ that showed the highest percentage of HCMV infected cells , and also in previous studies have been shown to be particularly vulnerable to HCMV infection [45] , produced the highest amount of intracellular cGAMP . Therefore , we hypothesize that the susceptibility to HCMV infection determines the accessibility of the viral genome to cGAS , thus directly affecting the magnitude of cGAMP responses . However , intracellular cGAMP levels did not always correlate with the magnitude of the resulting IFN-α responses . moDC expressed similar amounts of cGAS compared with moMΦ ( as shown by western blot and qPCR ) , and produced higher or similar levels of cGAMP compared with GM-CSF MΦ ( as shown by mass spectrometry analysis ) upon HCMV or MVA infection; however , moDC produced significantly less IFN-I than moMΦ . Additionally , transfection of synthetic cGAMP into cGAS knock-down monocyte-derived cells induced less IFN-β in moDC than in moMΦ . Importantly , upon recognition of VSV , which is recognized in a cGAS-independent manner , moDC and moMΦ mounted similar IFN-I responses . These experiments suggested that in moDC cGAS-dependent IFN-I induction was limited downstream of cGAMP . The comparably low STING expression detected in moDC may account for this limitation of cGAMP-dependent IFN-I expression . In conclusion , here we report that human pDC as well as monocyte-derived cells abundantly express cGAS and STING . Although pDC carry a functional cGAS/STING axis down-stream of STING , they sense HCMV in a TLR9-dependent manner . However , monocyte-derived cells are triggered by HCMV in a cGAS-dependent manner to mount IFN-I responses . Interestingly , IFI16 cannot compensate cGAS deficiency . Because individuals devoid of MyD88 function do not suffer from enhanced incidence or severity of herpesvirus infections [31 , 32] , it is likely that in the pathogenesis of HCMV infected humans pDC-derived IFN-I do not play a critical role . This study provides evidence that MΦ , which are targets for HCMV infection in vivo [73] , mount high amounts of MyD88-independent IFN-I and thus may contribute to the protection of MyD88 deficient patients . The moderate STING levels in moDC that might limit IFN-I responses even in the presence of ample cGAMP concentrations imply that moDC are not main providers of MyD88-independent IFN-I responses . So far it remains unclear whether antigen-presenting cells establish cell-cell contact with surrounding tissue cells to transfer cGAMP and thus spread antiviral protection . To address this question the direct cGAMP detection method used in this study might become instrumental for the analysis of cGAMP levels in ex vivo isolated infected tissues .
HCMV-GFP was generated on the backbone of the endotheliotropic BAC-cloned TB40/E strain [74–76] . For preparation , the virus was first passaged on HUVEC cells ( ATCC: PCS-100-010 ) and then expanded on MRC-5 cells ( ATCC: CCL-171 ) . Viral titers were determined on MRC-5 cells as described previously [19] . VSV-M2 [77] and VSV-eGFP [78] were expanded on BHK-21 cells and titers were determined by plaque formation on Vero cells . MVA-mCherry [79] was propagated and titrated on chicken embryo fibroblasts . Original THP-1 cells and clones carrying a CRISPR/Cas9-mediated knock-out of cGAS or STING [59] as well as of IFI16 were cultured in RPMI1640 containing 10% FCS and 1% sodium pyruvate . Differentiation of THP-1 cells was performed by stimulation with 200 nM phorbol 12-myristate 13-acetate ( PMA ) for 3 days . One day prior to usage medium was exchanged and cells were cultivated in fresh medium . IFI16 knock-out THP-1 cells were generated as previously described [80] . An early coding exon of the IFI16 gene was targeted using the following sgRNA target site: 5′-CGGACACCTTACTCCCTTTG-3′ . The sgRNA construct was obtained from the sgRNAKOLIBRY library [81] . Following limiting dilution cloning , cell clones harboring all-allelic frame shift mutants were identified using Outknocker [82] . Primary human pDC and monocytes were isolated from buffy coats of healthy blood donors provided by the Blutbank Springe ( Germany ) using ficoll density gradient centrifugation and subsequent magnetic activated cell sorting ( Diamond Plasmacytoid Dendritic Cell Isolation Kit , CD14+ Cell Isolation Kit; Miltenyi Biotec ) . Following isolation , 2 x 105 pDC were cultivated for 1 h in 200 μl of 10 ng/ml interleukin 3 containing serum-free DC medium ( CellGenix ) and were then treated as indicated . moDC , GM-CSF MΦ , and M-CSF MΦ were differentiated from 5 x 105/500 μl monocytes for 5 days in serum-free DC medium enriched with 1000 U/ml GM-CSF ( granulocyte macrophage-colony stimulating factor , CellGenix ) and 1000 U/ml IL-4 ( CellGenix ) , or 80 U/ml GM-CSF , or 100 ng/ml M-CSF ( macrophage-colony stimulating factor , Miltenyi Biotec ) , respectively . Primary human cells were stimulated with MVA-mCherry and VSV-M2 at MOI 1 or HCMV-GFP at MOI 3 , except otherwise indicated . THP-1 cells were stimulated with MVA-mCherry and VSV-eGFP at MOI 1 . HCMV-GFP infection was performed at MOI 50 in undifferentiated THP-1 cells and at MOI 10 in PMA-matured THP-1 cells . HCMV-GFP infection was enhanced by centrifugation at 300 g for 30 min . Cells were stimulated with LPS ( 100 ng/ml , Sigma-Aldrich ) , IFN-γ ( 10 ng/ml , Preprotech ) , and poly ( I:C ) ( 10 μg/ml , InvivoGen ) as well as with recombinant IFN-α2b ( 1000 U/ml , IntronA , MSD Merck Sharp & Dohme AG ) . 18–24 hpi cell-free supernatant was harvested for ELISA analysis and cells were kept for mRNA , western blot , and flow cytometry analysis . For intracellular cytokine staining , cells were treated with Brefeldin A ( BD Bioscience ) 6 h prior to intracellular IFN-α staining . For transfection of cells with synthetic cGAMP ( InvivoGen ) or siRNA directed against cGAS ( SMARTpool: siGENOME MB21D1 , GE Healthcare ) , or control ( siGENOME Non-Targeting siRNA Pool #2 , GE Healthcare ) , the Viromer BLUE Kit ( Lipocalyx ) was used . Final concentrations of 43 nM siRNA were packed in 22 μM Viromer following the manufacturer´s instructions and incubated with the cells for 72 h starting on day 2 after monocyte isolation and differentiation . 48 h post transfection and 1 h prior to stimulation differentiation medium was exchanged . Transfection of 3 μg/ml synthetic cGAMP packed in 22 μM Viromer was performed similarly and cells were analyzed after 12 h by qPCR and supernatants were analyzed by ELISA after 24 h . For inhibition of TLR9 signaling , 7 x 104 pDC were incubated with IRS869 ( PTO , 5’-TGCTTGCAAGCTTGCAAGCA-3’ ) [61] for 1 h and subsequently stimulated with HCMV-GFP at MOI 3 or 5 μg/ml R848 ( InvivoGen ) . Monocyte-derived cells were lysed 24 hpi with HCMV-GFP by addition of 300 μl of a 2/2/1 [v/v/v] methanol , acetonitrile and water ( HPLC-grade , J . T . Baker ) mixture containing 25 ng/ml tenofovir ( obtained through the NIH AIDS Research and Reference Reagent Program ) as an internal standard . Wells were rinsed twice . After incubation for 15 min at 95°C , protein precipitation of lysates was performed at -20°C over night and protein-free lysates were obtained by collection of supernatants after 10 min 20 , 000 g centrifugation . Supernatants were vaporized ( Concentrator plus , Eppendorf ) and remaining pellets were dissolved in water for mass spectrometry analysis . An HPLC-system ( Nexera , Shimadzu ) , consisting of two HPLC pumps , a temperature controlled autosampler , a degasser , an oven , and a control unit was employed for reversed phase chromatographic separation of cGAMP ( 2´-5´/3´-5´ ) and cGAMP ( 3´-5´/3´-5´ ) calibrators or sample extracts . A Zorbax eclipse XCB-C18 1 . 8 μm column ( 50 x 4 . 6 mm ) kept at 25°C from Agilent was used , connected to a C18 security guard ( Phenomenex ) and a 2 μm column saver ( Supelco ) . The mobile phases were 3/97 methanol/water [v/v] ( A ) and 97/3 methanol/water [v/v] ( B ) , each containing 50 mM ammonium acetate and 0 . 1% acetic acid . The following gradient was applied: 0 to 5 min , 0 to 50% B and 5 to 8 min , 0% B . The flow rate was 400 μl/min . Detection and quantification of cGAMP ( 2´-5´/3´-5´ ) was carried out by a tandem mass spectrometer , 5500QTRAP ( AB Sciex ) , equipped with an electrospray ionization source , operating in positive ionization mode . For SRM detection , the following mass transitions were identified for cGAMP ( 2´-5´/3´-5´ ) : m/z 338 . 1 [M+2H]2+ → 152 . 0 [M+H]+ ( quantifier ) , m/z 338 . 1 [M+2H]2+ → 119 . 0 or 136 . 0 [M+H]+ ( identifier ) and for tenofovir: m/z 288 . 0 [M+H]+ → 176 . 0 [M+H]+ ( quantifier ) , m/z 288 . 0 [M+H]+ → 159 . 1 [M+H]+ ( identifier ) . Stock solution of cGAMP ( 2´-5´/3´-5´ ) ( obtained from Biolog ) was prepared in HPLC-grade water . Calibration curves were constructed using seven calibrators ranging from 0 . 64 to 10 , 000 nM . Tenofovir was applied as internal standard . Intracellular IFN-α staining was performed according to the intracellular staining protocol from BD Bioscience using anti-human IFN-α antibody APC ( Miltenyi Biotec ) . Surface marker staining with anti-CD14 V450 ( BD Bioscience ) , anti-CD163 PE , anti-CD206 PE-Cy7 , and anti-CD209 APC ( BioLegend ) was performed for 20 min at 4°C . Data were acquired on a LSRII flow cytometer ( BD Biosciences ) and analyzed with FlowJo software ( Tree Star ) . Cells were lysed in SDS sample buffer , denatured at 95°C for 10 min , sonicated for 10 min , and lysates were separated by 10% SDS-PAGE . After transfer to nitrocellulose membranes , membranes were blocked in 5% milk ( TBS , 0 . 1% Tween ) before incubation with anti-cGAS ( 1:1 , 000; Sigma or 1:1 , 000; Cell Signaling ) , anti-STING ( 1:5 , 000; R&D Systems or 1:1 , 000; Cell Signaling ) , anti-IFI16 ( 1:2 , 000; Santa Cruz ) , anti-P-IRF3 ( 1:2 , 000; Cell Signaling ) , or anti-IRF3 ( 1:2 , 000; Cell Signaling ) antibodies over night or anti-β-Actin-Peroxidase ( 1:50 , 000; Sigma-Aldrich ) antibody for 1 h . After 2 h of incubation with secondary goat anti-rabbit-HRP , and goat anti-mouse-HRP ( KPL ) , or goat anti-mouse IgG1-HRP ( Southern Biotech ) membranes were developed with Amersham ECL Western Blotting Detection Reagent ( GE Healthcare ) . For detection of P-IRF3 SuperSignal West Femto Maximum Sensitivity Substrate ( Thermo Scientific ) was mixed 1:10 with Amersham ECL Western Blotting Detection Reagent . Cell-free supernatants were analyzed by using Human IFN-alpha Platinum ELISA ( eBioscience ) , human IFN Beta ELISA kit ( PBL ) , and human IL-12 , IL-10 and TNF-α ELISA ( BioLegend ) according to the manufacturer’s instructions . RNA extraction ( Macherey-Nagel ) and cDNA synthesis ( Takara ) were performed according to the manufacturer’s instructions . 5 ng of cDNA were analyzed by quantitative PCR using SensiFAST SYBR no-ROX Kit ( Bioline ) in a LightCycler 480 ( Roche ) . All data are presented as relative expression to hypoxanthine phosphoribosyl transferase 1 ( HPRT1 ) mRNA . The corresponding primers were: HPRT1 forward , 5’-GAACGTCTTGCTCGAGATGTG-3’ HPRT1 reverse , 5’-CCAGCAGGTCAGCAAAGAATT-3’ IFN-α forward , 5’-CGATGGCCTCGCCCTTTGCTTTA-3’ IFN-α reverse , 5’-GGGTCTCAGGGAGATCACAGCCC-3’ IFN-β forward , 5’-TGTGGCAATTGAATGGGAGGCTTGA-3’ IFN-β reverse , 5’-TCAATGCGGCGTCCTCCTTCTG-3’ cGAS forward , 5’-CCCAAGCATGCAAAGGAAGG-3’ cGAS reverse , 5’-ACAATCTTTCCTGCAACATTTCT-3’ STING forward , 5’-CACCTGTGTCCTGGAGTACG-3’ STING reverse , 5’-CATCTGCAGGTTCCTGGTAGG-3’ MxA forward , 5’-ACAGGACCATCGGAATCTTG-3’ MxA reverse , 5’-CCCTTCTTCAGGTGGAACAC-3’ Stimulation of 0 . 1 μM recombinant human cGAS ( residues 155–522 ) [58] was performed for 2 h at 37°C in the presence or absence of 1 mM ATP and 1 mM GTP in 40 mM TRIS pH 7 . 5 , 100 mM NaCl und 10 mM MgCl2 . To activate cGAS , 6 μM of a control 50 bp dsDNA ( 5’-GGATACGTAACAACGCTTATGCATCGCCGCCGCTACATCCCTGAGCTGAC-3’ , Eurofins Genomics ) was used . Furthermore , purified HCMV containing 6 . 5 x 106 infectious particles was used with and without DNA digestion by 1 μl Benzonase Nuclease ( Merck Millipore ) in the presence of 1 mM MgCl2 for 30 min at 37°C prior and post heat treatment for 10 min at 95°C . To isolate genomic HCMV DNA , purified HCMV was digested with Benzonase Nuclease for 30 min at 37°C to remove residual DNA that was not packaged into viral particles . Subsequently , digestion by Benzonase Nuclease was inhibited by treatment with 10 mM EDTA and genomic HCMV DNA was isolated using the DNeasy Blood & Tissue Kit ( Qiagen ) . Quantification of isolated HCMV genomes was performed using qPCR analysis in parallel with defined amounts of HCMV BAC DNA ( qPCR primers: HCMV forward: GGGTTCTCGTTGCAATCCTC; HCMV reverse: GGAAGGAGGTTAACAGTCAGC ) . Since this method might also detect fragments of genomic HCMV DNA that contain the amplified region , the calculated amount of HCMV genomes might be slightly overestimated . 10 pM isolated genomic HCMV DNA was used in the in vitro stimulation assay of recombinant human cGAS , except otherwise indicated . In vitro cGAMP synthesis was stopped by the addition of 300 μl of a 2/2/1 [v/v/v] methanol , acetonitrile and water mixture containing 25 ng/ml tenofovir as an internal standard . Subsequently , samples were prepared as described above to determine cGAMP concentrations by mass spectrometry analysis . Data were statistically analyzed using the software package GraphPad Prism Version 5 . 0 . Comparisons between monocyte-derived cells as well as stimulation induced responses were analyzed by non-parametric paired Wilcoxon signed rank test . Comparison of pDC with monocyte-derived cells was analyzed by non-parametric unpaired Mann-Whitney test . Human cGAS: UniProt Q8N884; human STING: UniProt Q86WV6; human IFI16: UniProt Q16666; human TLR9: UniProt Q9NR96; human IRF3: UniProt Q14653; | Human cytomegalovirus ( HCMV ) has been shown to induce type I interferon ( IFN-I ) responses in myeloid cells such as plasmacytoid dendritic cells ( pDC ) . Although these cells were reported to sense the viral DNA genome in a Toll-like receptor ( TLR ) -dependent manner , previous studies showed that individuals displaying a hypo-functional TLR axis do not show increased incidence of HCMV infection . This implies that in addition to TLR other sensing mechanisms played a role . Recently cytosolic cyclic GMP/AMP synthase ( cGAS ) was reported to sense cytosolic DNA and subsequently to induce IFN-I via the production of the cyclic di-nucleotide cGAMP , which activates the stimulator of interferon genes ( STING ) . However , the role of cGAS in recognition of HCMV by human immune cells has not been addressed , yet . In this study we found that pDC as well as monocyte-derived dendritic cells ( DC ) and macrophages express cGAS and STING . Although pDC expressed particularly high levels of cGAS , they sensed HCMV via TLR9 . In contrast , monocyte-derived DC and macrophages sensed the virus in a cGAS-dependent manner . Thus , different innate immune cell subsets deploy different recognition platforms for the detection of the same pathogen . Such information will help to better understand the complex host pathogen balance in humans . | [
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] | 2016 | cGAS Senses Human Cytomegalovirus and Induces Type I Interferon Responses in Human Monocyte-Derived Cells |
In 1997 , the World Health Assembly adopted Resolution 50 . 29 , committing to the elimination of lymphatic filariasis ( LF ) as a public health problem , subsequently targeted for 2020 . The initial estimates were that 1 . 2 billion people were at-risk for LF infection globally . Now , 13 years after the Global Programme to Eliminate Lymphatic Filariasis ( GPELF ) began implementing mass drug administration ( MDA ) against LF in 2000—during which over 4 . 4 billion treatments have been distributed in 56 endemic countries—it is most appropriate to estimate the impact that the MDA has had on reducing the population at risk of LF . To assess GPELF progress in reducing the population at-risk for LF , we developed a model based on defining reductions in risk of infection among cohorts of treated populations following each round of MDA . The model estimates that the number of people currently at risk of infection decreased by 46% to 789 million through 2012 . Important progress has been made in the global efforts to eliminate LF , but significant scale-up is required over the next 8 years to reach the 2020 elimination goal .
Lymphatic filariasis ( LF ) is a Neglected Tropical Disease ( NTD ) endemic in 73 tropical and sub-tropical countries [1] . It is caused by three species of filarial worms – Wuchereria bancrofti , Brugia malayi and Brugia timori – and is transmitted by multiple species of mosquitoes . The disease manifests as a spectrum of clinical conditions , the most prominent being hydrocele , chronic lymphoedema/elephantiasis of legs and arms , and sub-clinical lymphatic damage , even in young children . Affected individuals suffer from disability , stigma and associated social and economic hardship . Marginalized populations , particularly those living in areas with inadequate sanitation and sub-standard housing conditions are particularly vulnerable and thus most likely to be affected by the disease . According to the initial estimates compiled in 1996 , 1 . 2 billion people were living in areas where they were ‘at-risk’ of acquiring LF and 120 million people were infected , 40 million of whom experienced one or more chronic disease manifestations [2] . In 1997 , the World Health Assembly , through resolution 50 . 29 ( WHA 50 . 29 ) , urged member states and the WHO to capitalize on both new advances in the understanding of lymphatic filariasis and the opportunities for its elimination by developing national plans of action that would lead to the eventual elimination of the disease as a public health problem . In principal , the elimination strategy is relatively straightforward , involving a pre-implementation phase of epidemiological assessment ( ‘mapping’ ) followed by a minimum of five annual cycles of once-yearly mass preventive chemotherapy ( mass drug administration [MDA] ) to all eligible populations residing in geographic zones determined to be endemic . Each cycle of preventive chemotherapy employs a ‘single-dose’ co-administration of two anthelmintics that have impact on adult parasites but are especially effective at reducing microfilariae , the transmission stage of filarial parasites circulating in the blood , to very low levels for periods up to 12 months or more , thereby inhibiting transmission of the microfilaria to mosquitoes . By depleting the reservoir of infectious stages of the parasite across broad geographic areas for five years or more , the transmission cycle of the parasite is expected to be interrupted for long enough to significantly influence parasite population dynamics to the point where elimination is possible [3] . Utilizing a rapid diagnostic test that permits detection of adult filarial worm antigen in daytime blood , national programs first determined the geographic distribution of infection and then embarked on LF elimination [3] . Through early epidemiological assessments and modeling , the initial size of the global population requiring intervention was estimated; the 2013 revised estimate by WHO for the total population living in areas where filariasis had been endemic when the GPELF began in 2000 ( i . e . , were ‘at risk’ of infection and , thus , required preventive chemotherapy for lymphatic filariasis ) was 1 . 4 billion [1] , [2] . Since the beginning of the program in 2000 through the end of 2012 , however , 4 . 4 billion doses of anthelmintics had been administered to populations in 56 of the endemic countries , so it is clear that the number of at-risk individuals will have changed [1] . Indeed , the following analysis was conducted to estimate the impact that this global campaign ( the Global Programme to Eliminate Lymphatic Filariasis [GPELF] ) , after its first 13 years , has had on the number of people living at risk of LF infection .
Baseline at-risk population data in each country were acquired from the publically available Preventive Chemotherapy ( PCT ) Databank maintained by the NTD unit at WHO in Geneva [4] . The WHO compiles information submitted by endemic countries through their annual program reporting schedule . Early epidemiological assessments were based on determination of microfilariae circulating in the blood , a procedure requiring blood collection at night when the microfilariae are found in the blood but a challenging activity both for field teams and communities alike . The development and adoption of new diagnostics changed the program , such that now national estimates of the population requiring preventive chemotherapy are derived mostly from assessment of parasite-specific antigen prevalence in areas where Wuchereria bancrofti is the predominant species , or parasite-specific IgG4 antibody prevalence in areas where Brugia malayi or Brugia timori predominate . Following earlier experiences from the highly successful LF elimination program in China , mapping in the GPELF was designed to determine whether the population of the ‘Implementation Units’ ( IUs – usually health districts ) in countries should be considered to be ‘at risk’ of acquiring LF ( and , therefore requiring preventive chemotherapy ) based on whether there were areas within the IUs where at least 1% of the surveyed population were infected [5] , [6] . Different sampling strategies were utilized in different countries , but the total population of the IUs where there were areas of LF prevalence >1% was considered to be ‘at-risk’ and require MDA [6] . The recommended WHO strategy to eliminate LF is based on MDA to remove microfilariae from the blood , preventing parasite transmission to mosquitoes . Though programmatic evidence suggests that effective transmission of LF might cease very soon after the initiation of MDA [7]–[9] , entomologic studies linked with anti-filarial single dose treatment regimens suggest that the decline in vector infection is more gradual [7] , [8] , [10]–[14] . Our analysis is modeled off this entomologic information coupled with the assumption outlined previously that decreased vector infection leads to a proportionately decreased risk of infection to the endemic population [15] , [16] . Thus , the progressive influence of MDA can be estimated by using the progressive decrease in vector infection rates as an indicator for decreased transmission , and , therefore , reduced population at risk of LF . As populations are treated , their risk of infection diminishes progressively after each MDA . Specifically , the available empiric evidence yielded a relationship that describes an ‘average’ rate-of-decline of vector infection as 50% , 25% , 12% , 6% and 0% of pre-treatment levels following each of the first 5 rounds of yearly MDA , a relationship described and utilized in previous studies [15] , [16] . The population remaining at-risk of infection following a series of MDAs can be modeled for each LF-endemic country using the following general algorithm:where: A = Population still at-risk of infection B = Initial population at risk at baseline Tn = Population treated at nth round of MDA tmax = Maximum population treated in any round prior to tn n = Total number of MDAs Since data at the implementation Unit ( IU ) level is not available in the PCT Databank for each country , the model builds ‘bottom-up’ from the sub-national to the national level , predicated on the number of new persons treated each year ( i . e . , the treatment cohorts ) and followed over time . This approach permits the model to handle staggered MDA rounds on a sub-national level by assigning each cohort its appropriate rate of ‘at-risk’ decline based on the specific number of MDAs it has experienced , rather than applying an average round of treatments for the whole country ( see Fig . 1 ) . The model comprises three temporal components: Meanwhile , the same IU or additional IUs may be scaling up treatment in subsequent years . Again , the model determines only the new population treated in each MDA as [tn-tmax] where tn is the total population treated in the following year and tmax is the maximum total population treated in any previous year ( tmax ≠ 0 after the first year ) . Each group of [tn-tmax] can now be clearly seen in the model as a mutually exclusive cohort ( summed to the national level ) based on the number of MDA treatments it has received . This avoids double counting of populations while enforcing proper assignment of infection reduction rates to each treated cohort; the newest ones are closer to the left side of the equation and receive the smallest percent reduction . The model also accounts both for years without new MDA cohorts and for MDAs skipped altogether ( see Assumptions 3–5 below ) . Several key assumptions were made in the formulation of this model:
Aggregating the country populations requiring MDA for LF upon completion of mapping ( as described above ) yields a global total baseline of approximately 1 . 46 billion people at risk of infection . Applying the model and its assumptions to data from each individual country ( schematized in Figure 1 ) yielded the progressive decline in the at-risk population seen in Figure 2 . After 13 years of LF MDA , the global total of 1 . 46 billion people requiring treatment is estimated to have decreased by 46% to 789 million through 2012 .
During its first 13 years , WHO's Global Programme to Eliminate Lymphatic Filariasis achieved enormous progress by distributing 4 . 4 billion treatments in 56 countries and achieving ( from our calculations ) an estimated 46% reduction in the population at risk of LF from 1 . 46 billion to 789 million people . By the end of 2012 , 13 countries had entered the post-MDA surveillance phase . The available data show that all regions have made substantial progress towards achieving the elimination target of 2020 , and for most regions the preponderance of what is left to be achieved is restricted to a few countries with large populations . Overall , these impressive accomplishments were possible only because of strong global partnerships and the commitment of national governments in collaboration with pharmaceutical company partners donating billions of tablets of medicine ( Mectizan by Merck & Co . , Inc . ; albendazole by GlaxoSmithKline; diethylcarbamazine [DEC] by Eisai ) , bilateral and other donors , non-governmental organizations and researchers , all with guidance and leadership from the World Health Organization . Estimating the number of people freed from the risk of infection is important for all of these key constituencies in order to define the progress being made , while also calling attention to the need to speed the expansion of intervention efforts to ensure that the global elimination targets can be reached . The accuracy of the estimates from the present model , however , depends on the appropriateness of its underlying assumptions . There is potential both for overestimating the effect of the GPELF in decreasing the number of people at-risk of LF infection and for underestimating it . A major limitation of this model is its total reliance on the WHO PCT Databank for information on the numbers of people treated each year in the Global Programme . The data is self-reported by national programs , and while in most situations where it has been examined the reported coverage and the independently-surveyed coverage have been similar , there are areas where frequent over-reporting has been identified ( reviewed in [18] ) . Such over-reported coverage would lead the model to overestimate the reduction in at-risk population by the GPELF . On the other hand , for the program to be effective , LF MDA guidelines recommend treating at least 65% of the total population in each targeted implementation unit , recognizing that the untreated percentage of the population still garners some level of protection from the treatments in their communities [19] . Since our current model is based on actual treatments distributed , its estimates do not include this additional ‘herd protection’ in populations covered by MDA and , thus could be underestimating overall Programme effects in decreasing the number of at-risk individuals . Also leading the model potentially to underestimate the decrease in numbers of at-risk individuals is the technical convention that the model uses in not ‘zeroing out’ an at-risk population until all districts in the country have programmatically passed a Transmission Assessment Survey ( TAS ) or equivalent . The remaining population at-risk in a country with more than 5 rounds of MDA is likely , therefore , to be overestimated by the model , and the model will show sharp drops in the estimated at-risk population over the next several years as more countries implement the TAS , stop mass treatment , and enter a post-treatment surveillance phase . The model used in this study also has important additional limitations related to its initial assumptions . Despite these modeling constraints , assessing progress of the Global Programme is essential both for demonstrating the successes already achieved and for identifying the challenges remaining . Viewed from the perspective of understanding the population at risk of LF globally , this model has estimated a 46% decrease in those still at risk of acquiring LF infection after 13 years of the GPELF . Clearly , this implies much progress to be celebrated , especially since , for the reasons described above , the ‘true’ reduction in at-risk individuals is likely to be even greater . It is also clear , however , that much more needs to be done – particularly in two important domains . The first is in optimizing MDA drug uptake rates ( ‘coverage’ ) in programs already underway , and the second is in extending current programs to those endemic countries ( or regions within countries ) not already engaged in LF elimination . Where programs have been established , results have been remarkable , but it will be necessary now to maximize program coverage , both programmatically and geographically , in order to meet the global goal of achieving elimination of LF as a public health problem by 2020 . | Lymphatic filariasis ( LF ) is a widespread neglected tropical disease most frequently recognized as elephantiasis that is caused by parasitic worms and spread by mosquitoes . To overcome this public health problem , the World Health Organization created the Global Programme to Eliminate Lymphatic Filariasis ( GPELF ) in 2000 , subsequently committing to the elimination of LF as a public health problem by the year 2020 . Between 2000 and 2012 , GPELF provided over 4 . 4 billion treatments in 56 endemic countries . In order to assess the progress during the first 13 years of GPELF in reducing the at-risk population ( initially 1 . 2 billion ) requiring treatment for LF , we developed a model that indicates that the number of people remaining at-risk of infection has decreased by nearly half through 2012 to 789 million people globally . This is important progress in the global efforts to eliminate LF , but significant scale-up is still required over the next 8 years to achieve the 2020 elimination goal . | [
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] | 2014 | Assessing Progress in Reducing the At-Risk Population after 13 Years of the Global Programme to Eliminate Lymphatic Filariasis |
Organism cells proliferate and die to build , maintain , renew and repair it . The cellular history of an organism up to any point in time can be captured by a cell lineage tree in which vertices represent all organism cells , past and present , and directed edges represent progeny relations among them . The root represents the fertilized egg , and the leaves represent extant and dead cells . Somatic mutations accumulated during cell division endow each organism cell with a genomic signature that is unique with a very high probability . Distances between such genomic signatures can be used to reconstruct an organism's cell lineage tree . Cell populations possess unique features that are absent or rare in organism populations ( e . g . , the presence of stem cells and a small number of generations since the zygote ) and do not undergo sexual reproduction , hence the reconstruction of cell lineage trees calls for careful examination and adaptation of the standard tools of population genetics . Our lab developed a method for reconstructing cell lineage trees by examining only mutations in highly variable microsatellite loci ( MS , also called short tandem repeats , STR ) . In this study we use experimental data on somatic mutations in MS of individual cells in human and mice in order to validate and quantify the utility of known lineage tree reconstruction algorithms in this context . We employed extensive measurements of somatic mutations in individual cells which were isolated from healthy and diseased tissues of mice and humans . The validation was done by analyzing the ability to infer known and clear biological scenarios . In general , we found that if the biological scenario is simple , almost all algorithms tested can infer it . Another somewhat surprising conclusion is that the best algorithm among those tested is Neighbor Joining where the distance measure used is normalized absolute distance . We include our full dataset in Tables S1 , S2 , S3 , S4 , S5 to enable further analysis of this data by others .
A multi-cellular organism develops from a single cell – the zygote , through cell division and cell death , and displays an astonishing complexity of trillions of cells of different types , residing in different tissues and expressing different genes . The development of an organism from a single cell until any moment in time can be captured by a mathematical entity called a cell lineage tree [1]–[4] . Uncovering the human or even the mouse cell lineage tree may help to resolve many open fundamental questions in biology and medicine , as illustrated by our earlier work [5]–[9] . In the past few years , our lab developed a method for reconstructing the lineage relations among cells of multi-cellular organisms 1 , 10 and applied it to various questions of biological and medical importance [5]–[9] . The method is based on the fact that cells accumulate mutations during mitosis in a way that , with a high probability , endow each cell with a unique genomic signature , and distances between genomic signatures of different cells can be used , in principle , to reconstruct the organism's cell lineage tree [1] . Instead of examining the whole genome of all cells of an organism , which is currently not feasible , our method uses Microsatellite ( MS ) loci which are repeated DNA sequences of 1–6 base pairs . Slippage mutations , in which repeated units are inserted or deleted , occur at relatively high rates ( 10−5 per locus per cell division in both wild type mice and humans [1] , [11] ) , and thus provide high variation . These mutations are phenotypically neutral [11]–[13] and they are highly abundant in the genome ( composing 3% of the genome ) . Importantly , Mismatch-Repair ( MMR ) deficient mice display an even higher mutation rate ( 10−2 per locus per cell division [14] ) in MS and are available for experimentation and analysis [5]–[8] , [10] , [15] , [16] . By comparison , SNPs have a mutation rate of the order 10−8 per site per generation [17] , and thus about 10−10 per site per cell division . Besides the use of MS to reconstruct cell lineage trees which was proposed also by others [3] , [4] , [18]–[20] , several other retrospective methods to trace cell lineages in mammalians have been proposed . These methods include the genomic profiling of single nucleotide polymorphism ( SNP ) [21]–[23] , copy number variations ( CNV ) [24] and DNA methylation [25]–[27] . A common feature of all these methods is the use of a genomic property that accumulates mutations during cell divisions , thus making it suitable to be used as a genomic signature . While phylogenetic lineage tree reconstruction of cells is similar to that of organisms and species , it also has unique characteristics such as the existence of stem cells ( that influence the shape of the tree ) , a sometimes very shallow tree ( in the order of dozens of generations ) , a dramatic variation in the number of divisions the cells have undergone since the zygote ( which is much larger than what exists in species with different evolutionary paces ) , and the fact that the cells have undergone binary cell divisions . Besides the last feature , which has been widely investigated in population genetics [28] , these unique characteristics as well as the uncertainties about the exact nature of the mutational process in somatic cells require an assessment of the accuracy of known lineage reconstruction algorithms for this application . Another meaningful difference is that MS are usually used to define relationships between groups ( species or populations ) [29] , and not between individuals . Thus the mathematical measures defining the distance are different . The goal of this work is to test the existing algorithms on experimental cell data and to validate their use . In addition we want to test which of these methods ( even though not developed for the purpose of cell lineage reconstruction ) performs best . In order to accomplish this goal , we took experimental data from clearly known biological expectations and examined whether the reconstructed trees present this knowledge . The data was obtained by isolating cells from different mice and humans , and extracting their genomic signatures ( see Materials and Methods ) . Due to the fact that the real cell lineage tree is not known , we examined two aspects of the estimated tree . One aspect is the clustering of biologically distinct cell groups on the tree , and the second is the ability to distinguish between two groups of cells that are known to have different depths ( number of divisions the cell has undergone since the zygote ) .
As mentioned earlier the goal of this work is to validate and quantify the ability to reconstruct a cell lineage tree utilizing genomic signatures of individual cells that record mutations in microsatellites . Since precise inference of tree topology cannot be accomplished using our current limited number of loci ( See Table S6 ) , we examined in this work whether certain aspects of the inferred tree reflect known biological scenarios . The first is the clustering of different cell groups . The basic assumption of this test is that if a statistically significant clustering on the cell lineage tree is consistent with a biological characteristic , then such clustering is very likely to reflect a real biological phenomenon , and therefore the more significant the clustering found by an algorithm , the better the algorithm . The simplest possible grouping of cells can be according to which individual they belong . Investigating the lineage relations among cells of different individuals is normally not done , however it is useful as a benchmark to test the validity of cell lineage reconstruction algorithms , as cells from different individuals clearly should be clustered separately . The second aspect we examined is the depth separation between different types of cells that are known to have different depths . The tree reconstruction algorithms that we used are Neighbor Joining ( NJ ) [31] , UPGMA [32] , and a quartet-based method as implemented in the QMC tool [33] . The distance measures that we used are two versions of the Absolute genetic distance ( regular and normalized ) , Euclidian distance , Equal or Not distance , and six versions of likelihood distances – assuming equal mutation rates for all loci , assuming two different mutation rates for mono-nucleotide and di-nucleotide repeats , and assuming length dependent mutation rates . These three mutation models were tested on both the Stepwise Mutation Model – SMM , and the Multistep Mutation Model – MMM ( for more details regarding the reconstructing methods see Materials and Methods ) . In addition to distance-based algorithms , Bayesian methods can also be used to infer the cell lineage tree . Even though these Bayesian methods hold great promise , there are currently only a very limited amount of existing tools that can be used to analyze MS . In addition most of the existing tools ( such as MrBayes [34] , [35] , Migrate [36] and Beast [37] ) assume no linkage between the different loci . However , in the cell-lineage tree , the range of the linkage disequilibrium is infinite ( i . e . the whole chromosome is fully linked , as the mitotic recombination rate is very small compared to the MS mutation rate multiplied by the chromosome length and the depth of the trees ) . Moreover , in the special case of cells inside multicellular organisms , since each cell has only one single parent cell from which all its chromosomes derive , all the MS loci share the same history , and hence all the loci are fully linked ( including loci that are on different chromosomes ) . The only ML tool that we found to be applicable to our case is BATWING [38] which reads in multi-locus haplotype data , a model and prior distribution specifications . It may be worthwhile to test the performance of the other Likelihood\Bayesian algorithms , even though they assume that loci are not fully linked; however , these algorithms are highly computationally intensive , and therefore we could not test these . In this section we checked the clustering quality of the tree reconstruction methods . An example for a case where cells from different individuals are clustered distinctively on the tree can be seen in Figure 1 . We used all cell types ( see Table S1 for the list of cell types of each individual ) from three mice ( Figure 1A ) and from seven humans ( Figure 1B ) , with the Normalized Absolute genetic distance and the Neighbor Joining tree reconstruction algorithm . It can be seen that the cells of each individual are clustered separately on the tree . However , due to the many types of noise existing in the system , such a distinct clustering is not likely to happen in all cases , especially if the individuals are related to each other ( as in the case of some of the experimental mice ) , and their zygotes are genetically close . In such cases , due to the small panel of MS used , cells from different individuals can randomly accumulate mutations that reduce the genetic distance between them , and may become closer to each other than to other cells from the same individual . This effect depends on the ratio between the genetic distance between the zygotes of the mice and the number of divisions the cells in each mouse underwent . We showed via computer simulation ( Text S1 and Figures S1 , S2 , S3 ) that when this ratio is small it is very hard to distinguish which cells belong to which mouse . In addition , we showed that as the number of loci grows , the separation between the mice improves . Our panel contains ∼120 loci but we prefer to ignore loci where there are allelic dropouts- i . e . where there is amplification failure of one of the two heterozygous alleles while the other allele successfully amplifies , which may often be misinterpreted and lead to errors in allele size determination . We thus used an average of about 80 loci per cell . In addition , those 80 loci can be different between distinct cells , so the actual number of loci used for the distance calculation can be even smaller ( with a minimum limit of 25 ) . We showed that when the ratio is 0 . 2 and the mutation rate is 1/100 , a separation of 90% can be achieved using at least 200 loci . An example of such a case is shown in Figure 2 where we present the tree of five mice with all their cells . It can be seen that three of these mice ( M1 , M2 and M3 ) are separated quite well in the lineage tree , compared to the cells of the other two mice ( M7 and M8 ) which are strongly mixed . It may be due to their possible family relations; however since we do not know the real relations but rather estimation , other types of noise can mix the cells , such as errors in the amplification of the genetic sequence or in the PCR reaction . Nevertheless , not all the algorithms and distance measures suffer from this problem to the same degree . This may be due to the fact that some measures describe the mutational process more accurately , or due to some other robustness feature of the algorithm . An example for a different performance between different metrics is shown in Figure 3 where we applied two methods to the same dataset . It can be seen that the NJ- Normalized Absolute ( Figure 3A ) produced better cluster separation than the NJ- Equal or Not method ( Figure 3B ) . The difference between methods in the clustering separation of cell groups necessitates the quantification of their performance , in order to determine which method is the best ( if any ) . In order to do so , we used three measures to quantify the clustering quality of distinct groups: the Quality of the Largest Cluster ( QLC ) from each group , the Tree Entropy ( TE ) , and the probability of getting such a cluster under the assumption of hyper geometric sampling ( HS ) . ( A detailed explanation of the measures is given in the Materials and Methods ) . We analyzed the performance of the methods on all the information that we have available: cells from nine mice and seven humans , each containing a few types of cells ( Table S1 ) . We combined two or more individuals into one larger dataset , using all their cell types or a single type . For each dataset , we reconstructed the cell lineage tree using all the methods we have . Then we quantified the separation performance of each method with the measures listed above , and determined the method with the best performance . In the following section , we present the results of this test and its variants . As mentioned before , clustering is just one example of a feature of the tree in which we are interested . Another feature is the depth of specific types of cells . Different depths can indicate different biological scenarios , for example whether some types of cells divide only during the embryonic stage or also in the adult stage . In this section our goal is to quantify the performance of the different methods in identifying depth differences between groups of cells . In order to obtain cases where the depth separation between the cell groups is known , we used the same type of cells from individuals with a substantial gap between their ages . The list of cell types of each individual is given in Table S1 . The tree-reconstruction algorithm that was applied in this case is the NJ algorithm , since it is the only algorithm that allows different depths for different cells inside the same individual . Two examples of trees with depth difference are presented in Figure 6 , one with a good depth separation ( Figure 6B ) and one with a poor separation ( Figure 6A ) . The depth of each group of cells as reconstructed by the NJ varies even if all the cells of this group actually have exactly the same number of divisions . Therefore for each group of cells , the depth is described by a distribution rather than by a single number . In order to quantify the performance of a method in separating between the two groups , quantities that differentiate between distributions are needed . The most natural choice is the Kolmogorov-Smirnov ( KS ) test , which measures the similarities between two datasets . However , this test has some disadvantages for our purposes; the most significant one is the ability to determine that two datasets are different even if they have exactly the same average depth , in cases where their standard deviations are substantially different . Therefore in addition to the KS we added two other measures that focus on the separation between the two distributions . The first is the normalized distance between the mean of the two groups , and the second is the overlap percentage between the two distributions ( see Materials and Methods for more details ) . The difference between them is that the overlap percentage is affected by the behavior of the extreme cells , while the normalized average distance captures the behavior of the bulk . Another minor difference is that the average normalized distance can distinguish between the separation qualities of methods even in the case of fully separated groups . A summary of the depth separation tests' results is presented in Figure 7 and Figure S13 ( full results are given in Table S5 . 4 ) . In this case there is no one method which is superior over the others , but a few which are rather equally good: Normalized Absolute , Euclidean and SMM with length dependent mutational rates . This implies that for the depth separation there is no one tree which can be considered the correct one . The various inferred trees should be seen as approximate projections of the real tree , which cannot be inferred precisely as of yet , since the genetic identifier is not sufficiently informative . These results were validated by simulations in which lineage trees with different cell depths were reconstructed and the differences were evaluated using the depth measures described in Materials and Methods . In each iteration , two lineage trees were simulated and the difference between the depths was calculated , where we distinguished between cases in which the trees were relatively shallow , and cases in which the trees were relatively deep . The simulations show that when the trees are shallow , when using 100 loci , there is no one method which is uniformly better than the others , in accordance with our result obtained with real data . When using 50 loci , the Normalized-absolute has the worst performance , while with 500 loci; the Normalized-absolute performs best . When the trees are deep , with 500 loci there is no one method that is better than the others , whereas when using fewer loci , the Normalized-absolute has the worst performance ( results are shown in Table S8 and in Figure S14 ) . Bootstrap analysis was used in order to evaluate the robustness and reproducibility of the estimated trees , the clustering of the tree and the depth separation according to cell type . We performed this analysis on several mice and human datasets which showed a good clustering or depth separation using the NJ-Normalized Absolute method . The bootstrap values were obtained by generating 100 trees using MS values extracted from sampling with replacement of the loci from each dataset . The bootstrap showed that the robustness of any particular branch in the tree is low , but the robustness of the clustering results and depth separation according to cell type is high ( see Table S9 for all the results ) .
In the preliminary stages of the cell lineage research conducted by our lab , small-scale investigations of the ability to reconstruct cell lineage trees were done . The large amount of information that was gathered during the last few years enabled us to conduct this investigation in a much more comprehensive way . The main outcome of this research is that even though currently only a limited amount of microsatellite loci are available , preventing the reconstruction of the accurate cell-lineage tree , many biological conclusions can still be confidentially drawn . By this we mean that apart from specific noisy cases , almost always we are able to identify the correct biological scenario from the reconstructed cell lineage if the proper tree reconstruction algorithm and distance measure are used . Among the NJ methods , we have found that the NJ- Normalized Absolute method outperforms the other methods at inferring the clustering of distinct groups . Interestingly , this shows that clear cluster- separation is not necessarily correlated with the most precise description of the mutational process . A result with a similar spirit was obtained previously [29] for using MS allele frequency in order to infer the phylogenetic tree of different species/populations . They also found that the best method is not necessarily the one that described the mutational process in the most accurate way . However , such a measure cannot describe accurately cell depth because depth information is eliminated in the normalization procedure . It is not unreasonable to assume that a Likelihood ( or a Bayesian ) method tailored towards cell lineage analysis that will make use of all the cells' information , without summarizing it into distance measure , will enable one to infer simultaneously both the topology and the depth in an accurate way . We hope to follow such a path in the future . We expect that in the coming years next generation sequencing methods will provide us with a much richer genetic signature , and thus improve our ability to infer the cell lineage tree more accurately . This in turn will enable relying on even fine details of the cell lineage tree and not only its rough features .
All animal husbandry and euthanasia procedures were performed in accordance with the Institutional Animal Care and Use Committee ( IACUC ) of the Weizmann Institute of Science . All human patients signed an informed consent; the study has received Helsinki authorization and was approved by the Rambam Hospital IRB committee and by the Bioethics Committee of the Weizmann Institute of Science . Our aim is to quantify the performance of different tree reconstruction methods in inferring clustering and depth separation . Most of the methods we tested are distance-based algorithms which use a distance measure between the cells to iteratively join close samples together , such as the Neighbor-Joining algorithm ( the full list of methods and distance measures is given below ) . We tested each method on two features: The first distance based algorithm we used is UPGMA ( Unweighted Pair Group Method with Arithmetic Mean algorithm ) [32] which assumes that all lineages evolve at the same rate . This assumption limits us to reconstructing trees which contain cells that underwent the same ( or very similar ) number of divisions . The second algorithm we used is NJ ( Neighbor-Joining ) [31] . In order to fit the algorithm to our problem , we corrected the branch lengths such that they will not be negative . When negative branches appear during the running of the algorithm , we set its length to 0 , adjusting its sibling branch accordingly [40] . Note that this correction does not change the inference of the topology , since it depends only on the distance matrix , and is not affected by the branch lengths of the inferred tree . With the NJ algorithm , a rooted tree can be created by using an out-group , and the root can then effectively be placed on the point in the tree where the edges from the out-group connect . The root in our trees is usually a mix of a wide variety of cell types ( a description of the root's determination is given below , in the Data description section ) . The third algorithm we used is QMC ( Quartet MaxCut ) [33] , [41] . Quartets-based methods were initially proposed to provide an alternative to maximum likelihood methods , which are computationally intensive . These methods first estimate a set of trees on many four-leaf subsets of the taxa , and then combine them into a tree on the full set of taxa . The QMC method is based on a recursive divide and conquer algorithm that seeks to maximize the ratio between satisfied and violated quartets at each step . The common implementations of the quartet method ( including QMC ) produce only a tree topology without any explicit information about the branches lengths . Even though it is possible to add depth estimation to the QMC , we limited ourselves to assessing the quality of existing methods without any new features added . Apart from the distance based algorithms , we tested a Bayesian method for inferring the cell lineage tree . We used the computer software , BATWING [38] which reads in multi-locus haplotype data , a model and prior distribution specifications . This program uses a Markov Chain Monte Carlo ( MCMC ) method based on coalescent theory to generate approximate random samples from the posterior distributions of parameters such as mutation rates , effective population sizes and growth rates , and times of population splitting events . Even though there is currently no Likelihood or Bayesian tree reconstructing algorithm that uses microsatellites , which was developed to include the unique features of cell lineages , the growing population implementation of BATWING seemed most suited for our study . The priors we used are 1/100 for the mutation rate , and uniform distributions for the effective population size and the population growth rate per generation ( on the intervals [10 , 000 10 , 000 , 000] and [0 2] respectively ) . The distance-based methods require a distance measure between cells , which ideally should be linear with the actual number of divisions separating any two samples , and should provide the most robust tree reconstruction . We have tested several different distance functions . In these functions and are the number of repeats in the single allele of the and sampled cells , respectively , and is the set of alleles which were amplified for both samples and ( for autosomal loci , both alleles were included , and for chromosome X loci , one allele was included in male samples ) : We used three different measures to quantify the quality of the clustering separation ability: All these measures evaluate the quality of the separation of the distinct groups on the tree , but they measure parameters that are slightly different . The QLC focuses on the existence of one large cluster and ignores the behavior of the rest of the cells . The TE on the other hand is determined by the number of distinct clusters of each cell type , and ignores their sizes . Hence , the TE focuses on the global behavior of the tree and not just on one sub-tree . The HS , like the QLC , focuses on the existence of a large cluster , but does not ignore the rest of the cells as it detects a statistically significant clustering of a group of cells on the lineage tree . We used three different measures to quantify the quality of the depth separation ability: We simulated trees similar to the ones reconstructed using the real data , with some trees containing 3 individuals with 5 different cell types for each individual , and other trees composed of a single individual . We simulated several kinds of topologies , which were different from each other in branch length . For example , in one topology the distance between the leaves and their MRCA was high , compared to the distance between the root and these MRCAs , and in another topology this distance between the leaves and their MRCA was much lower . We repeated the simulation 1000 times , where in each iteration , we built a random tree and randomly added MS mutations according to a given mutation rate ( 1/100 , 1/1000 and 1/10000 ) using a binomial distribution . We then reconstructed the tree using all of our methods and compared it with the actual tree that was generated . The topology comparison between the inferred and the actual trees was done using Penny & Hendy's topological distance algorithm [47] . In this algorithm each internal edge confers a partitioning of the tree into two groups by removing the edge . We assigned a score equal to the ratio of equal partitions of the two trees to the total number of partitions . For each of the simulated trees we also calculated the clustering measures ( mentioned above ) . | The history of an organism's cells , from a single cell until any particular moment in time , can be captured by a cell lineage tree . Many fundamental open questions in biology and medicine , such as which cells give rise to metastases , whether oocytes and beta cells renew , and what is the role of stem cells in brain development and maintenance , are in fact questions about the structure and dynamics of that tree . Random mutations that occur during cell division endow each organism cell with an almost unique genomic signature . Distances between signatures capture distances in the cell lineage tree , and can be used to reconstruct that tree . On this basis , our lab developed a method for cell lineage reconstruction utilizing a panel of about 120 microsatellites . In this work , we use a large dataset of microsatellite mutations from many cells that we collected in our lab in the last few years , in order to test the performance of different distance measures and tree reconstruction algorithms . We found that the best method is not the one that gives the most accurate estimates of the mean distance , but rather the one with the lowest variance . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Comparing Algorithms That Reconstruct Cell Lineage Trees Utilizing Information on Microsatellite Mutations |
Schistosoma mansoni exists in a complex environmental milieu that may select for significant evolutionary changes in this species . In Kenya , the sympatric distribution of S . mansoni with S . rodhaini potentially influences the epidemiology , ecology , and evolutionary biology of both species , because they infect the same species of snail and mammalian hosts and are capable of hybridization . Over a 2-year period , using a molecular epidemiological approach , we examined spatial and temporal distributions , and the overlap of these schistosomes within snails , in natural settings in Kenya . Both species had spatially and temporally patchy distributions , although S . mansoni was eight times more common than S . rodhaini . Both species were overdispersed within snails , and most snails ( 85 . 2% for S . mansoni and 91 . 7% for S . rodhaini ) only harbored one schistosome genotype . Over time , half of snails infected with multiple genotypes showed a replacement pattern in which an initially dominant genotype was less represented in later replicates . The other half showed a consistent pattern over time; however , the ratio of each genotype was skewed . Profiles of circadian emergence of cercariae revealed that S . rodhaini emerges throughout the 24-hour cycle , with peak emergence before sunrise and sometimes immediately after sunset , which differs from previous reports of a single nocturnal peak immediately after sunset . Peak emergence for S . mansoni cercariae occurred as light became most intense and overlapped temporally with S . rodhaini . Comparison of schistosome communities within snails against a null model indicated that the community was structured and that coinfections were more common than expected by chance . In mixed infections , cercarial emergence over 24 hours remained similar to single species infections , again with S . rodhaini and S . mansoni cercarial emergence profiles overlapping substantially . The data from this study indicate a lack of obvious spatial or temporal isolating mechanisms to prevent hybridization , raising the intriguing question of how the two species retain their separate identities .
One of the world's most prevalent neglected diseases is schistosomiasis , which is caused by flatworms of the genus Schistosoma . It is estimated that 200 million people world wide are infected [1] . Schistosomiasis is notable for its chronic nature , for being difficult to control on a sustained basis , and for the limited options currently available for control [2] . Schistosoma mansoni is the most widespread and best known of the human-infecting schistosomes . It is a genetically diverse parasite with complex epidemiology , particularly in East Africa , which is also its hypothesized place of origin [3] . Epidemiological studies of S . mansoni understandably often focus on human infections [4] , but due to the longevity of schistosome infections in the human host and to the high vagility of humans , studies of humans alone make it difficult to detect when and where transmission actually occurs . By examining snails , the obligatory hosts for the larval stages of schistosomes , we can gain a much needed perspective , one that allows the determination of where human-infective cercariae are actually being produced , and thus identifies likely sites of active transmission . Also , during the molluscan phase of the schistosome life cycle , schistosome sporocysts may encounter other individuals of the same or a related schistosome species , or of unrelated species of digenetic trematodes ( see [5] for an overview of some of the possible interactions ) , potentially influencing the dynamics of transmission . Molecular epidemiological investigations have shown that S . mansoni infections tend to be overdispersed ( aggregated in a small proportion of host individuals ) in their molluscan hosts , with some snails harboring as many as 9 distinct parasite genotypes [6] , [7] . Such patterns could be the result of differing levels of susceptibility , acquired immunity [7] , [8] , microhabitat variation of snails and miracidia , and/or competitive interactions within the snail , and as they may influence transmission of infection to humans , should be further investigated . In western Kenya , where our studies were undertaken , S . mansoni is likely to encounter and interact with its sister species , S . rodhaini . This species is typically considered a parasite of rodents although it has been reported from wild felids , canids , and even humans , although this latter observation has not been confirmed with molecular techniques [9]–[14] . Evidence from experimental infections of baboons suggests S . rodhaini cannot infect these primates unless they are coinfected with S . mansoni [15] . Although S . mansoni is primarily a parasite of humans and secondarily other primates , rodents can serve as reservoir hosts , including in East Africa [16] . In some locations such as Guadeloupe , rodents are the exclusive definitive host for S . mansoni [17] . Overlap of both schistosome species in the same individual rodent host was reported by Schwetz [18] who found eggs of both species in rodents the Democratic Republic of the Congo , although he considered the eggs shaped like those of S . mansoni to be a different variety of this species . Both schistosome species infect the same species of Biomphalaria snails and past reports indicate that they can infect the same individual snail host [19]; therefore they potentially influence each other in terms of infection patterns , development , and cercarial release patterns . Also , these two species hybridize readily in the laboratory [20]–[22] and a natural hybrid has been found from a snail in the Lake Victoria region [23] . Hybridization is an important epidemiological concern because hybrids could directly infect humans or lead to gene introgression between the species , which both could alter their biology and capacity to cause pathology . However , in the face of possible hybridization and definitive and intermediate host overlap , these two species are apparently able to maintain their identity [23] , which unless contact is very recent , suggests the presence of isolating mechanisms including ecological , geographical , or temporal isolation . Théron and Combes [24] hypothesized that the time of day of cercarial emergence of each species could serve as an isolating mechanism since at different times of the day , different host species would be utilizing aquatic habitats . Most schistosome cercariae emerge from their snail hosts following a predictable circadian pattern [25]–[27] , one that is genetically controlled [28] . Schistosoma mansoni cercariae are diurnal and are typically released during daylight hours , but populations vary concerning their exact time of emergence ( [17] and references therein ) . Previous studies have shown that S . rodhaini is nocturnal and emerges after dark between 18:00–22:00 hours [27] , [29] . These emergence times correspond to times when their putative hosts are present in the water and available for infection , humans during the day and rodents at night . However , schistosome cercariae remain active and infective in the water column for up to 9 hours in an experimental setting [30] . This longevity creates the potential for overlap in actual transmission times , even if the cercariae emerge at different times . Using schistosome specimens derived from field collections of snails over a two year period in the Lake Victoria region of Kenya , and applying molecular techniques to these specimens , we addressed several questions concerning the epidemiology of S . mansoni and S . rodhaini , and investigated potential ecological , spatial , and temporal isolating mechanisms: 1 . Do S . mansoni and S . rodhaini co-occur spatially and temporally and how prevalent are they ? 2 . Does either species outnumber the other in terms of number of snails infected and number of cercariae produced per snail ? 3 . How common are hybrids in snails ? 4 . How are both species distributed within their snail hosts in terms of abundance ( number of genotypes per snail ) , and how does this correspond to the number of cercariae produced ? 5 . Can snails become coinfected with both species and is there any evidence the two species co-occur more or less often than expected by chance ? 6 . Do these species overlap on a microtemporal scale , or is there overlap in the circadian pattern of cercarial emergence for each species ? 7 . How are these patterns influenced when snails are coinfected with multiple multilocus genotypes or species ?
Snails were collected at various sites in western Kenya in the Lake Victoria Basin ( Table 1 ) . Snails were isolated in individual wells of tissue culture plates in aged tap water for 24–48 hours and examined for shedding cercariae . Infected snails were given an individual identification number and their cercariae were used to infect mice ( Swiss Albino , male and female , 6–7 weeks old ) , in most cases two mice per infected snail . Infections were performed via skin penetration of the abdomen while the mice were anesthetized with sodium pentobarbital . Infection doses of 10 to 200 cercariae were used depending on the number released by the snail . Infected snails were subjected to 24 hour cercarial release profiles every 4–7 days after collection for as long as they survived . Profiles were created by counting the number of cercariae released every hour for 24 hours as the snails were moved hourly between wells of 24 well tissue culture plates , each well with 1 mL of aged tap water . Snails were kept under natural lighting ( not direct sunlight ) in Kenya in a laboratory with east facing windows . Additional replicates were performed in a laboratory with west facing windows and the peak emergence times did not change . Cercariae were either counted directly using a stereomicroscope if few were released , or a subsample was counted by mixing the well with a pipette , removing a subsample of 200 µL , and counting them on a gridded plate after staining with iodine . The final count was then multiplied by 5 to estimate the number in 1 mL . To determine if snails were shedding multiple genotypes or multiple species at different time intervals , cercariae were pooled into 4 time intervals ( 3:00–9:00 , 9:00–15:00 , 15:00–21:00 , and 21:00–3:00 ) and used to infect 1–2 mice per time interval . Recovery of adult worms from mice 7 weeks post-exposure was accomplished by perfusion [31] . Gender of the worms was determined by examining adult morphology and was generally obvious with a few exceptions of infections with immature worms , which were scored as unknown . Adult worms were stored in 95% ethanol at 4°C until further use . The methodology described above has been fully approved for the use of animals by the University of New Mexico Institutional Animal Care and Use Committee ( Protocol #07UNM003 ) and Board of Animal Care and Use of the Kenya Medical Institute . Adult worms recovered from the mice were subsampled so that at least 16 individuals from every snail during each time interval were assayed if available . Snails did not shed during all time intervals and not all infections yielded at least 16 worms . The HotSHOT [32] method was used to prepare genomic DNA of the worms for PCR . To determine the number of genotypes of cercariae that were released from a snail , 7 previously published microsatellite loci [33] , [34] were amplified in 1 multiplexed PCR reaction , the P17 panel , as described by Steinauer et al [35] . PCR products were genotyped using an ABI3100 automated sequencer ( Applied Biosystems ) and scored with GeneMapper® v . 4 . 0 ( Applied Biosystems ) software . All genotype calls were verified manually . Individuals with the same genotypes at all 7 loci that emerged from the same snail were considered to be clones descended from a single miracidium and are referred to as a multilocus genotype , although the probability that identical individuals arose from sexual reproduction was also calculated with GENCLONE 1 . 1 [36] . Part of the 16S and 12S genes ( 16S-12S ) of the mitochondrial DNA from each multilocus genotype was amplified and sequenced using the method of Morgan et al . [23] . Sequences were submitted to GenBank Data Libraries ( Accession numbers EU513397-EU513598 ) Both the 16S-12S data and microsatellite data were used for species identification . Reference individuals from laboratory reared specimens and also field collected specimens of S . mansoni from Kenya , Egypt , and Brazil were used to establish species level differences with the markers . The 16S-12S data was aligned along with reference sequences from GenBank ( S . mansoni: AY446260 and AY446261 ( Madagascar ) ; AY446262 and AY446263 ( Kenya ) ; AY446259 ( Ghana ) , AF531310 ( Tanzania ) ; and S . rodhaini: AF531309 , AY446265 , and AY446264 ( Kenya ) . The total dataset included the following number of specimens for each species: S . mansoni , 190; S . rodhaini , 24; S . haematobium , 1; S . bovis , 2 . Sequences were aligned with ClustalX [37] using a gap opening penalty of 15 and extension penalty of 0 . 2 . Identical sequences were identified using Sequencher 4 . 6 ( Genecodes ) and redundant sequences were removed from the alignment . Phylogenetic analyses using the minimum evolution optimality criterion was performed on the data using the model of evolution selected by the likelihood ratio test implemented in MODELTEST 3 . 0 [38] . Tree searches were done heuristically using PAUP* 4 . 0b10 [39] with tree bisection reconnection ( TBR ) branch swapping on initial trees that were obtained by random stepwise addition of taxa , replicated 100 times . Node support for the node separating S . mansoni and S . rodhaini was assessed by bootstrap analysis [40] using the faststep option with 10 , 000 pseudoreplicates . Species identification was based on clustering with reference sequences from GenBank . Genetic divergence was calculated using MEGA version 2 . 1 [41] . Within clade divergences and net between clade divergences were calculated using uncorrected p-distances , which is the proportion of sites that differ between two taxa . For the microsatellite data , a population assignment test was performed with GenAlEx [42] using the “leave one out method” to assess whether the microsatellite markers agreed with the 16S-12S data and could differentiate the species using the 7 microsatellite loci . The loci were also compared by eye to determine which were able to differentiate the species . Prevalence , or percentage of infected snails , of schistosomes and of each schistosome species was calculated for each collection and also pooled across collections by site ( Table 2 ) . A proportion of infections ( 33% ) could not be identified to species because the snails never released enough cercariae to infect mice , the mice did not become infected by the cercariae , or the mice died before worms could be recovered . Therefore , estimated prevalence values were also calculated by apportioning the total prevalence value to each species based on their proportion in the known specimens at each site . Both raw prevalence and estimated prevalence values are given in Table 2 . To test if prevalence ( raw values ) and mean intensity ( number of genotypes per snail ) of infection was positively correlated as noted in previous studies [43] , a Pearson's correlation was calculated on the log transformed values using the same software . Also , an analysis of covariance ( ANCOVA ) that examined the difference in the total number of cercariae released between species and its relationship to snail size was performed . Only snails infected with a single genotype and that shed more than 90 cercariae were used in this analysis . The model included species as a categorical variable and snail size as a covariable as well as the interaction between the terms . To determine if coinfections in snails were random occurrences or if they were the product of a structured community , the observed parasite communities were compared to a null models of communities based on the observed values of the species' prevalence as described by Lafferty et al . [44] . Expected numbers of coinfected snails were calculated as the product of the number of snails collected and the prevalence ( as a proportion ) of each parasite species present in the population at the site of interest , during the time of interest ( not pooled spatially or temporally ) . The expected number was compared to the observed number using χ2 goodness of fit tests . Two-tailed Fisher's Exact tests were used to detect if the proportion of each genotype of cercariae shed from multiply infected snails varied among replicates over time using VassarStats ( www . faculty . vassar . edu/lowry/VassarStats . html ) . Only one snail yielded enough data to examine the three way relationship among genotype , replicate , and time of day ( most snails yielded adults mostly from a single time period , 9:00–15:00 ) . This snail was coinfected with both schistosome species , 3 genotypes of S . rodhaini and 1 genotype of S . mansoni . These data were analyzed with a 3-way contingency table with a log-linear analysis for goodness of fit using VassarStats , and the standardized deviates were examined to determine which categories contributed the most to observed significant values .
Alignment and removal of redundant sequences yielded 512 bp for 64 taxa: 61 S . mansoni and one each of S . rodhaini , S . bovis , and S . haematobium . The evolutionary model selected by the likelihood ratio test implemented by MODELTEST 3 . 0 [38] was the unequal-frequency Kimura 3-parameter model . Phylogenetic analysis yielded 9 trees that did not differ in their groupings of specimens between species ( Fig . 1 ) . Within S . mansoni 1 . 5% sequence divergence was detected; however , no variation was detected in S . rodhaini ( 24 specimens ) or S . bovis ( 2 specimens ) . The net between groups genetic distance between S . mansoni and S . rodhaini was 9 . 3% , which was greater than the distance between S . haematobium and S . bovis ( 7 . 6% ) . A population assignment test using the microsatellite markers yielded 100% assignment of the individuals of S . mansoni and S . rodhaini to their species based on the 16S-12S data ( Fig . 2 ) . Two loci were completely non-overlapping between S . mansoni and S . rodhaini ( SMD28 and SMD89 from [34] ) , and one locus ( SMD43 from [33] ) did not amplify in S . rodhaini . There was no evidence of hybrids based on the mtDNA and microsatellite markers which were concordant in their identification of each individual . Also , no individuals were found to have microsatellite signatures that were indicative of hybrids either in the nonoverlapping loci or the other loci as shown by the population assignment test , which placed the species in relatively tight groups ( Fig . 2 ) . A total of 22 , 641 snails were collected in the Lake Victoria basin over a 2 year period . Of these snails , 236 ( 157 B . sudanica and 79 B . pfeifferi ) were infected with schistosomes , a prevalence of 1 . 04% . Not all schistosome infections were identified , but of the 167 that were , 90% were S . mansoni and 8 . 1% were S . rodhaini , and 1 . 9% were mixed species infections . Most infections of S . rodhaini occurred in B . sudanica and only one individual of B . pfeifferi was infected with this species , which was a coinfection with S . mansoni . The sex ratio of adults obtained from mice of S . mansoni was male biased ( 2 . 36 ) , while that of S . rodhaini was more equivalent ( 1 . 11 ) . Prevalence of schistosome infection varied spatially and ranged from 0 . 11–3 . 65% among positive collection sites ( Table 2 ) . Prevalence was the highest for both S . mansoni and S . rodhaini at the Car Wash site , which is an area along the shore of Lake Victoria in the city of Kisumu , Kenya , where a population of car washers earns their living by washing vehicles in the lake and is known to be infected with schistosomes [45] . Schistosoma mansoni was more prevalent and widespread than S . rodhaini which was only present at 7 of the 14 collection sites where S . mansoni occurred , and there were no sites where only S . rodhaini occurred . Total prevalence ( added over time ) of S . rodhaini was not greater than S . mansoni at any one site , but was more prevalent in 7 of the 169 individual collections at Nawa , Nyabera , Usare Beach , and Lwanda . Seasonal patterns of prevalence were not evident , but prevalence for both species was low between November 2004 and March 2005 , and increased between September 2005 and March 2006 ( Fig . 3 ) . Examination of the number of genotypes per schistosome species per infected snail included a dataset that consisted only of snails that yielded 8 or more adult worms for DNA analysis and totaled 138 snails . The total number of adults genotyped was 4 , 777 , with a mean of 34 . 1 per snail ( 2 . 5 standard error ) , range of 8–217 , and median of 24 adults per snail . Many snails were sampled over multiple days or shedding intervals that were 4–7 days apart . Snails were sampled over a mean of 2 . 3 ( 0 . 18 standard error ) replicates , and ranged between 1 and 6 replicates . For S . mansoni , the 7 loci were adequate to determine that identical individuals were derived from clones and not separate sexual reproduction events . The Psex values ( probability that the same multilocus genotype was produced from independent sexual reproduction events ) ranged from 1 . 2×10−27 to 0 . 000735 for this species . For S . rodhaini , individuals were less diverse and Psex values ranged from 1 . 2×10−8 to 0 . 1442; however , this method does not take into account the probability that two individuals that are identical due to sexual reproduction infect the same individual snail host , which is 8 . 8×10−5 for S . rodhaini . Therefore , it is highly unlikely that we are missing genotypes of either species due to identical individuals in the same snail hosts . Of the snails that yielded at least 8 adults ( 128 for S . mansoni and 12 for S . rodhaini , with 2 of these snails coinfected with both species ) , most harbored only one genotype , but multiple infections of up to 4 genotypes were found ( Table 3 ) . A total of 152 genotypes of S . mansoni were found in 128 infected snails and 14 genotypes of S . rodhaini were found in 12 infected snails . There was a significant positive correlation between prevalence and mean intensity ( number of genotypes per snail ) r2 = 0 . 264 , p<0 . 05 . Three snails harbored genotypes of both S . rodhaini and S . mansoni , and were found at different sites during the last week of October of 2005 or 2006: Asembo Bay , Nyabera , and Asao . Statistical comparison with null communities indicated that the schistosome communities were structured and coinfections were more common than expected by chance at all three collecting sites , Nyabera ( χ2 = 49 . 3 , P<<<0 . 0001 ) , Asao ( χ2 = 140 . 1 , P<<<0 . 0001 ) , and Asembo Bay ( χ2 = 305 . 4 , P<<<0 . 0001 ) . According to the calculated expected values , one would have to collect 15 , 692 , 40 , 571 , and 94 , 769 snails at each site , respectively , to find one coinfected snail . Circadian cercarial emergence profiles were generated based on 226 replicates from 100 snails infected with S . mansoni and 27 replicates from 8 snails infected with S . rodhaini ( identified based on mtDNA sequences and microsatellite genotypes ) . Peak cercarial emergence of S . mansoni occurs between 8:00–13:00 and emergence of S . rodhaini was bimodal with a peak occurring between 5:00 and 8:00 and also 19:00 to 22:00 ( Fig . 4 ) . The ANCOVA revealed a significant interaction between parasite species and snail size , making the other effects difficult to interpret because of the uneven slopes ( species: F1 , 127 = 4 . 702 p = 0 . 032; size: F1 , 127 = 0 . 401 p = 0 . 528 , interaction: F1 , 127 = 5 . 087 p = 0 . 026 ) . Using separate regressions , S . mansoni cercarial abundance has a significant positive relationship with snail size ( F1 , 117 = 9 . 275 p = 0 . 003 r2 = 0 . 073 ) , and S . rodhaini does not ( F1 , 10 = 2 . 003 p = 0 . 187 ) , a result that could be an effect of sample size since there were far fewer snails infected with S . rodhaini . A T-test indicated that there was no difference in cercarial production by species ( Tdf = 19 = 1 . 237 , p = 0 . 231 ) . Snails that were infected with multiple genotypes differed in the ratios of each genotype released and the proportions of each ranged from 50% of each to 95% and 5% of each . A total of 11 snails were examined for independence between the genotypes released and replicates performed over time ( typically a week apart ) . Results indicated significant differences or non-independence between genotype and replicate for 6 of the snails ( Table 4 ) . The patterns of five of the six indicated a replacement pattern in which an initially dominant genotype is less represented in later replicates . The remaining snail showed variable proportions over 3 replicates; however , one genotype was always dominant . The five snails with nonsignificant values displayed a more constant pattern of cercarial release in which the proportions of each genotype did not change over time . For the mixed species infections , limited data were obtained from two of the three snails . For the Asao snail , only 7 worms of 2 female genotypes was recovered , 6 of which were S . mansoni and 1 was S . rodhaini . Interestingly , a single species infection of S . rodhaini was never found at this site . For the Asembo Bay snail , cercariae were collected twice , 28 days apart . In the first collection , 16 adults were genotyped and all were one female genotype of S . mansoni . Unfortunately for the second collection , only 3 adults were recovered: one was a male S . rodhaini and 2 were a male genotype S . mansoni , but a different genotype than released previously . More extensive data was obtained from the Nyabera snail , which shed 1 male genotype of S . mansoni and 3 genotypes of S . rodhaini , one male and two females . Each of 4 replicates of circadian cercarial emergence showed a peak from 8:00–10:00 hours , which corresponds to S . mansoni emergence , and also an earlier morning peak that corresponds to S . rodhaini emergence . Two replicates also showed nocturnal peaks that also correspond to S . rodhaini ( Fig . 5 ) . The number of adults obtained from infections of mice with cercariae collected from different time pools of these circadian profiles indicated that S . rodhaini was more common: 94% were of this species , and 43% of these were of the male genotype . The three-way contingency table analysis indicated that all variables , genotype ( G ) , replicate ( R ) , and time of day ( T ) and their interactions , were significant ( G by R: G2 = 17 . 3 , p = 0 . 0002; G by T G2 = 22 . 44 , p = 0 . 0002; R by T G2 = 18 . 84 , p<0 . 0001; G by T by R: G2 = 55 . 12 , p<0 . 0001 ) . The three largest standardized deviates by more than a value of 1 included the comparison of the S . mansoni genotype between 9:00 and 15:00 hours ( 3 . 256 ) , a female S . rodhaini genotype during 3:00 to 9:00 hours ( 2 . 111 ) , and the S . mansoni genotype between 21:00–3:00 hours ( −2 . 036 ) . These values indicate that the S . mansoni genotype was more common than expected during 9:00 and 15:00 , the peak emergence time for this species ( and the only time period that this species was collected ) and less common than expected during 21:00–3:00 , a time period when this species rarely emerges ( Figs . 4–6 ) . Also , one of the female genotypes of S . rodhaini ( R3 ) was more common than expected during the 3:00–9:00 time period , one of the peak emergence times of this species ( Fig . 6 ) .
Schistosoma mansoni and S . rodhaini both have spatially and temporally patchy distributions in snails in the Lake Victoria region of Kenya and active infections ( those producing cercariae ) are characterized by low prevalence of about 1% combined . Although this number may be characterized as low in a relative sense , given the prodigious number of snails supported by Lake Victoria and its environs , this level of infection in snails is responsible for relatively high levels of infection in humans around the lake that can reach up to 80% in school children [46] . Most of the snails were infected with S . mansoni , which was about 8 times more common and more widespread than S . rodhaini . At every site where S . rodhaini was collected , S . mansoni was also collected , but S . mansoni was the sole species collected at 7 of the 14 sites . Also , S . rodhaini was not collected during a large part of the entire sampling period , while S . mansoni was present at some sites during all collection periods . The difference in the abundance and distribution of the species likely is due to differential definitive host use . Schistosoma mansoni primarily infects humans , which generally have larger , less subdivided , and more widespread populations than do rodents , the putative definitive hosts for S . rodhaini . Also , humans , and therefore their worms , are much longer lived than rodents and their worms , and serve as a more stable reservoir that continuously passes eggs and maintains the population . This difference is also reflected in the patterns of genetic diversity in that S . rodhaini showed little variation relative to S . mansoni , even when sample sizes are taken into account , reflecting a small population size for S . rodhaini that potentially has been bottlenecked in the past . Although S . mansoni outnumbered S . rodhaini in terms of numbers of infected snails , there was no difference in the number of cercariae produced by either species per infected snail and this number was not influenced by snail size . Temporal patterns of prevalence were not obvious in the data , but prevalence varied spatially from 0 . 11–3 . 65% at positive sites , with the highest levels of infection occurring at Car Wash site . Although snails are in relative low abundance here due to the less than optimal habitat due to the clearing of vegetation for washing cars , human activity and fecal material are abundant so that the snails that are there are likely to be infected , including with multiple genotypes: 8 of the 21 snails with multiple infections were collected at this site . We also collected at two additional sites that were approximately 210 m and 585 m along the shore from the Car Wash site , Tilapia Beach and Powerhouse . Infection prevalence declined the further the sites were from the Car Wash site , even though snails are much more common at these sites . Both species were overdispersed in their snail hosts , a pattern that is typical for schistosome populations in snails when prevalence is low [43] . One of the factors that likely leads to the observed pattern is the aggregation of miracidia in microhabitats occupied by particular snails [47] and low probability of contact between miracidia and snails since infection is relatively rare in this system . The fact that mean intensity and prevalence are positively correlated also suggests that probability of encounter plays a large role in determining parasite distribution , or in other words , some snails are “unlucky” and happen to be in the microhabitat where feces are deposited and eggs are hatching . Excess of multiple infections can also be explained by variability in susceptibility of infection of individual snails . Some individuals may be more susceptible or “worm-prone” and are thus likely to acquire multiple genotypes , while other snails are resistant and acquire none . Also , acquired susceptibility of snails could also lead to an excess of multiple infections . In this case , a snail that acquires one genotype becomes more susceptible to additional infections . On the other hand , lack of multiple infections can be explained by probability of encounter , differential compatibility between hosts and parasites , acquired resistance , and competition [8] , [48]–[50] . One potential limitation of the methodology used in this study is the possibility of underestimating the number of genotypes that infect a snail . If rare genotypes occur in the sample ( in which case they would be difficult to detect by any method ) or if certain genotypes are rare due to low infectivity to mice , they may not be detected using our methodology . However , with a minimum sample of 16 worms , and a mean of 34 . 1 worms sampled per snail , this error likely is low . The schistosome populations are structured in a way that leads to snail co-species infection more commonly than expected by random infection . Interestingly , two of these snails were also infected with multiple genotypes of one of the species so that the three snails harbored 2 , 3 , or 4 total genotypes . This result could be explained by the unlucky snail hypothesis mentioned above since microhabitats that are hotspots of transmission for one species could also be a hotspot for the other species . The “Worm-Prone” and the “Acquired Susceptibility” hypotheses mentioned above could also explain this pattern , but would require interspecific facilitation , a phenomenon not unknown in trematode-snail interactions [48] . Experimental infections of snails with one or both species are underway to distinguish among these possibilities . It is also possible that coinfections of definitive hosts play an important role in determining community structure at the snail level because the progeny of both species would be deposited together in the same microhabitat . Our preliminary data from worm burdens of rodents in the region have revealed only one individual that was infected with S . rodhaini , and that individual also was infected with S . mansoni . Circadian cercarial release cycles were strongly tied to the light/dark cycle in that S . mansoni began to emerge as light intensity increased with the start of the daylight period , and S . rodhaini emerged immediately before and after the daylight period . Peak cercarial emergence of S . mansoni occurred earlier in the 24 hour cycle than most previously studied populations that typically undergo peak emergence when light intensity is the greatest , around noon or later , although this characteristic is known to vary among populations [29] , [51] , [52] . The bimodal cercarial release pattern of S . rodhaini has not been reported previously , and only twilight emergence was reported from populations from Burundi and Uganda [27] , [29] . A possible morning peak of emergence in a Ugandan isolate of S . rodhaini was reported by Fripp [53]; however , his results are unclear because the snails were not monitored over a 24 hour period . In the present study , emergence of S . rodhaini varied among individuals and among replicates of individuals in the number of emergence peaks that occurred . In some cases both peaks occurred , but in others , only one peak occurred . Intraspecific differences in emergence time may correspond to differential definitive host use as this characteristic is likely selected for by the time that definitive hosts are present in the water and available for transmission [17] , [54] . Therefore , we suspect that in Kenya S . rodhaini infects a host or group of hosts that are most active in the water just after sunset and right before sunrise . Three snails were coinfected with both S . mansoni and S . rodhaini , and data from the cercarial emergence profiles of one of these snails indicate that the presence of each species does not influence the other's cercarial release patterns , which is consistent with results from other studies that have examined snails infected with both S . haematobium and S . bovis [55] or with different populations or “strains” of S . mansoni [56] . However , the data from the adults obtained from infections with mice also suggest that S . mansoni emergence is not influenced by coinfection , but S . rodhaini emergence may be because more adults of one genotype of this species were obtained from mice infected with cercariae that emerged between 9:00 and 15:00 hours than adults of S . mansoni . This result is unexpected since this is not the typical emergence time for S . rodhaini . Also , it is anticipated that mechanisms that separate the temporal emergence of each species would evolve particularly if they coinfect the same individual snail host because cercariae released concurrently are likely to infect the same definitive host individuals , thus potentially leading to hybridization . An alternative explanation to the observed results is that the actual number of adults of each species may be biased due to infection success since S . rodhaini may be better adapted to rodents , which are their presumed principal definitive hosts in nature . However , even if the proportions are biased , the data still indicate that the two species are emerging from snails concurrently . The proportions of genotypes that emerged from snails infected with multiple genotypes varied among circadian cercarial emergence replicates ( typically 1 week apart ) for about half of the snails examined . Replacement of one predominant genotype by another was the most common pattern detected . It is hypothesized that infection of these snails by the different genotypes occurred sequentially with a large time interval between infections so that one genotype has developed and produces cercariae before the other has developed to the same stage . Possible complete replacement of genotypes was only detected in two snails , but was confounded by small sample sizes of worms and not included in the statistical analyses . An alternative explanation is that since cercarial production occurs in cohorts [57] , the genotypes are producing their cohorts asynchronously leading to a pattern that appears to be replacement particularly when only 2 replicates of data are collected . However , in all 7 of the snails where 3 or more replicates were performed , the genotype in majority did not alternate and instead followed a pattern of replacement . The alternative to a replacement pattern was a constant pattern in which the proportions of genotypes did not differ among replicates . This constant pattern may be indicative of infections that were acquired simultaneously and are therefore at the same stage of development within the snail . Interestingly , within these infections the proportions of genotypes were mostly skewed , with the most even ratio being 61:38 . This skew suggests that there are other mechanisms besides timing of infection that affect cercarial output possibly including competition between genotypes or variation in compatibility of snail and schistosome genotypes that directly affects cercarial production . These mechanisms are best addressed experimentally to determine the roles of infection timing and competition on genotype “success” , and can be performed to remove the effect of infection bias that may occur when the cercariae are introduced into mice . Among the factors examined , this study revealed no evidence for ecologically induced isolating mechanisms that prevent S . mansoni and S . rodhaini from encountering one another and hybridizing . These species overlap on a microgeographic scale ( individual sites and individual snails ) and also temporally both on a seasonal scale and a circadian scale . Even though the emergence peaks of the cercariae do not directly overlap , the cercariae of these two species certainly overlap to some degree since the cercariae remain in the water column and infective for up to 9 hours , and therefore it is difficult to imagine how this would effectively isolate the two species . Also , competition within or among individual snail hosts does not seem to play a large role since coinfections were more common than expected by random infection . If anything , this observation in conjunction with the fact that S . rodhaini was only found in habitats also occupied by S . mansoni , suggests a pattern of co-occurrence as opposed to isolation . The number of cercariae produced per individual snail did not differ between the species; however , if both species share the same host pools , and if there are no strong mating barriers , it is surprising that S . mansoni has not driven S . rodhaini to extinction through hybridization since snails infected with the former species are eight times more common . However , it is possible that our sampling area represents the edge of the range of S . rodhaini and sampling throughout the Rift Valley may reveal larger , more stable populations that disperse to less ideal habitats through movement of snail or mammal hosts . However , the lack of genetic diversity suggests that migration from larger populations is not occurring on a regular basis . It is unknown how long S . mansoni and S . rodhaini have been in contact in Kenya and if their original divergence was due to sympatric or allopatric speciation . If the latter has occurred and we are witnessing relatively recent secondary contact , then this situation seemingly parallels one occurring in Cameroon in which S . intercalatum is thought to be endangered due to its interactions with S . haematobium and S . mansoni [58] . Decline of S . intercalatum has occurred in recent years ( 1968-present ) and is directly correlated with the introduction of S . haematobium in the region [58] . However , the molecular data suggest S . mansoni and S . rodhaini diverged approximately 2 . 8 million years ago [3] , and it seems likely that they have coexisted in the Lake Victoria basin for a long time . The most likely isolating mechanism separating the two species is the difficulty of S . rodhaini in infecting non-human primates [15] and presumably humans as well , and the preponderance of S . mansoni infections in humans . We have collected both species in the same rodent hosts ( unpublished observations ) but the relative frequency with which such coinfections occur may be insufficient to break down the genetic differences between the two species , or mate recognition systems may hinder interspecific reproduction when they do encounter each other in a host . What is still lacking is a full understanding of the definitive hosts used by S . rodhaini to propagate itself , whether these hosts are routinely colonized by S . mansoni , and whether the species will hybridize if they encounter each other in the same host . Future monitoring of schistosome populations in Western Kenya and further studies on introgressive hybridization will give further insight on the interactions between these species . | One of the world's most prevalent neglected diseases is schistosomiasis , which infects approximately 200 million people worldwide . Schistosoma mansoni is transmitted to humans by skin penetration by free-living larvae that develop in freshwater snails . The origin of this species is East Africa , where it coexists with its sister species , S . rodhaini . Interactions between these species potentially influence their epidemiology , ecology , and evolutionary biology , because they infect the same species of hosts and can hybridize . Over two years , we examined their distribution in Kenya to determine their degree of overlap geographically , within snail hosts , and in the water column as infective stages . Both species were spatially and temporally patchy , although S . mansoni was eight times more common than S . rodhaini . Both species overlap in the time of day they were present in the water column , which increases the potential for the species to coinfect the same host and interbreed . Peak infective time for S . mansoni was midday and dawn and dusk for S . rodhaini . Three snails were coinfected , which was more common than expected by chance . These findings indicate a lack of obvious isolating mechanisms to prevent hybridization , raising the intriguing question of how the two species retain separate identities . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"evolutionary",
"biology",
"infectious",
"diseases/helminth",
"infections",
"ecology"
] | 2008 | Interactions between Natural Populations of Human and Rodent Schistosomes in the Lake Victoria Region of Kenya: A Molecular Epidemiological Approach |
Despite the global distribution and public health consequences of Taenia tapeworms , the life cycles of taeniids infecting wildlife hosts remain largely undescribed . The larval stage of Taenia serialis commonly parasitizes rodents and lagomorphs , but has been reported in a wide range of hosts that includes geladas ( Theropithecus gelada ) , primates endemic to Ethiopia . Geladas exhibit protuberant larval cysts indicative of advanced T . serialis infection that are associated with high mortality . However , non-protuberant larvae can develop in deep tissue or the abdominal cavity , leading to underestimates of prevalence based solely on observable cysts . We adapted a non-invasive monoclonal antibody-based enzyme-linked immunosorbent assay ( ELISA ) to detect circulating Taenia spp . antigen in dried gelada urine . Analysis revealed that this assay was highly accurate in detecting Taenia antigen , with 98 . 4% specificity , 98 . 5% sensitivity , and an area under the curve of 0 . 99 . We used this assay to investigate the prevalence of T . serialis infection in a wild gelada population , finding that infection is substantially more widespread than the occurrence of visible T . serialis cysts ( 16 . 4% tested positive at least once , while only 6% of the same population exhibited cysts ) . We examined whether age or sex predicted T . serialis infection as indicated by external cysts and antigen presence . Contrary to the female-bias observed in many Taenia-host systems , we found no significant sex bias in either cyst presence or antigen presence . Age , on the other hand , predicted cyst presence ( older individuals were more likely to show cysts ) but not antigen presence . We interpret this finding to indicate that T . serialis may infect individuals early in life but only result in visible disease later in life . This is the first application of an antigen ELISA to the study of larval Taenia infection in wildlife , opening the doors to the identification and description of infection dynamics in reservoir populations .
Singular among cyclophyllidean tapeworms , taeniid species parasitize mammals in both their adult and larval stages [1] . Taeniid adult stages infect humans and carnivorous species that include canids , felids , hyaenids , mustelids , and viverrids [1 , 3] and cause few severe symptoms in healthy hosts [2 , 4 , 5] . By contrast , taeniid larval stages ( metacestodes ) generally infect herbivorous artiodactyl , rodent , and lagomorph species [1 , 3] and regularly cause extensive muscular and visceral damage [2 , 4 , 6 , 7] . Intermediate hosts become infected when they ingest eggs shed by adult tapeworms harbored in the definitive host , and definitive hosts become infected when , via predation or scavenging , they ingest larvae in infected intermediate hosts [1 , 3] . The scientific study of T . serialis is marked by a tendency to make species-level designations that may not be warranted and , consequently , to underestimate the range of hosts that T . serialis infects . The T . serialis metacestode is a thin-walled , translucent structure ( coenurus ) containing multiple protoscolices , the precursor to the mature scolex that constitutes the attachment end of the adult tapeworm in the definitive host [6] . This metacestode morphology is indistinguishable from that of T . multiceps , a zoonotic parasite found primarily in sheep [2] . Before the relatively recent emergence of molecular tools [8–13] , cases of coenurosis were ascribed to either T . serialis or T . multiceps based on now-outdated morphological cues [2 , 14 , 15] or on infection site predilection ( e . g . , central nervous system or subcutaneous tissue ) [2 , 16 , 17] . Furthermore , some researchers employed synonyms for T . serialis ( e . g . , T . brauni , T . glomeratus ) based on geographic location or occurrence in a non-rodent or lagomorph host [17 , 18] . In addition to taxonomic confusion surrounding metacestode identification , the occurrence of coenurosis ascribed to T . serialis in non-rodent or lagomorph hosts has been largely overlooked . Although parasitological texts invariably refer to T . serialis as a parasite of rodents and lagomorphs in its larval stage , it has been reported in a wide range of phylogenetically and geographically diverse hosts . Case studies have described T . serialis coenurosis in three rodent species [14 , 19–22] , domestic cats [23–29] , two marsupial species [30 , 31] , two lagomorph species [32–37] , and two nonhuman primate species ( the greater spot-nosed guenon ( Cercopithecus nictitans ) [38] , and the gelada ( Theropithecus gelada ) [39–44] . To our knowledge , only two studies of naturally occurring T . serialis coenurosis have used molecular tools for species identification [42 , 43] . Given the lack of confirmed T . serialis diagnoses in the literature , including cases in ‘standard’ rodent and lagomorph hosts , it stands to reason that T . serialis may be more widespread and flexible in its selection of intermediate hosts than previously described . The historic difficulty of definitively diagnosing T . serialis coenurosis may have also led to an underestimation of its zoonotic potential . Coenurosis has been recorded in humans across the globe [45 , 46] , including in Europe [47–59] , Africa [60–65] , the Middle East [66 , 67] , and the Americas [68 , 69] . Certain authors declined to assign a species [17 , 65] , while the others ascribed infection to T . serialis or T . multiceps based on morphological analysis . Only one study used molecular tools , identifying T . serialis coenurosis in a man in Nigeria [46] . In sum , the taxonomic uncertainty of coenurosis occurring in animals , including humans , has led to a fragmented record of the global occurrence and distribution of T . serialis and a potential underestimation of its zoonotic potential and importance to public health . As humans come into increasing contact with wildlife , understanding the biology and zoonotic potential of T . serialis is crucial to preventing its transmission to humans and domestic animals . Little is known about the natural dynamics of Taenia spp . in wildlife hosts , largely because of the impracticality of obtaining and storing biological samples or performing medical imaging in remote settings and on wildlife . To obtain a more accurate assessment of the prevalence of larval T . serialis infection in wildlife host species , we adapted an existing monoclonal antibody-based sandwich enzyme-linked immunosorbent assay ( ELISA ) for the detection of Taenia antigen in dried urine samples [70–73] . The monoclonal antibodies ( B158C11 and B60H8 ) used in this assay are specific to the Taenia genus , which permits its use in the detection of larval infections of all taeniid species . Indeed , this assay has been used as an epidemiological tool , often complementary to other diagnostic methods , in studies of porcine , bovine , and human cysticercosis [70 , 71 , 74–78] . Because this assay detects circulating metacestode ( larval ) antigens , it identifies active infections rather than past exposure identified by antibody assays [75 , 77] . Despite the success of this assay in studies of cysticercosis in livestock , the difficulty of obtaining blood or serum samples from humans limited its use in human populations [77 , 78] . Thus , two teams [77 , 78] adapted the monoclonal antigen test to non-invasively diagnose these diseases in urine . However , the existing protocols for Taenia antigen detection in urine are still impractical for implementation in wildlife studies because they require that urine samples be stored at -20°C until processing [77 , 78] . Because many wildlife studies are carried out in areas where electricity is absent or inconsistent , the need for refrigeration limits the practicality of these tests in remote areas . We therefore validated the use of dried urine with a modified protocol to investigate sylvatic cycles of Taenia transmission . Geladas—herbivorous primates endemic to the Ethiopian highlands–are known to exhibit protuberant cysts characteristic of infection with the larval stage of T . serialis ( Fig 1 ) . Coenuri have been recorded in wild-caught captive geladas for nearly a century and were often ascribed to T . serialis based primarily on morphological cues [39–41 , 79–83] . Recently , this identification was confirmed with molecular diagnosis of cystic material obtained from protuberant cysts [42 , 43] . Prevalence of T . serialis-associated cysts in geladas ranges from 4–13% in an ecologically disturbed area [42 , 44 , 84] to 30% in an ecologically intact area [43] , and cysts in both areas are associated with significant increases in mortality and decreases in reproductive success [43 , 44] . However , not all infections necessarily manifest as conspicuous cysts , a point illustrated by the presence of non-protruding cysts revealed during necropsies on infected captive geladas . Thus , prevalence of T . serialis in geladas based on protuberant cysts is likely to be underestimated . We implemented the monoclonal antibody-based sandwich ELISA in a wild population of geladas in the Simien Mountains National Park ( SMNP ) , Ethiopia , where individuals are parasitized with T . serialis [42] . Recent work in this population demonstrated sex- and age- biased distribution of T . serialis cysts , with higher prevalence in adults and females [44] . This sex bias may reflect either patterns of data collection that bias towards observing infected females and uninfected males , or the estrogen affinity exhibited by the larvae of many taeniid species [85 , 86] . The increased prevalence of T . serialis cysts in adults compared to immatures may arise either from increased susceptibility of adults due to the immunosuppressive effects of hormones related to sexual maturity , or as a function of the time required for infection to develop into observable cysts . The adaptation of the urine antigen ELISA to non-invasively diagnose T . serialis in dried gelada urine allowed us to investigate infection dynamics that cannot be detected solely by analyzing the presence of observable cysts .
We conducted our study in the Sankaber area of the SMNP , Amhara Region , Ethiopia . The SMNP was established in 1969 and has been classified as a UNESCO World Heritage Site in Danger since 1996 due to substantial anthropogenic impact [87] . The park covers 13 , 600 hectares , is characterized by Afro-montane and Afro-alpine habitats , and contains a number of mammals of potential importance to the T . serialis life cycle . These include the black-backed jackal ( Canis mesomelas ) , the golden jackal ( Canis aureus ) , the spotted hyena ( Crocuta crocuta ) , the Ethiopian wolf ( Canis simiensis ) , Starck’s hare ( Lepus starcki ) , and the gelada [88] . The substantial human population in the SMNP has contributed to the loss of natural vegetation and the expansion of crops and grazing seen in many areas of the park [88 , pers . obs . ] . Dogs , jackals , hyenas , and Ethiopian wolves are among the carnivores living in the SMNP that potentially prey on or scavenge the corpses of geladas [88] , and are thus of potential importance for the T . serialis life cycle as definitive hosts . From August 2014 to June 2015 , we collected a total of 527 urine samples from 204 geladas ( 117 females , 87 males; 37 infants , 60 subadults , 107 adults ) in 2 habituated groups under long-term study by the Simien Mountains Gelada Research Project ( SMGRP ) in the SMNP . Geladas in the habituated groups are each assigned a three-letter code and are individually identifiable by the field team based on suites of morphological characteristics and corporeal idiosyncrasies [88] . Thus , all samples collected in this population were from known individuals , with most individuals sampled more than once over time ( n = 97 individuals; median: 2 samples/individual , range: 1–10 ) . Sampling included 58 samples from 10 individuals exhibiting the cysts characteristic of T . serialis infection to serve as ‘true positives’ , and 57 samples from 37 unweaned infants to serve as ‘true negatives’ ( unweaned infants are unlikely to ingest eggs because they do not yet eat grass; see below for further explanation ) . All other samples ( 412 from 158 individuals ) were collected for evaluation in the Ag-ELISA as samples of ‘unknown status’ ( median = 2 , range: 1–10 ) . These included 94 females and 64 males; 60 subadults and 98 adults . Urine samples were collected from the ground immediately after urination using Whatman Qualitative Filter Papers ( Grade 4 , 11 . 0 cm ) . After urination , as much urine as possible was soaked up from the ground with a filter paper . The filter paper was folded and stored in a 2-oz Whirl-Pak bag , which was labeled with the unique code associated with the individual , date , and time . Approximately 1 g of indicating silica desiccant was added to each bag to ensure samples remained dry and to prevent mold growth . Samples were processed and analyzed using the B158/B60 ELISA ( Institute for Tropical Medicine , Antwerp ) in the Immunochemistry Laboratory of the Division of Parasitic Diseases and Malaria at the Centers for Disease Control and Prevention ( CDC ) in Atlanta , Georgia . To aid in identifying urine stains on the filter papers , we viewed each paper under a UV light ( long-wave , 365 nm; Spectroline Model ENF-240c ) , and used an office hole puncher to remove four circles ( ~6 mm diameter ) from the part of each filter paper that was soaked on both sides . The hole puncher was sterilized and dried after each use to prevent cross-contamination . The four circles taken from each sample were placed into a single labeled 2 mL sample tube . Each sample was reconstituted with 1 mL blocking buffer ( PBS-Tween 20 + 1% newborn calf serum ( NBCS ) , existing CDC collection ) and vortexed . Following [73] , polystyrene ELISA plates ( Nunc Maxisorp flat-bottom 96 well ) were coated and incubated with the capture antibody ( B158C11A1 monoclonal antibody in a sensitization buffer ( carbonate bicarbonate buffer , pH 9 . 5 ) ) . Each plate included 80 unknown samples , 4 known negative human samples , and 2 positive control samples created by spiking known negative human samples ( existing CDC collection ) with 0 . 125 μg antigen/1 mL urine T . crassiceps antigen ( soluble protein extract ) . A standard curve ( 2-fold serial dilutions of known negative human urine samples spiked with T . crassiceps antigen ) was included on each plate as an additional control . After a washing step ( 1x ) , plates were coated and incubated with 150 μL/well of blocking buffer , and then loaded and incubated with 100 μL from each sample . After a washing step ( 4x ) , plates were coated and incubated with 100 μL of detecting antibody dilution ( B60H8A4 + blocking buffer ) . Plates were washed ( 1x ) and subsequently loaded and incubated with 100 μL of Streptavidin-horseradish peroxidase ( HRP ) dilution ( Peroxidase-conjugated Streptavidin 1:10 , 000 dilution , Jackson ImmunoResearch Laboratories , West Grove , PA , in blocking buffer ( 0 . 1ug/ml ) ) . Plates were washed ( 1x ) and then loaded with 100 μL of Tetramethylbenzidine ( TMB ) ( 1-step Ultra TMB-ELISA , ThermoFisher Scientific , USA ) and shaken at room temperature for two minutes . After the addition of 100 μL of stop solution ( 1M sulfuric acid; H2SO4 , EMD Millipore , Darmstadt , Germany ) to each well , the optical densities ( OD ) of samples were read in the VersaMax ELISA Microplate Reader ( Molecular Devices , Sunnyvale , CA , USA ) at 450 nm ( see S1 Text for detailed protocol ) . If more than one control on a plate failed , the entire plate was repeated . The index value ( IV ) for each sample relative to the positive and negative controls on each plate was calculated using the following formula: IV=SampleOD−Average ( NegativeControlsOD ) /Average ( PositiveControlsOD−Average ( NegativeControlsOD ) We assessed the sensitivity and specificity of the Ag-ELISA with a receiver operating characteristic ( ROC ) curve [89] . The nature of working in a wild system precludes establishing a negative ‘gold standard’ because we are unable to confirm negative diagnoses with serological or imaging techniques . Thus , we used unweaned infants as ‘true negatives’ ( n = 58 samples ) , because they do not yet consume grass and are thus minimally exposed to T . serialis eggs and can be considered likely to be negative . We used individuals presenting with T . serialis cysts as ‘true positives’ ( n = 58 samples ) . We selected the point on the ROC curve at the shortest distance from the coordinate ( 0 , 1 ) as the optimal threshold IV for classifying a sample as positive or negative . ROC analysis was performed with the package “pROC” [90] in R [91] . To investigate if sex and age predicted the occurrence of cysts among adults and subadults ( n = 158 individuals ) , we used logistic regression implemented in the ‘glm’ function in the R package ‘stats’ [91] . We coded age as a continuous variable based on known or estimated birthdates for individuals . Model selection was performed with Akaike information criterion ( AICc ) , which selects the optimal model based on maximum likelihood [92] with a finite sample size [93] . To investigate if sex and age predicted the occurrence of antigen-positive samples ( i . e . , those with an IV greater than the IV threshold from the ROC analysis ) among adults and subadults without cysts ( n = 412 samples , 158 individuals ) , we used a generalized linear mixed effects model ( GLMM ) implemented with the ‘glmer’ function in the ‘lme4’ package in R [94] . We used binomial errors with a logit link function , and included age and sex as fixed effects . Because individuals were sampled at varying intensities and may have had different individual risks of infection , we included individual identity as a random effect . We coded age in the following two ways: ( 1 ) as a continuous variable based on known and estimated birthdates; and ( 2 ) as an ordered categorical variable with two levels based on developmental stage ( i . e . , subadult or adult ) . Continuous age is expected to be a relevant predictor of infection if accumulated exposure to T . serialis eggs in the environment drives risk , whereas categorical age based on developmental stages may be more relevant if hormonal factors are a major driver of risk . We compared the fit of the continuous age and categorical age models using AICc and calculated averaged coefficients for each variable using model averaging . All research was approved by the University Committee on the Use and Care of Animals at the University of Michigan ( UCUCA protocol #09554 ) , the Duke University Institutional Animal Care and Use Committee ( IACUC protocol #A218-13-08 ) , and followed all laws and guidelines in Ethiopia . This research adhered to the standards presented in the Guide for the Care and Use of Laboratory Animals ( National Research Council of the National Academies , 8th Edition ) and the Animal Care Policy Manual ( United States Department of Agriculture , 2016 ) .
Our measurement of infection status using the described Ag-ELISA was highly accurate . The ROC analysis revealed the optimal threshold IV to be 42 . 1 , with 98 . 4% specificity ( 95% CI: 95 . 1–1 ) , 98 . 5% sensitivity ( 95% CI: 95 . 6–1 ) and an area under the curve ( AUC ) of 0 . 99 ( 95% CI: 0 . 9937–1; Fig 2 ) . We identified only one likely false positive ( i . e . , an infant with a positive sample ) ( 98 . 2% , 56/57 ) , and one false negative ( i . e . , an individual with a cyst and a negative sample ) ( 98 . 3% , 57/58 , Table 1 ) . Because sample quality was difficult to evaluate with our collection technique , we binned samples into either ‘positive’ or ‘negative’ categories based on the IV cutoff instead of conducting analysis at the level of sample OD . This conservative approach permits for the broad designation of samples as positive or negative for antigen presence , but precludes analyses that address fluctuations or activity in sample OD . Twenty-six of 158 individuals without visible cysts ( 16 . 4% ) tested positive at least once . This included 14 females and 12 males , of which 6 were subadults and 20 were adults . All but one sample from an individual with a visible cyst fell above the optimal cutoff ( Fig 3 ) , indicating that samples from individuals with cysts had generally higher logged index values ( IVs ) than individuals without cysts . Importantly , 2 individuals without cysts that tested antigen-positive developed observable cysts within 7 months of sampling . One of these individuals had one negative and one positive sample in the 3 months prior to exhibiting an observable cyst , after which all of his samples were positive . The other individual had one positive sample 7 months before exhibiting an observable cyst , after which all of her samples were positive . To search for evidence of established T . serialis infection in individuals without visible cysts , we focused on individuals that were sampled at least 5 times during the study period ( 21 adults , 2 subadults ) . We found that some individuals without cysts were consistently positive for T . serialis antigen , others were consistently negative , and still others switched between antigen-positivity and antigen-negativity throughout the study period . Twelve individuals showed no antigen-positive samples , 2 showed a clear majority of positive samples ( one with 8/9 positive samples , one with 9/10 positive samples ) , and 7 individuals had a single positive sample within a sequence of negative samples . The remaining 2 individuals showed an interesting mixture of positive and negative samples: one individual tested positive in 3 consecutive months , and then negative 7 months later . The other displayed a sequence of negative and positive samples within 6 months . We investigated the predictors of visible cysts , focusing on age , sex , and the interaction between these two variables . AICc model selection revealed the models with the most support to include age ( in years ) , sex , and an interaction between age and sex as predictor variables , with the model including only age garnering the most support ( Table 2 ) . The importance of age and lack of effect of sex were reinforced with the results of full model averaging , which showed that increasing age was the strongest predictor of cysts across all models ( Table 3 ) . We then investigated the predictors of antigen-positivity in urine samples , again including age , sex , and the interaction between these two variables as predictors . One analysis included age coded categorically , whereas the other included age coded continuously , and both included individual ID as a random intercept to account for repeated sampling from individuals . In the first analysis ( categorical age ) , AICc model selection showed that the model with the most support included only the random intercept ( individual ID ) and no fixed effects ( i . e . , age , sex , and the interaction did not appear as predictors of Taenia antigen-positivity in samples , Table 4 ) . A model including age and the random intercept was less supported than the model containing only the random intercept ( Table 4 ) . Full model averaging revealed age to be a weaker predictor of antigen-positivity than the random intercept ( Table 3 ) . Results were similar for the analysis that used age coded as a continuous variable . The model with the most support included only the random intercept and no fixed effects ( Table 4 ) , which was also reflected in the model averaging estimates ( Table 3 ) .
Positive antigen samples are highly likely to reflect active larval growth ( i . e . , true infections ) and not merely the presence of eggs passing through the gastrointestinal tract , because this assay identifies active infection by detecting glycoproteins produced by taeniid metacestodes and not oncospheres ( this also precludes the possibility that positive antigen samples reflect atypical growth of the adult stage of the tapeworm in geladas ) [75 , 95] . We postulate that individuals without cysts that presented with high log ( IV ) samples should be considered positive for Taenia antigen and are likely to harbor active infections that are not visible as cysts to observers , whether because ( 1 ) the infection is young and has not yet had time to develop into a visible cyst; or ( 2 ) the infection is advanced but is located deep in the abdominal cavity or somatic tissues and will never become visible . It is highly unlikely that the samples positive for antigen presence are all false positives: based on the false positive rate of 1 . 79% calculated using the “known negative” infant set ( in which 1 out of 57 samples from unweaned infants tested positive ) , the expected number of false positives is 8 . 4 , and the probability of observing 50 or more false positives in 412 samples is less than p = 10−25 . These two possibilities–that positive assay results indicate young infections or fully developed internal cysts–are not mutually exclusive . In support of the interpretation of a positive antigen result as ( 1 ) reflecting the presence of young cysts that are not yet observable externally , 2 individuals that tested positive with no external cysts at the time of sample collection developed cysts within a year of sampling . In support of the interpretation of a positive antigen result as ( 2 ) reflecting the presence of advanced infections in deep tissue that will never become visible to observers , early necropsies of wild-caught captive geladas revealed fully developed , non-protruding cysts in the abdominal cavities , deep musculature , and viscera [39–41 , 79–83] . Thus , positive assay results in the absence of observable cysts may reflect either young infections or advanced infections in undetectable locations . Interestingly , we observed switches in infection status ( antigen-positive or antigen-negative ) within individuals without cysts ( i . e . , positive to negative and vice versa ) . Among 23 well-sampled individuals without cysts ( i . e . , 5 or more samples ) , only 2 had a clear majority of antigen positive samples , whereas 12 had no positive samples , 7 had just 1 positive sample , and the remaining 2 flipped from positive to negative during the study period . The observed switches in infection status may reflect either ( 1 ) the inability of some larvae to persist; or ( 2 ) the ability of hosts to control or eliminate their infections through calcification ( although caveats in data certainty must also be considered , such as incorrect individual identification during sample collection ) . Importantly , the values of samples from these individuals were strikingly different enough ( i . e . , not close to the cutoff on either side ) to make it unlikely that variation in sample quality was behind this pattern . A similar phenomenon was described in humans with T . solium cysticercosis , with 3 . 5% of 867 participants exhibiting a single positive sample in between 2 negative samples [96] . The authors postulated that this short-term antigen presence could owe to incomplete parasite formation or to effective host defenses that enable clearance of the parasite . In geladas , short-term antigen presence may indicate low T . serialis egg viability or highly effective host immune responses that result in stunted infections or incomplete parasite establishment . Indeed , experimental infection of swine with T . solium eggs demonstrated low rates of infection establishment even with high infectious doses [97] . Attempts by the host immune system to control infection may not always be successful; for example , one individual tested positive once and negative once in the 3 months before developing an external cyst , after which he consistently tested positive . This may indicate a process in which the host attempted to mount an immune response and was fleetingly able to control the infection before succumbing . Early stages of infection may also release antigens less reliably , which would make early infection difficult to detect . Future work that combines frequent longitudinal urine sampling from known individuals while monitoring for external signs of disease is needed to better understand the frequency and health consequences of transient T . serialis infections . The higher occurrence of cysts among older individuals is consistent with previous studies of T . serialis cyst prevalence in geladas [43 , 44] , whereas the lack of support for a strong relationship between age and antigen-positive samples was unexpected . Together , these results suggest that susceptibility to infection does not vary strongly with age , and that cysts may take years to develop to a stage at which they protrude and are visible to observers . Contrary to our predictions based on the increased female susceptibility observed in other larval taeniid systems [85 , 86] or the female-bias in data collection , we found no evidence for a sex bias in either T . serialis cysts or antigen-positivity in samples . The lack of support for increased susceptibility with age or sex suggests that susceptibility to T . serialis in geladas may not be hormonally modulated . Further research is needed to elaborate the physiological and ecological drivers of susceptibility and exposure in this system . Ongoing research is exploring the relationship between co-occurrence of gastrointestinal parasites and T . serialis infection in geladas , and research is planned to investigate the associations between measurements of stress ( fecal glucocorticoid concentrations ) and susceptibility to T . serialis infection and the development of cysts . Future studies should additionally consider other potential drivers of susceptibility and exposure to T . serialis , such as seasonal changes in T . serialis egg distribution and gelada ranging patterns and differences in social behavior that affect risk . Articulating the risk factors associated with infection in geladas may inform the understanding of the danger T . serialis poses to other primates , including humans , as well as the control of infections . If exposure is the central driver of infection , then humans and nonhuman animals that overlap significantly with T . serialis definitive hosts may be at the highest risk for infection and can thus be targeted for control efforts . While research has shown that T . serialis cysts substantially increase gelada mortality [45] , there is no indication that this infection threatens population-level persistence . Continuous monitoring of T . serialis and mortality in this population will determine whether future interventions are necessary . The use of dried urine for larval Taenia infection diagnosis provides the substantial benefits of not requiring refrigeration or invasive procedures; thus , it is well suited to the identification of Taenia infections in wildlife inhabiting remote areas . However , this approach has one notable drawback: this assay is genus-specific , not species-specific , and will pick up antigens from any Taenia species . Thus , other methods must be used for species-level identification . If it is possible to obtain tissue from the cyst of an infected individual ( from a dead individual , as in [42] , or from leaked cystic material , as in [43] ) , genetic methods can be used to identify the parasite to the species-level . Non-lethal traps may be employed in studies of smaller species ( e . g . , lining the trap floor with filter paper for urine collection prior to release ) , and fecal analysis of carnivore hosts sympatric with the target intermediate host species may also be employed to identify the taeniid species active in a given system . In future applications of this method , the potential for cross-reactions should be considered . Infection with parasites in the Trypanosoma genus may give rise to a cross-reaction on this assay [98] , and thus must be taken into account in the interpretation of assay results in Trypanosoma-endemic areas . Because geladas inhabit cool , high-altitude habitats that are free of the tsetse flies that carry Trypanosoma parasites [99 , pers . obs . ] , and because infection with mechanically transmitted Trypanosoma spp . is unlikely in African primates , this cross-reaction was not considered in the interpretation of our results . In conclusion , the global distribution and flexibility in intermediate host selection of many taeniid species make them critically important to monitor for global human and animal health . The adaptation of a serum protocol for the detection of Taenia infections for use with dried urine samples is a useful and pioneering step towards a complete understanding of the dynamics of Taenia infection in wildlife . While this assay cannot be used as a stand-alone diagnostic technique , particularly given its genus-wide specificity , it holds great value for studies of infection dynamics in host populations where regular invasive monitoring is impractical and in areas where sample storage prohibits the collection of wet urine samples . | Although tapeworm parasites of the genus Taenia are globally distributed and inflict enormous socioeconomic and health costs on their hosts , which include humans , little is known about taeniid tapeworms that infect wildlife . This gap in knowledge prevents an assessment of the potential for these parasites to infect humans and production animals and is largely due to the difficulty of conducting standard diagnostic tests on wildlife . To address this gap , we adapted a standard diagnostic assay to be used with dried urine samples . We used urine from geladas , primates endemic to Ethiopia , which are frequently infected with the larval stage of a taeniid tapeworm and exhibit protuberant cysts during advanced infection . The use of this diagnostic test in a wild gelada population allowed us to detect that individuals can be infected without exhibiting observable cysts , and that some individuals may control infection in its early stages . This tool provides information about how a neglected tapeworm functions in a wildlife system and opens the door to the non-invasive identification of tapeworm reservoir hosts that may threaten humans . | [
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] | 2017 | Identifying wildlife reservoirs of neglected taeniid tapeworms: Non-invasive diagnosis of endemic Taenia serialis infection in a wild primate population |
Interferons ( IFNs ) target macrophages to regulate inflammation and resistance to microbial infections . The type II IFN ( IFNγ ) acts on a cell surface receptor ( IFNGR ) to promote gene expression that enhance macrophage inflammatory and anti-microbial activity . Type I IFNs can dampen macrophage responsiveness to IFNγ and are associated with increased susceptibility to numerous bacterial infections . The precise mechanisms responsible for these effects remain unclear . Type I IFNs silence macrophage ifngr1 transcription and thus reduce cell surface expression of IFNGR1 . To test how these events might impact macrophage activation and host resistance during bacterial infection , we developed transgenic mice that express a functional FLAG-tagged IFNGR1 ( fGR1 ) driven by a macrophage-specific promoter . Macrophages from fGR1 mice expressed physiologic levels of cell surface IFNGR1 at steady state and responded equivalently to WT C57Bl/6 macrophages when treated with IFNγ alone . However , fGR1 macrophages retained cell surface IFNGR1 and showed enhanced responsiveness to IFNγ in the presence of type I IFNs . When fGR1 mice were infected with the bacterium Listeria monocytogenes their resistance was significantly increased , despite normal type I and II IFN production . Enhanced resistance was dependent on IFNγ and associated with increased macrophage activation and antimicrobial function . These results argue that down regulation of myeloid cell IFNGR1 is an important mechanism by which type I IFNs suppress inflammatory and anti-bacterial functions of macrophages .
Interferons ( IFN ) are critical mediators of host immunity . These cytokines are classified into two major groups: type I and type II . Type I IFNs are a family of homologous proteins including various IFNα subtypes and a single IFNβ [1 , 2] . They are synthesized and secreted in response to ligation of transmembrane or cytosolic pattern recognition receptors ( PRR ) by a variety of microbial products [3] . All type I IFNs signal through a commonly expressed heterodimeric receptor , IFNAR . Ligation of IFNAR activates TYK2 and JAK1 kinases to phosphorylate signal transducers and activator of transcription ( STAT ) proteins 1 and 2 [2 , 4] . These activated STATs associate with IRF9 to induce transcription of numerous IFN-stimulated genes ( ISGs ) whose products restrict viral replication [1] . Type I IFNs additionally impact cell survival and can have anti-inflammatory effects [3] . They are used therapeutically in treatment of chronic viral infections , cancers , and the autoimmune disease multiple sclerosis . IFNγ is the only type II IFN . It signals through its own ubiquitously-expressed heterodimeric receptor ( IFNGR ) comprised of IFNGR1 and IFNGR2 subunits . IFNγ binds directly to IFNGR1 to aggregate receptor subunits such that one signaling complex contains two IFNGR1 and two IFNGR2 chains [2 , 4 , 5] . This aggregation activates JAK1 and JAK2 kinases to phosphorylate STAT1 proteins [2 , 4] . Phosphorylated STAT1 proteins homodimerize and translocate to the nucleus to initiate transcription of IFNγ-activated genes ( GAG ) [2 , 4] . Several antiviral proteins are both ISGs and GAGs , but IFNγ uniquely stimulates macrophages to express certain GAGs that promote inflammatory responses ( e . g . TNF-α , IL-12 , CXCL9 , CXCL10 ) , contribute to antigen processing and presentation to T cells ( e . g . MHC class I and II , CD40 , CD80 , CD86 , the high-affinity Fc receptor CD64 ) , and promote macrophage bactericidal activity ( e . g . nitric oxide synthase 2 , NOS2 , and NADPH oxidase subunits ) [6 , 7] . By inducing expression of these and other GAGs , IFNγ drives M1-type pro-inflammatory and microbicidal activation in macrophages . IFNγ is vital for host resistance to bacterial infections , while type I IFNs have the opposite effect of increasing host susceptibility . Thus , mice lacking IFNγ , IFNGR1 , or critical signaling components such as STAT1 are exquisitely susceptible to infections by Mycobacteria tuberculous , Listeria monocytogenes and other pathogens [8–12] . By contrast , mice unresponsive to type I IFNs are 100–1000 times more resistant to mucosal and systemic infections by M . tuberculosis , M . leprae , L . monocytogenes , and numerous other pathogenic bacteria [3 , 4 , 13 , 14] . These data indicate type I IFNs normally increase susceptibility . Precisely how they do so is debated . It is clear type I IFNs reduce production of several myeloid cell-active cytokines and chemokines with presumed protective functions and increase expression of anti-inflammatory factors [15–20] . Thus , detrimental effects of type I IFNs appear to impair myeloid cell immunity [3 , 4 , 13 , 14] . However , it is not agreed how type I IFNs mediate this suppression of myeloid cells . Type I IFNs may primarily act through induction of IL-10 [15–18 , 20–22] , or by suppressing inflammasome activation and IL-1 production in myeloid cells responding to infection [23–25] . Alternatively , we previously suggested type I IFNs may suppress macrophage activation due to their ability to silence myeloid cell ifngr1 transcription and down regulate macrophage IFNGR1 [26–28] . To distinguish between the above models and test the importance of type I IFN driven down regulation of IFNGR1 , we developed a transgenic mouse line in which a functional FLAG-tagged IFNGR1 ( fGR1 ) is selectively expressed in macrophages using the well-characterized c-fms promoter [29 , 30] . Staining for the FLAG epitope revealed the fGR1 transgenic IFNGR1 was expressed at very low levels selectively on macrophages and inflammatory monocytes . Total cell surface IFNGR1 on macrophages was equivalent for unstimulated control and fGR1 mice . Control and fGR1 macrophages also responded similarly to stimulation with IFNγ . When stimulated with type I IFN , fGR1 macrophages showed similar induction of canonical responses as control cells but selectively retained more cell surface IFNGR1 . The ability to resist IFNGR1 down regulation enabled cultured fGR1 macrophages to respond better to IFNγ following stimulation with type I IFN . In fGR1 mice , macrophages also showed increased evidence of M1-type activation during systemic infection with the type I IFN-stimulating bacterial pathogen L . monocytogenes . Further , bacterial burdens were significantly reduced in the fGR1 mice . Heightened resistance of fGR1 mice was dependent on host production of IFNγ and associated with significantly increased bacterial uptake and containment in lysosomes , as measured by reduced overall bacterial burdens in fGR1 cells and increased co-localization of GFP expressing L . monocytogenes with the lysosomal marker LAMP-1 . These data indicate that preventing type I IFN stimulated down regulation of myeloid cell IFNGR1 during bacterial infection increases host resistance by permitting increased macrophage responsiveness to IFNγ and thus increased macrophage activation and bactericidal functions . We conclude that down regulation of myeloid cell IFNGR1 contributes significantly to the enhanced susceptibility associated with type I IFN production during bacterial infection .
A c-fms promoter construct driving expression of cDNA encoding IFNGR1 with an N-terminal FLAG-epitope tag was used to generate fGR1 transgenic mice . The endogenous c-fms promoter drives expression of the CSF1R , which is selectively expressed in myeloid cells [30] . Transgenic mice developed normally and transmitted the fGR1 transgene with a Mendelian inheritance pattern . Leukocyte frequencies were similar in the spleen , peritoneum , liver , lungs , and intestines of naïve WT C57Bl/6 and fGR1 mice , as was expression of activation markers such as MHC II ( S1A and S1B Table ) . Thus , fGR1 expression failed to notably impact hematopoietic cell development or activation in the absence of infection . Commercial and custom antibodies directed at the FLAG epitope stained macrophages and monocytes but not other cell populations from the fGR1 mice . Fig 1A illustrates typical anti-FLAG staining on gated CD90 . 2- , CD11bhi , F480hi macrophages ( Left ) and CD90 . 2+ ( Thy1+ ) T cells ( Right ) isolated from the peritoneum of naïve WT and fGR1 mice . Staining for fGR1 was also weak and restricted to monocytes and macrophages in all other examined tissues ( S1 Fig ) . Total cell surface IFNGR1 staining was equivalent on fGR1 and WT C57Bl/6 macrophages ( Fig 1B ) , indicating fGR1 did not increase overall cell surface IFNGR1 . To confirm responsiveness of the fGR1 transgenic receptor to IFNγ , the fGR1 transgene was crossed to B6 . ifngr1-/- mice . Macrophages from the resulting fGR1 x ifngr1-/- ( fGR1 x GR1 KO ) mice were then stimulated with increasing concentrations of IFNγ . Phosphorylation of STAT1 ( tyrosine 701 ) was equivalent in fGR1 x GR1 KO and WT C57Bl/6 macrophages , even though total STAT1 was observed to be increased in ifngr1-/- macrophages regardless of whether they also expressed fGR1 ( Fig 1C ) . Collectively , these data demonstrated myeloid cell-restricted expression and functionality of the FLAG-tagged IFNGR1 in the fGR1 mice . We next evaluated STAT1 Y701 phosphorylation early after IFNγ stimulation of WT and fGR1 peritoneal macrophages . WT and fGR1 macrophages responded similarly to 30 min stimulation with various concentrations of IFNγ ( Fig 2A ) . Likewise , kinetics of STAT1 phosphorylation were not affected by fGR1 expression , as determined by quantitative immunoblotting of lysates from WT and fGR1 macrophages 5 , 30 , or 60 min after treatment with 100 units ( U ) /mL of IFNγ ( Fig 2B ) . As an indicator of later responses to IFNγ , we measured induction of MHC II in macrophages . MHC II is a GAG whose induction requires multiple rounds of ligand-receptor binding and subsequent receptor internalization [31] . Overnight stimulation with IFNγ induced similar upregulation of cell surface MHC II in WT , B6 . ifnar1-/- , and fGR1 macrophages ( Fig 2C ) . Thus , in the absence of type I IFN stimulation fGR1 expression did not appreciably alter early or delayed macrophage responsiveness to IFNγ . In contrast to our experiments above showing no effects of fGR1 on basal macrophage IFNGR1 , fGR1 macrophages were nearly as resistant as B6 . ifnar-/- macrophages to IFNβ stimulated down regulation of IFNGR1 ( Fig 3A ) . Indeed , IFNGR1 staining in the IFNβ-treated fGR1 macrophages were not significantly lower than unstimulated controls or staining on B6 . ifnar1-/- macrophages . This was not due to altered sensitivity of fGR1 macrophages to type I IFN . Rather , WT and fGR1 peritoneal macrophages responded similarly to IFNβ stimulation as measured by STAT1 phosphorylation ( Y701 ) at 5 , 30 , and 60 minutes ( Fig 3B ) , and similar upregulation of MHC I after overnight IFNβ stimulation ( Fig 3C ) . We next investigated how retaining IFNGR1 affects functional responses in the fGR1 macrophages . Even modest reductions in cell surface IFNGR1 associate with impaired responsiveness to IFNγ and MHC II upregulation [28 , 32 , 33] . We thus evaluated upregulation of cell surface MHC II as a sensitive measure of IFNγ responsiveness . Treatment of WT C57Bl/6 but not B6 . ifnar1-/- peritoneal macrophages with IFNβ significantly impaired upregulation of MHC II in response to IFNγ ( Fig 3D ) , consistent with reductions IFNGR1 ( Fig 3A ) . Induction of MHC II under these circumstances was significantly higher in fGR1 versus WT C57Bl/6 macrophages . This indicates that fGR1 expression overcame the suppressive effects of type I IFN . Nevertheless , IFNβ-treated fGR1 macrophages did not upregulate MHC II as effectively as B6 . ifnar-/- cells treated with IFNβ and IFNγ ( Fig 3D ) . This difference likely reflects the modest reduction in IFNGR1 observed in the IFNβ-treated fGR1 macrophages ( Fig 3A ) . In conclusion , fGR1 largely ( though not completely ) restored IFNGR1 expression and responsiveness to IFNγ signaling in macrophages exposed to type I IFN . Deficiency in IFNAR1 expression renders mice highly resistant to infection by diverse pathogens , including L . monocytogenes [20–22 , 28 , 34] . The resistance seen in type I IFN-unresponsive mice is characterized by similar L . monocytogenes burdens early after infection , but 100–1000 fold reductions by 3–4 dpi [20–22 , 28 , 34] . To assess how blunting IFNGR1 down regulation might impact bacterial infections , parallel groups of WT and fGR1 mice were inoculated with a sublethal dose of L . monocytogenes . Bacterial burdens in the livers ( Fig 4A ) and spleens ( Fig 4B ) were equivalent in both groups of infected mice through 48 hpi , though the ~1 . 5 fold reduction in the spleens of fGR1 mice at 24 hpi was significant due to tight grouping of the data . At 72 hpi and beyond , fGR1 mice showed significantly enhanced resistance as illustrated by >70-fold fewer bacteria at 96 hpi ( Fig 4A and 4B ) . Variability in bacterial burdens seen in the control and fGR1 mice likely results from pooling of data from experiments performed over an extended time period . These data indicate that fGR1 mice were consistently and significantly more resistant to systemic L . monocytogenes infection . Type I IFN signaling can stimulate leukocyte apoptosis and correlates with reduced numbers of CD11b+ cells in spleens early after L . monocytogenes infection [20–22] . Consistent with these reports , IL-17 , CXCL2 , and neutrophil accumulation were modestly increased in tissues of IFNAR1-deficient mice infected with Francisella tularensis or Streptococcus pneumoniae [17 , 35] . We thus asked if the enhanced resistance of fGR1 mice to L . monocytogenes might be associated with increased recruitment of inflammatory myeloid cells to infected tissues . Both CD11b+Ly6C+Ly6Glo monocytes and CD11b+Ly6C+Ly6Ghi neutrophils accumulated in L . monocytogenes infected spleens over the first 96 hpi , but the frequency and number of these cells were comparable between WT and fGR1 mice ( S2A and S2B Fig ) . Furthermore , fGR1 expression did not restore the loss of T cells during infection ( S2C Fig ) . These same results were also noted in the liver of WT and fGR1 mice ( S3A and S3B Fig ) . We nonetheless confirmed that numbers of neutrophils , monocytes and T cells in the spleens of infected mice were significantly increased by blockade of IFNAR1 with an anti-IFNAR1 antibody ( MAR-1 ) ( S4A and S4B Fig ) . These data indicate that type I IFNs similarly suppress recruitment or survival of neutrophils , T cells , and inflammatory monocytes in both WT and fGR1 mice . Therefore , restoration of myeloid cell IFNGR1 can increase host resistance even in the presence of these other suppressive effects . Anti-FLAG staining was observed on inflammatory monocytes , but not neutrophils , from spleens of L . monocytogenes-infected fGR1 mice ( Fig 5A ) . Focusing on this population , we evaluated the expression of IFNGR1 and several pro-inflammatory activation markers on monocytes within naïve WT and fGR1 mice and observed no differences ( S5A and S5B Fig ) . However , during infection FLAG staining correlated with an increase in cell surface IFNGR1 on monocytes from the infected fGR1 mice , when compared to those of infected WT C57Bl/6 mice ( Fig 5B ) . Albeit , IFNGR1 staining on fGR1 monocytes was still significantly lower than on monocytes from uninfected spleens ( Fig 5B ) . These results indicate that the low expression of fGR1 enables monocytes to retain higher amounts of IFNGR1 during L . monocytogenes infection . IFNγ signaling activates macrophages and monocytes to adopt an “M1” pro-inflammatory and anti-microbial state . This M1 phenotype is associated with increased expression of cell surface markers such as MHC II , CD80 , and FcγR1 ( CD64 ) . Consistent with their higher IFNGR1 staining , inflammatory monocytes from fGR1 mice had significantly increased cell surface staining for each of these IFNγ-inducible markers ( Fig 5C ) . The increased expression of these markers was not associated with increased production of IFNγ in the L . monocytogenes infected fGR1 mice ( S6 Fig ) . Rather , these results suggest that the increased retention of cell surface IFNGR1 on monocytes from the fGR1 mice enables them to receive a stronger signal from the IFNγ present . To ask more directly if the increased resistance of fGR1 mice was dependent on IFNγ signaling , we pre-treated groups of mice with 500 μg of anti-IFNγ ( XMG1 . 2 ) to neutralize IFNγ during early stages of L . monocytogenes infection . Mice were given a lower bacterial dose ( 5 x 103 CFU ) to prevent early deaths . Results showed that neutralizing IFNγ completely abrogated the heightened resistance of the fGR1 mice ( Fig 6A ) . Neutralization of IFNγ did not affect cell surface IFNGR1 staining on monocytes from the fGR1 mice , which remained significantly increased compared to infected WT C57Bl/6 mice ( Fig 6B ) . However , the increased expression of MHC II , CD80 , and CD64 seen in IFNγ-replete fGR1 monocytes was abrogated in the IFNγ-depleted fGR1 mice ( Fig 6C ) . These data demonstrate that IFNγ is necessary for the increased resistance to L . monocytogenes as well as the increased M1 activation of inflammatory monocytes in the fGR1 mice . We next asked if the increased resistance to systemic infection in fGR1 mice was associated with reduced ability of L . monocytogenes to persist in monocytes with increased IFNGR1 expression and M1 activation . Since bacterial burdens in the WT and fGR1 mice first significantly diverged between 48 and 72 hpi ( Fig 4 ) , we examined bioparticle uptake , phagosome maturation , and L . monocytogenes survival at 60 hpi . Bacterial burdens did not significantly differ between WT and fGR1 mice at this timepoint ( Fig 7A ) , though there was a modest reduction in burdens in the fGR1 spleens . Mice were first inoculated with GFP-expressing L . monocytogenes and we used flow cytometry to quantify GFP fluorescence in gated inflammatory monocytes . The frequency of monocytes with high amounts of GFP fluorescence was significantly higher in the WT cells ( Fig 7B and 7C ) . We then sorted monocytes and plated lysates to calculate CFU per monocyte . Again , we observed a significant ( ~3-fold ) reduction in the number of live L . monocytogenes recovered from the fGR1 cells ( Fig 7D ) . Together , these data demonstrate that fewer fGR1 monocytes were productively infected by L . monocytogenes . We considered that the reduced frequency of infected fGR1 inflammatory monocytes and the reduced bacterial burdens within these monocytes might reflect defective uptake of bacterial particles . To address this , we used a bead-based approach to enrich for CD11b+ cells from spleens of L . monocytogenes-infected mice ( see S7 Fig ) . The purified cells were then incubated with fluorescence-labeled Staphylococcus aureus Bioparticles for 30 min incubation at 37°C . The bioparticles were labeled with a pH-sensitive dye ( pHrodo ) that only fluoresces at an acidic pH . Hence , we used flow cytometry to evaluate uptake of the particles into acidified compartments in gated inflammatory monocytes . Results indicated that the proportion of fGR1 monocytes staining bright for pHrodo was increased significantly , as was the MFI of pHrodo staining in these cells ( Fig 7E and 7F ) . We interpret these data to indicate that inflammatory monocytes expressing fGR1 have an enhanced ability to phagocytose bacteria into compartments that are maturing . Thus , the reduced frequency of infected inflammatory monocytes in the fGR1 mice was not due to impaired phagocytosis . The pathogenesis of L . monocytogenes involves bacterial escape from phagosomal compartments into the cytosol of infected cells , where the bacterium undergoes rapid replication . L . monocytogenes that are retained in phagosomes are unable to replicate and can be degraded following phagosome fusion with LAMP-1+ lysosomes [36 , 37] . We thus next asked whether reduced bacterial burdens and heightened IFNγ responsiveness seen in fGR1 monocytes during systemic in vivo infection might be associated with improved ability of myeloid cells to restrict L . monocytogenes to phagosomal compartments . Here , we again used beads to negatively enrich myeloid cells ( S7 Fig ) from spleens of WT and fGR1 transgenic mice at 60 hpi with GFP-expressing L . monocytogenes . We then assessed the abundance of GFP bacteria and the frequency of their co-localization with the lysosomal marker LAMP-1 using fluorescence microscopy . Staining of the isolated cells revealed the presence of bacteria ( green ) in CD11b+ cells ( blue ) ( Fig 7G ) . In cells from WT B6 mice , bacteria were rarely observed to co-localize with LAMP-1 ( red ) . This result is consistent with bacterial persistence in immature phagosomes or escape into the cell cytoplasm . By contrast , in cells from infected fGR1 mice bacteria frequently co-localized with LAMP-1 ( Fig 7G , right ) . Using the cell function of the Imaris software suite we unbiasedly quantified these differences . The results showed that more CD11b+ cells from the fGR1 mice contained low numbers of L . monocytogenes bacteria and fewer of these cells contained high numbers of bacteria ( Fig 7H ) . Furthermore , the proportion of bacteria co-localized with LAMP-1 was significantly higher in CD11b+ cells from fGR1 mice that contained GFP-expressing L . monocytogenes ( Fig 7I ) . These data suggest that increased M1 activation enables fGR1 myeloid cells to better engulf and contain L . monocytogenes within mature phagosomes and phagolysosomes . Hence , despite the continued presence of other responses previously associated with “pro-bacterial” effects of type I IFNs our data demonstrate that attenuating the loss of IFNGR1 suffices to increase myeloid cell-intrinsic bacterial killing and host resistance during systemic L . monocytogenes infection .
Animal infection studies over the past decade have revealed the ability of type I IFNs to potently increase host susceptibility to diverse bacterial pathogens [3 , 14] . Gene expression patterns associated with type I IFNs have also been implicated in increased disease severity in humans with naturally-encountered M . tuberculosis and Mycobacterium leprae infections [15 , 38] . Improved understanding of the mechanisms responsible for the “pro-bacterial” effects of these cytokines thus has relevance to human health . Our studies here used a systemic L . monocytogenes infection model to provide new experimental evidence in support of the model that type I IFNs exacerbate infection , at least in part , by suppressing macrophage responsiveness to IFNγ . Based on our prior finding that type I IFNs silence de novo transcription of ifngr1 to down regulate myeloid cell expression of IFNGR1 [26 , 28] , we developed a transgenic mouse model to express FLAG-tagged IFNGR1 at low levels using the heterologous c-fms promoter . Characterization of these mice confirmed the transgenic “fGR1” receptor is selectively expressed in macrophages at a low level that did not increase overall IFNGR1 and did not alter macrophage responsiveness to IFNγ stimulation in the absence of type I IFNs . Nevertheless , macrophages expressing fGR1 resisted the loss of IFNGR1 that is normally induced by stimulation with type I IFNs and under these conditions showed increased ability to respond to IFNγ . Consistent with these findings , fGR1 mice better resisted systemic L . monocytogenes infection . The increased resistance of fGR1 mice was also dependent on IFNγ . These data support the model that suppression of macrophage IFNγ responsiveness contributes to type I IFN-dependent increase in susceptibility to L . monocytogenes and M . leprae infections [3 , 15 , 28] . In addition , our studies here showed that the increased resistance of fGR1 mice correlates with increased expression of IFNGR1 and pro-inflammatory markers by inflammatory monocytes . Further , bacterial loads were reduced in inflammatory monocytes from fGR1 mice and we observed increased association of L . monocytogenes with the lysosomal marker LAMP-1 in CD11b+ cells . These results argue that the increased ability of this myeloid cell population to retain IFNGR1 and respond to IFNγ led to increased activation of cell-intrinsic resistance mechanisms . Overall , our data suggest that blocking the ability of type I IFNs to suppress macrophage IFNGR1 restores host resistance to systemic L . monocytogenes infection by improving antimicrobial “M1” activation of monocytes recruited to sites of infection . As mentioned above , diverse other bacterial pathogens also benefit from type I IFN signaling–including M . tuberculosis [18 , 39 , 40] . Patients with untreated pulmonary tuberculosis were reported to have decreased IFNGR1 expression on circulating CD14+ monocytes compared to healthy uninfected controls [27] . Furthermore , IFNGR1 expression was restored upon antitubercular treatment in many of these patients [27] . Thus , M . tuberculosis bacteria might exploit type I IFN-induced suppression of IFNGR1 in myeloid cells . The extent to which this reduction in IFNGR1 is a cause of host susceptibility remains to be explored , however , others have proposed alternative mechanisms that likely also contribute . Specifically , type I IFNs also suppress interleukin-1 and prostaglandin production [23] , increase IL-10 production by macrophages [3 , 14] , suppress recruitment of inflammatory neutrophils [17 , 35] , and can promote cellular apoptosis [21 , 22] . Using antibody blockade of IFNAR signaling we confirmed that responsiveness to type I IFNs corresponds with suppressed recruitment or survival of inflammatory monocytes , neutrophils , and T cells in the spleens of L . monocytogenes-infected mice . However , the increased resistance seen in fGR1 mice was not associated with increased numbers of T cells or inflammatory monocytes and neutrophils . Thus , blockade of IFNGR1 down regulation appears sufficient to increase host resistance even in the presence of type I IFN-dependent reductions in T cell and inflammatory cell numbers . Previous work in our laboratory has also implicated NK cells as a major source of serum IL-10 detected early after L . monocytogenes infection [41] , and failed to see direct suppression of macrophage cell surface IFNGR1 by IL-10 [28] . These data suggest fGR1 expression and the preservation of IFNGR1 expression enhances macrophage activation independent of effects on IL-10 production . Nevertheless , additional future studies will be necessary to conclusively define the relative impact of these and other processes on type I IFN-driven increases in susceptibility to M . tuberculosis and other pathogens . The fGR1 mice may also be useful more generally to better define how altering regulation of myeloid cell IFNGR1 affects inflammatory and immune responses in other settings , including viral infections , cancer , and inflammatory/autoimmune diseases . Increased activation of myeloid cells could potentially improve host resistance by cell intrinsic , cell extrinsic , or both mechanisms . Since fGR1 inflammatory monocytes have increased expression of MHC II and CD80 we considered that the increased resistance of fGR1 mice might be associated with an increased activation of T cells . However , fGR1 expression reduced bacterial burdens within 72–96 hpi , which is prior to peak T cell activation . Instead , we observed that inflammatory macrophages had reduced bacterial burdens and evidence of increased L . monocytogenes containment within LAMP-1+ phagosomes/lysosomes . Early phagosomes are commonly marked by their association with the small GTPase Rab5a which then recruits lysosomal proteins , including LAMP-1 , to form a mature phagolysosome [36] . To escape the phagosome , L . monocytogenes must delay this phagosomal maturation by lysing the vacuole before it associates with impenetrable LAMP-1+ lysosomes . Such escape is a crucial determinant of pathogenicity [37 , 42 , 43] . Hence , improved containment of L . monocytogenes to phagosomes suffices to restrict infection . There is furthermore evidence that IFNγ stimulation of macrophages improves their ability to mediate such containment [44] . How IFNγ mediates this effect is not clear . IFNγ can modulate phagosome maturation by increasing Rab5a function [45] , but also upregulates expression of NADPH oxidase subunits [7] and guanosine triphosphatase ( GTPases ) . The latter are known to associate with bacteria-containing phagosomes [46] , and to alter vesicular trafficking and phagosomal maturation [47] . Additional studies are needed to distinguish the relative importance of these various mechanisms in mediating the resistance of fGR1 macrophages . Future studies comparing WT and fGR1 macrophages could identify additional mechanisms through which IFNγ signaling can increase macrophage resistance to L . monocytogenes infection . Humans are thought to encounter L . monocytogenes infection through ingestion of contaminated foods and clinical disease in humans is typically associated with bacteremia or meningitis . However , the impact of type I IFNs on human Listeriosis remains unclear . Mice are not highly susceptible to development of high-titer systemic L . monocytogenes infections following oral inoculation [48] . In an effort to create a better oral infection model , a L . monocytogenes strain was engineered to express a mutant InlA that binds more avidly to mouse E-cadherin [49] . L . monocytogenes expressing this “murinized” InlA ( InlAm ) show increased invasiveness for epithelial and certain non-epithelial cell types [50] , but they still fail to achieve high-titer systemic infection [51] . Using L . monocytogenes that express InlAm , two recent papers presented data suggesting ifnar1 deficiency fails to substantially increase resistance during the low-burden oral L . monocytogenes infection [51 , 52] . Low systemic bacterial burdens were correlated with low systemic concentrations of type I IFNs in these mice [51 , 52] . Because both IFN production and bacterial burdens were low , it is thus unclear how to interpret the fact that ifnar1-deficient mice did not have further reduced burdens in these studies . Low type I IFN production may have either caused or been the result of the low systemic bacterial burdens seen in these studies . Thus , while it remains to be seen how relevant the study of systemic L . monocytogenes infection is for understanding of human Listeriosis , it also remains unclear if the mouse oral infection model accurately reflects human disease . Given this and the fact that type I IFNs increase susceptibility to other bacterial infections through mucosal and non-mucosal epithelia colonized by microbes , such as the lung [18 , 23 , 25 , 40 , 53] , skin [15] , and urogenital tract [54] , we argue that it remains worthwhile to use the systemic L . monocytogenes infection model in studies of the mechanisms responsible for the pro-bacterial effects of these cytokines–as well as in studies dissecting other aspects of host/pathogen biology . In conclusion , our studies provide evidence that host type I IFNs increase susceptibility to systemic L . monocytogenes bacterial infection by promoting loss of IFNGR1 on myeloid cells . While not addressed in our work , it is plausible that this process may also be a contributing factor towards host susceptibility in other bacterial infections . Drugs that target and block this process may thus prove to be useful in the clinical management of severe disseminated Listeriosis and other systemic and mucosal bacterial infections . The development and spread of antibiotic resistance mechanisms is a worldwide problem that limits clinical options for treatment of common infections [55] . New therapies for treating bacterial infections are thus urgently needed . Development of host-directed therapeutics that target and inhibit deleterious effects of host type I IFNs appears to be an attractive approach as such host-directed therapies may fail to drive the continued development and dissemination of antibiotic resistance mechanisms and thus have potential for prolonged life in the clinic .
fGR1 transgenic mice were created by inserting IFNGR1 cDNA into BsrGI and AgeI restriction digest sites of the 7 . 2 fms-EGFP transgenic vector kindly provided by Dr . David A Hume [30] . DNA encoding a single FLAG-epitope ( DYKDDDDK ) was placed after a signal sequence between residues 6 ( glutamic acid , E ) and 7 ( aspartic acid , D ) in the N-terminal extracellular domain of the endogenous receptor [56] . Cleavage of the endogenous signal peptide is predicted to generate a mature fGR1 protein with the N-terminal sequence EDYKDDDDKD and not interfere with IFNγ binding . The plasmid was linearized and microinjected into C57Bl/6J blastocysts by the National Jewish Health transgenic mouse core facility . Mice were screened using primers specific for the transgene construct: 5’-GGAGGCGCCCACGTAGGTC and 5’-AGCTTTAACTCTGGCCCAGGC . All fGR1 transgenic mice used in these studies originated from a single founder . For experiments in Fig 1C , fGR1 transgenic mice were crossed onto the B6 . ifngr1-/- background such that myeloid cells express only fGR1 . WT C57Bl/6J control mice and B6 . ifngr1-/- mice were purchased through Jackson Laboratories and maintained in our specific pathogen free ( SPF ) colonies at National Jewish Health and the University of Colorado medical campus . B6 . ifnar1-/- were described previously [28] , and maintained in our SPF facilities . Spleens were harvested into media containing 1% penicillin/streptomycin then transferred to a solution of 1 mg/mL of collagenase type IV ( Worthington ) in HBSS plus cations ( Gibco ) . After a 25-min incubation at 37°C , spleens were passed through a 70 μM cell strainer and the cell suspension was treated with RBC lysis buffer ( 0 . 15 M NH4Cl , 10mM KHCO3 , 0 . 1 mM Na2EDTA , pH 7 . 4 ) for 3 mins . Similar to splenic preparations , livers were also harvested into penicillin/streptomycin containing media then transferred to a solution of 1 mg/mL of collagenase IV . After 30 mins at 37°C , livers were disrupted over a 70 μM cell strainer and the cells were re-suspended in 40% Percoll ( GE Healthcare ) . The 40% Percoll was underlayed with 60% Percoll , and after centrifugation liver cells were collected from the gradient interface . Red blood cells were lysed with RBC lysis buffer for 1–3 min . Blood was collected via submandibular or cardiac puncture and harvested into HBSS without cations and heparin ( Sigma ) . Cells were exposed to RBC lysis buffer for 1–3 min twice to eliminate red blood cells . Lungs were perfused with 10-mL ice cold PBS then harvested and processed the same as the spleen . The femurs were harvest and flushed with penicillin/streptomycin containing media to collect the bone marrow . The cell suspension was treated with RBC lysis buffer for 3 min . Peritoneal cells were harvested by injecting 10-mL ice cold PBS into the peritoneal cavity . Resected intestines were washed with ice-cold PBS to remove fecal content . The tissue was then cut longitudinally and minced into small 3–5 mm pieces . The intestinal pieces were vigorously vortexed in IEL solution ( HBSS minus cations , 15mM HEPES , 1 mM EDTA ) for 5 mins at least three times to remove mucus and epithelial cells . Tissues were then washed with ice-cold PBS and transferred into a LPL digestion solution containing 150 U/mL collagenase VIII ( Sigma ) , 10% FBS , 15mM HEPES , and 1% penicillin/streptomycin for 15–60 min at 37°C with vigorous shaking . Peritoneal cells were isolated from naïve mice by lavage using 10 mL of ice cold PBS from the peritoneal cavity . Non-tissue culture treated suspension plates were used to minimize cell adherence for experiments involving flow cytometric surface expression analysis . For experiments using western blot analysis , peritoneal cells were placed on tissue-culture treated plates for several hours to ensure adherence by the macrophages and non-adherent cells were removed by vigorous washes with PBS . Cells were cultured in DM10 media ( DMEM supplemented with 10% FBS , 1% sodium pyruvate , 1% L-glutamine , 1% penicillin/streptomycin ) . In experiments evaluating IFNGR1 , cells were treated with 100 Units ( U ) /mL murine IFNβ ( PBL , #12401–1 ) for 6–8 hrs . For experiments evaluating MHC II up regulation cells were treated ± 100 U/mL IFNβ for 6–8 hrs followed by treatment with 100 U/mL murine IFNγ ( LifeTechnology , #PMC-4031 ) for 18–24 hrs . For MHC I expression , cells were treated with 100 U/mL IFNβ for 24 hrs . Murine Fc receptors were blocked before staining using supernatant from hybridoma 2 . 4G2 ( rat anti-CD16/32 ) . The following mAbs were diluted in FACS buffer ( 1% BSA , 0 . 01% NaN3 , PBS ) : CD11b ( M1/70 , eBioscience ) , CD11c ( N418 , Biolegend ) , Ly6C ( Hk1 . 4 , eBioscience ) , Ly6G ( 1A8 , BioLegend ) , CD90 . 2 ( 53–2 . 1 , eBioscience ) , CD64 ( X54/5/7 . 1 , BioLegend ) , CD80 ( 16-10A1 , eBioscience ) , IgM ( II/4I , eBioscience ) , MHC II ( I-A/I-E ) ( M5/114 . 15 . 2 , eBioscience ) , F480 ( CL-A3-1 , BioRad ) , IFNGR1/CD119 ( 2E2 , BD Bioscience ) , MHC I ( H-2Db ) ( 28-14-8 , eBioscience ) . For FLAG-tag staining experiments either used mouse α-DYKDDDDK IgG-PE ( M2 , Columbia Bioscience ) or a custom Ab to fGR1 ( BioMatik ) that was biotinylated ( Pierce ) . FLAG-tag or IFNGR1 ( CD119 ) were stained using a secondary Streptavidin-APC ( eBioscience ) . Cells were analyzed on either a BD LSR II or BD LSR Fortessa ( BD Biosciences ) and data were processed with FlowJo software ( Treestar ) . For FACS sorting , splenocytes were processed as described above and enriched for myeloid cell populations by depleting lymphocytes . To deplete , splenocytes were incubated with PE-tagged antibodies ( specific for CD90 . 2 , IgM , and NK1 . 1 ) and α-PE Microbeads ( Miltenyi Biotec ) . Cell suspensions were added to LS magnetic columns ( Miltenyi Biotec ) and the flow-through collected and stained for FACS cell sorting . At least 1 x 105 cells were sorted on BD Aura Fusion with a purity of >90% . For determining CFUs from sorted populations , cells were lysed in 0 . 02% NP-40 and serial dilutions plated on Tryptic Soy Broth ( TSB ) agar plates with 50 mg/mL Streptomycin . Total CFUs were counted and normalized to the exact number of cells sorted and lysed . Naïve peritoneal macrophages were treated with indicated concentrations of IFNγ or IFNβ for either 0 , 5 , 30 , 60 minutes . Cells were lysed in 0 . 02% NP-40 supplemented with HALT protease and phosphatase inhibitors ( Thermo Scientific ) directly in the tissue culture dish . Protein concentrations were determined by BCA protein assay ( Pierce ) and 1x SDS-PAGE buffer ( 0 . 0625 M Tris-Cl , pH 6 . 8/2% SDS/10% glycerol/5% 2-ME/0 . 01% Bromophenol Blue ) was added . Equivalent protein amounts were loaded into 10% acrylamide gels and transferred onto PDVF membranes ( Millipore ) . Blots were probed for pY701 STAT1 ( 58D6 , Cell Signaling ) or Total STAT1 ( 91-C , Cell Signaling ) with β-Actin ( 8H10D10 , Cell signaling ) as a loading control on each blot . Blots were developed using the secondary antibodies , goat α-rabbit IR 800 ( 926–32211 , LI-COR ) and goat α-mouse IR 680 ( 926–68070 , LI-COR ) , and imaged on an Odyssey CLX ( LI-COR ) . All pSTAT1 bands were normalized to β-actin on the same blot using ImageStudio Ver 4 . 0 software ( LI-COR ) . Densitometry graphs are pooled from at least 3 independent pSTAT1 blots . Male and female gender-matched 8–12 week mice were infected with WT mouse-passaged 10403s L . monocytogenes ( Lm ) strain . For most experiments , a sub-lethal dose of 1–1 . 5 x104 CFUs/mouse was given via tail vein i . v . inoculation . Lm from thawed aliquots was grown to log phase in TSB media prior to infection . Spleen and liver were harvested at 24 , 48 , 72 , or 96 hrs post infection ( hpi ) to quantify bacterial burdens and assess cell populations by flow cytometry . To quantify cytokine production , serum was obtained from clotted blood collected via cardiac puncture and was stored at -20°C until use . Serum was diluted at least 1:2 and IFNγ cytokine production measured by ELISA ( BD Biosciences ) . Bacterial burdens were determined as previously described [28 , 41 , 57] . For GFP-Lm infections , a WT 10403s Lm strain expressing a GFP plasmid with Erm resistance was injected at a high dose of 2 x 105 CFUs/mouse for 60 hrs . For cytokine depletion experiments , PBS , 0 . 5 mg of α-IFNγ Ab ( XMG1 . 2 , BioXcell ) , or 0 . 5 mg α-IFNAR1 ( MAR-1 , BioXcell ) , was injected intraperitoneally ( I . P . ) 24 hrs prior to Lm infection . A lower dose of 5 x 103 CFUs/mouse was used when IFNγ was depleted . Splenocytes from 60 hpi mice were isolated and enriched for myeloid cells by FACS staining with PE-conjugated antibodies specific for CD90 . 2 , IgM , ad NK1 . 1 then incubating with α-PE beads ( Miltenyi Biotec ) to deplete lymphocytes as described above . The flow-through from the LS magnetic column ( Miltenyi Biotec ) was collected and 5 x 105 myeloid cells were plated in a 24-well plate ( Gibco ) and allowed to adhere for at least 3 hrs . pHrodo Staphylococcus aureus Bioparticles ( ThermoFisher ) were reconstituted as per manufacturer’s instruction in Phagocytosis Buffer ( HBSS without cations , 20 mM HEPES , 5 mM EDTA , pH 7 . 4 ) to a concentration of 1 mg/mL . The Bioparticles were added to the myeloid cells at a concentration of 30 Bioparticles per cell and incubated at 37°C ( experimental ) or 4°C ( control ) for 30 min . Phagocytosis was stopped by the addition of ice-cold Phagocytosis Buffer and the cells were placed on ice . Samples were washed twice in ice-cold Phagocytosis Buffer and once in ice-cold FACS Buffer and then fixed in 1% PFA for 30 min in the dark at room temperature before being stained for flow cytometric analysis . C57Bl/6 and fGR1 mice were infected with a high-dose of 2–3 x 104 CFUs/mouse of WT ( non-GFP ) Lm . Single cell suspensions were incubated with PE-tagged antibodies specific for CD90 . 2 , NK1 . 1 , IgM , and Ly6G for depletion using α-PE Microbeads and LS columns ( Miltenyi Biotec ) . The flow-through was collected and largely comprised monocytes and macrophages ( S7 Fig ) . These were stained for surface CD11b ( APC ) , fixed in 4% PFA , and permeabilized with saponin to stain intracellular Lm using Listeria-O rabbit antiserum ( Difco ) and a F ( ab’ ) 2 Anti-Rabbit IgG-FITC secondary ( eBioscience ) . Additional stains were LAMP-1/CD107a- PE ( 1D4B , eBioscience ) , and DAPI . The cells were mounted on charged slides by cytospin and images captured within 48 hrs of staining . Images were analyzed using the cell function in Imaris software ( Bitplane ) . The cell body and nuclei were detected by CD11b and DAPI staining , respectively , and Lm was detected on a per cell basis by using the vesicle function . Consistent parameters were applied to all images for unbiased Lm and LAMP-1 detection . Co-localization between these two parameters was determined for each Lm ( vesicle ) on a per cell basis by applying a consistent positive threshold for the mean intensity of LAMP-1 at each Lm vesicle . All experiments were repeated at least three times unless otherwise noted . In vitro Mean Fluorescent Intensity ( MFI ) were normalized as previously described [26] to facilitate analysis of data pooled from independent experiments . In vivo MFIs are representative figures with at least 3 mice per group , except GFP MFIs which were pooled . Statistical methods were performed using GraphPad Prism software . Significance was determined by paired two-tailed t-test unless otherwise noted . A p-value of < 0 . 05 was considered significant . These studies were conducted with approval by the Animal Care and Use Committees for both National Jewish Health protocol #AS2682-08-16 and the University of Colorado School of Medicine protocol # B-105614 ( 05 ) 1E . These protocols follow standards enacted by the United States Public Health Service and Department of Agriculture . | Interferon ( IFN ) γ promotes host resistance to invasive bacterial infections , but type I IFNs suppress macrophage activation and increase susceptibility to L . monocytogenes and other pathogenic bacteria . IFNγ promotes resistance by potently activating macrophages to become pro-inflammatory and antimicrobial . Type I IFNs have the opposite effect on resistance during many bacterial infections . They can also suppress macrophage responsiveness to IFNγ through the down regulation of its signaling receptor ( IFNGR ) . Here , we developed an experimental system to investigate the significance of IFNGR1 down regulation as a mechanism contributing to pro-bacterial effects of type I IFNs . A transgenic mouse ( fGR1 ) was generated in which macrophages resist type I IFN-induced down regulation of IFNGR1 . We report that macrophages from fGR1 mice retain responsiveness to IFNγ when exposed to type I IFNs . fGR1 mice thus have increase resistance to systemic infection by L . monocytogenes . This resistance is dependent on IFNγ and associates with increased evidence of macrophage activation and anti-bacterial activity . Our findings thus suggest modulation of IFNGR1 is an important mechanism by which type I IFNs increase host susceptibility to bacterial infections . | [
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] | 2017 | Down regulation of macrophage IFNGR1 exacerbates systemic L. monocytogenes infection |
Rabies is a neglected zoonotic disease that causes an estimated 60 , 000 human deaths annually . The main burden lies on developing countries in Asia and Africa , where surveillance and disease detection is hampered by absence of adequate laboratory facilities and/or the difficulties of submitting samples from remote areas to laboratories . Under these conditions , easy-to-use tests such as immunochromatographic assays , i . e . lateral flow devices ( LFD ) , may increase surveillance and improve control efforts . Several LFDs for rabies diagnosis are available but , except for one , there are no data regarding their performance . Therefore , we compared six commercially available LFDs for diagnostic and analytical sensitivity , as well as their specificity and their diagnostic agreement with standard rabies diagnostic techniques using different sample sets , including experimentally infected animals and several sets of field samples . Using field samples the sensitivities ranged between 0% up to 100% depending on the LFD and the samples , while for experimentally infected animals the maximum sensitivity was 32% . Positive results in LFD could be further validated using RT-qPCR and sequencing . In summary , in our study none of the tests investigated proved to be satisfactory , although the results somewhat contradict previous studies , indicating batch to batch variation . The high number of false negative results reiterates the necessity to perform a proper test validation before being marketed and used in the field . In this respect , marketing authorization and batch release control could secure a sufficient quality for these alternative tests , which could then fulfil their potential .
Rabies is an important zoonotic disease and exhibits the highest case fatality rate of any infectious disease in humans . Infection is usually transmitted by bites via saliva and it is invariably fatal once clinical signs develop . The etiological agents of the disease are the different lyssavirus species of the order Mononegavirales , family Rhabdoviridae [1] . The prototypical rabies virus ( RABV ) transmitted by dogs is responsible for an estimated 60 , 000 human deaths per year , especially in Asia and Africa [2 , 3] . The gold standard for rabies diagnosis is the fluorescence antibody test ( FAT ) [4] , which is internationally approved by OIE and WHO . Briefly , brain tissue is fixed on slides , stained with fluorophore conjugated antibodies and examined under a fluorescence microscope . Confirmatory tests are virus isolation in cell culture ( Webster and Casey , 1996 ) and the mouse inoculation test ( Koprowski , 1996 ) , the latter no longer being recommended by international organizations ( OIE/WHO ) . Alternative diagnostics include various assays to detect viral RNA or antigen [5 , 6] . However , particularly in those countries that are most affected the lack of resources results in inadequate availability of equipment , chemicals and trained staff . Also , the maintenance of a cold chain during shipment of samples is difficult especially in tropical and subtropical countries , and hampers the use of these standard laboratory tests [7] . Unfortunately , the resulting inadequate rabies surveillance contributes to a cycle of neglect with a very limited number of laboratory confirmed human and animal rabies cases and thus an underestimation of the real impact of this neglected zoonotic disease , particularly in Africa and Asia [8] . Therefore , WHO has called for better tests for the rapid and economical diagnosis of RABV , without loss of sensitivity or specificity [2] . One approach to address this issue is the development of tests for the diagnosis of rabies that are relatively easy to perform , e . g . the direct rapid immunohistochemical test ( dRIT ) , which was developed as an alternative to FAT using light microscopy [9] . Another approach is lateral flow devices ( LFDs ) , also called rapid immunodiagnostic tests ( RIDTs ) , immunodiagnostic assays or immunochromatographic strip tests that are interesting insofar as they have potential for field use . They are rapid and easy to use without the need for special training for implementation and evaluation . Another advantage is that these tests have no special storage requirements in terms of temperature , i . e . they can be shipped and stored at ambient room temperatures . Their basic principle behind such tests is the fluid migration of a sample along a nitrocellulose membrane [10] . Gold conjugated antibodies bind to antigen in the sample and the antigen-antibody complex is then immobilized by a second antibody which is fixed on the test strip [11] . LFDs are applied in many different fields [10 , 12] including the diagnosis of viral human and animal diseases , e . g foot-and-mouth disease [13] , avian influenza [14] , Ebola virus disease [15] , porcine epidemic diarrhea [16] , Hepatitis C [17] , and respiratory syncytial virus infection [18] . Recently , LFDs for rabies detection were developed and proof of principle studies yielded good results regarding sensitivity and specificity [19 , 20] , raising hope of extending rabies diagnostic capacity in resource-limited settings [6 , 9] . Since then only one prototype LFD [19] was extensively evaluated , including its diagnostic range , indicating that the test is able to detect rabies and non RABV-lyssaviruses in field samples [21–24] . The routine use of LFDs for rabies diagnosis , however , is hampered by the lack of data regarding its sensitivity and specificity compared to standard diagnostic assays . In addition to the initially published prototype LFDs numerous other rabies LFDs are also commercially available for diagnostic use . Unfortunately , they have never been comprehensively analysed . Therefore , following WHO recommendations , six commercially available LFDs were compared in this study for their diagnostic and analytical sensitivity , as well as specificity , in comparison with FAT and PCR using a range of samples from experimentally infected animals and field samples .
Six different commercial LFD test kits for rabies , i . e . Vet-o-test Rabies Ag ( BioGen Technologies , Germany; LOT NO: AI191301 ) , Anigen Rapid Rabies Ag Test kit ( Bionote , Korea; LOT NO: 1801088 ) , Quicking Pet Rapid Test ( Quicking Biotech , China; LOT NO: G140210303 ) , Rapid Rabies Ag Test Kit ( Creative Diagnostics , USA; LOT NO: CD8921 ) , Rabies Virus Ag Rapid test ( Green Spring , China; LOT NO: 20140210 ) , and quickVET Rabies Antigen Rapid test ( Ubio , India; LOT NO: UB0131303 ) . ) , were identified based on literature and internet searches and purchased . The price per test including tax and shipment varied between 3 . 14€ and 10 . 12€ . Sensitivity and specificity of the commercial LFDs were tested using three different sets of samples from already-existing collections of brain specimens , i . e no animals were used in this study . Samples from sample set I and sample set II were obtained from the virus archive of the Friedrich-Loeffler-Institut ( FLI ) . Sample set I comprised 51 samples from different parts of the brain of 17 raccoons experimentally infected with three virulent primary host adapted RABV-isolates from a European red fox , Eurasian dog and North American raccoon ( Table 1 ) [25] . Sample set II contained 31 samples from different naturally infected brains , or mouse brain homogenates generated from field strains after mouse inoculation test ( MIT ) , representing five different lyssavirus species . In addition to RABV variants of differing geographical origin , these species were European bat lyssavirus type 1 ( EBLV-1 ) , European bat lyssavirus type 2 ( EBLV-2 ) , Duvenhage virus ( DUVV ) and Bokeloh bat lyssavirus ( BBLV ) , each of which was represented by at least one sample . The RABV field strains originated from North and South America , Asia and Europe ( Table 2 ) . Specificity was determined using five non infected brain homogenates . For both sample sets FAT was repeated for each sample essentially as previously described [4] using a four-plus scoring system . Additionally , brain material was subjected to real-time RT-PCR for confirmation and to determine the viral genome load . The quantification was performed essentially as described before [26] . Briefly , a synthetic artificial control encoding corresponding fragments of RABV , EBLV-1 , EBLV-2 , and BBLV was used to generate a standard curve with the R14 multiplex RT-PCR so that cq-values could be transformed into genome copies per μl template , i . e . 50mg of brain . For other lyssavirus species , the N-gene based pan-lyssa system was used [26] . Testing of those samples was conducted at the national reference laboratory for rabies at FLI , Germany . Sample set III comprised 20 brain samples of naturally infected animals including seven different animal species obtained from six different provinces of the Republic of South Africa ( RSA ) during rabies routine surveillance in 2015 . These samples were tested with both FAT and the respective LFDs ( Table 3 ) . Test specificity was determined using 10 negative field samples . Testing of the African samples was conducted at the OIE reference laboratory at Onderstepoort Veterinary Institute , RSA using the same LOT number for each of the LFD kits tested . For all tests a preparation of a 10% brain homogenate ( in PBS ) was required after which the manufacturers’ instructions were followed . Briefly , a cotton swab was inserted into the brain suspension until saturated and then placed into the buffer solution where it was thoroughly mixed . Between two and four drops of the buffer solution were then added to the sample inlet using the disposable dropper . For the Creative Diagnostics test kit , no sample buffer was provided and PBS was used instead . The readout was made 10 min afterwards , as recommended by the manufacturers . The test and control lines on the strips were separately classified by two individuals using a three-plus scoring system representing the intensity of the reaction in the test line area . To mimic low antigen content in a potential rabid brain sample ( analytical sensitivity ) a two-fold positive-in-negative brain homogenate dilution series was prepared . From each of those prediluted preparations different brain suspensions in buffer were again derived , i . e . neat/undiluted , 40% , 20% , and 10% . Subsequently , the produced brain suspensions of each prediluted positive brain sample were tested by mixing 100μl with 100μl of buffer and adding 100μl to the test . Additionally , brain suspensions were subjected to real-time RT-PCR to determine the viral genome load as described above . To investigate whether further characterization of virus in LFD test strips is possible , RNA was extracted from 30 randomly selected LFD test strips which had been stored at room temperature for six weeks . A square piece of approximately 5mm length , in the area where the test line appears , was excised , and immersed in 1ml of TriZol ( Invitrogen ) . RNA extraction was done following manufacturer’s instructions , followed by real-time RT-PCR essentially as described [27] . Exemplarily , five of the RNA samples originating from Bionote test strips were amplified using a conventional PCR assay for subsequent partial nucleoprotein sequencing [28] . To assess the potential presence of viable virus on the LFD , each buffer solution supplied with the test kits was tested for virus inactivation . Briefly , buffer/brain suspensions were prepared from two rabies positive samples as for use on the LFDs . A volume of 0 . 5 ml of those suspensions was then subjected to virus isolation in cell culture using the rabies tissue culture infection test ( RTCIT ) [29] . Additionally , strips of all LFDs used , except the Bionote , with a positive sample were excised 10 minutes and one hour after use and added to the prepared cell suspensions for virus isolation in cell culture . Three consecutive serial passages were considered confirmative for a negative result .
In experimentally infected raccoons from sample set I , 44 out of 51 brain samples were positive in FAT with fluorescence scores ranging between + and ++++ , whereas all samples tested positive using RT-qPCR . Most of the FAT negatives comprised samples from the Ammon’s horn . The amount of RNA per sample as determined by real-time RT-PCR ranged from 2 . 26 up to 1 . 52*107 mean genome copies/μl template . The lowest amount of RNA in a FAT positive sample was 7 . 35 mean copies/μl template . Four of the seven FAT-negative samples had RNA content of 6 . 60*101 mean genome copies/μl template or lower . The remaining three FAT-negative samples contained more than 1 . 78*104 mean genome copies/μl template of RNA . Generally , the strength of agreement between results obtained by individual commercial LFDs and FAT with brain samples from experimentally infected raccoons was considered to be 'poor' . Of the 44 FAT positive samples , none tested positive using the test kits of Ubio and Quicking and one sample only tested positive using BioGen ( Kappa = 0 . 006 , 95% CI: -0 . 007–0 . 020 ) . The other test kits detected more samples , with Bionote displaying a positive result for seven samples ( Kappa = 0 . 049; 95% CI: -0 . 000–0 . 099 ) , Green spring for 10 ( Kappa = 0 . 075; 95% CI: 0 . 007–0 . 143 ) and Creative diagnostics for 14 samples ( Kappa = 0 . 064; 95% CI: -0 . 052–0 . 180 ) ( Table 1 ) . Another sample was positive with the Creative diagnostics test kit but negative using FAT , at an RNA-content of 1 . 31*105 mean copies/μl template . The lowest amount of viral RNA in a sample that tested positive in an LFD was 1 . 32*103 mean copies/μl template . Of 31 field samples from sample set II 30 tested positive and one inconclusive using FAT , while all were positive by pan-lyssa real-time RT-PCR . The amount of lyssaviral RNA in the samples ranged from 9 . 81*102 mean copies/μl template up to 3 . 44*108 mean copies/μl template per sample excluding one sample . Here the amount of RNA was 3 . 76 mean copies/μl template , presenting with only unspecific fluorescence in FAT . In contrast , all FAT positive samples were negative using Ubio and BioGen ( Kappa = -0 . 0283; 95% CI: -0 . 067–0 . 021 ) . Quicking displayed positive results for two samples ( Kappa = -0 . 028; 95% CI: -0 . 059–0 . 053 ) , while Bionote and Green spring detected 13 ( Kappa = 0 . 085; 95% CI: -0 . 012–0 . 320 ) and 15 ( Kappa = 0 . 196; 95% CI: 0 . 004–0 . 387 ) FAT positive samples , respectively . With 21 FAT positive samples recognized ( Kappa = 0 . 364; 95% CI: 0 . 094–0 . 633 ) by the Creative diagnostics test , the correlation was considered 'fair' ( Table 2 ) . No LFD displayed a positive result with the sample that showed inconclusive fluorescence in FAT . Lyssavirus species other than RABV were negative in all LFDs except for BBLV . Creative diagnostics was able to detect one and Bionote both BBLV positive samples . All LFDs displayed a negative result for the five rabies negative samples resulting in a specificity of 100% . With field samples from South Africa ( Sample set III ) all LFDs displayed a negative result for the ten rabies negative samples resulting in a specificity of 100% . The correlation between results obtained by FAT and individual commercial LFDs ranged between perfect and poor . Bionote and BioGen showed the best test results . While the correlation between FAT and Bionote was perfect , it was considered 'good' for BioGen , Green spring and Creative diagnostics . Compared to FAT , BioGen displayed a positive result for 17 ( Kappa = 0 . 791 , 95% CI: 0 . 571–1 . 000 ) South African samples . Green spring and Creative diagnostics each recognized 16 ( Kappa = 0 . 727; 95% CI: 0 . 488–0 . 967 ) RABV positive field samples . In contrast , Quicking and Ubio detected only five ( Kappa = 0 . 182; 95% CI: 0 . 013 to 0 . 350 ) and two ( Kappa = 0 . 069; 95% CI: -0 . 030–0 . 168 ) FAT positive samples , respectively ( Table 3 ) . All ‘spiked’ brain-suspensions were positive using FAT ( +—++++ ) and real-time RT-PCR . The amount of RNA in brain suspensions decreased as the dilution factor increased , starting in the undiluted positive brain at 1 . 24*107 mean genome copies/μl template and finishing with 1 . 60*105 mean genome copies/μl template at a dilution step of 1:128 , which was the highest dilution factor used . The cut-off point up to which the LFDs were able to detect the positive brain varied , as can be seen in Table 4 . Many of the test results for Bionote and Ubio could not be analyzed , since the samples did not reach either the test line or the control line . Ubio did not display a single positive result . The real-time RT-PCR was positive for all 30 LFD test strips with Cq values ranging between 19 . 12 and 37 . 11 . Partial sequencing of the N-gene was successful for two out of 5 samples tested . When comparing the Cq values derived directly from the samples with the mean Cq values from the test strips , an increase between 11 . 82 and 13 . 51 was observed . Viable virus could be detected after mixing of RABV positive samples with the buffer solutions of Quicking , Green spring and Ubio . Also , one virus isolation was positive when the buffer solution of BioGen was used , while no positive results were obtained with Bionote buffer . After 10 minutes all test strips except Quicking still contained viable virus , but after one hour only the test strip of Creative diagnostics still contained infectious virus particles .
Based on the need to improve rabies surveillance in many remote endemic areas , LFDs would be one promising alternative to laboratory testing . However , with their current limitations commercially available rabies LFDs cannot be recommended for routine diagnosis and surveillance . In particular , if animals were involved in a biting incident to a human being , false negative results may induce the patient and the doctor to refrain from appropriate post-exposure prophylaxis ( PEP ) . Although the leaflet may explain that the results of these tests are to be confirmed by a reference method , this may not be followed and given that the cost of PEP equals a high proportion of the income in developing countries , PEP may be omitted , thus causing unnecessary deaths . Generally , the observed limited sensitivity indicates a lack of quality control . Quality control is essentially establishing adequate performance characteristics ( sensitivity , specificity , negative predictive value , positive predictive value , cross reactivity , etc . ) of a given test [10 , 12] . Thorough validation including various circulating variants of RABV and other lyssaviruses has been recommended before those tests could be relied upon and be used as an alternative for the gold standard FAT [6] . However , it should be the responsibility of the producers and not of the customers to install a rigorous quality control system before the tests are released on the market . In some countries , e . g . Germany , any test used for the detection of a notifiable animal disease needs to obtain marketing authorization . None of the tests studied would have met the requirements for this marketing authorization and thus would not be allowed to be marketed in Germany . This study is not meant to discredit the use of LFDs for rabies diagnosis but rather to encourage producers to substantially improve and assure the quality of their products . In principle , if those tests show a high sensitivity and specificity they could be very valuable and with their advantages in e . g . speed , easiness and storage without maintaining a cold chain could help to improve rabies detection in some parts of the world . | Despite being preventable with adequate biologicals , rabies still causes an estimated 60 , 000 human deaths annually . The main burden lies on developing countries in Asia and Africa , where dog rabies surveillance is hampered by laboratory confirmation of disease due to a number of reasons , including laboratory infrastructure and logistics . Lateral flow devices ( LFD ) may increase surveillance and improve control efforts . Several LFDs for rabies diagnosis are available but , except for one , there are no data available regarding their performance . Therefore , we compared six commercially available LFDs for diagnostic and analytical sensitivity . With sensitivities ranging from 0% up to 100% depending on the LFD and the samples , none of the tests investigated proved to be satisfactory , and the results somewhat contradict previous studies , indicating batch to batch variation . The high number of false negative results reiterates the necessity to perform a proper test validation before being marketed and used in the field . Only when sufficient quality is assured for these alternative tests , then they can fulfil their potential . In this respect , we demonstrated that positive results in LFD can be further validated and characterized using RT-qPCR and sequencing . | [
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] | 2016 | Evaluation of Six Commercially Available Rapid Immunochromatographic Tests for the Diagnosis of Rabies in Brain Material |
Dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) are severe disease manifestations that can occur following sequential infection with different dengue virus serotypes ( DENV1-4 ) . At present , there are no licensed therapies to treat DENV-induced disease . DHF and DSS are thought to be mediated by serotype cross-reactive antibodies that facilitate antibody-dependent enhancement ( ADE ) by binding to viral antigens and then Fcγ receptors ( FcγR ) on target myeloid cells . Using genetically engineered DENV-specific antibodies , it has been shown that the interaction between the Fc portion of serotype cross-reactive antibodies and FcγR is required to induce ADE . Additionally , it was demonstrated that these antibodies were as neutralizing as their non-modified variants , were incapable of inducing ADE , and were therapeutic following a lethal , antibody-enhanced infection . Therefore , we hypothesized that avian IgY , which do not interact with mammalian FcγR , would provide a novel therapy for DENV-induced disease . We demonstrate here that goose-derived anti-DENV2 IgY neutralized DENV2 and did not induce ADE in vitro . Anti-DENV2 IgY was also protective in vivo when administered 24 hours following a lethal DENV2 infection . We were also able to demonstrate via epitope mapping that both full-length and alternatively spliced anti-DENV2 IgY recognized different epitopes , including epitopes that have not been previously identified . These observations provide evidence for the potential therapeutic applications of goose-derived anti-DENV2 IgY .
Almost half of the world is at risk for dengue virus ( DENV ) infections with up to 390 million infections occurring in nearly 100 endemic countries annually [1] . Dengue is a rapidly emerging disease with a 30-fold increase in disease incidence reported in the past 50 years [2] . Dengue has established itself globally in both endemic and epidemic transmission cycles and is currently regarded as one of the most important arboviral diseases internationally [1 , 3 , 4] . DENV is a member of the Flavivirus family of RNA viruses . There are four distinct serotypes ( DENV1 , DENV2 , DENV3 , DENV4 ) that differ at the amino acid level by 25–40% [5] . DENV is primarily transmitted by the Aedes aegyptii mosquito , and Aedes albopictus is a secondary vector . In the Americas , epidemic dengue was controlled in most of the region by the eradication program that eliminated the Aedes aegypti mosquito vector from 23 countries until the program was terminated in the early 1970s [6] . Following the termination of this program , the mosquito rapidly reestablished itself and all four DENV serotypes re-emerged , resulting in the co-circulation of multiple DENV serotypes [4] . It has become increasingly evident that in order to control the disease in the absence of a strong vector control program , the development of new antiviral therapies and vaccines is crucial . DENV can affect people of all ages including infants , children , adults and the elderly , but the interplay between the virus and host is what determines the clinical outcome . Disease manifestations from DENV infections range from asymptomatic infections , a mild febrile illness known as dengue fever ( DF ) , or the more severe dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) . During an initial infection , most children experience subclinical infection or mild undifferentiated febrile syndromes[7] . In this situation , lifelong immunity against the primary infecting serotype occurs . During a secondary infection , the pathophysiology of the disease can change dramatically , specifically if the secondary infection is with a different DENV serotype . Heterotypic secondary infections are the cause of 90% of the DHF/DSS cases reported [8] . One working hypothesis explaining the severity of dengue pathogenesis observed during secondary infection is antibody dependent enhancement ( ADE ) [7] . ADE occurs when sub-neutralizing antibodies following a primary DENV infection bind to an infecting viral particle from the secondary heterotypic infection . These antibody-virus complexes then bind to Fcγ receptors ( FcγR ) on macrophages and dendritic cells via the Fc portion of the antibody [9] . The result of ADE is a higher number of infected immune cells , leading to heightened immune response to the infection [9] . ADE also results when infants are born to dengue immune mothers after maternal anti-DENV antibodies have been catabolized to sub-neutralizing levels[10] . At present , there remains an unmet need for an effective dengue therapeutic that is able to shorten the duration of the illness , prevent the development of severe disease , and reduce the severity of common symptoms [11] . There are a number of institutions , both academic and pharmaceutical , that are currently engaged in the discovery and development of therapeutics [11] . One encouraging area of research has been the development of therapeutic anti-DENV monoclonal antibodies that block viral infection . Balsitis et al . verified using aglycosylated and F ( ab’ ) 2 DENV-specific IgG that the interaction between the Fc portion of the serotype cross-reactive antibodies and the FcγR is required to induce ADE . In this study , they demonstrated that the genetically modified DENV-specific antibodies did not enhance DENV2 infection and were able to neutralize DENV infection as well as the non-modified variants . Furthermore they demonstrated that these antibodies were therapeutic up to 48 hours following a lethal , antibody-enhanced infection [12] . We suggest that IgY , the avian homolog to mammalian IgG , which naturally fails to bind FcγR , could be used as alternative therapeutic antibodies to treat DENV-induced disease . IgY is the primary immunoglobulin isotype in oviparous animals and is the functional equivalent to mammalian IgG , but also has the ability to sensitize tissues to anaphylactic reactions [13] . There are two IgY isoforms present in anseriform birds: full-length IgY and alternatively spliced IgY , which lack two of the constant regions present in full-length IgY . The alternatively spliced IgY coexists with the full-length IgY and is a structural equivalent to a mammalian F ( ab’ ) 2 fragment [14] . Previous studies from our laboratory have confirmed the potential therapeutic efficacy of goose-derived IgY antibodies in neutralizing viral infections and preventing Hantavirus pulmonary syndrome ( HPS ) . Geese were vaccinated with a DNA vaccine encoding virus envelope glycoproteins , and Andes virus ( ANDV ) -specific IgY was isolated and purified from goose egg yolks . It was demonstrated that ANDV-specific IgY provided protection from HPS when administered to hamsters 5 days post-infection with ANDV ( 25 LD50 ) [15] . There are many advantages to using IgY for the treatment of DENV infections . One important advantage is the genetic background and phylogenetic distance that distinguishes birds from mammals . Avian derived IgY , as compared to mammalian IgG has a higher avidity for some mammalian antigens and has the ability to recognize different epitopes that may be non-immunogenic in mammals [16 , 17] . Another key advantage of IgY is its inability to bind mammalian FcγR; therefore , it is possible that anti-DENV IgY will be able to neutralize a viral infection without inducing ADE [18] . Furthermore , IgY does not interact with other Fc-binding receptors , which limits its ability to elicit an inflammatory reaction in humans [19–24] . Although IgY is able to activate complement in the avian system , its unique structure prevents it from being able to activate human complement [23] . Although the complement component C1q has been demonstrated to increase neutralizing potential of anti-West Nile virus antibodies in the absence of inducing ADE , it has also been proposed that complement activation during secondary heterotypic DENV infection by non-neutralizing antibodies contributes to immune enhancement . Furthermore , it has been demonstrated that products of complement activation , specifically C3a , C5a , and SC5b-9 are elevated in patients with DHF [25–28] . Therefore , the inability of anti-DENV IgY to induce complement activation could prove to be detrimental or beneficial depending on the individual disease state . Furthermore , the ability of IgY to be easily isolated from the egg yolk presents another key advantage to using IgY as an alternative to mammalian IgG . Eggs can be collected from laying hens and the egg yolks used as a source of IgY . This is a rapid process that avoids serum collection while still maintaining high antibody yields . The concentration of IgY present in the egg yolk of immunized birds depends on the species , age , and antigen injected . Whereas concentrations of antibody from chicken egg yolk ranges from 60-150mg per egg , the concentrations of antibody from goose egg yolk ranges from 100-500mg per egg . Furthermore , there are a number of industrial processes setup for the collection and separation of eggs , making large-scale production of IgY a feasible option [16] . In this study , we demonstrate the efficacy of goose-derived , purified anti-DENV2 IgY in vitro and in vivo . Specifically , we demonstrate that anti-DENV2 IgY is able to neutralize DENV2 in vitro and provide protection in the AG129 mouse model when administered therapeutically following lethal challenge . Anti-DENV2 IgY was not able to enhance DENV2 infection in vitro , thereby providing protection in the absence of ADE . Finally , we confirmed that anti-DENV2 IgY binds to previously uncharacterized epitopes , and furthermore full-length IgY and alternatively spliced IgY also bind epitopes that are unique from one another .
Research was conducted in compliance with the Animal Welfare Act and adheres to the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved by the Institutional Animal Care and Use Committee at the University of California Berkeley ( R252-1012B ) . The geese ( Anser domesticus , 25 months old ) used in this study were an inbred ( over 35 generations ) hybrid between the German Embden , the Royal Chinese , and the Royal English breeds . An existing 10 , 150 sq . ft . barn at Schiltz Goose Farm , Inc . was converted into a Specific Pathogen Free ( SPF ) facility . 3 , 900 sq . ft . of the barn was reserved as a clean room for the ISO egg production units . The ISO facility had additional HEPA filtration to remove particles as small as 0 . 3 microns with a 99 . 97% minimum particle collective efficiency . Only authorized personnel were involved in the cleaning of the barn in the SPF and ISO facilities and all animal handling . Dengue Type 2 Antigen was purchased from Microbix Biosystems Inc . and used to vaccinate ten female geese . According to the manufacturer , the Dengue Type 2 Antigen was prepared by infecting Vero cells with DENV 16681 and collecting virus-containing supernatants . Virus particles were inactivated by formaldehyde at room temperature and neutralized by the addition of sodium bisulfite . Virus particles were purified by ultracentrifugation over a sucrose cushion and resuspended in Medium 199 . Geese were vaccinated with 120 μg of Dengue Type 2 Antigen on day 0 and boosted with 60 μg at weeks 2 and 4 . Immunizations consisted of 2 x 200 μL subcutaneous injections at the back of the neck in two different injection spots . The eggs were collected starting from week 2 after the first immunization and stored at 4°C until further use . Yolks were isolated and rinsed with water and then punctured to drain the contents , which were diluted 1:10 with cold deionized water , stirred , and acidified to pH 5 . 0 . The diluted yolk was centrifuged at 10 , 000 x g for 30 minutes , and the supernatant was filtered . In order to separate the full-length IgY from the alternatively spliced IgY , a sequential series of 30% , 40% and 50% ammonium sulfate was used . The pellets were suspended in 50 mM Tris HCl pH 8 . 0 . Further purification was achieved via hydrophobic charge induction chromatography on 4-Mercapto-Ethyl-Pyridine-linked ( MEP ) HyperCel sorbent ( Pall Corporation ) followed by concentration using a Tangential flow filtration and diafiltration with 1x PBS buffer . Rabbit- anti-goose IgY-IgG ( obtained from rabbits immunized with purified naïve goose IgY ) , full-length anti-DENV2 IgY , and alternatively spliced anti-DENV2 IgY were separated by 4–15% gradient SDS-PAGE under non-reducing conditions . The gel was incubated in Bio-Safe Coomassie G-250 stain ( Bio-Rad Laboratories ) for 30 minutes and subsequently de-stained in deionized water for 2 h . Signals were captured using the AlphaView software and AlphaImager HP Imaging System ( Alpha Innotech ) . The antibody activity of anti-DENV2 IgY was determined by ELISA . Briefly , microtiter plates were coated with 100 μL of the capture antigen ( Dengue Type 2 Antigen , Microbix Biosystems Inc ) and stored at 4°C overnight . After washing the plates 3 times with wash buffer ( 1X PBS , 0 . 05% Tween-20 ) , they were blocked with 400 μL per well of blocking buffer ( 0 . 25% BSA , 0 . 05% Tween-20 , 1X PBS ) and incubated for 30 minutes at room temperature . The wells were washed 3 times with wash buffer and incubated with 100 μL of anti-DENV2 goose antibody ( isolated from DENV2 vaccinated geese ) serially diluted 1:2 down the plate in blocking buffer and incubated at 37°C for 30 minutes . Dengue virus E protein and naïve IgY control antibodies ( isolated and purified from geese vaccinated with PBS ) were included as standards on each plate . The plates were washed 3 times with wash buffer and blocked for 10 minutes at room temperature . Next , 100 μL of biotinylated rabbit anti-goose IgY antibody ( 500ng/mL , Covance Inc . ) was added to each well and incubated at 37°C for 30 minutes . After washing the plates 3 times with wash buffer , the wells were blocked for 10 minutes at room temperature . Following this , 100 μL of diluted streptavidin-HRP antibody in blocking buffer ( 1:2 , 000 ) was added to each well , and the plates were incubated at 37°C for 30 minutes . The plates were finally washed 3 times before adding 100 μL of prepared o-phenylenediamine dihydrochloride ( OPD , Invitrogen ) color substrate to each well and allowed to develop for 15 minutes at room temperature . The reaction was terminated by adding 50 μL of 1N H2SO4 , and the absorbance was read in a BioTek plate reader at A490 . Data are represented as endpoint titers . DENV was propagated in C6/36 cells ( ATCC ) , and the DENV2 D2S10 strain ( passaged 4 times in C6/36 cells ) was derived in the laboratory of Dr . Eva Harris at the University of California , Berkeley from the parental DENV2 PL046 Taiwanese isolate as described [29] . U937-DC-SIGN cells were obtained from A . de Silva ( University of North Carolina , Chapel Hill ) and grown in RPMI media ( Invitrogen ) at 37°C and 5% CO2 . K562 cells ( obtained from ATCC ) were grown in RPMI media ( Invitrogen ) at 37°C and 5% CO2 . The neutralization and enhancement titers for anti-DENV2 and control purified polyvalent IgY sera against DENV2 D2S10 were determined . Both neutralization and enhancement experiments were performed twice , each time in duplicate . In brief , the IgY sera were diluted to a starting concentration of 2 . 0 mg/mL . Twelve 4-fold dilutions were mixed with equivalent volumes of DENV2 D2S10 for 45 minutes before infecting U937 DC-SIGN cells , a DENV-permissive human monocytic cell line [12] . The cells were washed two hours following infection , and then fixed and stained for DENV E protein with monoclonal antibody 4G2-Alexa 488 24 hours later . The data were analyzed by flow cytometry [12] , and the dilution yielding 50% neutralization ( NT50 ) was calculated using GraphPad PRISM [30 , 31] . To test for potential enhancement , the serum was diluted and mixed with DENV2 as described above and used to infect K562 cells , an erythroleukemic cell line that is not naturally permissive for DENV infection , but can be infected via surface FcγRIIA when DENV virions are coated with sub-neutralizing concentrations of anti-DENV antibody . The cells were fixed , stained , and analyzed as described above 48 hours following infection [30 , 31] . The therapeutic potential of goose-derived anti-DENV2 IgY was tested using conditions that cause 100% mortality in AG129 mice . Six-to-eight week old IFN-αβR-/- and IFN-γR-/- ( AG129 ) mice were administered a lethal dose of DENV2 strain D2S10 ( 1 . 0x107 plaque forming units ( PFU ) ) intravenously ( i . v . ) [12] . Twenty-four hours after infection , mice were injected intraperitoneally with the indicated amounts of polyclonal anti-DENV2 IgY or the positive control mouse monoclonal antibody ( MAb ) E60 N297Q ( E60 MAb originally obtained from M . Diamond , and genetically modified using QuikChange mutagenesis ( Stratagene ) to abolish FcγR and C1q binding [12] ) in a volume of 200 μL , or 200 μL of PBS as a negative control . Mice were followed for 10 days and observed for morbidity and mortality twice daily . Anti-DENV2 IgY was administered as 50μg , 500μg , 1mg or 2mg per injection , control naïve IgY was administered as 1mg or 2mg per injection , and the previously identified protective anti-DENV monoclonal antibody IgG E60 N297Q was administered as 20μg per injection for a positive control [12] . Anti-DENV2 IgY epitopes were mapped using E , prM , NS1 and NS3 proteins via peptide arrays . Specifically , 11 amino acid overlapping 15mer peptides were covalently attached to a microarray slide ( JPT Innovative Peptide Solutions , Berlin , Germany ) . All Pepstar microarray protocols were provided by JPT . Briefly , a slide sandwich containing the microarray and a dummy slide was made , separated by spacers , in order to increase the incubation environment . The primary antibody serum was incubated at 30μg/mL on the slide at 4°C overnight in a moist environment . The slide was rinsed 5 times for 4 minutes each with T-TBS , then 5 times for 4 minutes each with ultra-pure water . The slide was incubated in the fluorescently labeled ( Cy5 ) goat-anti chicken IgY secondary antibody solution ( 1μg/mL , Abcam ) for 45 minutes , washed 5 times with T-TBS , then 5 times with ultra-pure water , and dried using a dust-free , oil-free , high-velocity canned air . Fluorescence was measured at a pixel size of 10μm using the Genepix 4000 microarray reader . The signal intensity mean values were calculated for each sub-array and background corrected values were used for interpretation in Microsoft excel . The microarray experiment was repeated with each antibody type on three separate but identical slides; anti-DENV full length IgY , anti-DENV alternatively spliced , and control naïve IgY .
Following the purification of anti-DENV2 IgY , a SDS-PAGE was performed to verify the purity of anti-DENV2 full length IgY and anti-DENV2 alternatively spliced IgY ( Fig 1A ) . We were able to detect both anti-DENV2 full length ( Fig 1A , Lane 7 ) and alternately spliced IgY ( Fig 1A , Lane 8 ) purified antibody populations and detected both full length and alternatively spliced IgY ( Fig 1A , Lane 9 ) in the serum that was not used to isolate the two separate antibody populations . Rabbit sera containing anti-IgY IgG was used for comparison ( Fig 1A , Lane 3 ) . An ELISA was then performed to confirm the presence of DENV2-specific IgY and to determine the antibody titer in each egg yolk . Goose egg yolk titers are considered indicative of serum titers because antibodies are transferred from the serum of the laying hen to the egg yolk during embryogenesis . Of the 104 eggs that were measured , the average serum endpoint titer across all weeks post vaccination was 1:924 , 807 with the highest titer being 1:6 , 400 , 000 at 7 weeks post vaccination ( Fig 1B ) . Polyvalent anti-DENV2 IgY were purified from serum of DENV2-immunized geese . These antibodies were tested for potential neutralization and enhancement in vitro . Serial dilutions of anti-DENV2 IgY were mixed with DENV2 D2S10 and used to infect DENV-permissive monocytic U937 DC-SIGN cells ( neutralization ) or an erythroleukemic cell line , K562 ( enhancement ) , which are not naturally permissive for DENV infection , but can be infected via surface FcγRIIA when DENV virions are coated with sub-neutralizing concentrations of anti-DENV antibody . The dilution of anti-DENV2 IgY serum yielding 50% neutralization ( NT50 ) was between 1 . 0 and 2 . 6 μg/mL in two independent experiments ( Fig 2 ) . The control IgY serum did not yield a measurable NT50 titer in either experiment . Previous studies have identified the NT50 titer of anti-DENV MAb E60 and its modified variant E60 N297Q as 49 ng/mL and 72 ng/mL , respectively [31 , 32] . In the enhancement experiment , whereas the positive control anti-DENV Mab E60 generated ~15% infection at its peak enhancement titer , neither the anti-DENV IgY nor control IgY sera were enhancing at any dilution tested ( Fig 3 ) . AG129 mice were challenged with a lethal dose of D2S10 and the in vivo protective capacity of anti-DENV2 IgY was determined . In Fig 4 , the results of six different experiments using a lethal dose ( 1 . 0x107 PFU ) of DENV2 D2S10 were combined . All mice challenged with D2S10 and therapeutically administered PBS as a negative control succumbed to disease by five days post-infection , while all animals infected but treated with positive control mAb E60 N297Q [12] survived ( p = 0 . 0003 as compared to PBS ) ( Fig 4A and 4B ) . The E60 N297Q control mAb is different from the E60 mAb used in the in vitro studies in that it has been genetically modified to abolish FcγR and C1q binding . Similar to the positive control E60 N297Q mAb , 100% therapeutic efficacy was also observed with 1 mg anti-DENV2 IgY ( n = 4 ) administered 24 hours post-infection ( p = 0 . 0019 ) , while 1 mg of control IgY yielded 25% protection ( n = 4 , p = 0 . 04 comparing equal amounts of anti-DENV2 IgY and control IgY ) ( Fig 4A ) . Next , we tested multiple decreasing amounts of anti-DENV2 IgY , including doses of 500 ug and 50 ug . Treatment with 500-μg anti-DENV2 IgY ( n = 6 ) provided 66% protection ( p = 0 . 0035 ) , and treatment with 50 μg anti-DENV2 IgY ( n = 10 ) provided 33% protection . These data , including the therapeutic efficacy of 2 mg anti-DENV2 IgY and control IgY , are provided in S1 Table . To determine the specificity of full length and alternatively spliced anti-DENV2 IgY , linear epitope mapping was completed using the DENV2 envelope ( E ) , pre-membrane ( prM ) , nonstructural 1 ( NS1 ) and nonstructural 3 ( NS3 ) proteins in a microarray . Microarray slides were covalently linked with 15-mer peptides with a 11 amino acid overlap , and 3 amino acid offset , spanning the entire sequence of either the E , prM , NS1 and NS3 proteins . Slides were incubated with either naïve IgY , anti-DENV2 full length IgY or alternatively spliced IgY . Reactivity was compared to negative control peptides ( scrambled peptide or AAAAAAAAAAAAAAA peptide ) on each slide; the average MFI of the negative controls was used for comparison . Unbiased heat maps were generated to display the epitope mapping data in order to compare the epitope binding regions between full length , alternatively spliced , and naïve IgY . The MFI is shown in a color gradient , with the red color representing the strongest binding of the IgY antibody to the indicated peptide ( Fig 5 ) . Epitopes recognized by full-length anti-DENV2 IgY and alternatively spliced anti-DENV2 IgY were compared to each other and to previously characterized anti-DENV2 IgG epitopes . Our results suggest that the majority of the peptide sequences that were recognized were within the E protein , whereas there were few in the NS1 and NS3 proteins , and one with very low mean MFI in the prM protein . Some of the peptides recognized by the full-length and alternatively spliced anti-DENV2 IgY were similar , while others appeared unique to each of the different antibody populations . Interestingly , some of the peptides recognized by the anti-DENV2 IgY appeared to be distinct from previously described epitopes targeted by either human or mouse anti-DENV2 sera [33–51] . Specifically , we found that the peptide KGMSYSMCTGKFK at position 295–307 in the E protein domain III N-terminal stem region was strongly recognized by both anti-DENV2 full-length and alternatively spliced IgY , whereas alternatively spliced IgY also displayed high reactivity to the peptides GEVVQPENLEYTI at position 127–139 in the E protein domain II and TGHLKCRLRMDKL at position 278–290 in the E protein domain I . Interestingly , both of these regions recognized by alternatively spliced IgY cover hinge regions between the E protein domains . Naïve IgY recognized some of the same peptides that both full-length and alternatively spliced anti-DENV2 IgY recognized; however , there were more peptides recognized that were unique to both of the DENV2 specific IgY populations . It is also apparent that the anti-DENV2 alternatively spliced IgY recognized more peptides than the full-length anti-DENV2 IgY .
In this study , we demonstrate that anti-DENV2 IgY purified from goose egg yolk is effective in neutralizing DENV2 D2S10 viral infection both in vitro and in vivo and does not induce ADE . Vaccination with DEVN2 antigen induced a strong humoral immune response in the geese , with antibody titers maintained for over six weeks and reaching as high as 1: 6 , 400 , 000 . We observed therapeutic efficacy with 1–2 mg anti-DENV2 IgY administered 24 hours post-infection in lethally infected AG129 mice . This protection was comparable to the therapeutic protection observed with the MAb E60-N297Q positive control . Our results also indicate some non-specific protection that may be provided by large amounts of naïve goose IgY , as indicated by both the in vivo challenge data and the epitope mapping . Experiments that were performed prior to epitope mapping were completed using unfractionated serum that included both the full-length IgY and alternatively spliced polyclonal antibody populations . Further studies will determine the neutralization capacity of both serum components individually as well as the neutralization capacity of the epitope-specific affinity-purified IgY . At present , there are no licensed therapies to treat the severe manifestations of DENV infection . It has been suggested that ADE resulting from pre-existing antibodies binding at sub-neutralizing concentrations to heterotypic DENV ultimately results in increased viral load , increased activation of cytokines , and the activation of complement . All of these phenomena increase the likelihood of vascular leakage that may result in mortality if not treated appropriately , and underscore the need to develop therapeutics that will not induce ADE , such as anti-DENV2 IgY . The development of a vaccine has been problematic , in part due to the possible risk of eliciting suboptimal immune responses that will lead to ADE and severe disease following infection with heterologous virulent strains . Passive immunotherapy with neutralizing antibodies may provide an alternative for the treatment of severe dengue . Our data indicate that anti-DENV2 IgY does not induce ADE , presumably because similar to aglycosylated IgG , IgY does not bind FcγRs . This characteristic is especially advantageous because it does not require any genetic modification or engineering to prevent enhancement , unlike other non-avian derived antibody therapeutics [12] . Humanized anti-DENV MAbs obtained from mice or non-human primates have been produced to treat dengue , but the majority of these antibodies are weakly neutralizing and serotype cross-reactive [34 , 52 , 53] . Potently neutralizing human MAbs are rare , indicating that only a small fraction of the total antibody response during natural infection is responsible for virus neutralization . Here we demonstrate that anti-DENV IgY is able to neutralize DENV infection in vitro and in vivo . We recognize that the amount of anti-DENV IgY required to induce full protection in the mouse model is much greater than the amount of the E60 N297Q monoclonal antibody . Further characterization of the IgY antibodies to determine which antibodies are responsible for providing protection and generating a more potent anti-DENV IgY cocktail may overcome this limitation . We also recognize that high doses of control IgY induced moderate protection , suggesting that there are nonspecific mechanisms of protection provided by IgY antibodies . One potential explanation may be that administration of large doses of IgY induces an immune response or hypersensitivity reaction in mice that may be facilitating the protection provided by the antibody therapeutic itself . It is unlikely that in the absence of binding to human Fc receptors and complement that either the anti-DENV2 IgY or the naïve IgY promotes serum sickness . It is also possible that there are mammalian receptors that are homologous to avian receptors that could facilitate the binding and activation of the immune system . Furthermore , we also hypothesize that there may be cross-reactivity between DENV2-specific neutralizing epitopes and other viruses or bacteria that these geese may have come in contact with . Epitope mapping of four complete DENV proteins—E , prM , NS1 , and NS3 –suggested that naïve , anti-DENV2 full-length , and anti-DENV2 alternately spliced IgY bind to similar epitopes , but that both anti-DENV2 IgY populations recognize epitopes not recognized by naïve IgY and that they are different from one another . The majority of the epitopes detected were located within the E protein , with some located in the NS3 protein , two located in the NS1 protein , and one with very low mean MFI in the prM protein . These data are consistent with the current literature suggesting that most neutralizing epitopes are located within the E protein . The E protein is located on the surface of the virus and plays a key role in host cell entry . In contrast , the NS3-specific epitopes presented are uncharacteristic . NS3 is a viral protease and helicase that is in part responsible for virus processing and replication , and normally would not induce NS3-specific antibodies during immunization with an inactivated virus , as was used in our immunization protocol . It is also unlikely that these anti-NS3 antibodies alone are able to neutralize DENV [54–56] . The NS1 epitopes were only recognized by the alternatively spliced anti-DENV2 IgY , demonstrating that full-length and alternatively spliced anti-DENV2 IgY bind different epitopes . It is also interesting that the alternatively spliced anti-DENV2 IgY recognized more peptides overall as compared to the full-length anti-DENV2 IgY , and this could be due to both the smaller size of the antibody and the recognition of diverse epitopes . The reactivity of naïve IgY to DENV2 peptides is surprising but offers insight to the protection provided by naïve IgY in the in vivo studies . It is possible that naïve IgY is binding to certain DENV2 peptides due to the nature of the amino acid characteristics , specifically if there are cross-reactive epitopes to other viruses or bacteria . At present , there are no identified mammalian receptors that bind to IgY , but it is also possible that these DENV2 epitopes share biochemical characteristics to a putative IgY receptor . In mapping linear epitopes , we also recognize that we have potentially missed biologically relevant conformational and structural epitopes that would have been recognized using an alternate epitope mapping approach with intact virions . Recent reports suggest that many DENV specific epitopes rely on a mature viral surface and conformational motions of the virus particle , which are dynamics that are absent in mapping linear epitopes [57] . We also recognize that by using polyclonal antibodies for epitope mapping that we were not able to determine the specific epitopes that are important for virus neutralization and protection . However , because these epitopes have not been previously demonstrated as neutralizing for mammalian or murine MAbs , when comparing other linear neutralizing epitopes [33–51] , it is important to consider further characterization of these and other conformational epitopes . At present , our data are consistent with literature demonstrating that IgY recognizes different epitopes than IgG [16 , 17] , suggesting that the anti-DENV antibody population generated in geese is different from what might be generated during infection or vaccination in humans or mice . The exploration for alternative therapies is critical in the absence of fully effective DENV vaccines or antivirals , and immunotherapy is a potential candidate . The therapeutic potential of polyclonal avian-derived IgY has been previously established for the treatment of Pseudomonas aeruginosa , which is in an ongoing phase III clinical trial under the auspices of the European Medicines Agencies [58–60] , in addition to treatment of Candida albicans , [61 , 62] , and many toxins and venoms [63–65] . Although the animal models used in our study and others demonstrate the therapeutic efficacy of IgY , we recognize that there are potential obstacles that may arise in translating IgY therapeutics for human clinical application . The immunogenicity of IgY has been tested previously [66–68] in both pigs and mice . Vega et al . and Torche et al . have both demonstrated that administration of IgY to pigs via both systemic and local routes induced an anti-IgY antibody response , primarily consisting of the IgG subclass . These data suggest that IgY is antigenic and although the biochemical properties of this antibody molecule do not facilitate considerable binding to mammalian Fc receptors , serum sickness is a theoretical possibility if IgY is administered in large amounts . Whether or not IgY elicits an allergic response in pigs is unknown , however Akita et al . demonstrated that administration of egg yolk containing IgY , purified IgY , and IgY Fab’ to mice failed to induce an IgE response . They further determined that there was very little cross reactivity between egg white protein , which is highly allergenic , and purified IgY . Future studies will need to be conducted to elucidate the antigenicity and allerginicty of IgY in more relevant models . In addition to these studies , we must also consider the timing and dose of administration and the myriad of co-morbidities that are present in dengue endemic countries where protection from disease is most needed . Overall , we determined the therapeutic potential of anti-DENV2 IgY and demonstrated that IgY does not induce ADE . Our results indicate that anti-DENV2 IgY is capable of neutralizing but not enhancing DENV2 infection and that anti-DENV2 IgY therapeutically administered to mice can protect against lethal DENV2 challenge . Further , we have confirmed that the antibody repertoire generated against DENV in geese is different from the repertoire generated by mammals . | Dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) are severe disease manifestations following secondary heterotypic dengue virus ( DENV ) infections . DENV infects almost 400 million people annually and there are currently no licensed therapies to treat DENV-induced disease . DHF and DSS are mediated by serotype cross-reactive antibodies that facilitate antibody dependent enhancement ( ADE ) by binding to viral antigens and then Fcγ receptors ( FcγR ) on surrounding cells . ADE results in a heightened immune response and in part mediates the pathogenesis of secondary DENV infections . Researchers have developed an animal model of ADE-induced severe DENV in which anti-DENV2 antibodies genetically engineered to eliminate FcR binding have therapeutic and prophylactic efficacy . Our study suggests that avian-derived anti-DENV2 IgY , without genetic modification , is able to provide protection both in vitro and against a lethal DENV challenge in vivo . IgY has emerged as an attractive , alternative therapeutic antibody because of its biological characteristics that enable it to prevent some of the adverse reactions that mammalian antibodies have when used for human immunotherapy . Specifically , IgY does not bind to mammalian complement or rheumatoid factor and has proven to be very safe for human consumption . Without modification , anti-DENV2 IgY can be rapidly produced and may serve as a good therapeutic option to treat dengue worldwide . | [
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] | 2017 | Dengue virus specific IgY provides protection following lethal dengue virus challenge and is neutralizing in the absence of inducing antibody dependent enhancement |
We previously reported a multigene family of monodomain Kunitz proteins from Echinococcus granulosus ( EgKU-1-EgKU-8 ) , and provided evidence that some EgKUs are secreted by larval worms to the host interface . In addition , functional studies and homology modeling suggested that , similar to monodomain Kunitz families present in animal venoms , the E . granulosus family could include peptidase inhibitors as well as channel blockers . Using enzyme kinetics and whole-cell patch-clamp , we now demonstrate that the EgKUs are indeed functionally diverse . In fact , most of them behaved as high affinity inhibitors of either chymotrypsin ( EgKU-2-EgKU-3 ) or trypsin ( EgKU-5-EgKU-8 ) . In contrast , the close paralogs EgKU-1 and EgKU-4 blocked voltage-dependent potassium channels ( Kv ) ; and also pH-dependent sodium channels ( ASICs ) , while showing null ( EgKU-1 ) or marginal ( EgKU-4 ) peptidase inhibitory activity . We also confirmed the presence of EgKUs in secretions from other parasite stages , notably from adult worms and metacestodes . Interestingly , data from genome projects reveal that at least eight additional monodomain Kunitz proteins are encoded in the genome; that particular EgKUs are up-regulated in various stages; and that analogous Kunitz families exist in other medically important cestodes , but not in trematodes . Members of this expanded family of secreted cestode proteins thus have the potential to block , through high affinity interactions , the function of host counterparts ( either peptidases or cation channels ) and contribute to the establishment and persistence of infection . From a more general perspective , our results confirm that multigene families of Kunitz inhibitors from parasite secretions and animal venoms display a similar functional diversity and thus , that host-parasite co-evolution may also drive the emergence of a new function associated with the Kunitz scaffold .
Cestodes are a neglected group of platyhelminth parasites , despite causing chronic infections to humans and domestic animals worldwide [1] . Together with other researchers around the world [2] , we have been using Echinococcus granulosus as a model to study the molecular basis of the host-parasite cross-talk during cestode infections [3 , 4 , 5] . E . granulosus is the agent of cystic echinococcosis , a medically and economically important worldwide zoonosis , with endemic foci in Central Asia , China , South America and Africa [6] . Like all taeniid cestodes , it has a life cycle involving two mammals: a non carnivore intermediate host ( harboring the larva ) and a carnivore definitive host ( harboring the hermaphroditic adult ) . Intermediate hosts ( ungulates such as sheep , cattle and pigs; and , accidentally , also humans ) become infected by ingestion of eggs containing oncospheres that develop at visceral sites into bladder-like metacestodes ( hydatid cysts ) . These latter are bounded by a wall whose inner germinal layer gives rise to larval worms ( protoscoleces ) by asexual budding; protoscoleces are bathed in hydatid fluid that includes host plasmatic proteins and parasite secretions . Infection in the definitive host ( always a canid , most often dogs ) arises from ingestion of protoscoleces that , upon activation by contact with stomach acid , enzymes and bile acids , evaginate and attach to the mucosa of the duodenum , where they develop into adult tapeworms that can reside in the gut for long periods without causing any apparent damage [7] . Specific anatomical structures allow such a close contact at the canid-worm interface that E . granulosus has been regarded as both a tissue and a luminal parasite [8] . The molecular mechanisms underlying its successful establishment and persistence in the hostile environment of the dog duodenum are unknown . With the aim of identifying molecules participating in the E . granulosus–dog cross-talk , we surveyed the genes expressed by protoscoleces and pepsin/H+-treated protoscoleces . We thus identified a multigene family of Kunitz-type inhibitors ( EgKUs ) . These molecules were associated mostly with treated protoscoleces , suggesting that they play roles at the initial phases of infection [3] . Kunitz inhibitors are a class of metazoan serine peptidase inhibitors , whose prototype is the bovine pancreatic inhibitor of trypsin ( BPTI; family I2 of the MEROPS database; http://merops . sanger . ac . uk/ ) [9] . They are competitive inhibitors acting in a substrate-like manner , that form very stable complexes of 1:1 stoichiometry with their target enzymes , devoid of activity . The interaction between the enzyme and the inhibitor is highly dependent on the residue located at the position P1 of the antipeptidase loop ( position 15 of mature BPTI ) [10] . In addition , families of Kunitz inhibitors are frequent components of the saliva and secretions from hematophagous animals and also of animal venoms . These “Kunitz-type toxins” have been described in the venoms from snakes [11] , sea anemones [12 , 13] , cone snails [14] , spiders [15] , scorpions [16 , 17] as well as in the saliva of blood-sucking arthropods [18 , 19] and in the secretions of hematophagous nematodes [20] . Interestingly , besides inhibiting peptidases , some Kunitz toxins , until now described only in animal venoms , block various types of cation channels . Furthermore , some act solely as channel blockers . A set of neurotoxins present in the venoms of mamba snakes ( ‘‘dendrotoxins” ) , whose function is to paralyze the prey , is the best known example [21] . We previously reported the molecular features of eight EgKUs ( that we named EgKU-1-EgKU-8 ) and provided evidence that some of them ( notably , EgKU-3 and EgKU-8 ) are secreted by protoscoleces . Although diverse , these EgKUs were found to group into three pairs of close paralogs ( EgKU-1/EgKU-4; EgKU-3/EgKU-8; EgKU-6/EgKU-7 ) , which would be the products of recent gene duplications . In addition , we carried out detailed kinetic studies with native EgKU-1 and EgKU-8 purified from protoscoleces that revealed their possible functionalities . EgKU-8 behaved as a slow , tight-binding inhibitor of trypsins , with global inhibition constants ( KI* ) in the 10−11 M range , and interacted with enzymes through a mechanism involving two reversible steps; an initial relatively fast formation of an enzyme-inhibitor complex followed by a slow transition to a tight complex . In sharp contrast , EgKU-1 did not inhibit any of the assayed peptidases . Interestingly , molecular modeling revealed that structural elements associated with activity in Kunitz cation-channel blockers are also present in EgKU-1 . Indeed , α-dendrotoxin ( α-DTX ) , a well characterized blocker of specific voltage-activated K+-channels ( Kv ) [21] , was—at the time—the best overall template of EgKU-1; and several amino acids important for toxin activity were found to be conserved in the consensus model of the parasite molecule , supporting the notion that it is a putative cation channel blocker . Presumed orthologs of the EgKUs ( peptidase inhibitors as well as channel blockers ) were also found to be present in the transcriptomes from the other medically important cestodes ( notably , E . multilocularis and Taenia solium , the agents of alveolar echinococcosis and cysticercosis , respectively ) , indicating that families of monodomain Kunitz inhibitors are also present in closely related organisms [3] . In this article , we characterize the activity of EgKU-1–EgKU-8 using enzyme kinetics and whole-cell patch clamp assays . We thus demonstrate that the E . granulosus Kunitz family is indeed functionally diverse . On the one hand , we show that all but EgKU-1 and EgKU-4 behave as high affinity inhibitors of either chymotrypsin or trypsin . On the other hand , patch-clamp assays on rat dorsal root ganglion ( DRG ) neurons confirmed that EgKU-1 , and also its close paralog EgKU-4 , block Kv . Furthermore , the two proteins also block pH-dependent sodium channels ( acid sensing ion channels , ASICs ) , a previously unreported activity for Kunitz inhibitors , that we recently described for α-DTX [22] . In addition , we provide further evidence of the presence of EgKUs in parasite secretions . We discuss the significance of these results taking into account available genomic and transcriptomic data from E . granulosus and related cestodes .
In our previous study , working with native EgKU-1 and EgKU-8 , we demonstrated that EgKU-8 is a high affinity inhibitor of trypsins , whereas EgKU-1 did not inhibit any of the assayed peptidases [3] . To further advance in the functional characterization of the family , we prepared recombinants of the eight EgKUs . We carried out a preliminary screening of the serine peptidase inhibition activity of recombinant EgKU-2–EgKU-7 . Of note , in the case of EgKU-8 both the native inhibitor and the recombinant protein behaved similarly ( KI* 60 ± 13 versus 50 ± 10 pM , for native and recombinant EgKU-8 , respectively ) . Using pancreatic enzymes , we analyzed whether the EgKUs showed the inhibition profiles that may be predicted from the respective amino acid at position P1: EgKU-2 ( Trp in P1 ) and EgKU-3 ( Leu in P1 ) are candidate chymotrypsin inhibitors , whereas EgKU-4-EgKU-7 ( Arg in P1 ) are predicted to inhibit trypsin . All six EgKUs showed the expected activities . We subsequently performed titration assays to analyze whether they behaved as high affinity inhibitors . These studies indicated that , except for EgKU-4 , the close paralog of EgKU-1 , the EgKUs are high affinity inhibitors of bovine chymotrypsin ( EgKU-2 and EgKU-3 ) or trypsin ( EgKU-5-EgKU-8 ) ( Table 1 and Fig 1 ) . In view of these results , we further characterized the inhibitory activity of EgKU-3 , the closest paralog of EgKU-8 . We carried out kinetic studies using bovine chymotrypsin A and also chymotrypsin purified from dog pancreas , i . e . chymotrypsin B ( chymotrypsin A is absent from dogs , see S01 . 001 at MEROPS—http://merops . sanger . ac . uk ) . EgKU-3 inhibited with high affinity both peptidases; Fig 2A shows a representative experiment with the bovine enzyme and Table 2 the global inhibition constants calculated for the two chymotrypsins . The values of KI* were of the same order ( 53 ± 19 and 84 ± 49 pM for the bovine and canine enzymes , respectively ) indicating no bias in specificity towards any of them . EgKU-3 also inhibited elastase , although with substantially lower affinity than chymotrypsins ( KI* of 5 ± 2 nM ) . In order to study the inhibition mechanism of EgKU-3 towards chymotrypsins , we carried out time course experiments with chymotrypsin A . The progress curves for the inhibition ( Fig 2B ) indicated that the enzyme-inhibitor complex reaches equilibrium in a time scale of minutes and that EgKU-3 is a slow-binding inhibitor as defined by Morrison [23] . The interaction of EgKU-3 with chymotrypsin was reversible , since progress curves reached appreciable slopes even at higher than stoichiometric inhibitor concentrations . This is the expected behavior for Kunitz-type inhibitors [10] and the one observed for EgKU-8 [3] . Similarly , the plot of the apparent rate constant ( kobs ) versus EgKU-3 concentration was hyperbolical ( Fig 2C ) , in accordance with a mechanism involving two steps , a fast initial binding of the inhibitor to the target enzyme followed by a slow transition [24] . The kinetic constants of EgKU-3 binding to chymotrypsin obtained from analyses of the progress curves are shown in Table 3 . Note that the value of KI* calculated from the kinetic constants compared very well with the value obtained through the fit of steady-state rate versus inhibitor concentration data to the Morrison equation ( Table 2 ) . As already mentioned , our results indicate that the paralogs EgKU-1/EgKU-4 do not show the typical serine peptidase inhibitory activity of Kunitz-type inhibitors . In fact , EgKU-1 did not inhibit any assayed peptidase [3]; whereas EgKU-4 inhibited trypsin albeit with low affinity , with a KI* of 47 ± 2 nM , i . e . 1000-fold higher than the KI* of EgKU-3 and EgKU-8 versus their target enzymes ( Table 2 ) . In view of these results and taking into account the structural similarity between EgKU-1 and α-DTX [3] , we analyzed whether EgKU-1 and EgKU-4 acted on Kv using whole-cell patch-clamp assays on neurons isolated from DRG . Both EgKUs inhibited Kv; Fig 3A illustrates the effect of recombinant EgKU-1 . The blockade was more pronounced over the steady-state component of the current than over the peak current: 25 ± 11% versus 20 ± 14% and 27 ± 12% versus 23 ± 7% for 200 nM of recombinant EgKU-1 and EgKU-4 , respectively ( n = 7 ) . We also tested the activity of native EgKU-1 and verified that the recombinant inhibitor reproduced reasonably well the behavior of the native inhibitor ( 100 nM of native EgKU-1 blocked the steady-state current by 19% and the peak current by 10%; n = 4 ) . Thus , the effect appeared to be stronger on the currents at the end of the pulse ( accounting for non inactivating -delayed-rectifier- K+ currents , IKDR ) than on those at the beginning of the pulse ( corresponding to fast -transient A-type- K+ currents , IKA ) . The effect was only partially reversible because about 60% persisted 3 min after washing ( Fig 3B ) . In addition , it was clearly observed on the currents elicited over -40 mV , as highlighted by the activity profile of the EgKUs over the K+ currents activated by different voltages ( Fig 3D–3F ) . In contrast , the perfusion of 1 μM EgKU-8 ( n = 7 ) produced no significant changes in the peak amplitude ( 2 . 6 ± 3 . 2% , P = 0 . 20 ) or the steady-state current ( 1 . 1 ± 3 . 5% , P = 0 . 38 ) , whereas 1 μM of EgKU-3 ( n = 10 ) produced a slight non-significant reduction of the peak current ( 6 . 3 ± 3 . 8% , P = 0 . 13 ) and had no effect on the steady-state current ( 4 . 1 ± 4 . 5% , P = 0 . 13 ) ( Fig 4 ) . We also analyzed the effect of EgKU-1 and EgKU-4 on voltage-activated sodium channels ( Nav ) and observed no effect ( S1 Fig ) . Subsequently , we further characterized the effect of EgKU-1 and EgKU-4 on Kv . For this purpose , we recorded the currents after a pre-pulse of -120 mV to activate all voltage-dependent K+ currents , transient IKA as well as slow-inactivating IKDR; and also those remaining when the pre-pulse was of -45 mV , voltage at which IKA is inactivated . Thus , the second recording corresponded to IKDR; whereas IKA could be deduced by subtracting IKDR from the first recording . A representative experiment with recombinant EgKU-1 is shown in Fig 5 . This setup allowed us to analyze the effect of the inhibitors on total K+ currents ( Fig 5A and 5C ) as well as on both types of isolated K+ currents , IKDR ( Fig 5B and 5D ) and IKA ( Fig 5E ) . As anticipated by the previous experiment , EgKU-1 principally affected IKDR , with virtually no effect on IKA ( Fig 5D versus 5E ) . Finally , we studied the concentration-response relationship for native EgKU-1 and estimated an IC50 of about 200 nM when acting on all K+ currents activated by a pulse of -100 to 0 mV ( Fig 6 ) . Although we did not determine the IC50 for EgKU-4 , its behavior was similar to the one of EgKU-1 . Taking into account the recently described activity of α-DTX on ASIC currents in DRG neurons [22] , we also analyzed the effect of EgKU-1 and EgKU-4 on pH-dependent Na+ currents . The sustained application of both EgKUs blocked the ASIC currents elicited by a pH change from 7 . 4 to 6 . 1 . The blocking effect was on the peak amplitude ( Ipeak ) and no significant effect was observed on the desensitization time course ( τdes ) . The effect on the current amplitude was fully reversible after 1 min washing ( Fig 7A–7C ) . We similarly analyzed the effect of EgKU-3 and EgKU-8: EgKU-3 ( n = 8 ) produced a slight but significant decrement of the Ipeak ( 6 . 4 ± 3 . 0%; P = 0 . 02 ) , whereas EgKU-8 ( n = 7 ) had no effect ( 6 . 2 ± 5 . 1%; P = 0 . 11 ) ( Fig 7D–7E ) . Finally , we studied the concentration-response relationship for native EgKU-1 ( Fig 8A ) ; the estimated IC50 value ( about 8 nM ) was 25-fold lower than the one determined for Kv ( about 200 nM ) , suggesting higher selectivity of EgKU-1 for ASICs than Kv . In our previous study , using mass spectrometry analysis , we showed that members of the Kunitz family , notably EgKU-3 and EgKU-8 , would be present in protoscolex secretions from untreated and pepsin/H+-treated larval worms [3] . To further approach the question of whether Kunitz inhibitors are secreted to the parasite-host interface , we similarly analyzed hydatid fluid and adult worm secretions . Fig 9A shows a representative MALDI-TOF MS profile ( 5000–10000 Da ) of hydatid fluid from bovine cysts . Peaks of m/z 6407 . 5 and 6519 . 6 , matching the predicted MH+ value for EgKU-3 ( 6406 . 4 Da ) and EgKU-8 ( 6520 . 4 Da ) , respectively , were observed . Furthermore , the intensity of the signals putatively corresponding to both EgKUs was substantially increased in the chymotrypsin A-affinity purified fraction from the same sample ( Fig 9B ) . In addition , the MS profile of an analogous fraction from the supernatant of in vitro cultured immature adults ( Fig 9C ) also showed peaks matching the predicted MH+ value for EgKU-3 and EgKU-8 ( m/z of 6409 . 8 and 6521 . 3 , respectively ) . Peptide mass fingerprinting of the components purified from cyst fluid allowed the detection of signals that could be assigned to tryptic peptides of these EgKUs . In particular , we detected signals with m/z values 1074 . 54 and 1491 . 63 that corresponded to EgKU-8 sequences 7LPLDPGFCR15 and 21WGFHQESGECVR32 with S-carboxymethylated cysteines ( theoretical m/z values 1074 . 53 and 1491 . 64 , respectively; see Fig 10C ) . In addition , a signal corresponding to EgKU-3 sequence 49–57 was detected in the same spectrum . MS/MS analysis of the corresponding ions further corroborated the amino acidic sequences ( S1 Dataset ) . Note that , although lower than towards trypsins , the affinity of EgKU-8 towards chymotrypsin A ( KI* 10−9 M; Table 2 ) was high enough to allow its purification from the secretions . In contrast , this approach is not suitable to purify EgKU-1 and EgKU-4 that have no affinity towards chymotrypsin; thus , even if they had been present in the original sample , we would not have detected them .
In the present study , we described the functional characterization of eight members of the E . granulosus family of secreted Kunitz inhibitors ( EgKUs ) . Using recombinant forms of EgKU-1-EgKU-8 and native EgKU-1 , we demonstrated that six EgKUs behave as high affinity inhibitors of either chymotrypsin ( EgKU-2 and EgKU-3 ) or trypsin ( EgKU-5-EgKU-8 ) , whereas the close paralogs EgKU-1/EgKU-4 act as cation channel blockers ( of Kv as well as ASICs ) , while showing either null ( EgKU-1 , our previous study [3] ) or marginal ( EgKU-4 ) serine peptidase inhibition activity . This degree of functional diversity , commonly observed in animal venoms , had not been previously described for Kunitz inhibitors present in parasite secretions . Regarding serine peptidase inhibition , detailed kinetic studies showed that the interaction of EgKU-3 with chymotrypsins mimics the one of the close paralog EgKU-8 with trypsins: it is slow , of very high affinity and involves two steps . EgKU-3 strongly inhibited isoforms A and B of chymotrypsin with KI* in the 10−11 M range . Notably , according to MEROPS , dogs have two chymotrypsins B ( with > 95% identity , encoded by CTRB1 and CTRB2 genes ) and lack chymotrypsin A . The values of KI* are among the smaller registered for chymotrypsin inhibitors . In fact , only two peptides have been reported to have similar affinity , both towards bovine chymotrypsin A ( see S01 . 001 in MEROPS ) [25 , 26] . No high affinity inhibitors of chymotrypsin B have been described so far , most likely because very few studies have been carried out with this isoform ( see S01 . 152 in MEROPS ) . Therefore , EgKU-3 appears as an interesting titration reagent for chymotrypsins A and B , especially for the latter because adequate titration reagents are currently unavailable . The stability of the EgKU-3-chymotrypsin A complex is similar to that of EgKU-8 complexes with trypsins , with k2/KI , the apparent second order rate constant for complex formation ( kon ) , in the 106 M-1 s-1 range; and the dissociation rate constant ( k-2 ) in the 10−4 s-1 range ( Table 3 and our previous study , [3] ) . These values of k2/KI are in good agreement with reports for other members of the family , including BPTI with bovine trypsin [27] , whereas those of k-2 are several orders faster than the one reported for BPTI ( 10−8 s-1 with bovine trypsin [27] ) . The activity of EgKU-3 as a strong tight-binding inhibitor of chymotrypsins and a less potent inhibitor of pancreatic elastase , as well as its lack of activity towards trypsin ( Table 2 ) are consistent with the presence of a Leu in P1 . In turn , similar to BPTI ( Lys in P1 ) , EgKU-8 ( Arg in P1 ) strongly inhibits trypsins , less potently chymotrypsin A and does not inhibit elastase ( Table 2 ) . Notably , neither EgKU-8 nor BPTI inhibit chymotrypsin B . The antipeptidase loops of EgKU-3 and EgKU-8 differ by 50% ( the corresponding mature polypeptides differ by only 37% ) ; differences are mainly on the P side of the loop ( residues 10 to 15 ) , and involve some non-conservative substitutions ( notably Asp10 instead of Lys10 in P6; see Fig 10C ) . Because the loop contributes to the inhibition specificity primarily determined by the P1 site , some of these residues are likely involved in the interaction with chymotrypsin B . In any case , consistent with the fact that isoforms A and B have similar affinities towards substrates with Phe as P1 residue [28] , the values of KM for the substrate we used were of the same order . Regarding cation channel inhibition , patch-clamp studies carried out on rat DRG neurons showed that EgKU-1 and EgKU-4 block voltage-activated potassium currents . The effect was voltage-dependent and , as described for dendrotoxins , it was not totally reversible ( Fig 3; [29] ) . The detailed characterization of EgKU-1 activity on isolated K+ currents indicated that it preferentially blocks IKDR , as compared to IKA . This behavior differs from the one of α–DTX and resembles the one of δ–DTX [29] . In any case , the IC50 determined for EgKU-1 was two orders of magnitude higher than those of dendrotoxins assayed on DRG neurons ( 10−7 versus 10−9 M [29] ) . This could be due , at least in part , to the fact that , although EgKU-1 shares with dendrotoxins several residues that would participate in channel interaction ( notably Leu7 , Lys26 and Lys27 ) , it lacks the Lys5 that has been described as a primary determinant of activity ( [21]; this is also the case for EgKU-4 that shares Leu7 and Lys27 with α–DTX and EgKU-1; see Fig 10C ) . Because we studied the effect of the EgKUs on total Kv currents of primary cultures , we cannot comment on their activity over specific Kv . Nevertheless , our results indicate that EgKU-1 could be more active over some Kv than others . Indeed , the dose-response curve does not start from zero ( Fig 6 ) , as if a specific Kv was highly blocked at low concentrations of the inhibitor . EgKU-1 and EgKU-4 also showed a potent dose-dependent blocking effect on the ASIC currents in DRG neurons , which was totally reversible after one minute washing ( Fig 7 ) . These neurons express at least two subpopulations of transient ASIC currents as judged by their inactivation constants [30] . One of them derives from channels of ASIC1a , ASIC1b and ASIC3 subunits; the other from channels of ASIC2a subunits ( reviewed by [31] ) , which are the least expressed in DRG [32] . Although our experimental setup does not allow us to conclude which channels are sensitive to the EgKUs , the dispersion of the values in the dose-response study with native EgKU-1 ( Fig 8A ) points to some variability of the blocking effect among different cells , suggesting that the effect could be stronger for some channel ( s ) . EgKU-1 could thus mimic the performance of other peptide blockers of ASICs , such as APETx2 ( reviewed by [31] ) . We recently reported that α-DTX , the well-known blocker of voltage activated K+ channels , also inhibits ASIC currents in rat DRG , although with significantly less potency than Kv ( IC50 ~ 10−7 M [22] versus 10−9 M [29] , respectively; see also Figs 4 and 8B ) . This result indicates that the Kunitz domain is yet another structural scaffold for ASIC-blocking polypeptides . Interestingly , an exposed basic-aromatic cluster identified in structurally different ASIC blocking peptides [33] was also found to be present in the structure of α-DTX [22] . Notably , this feature is observable towards one side of the model structures of EgKU-1 and EgKU-4 and not in those of EgKU-3/EgKU-8 ( Fig 10A ) . In any case , functionally distinct EgKUs differ mainly in surface charge distribution ( Fig 10B ) . The relatively low selectivity of Kunitz inhibitors towards cation channels contrasts with the high specificity of their interaction with serine peptidases . Not surprisingly , structure-activity analyses focused at identifying the “channel-blocking site” of Kunitz proteins have usually highlighted regions on their surface involved in channel interaction but not a defined structural motif comparable to the antipeptidase loop involved in serine peptidase interaction . Furthermore , key residues for channel blockade are frequently located in the N- or C-terminal extensions of the Kunitz domain [17 , 34] The availability of the E . granulosus genome [5 , 35] has allowed us to identify genes coding for at least eight additional monodomain Kunitz proteins with the same molecular architecture as EgKU-1–EgKU-8 , i . e . a signal peptide followed by a single Kunitz domain . Similar to the rest of the family , the newly-identified members are diverse and include several pairs of close paralogs , consistent with an accelerated evolution of the family . Fig 11 shows an unrooted phylogenetic tree of the Kunitz domains from the sixteen EgKUs together with eleven close paralogs from T . solium and five from functionally characterized monodomain Kunitz proteins from Lophotrochozoa , including four from trematodes . A true phylogenetic tree is not intended , as the signal might be blurred by homoplasy . Rather , the tree is aimed to mirror functional groupings of the sequences in an approximate evolutionary context . Not surprisingly , the sequences from T . solium pair with their close E . granulosus paralogs . The groupings roughly correlate with functional features , whereas EgKU-2 ( and a putative T . solium ortholog ) appears very distant from the rest . The red sub-clade includes several serine peptidase inhibitors: in addition to EgKU-3/EgKU-8 and EgKU-5 , EGR_07242 ( EgKI-2 in [36] ) and the schistosome proteins SjKI-1 [37] and SmKI-1 [38] . EGR_07242 ( Arg in P1 ) was recently found to inhibit trypsin , although with relatively low affinity ( KI ~ 10−9 M; [36] ) , probably due to the lack of Cys14 , i . e . the one forming the disulphide bond that stabilizes the antipeptidase loop . SjKI-1 and SmKI-1 ( both with Arg in P1 ) also inhibit trypsin with IC50 in the 10−10 and 10−8 M range , respectively [37 , 38] . The green sub-clade appears to group a different set of serine peptidase inhibitors ( EgKU-6/EgKU-7 and closely related proteins from T . solium ) . In turn , the blue sub-clade includes the channel blockers EgKU-1/EgKU-4 together with another pair of close E . granulosus paralogs ( EgrG001136600/EgrG001137000 ) , and two T . solium proteins ( TsM_000410200 and TsM_000513000 ) . Although it is difficult to predict their function without further data , these proteins could also act as channel blockers because , similar to EgKU-1/EgKU-4 , they feature the conserved Leu7 and a positively charged β–turn that form the Kv-blocking site of α-DTX and related toxins [34 , 39 , 40 , 41] . As to the other groupings , EgrG_001136500 ( Leu in P1 ) was recently found to be a potent inhibitor of neutrophil elastase ( KI ~ 10−11 M ) and cathepsin G ( KI ~ 10−10 M ) and , interestingly , to reduce neutrophil infiltration in a local inflammation model ( EgKI-2 in [36] ) ; thus , its close paralog ( EgrG_00113800; Arg in P1 ) could also be a serine peptidase inhibitor . Finally , the sequences from Fasciola hepatica ( FhKTM [42] and FhKT1 [43] , both with Leu in P1 , whose Kunitz domains differ in 3/51 amino acids ) define a basal , separate sub-clade , that could also reflect functional diversity: FhKTM was found to be a marginal inhibitor of trypsin with virtually no effect over chymotrypsin [42] but , notably , FhKT1 was recently characterized as an inhibitor of cysteine peptidases , including the major parasite cathepsin L secreted peptidases and related human peptidases [43] ) . Another interesting finding of our work refers to the demonstration of the presence of some EgKUs ( notably , EgKU-3 and EgKU-8 ) in cyst fluid ( from bovine cysts ) and secretions from immature adult worms , which complement our previous results with secretions from protoscoleces and pepsin/H+-treated protoscoleces [3] . EgKU-8 was also detected by proteomic analyses in fertile cyst fluids from ovine and human infections , but not in infertile cysts from infected cattle [44] . Members of the Kunitz family could thus be secreted to the E . granulosus-dog interface not only at the initial stages of infection ( as indicated by their presence in larval worm secretions ) but also at late stages , and contribute to the establishment and persistence of dog echinococcosis . In turn , their presence in cyst fluid would point to a role at the onset of infection in dogs and/or during the chronic stage of infection in intermediate hosts . In addition , available RNASeq data [5 , 35] indicate that members of the family are expressed in all the analyzed stages ( immature adult , activated oncosphere , cyst , protoscolex , pepsin/H+-treated protoscolex ) ; interestingly , most EgKUs are expressed in adults . Furthermore , several of them appear up-regulated in this stage , notably , EgKU-3 , EgKU-7 , EgKU-8 , and also EGR_07242 and EgrG_0001137000 [35] . In addition , the orthologs of EgKU-1 and EgKU-4 were found to be highly up-regulated in E . multilocularis gravid adults ( as compared to non-gravid worms ) [5] , whereas EgrG_001136500 and a close paralog ( EmuJ_001136900 ) were among the transcripts with higher expression in oncospheres from E . granulosus [35] and E . multilocularis [45] , respectively . Together with our results highlighting the presence of EgKUs in parasite secretions , these data support the concept that Kunitz proteins are involved in parasite interaction with definitive and intermediate hosts , and indicate that specific members of the family would be engaged in particular moments of the life-cycle . Given the activity profile of EgKU-1-EgKU-8 and the fact that they are mostly expressed and secreted by larval and adult worms [3 , 35] , we can speculate about their potential counterparts in the dog duodenum . The apical end of the scolex contains a gland ( the rostellar gland ) whose secretion is believed to play a key role in host-parasite cross-talk , due to the very intimate contact of the scolex with the mucosa ( reviewed by [2] ) . Interestingly , seminal studies demonstrated that the rostellar gland secretion is cystine-rich [46]; the gland could thus be the site of synthesis and concentration of the EgKUs . Pancreatic enzymes appear as clear targets of the EgKUs acting as serine peptidase inhibitors: those present in cyst fluid could initially protect the larval worms from digestion; whereas those secreted could protect the scolex ( whose glycocalix is thin ) after the parasite has attached to the mucosa . The EgKUs could also inhibit other serine peptidases , such as those secreted by immune cells ( see for example [47] ) and membrane peptidases from epithelial cells ( see for example [48 , 49] ) ; in turn , this effect could prevent the activation of proteinase-activated receptors ( PARs; [50] ) . As to putative targets of EgKU-1/EgKU-4 , very little is known about the expression and functional properties of Kv and ASICs in the gut . In any case , both types of channels participate in the physiology of epithelial cells and afferent neurons [51 , 52 , 53] . In addition , Kv and ASICs are involved in the activation and maturation of dendritic cells and macrophages . In particular , Kv1 . 3 and Kv1 . 5 modulate the Ca2+-dependent functions of these cells and their blockade down-regulates their activation [54] . Regarding ASICs , dendritic cells express ASIC1 , ASIC2 and ASIC3 , and extracellular acidosis induces currents that are blocked by ASIC inhibitors . In addition , acidosis triggers the activation of dendritic cells and macrophages and ASIC inhibitors block these effects [54 , 55] . Taking into account that extracellular acidosis is a hallmark of inflammation , the blockade of ASICs may be crucial to weaken the induction of innate immunity and to favor the development of a chronic infection . In this context , it is pertinent to mention that excretory-secretory ( E/S ) products from E . granulosus adults have recently been found to impair dendritic cell function and induce the development of regulatory T cells [56] . Furthermore , a similar result was observed with the Kunitz protein FhKTM from the trematode F . hepatica , which is known to be present in parasite E/S products [57] . As already mentioned , FhKTM showed a marginal serine peptidase inhibitory activity [42] but , notably , the very closely related FhKT1 ( > 90% overall identity with FhKTM ) was recently found to inhibit cysteine peptidases [43] . In our previous study , we included data from an extensive survey of platyhelminth ESTs available at the time , indicating that the expression of families of monodomain Kunitz proteins would be a distinctive trait of cestodes [3] . Genomic and transcriptomic data currently available ( accessible from WormBase ParaSite: http://parasite . wormbase . org/; [58] ) confirm and extend these initial observations , in particular , for the other medically important cestodes , E . multilocularis and T . solium , and indicate that this family is expanded in cestodes . Putative orthologs of virtually all the EgKUs are present in the E . multilocularis genome , and several are also predicted for T . solium ( Fig 11 ) . In addition , recent genome-wide analyses of E/S proteins showed that the Kunitz domain is either the most ( in E . multilocularis , 17/673 predicted E/S proteins; [59] ) or the third most ( in T . solium , 14/838 predicted E/S proteins; [60] ) represented domain in the predicted secretome of these parasites , whereas manual inspection of the putative secreted Kunitz proteins indicates that a majority of them contain a single Kunitz domain . In contrast , parasitic trematodes express only a few monodomain Kunitz inhibitors ( see for example [61] ) . The secretion of monodomain Kunitz proteins thus appears to be a strategy evolved by cestodes to block , through high affinity interactions , the function of host proteins ( either serine peptidases or cation channels ) and contribute to the establishment and persistence of infection . The putative immunomodulatory role of these molecules opens the way to further studies of their involvement in immunoevasion , acting as single molecules as well as synergistically . From a more general perspective , the data confirm that multigene families of Kunitz inhibitors from parasite secretions and animal venoms display a similar functional diversity . As we had previously mentioned , because the genes coding for parasite secretions and predator toxins arise from an arms race between different organisms , it is interesting to consider that both sets of molecules display analogous evolutionary patterns . Finally , the strong target specificity of some of these molecules makes them uniquely suited as tools for the characterization of biological processes as well as for the development of pharmaceuticals .
Native EgKU-1 and EgKU-8 were purified to homogeneity from a protoscolex lysate by cation exchange followed by reverse-phase chromatography , as previously described [3] . EgKU-1–EgKU-8 [3] were overexpressed as amino-terminal His6-tagged fusion proteins in Escherichia coli strain BL21 ( DE3 ) using pET28a recombinant plasmids prepared according to standard procedures . The expression constructs included the His6 leader sequence followed by the cDNA sequence encoding the corresponding predicted full-length mature EgKU . These were amplified from E . granulosus pepsin/H+-activated protoscolex cDNA [62] using Vent DNA polymerase ( New England Biolabs ) and specific primers containing restriction sites to allow directional cloning into the pET28a vector . BamHI and HindIII sites were used except for EgKU-6 and EgKU-7 whose coding sequences are cleaved by BamHI; EcoRI was used instead . The integrity of the expression constructs was checked by sequencing . The His6-tagged EgKU fusion proteins were expressed in transformed E . coli grown in LB containing 10 mg/L of kanamycin and induced at 37°C with 0 . 1 mM isopropyl thiogalactopyranoside . Induction of expression was at late-log-phase ( A600 0 . 6–1 . 0 ) during 4 h , in the case of the pairs of paralogs EgKU-1/EgKU-4 and EgKU-3/EgKU-8 that yielded good amounts of soluble recombinant peptides [63] . Expression of EgKU-2 , EgKU-5 and the paralogs EgKU-6/EgKU-7 , whose recombinants are recovered mostly as inclusion bodies [63] , was induced earlier ( A600 0 . 2–0 . 3 ) to maximize the yield of the soluble proteins . In all cases , the induced cells were harvested by centrifugation , the pellet was suspended in “lysis” buffer ( 50 mM NaH2PO4 , 300 mM NaCl , 10 mM imidazole ) , and the cells were lyzed by sonication . The lysates were centrifuged ( 20 , 000 g for 30 min at 4°C ) and the supernatants used to purify the His-tagged fusion proteins using a Ni2+-charged affinity matrix ( Ni-NTA , Invitrogen ) , following the manufacturer’s instructions . The soluble fraction of the bacterial lysates was loaded onto the column equilibrated with lysis buffer , washed with equilibration buffer including 30 mM imidazole , and the recombinant EgKUs eluted with the same buffer containing 250 mM imidazole , that was subsequently dialyzed . The purity of the EgKUs was checked by SDS-PAGE analysis and the protein concentration was determined with the bicinchoninic acid reagent ( BCA , Pierce , USA ) using bovine serum albumin as standard , or by A280 . The quality of the recombinants was further controlled by: i ) confirming the presence of three disulphide bonds ( i . e . that the proteins were fully oxidized ) through determination of the molecular masses of the EgKUs and their reduced and alkylated derivatives by MALDI-TOF MS , as described by Calvete [64] ( S2 Fig ) ; ii ) checking that recombinant EgKU-8 reproduced the performance of the native inhibitor towards bovine trypsin and thus , that the recombinant was properly folded and the N-terminal extension contributed by the expression vector did not interfere with enzyme interaction ( Ki* was 50 ± 10 pM for recombinant EgKU-8 and 60 ± 13 pM for the native inhibitor [3] ) ; iii ) during this study , we also verified that recombinant EgKU-1 reproduced reasonably well the performance of the native inhibitor acting on Kv and ASIC currents from DRG neurons ( Figs 3 and 7 , respectively ) . Usual yields of the EgKUs recovered as soluble recombinants and used for activity assays were as follows: ~5 mg/L of culture for EgKU-1/EgKU-4 and EgKU-3/EgKU-8; ~300 μg/L for EgKU-7 ( ~30% of the total ) ; and ~5 μg/L for EgKU-2 , EgKU-5 and EgKU-6 ( ~5% of the total ) . The purity of the proteins used for detailed activity studies ( EgKU-1 , EgKU-3 , EgKU-4 and EgKU-8 ) was always > 95% . The inhibitory activity of recombinant EgKU-1–EgKU-8 was tested against bovine trypsin ( EC 3 . 4 . 21 . 4 ) , bovine and canine chymotrypsins ( EC 3 . 4 . 21 . 1 ) , and porcine elastase ( 3 . 4 . 21 . 36 ) , essentially as previously described [3] . Bovine enzymes and porcine elastase were obtained from Sigma-Aldrich , whereas the canine peptidase was purified from the pancreas of a dog that had passed away due to an accidental cause , following the procedure of Waritani et al . [65] . According to MEROPS , dogs have two chymotrypsins B ( with > 95% identity , encoded by CTRB1 and CTRB2 genes ) ; the fraction we isolated from dog pancreas most likely contained a mixture of both enzymes . The following peptidases were thus assayed ( MEROPS—http://merops . sanger . ac . uk—identifiers are indicated in brackets; [9] ) : from Bos taurus , chymotrypsin A ( S01 . 001 ) and trypsin 1 ( cationic , S01 . 151 ) ; from Sus scrofa , elastase ( S01 . 153 ) ; from Canis familiaris , chymotrypsin B ( S01 . 152 ) ) . Prior to inhibition studies , proteolytic activity in enzyme preparations was determined with fluorogenic substrates using initial steady-state rate conditions at 37°C and pH 8 . 0 . Assays ( 200 μl ) were performed in black 96-well microplates ( Costar , Corning Life Sciences ) . Enzymes and substrates were dissolved in 50 mM Tris-HCl , pH 8 . 0 containing 0 . 01% Triton X-100 ( v/v ) , and reactions were initiated by the addition of enzyme . The changes in fluorescence intensity , corresponding to the formation of the hydrolysis product 7-amino-4-methylcoumarin ( AMC ) , were registered at excitation and emission wavelengths of 390 and 460 nm , respectively , with a microplate fluorescence reader ( FLUOstar* OPTIMA , BMG Labtechnologies ) . For trypsin activity , the artificial substrate N-t-BOC-Ile-Glu-Gly-Arg-AMC was used; for chymotrypsin , Suc-Ala-Ala-Pro-Phe-AMC; and for elastase , Suc-Ala-Ala-Ala-AMC . Calibration curves using AMC were carried out in each experiment . Initial steady-state rates of substrate hydrolysis were calculated from the linear portion of product ( AMC ) versus time plots when less than 10% of substrate had been consumed . The substrates and AMC were also obtained from Sigma-Aldrich . Protein concentrations of enzyme preparations were determined with the BCA reagent using bovine serum albumin as standard; and the active site concentration of trypsin and bovine chymotrypsin A by specific titration with the high affinity inhibitor BPTI . Initially , the active site concentration of canine chymotrypsin could not be estimated because , similar to bovine chymotrypsin B [66] , it was not inhibited by BPTI . The enzyme was subsequently titrated with EgKU-3 that inhibits chymotrypsins A and B with high affinity ( see results ) . The kinetic parameters for substrate and enzyme pairs were calculated from the non-linear fitting to the Michaelis-Menten equation . The values determined with the substrates specified above were: KM = 85 ± 9 μM and kCat = 50 ± 6 s-1 for bovine trypsin; KM = 30 ± 2 μM and kCat = 19 ± 2 s-1 for bovine chymotrypsin A; KM = 39 ± 2 μM and kCat = 6 ± 1 s-1 for canine chymotrypsin B . For inhibition studies , each of the enzymes was incubated with the purified recombinant EgKUs for 15 min at 37°C prior to the addition of the appropriate fluorogenic substrate , to allow for the equilibration of the enzyme-inhibitor complexes . The substrate concentration ( 5 μM ) was chosen so as to be well below the corresponding KM , as specified above . To check whether the EgKUs behaved as high affinity inhibitors , the purified recombinants were titrated against active-site titrated bovine trypsin and chymotrypsin , as described by Olson et al . [67] . The activities of EgKU-3 and EgKU-4 were further analyzed by characterizing the kinetics of enzyme inhibition , as previously described for EgKU-8 [3] . All experiments were carried out at least two independent times . Within each experiment , measurements were performed in duplicates . The inhibition and rate constants reported are the average ± standard error of independent experiments . The effect of EgKU-1 and EgKU-4 on voltage-gated ( Na+ and K+ ) and ASIC currents was studied using the whole cell patch-clamp technique in primary cultured rat DRG neurons . The effect of EgKU-3 and EgKU-8 on voltage-gated K+ and ASIC currents was similarly analyzed . α-DTX ( kindly donated by Dr . Carlos Cerveñansky from the Unidad de Bioquímica y Proteómica Analíticas , Institut Pasteur de Montevideo/Instituto de Investigaciones Biológicas Clemente Estable , Montevideo , Uruguay ) and Bovine Serum Albumin ( Sigma Chemicals ) were used as positive and negative controls , respectively . Fresh hydatid fluid was recovered under aseptic conditions from individual fertile cysts of the G1 genotype ( E . granulosus sensu stricto ) , present in the lungs of naturally infected bovines in Uruguay , and kept at -70°C . Cysts were collected during the routine work of local abattoirs in Montevideo . Cyst fluid was analyzed by MALDI-TOF MS using a Voyager DE-PRO spectrometer ( Applied Biosystems ) . The sample was concentrated by vacuum drying , desalted using C18 reverse phase micro-columns ( OMIX Pipette tips , Varian ) and eluted with matrix solution ( α-cyano-4-hydroxycinnamic acid in 0 . 2% trifluoroacetic acid in 50% v/v acetonitrile-H2O ) directly on the MALDI sample plate . An aliquot of adult worm in vitro secretions was kindly provided by MSc Noelia Morel; the culture was carried out in our department in the context of a project to develop a copro-ELISA kit for canine echinococcosis [72] . Worm secretions were prepared essentially as described by Casaravilla and coworkers [73] . In brief , adult worms were recovered from the intestine of dogs experimentally infected with E . granulosus ( euthanized 30 days post-infection ) , washed with sterile phosphate-buffered saline ( PBS ) and cultured in RPMI containing 105 UI/L penicillin , 100 mg/L streptomycin and 250 μg/L amphotericin B , at 37°C in 5% CO2 . The supernatant was collected every 8 h for two days and kept at -70°C . An aliquot was clarified by centrifugation at 10 , 000 g and analyzed as described for cyst fluid . EgKU-3 and EgKU-8 were affinity purified from hydatid fluid and worm secretions using chymotrypsin A agarose ( Sigma-Aldrich , USA ) and analyzed by MALDI-TOF MS ( 4800 MALDI TOF/TOF analyzer , ABi Sciex ) to confirm their presence in the parasite secretions . The resin containing agarose-bound bovine chymotrypsin A ( 1 . 5 mg ) was rehydrated and equilibrated with 10 mM Tris-HCl buffer pH 7 . 5 . Cyst fluid ( 1 ml ) was incubated in batch with the resin during 10 min at 20°C; the resin was then washed 3 times with 10 mM Tris-HCl pH 7 . 5 , and the EgKUs were eluted by incubation during 10 min with 25 μl of 0 . 2% trifluoroacetic acid . After centrifugation , 1 μl of the eluate was applied directly on the MALDI sample plate with 1 μl of the matrix solution ( α-cyano-4-hydroxycinnamic acid in 0 . 1% trifluoroacetic acid in 60% v/v acetonitrile-H2O ) . Mass spectra were acquired in positive ion linear mode and externally calibrated using protein standards ( Applied Biosystems ) . The EgKUs were purified from adult worm secretions with the same protocol , using 3 ml of the sample previously concentrated by vacuum drying . EgKU-3 and EgKU-8 purified from cyst fluid were characterized by peptide mass fingerprinting; the eluate was reduced and alkylated with iodoacetamide prior to treatment with trypsin ( Sequencing-grade , Promega ) . The sequence of selected peptides was confirmed by collision-induced dissociation MS/MS experiments , as previously described [3] . The full-length mature sequences of EgKU-1 , EgKU-3 , EgKU-4 and EgKU-8 were used to compute structural models using the i-Tasser server [74] . The C-scores for all models were higher than 0 ( EgKU-1 = 0 . 93; EgKU-3 = 1 . 23; EgKU-4 = 0 . 05; EgKU-8 = 1 . 13 ) . Typical C-scores range from [–2 , 5] , with higher scores meaning more reliable models . α-DTX did not stand amongst the top 10 threading templates used by i-Tasser , which allowed direct comparisons with no circularities in the analyses . Electrostatic properties were calculated at pH 7 . 4 with the Adaptive Poisson Boltzmann Solver [75] for the best i-Tasser models as well as for the crystal structure of α-DTX ( PDB access code 1DTX ) . Next , per-residue solvent-accessibility was computed with the “areaimol” program from the CCP4 suite [76] . Basic , acidic and aromatic residues with solvent accessibilities above 40 Å2 were displayed in van der Waals representation onto a cartoon backbone of the model , using VMD [77] . Electrostatic molecular surface representations were produced and rendered with PyMol ( http://pymol . sourceforge . net ) . The Kunitz domains from EgKU-1-EgKU-8 as well as from eight additional monodomain Kunitz proteins identified in the E . granulosus genome sequences ( encoded by EgrG_001136500 , EgrG_001136600 , EgrG_001136800 , EgrG_001137000 , EgrG_001137200 , EgrG_1137300 and EgrG_1137400 from the genome produced at the Wellcome Trust Sanger Centre [5]; and the protein EGR_07242 , from the genome produced at the Chinese National Human Genome Center [35] ) were multiply aligned with Mafft [78] ( L-insi option ) . Eleven close paralogs ( some of which are putative orthologs ) of the E . granulosus monodomain Kunitz proteins identified within genomic and transcriptomic data from the T . solium genome project [5] ( encoded by genes TsM_000321400 , TsM_000410200 , TsM_000513000 , TsM_000576900 , TsM_000647700 , TsM_000724900 , TsM_001022000 , TsM_001027800 , TsM_001085400 , TsM_001162600 retrieved from GeneDB—http://www . genedb . org/Homepage/Tsolium; and the EST EL746785 retrieved as TSE0004567 from PartiGeneDB—http://www . compsysbio . org/partigene/ ) were added to this set . A group of five functionally annotated sequences from other Lophotrochozoa was also included: FhKTM ( UniProt Q9TXD3; [42] ) and FhKT1 [43] , from F . hepatica; SjKI-1 ( Sjp_0020270 ) from S . japonicum [37]; SmKI-1 ( Smp_147730 ) from S . mansoni [38] and Conkunitzin-S1 ( UniProt P0C1X2 ) from the mollusk Conus striatus [14] . The alignment of these 32 proteins was used as input for MrBayes [79] for a Bayesian phylogenetic reconstruction using the Poisson substitution model in a run of 1 , 000 , 000 generations , discarding the first 100 , 000 for summarizing results ( mcmc ngen = 1000000; sump burnin = 100000; sumt burnin = 100000 ) . The short sequence length of the Kunitz domain prevents robust and reliable identification of many branching events . Furthermore , saturation events are guaranteed to occur and may be difficult to pinpoint . The tree is one of the best we can get with current methods . In fact , maximum likelihood reconstruction with 1000 bootstraps provided poorer support for branching events . The final dendrogram was visualized and rendered in FigTree ( http://tree . bio . ed . ac . uk/software/figtree ) . It should be noted that the proteins encoded by EgrG_001136500 , EgrG_001136600 , EgrG_001137000 , EgrG_001137200 , and EgrG_001137400 [5] correspond to EGR_08721 , EGR_08720 , EGR_08716 , EGR_9006 , and EGR_9007 [35] , respectively . | Parasite secretions are key players at host-parasite interfaces: parasite establishment and persistence rely , to a great extent , on interactions between these molecules and their host counterparts . We present the functional characterization of a multigene family of secreted Kunitz proteins from the cestode Echinococcus granulosus . Kunitz proteins are a class of metazoan high affinity serine peptidase inhibitors . In addition , families of Kunitz proteins are frequent components of animal venoms; besides inhibiting peptidases , some of these “Kunitz toxins” block cation channels and thus , provide a remarkable example of protein evolution where natural selection has driven the emergence of a new function associated with the same molecular scaffold . Using enzyme kinetics and electrophysiological assays , we demonstrated that the E . granulosus Kunitz family includes peptidase inhibitors as well as channel blockers . This diversity highlights an interesting similarity between animal toxins and parasite secretions that had not been previously described . Furthermore , the presence of analogous families of Kunitz proteins appears to be a distinctive trait of cestode genomes . We thus propose that these molecules contribute to a successful infection acting at the parasite-host interface . In addition , because they bear a strong specificity towards their targets , they are uniquely suited for the development of pharmaceuticals . | [
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] | 2017 | Functional diversity of secreted cestode Kunitz proteins: Inhibition of serine peptidases and blockade of cation channels |
Noroviruses are important human pathogens responsible for most cases of viral epidemic gastroenteritis worldwide . Murine norovirus-1 ( MNV-1 ) is one of several murine noroviruses isolated from research mouse facilities and has been used as a model of human norovirus infection . MNV-1 infection has been shown to require components of innate and adaptive immunity for clearance; however , the initial host protein that recognizes MNV-1 infection is unknown . Because noroviruses are RNA viruses , we investigated whether MDA5 and TLR3 , cellular sensors that recognize dsRNA , are important for the host response to MNV-1 . We demonstrate that MDA5−/− dendritic cells ( DC ) have a defect in cytokine response to MNV-1 . In addition , MNV-1 replicates to higher levels in MDA5−/− DCs as well as in MDA5−/− mice in vivo . Interestingly , TLR3−/− DCs do not have a defect in vitro , but TLR3−/− mice have a slight increase in viral titers . This is the first demonstration of an innate immune sensor for norovirus and shows that MDA5 is required for the control of MNV-1 infection . Knowledge of the host response to MNV-1 may provide keys for prevention and treatment of the human disease .
Norwalk virus and other human noroviruses are common human pathogens responsible for most of the nonbacterial epidemic gastroenteritis in both developed and developing countries [1] , [2] , [3] , [4] , [5] . In humans , norovirus infection can result in vomiting , diarrhea , fever , malaise , and abdominal pain within 24 hours after infection . These symptoms usually clear within 48 hours , but the virus can persist asymptomatically for 3–6 weeks post-infection [6] , [7] . Until recently the inability to culture human noroviruses has prevented investigation into its pathogenicity . The discovery and subsequent routine culture of murine norovirus-1 ( MNV-1 ) has led to advances in understanding of both the norovirus lifecycle as well as the host response to norovirus infection [8] , [9] . Noroviruses are in the Calicivirus family and are nonenveloped viruses containing a single-stranded positive-sense RNA genome . Norovirus genomes are covalently linked at the 5′ end to a viral nonstructural protein VPg [10] . Norovirus genomes encode three open reading frames ( ORFs ) [11] , [12] , [13] , [14] . ORF1 encodes a polyprotein that is cleaved into at least six nonstructural proteins by the viral 3C-like protease [15] , [16] , [17] , [18] . ORF2 encodes the major capsid protein , viral protein 1 [11] , [19] , while ORF3 encodes the small basic protein , viral protein 2 [20] , [21] . An additional ORF , ORF4 was recently discovered in the MNV genome although the function of this ORF has yet to be characterized [14] . The rapid clearance of MNV-1 infection in immunocompetent mice indicates an important role for the innate immune system , since clearance precedes the timeframe normally associated with the initiation of adaptive immunity [22] . Previous work has revealed that MNV-1 infection of mice lacking either the type I and type II interferon ( IFNα/β/γ ) receptors or the STAT-1 molecule results in lethality [9] , [22] . Several proteins are known to initiate the IFN response to viruses [23] , including Toll-like receptors ( TLR ) [24] , Rig-I-like helicases ( RLH ) [25] , [26] , PKR [27] , and RNase L [28] . However , the initial sensor responsible for recognition of noroviruses and subsequent activation of cytokine response has not been determined . TLRs are located on the plasma membrane and in endosomal compartments . Among the TLRs , TLR 7 and 8 recognize ssRNA [29] , [30] , [31] , TLR9 recognizes DNA [32] , [33] , while TLR3 signals in response to dsRNA [34] . The RLHs are sensors located within the cytoplasm [26] , which include Rig-I and MDA-5 [23] , [35] , [36] and signal through IPS-1/MAVS/Cardiff/VISA [37] , [38] , [39] , [40] . Rig-I has recently been shown to preferentially recognize 5′-phosphorylated RNA [41] , [42] , while MDA5 responds to dsRNA [43] . Recently it has been shown that the lack of Rig-I does not confer susceptibility to human norovirus in vitro [44] . Because MDA5 [45] , [46] , [47] , [48] , and TLR3 [49] , [50] have been shown to play a role in host response to other RNA viruses we investigated if these sensors might be involved in norovirus recognition in vitro and in vivo using the MNV-1 model system . In this study we demonstrate that indeed MDA5 is the predominant sensor of MNV-1 and initiates the innate immune response against the virus , and that TLR3 may also play a role in the response to MNV-1 in certain tissues .
Previous studies have shown a requirement for the type I IFN response for control of MNV-1 infection in vitro [8] . Since both MDA5 and TLR3 have been shown to be involved in type I IFN and cytokine signaling in response to infection with other RNA viruses , we were interested to see if they may play a role in MNV-1 infection . MNV-1 infection has a limited cell tropism- infecting only DC and macrophage lineages in vitro [8] , [51] . In order to test whether the MDA5 or TLR3 sensors were important , BMDCs from Wild Type as well as TLR3−/− and MDA5−/− mice were cultured for 7 days and then inoculated with various MOI of MNV-1 . After 24 hours supernatants from the in vitro infections were harvested and tested for cytokine secretion from the BMDCs . Interestingly , although WT and TLR3 DCs produced similar levels of IFNα and inflammatory cytokines in response to MNV stimulation , MDA5 deficient DCs produced significantly less IFNα , IL-6 , MCP-1 , TNFα ( Figure 1 ) and IFNβ ( data not shown ) . In this cell type MDA5 appears to be the primary sensor responsible for type 1 IFN production in response to MNV-1 , however , we cannot rule out that other sensors may play a role in other cell types . MNV-1 infection naturally occurs after fecal-oral transmission [8] . In order to test whether MDA5 and TLR3 play a role in MNV-1 detection in vivo we infected WT , MDA5−/− , or TLR3−/− mice with MNV-1 . CW3 perorally . Organs were then harvested from infected as well as mock-infected mice on days 1 , 3 , and 5 after inoculation and viral titers were determined for each sample . MNV-1 titers were highest at d3 post infection . At this time-point MDA5−/− animals had significantly increased viral titers compared to wild type animals in the mesenteric lymph nodes , spleen , and proximal intestine ( Figure 2 ) . Minimal or no virus titers was detected by plaque assay in the distal intestine or feces ( data not shown ) in WT and MDA5−/− animals on the 129/Svj background . Interestingly , although there did not appear to be an effect of TLR3 deficiency in the detection of MNV-1 in vitro , there was a slight , but significant increase in viral titers in the mesenteric lymph node of TLR3−/− mice compared to wild type mice . No difference in virus titers was detected in the spleen or distal intestine between WT and TLR3−/− animals ( Figure 2 ) . This may indicate a role for TLR3 in MNV-1 detection in a tissue-specific manner . Minimal or negative titers were seen in proximal intestines and feces ( data not shown ) in WT and TLR3−/− animals on the B6 background . That B6 and 129 strains of mice have higher titers of virus in the distal and proximal intestine respectively has been reported in previous studies [14] , [22] and may reflect the differential distribution of viral sensors along the gastrointestinal tract . Previous work has demonstrated that lack of innate immune components leads to disseminated MNV-1 infection and is ultimately fatal [9] . Therefore we determined whether the lack of MDA5 could also cause a more extensive spread of MNV-1 . However , we detected no virus titers in the lung or the liver in WT or MDA5−/− mice ( data not shown ) . Consistent with the lack of systemic infection , serum samples taken from WT and MDA5−/− mice at 1 , 3 , and 5 days after inoculation were tested by ELISA for IFNα , IFNβ , and IFNγ and found to be negative ( data not shown ) . The kinetics of clearance of MNV-1 infection was similar in WT and MDA5−/− mice in vivo , even though there was a significant increase in virus titers in the MDA5−/− animals at day 3 . By day 5 post-inoculation no virus titers were detected in MDA5−/− spleen , MLN , or proximal intestine . In addition , there was no difference in survival between WT , MDA5−/− , or TLR3−/− mice for 35 days post-inoculation . In contrast , STAT-1−/− mice and IFNαβγR−/− mice have shown a survival defect to MNV-1 infection . Since these mice have complete abrogation of IFN signaling pathways , this may indicate that MDA5 and TLR3 are redundant or that other sensors may also play a role in MNV-1 detection . Because the MDA5−/− and TLR3−/− mice had an increase in MNV-1 virus titers , we wanted to test the cellular basis of this deficiency . To address this issue , we infected BMDCs from WT , MDA5−/− , and TLR3−/− mice with MNV-1 and harvested samples at 6-hour time-points after inoculation . The infections were done at a high and low MOI to test for effects on viral replication and spreading . Viral titers were identical in WT , MDA5−/− , and TLR3−/− mice up to 12 hours after inoculation at the high MOI ( Figure 3a ) . However , starting at 18 hours pi , titers from MDA5−/− BMDCs began to increase over WT and TLR3−/− BMDCs , and leveled out to a significant difference at 24 and 48 hours , indicating that MDA5 recognition occurs late in viral infection . At a low MOI there was no significant difference between viral titers in WT and MDA5−/− cells until the 48 hour time-point ( Figure 3b ) , indicating that the requirement for MDA5 recognition requires multiple cycles of replication . Indeed , the peak level of IFN production in WT BMDCs occurs at 12 hours post-infection after a low MOI inoculation ( Figure 3d ) , suggesting that a cycle of viral replication is necessary to induce IFNβ . The increase in virus titers in the MDA5−/− cells reflects the defect in type I IFN produced in response to viral infection . IFN pretreatment of MDA5−/− BMDCs before infection with a low MOI reconstitutes the WT phenotype , preventing an increase in virus titers . In both MOIs the kinetics of MNV-1 infection appears similar in WT and MDA5−/− BMDCs; the difference mainly appears to be in the total amount of viral replication seen in the MDA5−/− BMDCs . Interestingly , although TLR3−/− mice had an increase in MNV-1 titers , TLR3−/− BMDCs had no significant increase in titers . This may reflect a cell type-specific role for viral sensors . Although we have demonstrated that MDA5 is required to recognize MNV-1 , it is unclear which RNA feature is essential for recognition . Another sensor , Rig-I , recognizes viruses mainly through 5′-phosphorylation , however , in noroviruses this feature is absent because of a 5′ VPg cap [10] . To test whether 5′ RNA configuration is essential for MDA5 recognition we purified viral RNA from MNV-1 virions . BMDCs from WT , MDA5 , or TLR3 deficient mice were then stimulated with the harvested RNA , as well as RNA treated with RNase A which degrades ssRNA , and proteinase K ( PK ) , which degrades proteins- in this case the VPg cap . As expected , RNase treatment degraded the viral RNA , while PK treatment did not degrade the RNA as seen in Figure 4a . Consistent with the results of in vitro MNV-1 infections shown in Figure 1 , both WT and TLR3−/− BMDCs produced type 1 IFN in response to purified viral RNA , while MDA5−/− BMDCs had a significant decrease in IFN response ( Figure 4b , c ) . However , the addition of PK or RNase to the RNA abrogated the cytokine response in WT and TLR3−/− BMDCs . This data demonstrates that VPg is required for MDA5 recognition of MNV-1 , and suggests that MDA5 either directly recognizes RNA linked to VPg or , since VPg is required for norovirus replication [44] , that MDA5 recognizes dsRNA generated during viral replication . Because MDA5 has been previously shown to recognize uncapped poly I∶C [46] , [47] , it is most likely that the result of PK treatment reflects the requirement for viral replication and the subsequent generation of dsRNA that is recognized by MDA5 . Consistent with this hypothesis , WT BMDCs inoculated with UV-inactivated MNV-1 did not produce IFNβ ( data not shown ) .
We have provided the first description of an initial sensor of norovirus infection . MDA5 recognizes MNV-1 and stimulates antigen presenting cells to produce type I interferon as well as IL-6 , MCP-1 , and TNFα that function to recruit other immune cells as well as activate antiviral pathways in host cells . Deficiency of this sensor results in lack of cytokine production as well as increased MNV-1 replication in deficient cells and mice . It is interesting to note that although MDA5 deficient cells have a severe defect in IFNα production , MDA5−/− mice contain and clear MNV-1 infection . This is in contrast the severe systemic infection and survival phenotype as the IFNαβγR or STAT1 deficient mice , which lack type I and type II IFN signaling pathways . STAT-1−/− and IFNαβγR−/− mice have a 4 log increase in viral titers in vivo and a 2 log increase in viral titers in vitro as seen in previously published data [22] . In our study MDA5−/− mice have a 1-log increase in viral titers in vivo and in vitro , while TLR3−/− mice have a 0 . 5 log increase , but only in one organ in vivo . This indicates to us that although MDA5 may be the dominant sensor in BMDCs , it is likely that in other cell types additional sensors can detect MNV-1 , such as Rig-I , PKR , TLR7 , and perhaps other unknown sensors . Further investigation is needed to determine if mice and cells that are deficient in multiple nucleic acid sensors lack all ability to respond to MNV-1 and whether they therefore have a more severe phenotype . Data from our lab and others [44] from in vitro experiments suggest that lack of TLR3 and Rig-I seem to have little effect on MNV-1 recognition individually , however , we cannot rule out that their involvement is masked by MDA5 . Although the putative recognition structure for Rig-I has previously been determined [41] , [42] , the RNA structure recognized by MDA5 in viral infection remains unclear . We demonstrated that MDA5 recognition of MNV RNA is abrogated by treatment with PK , which degrades VPg , preventing viral replication . This data suggests that VPg is essential for MDA5 recognition of MNV-1 . Although we cannot rule out the possibility that MDA5 recognizes the VPg-RNA structure itself , this is less likely because MDA5 is known to respond to poly I∶C which has no protein cap . It is more likely that since VPg is essential for viral replication of the ssRNA norovirus genome , loss of VPg prevents MDA5 recognition of dsRNA produced during viral replication . Learning more about which viruses are recognized by MDA5 may provide hints as to what this protein recognizes . This information could then be used to design adjuvants to manipulate the immune response for both vaccine design as well as in treatment of viral infection .
RAW264 . 7 cells were maintained in Dulbecco modified Eagle medium ( DMEM ) supplemented with 10% fetal calf serum ( Hyclone ) , 100 U penicillin/ml , 100 µg/ml streptomycin , 10 mM HEPES , and 2 mM L-glutamine . All experiments were performed with MNV-1 . CW3 [14] . Virus stocks were generated using RAW 264 . 7 cells that were inoculated with a multiplicity of infection ( MOI ) of 0 . 05 in VP-SFM media ( Gibco ) and harvested approximately 40 hours after inoculation . Infected cell lysates were frozen at −80°C and thawed three times . Cell lysates were clarified by low-speed centrifugation for 20 min at 3 , 000 rpm . To generate a concentrated virus stock , clarified cell lysates were concentrated by centrifugation at 4°C for 3 h at 27 , 000 rpm ( 90 , 000 g ) in a SW32 rotor . Bone marrow was flushed from the femurs of mice and cultured as described previously [52] . Briefly , cells were cultured in RPMI ( Gibco ) with 10% fetal calf serum ( Hyclone ) , Glutamax , Na Pyruvate , Non-Essential AAs , and Kanamycin for 7–8 days at 37 degrees . MDA5−/− mice were described previously [47] . For the infection studies mice backcrossed onto a pure 129/SVJ background were used . Control WT mice were age and sex matched and were obtained from littermate controls and from Jackson Lab for 129/SVJ and C57BL/6 . TLR3−/− mice were kindly provided by Richard Flavell [34] . All mice were bred and housed in a pathogen free facility and regularly tested for MNV-1 antibodies . BMDCs were counted and plated at 200 , 000 cells/well in a 96 well plate . MNV-1 was added at various MOI to the cultures , or alternatively 500 ng RNA was complexed with lipofectamine 2000 ( invitrogen ) and added according to manufactures instructions . After 20–24 hours supernatants were harvested and stored at −20 degrees until cytokine analysis . IFNα and IFNβ levels from the supernatants were measured by ELISA ( PBL Biomedical Laboratory , New Brunswick , NJ ) , while IL-6 , MCP-1 , and TNFα levels were determined by cytokine bead array ( BD Biosciences ) . WT , TLR3−/− , or MDA5−/− mice were infected perorally with 3×107 PFU MNV1 . CW3 [14] or mock-infected with media only . Three days post-infection the following organs were harvested and stored at −80 degrees until assayed: spleen , liver , mesenteric lymph node , lung , proximal intestine , distal intestine , stool , and serum . Tissue samples were homogenized in 1 ml complete DMEM by bead beating with 1 . 0-mm zirconia/silica beads ( BioSpec Products , Inc . ) . Tissue homogenates were diluted 1∶10 in complete DMEM and tested for viral titers by using a plaque assay that has been previously described [8] . Briefly , 2×106 RAW264 . 7 cells were seeded into each well of six-well plates , and infected the next day with 10-fold dilutions of tissue homogenate in duplicate . After a 1-hr infection , the inoculum was removed and wells were overlaid with 1 . 5% SeaPlaque agarose ( Cambridge Biosciences ) in complete minimal essential medium and incubated at 37°C . After 48 hrs , a second overlay was added containing 1 . 5% SeaKem agarose ( Cambridge Biosciences ) and 0 . 01% neutral red in complete minimal essential medium . After 8 hrs , plaques were then visualized . Total viral RNA was harvested from concentrated virus stock using Trizol reagent ( Invitrogen ) according to manufacturer's instructions . Purified RNA was incubated with either 10 units RNase A ( Sigma ) in NEB buffer 3 ( New England Biolabs ) or with 200 µg/ml proteinase K ( Sigma ) in 0 . 1 M NaCl , 10 mM Tris ( pH 8 ) , 1 mM EDTA , 0 . 5% sodium dodecyl sulfate or left untreated in NEB buffer 3 for 30 minutes at 37°C then stopped with 0 . 1 mM EDTA . To test for RNA degradation , samples were run on a 1% agarose gel and visualized using a UV light box . | Gastroenteritis is a common disease in both developed and developing countries . The two main causes of this affliction are bacteria and viruses . The primary viruses implicated in gastroenteritis have been shown to be noroviruses , which include Norwalk virus , notorious for numerous recent outbreaks on cruise ships . We are interested in how the innate immune system detects viral infection and prepares the host to respond to the threat . To understand how the host responds to norovirus infection , we studied two classes of proteins , both of which are thought to detect signs of viral infection . We discovered that one of these proteins , melanoma differentiation associated protein-5 ( MDA-5 ) , is responsible for detecting a mouse norovirus that is genetically related to the human pathogen . These findings allow us to better understand the pathogenesis of norovirus infection and may provide clues for controlling the human disease . | [
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] | 2008 | MDA-5 Recognition of a Murine Norovirus |
In higher eukaryotes , messenger RNAs ( mRNAs ) are exported from the nucleus to the cytoplasm via factors deposited near the 5′ end of the transcript during splicing . The signal sequence coding region ( SSCR ) can support an alternative mRNA export ( ALREX ) pathway that does not require splicing . However , most SSCR–containing genes also have introns , so the interplay between these export mechanisms remains unclear . Here we support a model in which the furthest upstream element in a given transcript , be it an intron or an ALREX–promoting SSCR , dictates the mRNA export pathway used . We also experimentally demonstrate that nuclear-encoded mitochondrial genes can use the ALREX pathway . Thus , ALREX can also be supported by nucleotide signals within mitochondrial-targeting sequence coding regions ( MSCRs ) . Finally , we identified and experimentally verified novel motifs associated with the ALREX pathway that are shared by both SSCRs and MSCRs . Our results show strong correlation between 5′ untranslated region ( 5′UTR ) intron presence/absence and sequence features at the beginning of the coding region . They also suggest that genes encoding secretory and mitochondrial proteins share a common regulatory mechanism at the level of mRNA export .
In humans , ∼35% of all genes have introns in their 5′ untranslated regions ( UTRs ) [1]–[3] . These introns differ from those in coding regions , for example , in typical length and nucleotide composition [1]–[3] . Previously , 5′UTR introns ( 5UIs ) were suggested to be evolving under a neutral model of random insertion and deletion events with the sole constraint of avoiding upstream open reading frames [3] . Recently , we showed that presence and length of 5UIs correlates with the level of expression across cells and tissue types [1] . More importantly , we observed an uneven distribution of 5UIs amongst genes across specific functional categories [1] . Genes with regulatory roles , including non-receptor tyrosine kinases , regulators of cytoskeleton , transcription and metabolism , were enriched in having 5UIs [1] . Our results suggested that many 5UIs are evolving under complex selective forces as opposed to a simple model of neutral evolution [1] . However , it is unclear whether there is any widely used mode of regulation that is unique to 5UIs . In eukaryotes , splicing is coupled to key mRNA metabolic processes . During the act of splicing , several different protein complexes are deposited onto mRNA . For example , the Transcription Export ( TREX ) complex promotes the nuclear export of fully processed transcripts [4] . In higher eukaryotes , the TREX complex is deposited primarily onto the 5′ end of nascent transcripts by the cooperative action of the cap-binding complex and the spliceosome [5] . Given that 5UIs are necessarily proximal to 5′ ends of transcripts , an intriguing possibility is that splicing of 5UIs could have a disproportionate impact on mRNA export by promoting TREX recruitment . Although the majority of transcripts follow the splicing-dependent export pathway , alternative pathways exist . Recently , Palazzo et al . demonstrated that mRNAs that encode secreted proteins can use an alternative route for mRNA export that is mediated by a nucleotide element within the signal sequence coding region ( SSCR ) [6] . In contrast to the splicing-dependent pathway , this alternative RNA export ( ALREX ) pathway does not require splicing or a 5′ cap [6] . Vertebrate SSCRs were found to be adenine-poor and silent mutations introducing adenines into the SSCR impair its ability to promote mRNA export [6] . However , beyond adenine-depletion this element has been poorly characterized . Furthermore , it has remained unclear which SSCR-containing transcripts use ALREX and to what extent , since the vast majority of SSCR-containing transcripts are also spliced and thus could potentially use the canonical export pathway . The fact that both ALREX signals and splicing signals are found near the 5′ end of genes , suggests the interesting possibility that competition between signals at the 5′end of transcripts determines how a given mRNA is exported . Here , we extend our computational analysis of 5UIs to identify functional groups of genes that preferentially lack these introns . We find that 5UIs are depleted in genes containing SSCRs or mitochondrial-targeting sequence coding regions ( MSCRs ) . We demonstrate that SSCRs and MSCRs derived from 5UI-lacking ( 5UI− ) genes contain sequence features associated with ALREX and promote export in vivo . In stark contrast , SSCRs and MSCRs derived from 5UI+ genes do not exhibit ALREX-associated features . Furthermore , we show that 5UI+ genes do not support splicing-independent mRNA export . We then characterize ALREX elements more fully by identifying and validating new ALREX-associated motifs . Taken together , our results support a model wherein the 5′-most element in a newly synthesized transcript , be it an intron or an ALREX element , dictates which pathway is employed for export . Furthermore , our results provide the first known regulatory role that is unique to 5′ UTR introns and suggest that it is widely used .
Using a high quality set of 5UI definitions for human , we observed a depletion of 5UIs amongst genes with certain Gene Ontology [7] ( GO ) annotations ( Table S1 ) . Examples of 5UI-depleted GO terms include “MHC class II protein complex” ( ratio of 5UI-containing genes to total genes annotated with particular GO term is 0/25 ) , “aspartic endopeptidase activity” ( 0/23 ) , “voltage-gated calcium channel activity” ( 2/35 ) , “growth factor activity” ( 33/180 ) , “electron carrier activity” ( 27/145 ) , and “extracellular space” ( 108/497 ) . In each case , these ratios are significantly lower than the ratio of ∼35% expected by chance ( p<0 . 05 after adjusting for multiple hypothesis testing ) . More generally , we observed a depletion of 5UIs among nuclear genes encoding three protein classes . The first class was composed of protein families encoded by mostly intronless genes . This group includes histone genes [8] , olfactory receptors , G-protein coupled receptors [9] , and keratins [10] , [11] . Depletion of 5UIs in these gene classes does not suggest any 5UI-specific phenomena , as these genes are more generally intron-depleted . The second class was composed of secreted or membrane-bound proteins that are trafficked through the endoplasmic reticulum ( ER ) . We compiled a list of all genes with signal sequence coding regions ( SSCRs ) , encoding N-terminal cleavable signal sequence peptides that target newly synthesized proteins to the ER [12] ( see Materials and Methods ) . We observed that 5UIs were generally depleted among SSCR-containing genes ( Figure 1; Fisher's Exact Test p = 8×10-8 , odds ratio 0 . 84 ) . The last class included proteins localized to mitochondria . Nuclear-encoded mitochondrial genes are translated in the cytoplasm and are targeted via an N-terminal leader peptide sequence to mitochondria [13] , [14] . We compiled a list of genes with mitochondrial-targeting sequence coding regions ( MSCRs ) , and observed that 5UIs were depleted in MSCR-containing genes ( Figure 1; Fisher's Exact Test p = 8×10-6; odds ratio 0 . 59 ) . This depletion is even stronger than that observed for SSCR-containing genes . Thus , our results showed a general depletion of 5UIs among genes encoding either ER-targeted or mitochondrial proteins . Next , we tested whether 5UI depletion in SSCR or MSCR-containing genes is a secondary effect of these genes having short 5′UTRs . Although 5UIs are more likely amongst genes with long 5′UTRs ( Figure S1A , Wilcoxon Rank Sum Test p <2×10-16; a 99 nt greater median 5′UTR length in 5UI+ than 5UI− genes ) , we observed that genes encoding secreted and mitochondrial proteins have 5′UTRs that are only slightly shorter than other genes ( Figure S1B , Wilcoxon Rank Sum Test p = 2×10-15 , p = 9×10-9; a 25 nt and 51 nt difference in median 5′UTR length for SSCR- and MSCR-containing genes , respectively ) . Even after correcting for the differences in 5′UTR length , SSCR- and MSCR-containing genes were significantly depleted of 5UIs ( see Text S1 ) . Similarly , the depletion of 5UIs did not reflect an overall decrease in intronic content , as the total number of bases in non-5′UTR introns did not differ between genes containing or lacking SSCRs ( Welch Two Sample t-test , p = 0 . 34; Figure S2 ) . A possible link between splicing and genes encoding secretory proteins is the nuclear export of mRNA . Several studies have indicated that export factors are loaded near the 5′ cap co-transcriptionally during the splicing of the more 5′-proximal intron [5] , [15] . SSCRs , which similarly promote mRNA export via ALREX [6] , are located at the 5′end of the open reading frame ( ORF ) and could also potentially be recognized by factors co-transcriptionally . Hence , we hypothesized that the 5′-most element in a given transcript , be it an intron or an SSCR , dictates the pathway by which that transcript is exported . Signal peptide sequences contain a hydrophobic core with amino acids that are naturally encoded by codons with low adenine content . In addition , for pairs of biochemically similar amino acids that differ in the adenine content of their corresponding codons , SSCRs tend to prefer the amino acid with low adenine content codons [6] . We previously showed that adenine depletion in SSCRs is functionally linked to ALREX as silent adenine mutations partially inhibit ALREX [6] . Our hypothesis of a competition between export pathways , driven by whether the 5′-most element is a 5UI or an ALREX signal , predicts that the selection pressure to maintain sequence features important for ALREX-dependent mRNA export would be relaxed in transcripts with 5UIs . We therefore tested whether adenine depletion in SSCRs is attenuated in genes containing 5UIs . Remarkably , we found that SSCRs from genes lacking 5UIs contain 18 . 2% fewer adenines when compared to SSCRs from genes carrying 5UIs ( Figure 2A; Wilcoxon Rank Sum Test p = 4×10-49 ) . Next , we analyzed the amino acid preference of SSCR-containing genes for pairs of biochemically similar amino acids . Specifically , we observed that SSCRs of 5UI− genes have a significantly increased ratio of leucine ( which has adenine-poor codons ) to isoleucine ( which has at least one adenine in all of its three codons ) and of arginine ( with relatively adenine-poor codons ) relative to lysine as compared to SSCRs of 5UI+ genes ( Figure 2B–2C; Fisher's Exact Test , p = 3×10-27 and 3×10-40 , 95% confidence interval of odds ratio 1 . 4–1 . 7 and 1 . 9–2 . 4 respectively ) . SSCRs also exhibit a bias towards synonymous codons that lack adenine [6] . Importantly , this bias diminishes for 5UI+ genes ( Figure 2D ) . This was true for codons for any given single amino acid , such as leucine or serine ( Figure S3 ) , or when all synonymous codons were aggregated ( Figure 2D; Fisher's Exact Test p = 2×10-42; 95% CI of odds ratio 1 . 3-1 . 4 ) . Taken together , our computational analysis indicates that the bias of SSCRs against adenines is relaxed in 5UI+ genes . Furthermore , this reduced bias appears to be due to a relaxation of nucleotide-level constraints , supporting the idea that the presence of 5UIs relieves selection maintaining ALREX signals . To experimentally investigate this intriguing connection between sequence features in the coding region and the presence or absence of 5UIs , we tested whether SSCRs derived from genes with 5UIs are defective in promoting mRNA export . We inserted SSCR elements into a fragment of the fushi tarazu ( ftz ) , just downstream of the start codon . Furthermore we generated versions of ftz that either contained ( ftz-i ) or lacked ( ftz-Δi ) its endogenous intron . Modified forms of these transcripts were previously used to study splicing- and SSCR-dependent mRNA nuclear export [6] , [16] . Polyadenylated forms of the ftz mRNA were microinjected into the nuclei of NIH 3T3 mouse fibroblasts . After incubating the cells for one hour , mRNA export was visually monitored by fluorescence in situ hybridization ( FISH , Figure 3A ) and the amount of mRNA nuclear export was quantified ( Figure 3B ) . Nuclear injection was confirmed by co-injecting fluorescently labeled 70 kD dextran , which is too large to passively diffuse through nuclear pores ( see insets , Figure 3A ) . As demonstrated by several groups , we found that a version of the ftz mRNA that encodes a cytoplasmic protein , but contains neither an intron nor an SSCR ( c-ftz-Δi ) , was not efficiently exported [6] , [16] ( Figure 3 ) . Nuclear export could be rescued if an intron was incorporated ( c-ftz-i ) . As reported previously , SSCRs from the MHC class 2 gene H2-k1 , which lacks a 5UI , promoted efficient export of an intronless version of ftz ( Figure 3 , MHC-ftz-Δi; see Palazzo et al . [6] and Figure S4 for all ftz variant sequences ) . We next examined the parathyroid hormone ( PTH ) and the prion protein ( PRP ) SSCRs , both derived from genes with 5UIs . Consistent with trends we observed for 5UI+ genes in general , neither PTH nor PRP SSCRs are depleted in adenine content . Furthermore , neither promoted efficient export ( Figure 3 , PTH-ftz-Δi and PRP-ftz-Δi ) . Interestingly , elimination of adenines from the PRP SSCR ( PRPΔA ) only marginally stimulated export ( Figure 3 , PRPΔA-ftz-Δi ) suggesting that this SSCR lacks other features crucial for stimulating export . In summary , only SSCRs from genes lacking 5UIs promoted efficient mRNA export , experimentally demonstrating a functional relevance for the computationally-discovered connection between coding sequence features and 5UI status . Our investigation into the relationship between 5UIs and alternative export began with the observation that 5UIs were depleted amongst secretory genes . Because 5UIs are also depleted amongst nuclear-encoded mitochondrial genes ( Figure 1 ) , we wondered whether related phenomena might be at play . Like secreted proteins , mitochondrial proteins contain a cleavable leader peptide that dictates the ultimate localization of the polypeptide chain [13] , [14] . We therefore wondered whether MSCRs exhibit the same nucleotide features that had been associated with ALREX in SSCRs . Indeed MSCRs , like SSCRs , were depleted in adenines overall . Also like SSCRs , this adenine depletion was restricted to MSCRs derived from 5UI− genes ( Figure 4A; Wilcoxon Rank Sum Test p = 2×10-9 ) . We found that MSCRs , like SSCRS , tend to encode leucine relative to isoleucine ( Figure 4B ) , and arginine relative to lysine ( Figure 4C ) . Just as with SSCRs , this phenomenon was more pronounced when the elements were derived from 5UI− genes ( Fisher's Exact Test p = 0 . 16 and 10−9 , 95% CI of odds ratio 0 . 9–1 . 9 and 1 . 9–3 . 7 respectively ) . Finally , only MSCRs from 5UI− genes displayed a bias for synonymous codons that lacked adenine ( Figure 4D ) . This was true for codons coding for any given single amino acid examined , such as leucine or serine ( Figure S3 ) , or when results for all synonymous codons were aggregated ( Figure 4D; Fisher's Exact Test p = 7×10-06; 95% CI for odds ratio 1 . 2-1 . 7 ) . We next experimentally tested whether MSCRs from 5UI− genes promoted mRNA export in tissue culture cells . Indeed , we found that MSCRs from both the F1 ATP Synthase A ( F1 ) and ferroredoxin reductase ( FR ) stimulated efficient nuclear export of the ftz transcript ( Figure 5A–5B , F1-ftz-Δi , FR-ftz-Δi – see Figure S4 for all modified ftz sequences ) . We note that the alternative export phenotype observed for these MSCRs is at least as robust as any previously observed for SSCR-containing genes . In contrast , we found that the MSCR from the mitochondrial translation initiation factor 2a ( MTIF ) , a 5UI+ gene , does not promote efficient export ( Figure 5A–5B , MTIF-ftz-Δi ) . Similar to previous observations with the MHC and Insulin SSCRs [6] , the introduction of seven silent adenine mutations in the FR MSCR ( FR7A ) partially inhibited its ability to promote export ( Figure 5 , FR7A-ftz-Δi ) . Microinjected mRNA may behave differently from mRNA that has been endogenously transcribed . Therefore , we microinjected plasmids encoding various ftz transcripts into the nuclei of NIH 3T3 cells . After allowing the plasmids to be transcribed ( 20 min ) , further mRNA synthesis was inhibited by treating cells with the RNA Polymerase II inhibitor α-amanitin . Export of the newly synthesized transcripts was assessed two hours after treatment . We found that transcripts produced from plasmids containing FR-ftz-Δi , but not c-ftz-Δi or PTH-ftz-Δi , were efficiently exported ( Figure 5C ) , as was previously seen for MHC-ftz-Δi [6] . Thus , we have shown that MSCR-containing transcripts are capable splicing-independent mRNA export in a manner that depends on 5UI status . This result suggests that the scope of the ALREX pathway extends from ER-trafficked genes to include nuclear mitochondrial genes . We next wished to assess whether export was dependent on the TAP/p15 nuclear transport receptor , which is required for both SSCR- and splicing-dependent export [6] . We co-injected the viral constitutive transport element ( CTE ) RNA ( known to inhibit TAP/p15 [17] ) with the plasmid and observed that export of in vivo-transcribed FR-ftz-Δi was inhibited . Taken together , these experiments indicate that MSCRs and SSCRs from 5UI− genes promote mRNA export using a similar if not identical pathway . Although our experimental findings supported the importance of adenine-depletion for ALREX , they also indicated that other sequence features may be involved . For example , the PRP SSCR ( from a 5UI+ gene ) did not promote efficient export even after adenines were eliminated ( Figure 3 , PRPΔA-ftz-Δi ) . Furthermore , the incorporation of silent adenines only partially inhibited export by the FR MSCR ( Figure 5 , FR7A-ftz-Δi ) , or the MHC SSCR [6] . Therefore , we wished to search for additional ALREX-associated sequence features . Identification of nucleotide motifs responsible for ALREX function is challenging , because enriched RNA-level motifs might arise due to recurrent patterns at the protein sequence level . Although numerous bioinformatics tools exist to search for nucleotide features ( such as transcription factor binding sites ) in non-coding regions , few are tailored to the problem of identifying RNA motifs within coding regions . We sought to exploit the idea that we have two collections of SSCRs that differ in the expected abundance of ALREX signals . Specifically , we compared SSCRs from genes with and without 5UIs to identify nucleotide signals exhibiting differential abundance between the sets . Although RNA-level features may be artifactually enriched relative to random RNA sequence due to protein sequence-level constraints , such an artifactual enrichment would not be expected in 5UI− relative to 5UI+ SSCR-containing genes . We first extended codon usage analyses of the SSCR and MSCR regions to identify other representative signatures . In addition to previously noted adenine depletion , 5UI− SSCR and MSCR genes strongly preferred codons lacking thymine , with a ∼1 . 4 and a ∼1 . 7 fold enrichment relative to 5UI+ SSCR and MSCR genes ( Figure 6A , Fisher's Exact Test p = 7×10-46 and 4×10-13; 95% CI for odds ratio 1 . 3–1 . 5 and 1 . 5–2 . 0 , for SSCRs and MSCRs respectively ) . Next , we searched for primary sequence elements using a discriminative motif finding approach . Specifically , we searched for nucleotide sequences that are significantly enriched among SSCR-containing 5UI− genes relative to 5UI+ genes using the DEME algorithm [18] . We found a likely candidate motif ( Figure 6B ) , which can be roughly described by the consensus sequence CGSSGC ( where S represents a mixture of C and G ) . This motif is highly depleted of adenines and thymines consistent with our analysis ( Figure 2 , Figure 3 , and Figure 6A ) and had high information content . The motif did not show a strong preference for a particular frame of translation ( Figure 6B ) suggesting that this signal is relevant at the RNA as opposed to protein level . The motif not only appeared in a higher fraction of 5UI− SSCR sequences ( 47 . 5% versus 22 . 2% in 5UI+ SSCRs; see Materials and Methods ) , but also was much more likely to occur in multiple copies in the SSCRs of 5UI− genes ( Figure 6C , 6D; 26 . 8% versus 7 . 14% ) . The CGSSGC motif also revealed a strong positional bias , occurring more frequently toward the 5′ end of coding regions from 5UI− genes ( Figure 6E , Figure S5 , Wilcoxon Rank Sum Test p = 0 . 002 , median position was 39 and 45 among 5UI− and 5UI+ genes , respectively; see Materials and Methods ) . We wished to further examine the question of whether the non-canonical mRNA export function of SSCRs is acting via the same mechanism as that of MSCRs . We therefore tested whether the CGSSGC motif ( which was enriched among 5UI− SSCR genes ) could also predict the absence of 5UIs among genes with an MSCR . We compared performance of the CGSSGC motif ( discovered without use of any MSCR-containing genes ) in discriminating 5UI− from 5UI+ MSCRs and found it to outperform at least 99% of 100 , 000 randomly generated motifs ( Figure 6F; False-positive Rate range 10% to 70%; see Materials and Methods ) . This result indicates that MSCRs and SSCRs , despite differences in the protein sequences they encoded , each play host to a common RNA-level motif associated both with the lack of 5UIs and the ability to support non-canonical mRNA export . To identify additional motifs , we used the AlignACE [19] algorithm on the set of SSCR sequences from 5UI− genes . This algorithm has the advantage that it can identify multiple nucleotide sequences and allows greater flexibility in motif length . We filtered the discovered sequences for their discriminative ability and found 19 motifs that were significantly enriched among 5UI− relative to 5UI+ genes ( Table S2 , see Materials and Methods ) . The discovered motifs displayed mutual similarity and included several close variants of the CGSSGC motif discovered by DEME ( See Figure S6 for the PSSM logos of the most discriminative AlignACE motifs ) . We next focused on the properties of the four most discriminative AlignACE motifs . All four motifs were more likely to occur in multiple copies among the 5UI− genes compared to 5UI+ genes ( Figure S7 ) . Even though these four motifs were discovered based on their ability to discriminate 5UI− from 5UI+ genes among those genes with SSCRs , these motifs were also predictive of 5UI absence for genes with MSCRs ( Figure S8 ) . All four motifs performed in the top quartile compared to 100 , 000 random motifs ( Figure S8; see Materials and Methods ) . However , unlike the CGSSGC motif , three of these motifs displayed a significant bias for occurring in a particular frame of translation . These three motifs may thus be detecting protein sequence-level differences between 5UI− and 5UI+ genes ( Figure S9 ) . In fact , consensus sequences of many AlignACE motifs included CTGs that can encode leucines , which were highly enriched among SSCRs and MSCRs from 5UI− genes relative to their 5UI+ counterparts . We next decided to test whether synthetic elements matching the discovered motifs could promote the export of ftz mRNA . We used versions of the ftz-Δi mRNA containing either three copies of an element matching the consensus CGSSGC motif ( M1-ftz-Δi ) , a CUG repeat-containing element ( M2-ftz-Δi ) , or a single copy of each ( M3-ftz-Δi see Figure 7A for the sequences of all these constructs ) . We chose CUG repeats as they appeared in many of the consensus sequences of AlignACE motifs ( Table S2 ) . In addition , there are several RNA binding proteins , such as CUG-BP1 [20] and the Muscleblind family of proteins [21] that are known to recognize CUG repeats . To assay for export activity we microinjected plasmids that contained versions of the ftz gene fused to segments containing elements matching ALREX-enriched motifs and their combinations ( Figure 7A ) into the nuclei of NIH 3T3 cells . After allowing the plasmids to be transcribed ( 20 min ) , further mRNA synthesis was inhibited by treating cells with α-amanitin . We found that all three motif-containing ftz constructs ( M1- , M2- , M3-ftz-Δi ) were exported more efficiently than c-ftz-Δi but substantially less efficiently than MHC-ftz-Δi mRNA ( Figure 7B ) . Adenine depletion was required for export , as mRNA generated from plasmid containing a mutant form of M3-ftz-Δi bearing four silent adenine mutations ( 4A-M3-ftz-Δi , see Figure 7A ) collectively disrupting each of the two component elements was not efficiently exported ( Figure 7B–7C ) . To further validate these results , we transfected plasmids encoding the motif-containing ftz genes with elements corresponding to these motifs into COS-7 cells and measured the steady state distribution of mRNA . In agreement with our microinjection experiments , we found that the three motif-containing ftz constructs were exported to a level that was clearly higher than c-ftz-Δi but lower than MHC-ftz-Δi ( Figure 7D ) . As observed for microinjected NIH3T3 cells ( Figure 5 ) , mRNA generated from a plasmid containing the 4A-M3-ftz-Δi construct was not efficiently exported from transfected COS-7 cells ( Figure 7D ) .
The function and evolution of introns has been intensely studied since their discovery ( reviewed in [22] , [23] ) . Despite the presence of a large number of introns in untranslated regions , especially in the 5′ untranslated regions of transcripts , these studies have been largely focused on introns in coding regions [3] . We established that the distribution of 5UIs in the human genome is non-random , with specific functionally related groups of genes being enriched [1] or depleted ( this study ) for 5UIs . Here we show that , in both secreted and mitochondrial genes , the presence or absence of 5UIs correlates with sequence features at the beginning of the coding region . Minimally , our results further support the conclusion that complex selective forces govern the evolution of 5′UTR introns . Moreover , our results are best explained by the existence of a regulatory mechanism that is both special to 5UIs and has relevance to thousands of genes across the genome . Our results show that nuclear transcripts encoding both secretory and mitochondrial proteins share RNA-level signals capable of directing mRNA export , even for an intronless message . It has frequently been observed that mRNAs of functionally related genes are co-regulated at the post-transcriptional level ( ‘the regulon hypothesis’ [24] ) . Our results suggest that , consistent with this phenomenon , the ALREX pathway can facilitate coordinated expression of functionally related genes at the level of mRNA export . Moreover , our analyses support a model whereby the first transcript element emerging from RNA Polymerase II during transcription—be it an intron or an ALREX-promoting element—determines which RNA export pathway is predominantly followed ( Figure 8 ) . Under this model , presence of a 5UI would supersede downstream SSCR or MSCR export signals and relax selection pressures that maintain ALREX-promoting sequence features . Although we have made progress in defining some sequence features that mediate the ALREX function ( see Figure 6 and Figure 7 ) , it is clear that a more extensive description of ALREX features individually and in combination will be quite useful . We found specific nucleotide-level motifs in the 5′ end of coding regions which discriminate between genes with and without 5UIs . Substantial future efforts will be required to combine information about 5UI absence with the presence and placement of ALREX signals within a unified framework that can predict ALREX activity . This information could be used to compile a full list of transcripts using the ALREX pathway . It will be interesting to determine whether other genes , such as those that encode membrane-bound proteins but lack a signal sequence ( and hence an SSCR ) , can use this alternative export pathway . The most fundamental challenge for future studies will be to understand the biological role or roles of the ALREX pathway . Why is its selection maintained even in transcripts that contain coding region introns and are therefore enabled to use the canonical mRNA export pathway ? Although the functional downstream consequences of using either the splicing-dependent or ALREX-pathway remain unknown , silent mutations within the SSCR not only impair mRNA export but also disrupt proper ER-targeting of the transcripts [6] . This suggests that multiple post-transcriptional events , such as mRNA export , mRNA transport in the cytoplasm and mRNA translation , are coupled [25] . Here , we have discovered and validated two motifs that promote mRNA export , suggesting that ALREX may recruit more than one nuclear factor . Such factors could not only dictate RNA export but perhaps also dictate how the mRNA is distributed and translated once in the cytoplasm . Investigation of these questions awaits identification of ALREX factors , and of mRNA localization or other phenotypes associated with disrupted ALREX function . One of the motifs we discovered is a long CUG repeat that could potentially bind to CUG binding proteins . However , MHC-ftz-Δi mRNA is exported from HeLa cells that were depleted of both MBNL1 and MBNL2 , two members of the muscleblind family of CUG-repeat binding proteins ( unpublished findings ) , suggesting that these are not the responsible factors . Identification of the ALREX-element binding protein ( s ) will shed light onto how ALREX operates and provide insight into the biological role of this pathway . The question of biological role is particularly intriguing in the case of nuclear-encoded mitochondrial genes . The textbook description of nuclear-encoded mitochondrial genes has translation of these genes occurring within the general pool of cytoplasmic proteins , with subsequent protein localization due solely to the mitochondrial targeting peptide sequence . However , there is evidence that nuclear-encoded mitochondrial transcripts can localize to the vicinity of mitochondria prior to translation [26] , [27] . Although we do not detect any mitochondrial targeting of MSCR-bearing transcripts ( Figure 5 ) , it is possible that a fraction of these mRNAs are indeed localized . It will be interesting to learn what role ALREX could play in the localization and translation of nuclear-encoded mitochondrial genes . Substantial future studies will be required to further explore mechanisms of the ALREX pathway . For example , it is unclear whether ALREX signals are inhibited by other complexes deposited on the transcript in a splicing dependent manner . One example is the Exon Junction Complex ( EJC ) , which potentiates the translation of properly spliced mRNA [28] , [29] and the nonsense-mediated degradation of improperly spliced transcripts [30] , [31] . Some mRNAs , such as those of PrP and PTH genes , encode secreted proteins but lack any ALREX-promoting element . For such mRNAs , it is possible that the proper ER- targeting and efficient translation of these transcripts requires the recruitment of the EJC or TREX components to the 5′UTR . Identification of the nuclear proteins that associate with ALREX elements , and how these factors are coupled to other processes , will yield significant insight into the role of ALREX in mediating gene expression , and localization of both mRNAs and proteins .
NCBI's human Reference Gene Collection ( RefSeq ) [32] and the associated annotation table , retrieved from the UCSC genome browser genome assembly May 2004 ( http://hgdownload . cse . ucsc . edu/downloads . html ) , were used to extract a high confidence set of 5UIs . The lengths of 5′UTR-associated genomic features were determined using RefSeq intron-exon definitions ( downloaded June 2007 ) . Out of a total ∼24 . 5 k RefSeq transcripts , ∼8 . 5 k contained at least one intron . Genomic coordinates of 5UIs examined were as previously described [1] . When multiple splice variants involving a given 5′UTR exhibited identical splicing patterns within that 5′UTR region , a single identifier was selected randomly as the representative for that 5′UTR . For the remaining transcripts , total lengths of coding region introns were determined from the RefSeq Annotation ( downloaded from UCSC genome browser , May 2004 genome assembly on May 15th 2009 http://hgdownload . cse . ucsc . edu/downloads . html ) . DNA constructs encoding ftz isoforms were assembled by first digesting the pBR322 plasmid containing c-ftz-Δi [6] with Nco I and ligating oligonucleotides encoding various SSCRs and MSCRs ( see Figure S4 ) so that the extra sequences were all inserted just downstream of the start codon . The constructs were then transcribed into mRNA , which was then polyadenylated , purified and then microinjected into NIH 3T3 fibroblast nuclei at 200 µg/ml with Alexa488 conjugated 70 kD dextran ( 1 mg/ml ) as previously described [6] , [33] . DNA microinjections were performed as previously described [6] . Briefly , ftz isoforms were subcloned into pCDNA3 using Hind III and Xho I and microinjected at 50 µg/ml with Alexa488-conjugated 70 kD dextran ( 1 mg/ml ) into NIH 3T3 fibroblast nuclei . After allowing the RNA to be transcribed for 20 min , the cells were treated with α-amanitin ( 50 ng/ml ) to prevent further transcription . CTE RNA was synthesized as previously described [6] and microinjected at a concentration of 200 µg/ml along with DNA and Alexa488-conjugated 70 kD dextran . All microinjected cells were incubated for the indicated time to allow for mRNA export , then fixed with 4% paraformaldehyde in phosphate buffered saline ( PBS ) . DNA transfections into COS-7 cells were performed as described previously [6] . Transfected cells were incubated for 12–18 hrs , then fixed with 4% paraformaldehyde in PBS . The ftz mRNA was stained by fluorescence in situ hybridization followed by imaging and quantification of RNA nuclear export as previously described [6] . Cell imaging and mRNA quantification were also performed as previously described [33] . FuncAssociate [34] , [35] beta version was used for Gene Ontology ( GO ) analysis , and Synergizer [36] was used for mapping RefSeq IDs into the ‘namespace’ of GO association files using Ensembl as the synonym authority . We restricted the space of genes in which GO correlations were sought to RefSeq because our 5UI genes were drawn only from this set . To quantify the effect size of GO correlations , the results in Table S1 were sorted according to their log10 odds ratio , with significance calculated by Fisher's Exact Test as previously described [35] . Multiple hypothesis correction was achieved via a resampling approach that preserves the dependency structure between the tested hypotheses [35] . Adjusted p-values were calculated using 10000 resampling simulations . We retrieved the complete set of transcripts with signal peptide annotations from the Ensembl 50 database using Biomart [37] ( downloaded on February 2009 http://www . ensembl . org/biomart/martview ) . Of the 38396 transcripts in this database , 4953 were annotated as having a signal peptide , and 4704 of these were in our set of RefSeq genes . The coding region sequences for all the genes in our set were downloaded from NCBI Refseq Collection release 33 ( ftp://ftp . ncbi . nih . gov/refseq/H_sapiens/mRNA_Prot ) . The ratio of the amino acids , total adenine counts and the codon usage bias were calculated for the first 69 nt and the rest of the sequences . There were 135 coding region sequences that had a length that was not a multiple of three . These sequences in addition to those with total length less than 150 nt were removed from further analysis . The list of mitochondrial genes was retrieved from the Organelle DB [38] website ( downloaded on February 2009 from http://organelledb . lsi . umich . edu/ ) . Identifiers were translated to RefSeq ID using Synergizer [36] . Nine genes were removed from this list as they were encoded by the mitochondrial genome . For some genes , Synergizer could not find a RefSeq ID corresponding to the “standard name” . These genes were manually inspected and the synonyms provided by Organelle DB website were used to find corresponding RefSeq IDs . When multiple splice variants were exact duplicates with respect to the first 69 nts of their coding region , a single identifier was selected as the representative . This procedure yielded 364 RefSeq transcripts out of ∼25 k transcripts having an MSCR . The manually edited list of mitochondrial genes is available in Dataset S1 . The software package R 2 . 6 . 0 was used for all the statistical analyses , except where otherwise noted . For motif discovery , the first 99 nt of SSCR-containing genes were used to ensure that all signal peptides were included in their entirety . Highly similar sequences were removed to avoid overweighting closely related sequences . Specifically , the first 99 nt from each sequence was aligned to all others using blastn [39] . A threshold ( E-value <10−25 ) was used to group similar sequences , and one randomly selected representative from each such set was used after this filter . We used the DEME [18] software to search for a motif that is highly enriched in the 5UI− set of sequences relative to the 5UI+ set . We also used the AlignACE software [19] to search for a set of highly enriched motifs in the 5UI− set . AlignACE searches for frequently occurring motifs in both the forward and complementary strands of DNA sequences . Choosing to focus on RNA motifs , we discarded 2 of the 20 motifs reported that were constructed from less than 10 representative forward-strand sites . Positional Specific Scoring Matrices ( PSSM ) of the discovered motifs were extracted from the forward-strand sites of each motif . For a given sequence s and a motif with length m , all windows of size m within the first 99 bases were scored using the PSSM of the motif . To avoid calling multiple overlapping motifs , only the highest scoring window in a contiguous series of overlapping windows was selected . For each motif , an initial PSSM score threshold ( t* ) was selected such that t* yields the highest enrichment of motif-containing sequences among the SSCR-containing and 5UI− genes on the p-value generated from Fisher's Exact Test using the 2×2 contingency table ( Table 1 ) . Given the total number of genes N , the number of 5UI− genes m , and the number of motif-containing sequences n , this test estimates the probability that k or more genes would be found to overlap between the 5UI− genes and the motif-containing sequences under the null hypothesis of independence:where the probability of observing exactly i overlaps given N , m and n follows from the hypergeometric distribution: Among the 18 AlignACE motifs , we focused on the four that were most enriched among 5UI− genes compared to 5UI+ genes based on the resulting p-value . Further analyses on motif occurrences and positional distributions were performed on these four AlignACE motifs and the DEME motif . While PSSM threshold selection using Fisher's Exact Test provided a quick way identify discriminative AlignACE motifs , the selection of thresholds did not take into account the likelihood that such discrimination may have occurred by chance . To account for this possibility , we randomly generated four sets of PSSMs matching the discovered motifs' lengths ( 6 , 10 , 14 , and 16 nt ) . We modeled each position of the PSSM as an independent sample from a Dirichlet distribution with parameters ( αi ) equal to the background nucleotide frequency such that Σαi = 1 . The background nucleotide frequency was calculated among the first 99 nts of either SSCR-containing or MSCR-containing 5UI− genes . For each given motif length , we generated 40 , 000 random PSSMs for SSCR set and 100 , 000 random PSSMs for the MSCR set . We generated receiver operating characteristic ( ROC ) plots to compare the discriminative performance of these randomly generated PSSMs with that of the discovered motifs . First , we scanned each sequence to find the maximum score for each PSSM . We classified a sequence as motif-containing if its maximum PSSM score was greater than a given threshold t* . For all random and discovered motifs , we calculated the true positive rate ( TPR ) as the fraction of motif-containing 5UI− genes , and the false positive rate ( FPR ) as the fraction of motif-containing 5UI+ genes as a function t* . Therefore , each point on an ROC plot corresponds to ( TPR , FPR ) of a particular PSSM at some threshold t* . These ROC plots are informative about the analyzed motif's power to discriminate 5UI− from 5UI+ genes . For each discovered motif , we used the ROC plots generated from SSCR-containing genes ( Figure S8 ) to choose the PSSM score threshold value ( t′ ) for subsequent analysis . The threshold t′ was chosen such that it maximizes the difference between the discovered motif's TPR and the median TPR of the random motifs was the most at the FPR value corresponding to t′ . Since we discovered motifs using the SSCR-containing set only , the ROC plots for the MSCR set were not subject to any overfitting that might have occurred during motif discovery . To assess whether there is any significant deviation in the positional distribution of motifs in the 5UI− set from that in the 5UI+ set , we performed the Wilcoxon Rank Sum test . We examined differences in distributions for the positions of all motif occurrences in each sequence . We also generated histograms for the reading frame at which motifs occur in the coding region to look for differences between the 5UI− and 5UI+ sets . | The function and evolution of introns have been topics of great interest since introns were discovered in the 1970s . Introns that interrupt protein-coding regions have the most obvious potential to affect coding sequences and their evolution , and they have therefore been studied most intensively . However , about one third of human genes contain introns within 5′ untranslated regions ( UTR ) . Here we observe that certain classes of genes , including those targeted to the endoplasmic reticulum and nuclear-encoded mitochondrial genes , are surprisingly depleted of 5′UTR introns . We offer and support a model that explains this observation and points to a surprising connection between 5′UTR introns and how mRNAs are exported from the nucleus . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
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] | [
"computational",
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"analysis",
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] | 2011 | Genome Analysis Reveals Interplay between 5′UTR Introns and Nuclear mRNA Export for Secretory and Mitochondrial Genes |
Dengue is one of the most important arboviral diseases caused by infection of four serotypes of dengue virus ( DEN ) . We found that activation of interferon regulatory factor 3 ( IRF3 ) triggered by viral infection and by foreign DNA and RNA stimulation was blocked by DEN-encoded NS2B3 through a protease-dependent mechanism . The key adaptor protein in type I interferon pathway , human mediator of IRF3 activation ( MITA ) but not the murine homologue MPYS , was cleaved in cells infected with DEN-1 or DEN-2 and with expression of the enzymatically active protease NS2B3 . The cleavage site of MITA was mapped to LRR↓96G and the function of MITA was suppressed by dengue protease . DEN replication was reduced with overexpression of MPYS but not with MITA , while DEN replication was enhanced by MPYS knockdown , indicating an antiviral role of MITA/MPYS against DEN infection . The involvement of MITA in DEN-triggered innate immune response was evidenced by reduction of IRF3 activation and IFN induction in cells with MITA knockdown upon DEN-2 infection . NS2B3 physically interacted with MITA , and the interaction and cleavage of MITA could be further enhanced by poly ( dA:dT ) stimulation . Thus , we identified MITA as a novel host target of DEN protease and provide the molecular mechanism of how DEN subverts the host innate immunity .
Dengue has emerged as a rapidly spreading vector-borne disease annually affecting 50 to 100 million people living in tropical and subtropical areas [1] , [2] . Dengue virus ( DEN ) infection of humans causes a spectrum of illnesses ranging from mild classical dengue fever to severe dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) . The pathogenesis of severe dengue diseases remains unclear , but magnitude of DEN replication is believed to be one of the major determining factors [3] . Type I interferons ( IFNs ) , mainly IFNα and IFNβ , play central roles in host defense against viral infection [4] , [5] . DEN replication was sensitive to IFN in both cell-based assays and infected animals [6] , [7] , and the IFN-induced 2′ , 5′-oligoadenylate synthetase ( OAS ) /RNase L pathway may contribute to host defense against DEN infection [8]–[10] . Thus , for DEN to survive and replicate in host cells , it likely needed to evolve a way to downregulate the cellular IFN system . DEN triggers IFNβ through a molecular mechanism involving the retinoic acid inducible gene I ( RIG-I ) signaling pathway [11] , [12] . RIG-I binding to viral RNA triggers conformational changes that expose the N-terminal caspase recruitment domain ( CARD ) [13] . Mitochondrial antiviral signaling ( MAVS ) [14] , [15] , also called VISA [16] , IPS-1 [17] , and Cardif [18] , relays the signal to activate the downstream kinases , thus resulting in activation of IFN regulatory factor 3 ( IRF3 ) , IRF7 , and NF-κB , and finally IFN production [13] , [19] . DEN is known to be a weak IFN inducer [11] , [20] and MAVS is cleaved by caspases in DEN-infected cells [21] . Furthermore , IFN induction in response to poly ( I:C ) transfection and infection by several viruses such as Newcastle disease virus , Sendai virus ( SeV ) , and Semliki Forest virus was reduced in DEN-infected human dendritic cells [22] . A catalytically active DEN NS2B3 protease was found to reduce the IFNβ promoter activation triggered by SeV infection and poly ( I:C ) transfection [22] . However , the molecular target of dengue protease in IFN induction remains elusive . Mediator of IRF3 activation ( MITA ) [23] , also known as stimulator of interferon genes ( STING ) [24] , endoplasmic reticulum IFN stimulator ( ERIS ) [25] , and transmembrane protein 173 ( TMEM173 ) , shares 81% similarity ( 68% identity ) with its murine homologue MPYS [26] . MITA is a membrane protein involved in IFN production triggered by viral RNA and dsDNA [23]–[25] . MITA interacts with RIG-I , forms a complex with MAVS , activates IRF3 phosphorylation , and is required for IFN induction triggered by RNA and DNA viruses [23] , [24] , [27] . MITA is positively and negatively regulated by multiple mechanisms . Phosphorylation of MITA by TBK1 is critical for virus-induced IRF3 activation [23] . K63-linked ubiquitination of MITA by TRIM56 induces MITA dimerization , and then recruits TBK1 for subsequent IFN induction [28] . MITA is downregulated by RNF5 , an E3 ubiquitin ligase , which targets MITA for ubiquitination and degradation [29] . MITA association with TBK1 with dsDNA stimulation was downregulated by Atg9a , an autophagy related protein [30] . Interestingly , amino acids 125–222 of MITA exhibit homology to flaviviral NS4B proteins , and yellow fever virus NS4B has been shown to suppress MITA-mediated IFN production [27] . To understand the molecular target of dengue protease in IFN pathway , we investigated the cleavage of MITA and MPYS in cells infected with DEN or with expression of DEN protease . MITA , but not murine homologue MPYS , was cleaved in a DEN protease-dependent manner , which led to impaired IRF3 activation stimulated by Japanese encephalitis virus ( JEV ) infection or by transfection with poly ( I:C ) or poly ( dA:dT ) . The potential cleavage site on MITA was mapped and the possible regulatory mechanisms and biological significance of torpid MITA signaling by dengue protease are discussed .
DEN antagonizes type I IFN response in human dendritic cells through a catalytically active protease complex , NS2B3 [22] . To further address the role of dengue protease in suppressing the IFN pathway , we established a stable DEN-2 protease NS2B3-overexpressing A549 cell line by lentiviral transduction . A single point mutation changing serine residue 135 of NS3 to alanine ( S135A ) abolished protease activity , as evidenced by loss of NS2B3 autocleavage ( Figure 1C and 1D ) . JEV , known to induce high level of IRF3 activation and trigger IFN induction [11] , whose replication was not affected by dengue protease ( Figure S2 ) was used as a viral IFN inducer in this study . In contrast to control cells overexpressing GFP , cells overexpressing dengue NS2B3 showed reduced phosphorylation of IRF3 triggered by JEV infection ( Figure S1 ) . JEV-induced IRF3 nucleus translocation ( Figure 1A , 1B and Table S1 ) , dimerization ( Figure 1C ) , and phosphorylation ( Figure 1C ) , were all reduced in cells overexpressing the wild-type but not enzyme-dead ( S135A ) NS2B3 , under similar levels of JEV replication ( Figure S2 ) . To ascertain the spectrum of this inhibitory effect of dengue protease , we transfected cells with poly ( dA:dT ) and poly ( I:C ) , two synthetic analogs of dsDNA and dsRNA , respectively , that mimic foreign nucleic acids from pathogens . Cells overexpressing the enzymatically active dengue NS2B3 showed attenuated IRF3 phosphorylation triggered by poly ( dA:dT ) or poly ( I:C ) transfection ( Figure 1D ) . Therefore , virus- , DNA- and RNA-triggered IRF3 activation could be subverted by dengue NS2B3 in a protease activity-dependent manner . To reveal the molecular target of dengue protease in the IFN pathway , three key molecules in pattern recognition receptor ( PRR ) signaling , RIG-I , MAVS , and MITA , were cotransfected with the wild-type ( WT ) or S135A-mutated NS2B3 . The protein expression of RIG-I and MAVS appeared to be similar in cells with WT or S135A-mutated NS2B3 overexpression ( Figure 2A ) . However , the protein bands corresponding to the full-length and the dimer [25] form of MITA were reduced in cells cotransfected with the wild-type but not S135A-mutated NS2B3 , and an extra protein band of smaller size was detected in NS2B3 ( WT ) -transfected cells ( Figure 2A ) , indicating a cleavage of MITA in dengue protease-overexpressing cells . This MITA-cleavage event seems to be specific for dengue protease , because JEV protease , which shares similar substrate sequences as DEN , did not cleave MITA ( Figure 2B ) . The cleavage of MITA could also be detected in cells infected with DEN-2 , PL046 and NGC-N strains or with DEN-1 Hawaii strain but not with JEV ( Figure 2C ) . Importantly , this cleavage impaired the normal function of MITA , because the viperin promoter-driven reporter , Vip-Luc , with expression under the control of ISRE [31] , was dose-dependently downregulated by cotransfection with the wild-type but not S135A-mutated dengue NS2B3 ( Figure 2D ) . The MITA-triggered activation of the IFNβ promoter-driven reporter p125-Luc ( Figure S3 ) and the induction of endogenous IFNβ mRNA ( Figure 2E ) were also reduced by dengue protease . However , Vip-Luc and p125-Luc triggered by TBK1 , a downstream kinase of IFN pathway [23] , [24] , were not affected by dengue protease ( Figure S4 ) . Furthermore , culture medium derived from A549 cells transfected with MITA plus S135A-mutated NS2B3 expression showed a stronger anti-VSV status than that from MITA plus the wild-type NS2B3 in Vero cells ( Figure S5 ) . We further used an IFN-sensitive recombinant sindbis virus containing a Firefly luciferase reporter gene ( dSinF-Luc/2A ) [32] to quantify the impact of DEN antagonizing antiviral activity . As expected , the luciferase activities derived from dSinF-Luc/2A were dose-dependently reduced by IFNα treatment ( Figure S6A ) . Furthermore , dSinF-Luc/2A replicated to higher level in cells pre-infected with DEN-2 ( Figure S6B ) , supporting the notion that DEN-2 dampens IFN pathway . Consistent with the VSV results ( Figure S5 ) , culture medium derived from cells with transfection of MITA and S135A-mutated NS2B3 expression showed a stronger antiviral activity against dSinF-Luc/2A as compared to that from transfection of MITA with wild-type NS2B3 expression ( Figure 2F ) . Our results suggest that DEN infection subverts the innate IFN immunity by cleaving MITA through a dengue protease-dependent mechanism . To determine the potential cleavage site of dengue protease in MITA , we added an HA-tag and a V5-tag to the N and C termini , respectively , of MITA ( Figure 3A ) . Western blot analysis with antibodies against the tags showed that the N- and C-terminal fragments of MITA produced with NS2B3 cotransfection were ∼1/4 ( ∼12 kDa ) and ∼3/4 ( ∼35 kDa ) of the full-length MITA ( 379 amino acids , ∼47 kDa with tags ) , respectively ( Figure 3B ) . Because the consensus cleavage sites for dengue proteases are two basic residues followed by a small amino acid , we suspected LRR↓96G as the most likely cleavage site of MITA by dengue NS2B3 . We constructed plasmids expressing the expected cleavage products: HA-MITA-N for the N-terminal residues 1–95 plus an HA tag and MITA-C-V5 for the C-terminal residues 96–379 plus a V5 tag . The smaller fragments of MITA produced by NS2B3 cotransfection co-migrated with HA-MITA-N ( a . a . 1–95 ) and MITA-C-V5 ( a . a . 96–379 ) ( Figure 3B ) , suggesting that LRR↓96G , located between the second and the third transmembrane domain of MITA , is likely the site cleaved by dengue NS2B3 . To understand the influence of this cleavage on MITA function , we determined whether these truncated MITAs were still able to transactivate the viperin promoter . Cotransfection of Vip-Luc with the MITA deletion constructs revealed that neither MITA-N nor MITA-C could trigger Vip-Luc expression as did the full-length MITA ( Figure 3C ) , so cleavage of MITA by dengue protease would dampen its normal cellular function . We further checked whether the murine homolog of MITA , MPYS ( Gene ID:72512 ) [23]–[26] , is sensitive to dengue protease . Different from the results for MITA , the expression pattern of MPYS was not changed by cotransfection with dengue NS2B3 ( Figure 3D ) . Furthermore , replacing the LRRG sequences of MITA with the corresponding sequence IHCM found in MPYS reduced the cleavage of MITA by dengue NS2B3 ( Figure 3D ) , which supports LRR↓96G as the target site of dengue protease in MITA . However , changing only one residue of the LRRG motif , L93I , R94A , R95A and G96P mutation , did not confer resistance to dengue protease of MITA ( Figure S7 ) . We also noted that some of these MITA mutants such as IHCM , R94A , R95A , and G96P greatly lost their ability to trigger Vip-Luc expression , suggesting that this LRRG region is important for MITA's function ( Figure S7 ) . To test whether introducing the cleavage site LRRG from MITA to MPYS would make MPYS susceptible to DEN protease , we made a MPYS-LRRG construct by replacing the IHCM sequences of MPYS with LRRG . MPYS-LRRG was still resistant to the cleavage of DEN protease ( Figure 3D ) . To address the interplay of dengue protease with MITA versus MPYS , we used immunoprecipitation-immunoblotting ( IP-western ) assay to determine whether these proteins physically interact with each other . Cells were cotransfected with V5-tagged MITA or MPYS plus the Flag-tagged enzyme-dead ( S135A ) dengue protease . Immunoprecipitation of MITA with anti-V5 antibody co-precipitated NS2B3 ( Figure S8 ) . DEN protease interacted with MITA , and to a lesser extent also with MITA-IHCM-mutant , but not much with WT and LRRG-mutant of MPYS ( Figure S8 ) . Cotransfection of MITA with dengue NS2B3 suppressed more than 50% of Vip-Luc activity as compared to transfection with the enzyme-dead S135A mutant; while Vip-Luc triggered by the dengue protease-resistant MPYS remained unaffected by NS2B3 cotransfection ( Figure 3E ) . Therefore , dengue protease subverts innate immunity by cleavage of human MITA . To determine whether the endogenous MITA is targeted by DEN infection , we detected the protein levels of MITA and several IFN signaling molecules in DEN-infected cells by western blotting ( Figure 4A ) . As expected , IRF3 phosphorylation and expression of DEN viral protein NS3 and IFN-induced RIG-I increased with DEN-2 infection . Furthermore , not only for MAVS that is cleaved by DEN-2-induced caspases [21] , the endogenous MITA protein levels were also reduced in cells with DEN-2 infection through an infection time- and infection dose-dependent manner ( Figure 4A and 4B ) . To ascertain whether cleavage of MITA discredits innate immunity in response to DEN infection , we established stable A549 cells overexpressing MITA or MPYS by lentiviral transduction . MITA is a DNA sensor and plasmid transfection activates its signaling [24] , [27] , however different from the transient transfection , cells stably expressing MITA or MPYS showed no sign of basal IRF3 activation ( Figure 4C ) . Upon stimulation with dsDNA , we noted higher IRF3 phosphorylation triggered by poly ( dA:dT ) in A549 cells expressing MITA or MPYS than the GFP control ( Figure S9 ) . In response to DEN-2 infection , cells with MPYS overexpression showed higher levels of IRF3 phosphorylation and lower dengue viral NS3 protein expression than with the GFP control ( Figure 4C ) . However , in MITA-overexpressing cells , MITA was cleaved , as indicated by the smaller protein fragments recognized by anti-HA and anti-V5 antibodies ( Figure 4C ) and no anti-DEN effect of MITA was noted because of similar levels of dengue viral NS3 protein expression between MITA and control GFP cells ( Figure 4C ) . Furthermore , the cleaved MITA products , HA-MITA-N or MITA-C-V5 , had no effect on IRF3 phosphorylation , no anti-DEN activity , and no further cleavage ( Figure S10 ) . Consistent with the viral protein data detected by western blotting , less infectious DEN-2 production was noted in MPYS-expressing cells that had higher IFNβ expression level ( Figure 4D ) and stronger antiviral activity against IFN-sensitive dSinF-Luc/2A ( Figure 4E ) , as compared with the MITA-expressing cells ( Figure 4F ) . To further address the role of endogenous MITA/MPYS in DEN infection , we knocked down the endogenous MITA expression in A549 cells by lentivirus-delivered shRNA targeting human MITA gene . A slight increase of DEN replication was noted in iMITA cells , especially when a low MOI was used ( Figure 5A and Figure S11 ) , probably reflecting that high MOI of DEN infection blunts MITA efficiently and MITA knockdown has little additional effect . We also noted that the levels of IRF3 phosphorylation and IFN-induced RIG-I expression were reduced in iMITA cells upon DEN-2 infection ( Figure 5A ) , supporting the notion that MITA plays a role in IFN signaling during DEN infection . Consistent with the protein expression data ( Figure 5A ) , culture medium derived from DEN-2-infected iMITA cells also exhibited lower antiviral activity against dSinF-Luc/2A ( Figure 5B ) as compared to control knockdown cells . To further demonstrate the contribution of endogenous MPYS on anti-DEN host defense , we reduced the endogenous MPYS expression of murine Hepa 1–6 cells that has been used for DEN study [33] by shRNA targeting MPYS . DEN-2 viral protein expression ( Figure 5C ) and viral progeny production ( Figure 5D ) were enhanced in cells with reduced MPYS , further supporting the antiviral role of MITA/MPYS and the need of DEN to subvert it by protease degradation . MITA is known to be critical for intracellular DNA-mediated IFN production but its role in dsRNA-triggered IFN production is more controversial [27] . Since DEN is a RNA virus , we are interested to know whether MITA/MPYS is activated in DEN-infected cells . Because MITA forms cytoplasmic punctate structures during activation [30] , we determined the cellular distribution of MITA and MYPS in DEN-infected cells . To avoid using dsDNA plasmid transient transfection that activates MITA , we used A549 cells stably expressing MITA or MPYS to examine the cellular localization of MITA/MPYS . At the early time point of DEN-2 infection , both MITA and MPYS showed homogenous cytoplasmic distribution . However , at the later time point , MITA was diminished likely through cleavage by DEN protease and then degradation by cell machinery , while MPYS formed punctate structures , suggesting that MPYS is activated by DEN-2 infection ( Figure 6A ) . We then established stable cells overexpressing the wild-type NS2B3 plus MITA or MPYS by lentivirus transduction . In the absence of stimulation , MITA/MPYS and dengue protease co-existed in the same cells ( Figure 6B ) even though MITA is cleavable by NS2B3 , suggesting that certain stimulation is required to facilitate this cleavage event . With dsDNA stimulation , MPYS formed punctate structures but not MITA with NS2B3 expression ( Figure 6B ) , supporting the notion that MITA but not MPYS is targeted by dengue protease . To further address the interplay of dengue protease with MITA versus MPYS , cells were cotransfected with V5-tagged MITA or MPYS plus the Flag-tagged WT or enzyme-dead ( S135A ) dengue protease . Immunoprecipitation of NS2B3 ( S135A ) with anti-Flag antibody readily brought down MITA but not much of MPYS ( Figure 7A ) . Similar results were noted by immunoprecipitation of MITA with anti-V5 antibody and then immunoblotting for NS2B3 with anti-Flag antibody ( Figure 7A ) . Interestingly , the interaction between MITA and dengue NS2B3 was enhanced by transfection with poly ( dA:dT ) but not much with poly ( I:C ) ( Figure 7B ) . To determine whether this interaction contributes to the cleavage event , stable cells overexpressing the wild-type NS2B3 plus MITA or MPYS were treated with poly ( dA:dT ) or poly ( I:C ) . A basal level of cleavage of MITA but not MPYS was detected ( Figure 7C , lanes 1 and 4 ) , and this cleavage was further enhanced by transfection with poly ( dA:dT ) but not with poly ( I:C ) ( Figure 7C ) . A reduced level of the full-length endogenous MITA was noted in cells with WT but not with enzyme-dead NS2B3 ( S135A ) regardless of dsDNA stimulation ( Figure 7D ) . We were not able to detect the cleaved products of MITA protein probably due to rapid degradation . The protein-protein interaction of endogenous MITA and dengue protease could be demonstrated by immunoprecipitation with anti-NS3 antibody and then western blotting with anti-MITA antibody , especially in cells with the enzyme-dead S135A NS2B3 ( Figure 7E ) . Overall , we found that dengue protease NS2B3 targets MITA , an important signaling molecule of host innate immunity in response to foreign nucleic acids , to downregulate the host defense mechanism .
In this study , we found that MITA , a key adaptor molecule in host innate immune response , is targeted by the DEN protease NS2B3 . MITA , also known as STING , MPYS and ERIS , is an ER-localized transmembrane protein essential for IFN induction triggered by DNA pathogens [27] , [34] , [35] and probably also by some RNA viruses [23] , [24] , [27] , [36] . Targeting MITA during DEN infection may result in reduced host defense against DEN , and maybe also against DNA pathogens such as bacterial infection . Even though concurrent bacteraemia in patients with dengue fever is rare , some reports have implicated bacterial infection in severe forms of dengue diseases . For example , secondary bacteraemia was a contributor to death in 4 of the 9 adult patients who died of dengue-related illness in Singapore [37] . A study in Taiwan indicated that 5 . 5% of the patients with DHF/DSS had concurrent bacteremia [38] , and a study of DHF infants in India showed 21% with bacterial co-infections [39] . Thus , our results showing that DEN may modulate the innate immunity predisposition to other infections provide a molecular explanation for the mortality of nosocomial bacteraemia in dengue patients . The protease activity of DEN NS3 depends on the association with NS2B cofactor [40]–[42] , and the viral NS2B3 protease cleaves the viral polyprotein precursor at the junctions of NS2A/NS2B , NS2B/NS3 , NS3/NS4A , and NS4B/NS5 [40] , [43] . These cleavage sites have the consensus sequence of two basic amino acids ( KR , RR , RK , and occasionally QR ) at the −2 and −1 positions , followed by a small amino acid ( G , A , or S ) at the +1 position [40] , [42]–[44] . Previously , by using an IFNβ-Luc reporter assay , IFNβ promoter activation triggered by SeV infection was reduced by DEN NS2B3 through a protease-dependent mechanism [22] . In this study , we further demonstrated that DEN protease reduced IFN induction and IRF3 phosphorylation triggered by JEV infection and by transfection with poly ( I:C ) and poly ( dA:dT ) . Furthermore , human MITA but not the murine homologue MPYS was cleaved in cells expressing an enzyme-active but not enzyme-dead NS2B3 . From the sizes of the cleaved products , LRR↓96G , which matches the flaviviral protease consensus sequences , was predicted to be the cleavage site of MITA by DEN protease . Mutation of LRRG to the corresponding sequence , IHCM found in murine MPYS that cannot be cleaved by dengue protease , attenuated the cleavage pattern of MITA , suggesting that MITA is a substrate of dengue NS2B3 . However , we cannot exclude the possibility that MITA is cleaved by a yet-to-be identified cellular protease that depends on the activity of dengue NS2B3 . The cleavage sites for JEV and DEN proteases share the same consensus sequences; but different from the data for DEN infection and DEN protease , JEV infection and JEV NS2B3 expression failed to trigger cleavage of MITA , suggesting that other factors , besides the presence of flaviviral protease , also govern this MITA cleavage event . Furthermore , this DEN protease-mediated cleavage of MITA could be enhanced by dsDNA stimulation , suggesting that some dsDNA-induced factor ( s ) also participate in this cleavage event . So far we could conclude that MITA , but not MPYS , is specifically cleaved by either DEN protease itself or by host protease ( s ) depending on the activity of DEN protease . This different response between human MITA and murine MPYS provides a potential clue to improve the animal models for DEN pathogenesis study . As outlined in Figure 8 , in unstimulated cells , MITA mainly localizes to the endoplasmic reticulum ( ER ) [24] , [25] , [30] . Following stimulation , MITA translocates from the ER to the Golgi and finally assembles with TBK1 in punctate membrane structures , which is required for activation of downstream signals [30] . Dengue NS3 protease also targets to ER by interacting with its cofactor NS2B [45] . Here , we found that MITA and dengue NS2B3 physically interact with each other , and this interaction could be further enhanced by stimulation with poly ( dA:dT ) . The cleavage of MITA by NS2B3 could also be enhanced by poly ( dA:dT ) , suggesting that NS2B3 would encounter the dsDNA-driven trafficking MITA [30] and execute the cleavage . The cleavage decreasing the function of MITA is further supported by loss of punctate structures with MITA triggered by dsDNA stimulation in cells with dengue NS2B3 . MITA is known to be critical for intracellular DNA-mediated IFN production but its role in RNA-triggered IFN production is not so clear [27] . However , MITA is required for IFN induction triggered by RNA viruses such as SeV [23] and VSV [27] . VSV [23] , [24] , [27] and Newcastle disease virus [25] are also sensitive to the antiviral activity mediated by MITA both in vitro and in vivo . Thus , MITA is likely not only a DNA sensor , but also involved in RNA viruses signaling . Consistent with the previous study [30] , poly ( I:C ) did not trigger MPYS activation marker , cytoplasmic punctate structures ( Figure S12 ) , however DEN-2 infection induced MPYS punctate structures . These results suggest that poly ( I:C ) transfection may not completely recapitulate the events occurring during DEN infection . Several possibilities may contribute to this discrepancy , for example viral RNA may possess of property more than poly ( I:C ) and/or the host , both genomic and mitochondrial , DNA release in virus-infected cells would activate the MITA/MPYS signaling pathway . DEN is a weak inducer of type I IFN , and DEN-infected human dendritic cells showed a reduced type I IFN response to infection with several viruses and to stimulation with poly ( I:C ) [11] , [20] , [22] . Several molecules of the IFN-inducing pathway are targeted by members of the Flaviviridae family . MAVS is cleaved by HCV NS3/4A protease [14] , [18] and by caspase-1 and caspase-3 induced by DEN infection [21] . A sequence homology between flaviviral NS4B and MITA ( a . a . 125–222 ) has been reported , and the IFN induction ability of MITA was downregulated by yellow fever virus NS4B [27] . DEN-encoded NS2B3 protease apparently also targets the human MITA . DEN replication levels were reduced in cells expressing the murine homologue of MITA , MPYS , which cannot be cleaved by dengue protease , suggesting that DEN replication benefits from MITA cleavage with dengue NS2B3 . Because dengue protease is essential for DEN replication , it has been extensively investigated as an antiviral target . Whether drugs that block DEN protease can affect DEN replication and rescue the MITA-mediated innate immune response and influence DEN pathogenesis is of great interest .
DEN and JEV propagation and titration were as described previously [21] , [46] . IFN-sensitive dSinF-Luc/2A sindbis virus [32] and vesicular stomatitus virus ( VSV ) were amplified in Vero cells as described [47] . IFNα-2a ( Roferon-A ) was from Roche . 293T/17 cells ( ATCC , CRL-11268 ) and murine hepatoma Hepa 1–6 cells ( ATCC CRL-1830 ) were cultured in DMEM medium supplemented with 10% FBS . A549 , a human lung carcinoma cell line , was cultured in F-12 medium supplemented with 10% FBS . African green monkey kidney Vero cells were cultured in MEM supplemented with 10% FBS and 2 mM L-glutamine . PolyJet transfection reagent ( SignaGen Laboratories ) and Lipofectamine 2000 ( Invitrogen ) were used according to the manufacturer's instructions . Poly ( dA:dT ) naked and Poly ( I:C ) LMW were obtained from InvivoGen and delivered into cells by transfection with Lipofectamine 2000 . pRK-HA-MITA and pRK-Flag-MITA were kindly provided by Dr . Hong-Bing Shu . The cDNA of MPYS was cloned by PCR with the primers: XhoI-mMITA ( 1–22 ) : 5′-ACCGctcgagATGCCATACTCCAACCTGCATC-3′ and mMITA ( 1136–1114 ) -XbaI: 5′-CTAGtctagaCAGATGAGGTCAGTGCGGAGTGG-3′ , and the sequences were identical to that of murine MPYS ( GenBank accession no . NM_028261 ) . To add tags to both ends of MITA , HA-MITA was subcloned into the V5-His/pcDNA3 . 1 vector ( Invitrogen ) . Primers for deletions and mutations of MITA or MPYS are listed in Figure S7 . The pTY lentiviral expression system was obtained from Dr . Lung-Ji Chang [48]–[50] . The lentivirus vector carrying the shRNA targeting human MITA ( 5′-CATGGTCATATTACATCGGAT-3′ , TRCN0000160281 ) , murine MPYS ( 5′-AGAGGTCACCGCTCCAAATAT-3′ , TRCN0000346319 ) or LacZ ( 5′-TGTTCGCATTATCCGAACCAT-3′ , TRCN0000072223 ) , and the lentiviral vectors for cDNA overexpression , pLKO . 1_AS3w . puro and pLKO . 1_AS3w . bsd , were from the National RNAi Core Facility , Taiwan . Lentivirus was prepared following the protocol of the National RNAi Core Facility ( Academia Sinica , Taiwan ) . The pCR3 . 1 vectors expressing DEN-2 NS2B3 and its S135A mutant have been described previously [46] . The cDNAs of NS2B3 and NS2B3 ( S135A ) were subcloned to pTY vector for lentivirus production . To assess the effect of DEN protease on MITA-mediated gene induction , A549 cells were cotransfected with viperin promoter-driven reporter Vip-Luc [31] ( 0 . 15 µg ) , IRF3/pCR3 . 1 [11] ( 0 . 15 µg ) , MITA/pcDNA3 . 1 ( 0 . 3 µg ) , internal control pRL-TK ( Promega ) ( 0 . 05 µg ) , and various doses of NS2B3/pCR3 . 1 ( WT or S135A-mutant ) ( 0 . 3 , 0 . 45 , and 0 . 6 µg ) for 24 h . GFP/pCR3 . 1 was used as plasmid control . The cell lysates were harvested and analyzed by dual-luciferase assay system ( Promega ) . The firefly luciferase activity ( Vip-Luc ) was normalized to that of renilla luciferase ( pRL-TK ) and the relative luciferase activities are shown . Total RNA was prepared with an RNeasy RNA Mini Kit ( Qiagen ) and the cDNA was reverse transcribed from 1 µg of total RNA with random hexamer primer using a ThermoScript RT kit ( Invitrogen ) . qPCR was then carried out using the specific primer sets for IFNβ ( 5′-CACGACAGCTCTTTCCATGA-3′ and 5′-AGCCAGTGCTCGATGAATCT-3′ ) and actin ( 5′-TCCTGTGGCATCCACGAAACT-3′ and 5′-GAAGCATTTGCGGTGGACGAT-3′ ) with the LightCycler FastStart DNA Master PLUS SYBR Green I kit ( Roche ) , according to the manufacturer's recommendations . The level of IFNβ was normalized to that of actin based on the second derivative maximum method ( Roche ) . Melting curves were used to verify the specificity of PCR products . Conditioned medium was harvested and two-fold serial diluted with fresh medium . 1×105 Vero cells were cultured with the diluted medium for 18 h in a 24-well plate and then infected with dSinF-Luc/2A sindbis virus ( 500 pfu/well ) . The cell lysates were harvested for luciferase activity assay ( Promega ) at 24 h p . i . The conditioned medium collected from DEN-2-infected cells was UV-inactivated [11] before serial dilution . For IRF3 nuclear translocation assay , cells were fixed with 4% paraformaldehyde in PBS and then permeabilized by 0 . 5% TritonX-100 . After blocking with skim milk in PBS with 0 . 1% Tween-20 ( PBS-T ) , antibodies against IRF3 ( 1∶200; Santa Cruz Biotechnology ) were added overnight . Biotin-conjugated goat anti-rabbit antibody and Cy3-conjugated streptavidin ( 1∶1000; Jackson ImmunoResearch ) was added sequentially on the next day for 1 h at room temperature . Primary antibodies against JEV and DEN NS1 and NS3 ( 1∶1000 ) and goat anti-mouse Alexa Fluor-488-conjugated secondary antibody ( 1∶1000; Molecular Probes ) were used for 1 h at room temperature . Cells were examined and photographed by use of an inverted fluorescent microscope . For the samples examined under a fluorescence laser scanning confocal microscope ( FV1000 , Olympus ) , cells were seeded in μ-Slides chamber slides ( ibidi ) overnight before treatments . Cells were lysed with RIPA buffer ( 10 mM Tris , pH 7 . 5 , 5 mM EDTA , 150 mM NaCl , 0 . 1% SDS , 1% TritonX-100 , 1% sodium deoxycholate ) containing a cocktail of protease and phosphatase inhibitors ( Roche ) . Protein samples were separated by SDS-PAGE and transferred to a nitrocellulose membrane ( Hybond-C Super , Amersham ) . The nonspecific antibody binding sites were blocked with skim milk in PBS-T and then reacted with the primary antibodies: anti-phospho-IRF3 ( S396 ) ( 1∶1000; Cell Signaling ) , anti-IRF3 ( 1∶1000; Santa Cruz Biotechnology ) , anti-actin ( 1∶10000; Chemicon ) , anti-TMEM173 ( 1∶2000; Novus ) , anti-RIG-I ( 1∶1000; Cell Signaling ) , anti-MAVS ( 1∶1 , 000; Axxora ) , anti-HA ( 1∶2000; Covance ) , anti-V5 ( 1∶5000; Sigma-Aldrich ) , and anti-Flag M2 ( 1∶5000; Sigma-Aldrich ) . Blots were treated with horseradish peroxidase-conjugated secondary antibody ( 1∶2500; Jackson ImmunoResearch ) , and signals were detected by enhanced chemiluminescence ( ECL , Pierce ) . For native PAGE , sample preparation and electrophoresis in the presence of deoxycholate ( DOC ) were performed as previously described [11] . Briefly , cells were lyzed in protein lysis buffer ( 50 mM Tris-HCl [pH 7 . 5] , 150 mM NaCl , 1 mM EDTA , 1% NP-40 ) containing a cocktail of protease inhibitors . Cell lysates ( 10 µg ) in native sample buffer ( 62 . 5 mM Tris–HCl [pH 6 . 8] , 15% glycerol , and 1% DOC ) were separated by 7 . 5% PAGE without SDS for 60 min at 25 mA . The electrophoresis buffer contained 25 mM Tris-HCl and 192 mM glycine ( pH 8 . 4 ) with and without 1% DOC in the cathode and anode chambers , respectively . Cells were lysed with protein lysis buffer containing a cocktail of protease and phosphatase inhibitors ( Roche ) . Cell lysates were immunoprecipitated with mouse anti-V5 antibodies ( 1∶1000; Sigma-Aldrich ) or mouse anti-Flag beads ( Sigma-Aldrich ) overnight at 4°C . The immunocomplex was captured by use of protein G-coated beads ( GE Healthcare ) at 4°C for 2 h , then precipitates were washed with protein lysis buffer and resuspended in sample buffer with 2-mercaptoethanol . For immunoprecipitation of endogenous MITA , cells were lysed with protein lysis buffer plus 1% TritonX-100 . The immunoprecipitated samples were examined by Western blot analysis . Data are presented as mean ± standard deviation ( SD ) . The results of the indicated groups were compared by two-tailed Student's t test . | The pathogenesis of severe dengue diseases remains unclear , but magnitude of dengue virus ( DEN ) replication is believed to be one of the major determining factors . Thus , revealing how DEN evades the host defense mechanism such as type I interferon ( IFN ) system is important for better understanding this devastating disease . Although several DEN viral proteins have been reported as IFN-resistant factors , without knowing the cellular targets , the mechanism of how DEN subverts IFN system is poorly understood . In this study , we found that the human mediator of IRF3 activation ( MITA ) , also known as STING and ERIS , was cleaved in cells infected with DEN and in cells expressing an enzymatically active DEN protease NS2B3 . MITA is known as a DNA sensor for IFN production and its antiviral role has also been demonstrated for several DNA and RNA viruses . DEN protease appears to cleave MITA but not its murine homologue MPYS , and this cleavage resulted in impaired MITA activation . Ectopic overexpression of MPYS but not MITA reduced DEN replication , and knockdown of endogenous MPYS enhanced DEN replication . Thus , we find that MITA/MPYS is involved in host defense against DEN replication and DEN protease targets MITA to subvert its antiviral effect . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"immunology",
"biology",
"microbiology"
] | 2012 | Dengue Virus Targets the Adaptor Protein MITA to Subvert Host Innate Immunity |
Stress-induced changes of gene expression are crucial for survival of eukaryotic cells . Regulation at the level of translation provides the necessary plasticity for immediate changes of cellular activities and protein levels . In this study , we demonstrate that exposure to oxidative stress results in a quick repression of translation by deactivation of the aminoacyl-ends of all transfer-RNA ( tRNA ) . An oxidative-stress activated nuclease , angiogenin , cleaves first within the conserved single-stranded 3′-CCA termini of all tRNAs , thereby blocking their use in translation . This CCA deactivation is reversible and quickly repairable by the CCA-adding enzyme [ATP ( CTP ) :tRNA nucleotidyltransferase] . Through this mechanism the eukaryotic cell dynamically represses and reactivates translation at low metabolic costs .
Environmental stress or suboptimal growth conditions reduce cell viability and put cells at risk . Cells maintain their internal homeostasis by adequate reprogramming of metabolic activities at all levels of gene expression , including chromatin remodeling , mRNA expression and degradation , translation and protein degradation . Given the considerable time needed to activate new genes and/or de novo synthesize mRNA , the translation of existing mRNAs provides the necessary plasticity for the cell to selectively and rapidly respond to stress [1] , [2] . Translation is divided into three distinct phases: initiation , elongation and termination . Translation initiation , as a rate-limiting process , is a major point to reprogram translation in response to stress [3] , [4] . A key mechanism to repress translation initiation is the phoshorylation of the alpha-subunit of translation initiation factor 2 ( eIF2 ) by stress-activated kinases [5] , [6] . However , a sizeable set of cellular mRNAs are initiated in an eIF2-independent manner , which allows for escaping the global kinase-dependent inhibition of translation initiation [3] , [4] . It remains elusive , which alternative mechanisms the cell employs to regulate translation during adverse environmental stress . Transfer RNAs ( tRNAs ) enter ribosome-mediated protein biosynthesis in a translationally competent state , which includes post-transcriptional modifications at various positions , including the anticodon loop , and the presence of an intact single-stranded CCA-sequence at the 3′-terminus that is required for amino acid attachment by the corresponding aminoacyl-tRNA-synthetase [7] . The CCA ends are generated and maintained by the CCA-adding enzyme [8] . Some bacteria carry tRNA genes encoding CCA termini , thus the CCA-adding enzyme is primarily involved in repairing damaged CCA ends in these organisms [9] . In contrast , all eukaryotic tRNA genes lack the CCA ends and the role of the CCA-adding enzyme is to attach post-transcriptionally the CCA overhang to the 3′-termini of all tRNAs [8] , [10] , [11] . The functional repertoire of the CCA-adding enzyme has been expanded by its recently discovered role in the quality control of hypo-modified tRNAs [12] . Mature , translationally competent tRNAs are very stable under normal growth conditions , with a half-life of approximately one to several hours [13] . However , environmental changes dynamically modulate the concentration of the tRNA pool . Some tRNAs are cleaved in the anticodon loop in response to various environmental stress factors ( e . g . , oxidative stress , heat shock or ultraviolet irradiation ) [14] , [15] , [16] , [17] . The endonucleolytic tRNA cleavage is a conserved feature in higher eukaryotes . Thereby , two tRNA-halves ( designated 5′- and 3′-tiRNAs ) are generated by a ubiquitously expressed enzyme , angiogenin [18] . This cleavage , however , does not significantly reduce the level of mature tRNAs , which implies that tiRNAs may rather act as a signal transducer to modulate translation of specific mRNAs , than to globally repress translation [18] . Furthermore , in response to external stimuli , retrograde translocation of mature tRNAs to the nucleus[19] or selective charging of different tRNA isoacceptors [20] transiently alter the pool of translationally active tRNAs in the cytoplasm . Consequently , these stress-induced alterations in the tRNA concentration will decrease the amount of ternary complex ( that is , the complex of charged tRNA with the GTP-loaded elongation factor ) . However , the primary mechanism , that triggers a general inhibition of translation elongation during stress , remains surprisingly elusive . Here , using high-sensitive approaches to probe the structural integrity of cellular tRNAs , we show that upon exposure to oxidative stress all tRNAs are rapidly deactivated by a cleavage within their 3′-CCA termini by oxidative stress-activated nuclease , angiogenin . Since 3′-CCA ends are ubiquitous for all tRNAs , angiogenin-induced deactivation of tRNAs provides a means for global repression of translation at the level of elongation . On a much slower scale , at longer times of exposure to stress , some tRNAs are also cut in their anticodon . The CCA ends deactivation is reversible and quickly repairable by the CCA-adding enzyme . We propose that this is a mechanism to dynamically repress and reset translation at low metabolic costs .
Angiogenin , the nuclease that endonucleolytically cleaves tRNAs during oxidative stress , is constitutively expressed , but kept inactive through an inhibitor RNH1 [18] . Oxidative stress dissociates the inhibitor and activates angiogenin [21] . To investigate the susceptibility of the cellular tRNAs to angiogenin-mediated cleavage , we exposed confluent HeLa cells to arsenite which elicits oxidative stress and activates angiogenin . A small amount of tiRNAs was generated , but only at prolonged exposure to arsenite ( >30 min ) ( Figure 1A ) . Next , we used tRNA microarrays [22] with immobilized oligonucleotide probes complementary to the full-length tRNA sequences to determine the susceptibility of each tRNA species to angiogenin . Only a subset of all tRNAs bearing a CA sequence in the anticodon loop was predominantly cleaved into tiRNAs; a minor fraction with UA or GC motifs in the anticodon loop was also cleaved ( Figure S1A ) . This cleavage pattern mirrors the substrate specificity of angiogenin: it targets single-stranded ribonucleic acid sequences with 10–30-fold higher preference for CA over UA [23] and 3-fold higher for CA over CG [24] . While there is a large variability in the composition of the anticodon loops of all tRNAs and only a fraction of tRNAs possesses a CA-motif in the anticodon loop ( Figure S1B ) , we realized that the ubiquitous , single-stranded 3′-CCA sequence post-transcriptionally attached to all eukaryotic tRNAs [8] , bears the strongest angiogenin recognition motif , the CA motif . To investigate whether the 3′-CCA ends can be targeted by angiogenin upon exposure to oxidative agents , we used enzymatic ligation of a fluorescent stem-loop oligonucleotide that complementary pairs only to the intact 3′-CCA end of tRNA ( Figure 1B , schematic inset ) . The fluorescent signal , which is proportional to the amount of tRNAs with intact 3′-CCA ends , decreased noticeably in conditions of severe oxidative stress while the total tRNA amount remained relatively constant ( 500 µM arsenite; Figure 1B ) , consistent with the idea that the 3′-CCA ends of tRNAs are primary substrates of angiogenin . Oxidative stress altered the structural integrity of the 3′-CCA termini of tRNAs in a dose-sensitive manner; at lower dose of stress ( 100 µM arsenite ) much smaller fraction of tRNAs than at high stress dose ( 500 µM arsenite ) was unable to ligate the fluorescent oligonucelotide ( Figure 1B ) . To our surprise , the removal of the 3′-CCA ends of the tRNAs occurred on a much faster time scale ( Figure 1B ) compared to the appearance of the tiRNA fragments ( Figure 1A ) . To confirm that angiogenin cleaves the 3′-CCA termini of tRNAs upon exposure to arsenite , we upregulated the level of aniogenin and analyzed the integrity of the 3′-CCA ends using the fluorescent oligonucleotide-ligation approach . A small increase in the cellular level of angiogenin led to a noticeable enrichment of tRNAs with cleaved 3′-CCA termini , confirming its role in the oxidative stress-mediated cleavage of the 3′-CCA ends ( Figure 1C ) . Note that angiogenin can be only moderately upregulated for short expression times ( Figure S2A ) ; longer expression perturbs the vitality of the cell . The ectopically enhanced levels of angiogenin , a fraction of it might be additionally deactivated by the excess of the RNH1 inhibitor in the cell , is most likely far below the concentration of stress-activated angiogenin , thus the effect of arsenite-induced 3′-CCA end cleavage ( +arsenite ) was much stronger ( Figure 1C ) . Intrigued by the different time scales of the stress-induced alterations of cellular tRNAs , we next analyzed the kinetics of 3′-CCA end cleavage and tiRNA generation in vitro . Total tRNA was isolated from confluent , non-stressed HeLa cells , radioactively labeled at their 5′- or the 3′-end and subsequently subjected to angiogenin treatment . Strikingly , while 5′-labeled tRNAs are still visible at 120 min of incubation with angiogenin , the 3′-labeled tRNAs completely disappeared after 30 min ( Figure 2A ) . The fast decay of the signal of the 3′-labeled full length tRNAs than the 5′-labeled tRNAs ( Figure 2A ) is consistent with a preferred and much faster cleavage in the 3′-CCA termini of the tRNAs . All tRNAs were equally sensitive to angiogenin cleavage at the 3′-CCA termini; the signal for all 3′-radioactively labeled tRNAs decayed almost simultaneously during the angiogenin treatment ( Figure 2B and S3 ) . In the fluorescent oligonucleotide-ligation approach ( Figure 1B , schematic inset ) , we observed a clear progressive decrease of the yield of the oligonucleotide ligated to the 3′-CCA end of the tRNAs upon angiogenin treatment ( Figure 2C ) . Notably , fragments migrating at the height of the tiRNAs appeared much later ( Figure 2C ) , suggesting that angiogenin degraded the 3′-CCA ends more rapidly than it cleaved tRNAs in the anticodon loop , thus recapitulating the observations in HeLa cells ( Figure 1 ) . To define the exact cleavage site in the 3′-CCA end , we used internally radioactively labeled variants of tRNAPhe ( GAA ) with intact 3′-CCA and truncated 3′-CC end . tRNAPhe ( GAA ) lacks the CA motif in the anticodon loop and hence , only the 3′-CCA terminus is susceptible to angiogenin cleavage . Angiogenin cleaved endonucleolytically within the 3′-CCA motif between the C and A nucleotide and removed exclusively the adenosine residue ( Figure S4 ) , implying a high CA-dependent endonucleolytic activity of angiogenin . While in vitro all tRNAs lost their 3′-ends after 10 min , as evidenced by almost complete signal extinction of the 3′-labeled full-length tRNAs ( Figure 2A ) , in vivo the signal plateaued at about 60% of the initial signal intensity ( Figure 1B ) . Translationally competent tRNAs are aminoacylated and complexed with elongation factor EF1α , which may protect tRNAs from angiogenin cleavage . Aminoacylation per se did not influence angiogenin cleavage ( Figure S5 ) . The crystal structure of the ternary complex from Thermus aquaticus indicates that EF1α contacts only the phosphate groups of tRNA bases 73–75 [25] . [Note , C75A76 is endonucleolytically targeted by angiogenin] . Thus , the elongation factor EF1α may marginally interfere with the angiogenin binding and partly protect the aminoacyl-tRNA . In cells , the CCA-adding enzyme repairs the partially degraded 3′-CCA ends of tRNAs without a nucleic acid template and highly discriminates between adding cytidine at position 75 and adenosine at position 76 [8] , [10] , [11] . Thus , we hypothesized that the lower amount of tRNAs with deactivated CCA termini in HeLa cells ( Figure 1B ) , compared to the in vitro angiogenin treatment ( Figure 2A ) , might represent a steady-state equilibrium between the angiogenin-mediated cleavage and simultaneous repair by the CCA-adding enzyme whose activity remained unchanged upon the arsenite treatment ( Figure S4C ) . To determine the effect of these two opposing processes , total HeLa tRNAs were successively treated with angiogenin and human CCA-adding enzyme . Indeed , the CCA-adding enzyme repaired the CCA termini ( Figure 3A ) by adding the cleaved adenosine ( Figure 3B ) , implying that stress-damaged 3′-CCA ends of tRNAs can be easily repaired and tRNAs are converted back to translationally-competent species . The angiogenin-catalyzed endonucleolytic cleavage of the CA motif results in a 3′-terminal 2′ , 3′-cyclic phosphate at the cytosine residue . Thus , prior to treatment with CCA-adding enzyme HeLa tRNAs ( Figure 3A ) or single tRNAPheCC were treated with T4 polynukleotide kinase ( PNK ) . In the cell , the 3′-end cyclization products are quickly hydrolyzed to 3′OH by 2′ , 3′-cyclic 3′-phosphodiesterases [26] , [27] . An attempt to reduce the cellular concentration of the CCA-adding enzyme was unsuccessful: even though de novo synthesis of the enzyme was significantly inhibited by targeting its mRNA with specific siRNA probe ( Figure S2B ) , the concentration of the mature CCA-adding enzyme remained unchanged ( Figure S2C ) . As the CCA-adding activity is essential for cell viability , an intrinsic robustness of this enzyme has the advantage of maintaining a constant function and permitting a prompt stress response . The 3′-CCA ends are indispensable for tRNA aminoacylation and subsequently for translation . What is the effect of angiogenin-induced deactivation of the 3′-CCA termini of cellular tRNAs on protein translation ? Exposure of HeLa cells to acute oxidative stress ( 500 µM arsenite ) altered the polysomal profile and shut down translation ( Figure 4A ) . Importantly , at low arsenite concentration ( 100 µM ) the cells retained some translation activity , detectable as a considerable polysomal fraction ( Figure 4A ) . The most potent inhibition of translation is mediated by eIF2α phosphorylation upon oxidative stress via haem-regulated inhibitor kinase ( HRI ) , which represses translation of mRNAs with scanning- or cap-dependent translation initiation [3] . By contrast , a sizeable subset of genes are translated through a cap-independent mechanism: internal ribosome-entry sites ( IRES ) direct translation initiation without the aid of canonical initiation factors and initiator Met-tRNA [28] . We hypothesized that cap-dependent translation will be influenced at much lower arsenite concentrations compared to mRNAs with scanning-independent initiation; the combined effect of oxidative stress on eIF2α phosphorylation and the deactivation of the 3′-CCA ends of all tRNAs will have much higher impact on mRNAs initiated in a scanning-dependent manner . In contrast , in the IRES-initiated translation , as only the 3′-CCA-end inactivation should play a role the effect should be less pronounced . We therefore tested the effect of two arsenite concentrations , representing severe ( 500 µM ) and moderate ( 100 µM ) oxidative stress using bicistronic mRNA encoding renilla luciferase ( Rluc ) , initiated in a cap-controlled manner , and firefly luciferase ( Fluc ) , initiated via cricket paralysis virus IRES ( CrPV-IRES ) ( Figure 4B ) , an IRES sequence described to confer translation independent of any initiation factor [29] . At a low arsenite concentration ( 100 µM ) , the Fluc activity remained at >80% , while Rluc activity progressively decreased , indicating much potent inhibition of cap-dependent initiation compared to IRES-dependent initiation ( Figure 4C , D ) . At a high arsenite concentration ( 500 µM ) , however , a similar decrease for both Rluc and Fluc activity was observed , implying that both IRES-dependent and scanning-controlled initiation were equally inhibited ( Figure 4C , D ) . This cannot be attributed to the decrease of mRNA levels , since the mRNA expression levels of the bicistronic construct remained similar upon stress exposure ( Figure S6 ) . Variations in the transfection efficiency are not likely; transfection efficiency was equal in all experiments as assessed with fluorescent reporter . This suggests that under acute oxidative stress translation of all mRNAs is globally repressed , while moderate oxidative stress affects more strongly the cap-dependent than the IRES-controlled initiation due to the combined effect on eIF2α phosphorylation and the tRNAs deactivation .
Here , we analyze the effect of oxidative stress on the structural integrity of the cellular tRNAs and define the mechanisms of oxidative stress-induced global repression of translation at the level of elongation . Our observations clearly suggest a sequential order of tRNA deactivation upon exposure to oxidative stress: the 3′-terminal CCA sequence is first targeted , while the deactivation into tRNA halves occurs much later . The first event , the CCA cleavage , is not restricted to specific tRNAs; the CCA ends of all tRNAs can be targeted by angiogenin . At severe oxidative stress ( 500 µM ) all tRNAs are rapidly deactivated which leads to a global repression of translational elongation of both mRNAs with scanning and non-scanning ( IRES ) controlled initiation . The deactivation of the 3′-CCA ends is a mechanism to reversibly repress translation at very low metabolic costs; the 3′-CCA tRNA ends are quickly repaired by the CCA-adding enzyme [8] , [10] , [30] and translation is reset . In contrast , cleavage in the anticodon loop proceeds on a much slower timescale and is specific for only a subset of tRNAs , so that tiRNAs with specific primary sequences can be generated . This mirrors the reported specificity of the tiRNAs to either selectively target translation of a defined fraction of mRNAs [14] or trigger formation of stress granules [31] . The role of some tiRNAs to silence specific functions [14] indirectly suggests a separation of the tRNA halves upon a cleavage in the anticodon loop . Regeneration of such tRNAs is more metabolically demanding for the cell , as the cleaved tRNAs can be replaced only through a new transcription cycle . A sizeable fraction of tRNAs with cleaved anticodons may not dissociate into tiRNA halves and undergo a repair by tRNA ligases [32] . In vertebrates , the tRNA-ligase activity is coupled to tRNA splicing and is mainly localized in the nucleus [33] , [34]; a cytoplasmic localization , although conceivable given the observation for cytoplasmic mitochondrial surface in yeast and plants [35] , [36] , has not yet been described . This , in turn , would require a translocation of the cleaved tRNAs into the nucleus . Stress-induced retrograde translocation to the nucleus has been shown for mature tRNAs [19] . If upon cleavage within the anticodon the tRNA structure is maintained nearly to the native one , the retrograde transport into the nucleus would be possible and tRNAs can be repaired by the tRNA ligases . Arsenite derived oxidative stress has also been shown to induce elevated tRNA misacylation with methionine [37] . However , Met-misacylation was obtained upon 1 µM arsenite which is much lower than the concentrations in the experiments presented here ( 100 or 500 µM ) . Furthermore , the duration of the treatment is much longer ( 4 hours ) [37] , which exceeds the time of the first response towards oxidative stress – the 5′-CCA-ends cleavage . Met-misacylation has been proposed to potentially serve as a protective mechanism for cell's own proteins against oxidative inactivation [37] . This mechanism is distinct from the 3′-CCA cleavage which is useful to regulate global translation activity . Our observation for selective translation of transcripts with scanning-independent initiation under moderate oxidative stress ( 100 µM arsenite ) adds another layer to selectively reprogram protein translation under stress at the level of elongation . The inhibition of translation initiation through eIF2α phosphorylation upon oxidative stress is a potent mechanism to repress translation of mRNAs with scanning-controlled initiation [3] , [16] . At low doses of arsenite ( 100 µM arsenite ) translation of mRNAs with cap-dependent translation initiation is compromised , while the non-scanning , IRES-dependent translation continues to function to a certain level ( Figure 4 ) as in the cell only a small fraction of the total tRNA pool is with deactivated 3′-CCA ends ( Figure 1B ) . Finally , as many transcripts involved in proliferation , differentiation and apoptosis [16] are initiated in a cap-independent manner , the observed differential inactivation of protein synthesis which allows these mRNAs to bypass the global translational repression and activate the selective stress response [3] , [16] , [38] .
HeLa cells were usually cultured in DMEM with 10% fetal bovine serum and L-Glu ( 2 mM ) to 80–90% confluency . Oxidative stress was exerted by adding 100 or 500 µM sodium arsenite ( Fluka ) for indicated times . Human angiogenin was cloned in pCDNA3 plasmid ( Invitrogen ) and transfected in sub-confluent HeLa cells using polyethylenimine ( PEI , Polysciences Europe GmbH ) . After 8 h angiogenin expression was detected with polyclonal antibodies ( 1∶1000 , Santa Cruz Biotechnology ) . To decrease the expression of CCA-adding enzyme , pSuper plasmid ( Oligoengine ) bearing shRNA ( 5′-CCGGCGCAGAGATCTCACTATAAATCTCGAGATTTATAGTGAGATCTCTGCGTTTTTG-3′ ) that targeted the CCA-adding enzyme mRNA was transfected using polyethylenimine and expressed for 12 h . shRNA with the same , but randomly scrambled sequence was used as a control . Prior to harvesting , an aliquot of cells was additionally exposed to 500 µM sodium arsenite for 1 h . mRNA was quantified by real-time qRT-PCR and the protein level with polyclonal antibodies ( 1∶200 , Santa Cruz Biotechnology ) against human CCA-adding enzyme . Statistical analyses were performed with Fisher's exact test . Differences were considered statistically significant when p<0 . 05 . A bicistronic construct was created by cloning renilla luciferase ( Rluc ) gene under the CMV promoter and downstream of it a firefly luciferase ( Fluc ) gene under the cricket paralysis virus IRES ( CrPV-IRES ) into pECFP-C1 ( Clontech ) ; note CFP was deleted from pECFP-C1 prior to cloning . HeLa cells were transfected with this bicistronic reporter construct using polyethylenimine ( PEI , Polysciences Europe GmbH ) and expressed for 8 h in total . Prior to harvesting cells were exposed to 100 and 500 µM sodium arsenite for various times and harvested . Luciferase activities were measured using Dual-Luciferase® Reporter Assay System ( Promega ) . For isolation of non-charged tRNAs , HeLa cells were harvested by mechanical scrapping and total RNA was isolated with TriReagent ( Sigma-Aldrich ) according to the manufacturer's protocol . For isolation of charged tRNAs , total RNA was isolated under acidic conditions . Briefly , HeLa cells were re-suspended in 0 . 3 M NaOAc 10 mM EDTA pH 4 . 5 and extracted two times with acidic phenol . The aqueous phase , containing RNA , was precipitated with one volume isopropanol and washed with 80% ethanol . The total uncharged or charged tRNAs were separated on 10% PAGE gels at 4°C . Bands corresponding to the tRNAs were visualized by UV-shadowing , cut and eluted from the gel overnight at 4°C , for uncharged tRNAs in elution buffer ( 50 mM potassium acetate , 200 mM potassium chloride , pH 7 . 0 ) or for charged tRNAs in acidic elution buffer ( 0 . 3 M NaOAc , 10 mM EDTA pH 4 . 5 ) . 1 . 5 Mio . HeLa cells were treated for 30 min with 100 or 500 µM arsenite . Ten minutes prior to harvesting , cycloheximide ( CHX ) to a final concentration of 100 µg/ml was added to the medium . Cells were trypsinized ( trypsin solution was also supplemented with CHX ) and collected by centrifugation at 232×g for 5 min . The cell pellet was resuspended in 320 µl of ice-cold lysis buffer ( 10 mM Tris-HCl pH 7 . 4 , 5 mM MgCl2 , 100 mM KCl , 1% Triton-X , 100 µg/ml , 2 mM DTT ) and cells were sheared with a 26-gauge syringe . After pelleting of the debris at 5000×g for 8 min at 4°C , the supernatant was layered onto 15 to 50% ( w/v ) sucrose gradient ( 20 mM HEPES-KOH pH 7 . 4 , 5 mM MgCl2 , 100 mM KCl , 100 µg/ml CHX , 2 mM DTT ) and centrifuged for 1 . 5 h at 35 , 000 rpm in SW 55Ti rotor ( Beckman ) at 4°C . The gradient was slowly pumped out from the bottom of the tubes and A254 nm was recorded via a flow-through UV spectrophotometer cell ( Pharmacia LKB-UV-M II ) . One µg of the total mRNA from HeLa cells was treated with DNase I ( Fermentas ) , the cDNA was synthesized with reverse transcriptase using oligo-dT primer ( both Fermentas ) and quantified using the 2× Fast SYBR® Green Master Mix ( Applied Biosystems ) and the 7500 Fast Real-Time PCR system ( Applied Biosystems ) . The following primers were used for amplification of the bicistronic Fluc-Rluc construct: forward ( 5′-GCTGTTTCTGAGGAGCCTTC-3′ ) and reverse ( 5′-GCACTCTGATTGACAAATACGATT-3′ ) , and for CCA-adding enzyme: forward ( 5′-GATCGCAAAAGAGGAGAAAAAC-3′ ) and reverse ( 5′-GCATCAGGTTCCCTAGAATC-3′ ) . mRNA expression was normalized to β-actin . The degree of aminoacylation of isolated HeLa tRNAs was tested using the periodate protection assay described previously [39] . Total tRNA sample was treated with 50 mM sodium periodate , which oxidizes the 3′-ends of uncharged tRNAs , which prevents the ligation of the fluorescent stem-loop DNA/RNA oligonucleotide . tRNAs are resolved on denaturing 10% PAGE and the ligation efficiency serves as a measure for the levels of charged tRNAs . tRNA probes covering the full-length sequence of 42 cytosplasmic tRNA species with sequences described previously [22] were spotted onto amino-coated slides . The probes for each tRNA are arranged in clusters of six replicates . Radioactively labeled tRNA samples were mixed with 0 . 17 mg/ml salmon sperm DNA ( Invitrogen ) , 0 . 17 mg/ml polyA ( Sigma-Aldrich ) in hybridization buffer ( Sigma-Aldrich ) and hybridized on the microarrays for 16 h at 60°C . Subsequently , the microarrays were washed three times in 6×SSC at 35°C and once in 2×SSC and 0 . 2×SSC at 30°C . The composition of the 20×SSC buffer was as follow: 3 M sodium chloride , 300 mM sodium citrate , 0 . 1% SDS . Radioactivity was detected on a FUJI BAS scanner . Yeast tRNAPhe with intact CCA ends or tRNAPheCC were generated according to the procedure described in [40] . Radioactive , internally labeled transcripts were synthesized in the presence of 3 µCi 32P-α-ATP . Total HeLa tRNA was dephosphorylated with calf intestine alkaline phosphatase ( Roche ) for 30 min at 37°C . The enzyme was removed by phenol/chloroform extraction and the tRNA was precipitated . Radioactive phosphate was incorporated by T4 polynukleotide kinase ( USB ) and 32P-γ-ATP for 30 min at 37°C . Radioactively labeled RNA was separated on a denaturing 10% PAGE gel , tRNA bands were cut and eluted in the elution buffer ( 4 h , 25°C ) . Total HeLa tRNAs were deacylated for 45 min at 37°C in 0 . 1 M TrisHCl , pH 9 . 0 and dephosphorylated with T4 polynucleotide kinase ( USB ) in the absence of ATP . 3′-CMP was phosphorylated with 32P-γ-ATP ( 30 min , 37°C ) using PNK ( Fermentas ) and ligated to the RNA with T4 RNA ligase ( NEB ) by incubation over night at 16°C . Radioactively labeled RNAs were separated on a denaturing 10% PAGE gel , tRNA bands were cut and eluted in the elution buffer ( 4 h , 25°C ) . To prepare the tRNA for subsequent digestions , isolated tRNA was heated at 90°C for 2 min and cooled down at room temperature in 30 mM HEPES 30 mM sodium chloride , pH 7 . 0 for 3 min . MgCl2 and BSA were added to final concentrations of 2 mM and 0 . 01% , respectively , and further incubated for 5 min at 37°C . Recombinant human angiogenin ( R&D systems ) was added to a final concentration of 0 . 2 or 1 µM to the total HeLa or yeast tRNAPhe and incubated at 37°C for the indicated times . In the radioactive experiments , total non-labeled HeLa tRNA was spiked with radioactive 5′- or 3′-labeled tRNA . The reactions were stopped by extraction with phenol/chloroform or adding gel loading buffer ( 95% formamide , 0 . 025% SDS , 0 . 5 mM EDTA , 0 . 25% ( w/v ) bromophenolblue , 0 . 25% ( w/v ) xylene cyanol ) and shock freezing in liquid nitrogen . To test the integrity of the 3′-CCA ends , a fluorescent stem-loop RNA/DNA oligonucleotide , with a sequence described previously [22] , was ligated over night at 16°C with T4 DNA ligase ( NEB ) . Full-length tRNA and tiRNAs were separated on a denaturing 10% PAGE gel . Human CCA-adding enzyme was purified as described [41] . Total HeLa tRNA and 3′-radioactive labeled yeast tRNAPhe ( 0 . 5 µM ) were treated with 0 . 2 µM angiogenin at 37°C for 4 h , dephosphorylated with PNK to convert the 2′ , 3′-cyclophosphate generated by angiogenin to 3′-OH [40] and subsequently treated with 50 nM human CCA-adding enzyme at 30°C for 30 min in 20 mM HEPES pH 7 . 6 , containing 20 mM KCl , 6 mM MgCl2 , 2 mM DTT and 1 mM NTPs . Oxidative stress was exerted by adding 500 µM sodium arsenite ( Fluka ) to confluent HeLa cells for indicated times . RNA was isolated using mirVana miRNA Isolation kit ( Ambion ) and subsequently deacylated in 0 . 1 M Tris . HCl buffer , pH 9 . 0 at 37°C for 30 min . Fluorescent stem-loop RNA/DNA oligonucleotide [22] ( Figure 1B , schematic inset ) was ligated over night at 16°C with T4 DNA ligase ( NEB ) . Ligation efficiency was analyzed by resolving the samples on denaturing 10% PAGE and detected by fluorescence ( Fujifilm LAS-4000 ) or SYBR Green ( Invitrogen ) staining . | Adequate reprogramming of metabolic activities by environmental stress or suboptimal growth conditions is crucial for cell survival . Cells employ a remarkable diversity of processes to maintain its homeostasis at all levels of gene expression , including chromatin remodeling , mRNA expression and degradation , translation and protein degradation . Each of these processes shapes cell response at different time scales . In this study , we analyzed the cellular response to oxidative stress at the level of translation . Translation , as one of the most downstream processes in gene expression , provides the necessary plasticity for immediate changes of cellular activities . Using high-sensitive approaches to probe the structural integrity of cellular tRNAs , we show that upon exposure to oxidative stress tRNAs are rapidly deactivated by a cleavage within their ubiquitous , single-stranded 3′-CCA termini by oxidative stress-activated nuclease , angiogenin . The CCA-ends deactivation is reversible and quickly repairable by a ubiquitous enzyme , CCA-adding enzyme , whose natural function is to attach post-transcriptionally the CCA overhang to the 3′-termini of all tRNAs in an mRNA template-independent manner . We propose that this is a mechanism to dynamically repress and reactivate translation at low metabolic costs . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Reversible and Rapid Transfer-RNA Deactivation as a Mechanism of Translational Repression in Stress |
Poxviruses encode a large variety of proteins that mimic , block or enhance host cell signaling pathways on their own benefit . It has been reported that mitogen-activated protein kinases ( MAPKs ) are specifically upregulated during vaccinia virus ( VACV ) infection . Here , we have evaluated the role of the MAPK negative regulator dual specificity phosphatase 1 ( DUSP1 ) in the infection of VACV . We demonstrated that DUSP1 expression is enhanced upon infection with the replicative WR virus and with the attenuated VACV viruses MVA and NYVAC . This upregulation is dependent on early viral gene expression . In the absence of DUSP1 in cultured cells , there is an increased activation of its molecular targets JNK and ERK and an enhanced WR replication . Moreover , DUSP1 knock-out ( KO ) mice are more susceptible to WR infection as a result of enhanced virus replication in the lungs . Significantly , MVA , which is known to produce non-permissive infections in most mammalian cell lines , is able to grow in DUSP1 KO immortalized murine embryo fibroblasts ( MEFs ) . By confocal and electron microscopy assays , we showed that in the absence of DUSP1 MVA morphogenesis is similar as in permissive cell lines and demonstrated that DUSP1 is involved at the stage of transition between IVN and MV in VACV morphogenesis . In addition , we have observed that the secretion of pro-inflammatory cytokines at early times post-infection in KO mice infected with MVA and NYVAC is increased and that the adaptive immune response is enhanced in comparison with WT-infected mice . Altogether , these findings reveal that DUSP1 is involved in the replication and host range of VACV and in the regulation of host immune responses through the modulation of MAPKs . Thus , in this study we demonstrate that DUSP1 is actively involved in the antiviral host defense mechanism against a poxvirus infection .
Poxviruses have evolved to efficiently counteract host immune responses through the modulation of extracellular and intracellular environment of the infected cell . Thereby , the ability of a poxvirus to produce a permissive infection in a specific cell type depends on the antiviral mechanisms that the virus is able to block once it infects the cell . VACV is the prototype member of the Poxvirus family , whose large genome of about 200 Kbp encodes a wide array of proteins involved in the control of apoptosis , differentiation , host range , host immune responses and stress-induced signaling pathways [1] , [2] , [3] , [4] , [5] , [6] , [7] . Protein phosphorylation is a conserved mechanism by which the activity of numerous proteins involved in different biological processes is switched on or off [8] . Transient activation of extracellular-regulated kinase ( ERK ) and jun-kinase ( JNK ) MAPKs is an essential step in the pathways involved in cellular survival . Therefore , several viruses have evolved to develop strategies that consist of the modulation of these proteins [9] , [10] , [11] . A specific modulation of cell signaling pathways by VACV is crucial to achieve a productive viral outcome and an efficient viral spread [12] , [13] . Hence , VACV not only encodes its own kinases and phosphatases [14] , [15] , [16] but it is also able to benefit from the activity of cellular proteins such as the ones belonging to the family of MAPKs which consists of p38MAPK , ERK and JNK . For instance , it has been reported that VACV requires ERK activation for an efficient viral replication [17] . MAPKs are involved in important cellular pathways by regulating both physiological and pathological responses that include proliferation , differentiation , stress responses , inflammation , growth arrest and apoptosis [18] , [19] , [20] . Since MAPKs suffer a context-dependent regulation , a tight control of the magnitude and duration of MAPK activation as well as their subcellular localization are essential for a balanced MAPK signaling [21] . Therefore , as MAPKs are activated by phosphorylation by upstream MAPKKs [22] , the cell encodes phosphatases that specifically bind to MAPKs and inactivate them by removing the phosphates on the Thr-Xaa-Tyr motif . Among these phosphatases , DUSPs provide an important negative feedback mechanism for MAPK activation [23] . The family of DUSPs shares a common kinase interaction motif ( KIM ) located at the N-terminal region and a catalytic domain at the C-terminal region . DUSP1 , as the archetypal member of the family , has been extensively studied . It is encoded by an immediate-early gene that was first discovered in 1985 as one of the genes expressed in cultured murine cells during the G0/G1 transition [24] . Since DUSP1 knock-out ( KO ) mice were healthy and presented no obvious phenotype related to MAPK activity [25] , it was not until the last decade that DUSP1 grew in importance due to the novel phenotype of DUSP1 KO mice found after treatment with corticoids . DUSP1 is ubiquitously expressed in the organism and it is known to bind p38MAPK , JNK or ERK depending on the cell status [26] , [27] . DUSP1 expression is strongly upregulated upon several stimuli such as oxidative stress , hypoxia , growth factors , glucocorticoids , heat shock and UV [28] , [29] , [30] , [31] , [32] . DUSP1 is tightly regulated at transcriptional , translational and post-translational levels . Some well-characterized examples are the phosphorylation of Serines 359 and 364 , which enhances the half-life of DUSP1 [33] or the acetylation of DUSP1 that mediates important events on the host inflammatory response [34] , [35] . As an important MAPK regulator , DUSP1 is involved in a wide variety of cellular processes such as obesity , transplantation , cancer , depressive behavior and inflammation [36] , [37] , [38] , [39] , [40] . By microarray analysis of VACV-infected cells , we [41] , [42] and others [43] , [44] have identified DUSP1 as one of the genes specifically induced by VACV infection . Although several reports have highlighted the importance of DUSP1 in parasite and bacterial diseases [45] , [46] , [47] , there is limited information on the involvement of DUSP1 in viral infections . The present study is focused on the role of DUSP1 in VACV infection and more specifically in the replication of wild-type ( WT ) VACV Western Reserve ( WR ) and VACV attenuated viruses Modified Vaccinia virus Ankara ( MVA ) and NYVAC . We have addressed this issue both in vitro , by using cultured DUSP1 WT and KO cells , siRNA technology and nucleofection , and in vivo through a DUSP1 KO mouse model . We showed that replication of WR and MVA is enhanced in the absence of DUSP1 and that , interestingly , DUSP1 is a key factor during MVA morphogenesis in murine cells . Moreover , we demonstrated the influence of DUSP1 in host innate and adaptive immune responses elicited by VACV . Overall , the present study reveals a novel role for DUSP1 in VACV infection and provides new insights into the still not fully understood MVA host restriction process .
Animal experimental protocols were approved by the Ethical Committee of Animal Experimentation ( CEEA-CNB ) of Centro Nacional de Biotecnologia ( CNB-CSIC , Madrid , Spain ) in strict accordance with Spanish national Royal Decree ( RD 1201/2005 ) and international EU guidelines 2010/63/UE about protection of animals used for experimentation and other scientific purposes and Spanish national law 32/2007 about animal welfare in their exploitation , transport and sacrifice and also in accordance with the Royal Decree ( RD 1201/2005 ) . Permit numbers: 10-018 and 10-023 . African green monkey kidney cells ( BSC40 ) from ATCC were grown in Dulbecco's Modified Eagle's medium ( DMEM ) with penicillin ( 100 U/ml , Invitrogen ) , streptomycin ( 100 µg/ml , Invitrogen ) , L-glutamine ( 2 mM; Merck ) and supplemented with 10% newborn calf serum ( NCS; Sigma ) . Human HeLa cells ( human cervix adenocarcinoma cell line ) , baby hamster kidney cells ( BHK-21 ) , DF-1 ( a spontaneously immortalized chicken embryo fibroblast cell line ) , all from ATCC , and DUSP1 wild-type and knock-out murine embryonic fibroblasts were cultured in complete DMEM supplemented with 10% fetal calf serum ( FCS; Sigma ) . Cells were maintained in a humidified air-5% CO2 atmosphere at 37°C or 39°C ( for DF-1 cell line ) . VACV wild-type Western Reserve ( WR ) was grown in monkey BSC40 cells and purified by sucrose gradient banding . NYVAC ( kindly provided by Sanofi-Pasteur ) and MVA ( kindly provided by G . Sutter ) were grown in BSC40 cells and in BHK-21 cells , respectively , and purified by two subsequent 36% sucrose ( w/v ) cushions . After all infections , complete DMEM supplemented with 2% NCS or FCS was added to the cultured cells . WR , MVA and NYVAC were titrated in DF-1 cells by immunostaining plaque assay as previously described [48] . Where indicated , purified WR was inactivated by ultraviolet light during 20 min [49] . HeLa cells were mock-infected or infected with WR , MVA or NYVAC at 5 PFU/cell . Where indicated , cells were infected in the presence of Cycloheximide ( CHX ) at a final concentration of 100 µg/µl . Total RNA was isolated using RNeasy Mini kit ( Qiagen ) following manufacturer's instructions . Total RNA ( 1 µg ) was digested with DNases to avoid genomic DNA contamination . Reverse-transcription PCR ( RT-PCR ) was performed using Superscript first-strand synthesis system ( Invitrogen ) . 50 ng of cDNA was assayed for human DUSP1 expression using specific Taqman probes ( Applied Byiosystems ) and TaqMan Universal PCR MasterMix ( No AmpErase UNG ) . Duplicates were assayed for each sample . Data were acquired with an ABI PRISM 7000 sequence detection system and analyzed with ABI PRISM 7000 SDS version 1 . 2 . 3 software ( Applied Biosystems ) . Cells were infected with WR , MVA or NYVAC at 5 PFU/cell , harvested at different times post-infection and protein extracts obtained using lysis buffer following manufacturer's recommendations ( Cell lysis Buffer , Cell Signaling ) . Equal amounts of protein ( 50 µg ) were fractionated by 10% SDS-polyacrylamide gel electrophoresis ( SDS-PAGE ) and then transferred to nitrocellulose membrane ( Bio-Rad Laboratories ) in a wet blotting apparatus ( Bio-Rad ) for 50 min . Membranes were blocked with TBS-Tween 20 0 , 1% BSA 5% and then incubated with specific antibodies against viral proteins such as E3 ( kindly provided by B . Jacobs ) , A10 and L4R ( kindly provided by D . Hruby ) , A14 ( CNB , CSIC ) , A17 ( CNB , CSIC ) , A17-N ( kindly provided by J . Locker ) and A17-C ( CNB , CSIC ) or cellular proteins such as α-tubulin , P-DUSP1 , P-ERK1/2 and P-JNK ( Cell Signaling ) or total DUSP1 ( Santa Cruz Biotechnology ) , ERK1/2 or JNK ( Cell Signaling ) . The anti-rabbit-HRPO ( Sigma ) was used as secondary antibody and the immunocomplexes were detected by enhanced chemiluminescence ( ECL , GE Healthcare ) . The viral DNA synthesis inhibitor Arabinofuranosyl Cytidine ( Ara-C ) and protein synthesis inhibitor CHX from ( SIGMA ) were added to cell culture media at 40 µg/ml and 100 µg/ml , respectively . To determine viral growth profiles , duplicate samples of monolayers of MEFs grown in 12-well tissue culture plates were infected at 0 . 01 PFU/cell with WR , MVA or NYVAC . At 0 , 24 and 48 hours post-infection , samples were collected and titrated as described previously [6] . Specific MAPKs inhibitors , ERK inhibitor UO126 ( Cell Signaling ) and p38MAPK inhibitor SB203580 ( Calbiochem ) , were added to the cell culture at a concentration of 10 or 50 µM and 5 µM , respectively , for 30 min or 1–8 h ( UO126 ) or 30 min ( SB203580 ) before the infection . Alternatively to KO cells , DUSP1 expression was suppressed with interference RNAs . Duplicate samples of subconfluent monolayers of HeLa cells grown in 12-well tissue culture plates were treated with DUSP1 specific siRNAs ( siGENOME SMARTpool , Dharmacon; target sequence 1: CCAUUGUCCCAACCAUUU; target sequence 2: CAACGAGGCCAUUGACUUC; target sequence 3: CCACCACCGUGUUCAACUUÇ; target sequence 4: GCAUAACUGCCUUGAUCAA ) or Scramble siRNA ( Applied Biosystems ) as a control , at a final concentration of 50 nM using Lipofectamine 2000 ( Invitrogen ) . After 48 hours of incubation with the siRNAs , cells were infected with WR or MVA at 0 . 1 PFU/cell . At 0 , 24 and 48 hours post-infection cells in medium were collected and virus titrated as previously described [6] . DUSP1 KO MEFs were nucleofected using 4D-Nucleofector ( Lonza ) and Amaxa P3 Primary Cell kit ( Lonza ) following manufacturer's instructions . Briefly , 2×106 cells were resuspended in 100 µl P3 Buffer Mix and nucleofected with 6 µg of pSG5-DUSP1 plasmid ( kindly provided by E . Perdiguero ) or pSG5-empty plasmid as a control . After 24 hours of incubation , MEFs were infected with WR or MVA at 0 . 1 PFU/cell and viral titers were determined at 0 , 24 and 48 hpi by immunoplaque assay in DF-1 cells . MEFs were infected with WR , MVA or NYVAC at 5 PFU/cell and at 5 . 5 or 16 hpi . Cells were fixed with 4% paraformaldehyde ( PFA ) and permeabilized with 0 . 05% saponin in 2% FCS-supplemented phosphate-buffered saline ( PBS ) . Specific antibodies against viral proteins A27 ( 1∶2000 ) , A17 and A14 ( 1∶1 . 000 ) were used ( CNB ) . Bound primary antibodies were detected with AlexaFluor-488 or −594 conjugated antibodies specific for mouse or rabbit ( Invitrogen 1∶500 ) . β-Actin was detected with a TRITC-conjugated probe anti-Phalloidin ( Sigma , 1∶500 ) . Cell nucleus was stained with 4′ , 6-diamino-2-phenylindole ( DAPI; Sigma , 1∶200 ) . To block DUSP1 in WT cells , siRNA treatment was performed on subconfluent monolayers of MEFs using Lipofectamine 2000 ( Invitrogen ) as transfection agent and a mix of two DUSP1 specific siRNAs ( Applied Biosystems ) at a final concentration of 100 nM . After 24 hours of incubation , cells were infected with VACV and specific viral proteins were detected by immunofluorescence and confocal microscopy as described above . Confluent monolayers of MEFs were mock-infected or infected with MVA at 5 PFU/cell and at 16 hpi cells were fixed for 2 hours with 4% PFA+2% glutaraldehyde in NaH2PO4/Na2HPO4 0 . 1M pH 7 . 4 buffer . Samples were then washed ( 3 times , 10 min each ) with NaH2PO4 pH 7 . 4 buffer , scrapped and harvested in 1 . 5 ml of the same buffer . Fixed samples were processed by conventional embedding in the epoxi-resin EM-812 ( Taab Laboratories , Adermaston , Berkshire , UK ) . Ultrathin sections of 70 nm thick were collected on formvar-coated parallel-bar copper grids and analyzed with a JEOL 1011 transmission electron microscope . Quantitative analysis of viral forms by electron microscopy was performed analyzing 30 infected cells per cell line ( DUSP1 WT and KO cells ) . The results represent the percentage of each viral intermediate in the total of viral particles quantified . DUSP1 KO mice were generated by Jackson laboratories using embryos with a 129S2/SvPas genetic background . Jackson Labs provided Dr . Caelles lab with the resulting mice and they were backcrossed in a C57BL/6 background for at least 11 mice generations . MEFs from wild type and KO mice were generated independently from mouse embryos by Caelle's and Esteban's labs following standard procedures . Groups of 8–10 week-old DUSP1 WT or KO mice were intranasally ( i . n . , n = 5 , 20 µl inoculum ) , intraperitoneally ( i . p . , n = 3–4 , 200 µl inoculum ) inoculated or skin scarified ( s . s . , n = 3–4 , 10 µl inoculum ) with different challenge doses of WR ( 5×105–5×106 PFU/mouse ) , MVA or NYVAC ( 5×106–2×107 PFU/mouse ) . Representative examples of three independent experiments are shown in the figures . Weight loss , mortality and signs of illness , such as reduced mobility and ruffled fur , were daily evaluated using a 0–3 scale ( healthy-ill ) . For in vivo viral replication assays , mice were sacrificed at various times post-inoculation and spleen , liver , ovaries ( i . p . ) , lungs ( i . n . ) or tail tissue ( s . s . ) were excised , washed with sterile PBS and stored at −70°C . Tissue extracts were obtained by homogenizing representative sections of each tissue . Homogenized samples were subjected to three cycles of freezing and thawing , sonicated and centrifuged ( 10 min/2000 rpm ) ; supernatants were collected and titrated by immunostaining plaque assay . Serum was obtained by submaximal vein bleedings 3 or 24 hours post-inoculation and was allowed to clot 1 hour at 37°C . After a 4°C overnight incubation , they were spun down in a microcentrifuge , serum was collected , aliquots were prepared , pooled and stored at −20°C . Sera from mock-infected or WR , MVA or NYVAC-infected mice were assayed for detection of IL-6 , TNF-α , IL-1β and IL-10 using LUMINEX technology according to the manufacturer's instructions . All samples were kept at −20°C until use . Recombinant viruses WR-Luc , MVA-Luc and NYVAC-Luc expressing luciferase have been previously described [50] , [51] . Samples from mice i . p . inoculated with these viruses at 2×107 PFU/mouse were homogenized in 200–400 µl of luciferase assay reagent ( Promega ) per sample . Clarified supernatants were used to measure luciferase activity in the presence of luciferine and ATP according to the manufacturer's instructions , using Lumit LB 9501 luminometer ( Berthold ) . The luciferase activity is represented as luciferase units per miligram of protein ( U/mg ) . The specific VACV-induced T cell response was analyzed by ICS and flow cytometry fluorescence-activated cell sorting analysis ( FACS ) . After isolation of splenocytes from infected mice , samples were resuspended in RPMI 1640 supplemented with 10% FCS and containing 1 µg/ml Golgiplug ( BD Biosciences ) to inhibit cytokine secretion . Cells were stimulated with E3 peptide ( sequence: VGPSNSPTF , CNB , CSIC ) added to the cells at a final concentration of 5 µg/ml . After 6 hours of incubation at 37°C and 5% CO2 , cells were washed , fixed , permeabilized , using the BD Cytofix/Cytoperm Kit ( Becton Dickinson ) , and stained intracellularly using the appropriate fluorochrome-conjugated antibodies . To analyze the adaptive immune responses , the following fluorochrome-conjugated antibodies were used: CD3-FITC or -PerCP , CD4-Alexa 700 or –APCCy7 , CD8-PerCP or –V500 , IL-2-PE or –PE-Cy7 , IFN-γ-APC or –PE-Cy7 and TNF-α-PE-Cy7 or -PE . All antibodies were from BD Biosciences . Cells were acquired using an LSRII flow cytometer ( Becton Dickinson ) . Dead cells were excluded using the violet LIVE/DEAD stain kit ( Invitrogen ) . Lymphocytes were gated on a forward scatter area versus side scatter area pseudo-color dot plot . To analyze the adaptive immune responses , CD4+ and CD8+ T cells ( previously gated on CD3+ cells ) were gated versus IFN-γ , TNF-α and IL-2 and then combined together using the boolean operator . Sample analysis was performed using FlowJo version 8 . 5 . 3 ( Tree Star , Ashland , OR ) . For the statistical analysis of the viral growth and the differences between groups of mice a Student T test was performed . Statistical significance is represented as p<0 . 05 ( * ) , p<0 . 005 ( ** ) or p<0 . 001 ( *** ) . For the statistical analysis of ICS data , we used a novel approach that corrects measurements for the medium response ( RPMI ) and calculates confidence intervals and p values of hypothesis tests [52] , [53] . Only antigen responses values significantly higher than the corresponding RPMI are represented and the background for the different cytokines in the unstimulated controls never exceeded 0 . 05% . We used the data analysis program Simplified Presentation of Incredibly Complex Evaluations ( SPICE , version 4 . 1 . 5 , Mario Roederer , Vaccine Research Center , NIAID , NIH ) to analyze and generate graphical representations of T cell responses detected by polychromatic flow cytometry . All values used for analyzing proportionate representation of responses are background-subtracted . - Tubulin: human , P68366 , UniprotKB . - DUSP1: human , P28562 , UniprotKB . - DUSP1: mouse , P2563 , UniprotKB . - ERK1: mouse , Q63844 , UniprotKB . - ERK2: mouse , P63085 , UniprotKB . - JNK1: mouse , Q01Y86 , UniprotKB . - JNK2: mouse , Q9WTU6 , UniprotKB . - p38MAPK: mouse , P47811 , UniprotKB . - IFN-γ: mouse , P01580 , UniprotKB . - IL2: mouse , P04351 , UniprotKB . - TNF-α: mouse , P06804 , UniprotKB . - IL6: mouse , P08505 , UniprotKB . - IL1β: mouse , P10749 , UniprotKB . - IL10: mouse , P18893 , UniprotB .
We and others have previously described using microarray technology that DUSP1 mRNA transcription is specifically upregulated during VACV infection [42] , [43] , [44] , [42] . To validate these previous observations , we carried out RT-PCR and Western-blot analyses from HeLa cells infected with the virulent WR strain or with the attenuated VACV mutants MVA and NYVAC . As shown by RT-PCR , in HeLa cells infected with WR , MVA or NYVAC , there was an increase in DUSP1 mRNA levels at the different times post-infection analyzed ( Fig . 1A ) . The induction was more pronounced at 6 hpi , especially for NYVAC infection . The increase in DUSP1 protein expression levels following VACV infection was confirmed by Western-blot analysis ( Fig . 1B ) . DUSP1 protein was upregulated during the infection with each one of the three viruses used and this increase peaked between 4 and 6 hpi . The highest levels of DUSP1 induction were observed upon NYVAC infection where an increase in protein expression was still detected at 8 hpi . This upregulation was VACV-specific since in uninfected control cells DUSP1 protein level was below the limit of WB detection . To determine if VACV not only triggers enhanced protein synthesis but also affects protein stability , we used an antibody that recognizes specifically phosphorylated serines 359 and 364 , which are the residues responsible for DUSP1 stabilization [33] . We observed phosphorylation of DUSP1 after the infection with each one of the three viruses used . The highest phosphorylation levels of DUSP1 were observed at 4 hpi ( Fig . 1B ) . The findings of Fig . 1 showed that DUSP1 mRNA transcription and DUSP1 protein expression as well as DUSP1 phosphorylation were specifically modulated by VACV infection in HeLa cells . To define the role of viral proteins on DUSP1 induction , we treated HeLa cells with a UV-inactivated WR virus which is unable to synthesize viral RNA or protein or with an inhibitor of viral DNA synthesis , Arabinofuranosyl Cytidine ( Ara-C ) . In the presence of Ara-C ( Fig . 2A , central panel ) , early VACV proteins , such as E3 ( p25 ) , were expressed , however , late viral proteins such as A17 ( p17 ) were not produced . In Ara-C-treated samples , DUSP1 expression showed the same kinetics as observed in WR-infected cells ( Fig . 2A , left panel ) , whereas DUSP1 induction was not observed in UV-inactivated WR-infected cells ( Fig . 2A , right panel ) nor with a 55°C inactivated WR ( not shown ) . These findings reveal that early but not late viral gene expression is required for VACV-mediated DUSP1 protein upregulation . To define which viral component is responsible for the induction of DUSP1 mRNA , we infected HeLa cells with WR in the presence of CHX , which allows viral mRNA but not protein synthesis , and determined the levels of DUSP1 mRNA by quantitative RT-PCR . As shown in Fig . 2B ( left panel ) , in the presence of CHX there was an increase of DUSP1 mRNA levels in comparison with untreated infected samples , indicating that DUSP1 mRNA expression is dependent on viral RNA synthesis . In order to analyze whether this result could also be observed with an attenuated VACV and to test another cell culture model , we infected DUSP1 WT MEFs with MVA and analyzed DUSP1 mRNA . Fig . 2B ( right panel ) shows for MVA in MEFs similar kinetics for DUSP1 mRNA to HeLa cells . In order to determine whether during VACV infection DUSP1 stabilization is mediated by ERK phosphorylation or other cellular kinase or if a viral-encoded kinase is involved , we used an ERK inhibitor ( UO126 ) . ERK inhibitor was added to the culture medium 30 min before infection and was present in the cell culture at all times during infection . In the presence of ERK inhibitor ( Fig . 2C ) , ERK phosphorylation and , consequently ERK activation , was completely abrogated and we could not detect DUSP1 phosphorylation . The findings of Figure 2 reveal that induction of DUSP1 mRNA and protein required viral mRNA synthesis and early viral gene expression . Moreover , DUSP1 phosphorylation during VACV infection is mediated by ERK . DUSP1 plays an important role in the negative regulation of MAPKs upon different stimuli , including bacteria and parasite infections [54] . Nevertheless , little is known about the function of DUSP1 in viral infections . To assess this issue , we decided to determine the effect of the absence of DUSP1 on the replication of WR , MVA or NYVAC . We observed a significant increase ( 24 hpi: p<0 . 05; 48 and 72 hpi: p<0 . 005 ) in WR replication in KO MEFs in comparison with WT MEFs ( Fig . 3A ) . Surprisingly , when we infected the cells with MVA , which also cannot replicate in most mammalian cell lines , we detected higher viral titers in KO MEFs in comparison with the WT MEFs; this significant difference ( 24 and 48 hpi: p<0 . 05; 72 hpi: p<0 . 005 ) in replication of about two logs higher was evident at 24 hpi and increased with time ( Fig . 3B ) . Productive MVA infection required DUSP1 KO MEF immortalization process , as MVA produced abortive infections in primary DUSP1 KO MEFs obtained from DUSP1 KO mice ( data not shown ) . However , we could not detect NYVAC replication in KO nor in WT MEFs ( Fig . 3C ) , which was consistent with the fact that NYVAC is unable to replicate in murine cell lines . In order to further confirm these results , we treated HeLa cells , which is a permissive cell line for WR but host-restricted to MVA replication , with specific siRNA to suppress DUSP1 expression . As shown in Fig . 4A , DUSP1 mRNA expression was efficiently suppressed ( lower panel ) and viral titers of both WR and MVA were significantly increased ( upper and middle panels ) ( WR , 24 hpi: p<0 . 05; MVA , 48 hpi: p<0 . 001 ) in HeLa cells treated with DUSP1 siRNAs compared to cells treated with a scramble siRNA . This phenotype mimics what it was that previously observed in DUSP1 KO cells infected with either WR or MVA ( Fig . 3 ) . In addition , to further prove the role of DUSP1 in VACV replication , we nucleofected a DNA vector expressing DUSP1 gene into DUSP1 KO MEFS , then cells were infected with WR or MVA and viral titers were determined at 0 , 24 and 48 hpi . As shown in Fig . 4B , DUSP1 expression was successfully restored into KO MEFs ( WB in lower panel ) and viral titers were significantly ( MVA , 48 hpi: p<0 . 05 ) reduced in cells infected with MVA expressing DUSP1 ( middle panel ) in comparison with control cells . This effect was not observed in the case of WR infection ( upper panel ) . The results of Fig . 4 establish that DUSP1 plays a role in the replication of VACV , and this effect is more prevalent in the case of the MVA strain . To further characterize the role of DUSP1 in MVA replication in MEFs , we performed a viral growth assay to evaluate the levels of cell-associated virus and released virus from infected cells . The titration of cell-associated virus showed a difference of over 2 logs in MVA production between KO and WT cells ( Fig . 5A ) . This difference was even more evident in the case of released virus since we could not detect the presence of virus in the cell supernatants of WT-infected MEFs . Moreover , when we performed immunoplaque assay of MVA-infected WT or KO MEFs , we could only observe virus plaques in the absence of DUSP1 at any time point analyzed ( Fig . 5B ) . There were differences in the cytopathic effect ( CPE ) produced by MVA infection between WT and KO cells ( Fig . 5C ) , with a more pronounced CPE in KO MEFs ( lower panels ) compared to WT MEFs ( upper panels ) . Altogether , these results reveal a distinct behavior of MVA in KO MEFS in comparison with WT MEFs indicating that the host restriction of MVA replication in murine cells is overcome in the absence of DUSP1 . Considering our previous data showing that MVA was able to complete its viral cycle in MEFs in the absence of DUSP1 , we next wanted to determine which of the events blocked during MVA morphogenesis is overcome by the action of DUSP1 . To address this issue , we studied early , intermediate and late events during MVA morphogenetic process in the absence of DUSP1 . First , we analyzed by immunofluorescence and confocal microscopy the expression of the viral membrane proteins A14 ( p16 ) and A17 ( p17 ) , which play key roles during the formation of crescents [55] , [56] . In DUSP1 WT and KO MEFs infected with WR , MVA or NYVAC at 5 PFU/cell ( Fig . 6A ) , we did not observe differences in the expression pattern of A14 or A17 between WT and KO MEFs . Black panels in Fig . 6A showed a punctuated pattern of both A14 and A17 in MVA-infected cells that localized with viral factories . Since the pattern of localization of A14 and A17 was similar in WT and KO-infected cells , we can suggest that this step of viral morphogenesis is not dependent on DUSP1 . During VACV morphogenesis , the processing of viral membrane and core proteins is required prior to formation of the mature virion . Some of the proteins that need proteolytic cleavage during infection are A10 ( p4a ) , L4 ( p25K ) and A17 ( p17 ) . In order to analyze this event , we infected WT and KO cells with WR , MVA or NYVAC and determined the expression of these three viral proteins at 2 , 6 , 16 and 24 hours post-infection by Western-blot analysis using antibodies that recognize the processed forms of the proteins: 4a , 25K , A17-C ( processed C-terminus ) and A17-N ( unprocessed N-terminus ) ( Fig . 6B ) . In the case of WR and MVA , processed forms of these proteins were found in both WT and KO-infected cells . However , as observed in the right panel , we could not detect any of these proteins in WT or KO cells infected with NYVAC , consistently with previous reports demonstrating that NYVAC does not produce certain late viral proteins in non-permissive cells lines [6] . These findings reveal that DUSP1 is not involved in proteolytic processing of viral proteins during the maturation of MVA progeny particles . To examine a later event in morphogenesis , we analyzed by immunofluorescence and confocal microscopy the distribution of the late viral membrane protein A27 ( p14 ) . As shown in Fig . 6C , in WT cells infected with MVA , a clear diffuse pattern of expression of A27 across the cytoplasm of the infected cell was observed indicating that A27 was expressed but it did not appear to be incorporated into virions . In contrast , in MVA-infected DUSP1 KO MEFs ( Fig . 6C , upper right panel ) , A27 showed both a diffuse and a punctuated pattern ( right-lower magnification ) characteristic of the labeling of mature virions . Similar punctuated pattern of A27 was observed when DUSP1 expression was blocked in WT MEFs with siRNAs and infected with MVA ( data not shown ) . These results suggest that DUSP1 might be involved in events that occur before the formation of mature virions . In order to identify the step of MVA morphogenesis affected by DUSP1 , we performed electron microscopy analysis of DUSP1 WT and KO MEFs infected with MVA for 16 h at 5 PFU/cell . Figures 7A–C showed the most characteristic viral forms present in MVA-infected WT cells represented by small and medium viroplasm foci ( V ) surrounded by nascent crescents ( C ) , immature virus ( IV ) and immature virus with nucleoid ( IVN ) . In addition , there were also several transition forms from IVN to mature virus ( MV ) as electro-dense particles being wrapped by a membrane ( Fig . 7D ) . These occasional intermediate forms have also been described during infection of HeLa cells with MVA [57] . Aberrant viral forms with multiple wrappings ( onion-like particles , Fig . 7E ) were also observed as well as virions with impaired nucleoid formation ( Fig . 7F ) and MV-like particles without a proper core condensation ( Fig . 7G ) . In the case of DUSP1 KO cells infected with MVA , we detected all the classical viral forms , from viroplasm foci ( V ) with crescents , IV , IVN , MV , wrapped virus ( WV ) and enveloped virus ( EV ) ( Figs . 7H–O ) . Groups of MV within a membrane were also observed ( Fig . 7L ) . To further characterize MVA outcome in WT and KO infected cells , we quantified the percentages of the different viral forms by counting sections of 30 cells from each cell line ( Fig . 7P ) . In MVA-infected WT cells , IV ( 80% ) and IVN ( 16% ) were the most abundant viral forms . The percentages of the different viral forms detected in MVA-infected DUSP1 KO cells were remarkably different with 59% of IV and almost the same amount ( 14% ) of IVN compared to WT-infected cells , whereas MV accounted for 20% , a percentage equivalent to VACV outcome in a permissive cell line [56] . In addition , we were able to detect the presence of WV ( 4% ) and EV ( 2% ) . The analysis by electron microscopy clearly showed that in DUSP1 KO cells , MVA is able to complete the morphogenesis process suggesting that DUSP1 is mainly acting at the stage of transition between IVN and MV . Since DUSP1 is an important MAPK regulator and VACV infection is able to trigger or block specific molecules of this pathway such as ERK , p38MAPK and JNK [58] , [59] , [60] , we next determined the effect of the absence of DUSP1 in the activation of these kinases during VACV infection . To address this issue , we infected DUSP1 WT and KO MEFs with WR , MVA or NYVAC at 5 PFU/cell and analyzed the expression of these proteins by Western-blot at 0 . 25 , 0 . 5 , 1 , 2 , 4 , 6 and 8 hours post-infection . As shown in Fig . 8A , the three viruses triggered phosphorylation of ERK as early as 15 min post-infection . In the case of WT-infected MEFs ( left columns ) , a modest phosphorylation of ERK was observed , while this activation was dramatically enhanced in the absence of DUSP1 ( right columns ) . In KO-infected MEFs , ERK phosphorylation peaked between 2 and 4 hpi and this activation was maintained along the time course analyzed . We also observed that VACV infection preferentially phosphorylated ERK-2 in MEFs . A similar pattern of activation was observed in the case of JNK , although in this case we could not detect activation at early times post-infection . In KO MEFs infected with WR , MVA or NYVAC , JNK phosphorylation was markedly enhanced after 2 hours post-infection , while low levels of phosphorylation were detected in WT MEFs infected with any virus . Altered activation of MAPKs during viral infection in the absence of DUSP1 might be the cause of the differences observed in VACV host range and replication . In order to analyze the specific contribution of each MAPK in the replication of MVA in DUSP1 KO-infected MEFs , we used MAPK inhibitors UO126 and SB203580 to block ERK and p38MAPK , respectively . The JNK inhibitor SP600125 could not be used because it has been recently described to exert anti-poxviral effects independent of its JNK inhibitory activity [61] . The MAPKs inhibitors were added to DUSP1 KO cells , previously infected with MVA , at 0 , 1 or 3 hours post-infection and 24 hours later viral titers were determined . We observed about 2 to 5-fold decrease in MVA replication in DUSP1 KO-infected cells treated with the inhibitors in comparison with mock-treated KO-infected cells ( Fig . 8B ) . From these findings , we conclude that activation of MAPKs by virus infection contributes to the replication of MVA in DUSP1 KO cells . The role of DUSP1 in bacterial and parasite diseases and clearance by the host has been well described [53] . However , little information has been reported regarding the involvement of DUSP1 in viral infections . To determine the function of DUSP1 during VACV infection , we inoculated DUSP1 WT and KO mice intranasally ( i . n ) with either 5×105 or 5×106 PFU/mouse of WR and monitored mice for different signs of disease like weight loss , illness ( reduced mobility and ruffled fur ) and mortality . DUSP1 KO mice experienced a significant ( higher dose , day 3: p<0 . 005; low dose , day 4: p<0 . 05 ) more accelerated weight loss than their wild-type littermates during the course of the infection , with a reduction of 25% ( high dose ) or 21% ( low dose ) at day 5 post-infection ( Fig . 9A ) . This susceptibility was also reflected by the worse disease prognosis evaluated daily during infection ( Fig . 9B ) . Moreover , the onset of mortality of DUSP1 KO mice began as soon as 4 days post-infection , with the death of 3 out of 5 mice and reached the top at 6 days post-infection ( Fig . 9C ) . In the case of DUSP1 WT mice it was not until day 6 when the first animal died and by the end of the experiment 20% were still alive ( Fig . 9C ) . The viral titers in the lungs of infected animals at days 2 and 5 post-infection were higher in DUSP1 KO infected mice in comparison with WT mice at both times post-infection ( Fig . S1 ) . Together , these results demonstrate that DUSP1 deficiency leads to an enhanced susceptibility of mice to WR infection and this susceptibility could be related with an enhanced viral replication in the lungs . In addition , we evaluated MVA and NYVAC infection of mice in the absence of DUSP1 administered by different routes of inoculation . By i . n route with a dose of 107 PFU/mouse , neither DUSP1 WT nor KO mice infected with MVA or NYVAC displayed any sign of illness during the time period analyzed . Furthermore , we could not detect virus replication in the lungs of infected animals ( data not shown ) . Next , we analyzed by a systemic i . p route with 2×107 PFU/mouse the replication of MVA and NYVAC recombinants expressing luciferase . We collected spleens , lymph nodes , testis and peritoneal washes from infected animals at 4 and 16 hpi and evaluated luciferase activity as an index of viral replication ( Fig . S2 ) . We did not find differences in luciferase gene expression between WT and KO mice in the tissues analyzed , neither at 4 nor at 16 hpi . Finally , we skin-scarified groups of DUSP1 WT and KO mice with MVA ( 2×107 PFU/mouse tail ) and after 8 days post-infection we observed that lesions in the tails of KO mice were more severe than in their WT counterparts ( Fig . S3 ) ; these lesions were not observed in media or PBS-scarified KO mice indicating that the inflammatory lesions were solely due to the virus inoculum . After virus titration of tail tissue samples from infected animals , we did not find evidence of MVA replication ( data not shown ) , suggesting that the more severe lesions observed in DUSP1 KO mice were related to a host inflammatory response triggered by MVA infection in the absence of DUSP1 . DUSP1 is known to be an essential regulator of the host inflammatory response that controls the production of several cytokines following different stimuli [40] . It has been well established that the enhanced inflammatory response , triggered by the hyper-phosphorylation of MAPK in the absence of DUSP1 , can even kill the host [62] . At present , little is known about the role of DUSP1 in host immune responses during viral infections . To further analyze this effect , we first focused on innate immune responses triggered after systemic ( i . p ) infection of DUSP1 WT and KO mice with 107 PFU/mouse of WR or 2×107 PFU/mouse of MVA or NYVAC . We determined production of IL-6 , TNF-α , IL-1β and IL-10 at 3 and 24 hpi in serum from infected mice . These cytokines were selected since they are the most commonly induced cytokines triggered by other stimuli in the absence of DUSP1 . As shown in Fig . S4 , most of these cytokines were not or poorly induced in WT and KO mice infected with WR . However , infection with MVA or NYVAC induced the expression of IL-6 , TNF-α and IL-10 , being this induction higher in KO mice . Differences in levels and in temporal induction of these cytokines were found between infections with MVA versus NYVAC in DUSP1 KO mice , an expected finding since these two viruses differ in the number of immunomodulatory genes present in their genomes [6] . Next , we characterized T cell responses after VACV infection , analyzing the pattern of cytokine secretion by intracellular cytokine staining ( ICS ) from splenocytes stimulated with the immunodominant VACV E3 peptide . DUSP1 WT and KO mice were i . p . inoculated with WR ( 107 PFU/mouse ) , MVA or NYVAC ( 2×107 PFU/mouse ) and animals sacrificed after 10 days post-infection . Alternatively , we inoculated mice by i . n . route with WR ( 105 PFU/mouse ) , MVA or NYVAC ( 5×107 PFU/mouse ) . After stimulating splenocytes from infected animals with the VACV-E3 peptide , we analyzed by ICS the production of IFN-γ , IL-2 and TNF-α . As shown in Fig . 10A , the magnitude of E3-specific CD8 T cell response after i . p . inoculation , determined as the percentages of IFN-γ and/or IL2 and/or TNF-α CD8 secreting cells , was significantly higher ( p<0 . 001 ) in DUSP1 KO infected mice with the three viruses , although it was more remarkable in infections with WR and NYVAC . The antigen-specific immune response elicited by the three viruses by i . p . inoculation was highly polyfunctional and mainly represented by triple and double positive T cells for TNF-α and IFN-γ with significant differences ( p<0 . 001 ) in the magnitude of those populations between DUSP1 WT and KO mice infected with WR or NYVAC but not with MVA ( Fig . 10C ) . Infections by the i . n . route revealed also significant differences ( p<0 . 001 ) in the magnitude ( Fig . 10B ) and polyfunctionality ( Fig . 10D ) of the E3-specific CD8 T cell responses between the three viruses , particularly for DUSP1 KO infected animals . In this case , the polyfunctional profile was represented by double positive CD8 T cells for IFN-γ and TNF-α and single positive CD8 T cells for IFN-γ ( Fig . 10D ) . The findings of Figs . 10 and S4 reveal that DUSP1 is involved in the regulation of innate and adaptive immune responses during a poxvirus infection , as shown by differential behavior of WR , MVA and NYVAC regarding cytokine production and by the higher percentage of cytokine-secreting CD8+ T cells induced in DUSP1 KO mice in comparison with WT animals .
At present , little is known about the role of DUSP1 in viral infections . Our study has addressed this issue by using the VACV system in both cultured murine cells and in mice lacking DUSP1 . In particular , we have shown the importance of DUSP1 in VACV infection , from viral replication and host range to host immune responses . In cultured cells , we have demonstrated that VACV specifically upregulates DUSP1 during infection and this induction is dependent on early viral protein synthesis . The higher upregulation of DUSP1 mRNA in cells infected in the presence of CHX suggests that viral RNAs released by viral cores ( as CHX blocks uncoating but allows RNA synthesis ) are responsible for DUSP1 mRNA induction . Moreover , VACV infection triggers DUSP1 phosphorylation through activation of ERK , discarding the possibility of phosphorylation by a viral kinase and demonstrating that VACV not only triggers DUSP1 upregulation but also DUSP1 protein stabilization . These results are consistent with previous reports , which showed that VACV promotes ERK activation allowing an efficient viral replication in the infected cells and successful viral progeny release [17] , [59] , [7] . Of relevance , we have observed an increase of VACV replication in DUSP1 KO immortalized MEFs compared to WT cells . Specifically , we have detected an increase in WR viral titers but more interestingly , in MVA viral titers . In addition , we have further confirmed this enhanced WR and MVA replication in DUSP1 KO MEFs using specific siRNAs against DUSP1 in WR or MVA infection in cells from different origin such as HeLa cells , and also restoring DUSP1 expression in DUSP1 KO cells . Permissiveness to poxvirus replication is mostly dependent on intracellular events after entering the cells , such as several host protein kinase cascades that can mediate subsequent viral replication events rather than on cell surface receptors . For example , VACV can even enter non-permissive cell lines as insect cells [63] . In addition , although many poxviruses show strict species specificities , these can vary markedly such that cells derived from species that are not considered permissive hosts can sometimes be productively infected in vitro [64] . Phosphorylation is an important event that numerous viruses use during their replication process [65] , [66] . In the context of this study , the absence of DUSP1 led to a change in the host range of MVA . This result might be related with the observed changes in the pattern of MAPK activation during MVA infection in the absence of DUSP1 . Therefore , we analyzed whether the inactivation of ERK and p38MAPK by specific inhibitors had an implication on MVA replication in DUSP1 KO cells . We have observed a reduction in MVA viral titers in comparison with untreated DUSP1 KO cells infected with MVA , suggesting that these MAPKs are directly involved in the phenotype observed in the absence of DUSP1 . There are several possible explanations for these results . First , MAPKs may be acting over cellular ligands to promote their stabilization , activation or changes in their subcellular localization . Thus , the MAPK-mediated hyper-phosphorylation in the absence of DUSP1 during MVA infection may be miss-regulating these processes in a manner that MVA is able to replicate in murine cells . Alternatively , this phenotype may be due to MAPK action over viral phosphoproteins . VACV has been widely reported to use phosphorylation as a mechanism to regulate the activity of several viral proteins [67] . Most of them are phosphorylated by VACV-encoded kinases B1 and F10 [14] , [68] but others are phosphorylated by cellular kinases [67] . Many of these phosphoproteins have an important role during VACV morphogenesis [69] , [70] , [71] , [72] . Then , the phosphorylation status of viral structural proteins may be affected by the absence of DUSP1 during MVA infection and this may have an effect on virus morphogenesis . By Western-blot and confocal microscopy on VACV-infected DUSP1 WT and KO cells , we did not observe changes in subcellular viral protein localization nor in proteolytic processing of major core and viral structural proteins , like L4 , A16 and A17 . This is consistent with previous studies that showed the same phenotype when MVA infects non-permissive cell lines such as HeLa [73] . However , when we analyzed the expression of the late membrane protein A27 , we found that A27 was incorporated in virions in DUSP1 KO cells infected with MVA but its expression was mostly diffuse across the cytoplasm in the case of DUSP1 WT infected cells . These results point to an implication of DUSP1 in the events that occur during the transition from IVN to MV . The majority of the mutations that affect virion morphogenesis are involved in this complex process [67] . Whether the role of DUSP1 in VACV morphogenesis is strictly dependent of viral protein assembly or it is associated with other cellular processes remains to be determined . In fact , electron microscopy data of the viral morphogenesis steps of DUSP1 WT and KO cells infected with MVA revealed that MVA is able to complete its viral cycle in the absence of DUSP1 , further supporting the findings of time course analysis and confocal microscopy . While all different viral forms ( crescents , IV , IVN , MV , WV and EV ) were produced in DUSP1 KO infected cells , similarly as it occurs during a productive VACV infection , in WT MEFS the mature viral forms MV and EV were greatly impaired , according to the phenotype of a non-permissive cell line for MVA infection [56] . Comparative data of the different viral forms shows quantitatively that the main block in WT cells occurs at the transition between IVN and MV . We have also defined the influence of DUSP1 during VACV replication in vivo in the mouse model . The increased weight loss , higher death rate and worse disease prognosis after i . n . inoculation with WR revealed that the absence of DUSP1 leads to an increase susceptibility of infected mice . One possible cause for this susceptibility could be an exacerbated pro-inflammatory cytokine production but we did not find detectable levels of these cytokines in lungs from DUSP1 WT or KO mice infected with WR . However , we observed differences between cytokine levels in serum from WT and KO mice infected with the attenuated viruses MVA or NYVAC . Hence , the main possible explanation for the susceptibility of DUSP1 KO mice to WR infection may be the higher viral titers found in the lungs of DUSP1 KO mice infected with WR in comparison with WT mice . Previous studies have shown the pivotal role of the modulation of MAPK in host immune responses [74] , [75] . Moreover , in recent years , there has been an increasing amount of literature on the role of DUSP1 in the inflammatory response against different pathogens [40] , [62] , [76] , [77] . Taking into account these data , we considered interesting to address the involvement of DUSP1 in innate and adaptive immune responses after VACV infection . Measurement of cytokine production in the serum of infected mice revealed that DUSP1 KO mice infected with NYVAC experienced enhanced pro-inflammatory cytokine release as soon as 3 hpi , whereas in DUSP1 KO mice infected with MVA this enhancement is observed at 24 hpi . These differences in the kinetics of cytokine production may be explained by the fact that these two VACV attenuated viruses lack different immunomodulatory genes and , consequently , their immunogenicity profile affects MAPK signaling pathway in a distinct manner . In adaptive immune responses , MAPKs also serve as critical regulators in the clonal expansion of effector T and B lymphocytes through modulation of cytokine production , cell proliferation and survival [78] . As a key MAPK modulator , DUSP1 may play an important role during antiviral adaptive immune responses . Therefore , we characterized the adaptive immune responses by ICS after i . n . and i . p . inoculation with VACV . The results indicate that even though there were differences in the magnitude of CD8+ T cell responses triggered by WR , MVA or NYVAC , viral-specific CD8 T cell responses were higher in DUSP1 KO mice infected than in WT animals , clearly suggesting that DUSP1 modulates adaptive immune responses during VACV infection . As WR had an enhanced replication in lungs from DUSP1 KO mice , the amount of viral antigen produced was higher and longer expressed in the organism with this fully competent replicating virus in DUSP1 KO mice than in DUSP1 WT mice . In the case of the attenuated MVA and NYVAC , the increased CD8+ T cell response observed in DUSP1 KO mice might be related to the different innate immune responses triggered by these viruses due to the absence of different immunomodulatory genes in their genomes [6] , [79] . The adaptive immune responses triggered can be shaped by the elicited innate immune response [80] . For instance , dendritic cells ( DCs ) are one of the first lines of defense against pathogens and the most important cells to bridge innate and adaptive immunity [81] . In this sense , it has been recently reported that DUSP1 has an essential role in the integration of DC-signals and T cell responses , coordinating protective immunity and immunopathology against bacteria and fungi [82] . Thus , it is also possible that the absence of DUSP1 in innate immune cells is responsible for the variations in T cell responses observed after VACV infection . In summary , this is the first report demonstrating the role of DUSP1 during VACV infection . We can conclude that DUSP1 expression is specifically upregulated and phosphorylated during VACV infection and that the activation of ERK by VACV is necessary to promote DUSP1 phosphorylation . More interestingly , host restriction of MVA replication in murine cells is overcome when DUSP1 is absent and activation of MAPKs by virus infection contributes to this enhanced replication . We have demonstrated that DUSP1 is mainly acting at the transition between IVN and MV . In addition , we have shown that DUSP1 deficiency results in an enhanced susceptibility of mice to WR infection and this susceptibility could be related with an enhanced viral replication in the lungs; meanwhile the more severe skin lesions observed in DUSP1 KO mice were likely due to a host inflammatory response triggered by MVA . Finally , we have demonstrated that DUSP1 is involved in the modulation of innate and adaptive immune responses during VACV infection . Overall , DUSP1 acts as an antiviral host defense factor against a poxvirus infection . | Phosphorylation is a post-translational modification that is highly conserved throughout the animal kingdom . Viruses have evolved to acquire their own kinases and phosphatases and to be able to modulate host phosphorylation mechanisms on their benefit . DUSP1 is an early induced gene that belongs to the superfamily of Dual-specificity phosphatases and provides an essential negative feedback regulation of MAPKs . DUSP1 is involved in innate and adaptive immune responses against different bacteria and parasites infections . The use of Knock-out technology has allowed us to understand the role of DUSP1 in the context of VACV infection both in cultured cells and in the in vivo mouse model . Here , we have showed that DUSP1 expression is upregulated during VACV infection and that DUSP1 plays an important role in VACV replication . Interestingly , we have demonstrated that the VACV attenuated virus MVA is able to grow in immortalized murine embryo fibroblasts in the absence of DUSP1 . In vivo results showed that VACV replication-competent WR pathogenesis is enhanced in the absence of DUSP1 . Furthermore , we have demonstrated that DUSP1 is involved in the host innate and adaptive responses against VACV . Altogether , we have presented a novel role for DUSP1 in VACV replication and anti-VACV host immune response . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | Involvement of the Cellular Phosphatase DUSP1 in Vaccinia Virus Infection |
Sex determination is remarkably dynamic; many taxa display shifts in the location of sex-determining loci or the evolution of entirely new sex-determining systems . Predominant theories for why we observe such transitions generally conclude that novel sex-determining systems are favoured by selection if they equalise the sex ratio or increase linkage with a locus that experiences different selection in males versus females . We use population genetic models to extend these theories in two ways: ( 1 ) We consider the dynamics of loci very tightly linked to the ancestral sex-determining loci , e . g . , within the nonrecombining region of the ancestral sex chromosomes . Variation at such loci can favour the spread of new sex-determining systems in which the heterogametic sex changes ( XY to ZW or ZW to XY ) and the new sex-determining region is less closely linked ( or even unlinked ) to the locus under selection . ( 2 ) We consider selection upon haploid genotypes either during gametic competition ( e . g . , pollen competition ) or meiosis ( i . e . , nonmendelian segregation ) , which can cause the zygotic sex ratio to become biased . Haploid selection can drive transitions between sex-determining systems without requiring selection to act differently in diploid males versus females . With haploid selection , we find that transitions between male and female heterogamety can evolve so that linkage with the sex-determining locus is either strengthened or weakened . Furthermore , we find that sex ratio biases may increase or decrease with the spread of new sex chromosomes , which implies that transitions between sex-determining systems cannot be simply predicted by selection to equalise the sex ratio . In fact , under many conditions , we find that transitions in sex determination are favoured equally strongly in cases in which the sex ratio bias increases or decreases . Overall , our models predict that sex determination systems should be highly dynamic , particularly when haploid selection is present , consistent with the evolutionary lability of this trait in many taxa .
We consider transitions between ancestral and novel sex-determining systems using a three-locus model , each locus having two alleles ( Fig 1 ) . A full description of our model , including recursion equations , is given in S1 Text . Locus X is the ancestral sex-determining region , with alleles X and Y ( or Z and W ) . Locus A is a locus under selection , with alleles A and a . Locus M is a novel sex-determining region , at which the null allele ( M ) is initially fixed in the population such that sex of zygotes is determined by the genotype at the ancestral sex-determining region , X; XX genotypes become females , and XY become males ( or ZW become females , and ZZ become males ) . To evaluate the evolution of new sex-determining systems , we consider the spread of a novel sex-determining allele ( m ) at the M locus . Here , we assume that the M locus is ‘epistatically dominant’ over the X locus such that zygotes with at least one m allele develop as females with probability k and as males with probability 1 − k , regardless of the X locus genotype . With k = 0 , the m allele is a masculiniser ( a neo-Y allele ) , and with k = 1 , the m allele is a feminiser ( a neo-W allele ) . With intermediate k , we can interpret m as an ESD allele , such that zygotes develop as females in a proportion ( k ) of the environments they experience . The assumption that derived sex-determining loci are epistatically dominant is motivated by empirical systems in which multiple sex-determining alleles segregate ( i . e . , X , Y , Z , and W alleles present ) , such as cichlid fish [21] , platyfish ( Xiphophorus maculatus [44] ) , houseflies ( Musca domestica [45] ) , western clawed frogs ( Xenopus tropicalis [46] ) , and Rana rugosa [20] . Nevertheless , our supplementary analysis file ( S1 File ) allows other dominance relationships between loci to be specified ( see also [35] supplementary material for a numerical analysis ) . We consider two forms of selection upon haploid genotypes , ‘gametic competition’ and ‘meiotic drive’ . During gametic competition , we assume that a representative sample of all gametes/gametophytes ( hereafter ‘gametes’ ) compete with others of the same sex for fertilisation , which implies a polygamous mating system . Relative fitnesses in sex during gametic competition are given by and ( see Table 1 ) . On the other hand , meiotic drive in our model only affects the segregation of gametes produced by heterozygotes . Specifically , gametes produced by Aa heterozygotes of sex bear allele A with probability . We note that competition between sperm produced by a single male ( e . g . , in a monogamous mating system ) would be appropriately modelled as male meiotic drive , as only the frequency of gametes produced by heterozygotes would be affected . However , we do not consider scenarios in which there is competition among gametes produced by a small number of males/females ( e . g . , [47] ) . In each generation , we census the genotype frequencies in male and female gametes before gametic competition . After gametic competition , conjugation between male and female gametes occurs at random . The resulting zygotes develop as males or females , depending on their genotypes at the X and M loci . Diploid males and females then experience viability and/or individual-based fertility selection , with relative fitnesses , , and . We do not consider fertility selection that depends on the mating partner , e . g . , sexual selection with variation in choosiness . The next generation of gametes is produced by meiosis , during which recombination and sex-specific meiotic drive can occur . Recombination ( i . e . , an odd number of crossovers ) occurs between loci X and A with probability r , between loci A and M with probability R , and between loci X and M with probability ρ . Any linear order of the loci can be modelled with appropriate choices of r , R , and ρ ( see Fig 1A and S1 Table ) . Our model is entirely deterministic and hence ignores chance fluctuations in allele frequencies due to genetic drift . The model outlined above describes both ancestral XY and ZW sex-determining systems . Without loss of generality , we refer to the ancestrally heterogametic sex as male and the ancestrally homogametic sex as female . That is , we primarily describe an ancestral XY sex-determining system , but our model is equally applicable to an ancestral ZW sex-determining system ( relabelling the ancestrally heterogametic sex as female and the ancestrally homogametic sex as male and switching the labels of males and females throughout ) . We use a superscript to specify the ancestral sex-determining system described , e . g . , ( XY ) for ancestral XY sex-determination . In the ancestral population , it is convenient to follow the frequency of the A allele among female gametes ( eggs ) , pX♀ , and among X-bearing , pX♂ , or among Y-bearing , pY♂ , male gametes ( sperm/pollen ) . We also track the fraction of male gametes that are Y-bearing , qY , which may deviate from 1/2 because of meiotic drive in males . We consider only equilibrium frequencies of alleles , , and Y-bearing male gametes , q^Y , when determining the invasion of new sex-determining factors . We use ξ to measure the sex ratio ( fraction male ) among zygotes , which is determined by the allele frequencies and haploid selection coefficients ( S2 Table ) .
The evolution of a new sex-determining system requires that a rare mutant allele , m , at the novel sex-determining locus , M , increases in frequency when rare . Specifically , m invades when λm ( XY ) >1 , in which λm ( XY ) is the leading eigenvalue of the system of eight equations describing m-bearing gamete frequencies , Eqs S1 . 1 . This system simplifies substantially for an epistatically dominant neo-Y ( k = 0 ) or neo-W ( k = 1 ) ; see S3 Text for details . Invasion by a neo-Y or a neo-W primarily depends on the ‘haplotypic growth rates’ ( denoted by Λmi ( XY ) ) of the neo-sex determination allele m on background i ∈ {A , a} , without accounting for loss due to recombination ( R = 0 ) ; see Table 2 . If both haplotypic growth rates are greater than 1 ( ΛmA ( XY ) , Λma ( XY ) >1 ) , then the new sex-determining allele invades regardless of the rate of recombination between the new sex-determining locus and the selected locus ( R ) . Conversely , if both haplotypic growth rates are less than 1 ( ΛmA ( XY ) , Λma ( XY ) <1 ) , then invasion can never occur . Finally , if only one haplotypic growth rate is greater than 1 , the new sex-determining allele can always invade when arising at a locus that is tightly linked to the selected locus ( R ≈ 0 ) . Furthermore , it can be shown that the leading eigenvalue declines with recombination rate , R , and invasion requires that R is sufficiently small such that χma ( XY ) / ( Λma ( XY ) −1 ) +χmA ( XY ) / ( ΛmA ( XY ) −1 ) <1 . ( 1 ) Here , χmi ( XY ) >0 is the rate at which mutant haplotypes on background i ∈ {A , a} recombine onto the other A locus background in heterozygotes ( which is proportional to R; see Table 2 ) . This is a ‘dissociative force’ that breaks down linkage disequilibrium . Condition 1 may or may not be satisfied for the full range of locations of the new sex-determining locus , including R = 1/2 ( e . g . , on an autosome ) , depending on the nature of selection . Interpreting this condition , if we assume that only the mA haplotype would increase in frequency when R = 0 ( i . e . , Λma ( XY ) <1<ΛmA ( XY ) ) , then the first term on the left-hand side of Eq ( 1 ) is negative , and invasion requires that the growth rate of mA haplotypes ( ΛmA ( XY ) −1>0 ) and the rate at which they are produced by recombination ( χma ( XY ) ) are sufficiently large relative to the rate of decline of ma haplotypes ( 1−Λma ( XY ) >0 ) and the rate at which m and A are dissociated by recombination ( χmA ( XY ) ) . The haplotypic growth rates and dissociative forces are listed in Table 2 for a neo-Y and neo-W invading an ancestrally XY system . From this table and the arguments above , we draw four main points about the generic invasion of neo-Y and neo-W mutations ( without specifying the ancestral equilibrium ) : ( 1 ) Fisherian sex ratio selection will favour the spread of a neo-W and inhibit the spread of a neo-Y if the ancestral zygotic sex ratio is biased towards males ( i . e . , the first factor of the Λmi ( XY ) is greater than 1 for a neo-W and less than 1 for a neo-Y when ξ > 1/2 ) . Thus , neo-sex-determining alleles that specify the rarer sex are favoured by fisherian sex ratio selection . ( 2 ) In addition , the new sex-determining allele has associations with alleles favoured by either haploid or diploid selection ( fitness terms in square brackets ) . Importantly , invasion by a neo-Y ( neo-W ) does not directly depend on the fitness of female ( male ) diploids . This is because a dominant neo-Y ( neo-W ) is always found in males ( females ) , and therefore the frequency of the neo-Y ( neo-W ) , m , only changes in males ( females ) , Fig 1C and 1D . ( 3 ) Haploid selection thus plays two roles , generating fisherian selection to equalise the ancestral sex ratio ( through ξ ) and generating selection for the neo-Y/neo-W through associations with haploid-selected loci , which can distort the sex ratio . Each role influences the invasion dynamics of a new sex-determining allele , allowing the sex ratio to become more or less biased during a transition ( as previously found in two special cases; [42 , 43] ) . ( 4 ) Finally , Table 2 shows that the genetic contexts that arise during cis- and trans-GSD transitions are qualitatively different . This is because , in an ancestrally XY system , a gamete with the neo-Y always pairs with a female gamete containing an X , Fig 1C . By contrast , a gamete with a neo-W can pair with an X- or Y-bearing male gamete , Fig 1D . Consequently , neo-W-bearing females obtain a different frequency of A alleles from mating compared to ancestral ( MM ) females ( p¯♂ versus p^X♂ , respectively ) . This can inhibit or favour the spread of a neo-W . In order to explicitly determine the conditions under which a new sex-determining allele spreads , we next calculate the equilibrium frequency of the A allele ( i . e . , p^X♀ , p^X♂ , and p^Y♂ ) and Y-bearing male gametes ( q^Y ) in the ancestral population . Because only the A locus experiences selection directly , any deterministic evolution requires that there be a polymorphism at the A locus . Polymorphisms can be maintained by mutation-selection balance or occur transiently during the spread of beneficial alleles . Here , however , we focus on polymorphisms maintained by selection for longer periods . Such polymorphisms can be maintained by heterozygote advantage , sexually antagonistic selection , ploidally antagonistic selection , or a combination [48] . We analytically calculate equilibrium frequencies using two alternative simplifying assumptions: ( 1 ) the A locus is tightly linked to the nonrecombining region around the ancestral sex-determining locus ( r ≈ 0 ) , or ( 2 ) selection is weak relative to recombination ( , , ) . The ancestral equilibria and their stability conditions are given in S2 Text . When there is complete linkage between the ancestral sex-determining locus and the selected locus A ( r = 0 ) , either the A allele or the a allele must be fixed in gametes containing a Y allele ( S2 Text ) . Because the labelling of alleles is arbitrary , we will assume that the a locus is fixed in gametes with a Y ( p^Y♂=0 ) , without loss of generality . If there are two alleles maintained at the A locus , the A allele can be fixed ( p^X♀=p^X♂=1 ) or segregating at an intermediate frequency ( 0<p^X♀ , p^X♂<1 ) in gametes with an X . We find that a neo-Y allele can never invade an ancestral XY system that already has tight linkage with the locus under selection ( λY′ ( XY ) ≤1 when r = 0; for details , see S1 File ) . In essence , through tight linkage with the A locus , the ancestral Y becomes strongly specialised on the allele that has the highest fitness across male haploid and diploid phases . It is thus not possible for a neo-Y to create males that have higher fitness than the ancestral Y , and cis-GSD transitions are never favoured . Neo-W alleles , on the other hand , can invade an ancestral XY system ( the full invasion conditions are given in S3 Text; Eqs S3 . 1 and S3 . 2 ) . Invasion occurs when neo-W females can have higher fitness than the XX females in the ancestral population . Neo-W invasion is possible under all forms of selection that can maintain a polymorphism ( sexually antagonistic selection , overdominance , ploidally antagonistic selection , or some combination , e . g . , S2 , S3 and S8 Figs ) . Thus , Conclusion 1: Selection on loci in or near the nonrecombining region around the ancestral sex-determining locus ( r ≈ 0 ) prevents cis-GSD transitions ( XY ↔ XY , ZW ↔ ZW ) but can spur trans-GSD transitions ( XY ↔ ZW ) . To clarify conditions under which trans-GSD transitions can occur , we focus here on cases in which there is no haploid selection ( and hence no sex ratio bias ) and discuss the additional effect of haploid selection in S3 Text . Broadly , it is possible for neo-W females to have higher fitness than XX females for two reasons . Firstly , because the ancestral X experiences selection in both males and females , the X may be unable to specialise strongly on an allele favoured in females . Secondly , an allele can be associated with the Y and yet favoured in females . In turn , a neo-W can spread because ( a ) it is only found in females and is therefore unleashed from counterselection in males ( corresponding to ΛW′A ( XY ) >1 ) , and/or ( b ) it allows Y-associated alleles into females ( corresponding to ΛW′a ( XY ) >1 ) . We first give an example in which neo-W-A haplotypes can spread because the neo-W is unleashed from counterselection in males ( case [a] , in which ΛW′A ( XY ) >1 ) . When A is female beneficial and a is male beneficial , the A allele can be fixed ( p^X♀=p^X♂=1 ) or polymorphic ( 0<p^X♀ , p^X♂<1 ) on the X . In this case , polymorphism on the ancestral X indicates suboptimal specialisation for females fitness , which occurs because the A allele is counterselected in males ( requires that wAa♂ be sufficiently small relative to waa♂ ) . Neo-Ws , however , spend no time in males and can build stronger associations with the female-beneficial A allele , allowing them to spread ( see grey region in Fig 2A ) . We next give an example in which neo-W-a haplotypes can spread because they bring in female-beneficial alleles associated with the Y ( case [b] , in which ΛW′a ( XY ) >1 ) . When there is overdominance in males , X-A Y-a males have high fitness , and the A allele is favoured by selection on the X background in males . Therefore , the A allele can be polymorphic or even fixed on the X background , despite selection favouring the a allele in females ( e . g . , see nonhatched region in Fig 2B and [49 , 50] ) . In such cases , neo-W-a haplotypes can spread because they create more Aa and aa females when pairing with an X-bearing gamete from males and because they bring more of the Y-a haplotype into females , in whom it has higher fitness ( Fig 1D ) . In some cases , both neo-W-A and neo-W-a haplotypes can spread . For example , when AA individuals have low fitness in females , yet the A is polymorphic or fixed on the X background due to overdominance in males ( Fig 2B and 2C ) , both neo-W-A and neo-W-a haplotypes produce fewer unfit AA females . This is true for the neo-W-A haplotype because it can pair with a Y-a haplotype and still be female . Whenever both haplotypic growth rates are greater than 1 , invasion by a neo-W is expected regardless of its linkage with the selected locus ( i . e . , for any R ) ; see S1 and S2 Figs for examples . As a consequence , evolution can favour a new sex determination system on a different chromosome ( R = 1/2 ) , despite the fact that this unlinks the sex-determining locus from the selected locus . When only one neo-W haplotype has a growth rate greater than 1 ( see Fig 2 ) , a neo-W allele can invade as long as Eq ( 1 ) is satisfied , which may require that the recombination rate , R , is small enough . Nevertheless , because we assume here that r is small , these results indicate that a more loosely linked sex-determining region ( r < R ) can spread . For example , tightly sex-linked loci that experience sexually antagonistic selection can drive trans-GSD transitions in which the new sex-determining locus is less closely linked ( R > r , Fig 3 ) , but the analysis in S1 File indicates that a new unlinked sex-determining allele ( R = 1/2 ) cannot invade when selection is purely sexually antagonistic ( directional selection in each sex and no haploid selection ) . Assuming selection is weak relative to recombination , van Doorn and Kirkpatrick [36] showed that invasion by a neo-W allele occurs under the same conditions as its fixation in females . An equivalent analysis is not possible when recombination rates are low . However , numerical simulations demonstrate that , with tight sex linkage , neo-Y or neo-W alleles do not necessarily reach fixation in males or females , respectively , which can lead to the stable maintenance of a mixed sex-determining system , in which X , Y , and neo-W alleles all segregate ( e . g . , S9B and S9C Fig ) . From the arguments above , we reach Conclusion 2: With tight linkage between a selected locus and the ancestral sex-determining locus ( r ≈ 0 ) , trans-GSD transitions ( XY ↔ ZW ) can be favoured by selection even if they weaken sex-linkage ( r < R ) , potentially shifting sex determination to a different chromosome ( R = 1/2 ) . Such transitions can also lead to the maintenance of multifactorial sex determination systems . With haploid selection , Conclusions 1 and 2 continue to apply ( S3 Text ) . The parameters for which neo-W-A and neo-W-a haplotypes spread under various forms of haploid selection are plotted in S4 , S5 , S6 and S7 Figs . In particular , we note that adding haploid selection allows shifts in sex determination to a different chromosome ( R = 1/2 ) even when selection is sexually antagonistic , with directional selection in each diploid sex , e . g . , S3 Fig . Furthermore , haploid selection allows variation to be maintained by ploidally antagonistic selection , under which trans-GSD transitions may also be favoured , S8 Fig . Some cases of XY → ZW transitions in which r = 0 , R = 1/2 , and selection is ploidally antagonistic ( meiotic drive in males opposed by diploid selection ) were studied by Kozielska and colleagues [42] , who found that sex ratio biases are reduced during these transitions . However , such transitions are not always driven by selection to reduce sex ratio bias . For example , with XY sex determination and haploid selection in females , sex ratios are not ancestrally biased , yet a neo-W can invade ( S8 Fig ) . We further discuss how the spread of neo-sex-determining alleles is influenced by associations with haploid-selected loci in the next section . Here , we assume that selection is weak ( , , of order ε , in which ε is some number much less than 1 ) and thus implicitly assume that all recombination rates ( r , R , and ρ ) are large relative to selection . To leading order in selection , λY′ ( XY ) =1+14p¯ ( 1−p¯ ) SA2 ( r−R ) rR+O ( ε3 ) ( 2 ) and λW′ ( XY ) =λY′ ( XY ) +[ ( 2αΔ♂−2αΔ♀+t♂−t♀ ) ( p^Y♂−p^X♂ ) /2]+O ( ε3 ) , ( 3 ) in which p¯ is the frequency of A , to leading order ( Eq S2 . 3 ) , and SA= ( s¯♂+αΔ♂+t♂ ) − ( s¯♀+αΔ♀+t♀ ) describes sex differences in selection for the A versus a allele across diploid selection , meiosis , and gametic competition . The diploid selection term , , is the difference in fitness between A and a alleles in diploids of sex . The difference in A allele frequency among Y-bearing sperm versus X-bearing sperm is , at equilibrium , p^Y♂−p^X♂=p¯ ( 1−p¯ ) SA ( 1−2r ) / ( 2r ) . Eq ( 2 ) demonstrates that , under weak selection , a neo-Y allele will invade an XY system ( λY′ ( XY ) >1 ) if and only if it is more closely linked to the selected locus than the ancestral sex-determining locus ( i . e . , if R < r ) . This echoes our results above , in which a neo-Y could never invade if r ≈ 0 . It is also consistent with the results of [35] , who considered diploid selection only and also found that cis-GSD transitions can only occur when the new sex-determining locus is more closely linked to a locus under sexually antagonistic selection . Conclusion 3A: New sex-determining alleles causing cis-GSD transitions ( XY ↔ XY or ZW ↔ ZW ) are favoured if they arise more closely linked with a locus that experiences ( haploid and/or diploid ) selection than the ancestral sex-determining locus ( R < r ) . Similarly , in the absence of haploid selection ( ) , Eq ( 3 ) indicates that trans-GSD transitions can occur if and only if the new sex-determining locus is more closely linked to a locus under selection , R < r , as found by [36] . With haploid selection , a neo-W is also usually favoured when it is more closely linked to the selected locus than the ancestral sex-determining region is ( R < r , e . g . , Figs 3B and 4 ) ; this is true unless the last term in Eq ( 3 ) is negative and dominant over the first , which requires relatively restrictive combinations of selection and recombination parameters . For example , with haploid selection , a neo-W will always be favoured if it arises in linkage with a selected locus ( R < 1/2 ) that is ancestrally autosomal ( r = 1/2 , leading to p^Y♂−p^X♂=0 ) . Conclusion 3B: New sex-determining alleles causing trans-GSD transitions ( XY ↔ ZW ) are usually favoured if they arise more closely linked with a locus that experiences ( haploid and/or diploid ) selection than the ancestral sex-determining locus ( R < r ) . However , with haploid selection and some ancestral sex linkage ( r < 1/2; allowing allele frequency differences on the X and Y ) , the term in square brackets in Eq ( 3 ) can be positive . This leads to Conclusion 3C: With haploid selection , new sex-determining alleles causing trans-GSD transitions ( XY ↔ ZW ) can spread even if they arise further from a locus that experiences selection than the ancestral sex-determining locus ( r < R ) . To clarify the parameter space under which neo-W alleles spread despite looser linkage with the selected locus ( R > r ) , we focus on cases in which dominance coefficients are equal in the two sexes , h♀ = h♂ , and haploid selection only occurs in one sex ( e . g . , during male meiosis only ) . Table 3 then gives the conditions required for unlinked ( R = 1/2 ) neo-W invasion when there is some ancestral sex linkage ( r < 1/2; e . g . , the selected locus is on the ancestral sex chromosome , and the novel sex-determining locus arises on an autosome ) . These special cases indicate that neo-W invasion occurs for a large fraction of the parameter space , even though the neo-W uncouples the sex-determining locus from a locus under selection . Fig 4 then demonstrates that under these conditions , neo-W alleles can spread when they are more loosely or more closely linked to the locus that experiences haploid selection , i . e . , Conclusions 3B and 3C ( compare with Fig 3A for diploid sexually antagonistic selection alone ) . We can also compare transitions among different GSD systems , as these are associated with different effects on the sex ratio , which can increase , decrease , or remain equal . For example , if there is meiotic drive in males only ( αΔ♂≠0 , αΔ♀=0 ) , without gametic competition ( t♀ = t♂ = 0 ) , the zygotic sex ratio is initially biased only when the ancestral sex-determining system is XY ( Figs 1B and 5A ) and not ZW ( Figs 1B and 5B ) . If fisherian sex ratio selection dominated , we would thus expect a difference in the potential for XY-to-ZW and ZW-to-XY transitions . However , invasion by a neo-W allele into an XY system and invasion by a neo-Y allele into a ZW system occur under the same conditions ( λY′ ( XY ) =λW′ ( ZW ) and λW′ ( XY ) =λY′ ( ZW ) , at least to order ε2 ) , implying that Conclusion 4: When selection is weak relative to recombination , trans-GSD transitions in the presence of haploid selection are favoured as often and as strongly , whether they erase ancestral sex ratio bias ( benefiting from fisherian sex ratio selection ) or generate sex ratio bias ( benefiting from associations with selected alleles ) . For example , in Fig 5A , neo-W alleles invade an ancestral XY system in which females are initially rare , equalising the sex ratio ( as occurs in [42] ) . However , Fig 5B shows that a neo-Y can invade the resulting ZW system under the same conditions . When R < 1/2 , the invading neo-Y becomes associated with the male meiotic drive allele , and the zygotic sex ratio evolves to become male biased ( as occurs in [43] , beginning from ESD ) . In this case , the neo-Y spreads because it is often found in males and can , if it carries the driven allele a , benefit from haploid selection in males ( Fig 5B ) . While equalising the sex ratio and benefiting from associations with selected alleles are two primary reasons why haploid selection spurs sex chromosome transitions , more complex situations also arise . For example , with R = 1/2 in Fig 5B ( green curve ) , the neo-Y allele spreads despite the fact that it cannot benefit from drive because free recombination moves it randomly between driven and nondriven backgrounds . Nevertheless , the unlinked neo-Y can spread because males bearing it more often carry the nondriven allele A and have higher average diploid fitness compared to ZZ males , which bear a high frequency of the driven allele a from their mothers . We next consider the case in which the new sex-determining allele , m , causes sex to be determined probabilistically or by heterogeneous environmental conditions ( ESD ) . In particular , we assume individuals carrying allele m develop as females with probability k ∈ ( 0 , 1 ) . In our deterministic model , this means the fraction of females in the subpopulation containing m is exactly k , even when m is rare ( i . e . , ESD does not introduce any additional variance in sex determination ) . We also assume that the environmental conditions that determine sex do not differentially affect the fitness of males versus females . Such correlations can favour environmental sex-determining systems by allowing each sex to be produced in the environment in which it has highest fitness; in the absence of these correlations , previous theory would predict that ESD is favoured when it produces more equal sex ratios than the ancestral system ( see reviews by [1 , 31 , 32] ) . The characteristic polynomial determining the leading eigenvalue ( Eqs S1 . 1 ) does not factor for ESD ( 0 < k < 1 ) as it does for a neo-Y ( k = 0 ) or neo-W ( k = 1 ) allele . We therefore focus on weak selection here , in which case the leading eigenvalue is λESD′ ( XY ) =1+ ( 1−2k ) 24p¯ ( 1−p¯ ) SA2r−RrR+k ( p^♂Y−p^X♂ ) 2[k ( 2αΔ♂−2αΔ♀+t♂−t♀ ) −2 ( 1−k ) SA]+O ( ε3 ) . ( 4 ) This reduces to λY′ ( XY ) when k = 0 , and λW′ ( XY ) when k = 1 . Of particular interest are ESD mutations that cause half of their carriers to develop as females and half as males ( k = 1/2 ) , creating equal sex ratios . The spread of such mutations is determined by λESD′ ( XY ) =1+12 ( λY′|R=1/2 ( XY ) −1 ) + ( λW′|R=1/2 ( XY ) −1 ) 2+O ( ε3 ) , ( 5 ) in which λY′|R=1/2 ( XY ) and λW′|R=1/2 ( XY ) represent λY′ ( XY ) and λW′ ( XY ) when evaluated at R = 1/2 ( Eqs 2 and 3 ) . That is , ESD with k = 1/2 behaves as if the M and A loci were unlinked , regardless of the actual value of R . This is because sex is randomised each generation in individuals bearing the m allele , preventing associations from building up between it and alleles at locus A . Eq ( 5 ) shows that the ESD mutation gets half of the fitness of a feminising mutation ( neo-W ) and half of the fitness of a masculinising mutation ( neo-Y ) but only has an effect one-half of the time ( the other half of the time it produces the same sex as the ancestral system would have ) . As discussed above , λY′|R=1/2 ( XY ) is necessarily less than or equal to 1 when selection is weak ( Conclusion 3A ) , but λW′|R=1/2 ( XY ) can be greater than 1 if there is haploid selection ( see Conclusion 3C ) . That is , with haploid selection , an allele causing ESD can invade an ancestrally XY system because it generates females that are either rare or have high fitness , in the same manner as a neo-W ( likewise , ESD invades a ZW system for the same reasons that a neo-Y can ) . Significantly , Eq ( 5 ) is the same whether ESD is invading an ancestrally XY or ZW system ( because λY′ ( XY ) =λW′ ( ZW ) and λW′ ( XY ) =λY′ ( ZW ) ) . Thus , focusing solely on fisherian selection to equalise the sex ratio does not fully explain GSD-to-ESD transitions . For example , when the ancestral sex-determining system is XY , the sex ratio is biased by male haploid selection . When the ancestral sex-determining system is ZW , the sex ratio is not biased . Nevertheless , ESD is equally likely to invade both XY ( through λW′ ( XY ) ) and ZW ( through λY′ ( ZW ) ) systems , equalising the zygotic sex ratio in the former case but potentially transiently biasing it in the latter . In addition , we note that ESD may not invade , even if the sex ratio is initially biased ( e . g . , with drive in males only , r < 1/2 , h♀ = h♂ , and s♀ s♂ < 0 , then λW′ ( XY ) <1 , see Table 3 ) . We conclude that , as with neo-W and neo-Y mutations , Conclusion 5: Transitions from GSD to ESD are not straightforwardly predicted by selection to balance the zygotic sex ratio when haploid selection is present .
New sex determination systems are typically expected to spread when they equalise the sex ratio and/or when they increase linkage with loci that experience sex differences in selection [33 , 34] . In accordance with the latter mechanism , we find that sex differences in selection at the haploid stage can favour cis- or trans-GSD transitions that tighten sex linkage ( Conclusion 3A and 3B ) . Contrary to this expectation , however , we find that trans-GSD transitions can be favoured that loosen linkage with the sex-determining locus , either when linkage is initially tight ( Conclusions 1 and 2 , Figs 2 and 3 ) or when there is haploid selection ( Conclusion 3C , Figs 4 and 5 ) . Furthermore , we show that the spread of new sex determination systems is not dominated by selection to balance the sex ratio ( Conclusions 4 and 5 , Fig 5 ) . On the one hand , sex ratio biases caused by haploid selection can facilitate trans-GSD transitions or transitions from GSD to ESD [42] . For instance , alleles favoured by haploid selection in males often become associated with the Y allele , which leads to an ancestral male-biased zygotic sex ratio . This male bias increases the potential for a neo-W or ESD allele to invade ( Table 2 ) , equalising the sex ratio ( e . g . , see Fig 5B; for related examples , see [42] ) . On the other hand , sex ratio selection can be overwhelmed by additional selective effects , preventing a neo-W or ESD allele from invading , even if it would balance the sex ratio ( e . g . , when selection also acts in opposite directions in male and female diploids , Table 3 ) . Indeed , transitions between sex-determining systems can generate stronger sex ratio biases ( e . g . , Fig 5A and step 1 in [43] ) . In one of our key results , we find that with weak selection , there is no difference in conditions allowing XY-to-ZW and ZW-to-XY transitions ( Conclusion 4 ) , even when haploid selection always acts in the same sex ( e . g . , males ) . That is , the sex ratio bias created by male haploid selection facilitates the spread of a neo-W allele into an XY system to the same degree that male haploid selection drives the spread of a neo-Y into a ZW system with a 1:1 sex ratio ( Fig 5 ) . Because both fisherian selection to equalise the sex ratio and the benefits of hitchhiking with driven alleles can facilitate transitions among sex chromosome systems , we predict that haploid selection should increase the lability of sex determination systems . Even in animal and plant species that have much larger and more conspicuous diploid phases than haploid phases , many loci have been shown to experience haploid selection through gamete competition and/or meiotic drive [38–41 , 51–56] , which can generate biased sex ratios [57–64] . In animals , a relatively small proportion of all genes are thought to be expressed and selected during competition in animal sperm [39 , 65 , 66] . Nevertheless , expression in the gamete is not required for haploid selection if the fitness of a gamete depends on its ability to condense DNA [67] . Furthermore , expression during gamete production often underlies systems of meiotic drive [68–70] , which may be a common form of haploid selection in animals [71] . Recent studies have demonstrated that sperm competition , even within a single ejaculate , can alter haploid allele frequencies and increase offspring fitness [72 , 73] . In plants , competition among gametophytes may be particularly important . It is estimated that 60%–70% of all genes are expressed in the male gametophyte , and these genes exhibit stronger signatures of selection than randomly chosen genes [74–76] . Furthermore , artificial selection pressures applied to male gametophytes are known to cause a response to selection ( e . g . , [77–80] ) . Linking haploid expression with the evolution of sex-determination , a recent transcriptome analysis in Rumex shows that pollen-biased expression ( relative to expression in flower buds or leaves ) is enhanced among XY-linked genes , compared to autosomal genes or compared to hemizygous genes that are only linked to the X [81] . In addition , Y-linked genes are overexpressed relative to X-linked genes in pollen ( but not in flower buds or leaves ) . This suggests that the spread of neo-Y chromosomes in this clade could have been favoured through linkage with haploid-selected genes rather than those under sexually antagonistic selection . Frequent turnovers driven by haploid selection may help to explain the relative rarity of heteromorphic sex chromosomes in plants . If haploid selection is strong , but selective differences between male and female diploids are weak , we specifically predict that trans-GSD transitions are favoured more strongly than cis-GSD transitions , with transitions to ESD intermediate ( e . g . , with |s¯♂−s¯♀|<<|αΔ♂−αΔ♀+t♂−t♀| , we have λW′ ( XY ) >λY′ ( XY ) ; Eq 3 ) . Among the relatively few dioecious clades in which multiple species have well-characterised sex chromosomes [6] , trans-GSD transitions have been inferred in Silene otites [15] and in Salicaceae [16 , 17] . Assuming that transitions from dioecy to hermaphroditism ( equal parental investment in male and female gametes ) are favoured in a similar manner to the ESD examined here ( equal probability of zygotes developing as males or females ) , our results suggest that competition among haploid pollen could drive transitions between dioecy and hermaphroditism , which are frequent in plants [82 , 83] . To further examine this link , future theory could also include inbreeding , which is an important consideration during transitions between dioecy and hermaphroditism [84] . Future empirical studies could look for evidence of haploid selection acting on former sex chromosomes in hermaphroditic species ( e . g . , a study such as [81] on ancestral , rather than derived , sex chromosomes ) . New sex-determining alleles have previously been shown to spread when they arise in linkage with loci that experience sex differences in selection because beneficial associations build up between alleles that determine sex and alleles that are favoured in that sex [35–37 , 43] . In support of this hypothesis , researchers have identified genes on recently derived sex chromosomes that might be under sexually antagonistic selection [21 , 85–87] . However , we show that , if selected loci are tightly linked to the ancestral sex-determining locus , they can drive trans-GSD transitions that reduce sex-linkage ( Conclusions 1 and 2 ) , thus widening the range of genomic locations where selection could be driving observed trans-GSD transitions . In addition , we find that polymorphic sex-determining systems ( X , Y , and neo-W alleles all segregating ) can be maintained when a selected locus is tightly linked to the ancestral sex-determining system ( e . g . , S9B and S9C Fig ) , which is not possible with loose linkage [36] . This pair of conclusions applies in cases with or without haploid selection . Our tight linkage result—in particular , the prediction that invasion can lead to polymorphic sex determination—is consistent with empirical data from species in which new feminising mutations are found segregating with ancestral XY loci . For example , in the platyfish ( X . maculatus ) , X , Y , and W alleles segregate at one locus ( or two closely linked loci ) near potentially sexually antagonistic genes for pigmentation and sexual maturity [44 , 88–90] . Furthermore , several rodent species maintain feminising alleles along with the ancestral X and Y sex determination alleles ( reviewed in [91] ) . In nine Akadon rodent species , it appears that male-determining sry expression is suppressed by an autosomal feminising allele ( a neo-W allele ) , creating XY females [92 , 93] . XY females have increased fitness relative to XX females [94] . However , it is not yet clear whether loci linked to the feminising factor or the ancestral Y cause this effect . Most convincingly , in Mus minutoides , females can have XX , XX* , or X * Y genotypes [95] . Previous theory would predict that the dominant X* chromosome ( potentially an autosome that has fused with the sex chromosome ) harbours female-beneficial alleles , driving its spread . However , XX and XX* females have similar fitness , whereas X * Y female fitness is enhanced [96–98] . Although Y-linkage of female-beneficial alleles is counterintuitive , our model suggests that it can be stably maintained when linkage is initially tight between the sex-determining region and the selected locus , subsequently favouring new feminising mutations , which would be a parsimonious explanation for the spread of feminising alleles in this case . Our models assume that sex-determining alleles do not experience direct selection except via their associations with sex and selected alleles . However , in some cases , there may be significant degeneration around the sex-limited allele ( Y or W ) in the ancestral sex-determining region because recessive deleterious mutations and/or deletions accumulate in the surrounding nonrecombining regions [99–102] . During trans-GSD transitions , but not cis-GSD transitions , any recessive deleterious alleles linked to the Y or W are revealed to selection in YY or WW individuals [4] . This phenomenon was studied by van Doorn and Kirkpatrick [36] , who found that degeneration can prevent fixation of a neo-W or a neo-Y allele , leading to a mixed sex-determining system in which the ancestral and new sex-determining loci are both segregating . However , they noted that very rare recombination events around the ancestral sex-determining locus can allow the completion of trans-GSD transitions . Degeneration around the Y or W could explain why trans-GSD transitions are not observed to be much more common than cis-GSD transitions despite the fact that our models demonstrate that they are favoured under a wider range of conditions , especially with haploid selection . For example , there are a dozen sex chromosome configurations among dipteran species but only one transition between male and female heterogamety [9] , but Y degeneration or absence is also very common among Diptera [9] . In this study , we have only considered new sex-determining alleles of large effect . However , we expect similar selective forces to act on masculinising and feminising alleles of weaker effect . For example , small-effect masculinising and feminising alleles within a threshold model of sex determination can be favoured when linked to loci that experience sexually antagonistic selection [37] . These results echo those for large-effect neo-Y and neo-W alleles [35 , 36] . It should be noted , however , that the dynamics of sex-determining alleles with very weak effect will be influenced by genetic drift , which itself has been shown to bias transitions towards epistatically dominant sex-determining systems when there is no direct selection [103] .
We have shown that tight sex linkage and haploid selection can drive previously unexpected transitions between sex-determining systems . In particular , both can select for new sex-determining loci that are more loosely linked to loci under selection ( Conclusions 2 and 3C ) . In addition , haploid selection can cause transitions in GSD analogous to those caused by purely sexually antagonistic selection , eliminating the need for differences in selection between male and female diploids ( Conclusion 3A , 3B , and 3C ) . We conclude that haploid selection should be considered as a pivotal factor driving transitions between sex-determining systems . Further , transitions involving haploid selection can eliminate or generate sex ratio biases; to leading order , selection to balance the sex ratio and the benefits of hitchhiking with haploid-selected alleles , leading to a biased sex ratio , are of equal magnitude ( Conclusions 4 and 5 ) . Overall , our results suggest several novel scenarios under which new sex-determining systems are favoured , which could help to explain why the evolution of sex-determining systems is so dynamic . | Systems of sex determination are strikingly diverse and labile in many clades . This poses the question: what drives transitions between sex-determining systems ? Here , we use models to derive conditions under which new sex-determining systems spread . Prevailing views suggest that new sex-determining systems are favoured when they equalise the sex ratio and/or when they are more closely linked to genes that experience differential selection in males and females . Our models include selection upon haploid genotypes ( meiotic drive or gametic competition ) , which biases the sex ratio and occurs differently in male and female gametes . Surprisingly , we find the two forces ( selection to equalise the sex ratio and the benefits of hitchhiking alongside driven alleles that distort the sex ratio ) will often be equally strong , and thus neither is sufficient to explain the spread of new sex-determining systems in every case . We also find that new sex-determining alleles can spread despite being less closely linked to selected loci , as long as initial linkage is tight or haploid selection is present . Our models therefore predict that loci in previously unexpected genomic locations and/or experiencing various types of selection ( including haploid selection ) can now be implicated as drivers of transitions between sex-determining systems . | [
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] | 2018 | Haploid selection, sex ratio bias, and transitions between sex-determining systems |
A central question in neuroscience is to understand how noisy firing patterns are used to transmit information . Because neural spiking is noisy , spiking patterns are often quantified via pairwise correlations , or the probability that two cells will spike coincidentally , above and beyond their baseline firing rate . One observation frequently made in experiments , is that correlations can increase systematically with firing rate . Theoretical studies have determined that stimulus-dependent correlations that increase with firing rate can have beneficial effects on information coding; however , we still have an incomplete understanding of what circuit mechanisms do , or do not , produce this correlation-firing rate relationship . Here , we studied the relationship between pairwise correlations and firing rates in recurrently coupled excitatory-inhibitory spiking networks with conductance-based synapses . We found that with stronger excitatory coupling , a positive relationship emerged between pairwise correlations and firing rates . To explain these findings , we used linear response theory to predict the full correlation matrix and to decompose correlations in terms of graph motifs . We then used this decomposition to explain why covariation of correlations with firing rate—a relationship previously explained in feedforward networks driven by correlated input—emerges in some recurrent networks but not in others . Furthermore , when correlations covary with firing rate , this relationship is reflected in low-rank structure in the correlation matrix .
One prominent goal of modern theoretical neuroscience is to understand how the features of cortical neural networks lead to modulation of spiking statistics [1–3] . This understanding is essential to the larger question of how sensory information is encoded and transmitted , because such statistics are known to impact population coding [4–8] . Both experimental and theoretical inquiries are complicated by the fact that neurons are widely known to have heterogeneous attributes [9–14] . One family of statistics that is implicated in nearly all population coding studies is trial-to-trial variability ( and co-variability ) in spike counts; there is now a rich history of studying how these statistics arise , and how they effect coding of stimuli [15–19] . Recent work by numerous authors has demonstrated that the information content of spiking neural activity depends on spike count correlations and its relationship ( if any ) with stimulus tuning [15 , 17 , 19–21] . Since a population of sensory neurons might change their firing rates in different ways to stimuli , uncovering the general mechanisms for when spiking correlations increases with firing rate ( or when they do not ) is important in the context of neural coding . Thus , we study this question in a general recurrent neural network model . One observation that has been made in some , but not all , experimental studies is that pairwise correlations increase with firing rates . This relationship has been observed in vitro [22] and in several visual areas: area MT [23] , V4 [24] , V1 [25 , 26] , and notably , in ON-OFF directionally sensitive retinal ganglion cells [21 , 27] . The retinal studies involved cells with a clearly identified function , and therefore allowed study of the coding consequences of this correlation/firing rate relationship . Both studies found that the stimulus-dependent correlation structure observed compared favorably to a structure in which stimulus-independent correlations were matched to their ( stimulus- ) averaged levels . This finding reflects a general principle articulated in other studies [17 , 19] , that stimulus-dependent correlations are beneficial when they serve to spread the neural response in a direction orthogonal to the signal space . While many studies have illustrated the connection between stimulus-dependent correlation structure and coding , these have ( until recently: see [21 , 25 , 27] ) largely taken the correlation structure as given , leaving open the question of how exactly a network might produce the hypothesized correlation structure [6 , 7] ( see also the theoretical calculations in [21 , 27] ) . Theoretical studies of the mechanisms that contribute to correlation distributions have largely analyzed homogeneous networks ( i . e . cells are identical , aside from E/I identity ) [2 , 3 , 28 , 29] , which does not allow an exploration of a correlation/firing rate relationship . Thus , how correlation coefficients can vary across a population of heterogeneously-tuned neurons is not yet well understood despite its possible implications for coding . In this paper we investigated the relationship between correlations and firing rates in conductance-based leaky integrate-and-fire ( LIF ) neural network models , consisting of excitatory ( E ) and inhibitory ( I ) cells that are recurrently and randomly coupled . We introduced neural heterogeneity by allowing thresholds to vary across the population , which induced a wide range of firing rates , and explored different firing regimes by varying the strength of recurrent excitation . We found that with relatively strong excitation , pairwise correlations increased with firing rate . In theoretical studies , this correlation-firing rate trend has been explained in feed-forward networks driven by common input [22 , 30 , 31] . Here we investigated whether the correlation/firing relationship in recurrent networks can be explained by this theory , but where the source of input correlations is internally generated; i . e . , from overlapping projections within the recurrent network . We first adapted a network linear response theory , to decompose predicted correlations into contributions from different graph motifs , which are subgraphs which form the building blocks of complex networks [28 , 32 , 33] . We found that in all networks studied here , second-order motifs—and specifically inhibitory common input—were the dominant contributor to overall pairwise correlations . This allowed us to generalize theory from [22] , and describe pairwise correlations in terms of a single-cell susceptibility function . Surprisingly , we found that correlations from inhibitory common input could either increase or decrease with firing rate , depending on how cells responded to fluctuations in inhibitory conductances . We further show that a correlation-firing rate relationship has an important consequence for heterogeneous networks; it can shape low-dimensional structure in the correlation matrix . Low-dimensional structure—often modeled with a low-rank approximation to the correlation matrix—is important because it can be used to improve estimation [34] and even to reconstruct full correlation matrices from incomplete data [35–37]; such structure has been observed in experimental data [25 , 38–41] but its origin is not always known . We demonstrate in our networks that when correlation co-varies with firing rate , the ( E-E ) correlation matrix could be accurately modeled with a low-rank approximation , and the low-rank projection in this approximation was strongly associated with firing rate . Thus we demonstrate that low-rank structure can result from recurrent activity modulated by single-cell characteristics , as well as from a global input or a top-down signal [38] .
We performed Monte Carlo simulations of recurrent , randomly connected E/I networks , as described in Methods: Neuron model and network setup . To connect to previous literature on asynchronous spiking , we compared networks with and without single-cell variability—referred to as heterogeneous and homogeneous respectively . Heterogeneity was introduced by allowing cell threshold to vary , which induced a corresponding range of firing rates ( see Methods: Neuron model and network setup for details ) . We first chose parameters so that the networks exhibited the classical asynchronous irregular ( Asyn ) regime , in which each neuron has irregular Poisson-like spiking , correlations are low , and the population power spectra are flat [42] . In Fig 1A we show raster plots from both the heterogeneous and homogeneous networks , in this regime . The heterogeneous network shows a gradient in its raster plot , because cells are ordered by decreasing firing rate . The population power spectra were flat , for both E and I cells and in both homogeneous and heterogeneous networks ( Fig 1C ) . When we increased excitation ( by increasing both WEE and WIE , where WXY is the conductance strength from type Y to X; see Table 1 for parameter values ) , we observed occasional bursts of activity . However , the bursts do not occur at regular intervals and do not involve the entire population ( we found excitatory bursts involved at most 25% of the population ) . The network is still moderately inhibition-dominated and neurons are spiking irregularly; example raster plots are shown in Fig 1B . The population power spectra ( Fig 1D ) are no longer flat ( compare to the asynchronous regime , Fig 1C ) ; they show local maxima around 8 Hz , but it is not a pronounced peak . We will refer to this as the strong asynchronous ( SA ) regime [43] . In both Fig 1C and 1D , we note that—despite the apparent differences in the distribution of spikes across the network , evident in the raster plots—both the autocorrelation functions ( Fig 1C and 1D , insets ) and the power spectra from the heterogeneous and homogeneous networks are very similar . Thus , we have a fair comparison to examine the role of heterogeneity , independent of other characteristics of the network . The distribution of both excitatory and inhibitory firing rates are extremely narrow in the homogeneous network , but broad in the heterogeneous network ( Fig 1E ) . This is expected , as each excitatory ( inhibitory ) cell in the homogenous network has the same uncoupled firing rate; because the number of synaptic inputs is likewise fixed , population variability in synaptic input is limited . The heterogeneous networks have a range of firing rates , which allows us to investigate the possibility of a relationship between ( variable ) firing rate and pairwise correlations . Population-averaged firing rates were very similar between the heterogeneous and homogeneous networks: in the asynchronous regime ⟪νE⟫ = 10 . 6 Hz ( heterogeneous ) and ⟪νE⟫ = 10 . 1 Hz ( homogeneous ) , while ⟪νI⟫ = 44 . 3 Hz ( heterogeneous ) and ⟪νI⟫ = 43 . 5 Hz ( homogeneous ) . In both regimes Fano factors ranged between 0 . 9 and 1 . 1 , consistent with Poisson-like spiking ( more statistics are given in S1 and S3 Tables ) . We next sought a possible relationship between pairwise correlations—quantified via the Pearson’s correlation coefficient for spike counts , ρ i j ≡ Cov T ( n i , n j ) / Var T ( n i ) Var T ( n j ) —and single-cell firing rates . Such relationships have been found in feed-forward networks [22 , 30 , 31] , and impact information transfer when considered in concert with stimulus selectivity ( i . e . signal correlations ) [7 , 8 , 15 , 19] . In heterogeneous networks , the large range of firing rates—equivalently the large range of operating points—admits the possibility that cells at different operating points may differ in their ability to transfer correlations . To investigate this we plotted pairwise correlations for each distinct excitatory pair ρij , versus the geometric mean of the firing rates ν i ν j , in both regimes ( asynchronous and strong asynchronous ) , for a range of time scales ( blue stars in Fig 2 ) . We focus here on excitatory-excitatory ( E-E ) pairs , because excitatory synaptic connections provide the predominant means of propagating cortical sensory information to higher layers . Our results show a striking difference between the two spiking regimes; while there is no clear relationship with firing rate in the asynchronous regime ( Fig 2 , top row ) , the strong asynchronous regime shows a distinct positive trend with firing rate ( Fig 2 , bottom row ) . We can quantify a hypothesized relationship between ν and ρ with linear regression , and indeed find that geometric mean firing rate explains a substantial part of the variability of correlations in the strong asynchronous regime obtained from the Monte Carlo simulations , with R2 values ( i . e . percentage of variability explained ) of 0 . 41 , 0 . 37 , and 0 . 34 for time windows of T = 5 , 50 , and 100 ms respectively ( in contrast , R2 values for the asynchronous network are below 0 . 005 ) . In recurrent networks , the response of each cell is shaped by both direct and indirect connections through the network . We used the linear response theory described in Methods: Linear Response Theory and Methods: Computing statistics from linear response theory to predict the full correlation matrix CT at various time scales , including the limit of long time scales: C ˜ ( 0 ) = lim T → ∞ 1 T C T . We found that this theory successfully captured E-E correlations , both the full distribution of values and coefficients of individual cell pairs ( details , including figures , can be found in: Supporting Information: S1 Text ) . We then plotted the predicted correlation , C ˜ i j / C ˜ i i C ˜ j j , vs . geometric mean firing rate ν i ν j ( magenta circles in Fig 2 ) . The predicted correlations captured the same positive relationship observed in Monte Carlo results , with R2 values of 0 . 47 , 0 . 4 , and 0 . 36 . Why does a correlation/firing rate relationship emerge in one spiking regime , but not the other ? In feed-forward networks , a positive correlation/firing rate relationship results from transferring common input through fluctuation-driven , asynchronously-firing cells [22 , 30] . In contrast , the amount of shared input into two cells in a recurrent network is determined by both direct and indirect connections through the network . To separate the impact of different network pathways , we decomposed the linear response-predicted correlations at long time scales ( i . e . C ˜ ( 0 ) = lim T → ∞ 1 T C T ) into normalized contributions from n-th order motifs , as described in Methods: Quantifying the role of motifs in networks . Common input from a divergent connection , for example , results from the 2nd-order motif K*C0K . In Fig 3 , we plot the summed contributions up to sixth order—i . e . R ˜ i j k , for k = 1 , 2 , …6—versus geometric mean firing rate , ν i ν j . The total normalized correlation , C ˜ i j / C ˜ i i C ˜ j j , is shown as well . In all cases , we plot long time scale correlations ω = 0; each distinct E-E pair is represented . In the asynchronous regime ( top panel of Fig 3A ) , first-order contributions ( R ˜ 1 ) separate into three distinct “curves” , reflecting a 1-1 relationship with firing rate conditioned on first-order connectivity ( no connection between i and j; one connection between i and j; bidirectional connection between i and j ) . Second-order contributions are overall positive while third-order contributions are overall negative ( consistent with [28] ) ; neither appear to have a relationship with firing rate . Second-order contributions are conspicuously dominant; fifth and sixth order terms are near zero . This qualitative picture changes when we consider the strong asynchronous regime ( bottom panel of Fig 3A ) . First-order contributions follow a similar pattern as in the asynchronous regime , and second-order contributions are likewise positive . However , third-order contributions are positive , and in the heterogeneous network they have a distinctly positive relationship with firing rate ( top panel ) . Thus , in the asynchronous regime , negative third-order contributions partially cancel with positive second-order contributions; in the strong asynchronous regime , first , second , and third-order motifs reinforce each other , contributing to an overall positive relationship with firing rate ( black dots ) . Despite these differences , second-order contributions are the major determinant of total correlation in both regimes . In Fig 3B we plot the same data ( R ˜ i j k ) vs . total correlation , rather than geometric mean firing rate . In the asynchronous regime , second-order contributions cluster near the unity line , suggesting they are strongly predictive of total correlation . To quantify this intuition we computed the fraction of variance explained ( R2 ) by performing a linear regression of total normalized correlation ( C ˜ i j / C ˜ i i C ˜ j j ) against contributions of each order ( Fig 3C ) ; in the asynchronous regime R2 values for R ˜ i j 1 , R ˜ i j 2 , and R ˜ i j 3 were 0 . 004 , 0 . 969 , and 0 . 0002 , respectively . R2 values for higher orders were likewise small: for R ˜ i j 4 , R ˜ i j 5 , and R ˜ i j 6 they were 0 . 047 , 0 . 034 , and 0 . 074 . This statistic was more ambiguous in the strong asynchronous regime , where R2 values for R ˜ i j 1 , R ˜ i j 2 , and R ˜ i j 3 were 0 . 595 , 0 . 474 , and 0 . 509 respectively . However , note that R ˜ i j 1 and R ˜ i j 3 were positive for all cell pairs; R ˜ i j 2 and total correlation were negative for less than 0 . 3% of cell pairs . Thus , we considered how each motif contributed to the total correlation by taking the ratio of each contribution to the total , averaged over all cell pairs ( Fig 3D ) . By this measure , second-order contributions were largest; fraction explained for R ˜ i j 1 , R ˜ i j 2 , and R ˜ i j 3 were 0 . 239 , 0 . 601 , and 0 . 420 , respectively . Note that this measure cannot be used for the asynchronous ( Asyn ) regime because of the negative values of R ˜ i j k . Taken together , this evidence points to a distinguished role for second-order motifs ( R ˜ i j 2 ) in determining total correlation . In the asynchronous regime in particular , R ˜ i j 2 is a near-perfect predictor of total correlation . We next analyzed contributions from specific second-order motifs in Fig 4 . There are four distinct second-order motifs that can correlate two E cells . There are two types of chains , from K2C0 and C0 ( K* ) 2 . An E → E → E chain tends to positively correlate , while an E → I → E chain will negatively correlate; these are shown as blue and green respectively . There are two types of common input , from KC0 ( K* ) ; they correspond to common input from E and I cells , i . e . E ← E → E and E ← I → E . They both lead to positive correlations and are shown as red and magenta respectively . In the asynchronous regime ( left panel of Fig 4A ) , the dominant contributions are I common input ( magenta ) and negative ( E → I → E ) chains ( green ) ; correlating chains ( blue ) and excitatory common input ( red ) are barely visible , as they are clustered near zero . In the strong asynchronous case ( right panel ) , blue and red dots are now visible and show a clear 1-1 trend with firing rate . In both regimes , inhibitory common input appears to be the dominant second-order motif . In Fig 4B we plot the contribution from different second-order motifs vs . the total contribution from second-order motifs , R ˜ i j 2 ( rather than geometric mean firing rate , ν i ν j ) . In both panels , the inhibitory common input ( magenta ) clusters around the unity line , showing it is the best predictor of the total second-order contribution . In Fig 4C we quantify this observation by reporting fraction of variance explained ( R2 ) from linear regressions: the R2 value for inhibitory common input exceeds 0 . 8 in both networks , while the R2 values for all other motifs types are less than 0 . 1 . In conclusion , decomposition of pairwise correlations into graph motifs has shown us two important things: first , while third-order motifs probably contribute to the positive correlation/firing rate relationship observed in the SA regime , second-order motifs still dominate in both regimes . Second , inhibitory common input is the most important second-order motif in both regimes ( Fig 4B ) . In feedforward networks—i . e . , in the absence of a path between two cells—correlations in outputs ( i . e . spike trains ) must arise from correlations in inputs; for example , through shared or common inputs . We have found that inhibitory common input is the dominant contributor to pairwise correlations in both the asynchronous and strong asynchronous regimes; we now turn our attention to modeling this term ( inhibitory common input ) specifically . Previous work that analyzed the relationship between the long-time correlation and firing rate in feedforward networks [22 , 30] quantified a susceptibility function that measures the ratio between output and input correlations: S ≈ ρ c . ( 1 ) If both cells receive a large ( but equal ) number of uncorrelated inputs , c would be the fraction of inputs that are common to both i and j . In the networks examined here , each cell had a fixed in-degree for both excitatory and inhibitory cells; however , for any given pair of cells i and j , the number of E and I inputs that synapsed onto both cells will vary from pair to pair . Thus , we next considered the possibility that our ( negative ) finding in the asynchronous network could be explained by accounting for variable cij . We focus on inhibitory common input , which is the dominant second-order contribution in the asynchronous network ( Fig 4 ) . We segregated pairs by whether they had 0 , 1 , 2 , etc . . common inhibitory inputs; we then use this number as a proxy for c ( recall that each excitatory cell had exactly 7 inhibitory inputs , so that this number divided by 7 approximates the common input fraction; two common inputs imply c ≈ 0 . 28 for example ) . We plot the results for the asynchronous network in Fig 5A , top panel ( data for each distinct value of c is presented by color ) . As we might expect , correlation increases as c increases . However , for a fixed c , there is not an apparent relationship between firing rate and correlation; if anything , there appears to be a slight decrease . Correlation also increases with c in the strong asynchronous network ( Fig 5A , bottom panel ) ; however , here we also see a modest increase with geometric mean firing rate ν i ν j . Previous theoretical work [22 , 30] identified an increase in susceptibility with firing rates in current-driven neurons; we next considered the possibility that this fails to hold for conductance-driven neurons . As described in Methods: Quantifying correlation susceptibility , we estimated correlation susceptibility for each pair of neurons , by using the susceptibility function for each neuron to conductance fluctuations ( computed as part of the linear response theory ) , divided by a measure of the long-timescale spike count variance: S i j ⟨ g I ⟩ = A ˜ ⟨ g I ⟩ , i ( 0 ) A ˜ ⟨ g I ⟩ , j ( 0 ) C ˜ i i ( 0 ) C ˜ j j ( 0 ) ( 2 ) We plotted the results for both networks in Fig 5B; while susceptibility increases with firing rate in the strong asynchronous network ( except for the largest firing rates ) , it actually decreases with firing rate in the asynchronous network . We can contrast with the estimated susceptibility to current fluctuations ( i . e . Aμ , i , with μi , τeff , i , and σeff , i as in Eq 29 ) which we also computed for the same set of cell pairs , shown in Fig 5C . S i j μ = A ˜ μ , i ( 0 ) A ˜ μ , j ( 0 ) C ˜ i i ( 0 ) C ˜ j j ( 0 ) ( 3 ) Here , we see that S i j μ increases with firing rates , in both networks . We next sought to understand how susceptibility depends on neural parameters; that is , we define the single-cell susceptibility S i ⟨ g I ⟩ ≡ A ˜ ⟨ g I ⟩ , i ( 0 ) ν i ( 4 ) where ν i = f ⟨ g I , i ⟩ , σ I , i , ⟨ g E , i ⟩ , σ E , i , σ i , θ i ( 5 ) A ˜ ⟨ g I ⟩ , i ( 0 ) = ∂ f ∂ x 1 ⟨ g I , i ⟩ , σ I , i , ⟨ g E , i ⟩ , σ E , i , σ i , θ i . ( 6 ) ( “∂ ∂ x 1” indicates that derivative is taken with respect to the first argument , 〈gI , i〉 ) . We have also used the asynchronous spiking assumption , that C ˜ i i ( 0 ) ≈ ν i ( compare with Eq 3 ) . This quantity is shown in Fig 6 , where it is plotted vs . firing rate νi ( blue stars ) . Note that this is a negative quantity; since the susceptibility for a neuron pair S i j 〈 g I 〉 = S i 〈 g I 〉 S j 〈 g I 〉 is the product ( and therefore positive ) , an increase in S i 〈 g I 〉 will result in a decrease in S i j 〈 g I 〉 and vice versa . In principle , the firing rate function ( Eq 5 ) —and therefore susceptibility—can depend on all six parameters defining the cell: our next step was to reduce the dimensionality of the problem . We first looked for any possible relationship between single-cell firing rates and cell parameters ( see S1 Text: Approximating single-cell susceptibility in a heterogeneous network , S6 and S7 Figs ) : in both networks , only threshold θi had an obvious relationship with firing rate . Among the remaining parameters , the mean inhibitory conductance 〈gI〉 had the greatest relative range of values in the asynchronous network ( S6A Fig ) . Therefore , we hypothesized that we could accurately capture S i 〈 g I 〉 , by approximating it as a function of the two parameters θi and 〈gI〉 . We reevaluated the firing rate function , where σI , i , 〈gE , i〉 , σE , i and σi have been replaced by their average values: i . e . S ^ i ⟨ g I ⟩ ≡ 1 F ( ⟨ g I , i ⟩ , θ i ) ∂ F ∂ x 1 ⟨ g I , i ⟩ , θ i ( 7 ) where F ( ⟨ g I , i ⟩ , θ i ) ≡ f ⟨ g I , i ⟩ , ⟨ σ I , i ⟩ p , ⟨ ⟨ g E , i ⟩ ⟩ p , ⟨ σ E , i ⟩ p , ⟨ σ i ⟩ p , θ i ( 8 ) and 〈 ⋅ 〉p denotes the population average . The results are also illustrated in Fig 6 ( red triangles ) . In the asynchronous regime ( Fig 6A ) , the results are remarkably close to the original quantities , indicating that using average parameter values has little effect; in the strong asynchronous regime ( Fig 6B ) the difference is larger , but the points appear to occupy the same “cloud” . However , we can now visualize the susceptibility as a function of only two parameters , and we do so in Fig 7 by evaluating S ^ i 〈 g I 〉 on a ( θ , 〈gI〉 ) grid; the points corresponding to the actual excitatory cells in our network are illustrated in red . In both the asynchronous and strong asynchronous regimes , the red stars form a scattered cloud around the average value 〈〈gI , i〉〉p , with no obvious relationship with θi . This fact motivated a further simplification , S ^ ^ i ⟨ g I ⟩ ≡ 1 F ( ⟨ ⟨ g I , i ⟩ ⟩ p , θ i ) ∂ F ∂ x 1 ⟨ ⟨ g I , i ⟩ ⟩ p , θ i ( 9 ) i . e . , we replaced 〈gI , i〉 with its population average , 〈〈gI , i〉〉p , in essence approximating a one-dimensional “path” that the cells take through parameter space . The results are shown in Fig 6 ( gold squares ) and , as we should expect , allow us to discern a functional relationship with firing rate νi; importantly , it appears to capture the average behavior of the actual susceptibility values S i 〈 g I 〉 . Here , we can see clearly that in the asynchronous regime , correlations should actually decrease with firing rate , for νi > 5 Hz . In the strong asynchronous regime , correlations will increase with firing rate , saturating around 10–15 Hz . Finally , recall that our actual network sampled a relatively small part of the ( θ , 〈gI〉 ) plane . This may be attributed to the fact that we generated firing rate diversity ( and therefore heterogeneity ) , by modulating cell excitability through the cell threshold θi . How might our results have changed , if we had generated firing rate diversity through some other mechanism ? In both regimes , we can increase firing rates by either decreasing 〈gI , i〉 , or by decreasing θ ( see S7 Fig ) . To explore this , we computed susceptibility values along another curve in the ( θ , 〈gI〉 ) plane; specifically , we held θ fixed and instead varied 〈gI〉 ( illustrated with black squares on Fig 7 ) ; i . e . S ^ θ = 1 ⟨ g I ⟩ ( ⟨ g I ⟩ ) ≡ 1 G ( ⟨ g I ⟩ , θ ) ∂ G ∂ x 1 ⟨ g I ⟩ , θ | θ = 1 ( 10 ) where G ( ⟨ g I ⟩ , θ ) = f ⟨ g I ⟩ , ⟨ σ I , i ⟩ p , ⟨ ⟨ g E , i ⟩ ⟩ p , ⟨ σ E , i ⟩ p , ⟨ σ i ⟩ p , θ Results are shown in Fig 6 ( purple diamonds ) and show a strikingly different relationship with firing rate; in the asynchronous regime , correlations should increase with firing rate for ν < 15 Hz; in the strong asynchronous regime correlations will increase with firing rate , saturating near 20 Hz . To summarize the previous two subsections , we first defined a single-cell susceptibility function ( Eq 4 ) , which captures a linear approximation to the cell’s response to input . This quantity relies on an underlying firing rate , which is a function of all parameters that define single-cell dynamics; in this case , six . Each cell occupies a point in this six-dimensional parameter space . We found that in each network studied here , the occupied points approximately lie along a one-dimensional path through this parameter space , along which we could visualize the susceptibility . Finally , we considered the consequences of taking other paths through this parameter space: these paths can be interpreted as generating firing rate heterogeneity using other network mechanisms . Previous work has identified low-dimensional structure in neural correlation matrices [25 , 38–41]; its origin is not always known [3] . We next hypothesized that the positive correlation-firing rate relationship we observed in the strong asynchronous regime , might be reflected in low-dimensional structure in the correlation matrix . For simplicity , suppose that correlations were really represented by a function of firing rate ( as in [22] ) : i . e . ρij = cS ( νi ) S ( νj ) . Then we could represent the off-diagonal part of the correlation matrix as CT = cSST , where S is a length N vector such that Si = S ( νi ) ; that is , CT would be a rank-one matrix . We followed the procedure outlined in Methods: Low-rank approximation to the correlation matrix to approximate each correlation matrix , CT , as the sum of a diagonal matrix and low-rank matrix: C T ≈ C T diag + R 1 = λ I + ( σ 1 - λ ) u 1 u 1 T ( 11 ) where λ is given in closed form by the eigenvalues of CT: λ = λ 1 - ∑ j > 1 ( λ 1 - λ j ) 2 ∑ j > 1 λ 1 - λ j ( 12 ) and σ1 , u1 are the first singular value and singular vector of CT . In Fig 8 , we show the results from heterogeneous networks in both the asynchronous ( top panel in each subfigure ) and strong asynchronous ( bottom panel in each subfigure ) regimes . We first show CT − λI , where λ is given by Eq 12 , in Fig 8A . Cells are ordered by ( decreasing ) firing rate . While no pattern is visible in the asynchronous state ( top panel ) , the strong asynchronous state ( bottom panel ) shows larger values in the upper left corner , suggesting that correlation increases with firing rate . This is even more visible in the rank one approximation , ( σ 1 - λ ) u 1 u 1 T , shown in Fig 8B . We now use C T diag + R 1 to approximate CT , and compare the results , cell pair-by-cell pair ( Fig 8C ) . In the asynchronous network , the approximated correlations take on a narrow range ( between 0 and 0 . 01 , compared to between −0 . 015 and 0 . 03 for the measured coefficients ) and do not show an obvious positive relationship . In the strong asynchronous regime , the range is more accurate ( between 0 . 02 and 0 . 1 , vs . 0 . 01 and 0 . 15 for the measured coefficients ) and the points cluster around the unity line . In Fig 8D , we plot the weight of each cell in the first singular vector , ( u1 ) j vs . the firing rate νj . We can clearly see a positive relationship in the strong asynchronous regime ( bottom panel ) , suggesting that the positive relationship between correlation and firing rate is related to the success of the low-rank approximation .
The networks studied here were not encoding a stimulus; correlations were generated by recurrent activity , given that each neuron had a baseline firing rate in the absence of recurrent input . However , we can readily connect this network to a stimulus coding task , in order to understand how the correlation-firing rate relationship can impact coding . Consider a population of cells that is responsible for encoding a single scalar stimulus θ , such as movement direction or orientation of a visual stimulus , and that each cell has roughly a bell-shaped tuning curve . Furthermore , we model an incoming stimulus by modulating a stimulus-dependent background current Ii , ( θ ) ; i . e . , cells which prefer the current stimulus have a higher level of current , and thus a higher firing rate , than cells which prefer an orthogonal or opposite stimulus . The network we studied here would model the response to a single stimulus θ0; that is , the firing rate diversity we observe is present because some cells are strongly tuned to the current stimulus , while others are not . We could extend this model , by resetting background currents to model a complete set of stimuli {θ1 , θ2 , …θn − 1} . For each stimulus θj , correlations would show the rough firing rate dependence displayed in the strong asynchronous network , resulting in a stimulus-dependent correlation structure in which pairwise correlations vary like geometric mean firing rate . This is the structure analyzed in [21 , 27]: the authors found that such a stimulus-dependent correlation code enhances information , when compared to a stimulus-independent code with the same average correlation level . Intuitively , the mean population response lives on the surface of a ( hyper- ) sphere in neural response space; the population encodes location on this surface . Positive correlations between similarly tuned cells produce response distributions that are stretched in the radial direction , “orthogonal” to this sphere , and thus have a minimal impact on the encoded variable . Moreover , the mechanism that produced stimulus-dependent correlations in [21 , 27] was similar to that shown here ( see also [25] ) ; common input modulated by stimulus-dependent gain factors . Here , we demonstrated how these stimulus-dependent gain factors might arise ( or not ) in a recurrent network . If excitation is tuned to put the network in the strong asynchronous regime , then the ( stimulus-dependent ) correlation structure that results will be favorable to coding . If excitation is tuned to put the network in the asynchronous regime , then correlations are overall low and not stimulus-dependent ( although , given that average correlations are not matched , we do not here compare information contained within the two networks ) . This work has , necessarily , focused only on a subset of network attributes that might affect firing statistics . One important feature is the frequency of higher-order graph motifs; experiments have shown that specific motifs will occur more frequently , than would be expected in an Erdős-Rényi network with fixed single-cell connection probability [49] . Theoretical work has found that in networks of integrate-and-fire neurons , an overabundance of divergent and chain motifs will lead to enhanced correlation [33] ( this finding does depend on the dynamical regime; different motifs impact correlations in networks of coupled oscillators [32] ) . In [33] , the authors use the assumption of homogeneous single-cell characteristics to find parsimonious and instructive formulae for the average correlation , and give a roadmap for how this might be generalized to heterogeneous networks . We look forward to considering the combined effect of single-cell and network heterogeneity in future work . Another source of cell-to-cell heterogeneity is how cells respond to stimuli , as emphasized in the previous discussion [17 , 20 , 21 , 27 , 50] ( see [19] for a review ) . Here , we did not consider a specific sensory system with tuning but rather focus on the general question of how the distribution of correlation values arise in recurrent networks . Given the previous discussion , one next step will be to investigate how correlations covary with firing rates , when cell-to-cell heterogeneity is produced by stimulus tuning in a structured network responding to a single variable ( such as direction or orientation ) . Finally , for numerical tractability our simulations here were performed in relatively small networks . While high average correlations have been measured in experiments [51] , theoretical models of asynchronous networks have found that correlations must go to zero as the system becomes large ( N → ∞ ) [2] . However , recent work has found that this does not have to be true , as long as spatial structure is introduced into the network [52] . We anticipate that this may carry over to other forms of heterogeneity , such as single-cell variability , and that therefore the effect we observe here persists for larger networks . We look forward to reporting on this in future work .
We considered randomly connected networks of excitatory and inhibitory neurons . Each cell was a linear integrate-and-fire model with second-order alpha-conductances , i . e . membrane voltage νi was modeled with a stochastic differential equation , as long as it remained beneath a threshold θi: τ m d ν i d t = - ν i - g E , i ( t ) ( ν i - E E ) - g I , i ( t ) ( ν i - E I ) + σ i τ m ξ i ( t ) , ( 13 ) When νi reaches θi , it is reset to 0 following a refractory period: ν i ( t + τ ref ) → 0 , ν i ( t ) ≥ θ i ( 14 ) Each neuron was driven by a Gaussian , white background noise , with magnitude σi depending only on the cell type; that is , 〈ξi ( t ) 〉 = 0 and 〈ξi ( t ) ξi ( t + s ) 〉 = δ ( s ) . The membrane time constant , τm , and excitatory and inhibitory synaptic reversal potentials , E E and E I , are the same for every cell in the network . Each cell responded to synaptic input through conductance terms , gE , i and gI , i , which are each governed by a pair of differential equations: τ d , X d g X , i d t = - g X , i + g X , i ( 1 ) ( 15 ) τ r , X d g X , i ( 1 ) d t = - g X , i ( 1 ) + τ r , X α X W Y X N Y X ∑ j ∈ X , j → i ∑ k δ ( t - t j , k ) ( 16 ) where Y = {E , I} denotes the type of cell i and X = {E , I} denotes the type of the source neuron j . Each spike is modeled as a delta-function that impacts the auxiliary variable g X , i ( 1 ) ; here tj , k is the k-th spike of cell j . The rise and decay time constants τr , X and τd , X and pulse amplitude αX depend only on the type of the source neuron; i . e . they are otherwise the same across the population . The parameter WYX denotes the strength of X → Y synaptic connections , which are ( once given the type of source and target neurons ) identical across the population . The “raw” synaptic weight ( listed in Table 1 ) is divided by NYX , the total number of X → Y connections received by each Y-type cell . We chose connections to be homogeneous and relatively dense , consistent with the local architecture of cortex . Connection probabilities ranged from 20%–40% , consistent with experimentally measured values [53–55] . For our baseline network state , we then chose synaptic weights so the network is moderately inhibition-dominated ( αEWIE < αIWII and αEWEE < αIWEI ) ; that is both E and I cells receive more inhibition than excitation ) and shows noisy spiking consistent with the classical asynchronous state . Each neuron receives a fixed number of incoming connections , the identities of which are chosen randomly . ( The specific cell ID numbers differ in the different simulations shown below . ) For most of the networks we discuss here N = 100 with the 80/20 ratio typical of cortex ( i . e nE = 80 , nI = 20 ) . Each excitatory cell receives NEE = 32 ( 40% ) excitatory and NEI = 7 ( 35% ) inhibitory connections; each inhibitory cell receives NIE = 16 ( 20% ) and NII = 8 ( 40% ) inhibitory projections . In heterogeneous networks , the threshold θi varied across the population . For both excitatory and inhibitory neurons , the thresholds θi were chosen from a log-normal distribution between 0 . 7 and 1 . 4 ( where the rest potential , Vr = 0 ) . To be precise , log θi was chosen from a ( truncated ) normal distribution with mean - s θ 2 / 2 and standard deviation sθ . With this choice , θi has mean 1 and variance: e s θ 2 - 1 . Thus we can view sθ as a measure of the level of threshold heterogeneity . Throughout this paper , we set sθ = 0 . 2 , which results in a wide range of firing rates compared to the homogeneous case . This was the only source of cell-to-cell heterogeneity; all other parameters were identical across the population , conditioned on neuron type ( values listed in Table 2 ) . In homogeneous networks , the threshold was the same across the population: θi = 1 . Monte Carlo simulations were performed using the stochastic forward- Euler method ( Euler-Maruyama ) , with a time step much smaller than any time scale in the system ( Δt = 0 . 01 ms ) . Each network was simulated for one second of simulation time , after an equilibration time . Then , a large number of realizations of this interval ( nR = 105 ) were simulated . Spike counts were retained in each 1 ms window ( for a total of 1000 windows ) within a realization . With this large number of realizations/trials , the error bars on the resulting time-dependent firing rates were small . Therefore we emphasize that the firing rate pattern is largely driven by network connectivity; while firing is driven by random fluctuations in the background noise , any cell-to-cell variability in the trial-averaged firing rates are not an artifact of the finite number of trials . In general , computing the response of even a single neuron to an input requires solving a complicated , nonlinear stochastic process . However , it often happens that the presence of background noise linearizes the response of the neuron , so that we can describe this response as a perturbation from a background state . This response is furthermore linear in the perturbing input and thus referred to as linear response theory [56] . The approach can be generalized to yield the dominant terms in the coupled network response , as well; we will use the theory to predict the covariance matrix of activity . We first consider the case of a single cell: an LIF neuron responding to a mean zero current ϵXi ( t ) τ m d ν i d t = - ( ν i - E L ) + E i + σ i τ m ξ i ( t ) + ϵ X i ( t ) . ( otherwise , the mean of Xi can simply be absorbed into Ei ) . For a fixed input ϵXi ( t ) , the output spike train yi ( t ) will be slightly different for each realization of the noise ξi ( t ) and initial condition νi ( 0 ) . Therefore we try to work with the time-dependent firing rate , νi ( t ) ≡ 〈yi ( t ) 〉 , which is obtained by averaging over all realizations and initial conditions . Linear response theory proposes the ansatz that the firing rate can be described as a perturbation from a baseline rate proportional to the input ϵXi: ν i ( t ) = ν i , 0 + ( A i * ϵ X i ) ( t ) ; ( 17 ) νi , 0 is the baseline rate ( when X = 0 ) and Ai ( t ) is a susceptibility function that characterizes this firing rate response up to order ϵ [22 , 29 , 57] . We now consider the theory for networks; here cell i responds to the spike train of cell j , yj ( t ) , via the synaptic weight matrix W , after convolution with a synaptic filter Fj ( t ) : τ m d ν i d t = - ( ν i - E L ) + E i + σ i τ m ξ i ( t ) + ∑ j W i j F j * y j ( t ) In order to consider joint statistics , we need the trial-by-trial response of the cell . We first propose to approximate the response of each neuron as: y i ( t ) ≈ y i 0 ( t ) + A i * ∑ j ( J i j * y j ) ( t ) ; ( 18 ) that is , each input Xi has been replaced by the synaptic input , and Jij = WijFj ( t ) includes both the i ← j synaptic weight Wij and synaptic kernel Fj ( normalized to have area 1 ) ; Ai ( t ) is the susceptibility function from Eq 17 . In the frequency domain this becomes y ˜ i ( ω ) = y ˜ i 0 + A ˜ i ( ω ) ∑ j J ˜ i j ( ω ) y ˜ j ( ω ) ( 19 ) where y ˜ i = F [ y i - ν i ] is the Fourier transform of the mean-shifted process ( νi is the average firing rate of cell i ) and f ˜ = F [ f ] for all other quantities . In matrix form , this yields a self-consistent equation for y ˜ in terms of y ˜ 0: I - K ˜ ( ω ) y ˜ = y ˜ 0 ⇒ y ˜ = I - K ˜ ( ω ) - 1 y ˜ 0 ( 20 ) where K ˜ i j ( ω ) = A ˜ i ( ω ) J ˜ i j ( ω ) is the interaction matrix , in the frequency domain . The cross-spectrum is then computed ⟨ y ˜ ( ω ) y ˜ * ( ω ) ⟩ = I - K ˜ ( ω ) - 1 ⟨ y ˜ 0 ( ω ) y ˜ 0 * ( ω ) ⟩ I - K ˜ * ( ω ) - 1 ( 21 ) To implement this calculation , we first solve for a self-consistent set of firing rates: that is , νi is the average firing rate of τ m d ν i d t = - ( ν i - E L ) + ( E i + E [ f i ] ) + σ i τ m ξ i ( t ) ( 22 ) where E[fi] = ∑j Wijνj . We must then compute the power spectrum 〈 y ˜ 0 ( ω ) y ˜ 0 * ( ω ) 〉 and the susceptibility Ai ( ω ) , which is the ( first order in ϵ ) response in the firing rate r i ( t ) = r i 0 + ϵ A i ( ω ) exp ( ı ω t ) in response to an input current perturbation X ( t ) = ϵ exp ( ıωt ) ( here ı is used for - 1 , while i denotes an index ) . Both can be expressed as the solution to ( different ) first-order boundary value problems and solved via Richardson’s threshold integration method [47 , 58] . In our simulations , we used conductance-based neurons; this requires modification , compared with the simpler current-based models . We first approximate each conductance-based neuron as an effective current-based neuron with reduced time constant , following the discussion in [59] . First , separate each conductance into mean and fluctuating parts; e . g . gE , i → 〈gE , i〉 + ( gE , i − 〈gE , i〉 ) . Then we identify an effective conductance g0 , i and potential μi , and treat the fluctuating part of the conductances as noise , i . e . gE , i − 〈gE , i〉→σE , i ξE , i ( t ) : τ m d ν i d t = - g 0 , i ( ν i - μ i ) + σ E , i ξ E , i ( t ) ( ν i - E E ) + σ I , i ξ I , i ( t ) ( ν i - E I ) + σ i 2 τ m ξ i ( t ) ( 23 ) where g 0 , i = 1 + ⟨ g E , i ⟩ + ⟨ g I , i ⟩ ( 24 ) μ i = E L + E i + ⟨ g E , i ⟩ E E + ⟨ g I , i ⟩ E I g 0 , i ( 25 ) σ E , i 2 = Var g E , i ( t ) = E g E , i ( t ) - ⟨ g E , i ⟩ 2 ( 26 ) σ I , i 2 = Var g I , i ( t ) = E g I , i ( t ) - ⟨ g I , i ⟩ 2 ( 27 ) We next simplify the noise terms by writing ν i - E E = ν i - μ i + μ i - E E ( 28 ) and assume that the fluctuating part of the voltage , νi − μi , is mean-zero and uncorrelated with the noise terms ξE , i ( t ) [59] . That allows us to define an effective equation τ eff , i d ν i d t = - ( ν i - μ i ) + σ eff , i 2 τ eff , i η eff , i ( t ) ( 29 ) where τ eff , i = τ m g 0 , i ( 30 ) σ eff , i 2 = σ E , i 2 ( μ i - E E ) 2 + σ I , i 2 ( μ i - E I ) 2 + σ i 2 τ m g 0 , i τ m ( 31 ) and the fluctuating voltage , νi ( t ) − μi , now makes no contribution to the effective noise variance . Finally , we consider how to model the conductance mean and variance , e . g . 〈gE , i〉 and σ E , i 2 . In our simulations , we used second order α-functions: each conductance gX , i is modeled by two equations that take the form τ r , X d g X , i ( 1 ) d t = - g X , i ( 1 ) + τ r , X α ^ X , i ∑ k δ ( t - t k ) ( 32 ) τ d , X d g X , i d t = - g X , i + g X , i ( 1 ) ( 33 ) where X = E , I and the summation is over all type-X spikes incoming to cell i . ( For notation purposes , α ^ X , i includes all factors that contribute to the pulse size in Eq 16 , including synapse strength and pulse amplitude . ) The time constants τr , X , τd , X may depend on synapse type; the spike jumps α ^ X , i may depend on synapse type and target cell identity . We assume that each spike train is Poisson , with a constant firing rate: i . e . each spike train is modeled as a stochastic process S ( t ) with ⟨ S ( t ) ⟩ = ν ⟨ S ( t ) S ( t + τ ) ⟩ - ν 2 = ν δ ( τ ) Then a straightforward but lengthy calculation shows that ⟨ g X , i ( t ) ⟩ = α ^ X , i ν X , i τ r , X ( 34 ) Var g X , i ( t ) = 1 2 α ^ X , i 2 ν X , i τ r , X τ r , X τ r , X + τ d , X ( 35 ) where νX , i is the total rate of type-X spikes incoming to cell i . We now describe how these considerations modify the linear response calculation . First , for the self-consistent firing rate calculation , Eq 22 is replaced by an equation with a modified time constant , conductance , and noise ( Eq 29 ) . We next compute the susceptibility in response to parameters associated with the conductance , i . e . 〈gE , i〉 and σ E , i 2 . This differs from the current-based case in two ways: first , there is voltage-dependence in the diffusion terms , which results in a different Fokker-Planck equation ( and thus a different boundary value problem to be solved for the power spectrum 〈 y ˜ 0 ( ω ) y ˜ 0 * ( ω ) 〉 ) . Second , modulating the rate of an incoming spike train will impact both the mean and variance of the input to the effective equation , Eq 23 ( via μi and σX , i ) . Furthermore , this impact may differ for excitatory and inhibitory neurons , giving us a total of four parameters that can be varied in the effective equation . However , neither consideration presents any essential difficulty [47] . Therefore we apply Richardson’s threshold integration method directly to Eq 23: τ m d ν i d t = - g 0 , i ( ν i - μ i ) + σ E , i ξ E , i ( t ) ( ν i - E E ) + σ I , i ξ I , i ( t ) ( ν i - E I ) + σ i 2 τ m ξ i ( t ) ( 36 ) When we compute susceptibilities , the parameter to be varied is either a mean conductance—〈gE , i〉 → 〈gE , i〉0 + 〈gE , i〉1 exp ( ıωt ) or 〈gI , i〉 → 〈gI , i〉0 + 〈gI , i〉1 exp ( ıωt ) —or a variance—σ E , i 2 → ( σ E , i 2 ) 0 + ( σ E , i 2 ) 1 exp ( ı ω t ) or σ I , i 2 → ( σ I , i 2 ) 0 + ( σ I , i 2 ) 1 exp ( ı ω t ) . Thus we have a total of four susceptibility functions A ˜ 〈 g E 〉 , i ( ω ) , A ˜ 〈 g I 〉 , i ( ω ) , A ˜ σ E 2 , i ( ω ) , and A ˜ σ I 2 , i ( ω ) . Since the Fokker-Planck equation to be solved is linear , we can compute both susceptibilities separately and then add their effects . We now have the interaction matrix: K ˜ i j ( ω ) = A ˜ ⟨ g E ⟩ , i ( ω ) J ˜ i j ( ω ) + A ˜ σ E 2 , i ( ω ) L ˜ i j ( ω ) , j excitatory A ˜ ⟨ g I ⟩ , i ( ω ) J ˜ i j ( ω ) + A ˜ σ I 2 , i ( ω ) L ˜ i j ( ω ) , j inhibitory ( 37 ) where L ˜ ( ω ) plays a similar role as J ˜ , but for the effect of incoming spikes on the variance of conductance . Its relationship to J ˜ ( either in the frequency or time domain ) is given by the same simple scaling shown in Eq 35: i . e . , for j excitatory , L ˜ i j ( ω ) = J ˜ i j ( ω ) × α ^ E , i 2 × τ r , E τ r , E + τ d , E ( 38 ) where the first factor comes from the effective spike amplitude α ^ E , i ( and is the scale factor proposed in [47] , Eq ( 64 ) ) , and the second arises from using second-order ( vs . first-order ) alpha-functions . We use a modified version of the implementation given by [29] for Richardson’s threshold integration algorithm [47 , 58] to compute rate νi , power 〈 y ˜ i 0 ( ω ) y ˜ i 0 * ( ω ) 〉 , and the various susceptibilities ( A ˜ 〈 g E 〉 , i ( ω ) , A ˜ 〈 g I 〉 , i ( ω ) , A ˜ σ E 2 , i ( ω ) , and A ˜ σ I 2 , i ( ω ) ) for an LIF neuron . We validated our code using exact formulas known for the LIF [60] , and qualitative results from the literature [61] . Linear response theory yields the cross spectrum of the spike train , 〈 y ˜ i ( ω ) y ˜ j * ( ω ) 〉 , for each distinct pair of neurons i and j ( see Eq 21 ) . To recover a representative set of statistics , we rely on several standard formulae relating this function to other statistical quantities . The cross correlation function , Cij ( τ ) , measures the similarity between two processes at time lag τ , while the cross spectrum measures the similarity between two processes at frequency ω: C i j ( τ ) ≡ ⟨ ( y i ( t ) - ν i ) ( y j ( t + τ ) - ν j ) ⟩ ( 39 ) C ˜ i j ( ω ) ≡ ⟨ y ˜ i ( ω ) y ˜ j ( ω ) ⟩ ( 40 ) The Weiner-Khinchin theorem [56] implies that { C i j , C ˜ i j } are a Fourier transform pair: that is , C ˜ i j ( ω ) = ∫ - ∞ ∞ C i j ( t ) e - 2 π ı ω t d t ( 41 ) In principle , the crosscorrelation C ( t ) and cross-spectrum C ˜ ( ω ) matrices are functions on the real line , reflecting the fact that correlation can be measured at different time scales . In particular , for a stationary point process the covariance of spike counts over a window of length T , ni and nj , can be related to the crosscorrelation function Cij by the following formula [4]: Cov T ( n i , n j ) = ∫ - T T C i j ( τ ) T - ∣ τ ∣ d τ ( 42 ) The variance of spike counts over a time window of length T , ni , is likewise given by integrating the autocorrelation function Cii: Var T ( n i ) = ∫ - T T C i i ( τ ) T - ∣ τ ∣ d τ ( 43 ) It can be helpful to normalize by the time window , i . e . Cov T ( n i , n j ) T = ∫ - T T C i j ( τ ) 1 - ∣ τ ∣ T d τ ; ( 44 ) we can now see that for an integrable cross correlation function ( and bearing in mind that the cross-spectrum is the Fourier transform of the cross correlation ) , that lim T → ∞ Cov T ( n i , n j ) T = ∫ - ∞ ∞ C i j ( τ ) d τ = C ˜ i j ( 0 ) ( 45 ) while lim T → 0 Cov T ( n i , n j ) T 2 = 1 T ∫ - T T C i j ( τ ) 1 - ∣ τ ∣ T d τ ≈ C i j ( 0 ) ( 46 ) Thus , we can use C ˜ i j ( 0 ) and Cij ( 0 ) as measures of long and short time correlations respectively . Finally , the Pearson’s correlation coefficient of the spike count defined as: ρ T , i j = Cov T ( n i , n j ) Var T ( n i ) Var T ( n j ) ( 47 ) is a common normalized measure of noise correlation , with ρ ∈ [−1 , 1] . While CovT and VarT grow linearly with T ( for a Poisson process , for example ) , ρT , ij in general will not ( although it may increase with T ) . In general , ρT , ij depends on the time window T; however for readability we will often suppress the T-dependence in the notation ( and use ρij instead ) . We next explain how we can use the results of linear response theory to give insight into the role of different paths in the network . We begin with our predicted cross-spectrum ( Eqs 21 and 40 ) and apply a standard series expansion for the matrix inverse: C ˜ ( ω ) = I - K ˜ ( ω ) - 1 C ˜ 0 ( ω ) I - K ˜ * ( ω ) - 1 ( 48 ) = ∑ k = 0 ∞ K ˜ ( ω ) k C ˜ 0 ( ω ) ∑ l = 0 ∞ K ˜ ( ω ) l ( 49 ) = ∑ k = 0 ∞ ∑ l = 0 ∞ K ˜ ( ω ) k C ˜ 0 ( ω ) K ˜ ( ω ) l ( 50 ) where C ˜ 0 ( ω ) is a diagonal matrix containing the power spectra of the unperturbed processes; i . e . C ˜ i i 0 ≡ 〈 y ˜ i ( ω ) y ˜ i ( ω ) 〉 . This double sum will converge as long as the spectral radius of K ˜ is less than 1 [29] . By truncating this double sum to contain terms such that k + l ≤ n , we define the nth approximation to the cross-spectrum: C ˜ ( ω ) ≈ C ˜ n ( ω ) ( 51 ) = C ˜ 0 ( ω ) + ∑ k = 1 n ∑ l = 0 k K ˜ ( ω ) k - l C ˜ 0 ( ω ) K ˜ * ( ω ) l ( 52 ) Each distinct term in the inner sum can be attributed to a particular undirected path of length k . Terms of the form K ˜ k C ˜ 0 and C ˜ 0 ( K ˜ * ) k account for unidirectional paths from j → i and i → j respectively; the term ( K ˜ ( ω ) ) k - l C ˜ 0 ( ω ) ( K ˜ * ( ω ) ) l captures the contribution from a cell that has a length l path onto cell j and a length k − l path onto cell i . Thus , we can use Eq 52 to decompose the correlation into contributions from different motifs ( [28] , see also [31 , 62] ) . We can also consider the contribution from all length-n paths; that is , P ˜ n = C ˜ n ( ω ) - C ˜ n - 1 ( ω ) = ∑ l = 0 n K ˜ ( ω ) n - l C ˜ 0 ( ω ) K ˜ * ( ω ) l If the sum in Eq 50 converges , we should expect the magnitude of contributions to decrease as n increases . We will also show the normalized contribution from length-n paths , which we define as follows: let Λ ( ω ) be the diagonal matrix with Λ i i ( ω ) = C ˜ i i ( ω ) . Then we define the matrix of contributions from length-n paths R ˜ n as follows: R ˜ n ( ω ) = Λ - 1 / 2 ( ω ) P ˜ n ( ω ) Λ - 1 / 2 ( ω ) ( 53 ) Equivalently , R ˜ i j n ( ω ) = P ˜ i j n ( ω ) / C ˜ i i ( ω ) C ˜ j j ( ω ) . This effectively normalizes the cross correlation by the autocorrelation; in particular , we can use this to decompose the correlation coefficient ( Eq 47 ) for long time windows , because lim n → ∞ ∑ k = 0 n R ˜ k ( 0 ) = lim T → ∞ ρ T , i j . In general , we will show long-timescale correlation ( e . g . C ˜ ( 0 ) or R ˜ n ( 0 ) ) ( Eq 45 ) ; results were qualitatively similar for other timescales . We next consider how to quantify the ( linear ) susceptibility of correlation to a change in parameter . Returning to Eq 17 , but written in terms of the single-cell response: y i ( t ) = y i , 0 + ( A μ , i * X μ ) ( t ) ⇒ ( 54 ) y ˜ i ( ω ) = y ˜ i , 0 ( ω ) + A ˜ μ , i ( ω ) X ˜ μ ( ω ) ( 55 ) Here , Xμ ( t ) is a ( possibly ) time-dependent change in a parameter , such as input current or mean inhibitory conductance; yi , 0 is the baseline spike train ( when X = 0 ) . Aμ , i ( t ) is a susceptibility function that characterizes the cell’s response ( to the parameter variation ) as long as Xμ ( t ) is small [22 , 29 , 57] . Following [22] , the cross-spectrum of y can now be approximated as: C ˜ i j ( ω ) ≡ ⟨ y ˜ i * y ˜ j ⟩ ≈ ⟨ y ˜ i , 0 * y ˜ j , 0 ⟩ + ⟨ A ˜ μ , i * X ˜ μ * y ˜ j , 0 ⟩ + ⟨ A ˜ μ , j X ˜ μ y ˜ i , 0 * ⟩ + A ˜ μ , i * A ˜ μ , j ⟨ X ˜ μ * X ˜ μ ⟩ ( 56 ) = A ˜ μ , i * ( ω ) A ˜ μ , j ( ω ) C ˜ μ ( ω ) ( 57 ) where C ˜ μ ( ω ) is the spectrum of the parameter variation . The susceptibility has an appealing interpretation in the limit ω → 0 , as the derivative of the classical f-I curve: lim ω → 0 A ˜ μ , i ( ω ) = d ν i d μ ( 58 ) where νi is the steady-state firing rate of cell i , assuming we can measure it for specific values of the parameter μ . lim T → ∞ ρ T , i j = lim T → ∞ Cov T ( n i , n j ) Var T ( n i ) Var T ( n j ) = C ˜ i j ( 0 ) C ˜ i i ( 0 ) C ˜ j j ( 0 ) ( 59 ) ≈ A ˜ μ , i ( 0 ) A ˜ μ , j ( 0 ) C ˜ i i ( 0 ) C ˜ j j ( 0 ) C ˜ μ ( 0 ) ( 60 ) This motivates the definition of a correlation susceptibility , which approximates the change in pairwise correlation induced by a parameter change experienced by both cells i and j: S i j μ = A ˜ μ , i ( 0 ) A ˜ μ , j ( 0 ) C ˜ i i ( 0 ) C ˜ j j ( 0 ) ( 61 ) If this increases with firing rate—that is , if d S i j μ d ν > 0—then correlations will also increase with firing rate . We can further analyze this quantity by making an assumption for asynchronous spiking , that spike count variance is equal to spike count mean; i . e . Var T ( n i ) = T ν i ⇒ C ˜ i i = ν i . Then S i j μ ≈ 1 ν i ν j A ˜ μ , i ( 0 ) A ˜ μ , j ( 0 ) = A ˜ μ , i ( 0 ) ν i A ˜ μ , j ( 0 ) ν j ( 62 ) which motivates the definition of the single-cell quantity S i ⟨ g I ⟩ ≡ A ˜ ⟨ g I ⟩ , i ( 0 ) ν i In general , the firing rate depends on all single cell parameters included in Eqn . ; i . e . there exists some function f such that ν i = f ⟨ g I , i ⟩ , σ I , i , ⟨ g E , i ⟩ , σ E , i , σ i , θ i ( 63 ) A ˜ ⟨ g I ⟩ , i ( 0 ) = ∂ f ∂ x 1 ⟨ g I , i ⟩ , σ I , i , ⟨ g E , i ⟩ , σ E , i , σ i , θ i ( 64 ) ( recall that the susceptibility for ω = 0 is the derivative of the firing rate with respect to the appropriate parameter ( here , mean inhibitory conductance 〈gI〉 ) . We consider the correlation matrix of spike counts , as measured from Monte Carlo simulations; while these are in principle related to the cross-correlation functions C ( t ) defined in Methods: Computing statistics from linear response theory we will use CT to denote the matrix of correlation coefficients measured for time window T; i . e . C T i j = ρ T , i j ( 65 ) Furthermore , we will restrict to the E-E correlations; i . e . CT will be a nE × nE matrix , with ones on the diagonal ( as ρT , ii = 1 ) . When we examined the singular values of the E-E correlation matrices obtained from Monte Carlo simulations , we noticed a consistent trend: there was usually one large cluster with one positive outlier . This motivates the following simple idea: by subtracting off a multiple of the identity matrix , λI , we shift the cluster towards zero; consequently CT − λI is close to a rank-1 matrix . We then propose to use the sum of the two as an approximation to CT: C T ≈ λ I + ( σ 1 - λ ) u 1 u 1 T . ( 66 ) We seek the value λ which maximizes the fraction of the Frobenius norm explained by the first singular vector: i . e . in terms of the singular values , λ = max λ σ ˜ 1 2 ∑ j = 1 r σ ˜ j 2 ( 67 ) = max λ ( σ 1 - λ ) 2 ∑ j = 1 r ( σ j - λ ) 2 ( 68 ) Since CT is symmetric semi-positive definite , the singular values σj are equal to the eigenvalues λj: here σ1 ≥ σ2 ≥ ⋯ ≥ σr ≥ 0 and r is the rank of CT . This has an exact solution: λ = λ 1 - ∑ j > 1 ( λ 1 - λ j ) 2 ∑ j > 1 λ 1 - λ j ( 69 ) Because we have subtracted a multiple of the identity matrix , none of the singular vectors will have changed . We then have C T ≡ λ I + ( C T - λ I ) ( 70 ) = λ I + ∑ i = 1 r ( σ i - λ ) u i u i T ( 71 ) By truncating this sum , we approximate C with a shifted low-rank matrix: C T ≈ C T diag + R 1 ≡ λ I + ( σ 1 - λ ) u 1 u 1 T ( 72 ) This procedure is similar to factor analysis , in which one seeks to explain a data vector as the sum of a random vector ( u ) and the linear combination of some number of latent factors ( z ) [48]: x = Λ z + u ; the entries of x would then have the correlation matrix Ψ + ΛΛT , where Ψ is a diagonal matrix containing the variances of u . | A central question in neuroscience is to understand how noisy firing patterns are used to transmit information . We quantify spiking patterns by using pairwise correlations , or the probability that two cells will spike coincidentally , above and beyond their baseline firing rate . One observation frequently made in experiments is that correlations can increase systematically with firing rate . Recent studies of a type of output cell in mouse retina found this relationship; furthermore , they determined that the increase of correlation with firing rate helped the cells encode information , provided the correlations were stimulus-dependent . Several theoretical studies have explored this basic structure , and found that it is generally beneficial to modulate correlations in this way . However—aside from mouse retinal cells referenced here—we do not yet have many examples of real neural circuits that show this correlation-firing rate pattern , so we do not know what common features ( or mechanisms ) might occur between them . In this study , we address this question via a computational model . We set up a computational model with features representative of a generic cortical network , to see whether correlations would increase with firing rate . To produce different firing patterns , we varied excitatory coupling . We found that with stronger excitatory coupling , there was a positive relationship between pairwise correlations and firing rates . We used a network linear response theory to show why correlations could increase with firing rates in some networks , but not in others; this could be explained by how cells responded to fluctuations in inhibitory conductances . | [
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] | 2017 | When do correlations increase with firing rates in recurrent networks? |
Purpureocillium lilacinum of Ophiocordycipitaceae is one of the most promising and commercialized agents for controlling plant parasitic nematodes , as well as other insects and plant pathogens . However , how the fungus functions at the molecular level remains unknown . Here , we sequenced two isolates ( PLBJ-1 and PLFJ-1 ) of P . lilacinum from different places Beijing and Fujian . Genomic analysis showed high synteny of the two isolates , and the phylogenetic analysis indicated they were most related to the insect pathogen Tolypocladium inflatum . A comparison with other species revealed that this fungus was enriched in carbohydrate-active enzymes ( CAZymes ) , proteases and pathogenesis related genes . Whole genome search revealed a rich repertoire of secondary metabolites ( SMs ) encoding genes . The non-ribosomal peptide synthetase LcsA , which is comprised of ten C-A-PCP modules , was identified as the core biosynthetic gene of lipopeptide leucinostatins , which was specific to P . lilacinum and T . ophioglossoides , as confirmed by phylogenetic analysis . Furthermore , gene expression level was analyzed when PLBJ-1 was grown in leucinostatin-inducing and non-inducing medium , and 20 genes involved in the biosynthesis of leucionostatins were identified . Disruption mutants allowed us to propose a putative biosynthetic pathway of leucinostatin A . Moreover , overexpression of the transcription factor lcsF increased the production ( 1 . 5-fold ) of leucinostatins A and B compared to wild type . Bioassays explored a new bioactivity of leucinostatins and P . lilacinum: inhibiting the growth of Phytophthora infestans and P . capsici . These results contribute to our understanding of the biosynthetic mechanism of leucinostatins and may allow us to utilize P . lilacinum better as bio-control agent .
Plant parasitic nematodes with wide host ranges cause enormous crop and economic losses amounting to $157 billion annually worldwide [1 , 2] . Biological control by fungi has become increasingly popular due to nematicides’ risks of environmental toxicity and adverse effects on human health [3] . One of the most promising and commercialized agents , Purpureocillium lilacinum , has been evaluated to assess its bio-control activity against plant nematodes in a number of studies [2 , 4] . In particular , P . lilacinum has been reported to effectively control such species as the cotton aphid Aphis gossypii [5] , the greenhouse whitefly Trialeurodes vaporariorum , the glasshouse red spider mite Tetranychus urticae [6] , and the leaf-cutting ant Acromyrmex lundii [7] . The genus Purpureocillium was recently proposed for of Ophiocordycipitaceae , based on the internal transcribed spacer ( ITS ) and translation elongation factor 1-α ( TEF ) sequences of P . lilacinum , although it was originally classified in the genus Paecilomyces [8] . P . lilacinum is commonly isolated from soil , plant roots , nematodes and insects , and it occasionally infects people . This fungus employs flexible lifestyles , including soil-saprobes , plant-endophytes and nematode pathogens . Opportunistic infection occurs when nematode eggs encounter P . lilacinum; therefore , parasitism can be a mechanism for nematode bio-control ( Fig 1A ) . It has now been confirmed that a serine protease [9] , a cuticle-degrading protease [10] and chitinase [11] play important roles in infection by degrading nematode eggshells . Recently , the production of SMs has been shown to be a mechanism that kills nematodes . For example , culture filtrates of P . lilacinum , in which leucinostatins were produced , caused strong mortality and inhibited nematode reproduction [12] . In addition to leucinostatins , a few other SMs have been isolated from P . lilacinum . The novel pyridone alkaloid paecilomide , an acetylcholinesterase inhibitor , was produced when this fungus was co-cultured with Salmonella typhimurium [13] . Two xanthone-anthraquinone heterodimers , acremoxanthone C and acremonidin A , were isolated in the course of a search for calmodulin ligands [14] . The leucinostatins ( Fig 1B ) are a family of lipopeptide antibiotics isolated from P . lilacinum [15] , Paecilomyces marquandii [16–18] and Acremonium sp . [19] . Leucinostatin A contains nine amino acid residues , including the unusual amino acid 4-methyl-L-proline ( MePro ) , 2-amino-6-hydroxy-4-methyl-8-oxodecanoic acid ( AHyMeOA ) , hydroxyleucine ( HyLeu ) , α-aminoisobutyric acid ( AIB ) , β-Ala , and a 4-methylhex-2-enoic acid at the N-terminus as well as an N1 , N1-dimethylpropane-1 , 2- diamine ( DPD ) at the C-terminus . Twenty-four different structures have been described in the leucinostatin series[20] . Leucinostatin A significantly suppressed prostate cancer growth in a coculture system in which prostate stromal cells stimulated the growth of DU-145 human prostate cancer cells through insulin-like growth factor I [21] . When screening for antitrypanosomal compounds among several peptide antibiotics , leucinostatins showed the most potent activity against trypanosomes . Trypanosome infection causes human African trypanosomiasis , which is one of the world’s most neglected diseases lacking satisfactory drugs [22] . Furthermore , leucinostatins have displayed broad bioactivity against bacteria and fungi . These antibiotics’ functions are based on their ability to inhibit ATP synthesis in the mitochondria as well as different phosphorylation pathways [23] . These findings drew our attention to the relationships between the bio-control function of P . lilacinum and leucinostatins . Furthermore , genetic and molecular information regarding the biosynthesis of this family of lipopeptide antibiotics , of which little was known to date , could contribute to increasing its production and screening for more efficient derivative compounds . Genome sequences have shed light on the mechanism of the endoparasitic lifestyle or nematode control beyond biological research . During the preparation of our manuscript , the genome sequence of P . lilacinum was published [24] . Two other plant nematode endoparasitic fungi , Pochonia chlamydosporia [25] and Hirsutella minnesotensis [26] , were recently sequenced . Genome sequencing revealed that P . chlamydosporia encoded a wide array of hydrolytic enzymes and transporters expressed at the mRNA level , which supported its multitrophic lifestyle , and H . minnesotensis , which mainly invades juvenile stage cyst nematodes , putatively conducted its parasitic process through lectins , secreted proteases and SMs . Thus , the genome sequence of P . lilacinum provides an opportunity to better understand its mechanism in controlling plant nematodes , and it would be useful to enhance its capabilities as a bio-control agent . At the same time , the genome sequence has the potential to solve the biosynthetic puzzle of leucinostatins as well as to detect novel genes and metabolites that might be of value in agriculture and medicine . Here , we present the results of genome sequencing of the PLBJ-1 and PLFJ-1 strains of the bio-control agent P . lilacinum , and we increased our knowledge of its bio-control capabilities by comparing the sequences of P . lilacinum with those of other fungi . The genome revealed a repertoire of SM-encoding genes that illustrated the potential for using this fungus to discover natural products . Furthermore , we identified the leucinostatin gene cluster ( lcs cluster ) and proposed a hypothetical pathway for biosynthesis through genetic manipulation . In the course of screening for new activities of leucinostatins , we found that they inhibited the most notorious oomycetes P . infestans , which causes potato late blight and results in global yield losses of 16% [27] .
Two P . lilacinum isolates , PLBJ-1 and PLFJ-1 , were sequenced to ensure the accuracy of the genome information and the subsequent analysis . PLBJ-1 and PLFJ-1 were assembled into 144 and 163 scaffolds , respectively , with total sizes of 38 . 14 and 38 . 53 Mb , while the published TERIBC I was assembled into 301 scaffolds with a total size of 38 . 82 Mb ( Table 1 ) . The comparative genome sizes of related fungi species are listed in S1 Table . A total of 11 , 773 and 11 , 763 gene models were predicted in both genomes , respectively , parallel to other ascomycetes fungi ( S1 Table ) . BLASTN analysis was performed between the two genomes and demonstrated that 83 . 56% of the PLBJ-1 genome and 82 . 79% of the PLFJ-1 genome shared high synteny ( Fig 2A ) . According to the syntenic relationship of PLBJ-1 and PLFJ-1 , we reconstructed 10 super-scaffolds ( S2 Table ) , which illustrated the physical ubieties of the assembled scaffolds; e . g . , scaffold 00006 , scaffold 00016 and scaffold 00015 in PLFJ-1 were combined into a super-scaffold ( Fig 2B ) . The overall syntenic relationship of PLBJ-1 and TERIBC 1 showed that 76 . 52% of the PLBJ-1 genome and 75 . 12% of the TERIBC 1 genome shared high synteny ( S1 Fig ) . Approximately 6 . 07% of the repeat sequences that included transposon elements ( TEs ) ( ~4 . 37% ) and tandem repeats ( ~1 . 70% ) were identified in PLBJ-1 . The Class I ( retrotransposons ) and Class II ( DNA transposons ) TEs occupied ~1 . 80% and ~0 . 76% of the genome , respectively . The PLFJ-1 isolate harbored a similar number of repeat sequences ( 6 . 00% ) . The distribution of the TE families was similar in the two isolates , with the exception of certain families , e . g . , I , Gypsy , Penelope , Tc1-Mariner and hAT ( S3 Table ) . In total , the two isolates of P . lilacinum contained a larger number of retrotransposons than DNA transposons . P . lilacinum exhibited expansion of repeat content comparable to other ascomycetes fungi , with the exception of H . minnesotensis , Ophiocordyceps sinensis and Fusarium oxysporum ( fol ) , in which the repeat sequences accounted for more than one quarter of the genome ( S1 Table ) . In TERIBC 1 , approximately 1 . 68% of the genome sequence was identified as repeat content . Among the predicted genes of PLBJ-1 , 90 . 4% were supported by RNASeq data from mycelia cultured in PDB . Both strains exhibited a consistent KOG pattern . Except for the category “General function prediction only” , which was ambiguously sorted to a certain group , the most abundant KOG categories were “Signal transduction mechanisms” , “Posttranslational modification , protein turnover , chaperones” , “Lipid transport and metabolism” , and “Intracellular trafficking , secretion , and vesicular transport” ( S2 Fig ) . A signal peptide analysis showed that 1 , 410 genes of PLBJ-1 and 1 , 448 genes of PLFJ-1 encoded putatively secreted proteins . CAZymes that cleave and build polysaccharides could be required when P . lilacinum degraded the structural polysaccharide armor of nematode eggshells , such as chitin , during the course of its parasitism . The protease could stop the development of nematode eggs and drastically alter the eggshell structures when applied individually or in combination with chitinases [28 , 29] . A detailed examination of the CAZymes and proteases of P . lilacinum was performed and compared with other fungi , including nematode parasitic fungi ( P . chlamydosporia and H . minnesotensis ) , nematode-trapping fungi ( Arthrobotrys oligospora and Monacrosporium haptotylum ) , entomopathogenic fungi ( T . inflatum , Beauveria bassiana , Cordyceps militaris , Metarhizium robertsii , and O . sinensis ) , a mycoparasitic fungus ( T . ophioglossoides ) , a saprotrophic fungus ( T . reesei ) and a plant pathogenic fungus ( F . oxysporum ) . We identified 53 families containing 239 genes in PLBJ-1 and 55 families containing 253 genes in PLFJ-1 that encoded glycoside hydrolases ( GH ) , which was more than the other fungi ( an average of 213 ) ( S4 Table ) . The most abundant family in PLBJ-1 and PLFJ-1 was GH18 , which was represented by 32 and 41 chitinases , respectively , that degrade the chitin present in the chitin protein complex of the nematode eggshell [30] . Consistent with GHs , PLBJ-1 and PLFJ-1 contained relatively more carbohydrate-binding modules ( CBMs ) ( 59 and 64 , respectively ) ( S5 Table ) , which were frequently appended to the enzymes involved in polysaccharide depolymerization . A series of carbohydrate esterase ( CE ) -encoding genes were also detected in the P . lilacinum genomes ( 33 and 32 , respectively ) , including the most abundant sterol esterases ( CE10 ) and cutinases ( CE5 ) , which are virulence factors of some plant pathogens [31] ( S6 Table ) . Another major class of CAZymes , the glycosyltransferases ( GT ) , establish natural glycosidic linkages across a broad range of small and macromolecules , and they were represented in the PLBJ-1 genome with 115 members in 32 families and in the PLFJ-1 genome with 124 members in 32 families ( S7 Table ) . These enzymes’ classification demonstrated that they exhibited less variability in ascomycetes than did GHs , a trend that was maintained in a previous analysis [32] . The P . lilacinum genome contained more proteases ( 430 and 443 , respectively ) than other fungi ( an average of 396 ) . The largest category of proteases encoded in PLBJ-1 and PLFJ-1 were serine proteases ( 194 and 198 , respectively ) ( S8 Table ) , 76 and 81 of which were secreted proteins , respectively . Among the serine proteases , we identified 34 subtilisins ( S8 ) and ten serine carboxyproteases ( S10 ) in the PLBJ-1 genome ( 36 and 11 , respectively , in PLFJ-1 ) , which were reported to be involved in infection and the lethal activity of nematodes [28 , 33] . The metalloprotease ( 108 in PLBJ-1 and 109 in PLFJ-1 ) and cysteine protease ( 66 in PLBJ-1 and 68 in PLFJ-1 ) families also accounted for a significant proportion of the proteases . A whole genome analysis was conducted against the pathogen-host interaction ( PHI ) gene database to identify potential virulence-associated genes , under the assumption that the homologue of an experimentally validated pathogenic gene suggested that it played a pathogenic role [34] . We demonstrated that 2 , 844 ( 24 . 1% ) and 2 , 892 ( 24 . 6% ) proteins of PLBJ-1 and PLFJ-1 , respectively , showed sequence similarity to those in the PHI database . Among these proteins , 299 and 317 proteins of PLBJ-1 and PLFJ-1 , respectively , were classified as putatively secreted proteins . The KOG functional class distribution of genes related to PHI showed a similar pattern to the whole genome KOG analysis ( S2 Fig ) . The PHI database search yielded 195 CAZymes in PLBJ-1 and 217 in PLFJ-1 , 28 and 36 of which were chitinases ( GH18 ) , respectively . Of the proteases , 125 in PLBJ-1 and 132 in PLFJ-1 were pathogenic genes according to the PHI database , of which 64 and 72 were identified as secreted proteins , respectively , and these proteins were more likely to function during the infection process [35] . A phylogenomic tree was constructed based on 855 single-copy orthologues of P . lilacinum and 34 other filamentous fungi , with Saccharomyces cerevisiae as the outgroup . The results verified that P . lilacinum belongs to Ophiocordycipitaceae , as described by Jennifer Luangsa-ard [8] , and it formed a clade with T . inflatum , T . ophioglossoides , O . sinensis [36] , O . unilateralis [37] and H . minnesotensis ( Fig 3A ) . The inferred phylogeny illustrated that T . inflatum and T . ophioglossoides were most closely related to P . lilacinum , and they diverged after their split with O . sinensis , H . minnesotensis and O . unilateralis . This phylogeny also reinforced the previous analysis that found that the split between Cordycipitaceae ( including B . bassiana and C . militaris ) and Clavicipitaceae ( including P . chlamydosporium and M . anisopliae ) occurred before Ophiocordycipitaceae diverged from Clavicipitaceae ( Fig 3A ) . The three nematode parasitic fungi P . chlamydosporium , H . minnesotensis and P . lilacinum clustered with insect pathogens , indicating that nematode and insect pathogens might share a common ancestor . A comparative genomic analysis was performed between P . lilacinum and other nematode-related fungi ( the nematode parasites P . chlamydosporia and H . minnesotensis and the nematode-trapping fungi A . oligospora and M . haptotylum ) . A total of 17 , 995 orthologous clusters consisting of 76 , 151 proteins were identified , of which 4 , 652 clusters containing 35 , 972 proteins were mapped to all four of the fungi types ( Fig 3B ) . On the whole , the nematode-trapping fungi , which capture nematodes through an entirely different mechanism compared to P . lilacinum [26] , possessed the largest number of unique gene clusters , although they had a more distant phylogenetic relationship with the other fungi in Hypocreales ( Fig 3B ) . P . lilacinum contained a large number ( 3651 ) of species-specific clusters , while P . lilacinum shared 7 , 700 , 6 , 673 and 5 , 253 clusters with P . chlamydosporia , H . minnesotensis and the nematode-trapping fungi , respectively . Lineage-specific expansions could provide material for the evolution of a specific functional system or adaptation in eukaryotes [38] . To study gene family expansions in P . lilacinum , a comparative genomic analysis of 15 fungal species ( PLBJ-1 , PLFJ-1 , P . chlamydosporia strain 123 , P . chlamydosporia strain 170 , H . minnesotensis , A . oligospora , M . haptotylum , T . inflatum , B . bassiana , C . militaris , M . robertsii , O . sinensis , T . ophioglossoides , T . reesei , and F . oxysporum ) was performed . In total , 1 , 963 gene families with more than one gene expansion were identified in both PLBJ-1 and PLFJ-1 , of which 1 , 761 gene families were only present in P . lilacinum , and some gene families with significant expansion are listed in S9 Table . However , most families were annotated as reverse transcriptases and transposases , and the others were related to transporters or lyases . When the nematode parasitic fungi P . chlamydosporium and H . minnesotensis were considered , 2 , 936 orthologous clusters showed expansion in the five isolates . The largest paralogous expansion contained protein families associated with SMs , such as cytochrome P450s , oxidoreductases , and transporters . In addition , these families also contained transcription factors , glycosyl hydrolases , the hAT family , the majority of which are listed in S10 Table . To evaluate the capability of P . lilacinum to produce SMs , we searched the genome of PLBJ-1 and PLFJ-1 for biosynthetic genes encoding the four classes of the main SM-associated synthetases , including polyketide synthase ( PKS ) , non-ribosomal peptide synthetase ( NRPS ) , terpene synthase ( TS ) and dimethylallyl tryptophan synthase ( DMATS ) [26] . A uniform SM profile with parallel categories and numbers was presented in the two genomes ( S11 Table ) . In total , 13 PKSs , 10 NRPSs , two PKS-like enzymes , 10 NRPS-like enzymes , one DMATS , 4 TSs and one PKS-NRPS hybrid were identified in the PLBJ-1 genome , as described in S11 Table . Compared to sequenced species in Ophiocordycipitaceae , the number of SMs in P . lilacinum ( 41 ) was similar to the 45 SMs in T . ophioglossoides , 39 SMs in O . unilateralis , more than 30 SMs in Ophiocordyceps sinensis , fewer than 55 SMs in T . inflatum [39] , and 101 SMs in the nematode endoparasitic fungus H . minnesotensis [26] . These core backbone genes were dispersed among 39 clusters with other enzymes , such as transcriptional regulators , P450s and transporters , as predicted by antiSMASH ( antibiotics and Secondary Metabolite Analysis SHell ) [40] ( S11 Table ) . According to the BLAST results from the NCBI NR database , no homologues of functionally characterized SMs were detected . Among them , we detected the expression of 29 core genes with FPKM ( fragments per kilobase of transcript per million mapped fragments ) values > 0 . 5 , using an RNA-seq analysis of PLBJ-1 cultured in PDB medium for 8 days . A phylogenetic tree was constructed based on the KS domain amino acid sequence of the PKSs in P . lilacinum and the products of known PKSs , which were divided into three main clades: non-reducing ( NR ) PKSs , partially reducing ( PR ) PKSs and highly reducing ( HR ) PKSs ( S3 Fig ) . VFPBJ_05021 , VFPBJ_09342 , VFPBJ_09755 , and VFPBJ_10843 were predicted as NR PKS-encoding genes , and they shared the highest homology with the non-reducing biosynthetic genes , such as citrinin [41] and griseofulvin [42] . VFPBJ_00212 , VFPBJ_02527 , VFPBJ_02532 , VFPBJ_03442 , VFPBJ_05962 , VFPBJ _06473 , VFPBJ _07567 and VFPBJ_09314 were distributed in the HR PKS clade in close relationship with HR polyketides , such as fumonisin synthase Fum1p [43] . The phylogenetic analysis was consistent with the domain structure analysis of degree of reduction , in which the HR PKS contained the reductive domains KR ( keto-reductase ) , ER ( enoyl reductase ) and DH ( dehydratase ) , while the NR PKS did not contain these domains ( S3 Fig , S11 Table ) . VFPBJ_05021 and VFPBJ_09342 were grouped with the antibiotics griseofulvin and citrinin with a bootstrap value of 100% , and they shared a common domain structure . This finding suggested that griseofulvin/citrinin or structurally related compounds could be produced by P . lilacinum . However , we did not detect these compounds when P . lilacinum was cultured in PDB for 8 days . Among the 10 NRPSs , six contained one module or an incomplete module , which could encode products with one amino acid . Four NRPSs were multi-module enzymes , which could encode products composed of more than one amino acid . To examine the potential NRPS orthologues of P . lilacinum and to detect the feasible NRPS evolutionary mechanism in the family Ophiocordycipitaceae , a genealogy was created based on the A-domains from the NRPS of fungi in Ophiocordycipitaceae and several functionally characterized products ( S4 Fig ) . The tree depicted an intricate evolutionary relationship for the NRPS genes . A general trend throughout the tree was that , in Ophiocordycipitaceae , many A-domains clustered with orthologues in other species than with in the same protein . Notably , the 11 A-domains of the cyclosporine synthetases from T . inflatum clustered separately ( S4 Fig , node 3 ) , indicating that other species were incapable of encoding cyclosporine and that its evolution occurred after T . inflatum diverged from these fungi in Ophiocordycipitaceae . This phylogenetic analysis of the A-domains for P . lilacinum detected a series of homologous A-domains: four of the mono-module NRPSs had functionally uncharacterized homologues . VFPBJ_05068 was identified as siderophore synthetase , of which three of the A-domains were grouped with homologues to form a sub-clade ( S4 Fig , node 2 ) . The three A-domains of VFPBJ_06596 were grouped with TINF2556 , annotated as an ergot alkaloid in T . inflatum , while TINF2556 contained four modules . The peptaibiotics , a class of linear NRPSs that are abundant of AIB [44] , were clustered into one sub-clade ( S4 Fig , node 1 ) , mainly including the peptaibiotics from T . ophioglossoides [45] , T . inflatum and P . lilacinum . The ten A-domains from VFPBJ_02539 ( identified as the leucinostatin biosynthetic gene lcsA in this study ) , clustered with the ten A-domains from the peptaibiotic TOPH_08469 in T . ophioglossoides , with bootstrap values of 100% , and a global BLAST analysis revealed that the sequence identity of the two homologues was 65% . Neither orthologue was identified in other species of Ophiocordycipitaceae . The single A-domain of lcsA was scattered in the peptaibiotic sub-clade of the tree , while A2 , A5 and A6 , which activated Leu or related amino acids , were identified in the subsequent study and were grouped together with a bootstrap value of 60% , suggesting that both lineage-specific changes and module duplication contributed to the evolution of the leucinostatin metabolites . In the previous study , A4 , A7 and A8 of TOPH_08469 were distributed in a sub-clade enriched in A-domains encoding AIB [45] , and our study demonstrated that A4 , A7 and A8 of lcsA were encoded for AIB . In T . ophioglossoides , the TOPH_08469 gene cluster was predicted to contain 28 genes from TOPH_08452 to TOPH_08478 that were located in an ~124 kb region [45] . A comparative analysis of genes surrounding lcsA and TOPH_08469 cluster revealed a high synteny ( Fig 4A ) . VFPBJ_02521 ( designed as lcsG ) shared 68% sequence identity with TOPH_08452 , and lcsA shared 66% sequence identity with TOPH_08469 . Interestingly , no homologues of the genes next to the cluster , VFPBJ_02510 to VFPBJ_02520 and VFPBJ_02540 to VFPBJ_02550 , were identified in the T . ophioglossoides genome . Within the lcs cluster , two genes , cytochrome P450 lcsI and a protein with unknown function , lcsM , did not possess homologues in the TOPH_08469 cluster , while all of the leucinostatin biosynthetic genes in T . ophioglossoides ( TOPH_08452 to TOPH_08469 ) had homologues within the lcs cluster . These results suggested that this nearly 100 kb region might have been horizontally transferred from other fungal or bacterial species . However , leucinostatins have not been reported to be produced by T . ophioglossoides to date . The lipopeptide leucinostatin A contains ten amide bonds that divide the molecule into 11 moieties , including 4-methylhex-2-enoic acid , 9 amino acid residues and DPD . The property of the mixture of the polyketide and peptide moieties in the leucinostatins indicated a PKS , NRPS or hybrid PKS-NRPS origin . It is logical to consider that a single reducing PKS encodes the 4-methylhex-2-enoic acid , and a NRPS enzyme encodes the remaining portion , as in the models for emericellamide synthesis in Aspergillus nidulans[46] and pneumocandin in Glarea lozoyensis[47] . Among the multi-module NRPSs in P . lilacinum , VFPBJ_05068 contains 13 domains grouping into 3 modules , VFPBJ_06596 contains seven domains grouping into three modules , and VFPBJ_11400 contains six domains grouping into two modules . These enzymes were insufficient for the assembly of nine amino acids of leucinostatins . Thus , VFPBJ_02539 was left as the only plausible candidate . VFPBJ_02539 ( LcsA ) consists of 11 , 872 amino acids and was encoded by a gene with five introns . The domain structure of LcsA was comprised of 10 C-A-PCP modules and carried the correct number of amino acids for the assembly of leucinostatins . The NRPSpredictor2 [48] offered little insight into the substrates except that the substrates of A1 and A3 were proline and leucine , respectively ( S12 Table ) . Two PKSs , lcsB and lcsC , located not far upstream of lcsA , which could encode 4-methylhex-2-enoic acid , indicated that this cluster is responsible for leucinostatin production . Furthermore , lcsD ( VFPBJ_02533 ) , located between lcsA and the PKSs , was annotated as an acyl-CoA ligase , offering a conceivable route for connecting the fatty acid and peptide . To verify the associations between the putative lcsA and leucinostatins , a gene deletion method was developed for P . lilacinum based on the previous method for Fusarium oxysporum[49] , with the G418 sulfate-resistance gene neo as the selection marker . A portion of lcsA ( 2 , 613 bp , including 236 bp upstream of the ORF ) was knocked out by double homologous deletion cassettes with the neo marker via PEG-mediated transformation , and the resulting G418 sulfate-resistant isolates ( S5A and S5B Fig ) were verified by diagnostic PCR , using the primers in neo and outside the knockout cassette ( S5C Fig ) ( S13 Table ) . Finally , one mutant ( ΔlcsA ) of PLBJ-1 was isolated with correct PCR amplification products from 320 G418 sulfate-resistance mutants ( S5C Fig ) , and the remaining isolates resulted from ectopic integration of the neo gene cassette into the genome . The wild type of P . lilacinum and the ΔlcsA mutant of PLBJ-1 were cultured in PDB medium for 8 days , and the ethyl acetate extracts were analyzed by HPLC-MS . The MS spectrum of the wild type displayed two overlapping peaks at 15 . 6 and 16 . 0 min , with m/z [M+H]+ of 1218 . 9 and 1204 . 9 , respectively , which were assigned to leucinostatins A and B and were absent in the ΔlcsA mutant ( Fig 5 , S6 Fig ) . A comparison with the authentic standard confirmed that the missing compounds of the ΔlcsA mutant were indeed leucinostatins A and B ( Fig 5 , S6 Fig ) . As expected , these results demonstrated the essential roles of lcsA in the biosynthesis of the leucinostatins . Different boundaries of the lcs cluster were defined by the SMURF and antiSMASH programs ( Fig 4A ) . Nine genes flanking lcsA from VFPPL_02532 to VFPPL_02540 spanning 62 Kb were predicted to be in the cluster by SMURF ( Secondary Metabolite Unique Regions Finder ) [50] , while a larger cluster comprising 26 genes from VFPBJ_02521 to VFPBJ_02546 , spanning 120 Kb , was predicted by antiSMASH . Therefore , it was necessary to explore the genes that were involved in the pathway using a biological approach . Changes in the culture medium could impact the general metabolic profile of an organism , based on the “OSMAC” ( one strain-many compounds ) hypothesis[51] . Indeed , we found that P . lilacinum produced leucinostatins A and B when cultured with our lab recipe of PDB but did not produce leucinostatins when cultured in PDB-BD ( see the Materials and Methods section ) . This result provided clues to identify the boundary of the lcs cluster using producing versus non-producing media . qRT-PCR analysis was conducted to compare the expression patterns of genes flanking lcsA when PLBJ-1 was grown in the two types of media for 8 days . Furthermore , RNA-Seq of PLBJ-1 under leucinostatin-inducing conditions ( PDB medium ) was performed . As expected , the expression level of NRPS lcsA when P . lilacinum was grown in leucinostatin-inducing medium was upregulated 95-fold , compared to those grown in non-inducing medium ( Fig 4B ) . The genes downstream of lcsA , including the putative transporter ABC gene VFPBJ_02540 , did not display a higher expression level in the leucinostatin-inducing medium , indicating that they were not involved in the leucinostatin biosynthesis pathway . Correspondingly , the RNA-Seq expression profile during leucinostatin production showed a low FPKM value of VFPBJ_02540 ( 2 . 01 ) ( S7A Fig ) , while the FPKM value of lcsA was 65 . 4 . These results indicated that the 3’ edge of the cluster was lcsA . The genes upstream of lcsA from VFPBJ_02520 to VFPBJ_02538 ( lcsT ) were upregulated at different levels in the leucinostatin-inducing medium . A 16- to 2692-fold increase in expression was observed ( Fig 4B ) , except for three genes , VFPBJ_02520 , LcsM , and lcsQ , which showed less than 10-fold increase and low FPKM values in the transcriptional data ( S7A Fig ) . VFPBJ_02520 was annotated as a phosphohydrolase that appeared to be involved in nucleic acid metabolism and signal transduction , instead of secondary metabolism[52] . Thus , we speculated that the 5’ boundary of the cluster was VFPBJ_02521 ( lcsG ) . To support this hypothesis , the expression patterns of the genes flanking the cluster were analyzed using qRT-PCR analysis in wild type PLBJ-1 and ΔlcsA grown in leucinostatin-inducing medium . We observed an increase in the expression of wild type P . lilacinum ranging from four- to 79-fold ( S7B Fig ) . Thus , a series of genes from VFPBJ_02521 to VFPBJ_02539 , designated as lcsA to lcsT , included the core enzymes , modifying enzymes and transporter enzymes coding for the biosynthesis of leucinostatins ( Fig 4A , Table 2 ) . Considering the structural similarities of leucinostatin A with emericellamide A [53] and pneumocandin [47] , we reasoned that a similar biosynthetic mechanism might be required to form the skeletons of lipopeptides and peptides . As reported , a single module polyketide synthase iteratively catalyzes the formation of the linear polyketide chain; in daptomycin [54] and echinocandin B [55] , acyl-CoA ligase converts the fatty acid to fatty acyl CoASH; in compound W493 B [56] , a thioesterase was proposed to hydrolyze the thiol bond and shuttle the product to the first module of NRPS . To determine whether the same enzymes play critical roles in the leucinostatin biosynthesis pathway , we disrupted the PKS ( lcsC ) , ligase ( lcsD ) and thioesterase ( lcsE ) -encoding genes in the cluster by homologous recombination ( S5A Fig ) and verified the mutants by PCR amplification ( S8A Fig ) . After culturing the fungi in PDB medium and comparing the extracts with the PLBJ-1 wild type and ΔlcsA by HPLC-MS , we showed that leucinostatins A and B disappeared in ΔlcsC , ΔlcsD and ΔlcsE , similar to ΔlcsA ( Fig 5 , S6 Fig ) . A powerful approach to enhancing the production of leucinostatins was to express transcription factors constitutively that were used for other SMs [57] . lcsF encodes a putative transcription factor with a bZIP domain structure , and it is associated with secondary metabolism [58] . To assess the function of lcsF , we cloned it into the KSTNP vector under the control of the TrpC promoter . The resulting plasmid , KSTNP-OElcsF , was randomly integrated into the genome of wild type P . lilacinum ( S8B Fig ) . The positive transformants were screened by G418 sulfate and were diagnosed by PCR amplification of the expression cassette ( S8C Fig ) . Transformants with an intact overexpression cassette were cultured in leucinostatin-inducing PDB medium for 8 days . The expression level of lcsF in the mycelia was analyzed by qRT-PCR , and six of ten transformants demonstrated more than 20-fold upregulation . Finally , three transformants without changes in their physiological indices were selected for the downstream test . As expected , all 20 genes in the cluster were upregulated to some extent by lcsF , with the exception of lcsL and lcsP , which were downregulated three- and five-fold ( S9A Fig ) . In addition to the 30-fold increase in lcsF expression , the expression of the three PKS/NRPS synthase encoding-genes ( lcsB , lcsC and lcsA ) were increased by ~3- to 4-fold . For O-methyltransferase ( lcsG ) , ABC transporter ( lcsH and lcsO ) , thioesterase ( lcsE ) , epimerase ( lcsT ) and the unknown function genes lcsM and lcsS , we observed ten-fold or higher upregulation . The other genes in the cluster displayed a two- to ten-fold increase in expression . Genes adjacent to the lcs cluster , VFPBJ_02520 and VFPPL_02540 , were downregulated three-fold . After the wild type and OE::lcsF P . lilacinum were grown in PDB medium with shaking for 8 days , the resulting HPLC profile showed that the titers of leucinostatins A and B were elevated by at least 50% ( S9B Fig ) . These results provided evidence that the pathway-specific transcription factor lcsF was capable of regulating the entire gene cluster and leucinostatin biosynthesis , further verifying the boundary of this cluster . The deletion and overexpression of the genes in the lcs cluster had no apparent effects on the fungal hyphae or spore phenotypes of P . lilacinum and did not cause any growth defects . It is well known that leucinostatins are antibiotics used to combat fungi and bacteria . Here , we found that leucinostatins contributed to the inhibition of oomycetes , which had not previously been reported . The growth of P . infestans and P . capsici was inhibited in a confronting incubation with wild type P . lilacinum and OE::lcsF , while the inhibition disappeared when they were grown in a confronting incubation with ΔlcsA ( Fig 6 ) . Similar to ΔlcsA , P . infestans could grow normally in a confronting incubation with ΔlcsC , ΔlcsD and ΔlcsE ( S10 Fig ) . The results indicated that leucinostatins A and B inhibited the growth of some oomycetes . A gradient inhibitory zone was explored with leucinostatins A and B in different concentrations to find quantitative evidence of inhibition against P . infestans ( S11 and S12 Figs ) .
The genomes of P . lilacinum strains PLBJ-1 and PLFJ-1 were sequenced , completely assembled , annotated , and comparatively analyzed with related fungi . Phylogenomic analysis showed that P . lilacinum was most closely related to T . inflatum and T . ophioglossoides , and the cluster of nematode parasitic fungi and insect pathogens indicated their common origin . PKS and NRPS-encoding genes were thoroughly characterized and analyzed by phylogenetic analysis , from which we found that lcsA was specific to P . lilacinum and T . ophioglossoides . Furthermore , lcsA was proved to be responsible for leucinostatin biosynthesis by homologous deletion . The boundary of the lcs cluster was identified by comparison of gene expression levels when P . lilacinum was cultured in leucinostatin-inducing and non-inducing medium as well as RNA-Seq analysis . Disruption of lcsC , lcsD and lcsE demonstrated the critical roles of PKS , acyl-AMP ligase and thioesterase in the biosynthetic pathway of leucinostatins . Overexpression of the transcription factor lcsF increased the production of leucinostatins A and B through regulated expression levels of genes in the lcs cluster . We also demonstrated that leucinostatins could enable the fungus with antagonistic activity against the oomycetes .
The leucinostatin-producing P . lilacinum strain PLBJ-1 ( CGMCC3 . 17492 ) was isolated from tomato roots in Beijing , China , and PLFJ-1 ( CGMCC3 . 17493 ) was isolated from tomato roots in Fujian , China . Both strains were sequenced to obtain the common features of P . lilacinum and to ensure the information accuracy of the lcs cluster . PLBJ-1 was used as the wild type recipient for the subsequently genetic manipulations because PLFJ-1 was insensitive to the antibiotics used as selection markers . P . infestans and P . capsici were maintained at the Chinese Academy of Agricultural Sciences . The pKOV21 vector used for homologous deletion and the KSTNP vector used for overexpression came from Prof . Youliang Peng , China Agricultural University . The leucinostatin A standard came from Bioaustralis , Inc . ( NSW , AUS ) . G418 sulfate was purchased from Amresco , Inc . ( OH , USA ) . The non-inducing PDB-BD medium ( Potato Dextrose Broth ) came from Becton , Dickinson and Company ( NJ , USA ) . Leucinostatin-inducing PDB medium was prepared in the lab . Briefly , 200 g of potatoes were boiled for 30 min , and then 20 g of glucose were dissolved into the filtrate and diluted to 1 L . The rye agar medium contained 50 g of crushed rye , 20 g of sucrose and 15 g of agar per liter . PDB cultures with 1×105 conidia per mL of PLBJ-1 were grown at 28°C on a shaker at 150 rpm for 8 days before DNA/RNA isolation . The mycelium tissues of the PLBJ-1 and PLFJ-1 isolates were harvested via filtration . Genomic DNA was isolated using a Qiagen DNeasy kit , according to the manufacturer’s protocol . The PLBJ-1 tissue for RNA isolation was grown in the leucinostatin-inducing medium . RNA was extracted using TRIZOL reagent ( Invitrogen , USA ) following the manufacturer’s protocol . The raw sequencing data ( Illumina HiSeq 2000 ) from the PLBJ-1 and PLFJ-1 strains were generated by BGI-Shenzhen ( China ) and Berry Genomics Co . , Ltd . ( China ) , respectively . A total of 13 . 27 Gb bases for the PLBJ-1 strain from three libraries , with average insert sizes of 165 bp , 758 bp and 5 , 490 bp , were obtained , and 5 . 88 Gb bases for the PLFJ-1 strain from two libraries with the average insert sizes of 175 bp and 4 , 760 bp were obtained . Both of the genomes were assembled using ALLPATHS-LG revision 42305 [81] . The repeat sequences were identified as previously described [82] , based on de novo and homology methods . For the de novo method , Piler [83] and RepeatScout , version 1 . 0 . 5 [84] , were used to construct the repeat sequence families; then , RepeatMasker , version 4 . 0 . 5 , was used for repeat analysis . For the homology method , the sequence families from Repbase , version 19 . 06 [85] , were used for annotation by performing RepeatMasker analysis . For gene prediction , the Augustus algorithm , version 2 . 7 [86] , identified 11 , 404 and 11 , 554 complete genes for the PLBJ-1 and PLFJ-1 strains , respectively , and the GeneMark-ES algorithm , version 2 . 3f [87] , discovered 11 , 001 and 11 , 070 complete genes for the PLBJ-1 and PLFJ-1 strains , respectively . The comparison showed that 9 , 509 and 9 , 562 genes of the PLBJ-1 and PLFJ-1 strains were predicted by both Augustus and GeneMark-ES . These consensus genes were considered to be high quality predicted genes and were used in this study . The additional 2 , 264 and 2 , 201 genes of the PLBJ-1 and PLFJ-1 strains were obtained according to the method in [82] . EuGene , version 4 . 1 [88] , was used to integrate multiple sources , including transcription start sites identified by Netstart [89] , homologous proteins identified from the Swiss-Prot database , version 2015-07-22 , by BLAST , version 2 . 2 . 26 , the assembled transcripts generated by IDBA-tran , version 1 . 1 . 1 [90] , and the exon junctions identified from RNA-seq by Tophat , version 2 . 0 . 13 [91] . The gene expression values were presented by the expected FPKMs using Cufflinks , version 2 . 2 . 1 [92] , based on the Tophat [91] analysis . Proteins were annotated by aligning their sequences to the NCBI fungi refseq , version 2015-07-10 , and SwissProt , version 2015-07-22 , with an E-value cutoff of 1e-5 using BLASTP . In addition , the Pfam database , version 27 . 0 , was used for domain annotation by HMMER , version 3 . 1b1 ( http://hmmer . janelia . org/ ) . The putative proteins were further classified by Gene Ontology ( GO ) [93] , using Blast2Go [94] , and the euKaryotic Clusters of Orthologous Groups ( KOG ) [95] , using BLAST ( E-value of 1e-5 ) . The Web server of the CAZymes Analysis Toolkit ( CAT ) [96] was used to identify CAZymes in P . lilacinum with E-values ≤ 1e-50 . The proteases were discovered by the MEROPS batch BLAST online server [97] . Proteins with sequences that matched the cytochrome P450 genes [98] with E-values ≤ 1e-50 were annotated as P450 enzymes . Candidate pathogenic factors were predicted by sequence alignment against the Pathogen Host Interactions ( PHI ) database , version 3 . 5 [99] , with E-values ≤ 1e-50 . In addition , the secretomes were identified based on recognizing the signal peptide and transmembrane sequences . Proteins were considered to be secreted proteins if the signal peptides were identified by at least two methods among SignalP , version 4 . 0 [100] , TargetP , version 1 . 1 [101] , Phobious , version 101 [102] , and Predisi [103] , and transmembrane sequences were not identified by at least one of the methods among SignalP , Phobious and TMHMM , version 2 . 0c [104] . Orthologous groups of genes from P . lilacinum and the other fungi listed in S14 Table were detected by OrthoMCL , version 2 . 0 . 9 [105] , and then were filtered to identify the single copy orthologues . The single copy orthologues were aligned with MUSCLE [106] . The poor alignment regions of the concatenated sequences were removed using Gblock , version 0 . 91b [107] , and then the high quality sequences were used for the maximum likelihood phylogeny analysis with the Dayhoff model implemented in the TREE-PUZZLE program [108] . Bootstrap support value was calculated by analyzing 1 , 000 replicates . The secondary metabolite genes were discovered by performing SMURF [50] and antiSMASH [40] analyses . PKS and NRPS domain structures were characterized by antiSMASH and Pfam , or were visually identified by multiple alignments . The KS domains extracted from PKS and the A-domain from NRPS were aligned by MUSCLE [106] , and then a maximum likelihood phylogeny was constructed by treeBeST ( http://treesoft . sourceforge . net/treebest . shtml ) using 1 , 000 bootstrap replicates . Three biological replicates were performed for each analysis of the relative expression levels . The cDNAs were synthesized with a TIANScript Ⅱ RT Kit ( TIANGEN , China ) . The cDNA was analyzed by qRT-PCR using SYBR Premix Ex Taq ( TAKARA , Japan ) on a BIO-RAD CFX96 ( BIO-RAD ) . The housekeeping actin gene designed from VFPBJ_07912 , which was similar to the reported GU299860 . 1 , was used for normalization . The relative expression values were calculated using the 2-∆∆Ct method . The primers are listed in S13 Table . Polyethylene glycol-mediated protoplast transformation of PLBJ-1 was performed as previously reported [49 , 109] , with the following modifications: the protoplast was produced by 20 gL−l Driselase ( Sigma ) digestion for 4 h at 31°C . The regeneration medium was PDA medium containing G418 sulfate ( 400 μg/L ) , supplemented with molasses ( 10 g/L ) , saccharose ( 0 . 6 M ) , yeast extract ( 0 . 3 g/L ) , tryptone ( 0 . 3 g/L ) , and casein peptone ( 0 . 3 g/L ) [10] . The construction of knockout and overexpression plasmids originated from pKOV21 and KSTNP , and the primers are listed in S13 Table . A quick method for isolating the fungal genomic DNA was developed to screen for a large number of transformants . Briefly , a nip of mycelia was transferred to 50 μL of NaOH ( 50 mM ) and was incubated at 95°C for 20 min . The solution was directly used for PCR amplification after 5 μL of Tris-HCl ( 1 M ) were added to neutralize the base . Cultures of 1×105 conidia per mL of P . lilacinum and its mutants were grown in leucinostatin-inducing PDB medium at 28°C on a shaker at 150 rpm for 8 days . Culture medium ( 7 . 5 L ) was extracted with the same volume of EtOAc three times ( each 1 h ) ultrasonically . The combined EtOAc extracts were concentrated to afford a crude extract ( 0 . 4 g ) , which was subjected to reversed-phase ODS column chromatography eluting with MeOH-H2O ( from 40% to 100% ) to afford 6 fractions ( Fr . A–Fr . F ) . Fr . E ( 40 mg ) was passed through a Sephadex LH-20 column ( MeOH ) and yielded mixtures of 5 . 0 mg of leucinostatins A and B . The structure of the mixtures was further identified by standard substance using LC-MS analysis . Approximately 200 mL of culture medium were used for comparative LC-MS analysis between PLBJ-1 and its mutants . LC-MS was performed on an Agilent Accurate-Mass-QTOF LC/MS 6520 instrument . HPLC analysis was performed on a Waters HPLC system ( Waters e2695 , Waters 2998 , Photodiode Array Detector ) using an ODS column ( C18 , 250 × 4 . 6 mm , YMC Pak , 5 μm ) . The ODS ( 50 μm ) column was produced by YMC Co . Ltd . ( Kyoto , Japan ) . The Sephadex LH-20 was purchased from GE Healthcare . Analytical HPLC was conducted with a Waters HPLC system ( Waters e2695 , Waters 2998 , Photodiode Array Detector ) using an ODS column ( C18 , 250 × 4 . 6 mm , YMC Pak , 5 μm ) with a flow rate of 1 mL/min . The fresh extracts were dissolved in methanol before being separated on a linear gradient of MeOH:H2O ( 0 . 1% formic acid ) at a flow rate of 1 mL/min . Fresh extracts of mutant strains were detected for 30 min using a linear gradient of 20% to 100% ( 0–20 min ) , 100% MeOH ( 20–25 min ) , and 20% MeOH ( 25–30 min ) . The LC-MS analysis method was consistent with analytical HPLC . Confronting incubation of P . lilacinum ( wild type , ΔlcsA and OE::lcsF ) with P . infestans was performed on rye agar medium in 9 cm Petri plates , incubated simultaneously and cultured at 28°C for 24 h and then at 18°C , the optimum temperature for P . infestans , for 9 days , while confrontation with P . capsici was performed on lab-made PDA medium , cultured at 28°C for 7 days . For the inhibitory zone experiment , freshly produced sporangia of P . infestans was suspended in sterile water at a concentration of 2×105 sporangia/mL . One milliliter of the suspension was smeared on 15 cm Petri plates , followed by Oxford cups with a diameter of 1 cm being placed . From 5 to 60 μg ( increment 5 μg ) of leucinostatins A and B dissolved in 20% methanol were added to the Oxford cup , and 20 μL of 20% methanol were used a control . Then , the Oxford cups were removed after the solution was absorbed by media . Five days later , the area was calculated by drawing circles of the inhibitory zone on metric graph paper and counting the number of square millimeters within the circle [110 , 111] . Three biological replicates were performed . At the same time , the effects of 50 μg , 33 μg , 17 μg and 8 . 5 μg of leucinostatins are demonstrated in S11 Fig . The genome sequences of PLBJ-1 , PLFJ-1 , and P . chlamydosporium strain 170 used for comparative analysis have been deposited at GenBank under the accession numbers LSBH00000000 , LSBI00000000 and LSBJ00000000 , respectively . | Purpureocillium lilacinum , a well-known bio-control agent against various plant pathogens in agriculture , can produce antibiotic leucinostatins—peptaibiotic with extensive biological activities , including antimalarial , antiviral , antibacterial , antifungal , and antitumor activities , as well as phytotoxic . We have sequenced the genomes of two P . lilacinum isolates , and compared them with other fungi , focusing on their bio-control characteristics . We discovered a rich repertoire of CAZymes , proteases , SMs and pathogenesis related genes . We also identified a gene cluster containing 20 genes involved in the leucinostatins A and B biosynthesis by gene deletion , qRT-PCR and RNA-seq analyses . A transcription factor in the pathway was overexpressed , resulting in the upregulation of the related genes and a 1 . 5-fold increase in leucinostatins A and B . A new bioactivity of leucinostatins , inhibition of the growth of the notorious Phytophthora , was identified in this study by confronting incubation with P . lilacinum . These results provided new strategies for the agricultural development of leucinostatins and improving P . lilacinum strains . | [
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] | 2016 | Biosynthesis of Antibiotic Leucinostatins in Bio-control Fungus Purpureocillium lilacinum and Their Inhibition on Phytophthora Revealed by Genome Mining |
The persistence of a spatially structured population is determined by the rate of dispersal among habitat patches . If the local dynamic at the subpopulation level is extinction-prone , the system viability is maximal at intermediate connectivity where recolonization is allowed , but full synchronization that enables correlated extinction is forbidden . Here we developed and used an algorithm for agent-based simulations in order to study the persistence of a stochastic metapopulation . The effect of noise is shown to be dramatic , and the dynamics of the spatial population differs substantially from the predictions of deterministic models . This has been validated for the stochastic versions of the logistic map , the Ricker map and the Nicholson-Bailey host-parasitoid system . To analyze the possibility of extinction , previous studies were focused on the attractiveness ( Lyapunov exponent ) of stable solutions and the structure of their basin of attraction ( dependence on initial population size ) . Our results suggest that these features are of secondary importance in the presence of stochasticity . Instead , optimal sustainability is achieved when decoherence is maximal . Individual-based simulations of metapopulations of different sizes , dimensions and noise types , show that the system's lifetime peaks when it displays checkerboard spatial patterns . This conclusion is supported by the results of a recently published Drosophila experiment . The checkerboard strategy provides a technique for the manipulation of migration rates ( e . g . , by constructing corridors ) in order to affect the persistence of a metapopulation . It may be used in order to minimize the risk of extinction of an endangered species , or to maximize the efficiency of an eradication campaign .
In recent years , many studies in the field of biodiversity maintenance were focused on spatially structured populations [1]–[14] . Of particular importance are Levins type metapopulations [1] , [2] , [15] , where distinct subpopulations occupy spatially segregated patches of habitats connected by migration . The principle aim of our research is to understand the effect of spatial structure on the persistence of the population; this will allow one to predict the impact of habitat fragmentation , to suggest systematic reserve design strategies [16] , and to forecast the effect of conservation corridors [3] . The population of an isolated patch is usually unstable , as demographic and environmental fluctuations may drive the colony to extinction . Migration among subpopulations allows recolonization of vacant habitat patches ( turnover events ) and reduces the risk of correlated extinction [2] . If the dynamic of a large , well-mixed population is stable , spatial segregation is always harmful . To avoid global extinction , one should increase the migration among patches to allow for a maximal “rescue effect” [17] , . Can one desire too much of a good thing ? Greater mixing , or even patch merging , is the optimal conservation strategy; this is the fundamental assumption behind the reserve design guidelines of Diamond [18] , for example . The situation becomes much more complicated if the local dynamics of a large , well-mixed population is also extinction-prone . In such a case , strong dispersal , which is equivalent to patch merging , increases spatial coherence and leads to global extinction . Many recent experiments on predator-prey [9]–[11] , [6] , host-parasite [5] , and single species [12] , [13] systems suggest that migration is a two-edged sword: it should not be too weak , so that it could allow for recolonization of empty patches by their neighbors , but if it becomes too large , the system synchronizes , the effect of local refuges is reduced , and all the patches undergo extinction together [3] , [4] . The typical outcome is the “bell shape” demonstrated in panels b , c of Figure 1 , where the average lifetime of a spatial stochastic system [the stochastic-logistic map , see Materials and Methods] is plotted against the ( density-independent ) migration rate . The left shoulder of the bell indicates an increase in the persistence with dispersal due to the rescue effect; along the right shoulder , migration becomes harmful as it leads to coherence and correlated extinction . Similar observations have been reported in several fields , ranging from evolutionary game theory [19] to the way globalization induces coherence among economic markets thus jeopardizing their stability [20] . There is a substantial literature on the two edges of the bell shape: the extinction transition that takes place as migration becomes too small [2] , [21]–[24] and the synchronization transition when the mixing exceeds some threshold value [9] , [3] , [25] , [26] . Here we intend to identify where the peak of the curve is , i . e . , under what parameters the system achieves maximum sustainability such that the chance of extinction is minimal . For this purpose , we have developed a numerical technique that allows one to consider the effect of demographic stochasticity and the possibility of extinction for a spatially structured population . To demonstrate the scope of our results , we have considered first the most studied system in the field , namely , the logistic map , and then two other paradigmatic systems: the Ricker map and the well-known Nicholson-Bailey host-parasitoid dynamics . Our main result is the identification of the conditions for maximum sustainability . Surprisingly , it turns out that the optimal point for the stochastic system has nothing to do with the stability properties of the deterministic ( noise-free ) dynamics . Instead , it always appears when the spatial system arranges itself in a checkerboard pattern . In the following section , we show that the maximal persistence time appears when the decoherence peaked , as this is the underlying mechanism beyond stability . The three different systems ( logistic , Ricker , and Nicholson-Bailey ) are analyzed in detail , and we demonstrate consistently that in each of them the maximum sustainability is associated with a checkerboard pattern . Along this paper we deal solely with demographic stochasticity . However , it should be emphasized that our results hold in the presence of other types of noise , like the environmental stochasticity considered by [27] , [28] , [29] - see Text S1 . The checkerboard strategy breaks down only when the population size is unrealistically high ( in which case the system follows its deterministic dynamics ) or extremely low ( where the question of coherence among patches is irrelevant , see Materials and Methods ) .
First let us present the numerical technique used in order to study the effect of demographic stochasticity on the sustainability of a spatially segregated population . We demonstrate this technique for the logistic system; the generalization of this method to any other dynamics is presented in the Materials and Methods section . We consider a metapopulation with local habitat patches , where the carrying capacity of a patch is . The dynamics is described by a discrete generation island model: local population of size at time produces local individuals in the next generation . Any agent may then decide to emigrate from its local habitat with probability ; upon migration it chooses its destination with equal probability among possible habitat patches . Each of the individuals in a local community produces offspring , but the chance of an offspring to survive local competition is , and thus the total population by the time of the next generation is on average . To consider demographic stochasticity we utilized the fact that , the probability that individuals ( out of ) survive to the next generation is given by the binomial distribution , ( 1 ) where B ( k;n , p ) is the chance to get exactly successes in trials , if the chance of success in an individual trial is . To avoid the possibility of an increase of the local community above we impose . Indeed , controls the strength of demographic stochasticity . If the population density is defined as the rescaled number of individuals , , it is clear that the map describes the dynamics of the average density for the stochastic process ( 1 ) . Moreover , since the variance is proportional to the population size , the stochastic map converges to the deterministic one in the limit , with fluctuations that scale like [30] . The deterministic limit of the stochastic-logistic system corresponds , thus , to the paradigmatic model of diffusively coupled logistic maps , considered already in the context of population dynamics [3] . In its spatially explicit form , this system obeys ( here is the population density after the dispersal/migration step ) : ( 2 ) where is the proportion of individuals from patch that disperse to patch , where is the number of patches , , and where is the maximal intrinsic growth rate ( maximum fecundity ) of the population . In this work , we have considered only local dynamics , where and is zero unless the and the sites are nearest neighbors , in which case , where is the connectivity of the th site . Note that this deterministic model per se may support chaotic dynamics where the population assumes an arbitrarily small value , but it never allows for extinction . To consider the possibility of extinction , one must adds demographic stochasticity to the model , i . e , use ( 1 ) instead of ( 2 ) . For a small system , the average time to extinction may be estimated by averaging over many runs with different initial conditions and different histories; this is the method used to obtain the graphs presented in the next figures . For large systems , or alternatively when the time to extinction is relatively large , we have used other estimation techniques ( see the Materials and Methods section for the details of these techniques and a comparison between them . ) Figure 1 exemplifies the situation for the simplest case , namely , a two-patch system . In that figure , two models are presented: in the upper panel the deterministic dynamic of a coupled logistic map [Eq . ( 2 ) ] , and below it , two panels with different of its stochastic , agent based analog [described by Eq . ( 1 ) ] . The orbit diagram of the deterministic map shows that , for some intermediate migration rate , the system supports an attractive , period-2 orbit [31] , [32] . This orbit is characterized by an “up-down” dynamic: when one patch is “up” ( admits a larger population ) the second is “down” ( in the low-density state ) and vice-versa . Interestingly , the peak of the bell shape for the persistence time in the cases of the stochastic ( agent-based ) system happens to be in that same “up-down” region . Does this fact indicate that the peak should be attributed to the features of the spatial deterministic dynamics , namely , to the existence of an attractive manifold ? Not really . Let us take a look at Figure 2 . Here plots are given for a four-patch system with periodic boundary conditions ( a square with no diagonal connections ) . There are two regions in the orbit diagram presented in the upper panel that correspond to period-2 attractive manifold . The first is the “up-down-up-down” ( UDUD ) region ( two up-down patches attached to each other ) and the second is an “up-up-down-down” ( UUDD ) configuration , where diffusion is strong enough to synchronize adjacent pairs . In the second panel , the Lyapunov exponent of the orbits is presented , and one finds that the UUDD is slightly more attractive than the UDUD region . However , as can be seen in the third panel , the peak of the persistence time is still found in the UDUD region , and the bell-shape is smooth , completely unaffected by the appearance of the UUDD periodic orbit . There is no direct correspondence between the appearance of attractive orbits of the deterministic map and the persistence of the stochastic system . Figure 3 explains why the analysis of stability using Lyapunov exponent is irrelevant for the prediction of the maximum sustainability point . The UDUD and the UUDD orbits are indeed attractive , but their basin of attraction is narrow , and small perturbations take the system to long excursions until it reaches the stable manifold again . [In the theory of nonlinear dynamics such systems are known as excitable [33] , and the excursion defines a homoclinic trajectory] . What determines the chance of extinction is not the local stability properties of the orbits but the probability of extinction during the excursion . This chance is proportional to the minimum distance to default along the excursion path . These individual particle simulations show that for realistic values of , up to 5000 agents per site , demographic stochasticity is strong enough to kick the system occasionally from the attractive orbit , sending it to a long excursion in phase space . It turns out that since the underlying dynamics is chaotic , the kicked system visits any possible point in phase space with almost equal chance . There is no need to make a distinction between asymptotic states of different initial conditions: what matters is the minimal total population along the transient . Indeed one may simply average the distance to default over many initial conditions of the deterministic system to get roughly the same bell-shape obtained from the individual-based simulations ( see Materials and Methods and Video S1 ) . The fractal basin boundary and the dependence of the asymptotic behavior on initial conditions have already been pointed out by Adler [34] for the Nicholson-Bailey map and by Hastings [32] for Ricker and logistic systems . Indeed , it is this feature of the deterministic model that makes stochasticity an important factor . In general , one may guess that there is no need to add stochasticity to the already random , erratic dynamics of a chaotic system , and on the other hand , that the effect of weak stochasticity on a system that admits an attractive orbit is small . Here we find that these two arguments fail when a stable orbit results from the interplay between chaotic subpopulations: stochasticity is still important and its little affect is amplified by the underlying chaotic motion that yield these long excursions .
The exceptional stability of the checkerboard pattern has to do with the fact that in this state the decoherence among neighboring habitat patches is maximal . To understand this we briefly review some elements of previous studies . A generic mechanism that leads to sustainability in spatially structured populations has been discovered recently [35] , [8] in the context of a two-patch system . The basic ingredients needed for its applicability are migration , stochasticity and an unstable dynamic . ( Abta and Shnerb [8] have discussed other stabilization mechanisms that depend on spatial heterogeneity of the local dynamic or , for victim-exploiter systems , on the difference in the migration rates of the species; these attributes do not exist in the models discussed here ) . In order to grasp the essence of the stabilizing mechanism , let us look at Figure 8 . For a simple victim-exploiter 2-patch system , this figure emphasizes that if the oscillations on these two patches are incoherent , then migration between patches drives the whole system inward toward the coexistence fixed point , yielding sustained oscillations . However , one should bear in mind that dispersal itself tends to reduce population gradients and induces synchronization . In order to gain stability , the migration among patches should be weak enough to allow for noise-induced desynchronization , yet strong enough to stabilize incoherent patches . As discussed in [8] , this general statement is valid for any unstable model that supports oscillations close to the unstable fixed point , these oscillations appear naturally in victim-exploiter ecologies [36] . The logistic map ( and other unimodular maps like Ricker ) also belong to the same class , as the population spirals out of the unstable fixed point [37] . Stable orbits , thus , may appear due to the presence of noise . The role of noise is to perturb the system from its fully synchronized phase . Once this perturbation happens , the differences between patches are amplified by the underlying unstable dynamic . This yields an effective decoherence between patches and , as a result , the dynamic stabilizes . Based on this insight , we suggest that the optimal persistence is always achieved at the point of maximum decoherence . The basic unit is a two-patch model in the “up-down” phase , and the whole system should be tiled with these dominoes in a checkerboard array that allows for maximum rescue effect . As demonstrated in Figure 9 , this conjecture explains the optimal patterns for larger arrays in one and two dimensions . The attractive up-down orbit of the deterministic model and the optimal persistence of the stochastic dynamics coincide , as both manifest the point of maximum efficiency of the stabilizing mechanism [35] , [8] . As shown above , the very same result holds for different dynamics that acquire stability through spatial structure , like the Ricker map considered by [12] and the classic Nicholson-Bailey model [38] for host-parasitoid dynamics . The results of the Drosophila experiment [12] also support our conjecture . Although global extinction has not been observed during the experiment , the authors have quantified the constancy stability of the metapopulations by measuring the amplitude of fluctuation in population size over time . This is equivalent to the second method for estimating persistence time explained in the Materials and Methods section below . For the most persistent scenario ( optimal migration ) the mean nearest neighbor cross-correlation was negative , indicating that the system is indeed in the checkerboard state . All these considerations fail when the number of individuals per site becomes extremely large ( the system follows the deterministic dynamics and the stability of an orbit is governed by the Lyapunov exponent ) or small ( where synchronization is no longer important and migration always helps ) . These limits are discussed below . Within the general framework suggested by Earn , Levin and Rohani [3] , our results admit a wide scope of implications . Once the density-dependent local dynamic of the population is known - e . g . , by estimating the maximum fecundity parameter or by retrieving the recruitment curve for a well-mixed population ( see , e . g . , the use of this technique in an annual plant metapopulation experiment [13] ) - one may use this deterministic , spatially explicit dynamic to find out which migration rate corresponds to the checkerboard state . This may be used for the design of conservation corridors and for evaluating the impact of habitat fragmentation . For the opposite effect , it may also help in the eradication of infectious diseases . Even without any knowledge of the local dynamics , tracing the patch density vs . time allows one to recover the correlations between neighboring patches; sustainability is optimal when neighbors' correlation reaches its minimum value . The checkerboard solution manifests itself even if the topology of the system does not allow for “perfect” partition , as in the case of an odd number of patches or an imperfect lattice . As demonstrated in the Video S2 , the system develops a local defect that “screens” the problematic region while the rest of the plane is covered by a checkerboard pattern . We have already carried out a preliminary study of other topologies , like equal coupling systems , for which dispersing individuals are equally likely to move to any of the other patches , random networks , triangular lattice etc . Our results suggest that the persistence time is maximal when the system reaches the most incoherent state , which in some aspect resembles the checkerboard strategy; we will return to this issue elsewhere .
The numerical procedure used along this work is a generic individual-based generalization of the deterministic approach for coupled map lattice [39]–[41] . A very similar island model has been used by Hamilton and May [42] who have considered the evolution of dispersal rates for a population with spatial structure and kin competition . However , the model of Hamilton and May , as well as other studies of the persistence of a metapopulation , neglects the demography of the local population: a habitat patch is either occupied or extinct . Under these conditions; the stochastic dynamics is equivalent to a contact process [22]–[24] , and the persistence receives only benefit from an increase of the migration rate . To say it another way , simple extinct/occupied dynamics supports , in the deterministic ( large ) limit , an attractive fixed point with a finite population density . Such a system goes extinct in the presence of stochasticity due to large fluctuations; since larger dispersal acts to decrease the amplitude of these fluctuations it must be advantageous for its persistence . On the contrary , here we consider the case where in the deterministic limit , the dynamics are unstable ( chaotic or otherwise extinction-prone ) , and thus stochasticity induces fluctuations , and their interference with the spatial structure plays a crucial role in the persistence of the population . We assume a population dynamics with nonoverlapping generations , where any generation involves two consecutive steps . The first step involves the “local reaction” ( birth , death , competition etc . , at which any patch is affected only by the local population ) , and the second is the density independent “migration” ( dispersal ) step , where individuals are allowed , with a certain probability , to leave their local community and migrate to another patch . No “dispersal cost” is introduced , so any emigrant reaches its chosen destination . In the reaction step , the number of agents on a patch at the generation , , is determined by . If the numbers of agents are very large ( e . g . , if one deals with a bacterial system ) , it is possible to neglect the discrete character of the system , as the effect of demographic stochasticity falls like . Under these conditions , one can write down a simple map of the form: ( 5 ) where is the population density . Along this work we deal with two particular examples of ( 5 ) , namely , the logistic map: ( 6 ) and the Ricker map: ( 7 ) Both maps are chaotic , and after a while the system reaches population levels that are very close to zero . The deterministic formalism has no problems with that: since is always above zero , the population survives forever . We know , however , that this is wrong . As the population is made of discrete individuals , too close to zero corresponds to no individuals at all , in which case the dynamics should halt ( this is the “absorbing state” in the stochastic processes terminology ) . To consider the effect of extinction , one should generalize the deterministic dynamics to include the effect of demographic stochasticity . This is done here by using: ( 8 ) where is a stochastic process that converges to its deterministic equivalence ( 5 ) in the large population limit . For the logistic map: ( 9 ) and for the Ricker map: ( 10 ) where stands here , for the sake of brevity , for a number taken from a binomial distribution ( i . e . , for where the chance to get successes from trials is given by the binomial distribution ) . In the large limit , the fluctuations around the mean are negligible , and since the mean of is , these maps converge to their corresponding deterministic values . In order to facilitate the numerics , we have chosen the value of such that the first argument of the binomial distribution will be an integer ( for the logistic map , for the Ricker ) , but it is easy to generalize ( 9 ) and ( 10 ) ) to the case of noninteger values of . After the reaction step , a migration step takes place . In the deterministic limit , a fraction of ( the population on the i-th site ) is subtracted from any site population , and is divided between all possible destinations . In the individual-based model , any individual on the -th site is chosen to emigrate with a chance , and it then it chooses its destination randomly; for a one dimensional chain it will arrive at the left or at the right neighboring patches with probability 1/2 . In order to avoid an artificial drift , the migration takes place via a parallel update scheme , and the site population is updated only after the whole diffusion cycle is completed ( failing to do so , one may choose an individual to migrate from the first to the second site , then choose again the same individual to jump from the second to the third patch; this introduces a residual drift in the direction along which the updates take place ) . Another example we consider here is the non-chaotic ( yet extinction-prone ) Nicholson-Bailey [38] model for host-parasitoid interaction . Here there are two species , the host and the parasitoid , that on a single patch satisfy the deterministic map: ( 11 ) where is the escape probability . In the model with demographic stochasticity , and are integers . To simulate the local dynamics the number of uninfected hosts , , at a certain generation is given by: ( 12 ) and the rest of the hosts are infected . Any uninfected host produces offspring in the next generation , while an infected host yields parasitoids . The migration procedure is the one presented above where it is now applied separately to the host and to the parasite . Along this work we have used three procedures in order to estimate , the persistence time of a system . As discussed in [35] , [8] , the distribution of lifetimes is exponential with an average ( see Text S1 ) . This has to do with the appearance of an attractive manifold , as opposed to the broad distribution of lifetimes at criticality as discussed in[43] . There are two extreme cases of a too large and too small noise , where the checkerboard strategy fails . In the weak noise limit ( that corresponds to the large case of the agent-based system ) the dynamics is very close to the deterministic one and the results of the deterministic modeling most hold . It turns out that the size of needed to reach this limit is huge , and any ecosystem ( except , maybe , bacterial colonies ) is far from this extreme . In particular , local extinction happens only if the deterministic dynamics takes the population to very small values , between zero and . This is a relevant process only in the weak migration regime ( the rate of recolonization approaches zero ) or in the fully synchronized case; otherwise , extinction simply never happens in this regime . The other limit , that of large noise , appears when is very small . In this case the rate of local extinctions is so high that the real degrees of freedom of a habitat patch are simply occupied or empty , and coherence among patches makes no difference . Our island model with complex dynamics becomes equivalent , in the strong noise limit , to a contact process . As explained above , in that case the higher the migration rate , the more sustainable is the metapopulation , and thus the optimal migration rate grows as approaches one . This phenomenon is demonstrated in Figure 11 . | No one can produce all his needs by himself . Personal autarky poses a serious danger of collapse in cases of illness , drought , etc . Trade reduces the impact of local catastrophes , thus increasing economic stability . However , the recent series of econo-crises revealed that globalization induces coherence among markets and jeopardizes their sustainability against global failures . Economists try to identify the optimal tariff that balances between the dangers of autarky and the risk of correlated failure . The same problem appears in ecosystems with a population divided among local habitat patches . “Optimal tariff” is translated to optimal migration rate: how should one manipulate connectivity among patches in order to achieve maximum sustainability ? Recolonization of habitats that undergo extinction is essential for survival , yet a too strong dispersal leads to coherence and correlated extinction . Here we use individual-based models in order to find the optimal migration rate . We show that this optimum appears when the the system takes a spatial “checkerboard” pattern that maximizes the decoherence . The insights gleaned allow for improved policies for conservation of endangered species ( optimizing the effect of corridors , predicting the impact of habitat fragmentation ) and , on the other hand , eradication campaigns ( like vaccination or pest control ) . | [
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"ecology"
] | 2010 | Optimizing Metapopulation Sustainability through a Checkerboard Strategy |
Local field potentials ( LFPs ) are widely used to study the function of local networks in the brain . They are also closely correlated with the blood-oxygen-level-dependent signal , the predominant contrast mechanism in functional magnetic resonance imaging . We developed a new laminar cortex model ( LCM ) to simulate the amplitude and frequency of LFPs . Our model combines the laminar architecture of the cerebral cortex and multiple continuum models to simulate the collective activity of cortical neurons . The five cortical layers ( layer I , II/III , IV , V , and VI ) are simulated as separate continuum models between which there are synaptic connections . The LCM was used to simulate the dynamics of the visual cortex under different conditions of visual stimulation . LFPs are reported for two kinds of visual stimulation: general visual stimulation and intermittent light stimulation . The power spectra of LFPs were calculated and compared with existing empirical data . The LCM was able to produce spontaneous LFPs exhibiting frequency-inverse ( 1/ƒ ) power spectrum behaviour . Laminar profiles of current source density showed similarities to experimental data . General stimulation enhanced the oscillation of LFPs corresponding to gamma frequencies . During simulated intermittent light stimulation , the LCM captured the fundamental as well as high order harmonics as previously reported . The power spectrum expected with a reduction in layer IV neurons , often observed with focal cortical dysplasias associated with epilepsy was also simulated .
Neuronal activity changes the distribution of electric potentials in the brain [1] , [2] . Local field potentials ( LFPs ) are the low-frequency ( <100 Hz ) fluctuations in electric potentials in the extracellular space of the brain [2] , [3] . They represent a weighted average of the potential changes produced by neuronal activity in a small volume around the measuring electrode [4] , [5] . Concurrent electrophysiological and functional MRI experiments have also demonstrated that LFPs are correlated with signal change in functional magnetic resonance imaging , a method of detecting neuronal activity through changes in blood-oxygen-level-dependent signal [6]–[8] . Previous electrophysiological experiments investigating the neuronal processes underlying LFPs have measured the membrane potential of neurons and extracellular field potentials simultaneously [9] , [10] . A major difficulty with this paradigm is that LFPs reflect the activity of more than 10 , 000 neurons [11] within 250 micrometres of the recording electrode [4] , [5] . Simultaneous measurement of such a large number of neuron activities has not been achieved to date . Furthermore , multiple concurrent processes contribute to LFPs , including action potentials , synaptic transmission , glial activity , and even extracellular space diffusion [12] and are difficult to disambiguate . Computer simulations have widely been adopted to predict changes in neuronal activity associated with corresponding LFPs . A previous study simulated the membrane potential changes of a large number of individual neurons as means of reconstructing the LFP [for example , see 13] . Simulating the dynamics of a large number of neurons faces the challenge of specifying the physiological parameters in large , inhomogeneous populations with diverse physiological properties [14] . An alternative approach to simulating individual neuronal activity has been to simulate the activity in an ensemble of neurons . An example of this is the continuum cortex model , developed by Wright et al [15]–[18] , which has been used to simulate ensemble activity at different scales [17] . Existing continuum cortex models do not take into account the laminar architecture of the cerebral cortex . They are , therefore , limited in their ability to model the distribution of electric potential of the brain in three dimensions . Cortical neurons are organized in columns comprising as many as 20 , 000 neurons [19] , [20] . Functionally , neurons in a column display similar responses to specific stimuli [21] . In this paper , we build on this notion to expand the continuum cortex model by incorporating the laminar connection architecture of the cortex and simulating the collective of neuronal ensembles within cortical columns . We have used the new laminar cortex model ( LCM ) to simulate LFPs within the visual cortex under different conditions of visual stimulation .
We give a brief overview of the continuum cortex model for completeness , but for specific details refer to [17] . The continuum cortex model simulates the collective electrophysiological activities of the cerebral cortex . A population approximation is used to overcome the difficulty of simulating a large number of individual neurons , and to capture the essential aspects of cortical dynamics [15] , [16] . The continuum cortex model divides the simulated cortical area into a grid of elements , where is an integer . Each element consists of two populations of neurons: excitatory and inhibitory [17] . Each population is treated as a single entity capable of receiving spikes , changing membrane potential , and generating and propagating spikes [17] . The numbers of spikes propagating between neurons of two groups at any one time varies . In the continuum cortex model the effects of action potential shape and its temporal evolution are ignored . Instead , the average afferent spike rate ( ) is used to measure interaction between the two groups of neurons . The spike rate is defined as the average number of spikes a neuron of one group receives from a neuron of the other group per unit time . The continuum cortex model contains four main components: 1 ) spike generation , 2 ) spike propagation , 3 ) generation of the postsynaptic potential , and 4 ) membrane potential aggregation . The equations describing each component are provided in Text S1 and were developed either by using theoretical approaches or by experimentally fitting observed data using an appropriate function . The mean field approximation was employed during this procedure [17] . The LCM exploits the laminar architecture of the cortex . Five cortical layers ( layer I to VI ) are considered ( cortical layers II and III are combined ) . Each layer is simulated using the continuum cortex model , and the layers are connected by laminar synaptic connections ( see Figure 1 ) . A synaptic connection map is created and used to control the connection between and within cortical layers ( see Table S1 in Text S2 ) . This connection map was based on empirical observations of the number of synapses formed between different types of neurons by Binzegger [22] ( see Text S2 ) . The connection map classifies the afferent synapses on each group of cortical neurons into three categories: 1 ) intracortical synapses , from within the visual cortex ( ) , 2 ) cortico-cortical synapses , from other cortical areas ( ) , and 3 ) thalamic synapses , projections from neurons in the lateral geniculate nucleus ( LGN , ) . The LCM allows simulation of centimeter and column scale ( micrometer ) cortical regions [17] . Since the grid elements of the centimeter scale model correspond to the size of cortical columns , the connections between cortical laminae are assumed to be local . This means that elements in the same horizontal position of all cortical layers are connected vertically ( see Figure 1B ) . In contrast , the column scale implementation is approximately the size of one cortical column . Therefore , connections between cortical layers are global , and the average spike rate of a cortical layer is the input to other cortical layers . The work here is focused on simulating LFPs produced in the visual cortex . Hence , results are limited to the application of the centimeter scale model . We simulated the effect of visual stimulation on LFPs using the LCM . Different forms of visual stimulation were assumed to form different spike trains projecting from the LGN to deeper cortical layers of the visual cortex ( Layer IV , V and VI , see Table S1 in Text S2 ) . Three states of visual stimulation were examined in the model: 1 ) spontaneous activity without visual stimulation , 2 ) constant visual stimulation , and 3 ) intermittent light stimulation . As illustrated in Figure 2 , these conditions correspond to afferent spike trains with the shape of small amplitude white noise , large amplitude white noise ( the random number generator from [23] was adopted ) , and recurring Gaussian peaks , respectively . Apart from the synapses projecting from neurons in LGN and the visual cortex , there are also a large number of synapses originating from other cortical areas ( see Table S1 in Text S2 ) . We assume that spikes from these synapses contribute to background noise , which was modeled as low-amplitude white noise . The LCM has over 150 parameters , which fall into four categories relating to: 1 ) electrophysiological properties of neurons , 2 ) spike propagation , 3 ) synaptic transmission , and 4 ) connections between cortical laminae . Most of these parameters were estimated from experimental data , while others were left as free parameters . However , the cortex is complex , to the extent that our simplified parameters may not represent its physiology , morphology and architecture exactly . We found that a small deviation of the parameter values do not change the results reported here significantly . This is because a similar LFP outcome can be achieved by tuning free parameters . Parameters relating to the electrophysiological properties of neurons are well established in the literature . We used the same values , derived from experimental data , as the continuum cortex model [17] . Spike propagation parameters and their values used are listed in Table 1 . The propagation speed of spikes in the horizontal ( lateral ) direction ( ) was set to 0 . 24 m/s , which is consistent with experimental measurements of the speed of spread of spikes in the cortex [24] , [25] . Since collateral branches are usually smaller in diameter than the main axon , the speed of vertical ( inter-laminar ) propagation of spikes ( ) was set to 1 . 2 times the speed of horizontal propagation . The spike propagation range parameters were set to the similar values as continuum cortex model [17] . There is a wide range of published values for synaptic transmission parameters [26] , [27] . We chose the middle parameter value when a range was provided and the average when multiple values were reported . The excitatory and inhibitory synaptic gains and , were treated as free parameters . Their values were determined by fitting experimental data to the LFPs generated using the LCM . The best set of parameter values was selected as those fulfilling the following criteria: 1 ) the LFP power spectrum fitted the function with [28] . 2 ) with simulated visual stimulation , there was an increase in gamma frequency in the power spectrum; 3 ) membrane potentials of neuron groups were less than 10 mV above their resting membrane potentials [29] . The simulation program was written using the ANSI C language and compiled with the Intel C compiler ( http://software . intel . com/intel-compilers/ ) . The program was compiled and executed on a Linux workstation ( Dell® Precision T7500 ) with Ubuntu version10 . 10 ( ×86_64 , http://www . ubuntu . com ) . OpenMP ( http://www . openmp . org ) , a shared-memory parallel programming library , was used to parallelize the code to speed up program execution . In this paper , the LCM was used to simulate a cortical area of size cm2 . The domain was discretized to a grid . At the beginning of each execution of the program , the simulation time was initialized to zero , and every neuron state variable was set to its resting state value ( see Text S2 ) . The iteration time step was one millisecond . After initialization , the program executed without particular visual stimulation for 60 seconds at which time the system is assumed to have reached steady state . Constant visual stimulation or intermittent light stimulation was then applied for 20 seconds ( time = 60–80 sec ) . LFPs were simulated for conditions of spontaneous activity and for each mode of visual stimulation . In the simulation , the membrane potentials of all neuron groups in the middle element of a layer are recorded during the entire execution . Data of the last 1 . 024 second prior to visual stimulation and after stimulation were used for frequency spectrum analysis . For comparisons with experimental data , the LFPs of the simulated cortical area are assumed to be the average of neuronal membrane potentials of the central elements of all layers , stated as: ( 1 ) where are the numbers of neurons in the central element of layer and are the potentials of the central elements of layer , which is the average of membrane potentials of neurons in the element , that is ( 2 ) where , are the numbers of excitatory and inhibitory neurons and and are the ( average ) membrane potentials of excitatory and inhibitory neuron populations respectively . The frequency spectrum of the LFPs was computed using the fast Fourier transform as implemented in MATLAB 2010a ( http://www . mathworks . com ) . The LFP frequency power spectra were compared with experimentally measured data . LFPs produced by LCM were also used to estimate current source density . The standard one-dimension current source density calculation method was used [30] , [31] ( 3 ) Here is electric conductance of the cortex , and was set to 0 . 3 S/m , is the potential at the point , and is the distance between two adjacent points . To reduce spatial noise , the three-point Hamming filter was applied [32] , [33] ( 4 )
We examined the behaviour of the LCM using different parameter values . For each parameter combination , around 100 executions of the LCM were conducted , and the average LFP frequency spectrum was computed . Figure 3 shows the power spectra of the LFPs obtained with different synaptic gains . The LCM was able to generate LFPs with different types and envelopes of oscillation , depending on the combination of excitatory and inhibitory synaptic gains used in the simulation . For example , when either excitatory or inhibitory synaptic gain was small , the frequency spectrum of background activity had an inverse-frequency shape . Stimulation resulted in an increase in gamma frequency . In contrast , when the excitatory and inhibitory synaptic gains were both large , particular frequency peaks dominated the LFP power spectra . Thus , variations of synaptic gains had a strong impact on LFP frequencies . For large synaptic gains , the peaks in the power spectra did not change position with variation in synaptic gain . Dependence of peak position on other parameters was also examined by generating LFP power spectra with different parameter values . The time course of the postsynaptic potential ( PSP ) was found to be strongly correlated with the positions of the peaks . Peak frequency decreased with increasing PSP time course . ( Four examples of LFP power spectra with different PSP time courses are shown in Figure S2 ) . This suggests that the dominant oscillation frequency is controlled by the feedback between excitatory and inhibitory neurons . The shape of the power spectrum of LFPs generated by the LCM is controlled by the balance between excitatory and inhibitory postsynaptic potentials ( PSPs ) . These are influenced by many parameters simultaneously , including synaptic gains , spike propagation ranges and synapse numbers . Changes in PSPs caused by variation of one parameter could be compensated by other parameters . For example , increase of synaptic gains may not change PSP when the corresponding synapse number is decreased . Therefore , the LCM could produce similar LFPs using different combinations of parameter values . Experimental models of neocortical epileptic foci suggest that reduced synaptic inhibition in layer IV plays an important role in epileptogenesis [34] , [35] . Focal cortical dysplasias characterized by an absence or significant reduction in layer IV are also very frequently associated with epilepsy [36] . Figure 4 shows the LFP power spectrum shapes generated by the LCM when the numbers of synapses formed with presynaptic neurons in layer IV are decreased by 50% . Compared to Figure 3A , the power spectra show a small shift to small inhibitory gain . For example , for LFPs produced using excitatory and inhibitory synaptic gains of V/spike , the power spectrum changed from a frequency-inverse shape to one with spectral peaks as would be expected with seizures when presynaptic neurons of layer IV decrease by 50% . This suggests that , changes in neuron or synapse density may change the way LFPs oscillate dramatically . These alterations in dynamics may increase our understanding of how abnormalities in cortical architecture lead to seizures . Figure 5 shows the time courses of membrane potentials in a single run of the LCM . We found that in every cortical layer , membrane potentials oscillated with amplitudes of 0 . 05–0 . 2 mV; the amplitudes are much larger in layers IV and VI ( around 0 . 1 mV ) than in other layers ( around 0 . 05 mV ) . During stimulation , the membrane potentials and its oscillation amplitudes increased in all layers except layer I . The power spectra in all layers , as provided in Figure 5 , all showed inverse-square decreasing frequency background activities , which is observed experimentally [37] . Stimulation also increased high-frequency membrane potential oscillation of all deep layers . The laminar distribution of the LFP power spectrum amplitude was examined . Figure 5C shows the laminar distribution of the average of the LFP power distribution in the gamma frequency ( 30–100 Hz ) and sub-gamma frequency ( 5–20 Hz ) ranges for spontaneous activity and general stimulation . Higher frequency powers were observed in layers IV and VI . This is in agreement with experimentally measured laminar LFP amplitude profiles in the primary visual cortex [38] . Since layers IV and VI are the main layers of the visual cortex receiving and sending projections to the LGN , the observed variation in LFP power spectra amplitudes between layers most likely results from these projections . We simulated the propagation of one spike source in the cortex using LCM . In Figure 6 we provide the result when a spike source is placed in the four central elements of layer IV for 20 milliseconds after 60 seconds of spontaneous activity . Following spike onset , a strong potential is observed in the center of all cortical layers except layer I . The potential is decreased in elements surrounding the source , simulating surround inhibition . We display the temporal profiles of current source density along a transverse line through the central point in layer IV and for the central elements of each cortical area in Figure 7 . Many electrophysiological experiments have demonstrated that with intermittent light stimulation , neuronal activity in the visual cortex synchronizes with stimulus frequency [39]–[42] . Furthermore , EEG responses are enhanced at this frequency ( fundamental harmonics ) , as well as at half the stimulus frequency ( first sub-harmonic ) , and at multiples of the stimulus frequency ( multiple harmonics ) . The responses to visual stimulation at specific frequencies , termed steady-state visual evoked potentials ( SSVEPs ) , can be observed on both scalp EEG recordings [39] and invasive recordings of LFPs [40] . We used SSVEPs to examine the effect of cortical architecture on LFPs . The LCM was used to simulate LFPs with 10 Hz intermittent light stimulation represented by a Gaussian distribution of spike rates for neurons projecting from the LGN to the visual cortex . The peak and standard deviation of the Gaussian shape was 30 spikes/second and 6 . 25 milliseconds , respectively ( see Figure 2 ) . Figure 8 shows the variation of LFPs with time and the associated power spectra . Simulations using the LCM reproduced the power spectra reported in experimental data [39] . The LFP power spectrum had peaks at frequencies that were multiples of the stimulus frequency ( i . e . capturing multiple harmonics ) . Notably , the amplitude of fundamental harmonic ( i . e . frequency peak at 10 Hz ) was smaller in layer II/III than other layers . This is probably because there are fewer projections from LGN to layer II/III than other layers . Experimentally observed sub-harmonics were not obvious in simulations using the LCM [39] .
This paper introduces the LCM and describes its use to simulate LFPs in the primary visual cortex . The LCM has the advantage that it incorporates the architecture of the visual cortex allowing the simulation of LFPs with high spatial and temporal resolution . We were able to simulate the membrane potential in each cortical layer , as well as its temporal variations . We used the LCM to investigate the relationship between visual stimulation and LFPs . We validated the model using two different experimental simulations: constant visual stimulation and intermittent light stimulation . Our results were comparable to relevant experimental measurements . We also simulated the effects of changes in neuronal density in layer IV , often observed in epileptic cortical dysplastic tissue . For certain parameter combinations the changes in the power spectra were those expected in seizures . CSD maps showed comparable features to experimental data and intralaminar CSD profiles following transient LGN input had the appearance of surround inhibition . With constant visual stimulation , the LCM produced LFPs oscillating in two different ways determined by the combination of parameters used in the simulation . When the cortex was activated with low levels of background noise and stimulus input ( small synaptic gains ) , the LFP oscillation was governed by the pool of excitatory neurons . Synaptic transmission acts as a filter due to the convolution in the membrane potential aggregation function of LCM ( refer to Equation S1 . 8 in Text S1 ) . Effectively , this dampens high frequency oscillations and results in an inverse-squared decreasing LFP spectrum . However , when the cortex is highly activated , inhibitory neurons play a more dominant role , resulting in oscillations in which initial activation of inhibitory neurons leads to suppression of the membrane potential of all neurons , including the inhibitory pool followed by a burst of activity cause by excitatory input . The LFPs produced using low synaptic gains are comparable to experimentally observed LFPs in the normal brain , while the LFPs obtained with large synaptic gains are similar to those measured during seizures [37] . This suggests that changes in neuronal physiology can result in a change in the LFP power spectrum and may help to explain frequency changes in the EEG observed in certain neurological disorders . There are some differences between LFPs from the LCM and experimentally measured LFPs . The amplitude of low frequency ( <10 Hz ) LFPs produced by the model is lower than measured experimentally . A possible explanation is that the low frequency oscillation results from feedback loops between the visual cortex and other brain areas [43] , which are not considered in the LCM . The gamma frequency ( 40–200 Hz ) power of stimulated LFPs is also smaller than experimental measurements . We postulate that this is because extracellular potential changes caused by synaptic activities and spike conduction are not included in the calculation of LFPs . These are reported to have a greater influence on high frequency LFPs [44]–[46] . The LCM simplifies synaptic processes and spike propagation to a signal delivery level . It does not simulate the burst of synaptic transmission and spikes . The CSDs calculated from LCM recreates several features from experimental observations [47] . Within layers , the CSD profile simulated surround inhibition [48] . Across cortical layers , the temporal profile of CSDs was similar to those observed by Schroeder et al . [47] with transition from sink to source following stimulation . We used SSVEPs , to test the effects of incorporating cortical architecture on simulation output . In our intermittent light stimulation study , we used the LCM to reproduce the behaviour of SSVEPs . The fundamental and high order harmonics were apparent in the visual cortex . The first sub-harmonics , shown to be present empirically [39] , may be brought about by feedback loops between the primary visual cortex and other visual cortical areas . These connections are not included in the LCM . The LCM may be used to simulate abnormal responses to intermittent light stimulation such as the photoparoxysmal response observed in forms of genetic generalized epilepsy . This can be achieved by varying LCM parameters , and comparing the simulation output with measured EEG data . This has the potential to generate testable hypotheses relating to underlying neurophysiological mechanisms . Although we showed that LCM is able to reproduce some of the results of electrophysiological experiments , it has some limitations . Firstly , only two populations of neurons ( excitatory and inhibitory ) are considered . The behaviour of excitatory neurons may not be best captured by a single category . For example , fast-spiking neurons generate spikes differently from other excitatory neurons [27] . In future work we will extend the LCM to include multiple categories of excitatory neurons . Secondly , simulation of neurotransmission in the LCM may be oversimplified . For example , in its current form it cannot simulate the effects of activating fast ( AMPA ) and slow ( NMDA ) excitatory glutamatergic receptors on LFPs . Thirdly , the physiological parameters used in our simulation were obtained from the results of experiments conducted in different species . In our simulations , LFPs were calculated as the aggregate membrane potential dynamics of populations of neurons , an approach commonly employed in simulation studies e . g . [49] . This approach may be inaccurate because it does not take into account the filtering properties of the neural membrane [44] , [45] . Methods based , for example , upon summation of conductance of synapses to pyramidal neurons [2] , [45] , [50] are inapplicable to the LCM , which simulates the collective activity of neuron groups . A future hybrid model is required to link continuum cortical models and models based on simulating the properties of individual neurons . | Local field potentials ( LFPs ) are low-frequency fluctuations of the electric fields produced by the brain . They have been widely studied to understand brain function and activity . LFPs reflect the activity of neurons within a few square millimeters of the cerebral cortex , an area containing more than 10 , 000 neurons . To avoid the complexity of simulating such a large number of individual neurons , the continuum cortex model was devised to simulate the collective activity of groups of neurons generating cortical LFPs . However , the continuum cortex model assumes that the cortex is two-dimensional and does not take into account the laminar architecture of the cerebral cortex . We developed a three-dimensional laminar cortex model ( LCM ) by combining laminar architecture with the continuum cortex model . This expansion enables the LCM to simulate the detailed three-dimensional distribution of the LFP within the cortex . We used the LCM to simulate LFPs within the visual cortex under different conditions of visual stimulation . The LCM reproduced the key features of LFPs observed in electrophysiological experiments . We conclude that the LCM is a potentially useful tool to investigate the underlying mechanism of LFPs . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"circuit",
"models",
"computational",
"neuroscience",
"biology",
"computational",
"biology",
"sensory",
"systems",
"neuroscience"
] | 2012 | The Laminar Cortex Model: A New Continuum Cortex Model Incorporating Laminar Architecture |
Bacterial cyclic glucans are glucose polymers that concentrate within the periplasm of alpha-proteobacteria . These molecules are necessary to maintain the homeostasis of the cell envelope by contributing to the osmolarity of Gram negative bacteria . Here , we demonstrate that Brucella β 1 , 2 cyclic glucans are potent activators of human and mouse dendritic cells . Dendritic cells activation by Brucella β 1 , 2 cyclic glucans requires TLR4 , MyD88 and TRIF , but not CD14 . The Brucella cyclic glucans showed neither toxicity nor immunogenicity compared to LPS and triggered antigen-specific CD8+ T cell responses in vivo . These cyclic glucans also enhanced antigen-specific CD4+ and CD8+ T cell responses including cross-presentation by different human DC subsets . Brucella β 1 , 2 cyclic glucans increased the memory CD4+ T cell responses of blood mononuclear cells exposed to recombinant fusion proteins composed of anti-CD40 antibody and antigens from both hepatitis C virus and Mycobacterium tuberculosis . Thus cyclic glucans represent a new class of adjuvants , which might contribute to the development of effective antimicrobial therapies .
Cyclic glucans are intrinsic components of the envelopes of Gram negative bacteria such as Agrobacterium , Rhizobium , Ralstonia solanacearum , Xanthomonas campestris , Rhodobacter sphaeroides and Brucella spp . [1] . Brucellae are intracellular pathogens of mammals , including humans [2] . The pathogenesis of brucellosis is linked to the ability of Brucella to survive and replicate inside host cells [3] through the expression of several effector molecules [2] . In particular , the periplasmic cyclic glucan is required for B . abortus intracellular trafficking [4]–[6] through the recruitment of the raft protein flotilin-1 at the site of the Brucella-containing vacuole [6] . This polysaccharide is built of a cyclic backbone of 17 to 25 glucose units in β-1 , 2 linkages ( CβG ) . CβG are abundant as they represent 1–5% of the bacteria dry weight . If the CβG content of a single bacterium is released inside a Brucella-containing vacuole , its concentration in the vacuole can reach 10 mM . The release of CβG in µM concentration upon bacterial killing through immune mechanisms [6] might alter the host immune responses . While linear ( 1 , 3 ) β-glucans have been shown to elicit anti-tumor [7] , [8] and anti-infective [9]–[12] responses [13] , [14] , we do not know whether CβG displays immunomodulatory functions . In this study , we have analyzed the effects of CβG on mouse and human dendritic cells ( DC ) and its consequences on immune responses . The discovery of vaccination is one of most important medical discoveries in the history and a turning point in the war between microbes and humans [15] , [16] . The goal of vaccination is to induce long-lasting protective immunity from infection and prevent infectious diseases . Vaccines operate through the activation of antigen-presenting cells such as DC that eventually stimulate antigen-specific T and B lymphocytes . Unlike attenuated live vaccines , killed whole organisms or subunit vaccines generally require the addition of adjuvants to be effective . Adjuvants promote and enhance immune responses to vaccine components [15] , [16] . It is now clear that adjuvants activate DC [15] . Adjuvants derived from microorganisms stimulate DC directly , leading to the up-regulation of cytokines , MHC class II , and co-stimulatory molecules and to their migration to the T cell area of lymph nodes . These pathogen-associated molecular patterns ( PAMP ) activate pattern-recognition receptors ( PRR ) , which act as microbial sensors and are expressed by DC and other leukocytes [13] , [14] . Most of PAMP used as vaccine adjuvants , like CpG oligonucleotides and monophosphoryl lipid A ( MPLA ) , are agonists of Toll-like receptors ( TLR ) [17] , [18] . For infectious as well as for noninfectious diseases , TLR activation have been used in both established and experimental vaccines [17] , [18] . Bacterial components are often potent immune activators trough commonly associated with toxicity , for example , bacterial DNA with immunostimulatory CpG motifs that bind TLR-9 are potent cellular adjuvants . Overall , several hundred natural and synthetic compounds have been identified to have adjuvant activity such as microbial products , mineral salts , emulsions , microparticles , and liposomes . Although many are more potent than alum , the almost universal human vaccine adjuvant , toxicity is the limiting step for their use in humans . Consequently there is a major unmet need for safer and more effective adjuvants suitable for human use [15]–[17] . We show here that Brucella CβG is neither toxic nor immunogenic when compared to LPS . It is a potent activator of DC thereby triggering antigen-specific CD8+ T cell responses in vivo . Brucella CβG enhance antigen-specific CD4+ and CD8+ T cell responses including cross-presentation by different human DC subsets .
Wild type B . abortus triggers limited activation of mouse bone marrow-derived dendritic cells ( BMDC ) [19] . Infection of BMDC with B . abortus cgs- ( cyclic glucan synthase ) mutant failed to activate BMDC as measured by the production of TNF-α and IL-12 ( Figure S1A and S1B ) . Likewise these infected DC displayed a low expression of immune co-stimulatory molecules such as CD80 , CD83 and CD86 ( not shown ) . This pilot findings led us hypothesize that CβG indeed acts as a DC activation molecule . To this end we had to ensure that the CβG preparations would not be contaminated by the potent DC activators LPS and lipid A . Whereas CβG are highly soluble in water , B . abortus and B . melitensis LPS partition in the phenol phase of the classical Westphal hot water-phenol extraction procedure . Thus , this extraction method was applied twice to a CβG water extract previously digested with nucleases and proteinase K . The identity of the CβG was established by several methods , including 13C-NMR , and the absence of LPS tested by both conventional analytical methods ( SDS-PAGE , inability to elicit anti-LPS antibodies , and Kdo analysis ) . MALDI-TOF analysis further showed both the spectra expected from Brucella CβG and the absence of molecular species signalling like Brucella lipid A ( Figure S2A and S2B ) . Mouse BMDC were incubated with synthetic methyl-β-cyclodextrin ( MβCD ) and cyclic glucans purified from Brucella melitensis , Brucella abortus and Ralstonia solanacearum . Brucella CβG consists of a cyclic backbone of 17–25 glucose residues linked in β- ( 1→2 ) associated with O-succinyl modifications [20] , [21] . Ralstonia cyclic α-glucan ( CαG ) is composed of 12 glucoses linked in eleven β- ( 1→2 ) plus one α- ( 1→6 ) linkages [20] . MβCD consists of 7 O-methyl substituted glucoses in β- ( 1→4 ) linkages . Both B . melitensis and B . abortus CβG induced DC to express levels of CD80 , CD86 , CD40 and MHC II molecules , comparable to those elicited by E . coli LPS ( Figure 1A ) . The two Brucella CβG induced the secretion of high levels of pro-inflammatory cytokines such as TNF-α and IL-12 ( Figures 1B ) . The induction was dose-dependent ( Figure S3A and S3B ) . These findings contrast with the poor DC-activating ability of Brucella LPS [2] . When compared to Brucella abortus CβG , the synthetic MβCD did not stimulate DC ( **p<0 . 01 ) and the Ralstonia CαG hardly induced ( **p<0 . 01 ) the production of TNF-α and IL-12 ( Figure 1B ) . Thus Brucella melitensis CβG is a potent activator of mouse DC . We then asked whether CβG , like LPS would activate DC through TLR4 , MyD88 and TRIF pathways . Thus , BMDC were derived from TLR4−/− , TLR2−/− , MyD88−/− , TRIF−/− , TRIF/MyD88−/− and CD14−/− mice . These DC were activated with either CβG or different TLR agonists such as CpG ( TLR9 agonist ) , Pam2CSK4 ( TLR2/6 agonist ) , curdlan ( linear β-1 , 3 glucan from Alcaligenes faecalis agonist of Dectin-1 [22] , [23] ) and E . coli LPS ( TLR4/MD2/CD14 agonist ) . Neither E . coli LPS nor B . melitensis CβG were able to induce the expression of co-stimulatory molecules ( Figure 2A ) and secretion of IL-12 ( Figures 2B , 2C ) by BMDC from TLR4−/− , Myd88−/− , Myd88/TRIF−/− and TRIF−/− mice . In addition , transfection of HEK 293T cells with vectors coding for TLR4/MD2 , TLR2 , TLR3 and TLR9 showed that CβG effect is dependent on TLR4/MD2 ( not shown ) . Moreover , CβG-treated human blood plasmacytoid DC ( pDC ) known to be devoid of surface TLR4 expression [24]–[26] were not activated by any of these agents ( data not shown ) . These results show that DC activation by CβG is TLR4-dependent and that DC activation is dependent on both MyD88 and TRIF adaptors ( Figure 2B , left panel ) . We then compared DC activation induced by B . melitensis CβG to that induced by linear ß1-3 glucans ( curdlan ) , which bind to Dectin-1 and activate DC in a MyD88/TRIF-independent manner [22] , [23] . First , several monoclonal antibodies specific for Dectin-1 that can inhibit curdlan-mediated activation failed to inhibit CβG-mediated DC activation ( not shown ) . While double MyD88/TRIF−/− BMDC did not respond to CβG ( **p<0 . 01 ) , they secreted high levels of IL-12 in response to curdlan ( Figure 2C ) . These results indicate that CβG and curdlan use different signalling pathways and that Dectin-1 is not the receptor for CβG . LPS recognition involves the LPS-binding protein ( LBP ) , the TLR4/MD2 complex and CD14 [27] . Accordingly , CD14−/− DC did not secrete IL-12 upon exposure to E . coli LPS ( Figure 2D ) . CD14−/− DC also failed to upregulate co-stimulatory molecules and MHC-II in response to LPS ( not shown ) . Strikingly , CβG was able to stimulate CD14−/− DC to secrete IL-12 ( Figure 2D ) . Altogether , these data show that CβG signalling is dependent on TLR4 but does not use CD14 as a co-receptor . LPS displays a toxicity that precludes its use as a vaccine adjuvant . Previous studies in cell cultures revealed that CβG , when compared to MβCD was not cytotoxic even at very high concentrations [6] . In addition , the Limulus Ameobocyte Lysate ( LAL ) test showed that CβG preparations did not contain significant endotoxin levels . To assess CβG toxicity in mice LD50 ( Lethal dose 50% ) was determined by injecting increasing amounts with death being recorded at 12 h , 24 h , 48 h , 72 h post-injection . Results showed that more than 500 µg of Brucella CβG were required to kill 50% of mice , when compared to 65 µg of E . coli LPS . To determine the immunogenicity of CβG , Balb/C mice were injected with by PBS , E . coli LPS , B . melitensis LPS or B . melitensis CβG . Primary and secondary antibody responses were analyzed and total immuoglobulin levels quantified by ELISA . Unlike E . coli LPS or B . melitensis LPS , B . melitensis CβG did not induce the generation of specific antibodies ( Figure S4A ) . We also analyzed CβG-mediated ability to induce pro-inflammatory cytokines in the sera of immunized mice by Cytokine Bead Array ( CBA ) assay ( Figure S4B ) . C57Bl/6 mice were injected with PBS , CβG , monophosphoryl-lipid A ( MPLA ) , LPS or Poly I/C . After 6 h , 24 h and 72 h of immunization , mice were bled and cytokine levels were measured . At 6 h post-immunization CβG , MPLA and PolyI/C did not induce pro-inflammatory cytokine secretion in contrast to E . coli LPS ( Figure S4B ) . Altogether , these data show that Brucella CβG is neither toxic nor immunogenic in mice . Given its ability to activate DC and its low toxicity profile we wondered whether administration of CβG to mice would enhance antigen-driven cellular immune responses in vivo . We used transgenic mice ( OT-I Rag−/+ ) that express a CD8+ T cell population specific for ovalbumin ( OVA ) as well as a congenic Ly5 . 1/Ly5 . 2 mouse model with 2 allelic forms of CD45 . We transferred CD8+ Ly5 . 2 CFSE+ OT-I T cells into C57Bl/6 Ly5 . 1 mice , and immunized them subcutaneously with either OVA alone , or a mix of OVA with CβG or OVA with Poly I:C or OVA with MPLA [15] . At day 3 post-immunization , OVA-specific OT-I T cell proliferation of all immunized mice was detected in the draining lymph nodes ( Figure 3 ) . OVA-specific T cells from mice immunized with Poly I:C , MPLA and CβG showed an up-regulation of CD25 and a down-regulation of CD62L , which correlated with T cell migration from lymph nodes to the sites of infection . OVA-specific T cells from mice treated with CβG showed a stronger down-regulation of CD62L expression than those treated with Poly I:C or MPLA ( Figure 3 ) . At day 6 post-immunization ( Figure S5 ) , OVA-specific T cells from CβG+OVA-immunized mice displayed stronger antigen-specific OT-I T cell proliferation and activation than those from mice immunized with OVA alone . Comparable responses were detected using the three adjuvants ( Figure S5 ) . We concluded that CβG is able to enhance antigen-specific CD8+ T cell responses in vivo . To study CβG capacity to induce local inflammation , mice were immunized either with MPL , LPS , CβG or Poly I/C by skin intradermal injection . At 48 h post-treatment , both the adjuvant-treated and untreated ears were collected for histological analysis of cutaneous inflammation ( Figure S6 ) . The skin of adjuvant-treated mice revealed marked increase in ear thickness accompanied by inflammatory cell infiltration ( Figure S6 ) . The inflammation was observed in all mice immunized with different adjuvants . Animals developed acute diffuse dermatitis characterized by heavy neutrophilic infiltration associated with hyperemia of dermal capillaries with neutrophilic margination and well-developed edema of the dermis ( Figure S6 ) . Similarly to other known adjuvants , CβG is capable of inducing local inflammation . We next analysed whether CβG was able to activate different human DC subsets ( Table 1 ) . We included myeloid DC isolated from blood , dermis and epidermis [28] , [29] as well as DC generated in vitro by culturing monocytes in the presence of GM-CSF with either IL-4 or IFNα . In response to CβG , all tested DC showed increased expression of HLA-DR , CD40 , CD86 , and CD83 and increased secretion of IL-12 , IL-6 and TNF-α ( Figures 4 and 5 ) . Notably , CβG did not activate pDC as measured by cell surface phenotype and secretion of IFN and pro-inflammatory cytokines such as TNF-α and IL-12p70 . To eventually distinguish the effects of LPS from those of CβG , blood mDC from five donors were activated for 6 h with either LPS or CβG and the early transcriptional responses was assessed using microarray profiling . 562 transcripts were significantly modulated in CβG-treated mDC as compared to media controls ( Figure 4A ) . These genes displayed a similar expression profile in LPS-treated mDC , albeit with lesser global intensity as measured by the molecular distance to media . Statistical comparison ( Ttest , p-value 0 . 01 , no correction ) between CβG-treated and LPS-treated mDC yielded 133 differentially regulated transcripts ( data not shown ) , highlighting the similarities and the differences between the two stimuli . Ingenuity Pathway Analysis ( IPA ) identified DC maturation as the most significantly represented canonical pathway among genes over-expressed in CβG-treated mDC ( Figure 4B ) , and PDFG signalling as the most represented pathway among genes under-expressed in CβG-treated mDC ( Figure 4C ) . Furthermore , CβG-treated mDC displayed increased transcription of the co-stimulatory molecules CD40 , CD80 , CD86 , CD70 and 4-1BBL , but decreased transcription of the Th2 co-stimulatory molecule OX40-L ( Figure 4D ) . The most significantly over-expressed transcripts in CβG-treated mDC are represented as a network ( Figure 4E ) depicting a strong pro-inflammatory response network . Overall , the global transcriptional changes that CβG elicit in mDC support the concept that this molecule might enhance DC-dependent T cell responses . We focused our attention to the effects of CβG on human blood mDC . Like E . coli LPS , CβG increased the surface expression of CD40 , CD83 , CD86 and MHC II ( Figure 5A ) . Furthermore , CβG-treated mDC secreted high levels of IL-6 , TNF-α and IL-12 ( p40 ) ( Figure 5B ) and were efficient at inducing allogeneic naïve CD4+ and CD8+ T cell proliferation ( Figure 5C ) . E . coli LPS-activated DC were slightly more potent than CβG-activated DC at inducing the proliferation of naïve allogeneic T cells ( Figure 5C ) . DC loaded with heat inactivated influenza virus can cross-present the Flu MP antigen to CD8+T cells as 0 . 8–1 . 38% of cocultured CD8+ T cells are specific for Flu-MP as assessed using peptide-MHC Class I tetramers . Activation of the mDC with both CβG and E . coli LPS resulted in a considerably increased MP-specific CD8+ T cell response ( Figure 5D ) . This indicates that CβG can enhance secondary CD8+T cell responses . To assess whether CβG can enhance the priming of naïve CD8+ T cells by DC , we chose the melanoma-derived antigens MART-1 and gp100 . The experiments illustrated in Figures 6A and 6B have respectively been performed with human skin CD1a+ and CD14+ DC [28] . Priming of naïve CD8+T cells requires activation of the DC through CD40 and addition of IL-2 and IL-7 to the cultures . Further activation of DC with LPS or CβG did not enhance the response to the relatively abundant MART-1 T cells . However , CβG enhanced the priming to the less frequent gp100-specific naïve T cells ( Figure 6 ) . Naïve CD8+ T cells were also cultured for seven days with allogeneic mDC that were activated or not with either LPS or CβG ( Figure 7A ) . CβG-activated mDC were also capable of inducing naïve CD8+ T cells to express high levels of IFNγ and granzyme B in CD8+ T cells when compared to unactivated mDC ( Figure 7A ) . Thus , CβG-activated mDC are able to activate CD8+ T cells . To characterize the CD4+ T cells exposed to CβG activated mDC , CD4+ T cells were cultured with allogeneic blood mDC activated or not with either LPS or CβG for seven days ( Figure 7B ) . The cultured T cells were then restimulated with PMA/Ionomycin and stained for intracellular IFN-γ , IL-13 , and IL-17 . Both LPS and B . melitensis CβG-activated mDC polarized CD4+ T cells into IFN-γ-expressing Th1 cells ( Figure 7B ) . In addition , both E . coli LPS and CβG-activated DC induced a minor sub-population of naïve CD4+ T cells to differentiate into IL-13+ CD4+ T cells . Naïve CD4+ T cells co-cultured with either E . coli LPS-activated or CβG-activated mDC did not express IL-17 ( Figure 7B ) . Thus , CβG-activated mDC induce Th1 responses . DC targeting approach consists in delivering antigens directly to DC in vivo using chimeric proteins composed of an anti-DC receptor antibody coupled to a selected antigen [30] . The selection of the appropriate adjuvant is a critical parameter for the induction of the desired type of immune response . PBMC from cured chronic HCV infected patients were cultured for ten days with monocyte-derived DC and recombinant humanized anti-CD40 or anti-DCIR fused to the HCVNS3 antigen with or without CβG or Poly-IC . Antigen-specific responses were measured by exposing the cultured cells to specific peptides clusters and stained for intracytoplasmic IFNγ expression . Low IFN-γ levels were observed in PBMC cultured with DC and control IgG4 with or without the adjuvants ( Figure 8A ) . Moreover DC targeting by anti-CD40 and anti-DCIR enhanced IFN-γ production by CD4+ T cells . Potent memory CD4+ T ( Figure 8A ) , but not CD8+ T cell ( not shown ) responses were observed after DC-PBMC co-culture in these conditions . Few CD4+ T cell responses could be induced when anti-CD40 targeted-DC were treated with Poly I:C , as demonstrated by the presence of some CD3+CD4+IFN-γ+ T cell ( 1 . 21% of parents cells , Figure 8A ) . However , CβG-treated DC using anti-CD40 targeting induced a dramatic increase of CD3+CD4+INF-γ+ cells ( 3 . 53% of parent cells compared to 0 . 85% without stimulation , Figure 8A ) . When anti-DCIR vaccine targeting was used , we could not observe any difference in CD4+INF-γ+ T cell population with or without stimulation ( Figure 8A ) . We next studied CβG effect in DC targeting experiments with PBMC from acute TB patients . For this , we used humanized anti-CD40 or anti-DCIR antibodies coupled to Ag85BD41-ESAT6-Rv1980D24 Mycobacterium tuberculosis antigens developed by the ANRS ( French agency of research against AIDS and viral hepatitis ) . DC targeting with control IgG4 and the specific peptides in the presence of adjuvants triggered IFN-γ production by CD4+ T cells ( Figure 8B ) . This induction was increased in the presence of CβG upon anti-CD40 and anti-DCIR targeting . The stimulation by Poly I:C slightly enhanced IFN-γ production only upon anti-DCIR targeting ( Figure 8B ) . Taken together , these data show that Brucella CβG increases CD4+ T memory responses after DC targeting of PBMC in HCV cured and acute TB patients .
Vaccination represents the most effective strategy to combat infectious diseases . Vaccines are composed of antigens and adjuvants , which activate antigen-presenting cells which then triggers the activation , differentiation and expansion of antigen-specific T and B lymphocytes [15] , [31]–[33] . The number of approved adjuvants effective in humans is very limited since they are mostly based on alum and on emulsions . Ideally , adjuvants should elicit a selected immune response ( i . e . cellular or humoral immunity depending on the requirements for protection ) , be safe ( sufficiently immunogenic , without excessive inflammation ) and cost-effective [16] , [18] , [31] . The benefits of adjuvant incorporation into any vaccine formulation should significantly outweigh the risks of adverse reactions . Unfortunately , potent adjuvant action is often correlated with increased toxicity , as exemplified by Freund's complete adjuvant or LPS . Thus , one of the major challenges in human adjuvant development is to identify compounds that enhance vaccine antigen induced responses with maximum tolerability and safety [31] . In particular , there is a high demand for adjuvants that stimulate cellular immunity [15] , [16] , [18] . Here , we demonstrate that Brucella CβG is a non-immunogenic and non-toxic molecule capable of triggering the activation of cellular responses in vivo . Moreover , Brucella CβG dramatically increases specific memory CD4+ T cell responses of human PBMC induced by DC-targeting fusion proteins expressing either HCV antigens or Mycobacterium tuberculosis antigens . Based on these data , we propose that Brucella CβG is a candidate adjuvant that might be used in humans . There has been an interest to identify TLR4 agonists with a dampened toxicity . A recent example of lipid A analog is MPLA known to activate immune cells with similar properties of the LPS but less toxic and non immunogenic [34] , [35] . Our results show that CβG-induced DC maturation is dependent on TLR4 as well as Myd88 and TRIF adaptor molecules . While CβG activates all human mDC subsets , it does not activate pDC , which is consistent with their lack of TLR4 [24]–[26] . However , what differentiates CβG from MPLA and Ploy I:C is that CβG induces an early immune response ( Figure 3 ) . This might have a significant impact on the quality of the immune response to vaccines [36] . We previously showed that Brucella CβG is capable of interacting with lipid rafts and modulate their organization [6] . Lipid rafts are plasma membrane microdomains enriched in cholesterol and sphingomyelin that are involved in intracellular signalling and membrane transport . In particular , lipid rafts are involved in the regulation and activation of several important immune receptor complexes such as the TLR4 complex [37] . [38] . It is thus possible that Brucella CβG triggers TLR4-dependent signalling through its effect on cholesterol in lipid rafts as it has been suggested for alum [15] . Indeed , alum recognition may occur indirectly through the release of endogenous uric acid . Recently , it was shown that monosodium urate crystals activate DC by interacting with the cell membrane , which leads to plasma membrane lipid sorting probably via interaction with cholesterol [15] . Herein , we observed that in contrast to E . coli LPS [27] , Brucella CβG-induced BMDC maturation was independent of the GPI-anchored protein CD14 . Actually , the synthetic lipid A compound CRX-527 does not require CD14 to engage MyD88-dependent and TRIF/IRF3-dependent pathways downstream TLR4 [39] . Furthermore , the uropathogenic E . coli ( UPEC ) triggers innate responses during urinary tract infection in a TLR4-dependent and CD14-independent manner both in mice and humans [40] . These results clearly indicate that CD14 is not required for TLR4-dependent cell activation . The detailed signalling pathway involved in CβG-dependent cell activation will require further studies including the discovery of putative co-receptors associated to TLR4 . Until now , we know that neither dectin-1 nor TLR2 contribute to the activation . Additional studies are planned to determine whether the CD14-independent CβG-dependent DC activation will have any therapeutic value . CβG displays interesting properties such as water solubility , limited toxicity and lack of immunogenicity together with a potent DC activation capacity that can trigger CD4+ and CD8+ T cell responses . Therefore , CβG might constitute a new class of adjuvants for future vaccines .
Animal experimentation was conducted in strict accordance with good animal practice as defined by the French animal welfare bodies ( Law 87–848 dated 19 October 1987 modified by Decree 2001-464 and Decree 2001-131 relative to European Convention , EEC Directive 86/609 ) . INSERM guidelines have been followed regarding animal experimentation ( authorization No . 02875 for mouse experimentation ) . All animal work was approved by the Direction Départmentale des Services Vétérinaires des Bouches du Rhônes ( authorization number 13 . 118 ) . For animal exerimentation in Costa Rica , animals were handled and sacrificed according to the approval and guidelines established by the “Comité Institucional para el Cuido y Uso de los Animales” of the Universidad de Costa Rica , and in agreement with the corresponding law “Ley de Bienestar de los Animales No 7451” of Costa Rica ( http://www . micit . go . cr/index . php/docman/doc_details/101-ley-no-7451-leyde-bienestar-de-los-animales . html ) . The animal handling and procedures were in accordance with the current European legislation ( directive 86/609/EEC ) supervised by the Animal Welfare Committee of the institution ( protocol number R102/2007 ) . Patients were recruited at the Baylor Hospital Liver transplant Clinic ( BHLTC , Dallas , TX ) after obtaining informed consent . The study was approved by the Institutional Review Board of the Baylor Health Care System ( Dallas , TX ) . Antibodies used for immunofluorescence labelling included rabbit Rivoli antibody against murine I-A [41] . CpG ( Invivogen ) , Pam2CSK4 ( Invivogen ) and curdlan ( Megazyme ) were used to activate DC . Antibodies used for flow cytometry included APC-CD11c , FITC-CD40 , FITC-CD80 , PE-CD86 , PE-IA-IE ( MHC class II ) ( Pharmingen ) , as well as PB-CD8 , A700-CD45 . 2 , APC-CD44 , PE-Cy7-CD25 , APC-CD62L ( BD Biosciences and eBiosciences ) . The Aqua Dead Cell Stain ( Invitrogen ) was used to eliminate dead cells . Human mDC were sorted from PBMC of blood from healthy donors using lineage cocktail-FITC ( BD Biosciences ) , CD123-PE ( BD Biosciences ) , CD11c-APC ( Biolegend ) , HLA-DR-Quantum Red ( Sigma ) . Human mDC were stained with CD86-PE , CD83-FITC , CD40-APC and HLA-DR-PB ( eBiosciences or Biolegends ) . 7-AAD was used to exclude dead cells . For intracellular labelling IL13-APC , INF-γ-PE-Cy7 , IL-17-PE and Granzyme B-APC antibodies were used . Isotype matched controls were used appropriately . At least 100 . 000 events were collected on flow cytometry Canto II ( BDBiosciences ) or FACSAria ( BDBiosciences ) . Flow cytometry analysis was performed using the FlowJo software . Purified cyclic glucans were obtained from B . melitensis 16 M or Brucella abortus 2308 [42] and from Ralstonia solanacearum ( gift from Dr . J . -P . Bohin , CNRS UMR8576 , Lille , France . Cells were stimulated with 100 ng/ml of E . coli LPS and 10 µg/ml of B . melitensis CβG to have the equivalent molarity of reagents ( 0 . 25 µM ) . CD-1 and C57Bl/6 Ly5 . 1 mice from Jackson Laboratory and OT-I TCR transgenic Ly5 . 2 mice on C57Bl/6 background were used . C57BL/6 , TLR4−/− , TLR2−/− , MyD88−/− , TRIF−/− , MyD88/TRIF−/− mice were maintained at CIML animal house , France . CD14−/− mice were obtained from CDTA , Orleans , France . All mice were from a C57BL/6 genetic background . Mouse bone marrow-derived DC ( BMDC ) and macrophages ( BMDM ) were prepared from 7–8 week-old female C57BL/6 mice as previously described [43] . Human monocyte-derived DC were generated from Ficoll-separated PBMC from healthy volunteers [44] . Monocytes were enriched from the leukopheresis according to cellular density and size by elutriation as per manufacturer's recommendations . For DC generation , monocytes were resuspended in serum-free Cellgro DC culture supplemented with 100 ng/ml GM-CSF and 500 UI/ml IFN-α . mDC ( HLA-DR+CD11c+CD123−Lin− ) and pDC ( HLA-DR+ , CD11c− , CD123+ , Lin− ) were sorted from fresh PBMC using FACSAria cytometer ( BD Biosciences ) . Naïve CD4+ and CD8+ T cells ( CD45RA+CD45RO−CCR7+ ) ( purity>99 . 2% ) were purified by flow cytometry sorting . CβG were obtained from B . melitensis 16 M or B . abortus 2308 grown and inactivated as described before [42] . For CβG extraction and purification , the protocol described before [6] was used . Briefly , a CβG-rich crude fraction was first obtained by ethanol precipitation of a hot water extract of killed bacteria , and freed from nucleic acids or proteins by digestion with DNase and RNase proteinase K . To remove LPS , the fluid was extracted with a volume of phenol ( in contrast to most LPS , Brucella LPS and lipid A partition into the phenol phase [42] at 70°C for 30 min , the mixture chilled and centrifuged ( 8000× g , 0°C , 15 min ) , and the aqueous phase collected and re-extracted again with phenol under the same conditions . The new aqueous phase was dialyzed , clarified by brief centrifugation and freeze-dried . The identity of CβG was demonstrated by 13C-NMR spectroscopy and high-performance TLC , and the absence of Brucella LPS or other contaminants was demonstrated by UV-spectrophotometry , SDS-PAGE , gel immunoprecipitation , and 3-deoxy-d-manno-2-octulosonic acid analyses [42] . To further demonstrate the absence of lipid A , purified CβG were analyzed by MALDI-TOF mass spectrometry as described before [45] . Briefly , 5 mg of lyophilized Brucella's CβG were resuspended in 100 µl of chloroform-methanol-water ( 3∶1 . 5∶0 . 25 [vol/vol/vol] ) and 1 µl aliquot of was deposited on the target and covered with the same amount of 2 , 5-dihydroxybenzoic acid matrix ( Sigma ) dissolved in chloroform-methanol-water ( 3∶1 . 5∶0 . 25 [vol/vol/vol] ) . Different ratios between the samples and dihydroxybenzoic acid were used . Alternatively , matrix solution was prepared by dissolving 1 mg of 2 , 5-dihydroxybenzoic acid with 0 . 1 ml of CH3CN/H2O ( 3∶2 , vol/vol ) . Analyses were performed in reflector modes and in both the positive and the negative ion modes on a Bruker Autoflex II MALDI-TOF mass spectrometer ( Bruker Daltonics , Inc . ) . A peptide calibration standard ( Bruker Daltonics ) was used to calibrate the MALDI-TOF . Spectra were recorded between 1900 and 3900 Da . Each spectrum was an average of 500 shots . Mark the absence of the ion species at m/z 2073 , 2145 , 2173 that are characteristic of Brucella lipid A [46] . LAL ( Limulus ameobocyte lysate ) test was used for the detection and quantification of bacterial endotoxins . Briefly , the samples were incubated with the circulating blood of horseshoe crab ( the LAL ) and a synthetic color producing substrate to detect endotoxins . LAL contains enzymes that are activated in a series of reactions in the presence of endotoxins . This assay is quantitative and the color intensity developed upon addition of the sample to the LAL is proportional to the amount of endotoxin in the sample and can be calculate from a standard curve . Immunogenicity of purified CβG was tested in mice and rabbits following described protocols [47] . LD50 ( Lethal dose 50% ) was measured in CD-1 mice injected with appropriate amounts of reagents . Animal death was recorded at 12 h , 24 h , 48 h , and 72 h post-injection . To determine the immunogenicity of B . melitensis CβG , Balb/C mice were immunized by either PBS or E . coli LPS , ( 10 µg/mouse ) or B . melitensis LPS ( 10 µg/mouse ) or B . melitensis CβG ( 10 µg/mouse ) . The primary antibody response was measured 21 days after immunization . Mice were boosted 45 days with the same molecules ( 5 µg/mouse ) to measure the secondary antibody response . Antibody responses were determined by an indirect ELISA . After culture , human mDC were lysed in RLT buffer and stored at −80°C until further processing . Total RNA was extracted using the mirVana miRNA Isolation Kit , from Ambion . Following RNA extraction , RNA concentration was measured using a Nanodrop 1000 ( Nanodrop Technologies , Wilmington , DE ) and the RIN was measured with an Agilent 2100 Bioanalyzer ( Agilent , Palo Alto , CA ) for quality control purposes . All samples with RIN values greater than seven were retained for further processing . 250 ng of total RNA were amplified and labelled with the Illumina TotalPrep-96 RNA amplification kit ( Ambion , Austin , TX ) . 750 ng of amplified labelled RNA were hybridized overnight to Illumina HT12 v4 Beadchip arrays ( Illumina , San Diego , CA ) . Following hybridisation , each chip was washed , blocked , stained and scanned on an Illumina iScan following the manufacturer's protocol . The dataset described in this manuscript is deposited in the NCBI Gene Expression Omnibus ( GEO , http://www . ncbi . nlm . nih . gov/geo , GEO Series accession number GSE32023 ) . Transcripts present in at least one of the samples as defined by significant chip detection value were used as a starting list . Non-parametric test was applied between CβG-treated mDC and medium-treated mDC from 5 donors , false discovery rate: 0 . 01 , with Benjamini-Hochberg correction . Pathway analysis was conducted with Ingenuity Pathway Analysis ( IPA ) software , Ingenuity Systems , Inc , Redwood City , CA . The molecular distance to medium ( MDTM ) quantifies the global transcriptional perturbation of a group of transcripts in a specific sample as compared to its reference control ( s ) . It is calculated as follows . For an arbitrary list of transcripts normalized to their reference control ( s ) , the absolute fold changes greater or equal to 2 for a specific sample are summed up . Murine IL-12 and TNF-α were quantified in culture supernatants of stimulated DC by sandwich enzyme-linked immunosorbent assays ( ELISA ) according to the manufacturer's protocol ( Abcys ) . Human cytokine ( IL-6 , TNF-α , and IL-12p40 ) were determined using the BeadLyte cytokine assay kit ( Upstate , MA ) . Mice were divided into groups each of 5 mice and were immunized i . p . with either: 50 µl of PBS , 20 µg of Monophosphoryl Lipid A ( MPLA ) ( InvivoGen ) in 50 µl of PBS , 200 µg of CβG in 50 µl of PBS , 10 µg of LPS in 50 µl of PBS , 50 µg of Poly I/C in 50 µl of PBS . For in vivo cytokine measurements , mice were bled submandibularly at 6 h , 24 h and 72 h after immunization and sera collected . Supernatants were then harvested at each time point and kept at −20°C . To determine the amounts of cytokines produced in the sera , a CBA assay ( BD Biosciences ) that detects IL-6 , IL-10 , MCP-1 , IFN-γ , TNF-α and IL-12p70 in a single sample was used . The sera from immunized mice were incubated with a mixture of specific capture antibodies that were coupled to the beads containing specific amounts of PE fluorescence intensity . The 6 different fluorescence intensities of PE were detected by flow cytometry and cytokine concentration in the samples was quantified from a standard curve according to manufacturer's protocol . 10 . 000 events were analyzed by flow cytometry Canto II ( BDBiosciences ) and the data were analyzed using the FlowJo software . Blood mDC were co-cultured with CFSE-labeled allogeneic naïve CD4+ T and CD8+ T cells . The expression of intracellular cytokines and granzyme B were measured after 6 h of cell stimulation by PMA and Ionomycine , in the presence of Brefeldin A . Blood mDC from HLA-A0201+ healthy donors were loaded with a multiplicity of infection ( MOI ) of 0 . 2 of heat-inactivated influenza virus ( PR8 ) for 2 h at 37°C . Autologous CD8+ T cells were mixed and cultured for 7 days in the presence of 20 units/ml IL-2 . Cells were then stained with anti-CD8 antibody and tetramer ( HLA-A*0201-Flu M158–66 ) . MART-1-specific CD8+ T cell responses were measured after co-culturing with CD1a+ and CD14+ skin DC loaded with 10 µM 15-mer MART-1 peptide- containing the immunodominant epitope MART-126–35 ( 27L ) or with gp100 peptide epitope for 10 days . Skin DC were purified as previously described [28] . To activate individual DC subsets we used 0 . 25 mM of either E . coli LPS or CβG . Autologous IFNα DC loaded with vaccine candidates ( for Mycobacterium: ESAT6 protein , for HCV: antigens from NS3 Helicase helB construct ) were co-cultured with PBMC from HCV or TB patients and incubated for 10 days . T-cell specific responses elicited by vaccine candidate loaded-DC were assessed by restimulating PBMC with peptide clusters OT-I transgenic cells that express TCR specific for an H-2Kb restricted CD8+ T cell epitope from OVA were used . Lymph nodes from OT-I Ly5 . 1 mice were harvested and digested with collagenase type I ( Sigma ) at 37°C for 30 min . CD8+ T cells were then negatively sorted by using mouse CD8 negative isolation kit ( Dynal ) . CD8+ T cells were labeled with 10 µM CFSE ( Invitrogen ) and transferred intravenously ( i . v . ) into naive congenic C57Bl/6 Ly5 . 2 recipient mice . At 24 h , recipient mice were immunized subcutaneously ( s . c . ) either with 30 µg OVA ( EndoGrade ) alone in endotoxin free PBS or 30 µg OVA mixed with 200 µg of CβG or 30 µg OVA mixed with 50 µg poly I:C ( Sigma ) or 30 µg OVA mixed with 20 µg MPLA ( InvivoGen ) . For histological assessment of cutaneous inflammation , intradermal injection of mouse ear skin was performed . C56BL/6 mice were immunized in the right ear with either: 20 µg of Monophosphoryl Lipid A ( MPL ) in 10 µl of PBS , 200 µg of CβG in 10 µl of PBS , 10 µg of LPS in 10 µl of PBS , 50 µg of Poly I/C in 10 µl of PBS . PBS was injected in the left ear as negative control . The animals were sacrificed 48 h later and both the adjuvant-treated and untreated ears were collected for further determination . Ears were fixed in 10% neutral buffered formalin for 48 h , embedded in paraffin , then sectioned at 4 µm at two levels separated by 1 mm interval and stained with hematoxylin and eosin . Normal skin histology was evaluated in PBS-inoculated contralateral ear for each mouse . All experiments were carried out at least 3 independent times and all the results correspond to the means ± standard errors . Statistical analysis was done using two-tailed unpaired Student's t test . Significance was defined when P values were <0 . 05 . See legends of Figures S1 , S2 , S3 , S4 , S5 and S6 . | Vaccination is one of the key strategies to fight against infectious diseases though numerous diseases remain without appropriate vaccines . The challenge is to generate potent vaccines capable of inducing long-lasting immunity in humans . Successful vaccines include adjuvants that enhance and appropriately skew the immune response to given antigens . The development of new adjuvants for human vaccines has become an expanding field of research . Here we show that bacterial cyclic β-glucans can be used to enhance cellular immunity by activation of dendritic cells , from both mice and humans . In particular , Cyclic-β glucans enhance the in vitro memory CD4+ T cell responses of patients suffering from hepatitis C and tuberculosis . Thus cyclic-β glucans are new adjuvants , which might be used in vaccines . | [
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] | 2012 | Brucella β 1,2 Cyclic Glucan Is an Activator of Human and Mouse Dendritic Cells |
Outer membrane vesicles are nano-sized microvesicles shed from the outer membrane of Gram-negative bacteria and play important roles in immune priming and disease pathogenesis . However , our current mechanistic understanding of vesicle-host cell interactions is limited by a lack of methods to study the rapid kinetics of vesicle entry and cargo delivery to host cells . Here , we describe a highly sensitive method to study the kinetics of vesicle entry into host cells in real-time using a genetically encoded , vesicle-targeted probe . We found that the route of vesicular uptake , and thus entry kinetics and efficiency , are shaped by bacterial cell wall composition . The presence of lipopolysaccharide O antigen enables vesicles to bypass clathrin-mediated endocytosis , which enhances both their entry rate and efficiency into host cells . Collectively , our findings highlight the composition of the bacterial cell wall as a major determinant of secretion-independent delivery of virulence factors during Gram-negative infections .
Outer membrane vesicles ( OMVs ) are nano-sized proteoliposomes released from the bacterial cell envelope [1] . OMV release is a highly conserved process , occurring in all growth phases and environmental conditions [2] . OMVs contain and deliver a broad range of cargos , from large hydrophobic molecules to DNA , making them a versatile and generalised form of secretion that enhances bacterial fitness in hostile environments [3–6] . They also contribute significantly to pathogenesis , via the delivery of virulence factors such as toxins , adhesins and immunomodulatory compounds directly into the host cell [7–9] . In a mouse model , purified OMVs from Escherichia coli were sufficient to cause lethal sepsis in the absence of intact bacterial cells , indicating their potency in enhancing infection and inflammatory processes [10] . The immunogenicity and ubiquitous production of OMVs has also led to their clinical use in vaccine preparations [11] , representing an application for OMVs in generating immunity against bacterial infections without the risks associated with live cell vaccines . Whilst many virulence factors are known to be OMV cargos , the processes underlying their delivery to host cells during infection are not well characterized . Understanding these mechanisms could help to identify targets for inhibition of OMV-associated toxin delivery and lead to attenuation of bacterial infections , as well as helping to achieve their therapeutic potential in medicine , via vaccines and engineered delivery vehicles [12–14] . Release of OMVs occurs during infection , and has advantages over other secretion systems . They can carry a broad range of cargos , from protein toxins to hydrophobic small molecules such as the Pseudomonas aeruginosa quorum sensing molecule quinolone signal ( PQS ) , and vesicular cargos are protected from environmental insults [15 , 16] . In addition , OMV-mediated delivery of virulence factors can function over longer distances than contact-dependent secretory pathways [17] . While much is known about the cargos contained within OMVs , the small size of OMVs ( 20–200 nm ) and rapid kinetics of entry ( cargo-specific effects can often be detected within minutes ) have made studying their interactions with host cells difficult . Previous work has often relied on OMVs labelled with dyes , non-discriminate probes that modify vesicular contents during labeling . While such probes allow real-time analysis of OMV entry and cargo delivery , their potential to modify vesicle components may interfere with the vesicle’s physicochemical characteristics , and alter the mechanism of OMV recognition , entry and cargo release [18–20] . Other approaches rely on immunolabelling of OMV-associated epitopes , but this often requires fixation of cells at pre-determined time points , and makes assumptions about OMV cargo , which may ignore natural sub-populations of OMVs [21] . Some experiments have used specific changes in host cell phenotypes in response to OMV contained toxins as an indicator of OMV uptake [4] . However , such changes in host cell responses have distinct dynamics from the OMV entry event , and allow only indirect conclusions about entry kinetics [22] . These challenges have often led to discrepancies in observations of OMV entry and cargo delivery [14] , demonstrating the need for an assay that can detect OMV entry processes in a consistent and repeatable manner . In this paper we describe a novel assay to continuously measure OMV entry and cargo release to host cells with high sensitivity , and in a format that is adaptable for high throughput screening . Using this assay to study entry of OMVs from different E . coli serotypes and pathovars into host cells , we identified key bacterial and host factors that determine the route of entry , and thereby kinetics and efficiency of vesicular cargo delivery and trafficking .
We set out to develop a highly sensitive and dynamic assay that would allow us to monitor the kinetics of OMV entry into host cells . We used a genetically encoded hybrid reporter probe that is incorporated into the bacterial outer membrane and subsequently targeted to the OMV surface . ClyA , a cytolysin that is sorted into OMVs produced by pathogenic E . coli , acts as the targeting component , and is fused to the TEM domain of β-lactamase ( Bla ) , which acts as an enzymatically active probe ( Fig 1A ) , and prevents assembly of the toxin into its biologically active oligomeric conformation [12] . Host cells were incubated with CCF2-AM , a dye composed of a covalently linked coumarin and fluorescein molecule , resulting in FRET and green fluorescence emission , specifically in the eukaryotic cytoplasm where it is processed by esterases . Esterification decreases the hydrophobicity of the FRET probe , thus decreasing its membrane permeability and trapping the probe in the host cell cytoplasm . When OMVs isolated from the producing bacterial strain enter host cells , their Bla cargo is able to cleave CCF2-AM , abolishing FRET and resulting in a shift in emission from green ( 530 nm ) to blue ( 460 nm ) fluorescence ( Fig 1A ) . We monitored the FRET kinetics upon incubation of OMVs with host cells , and analyzed efficiency of OMV uptake by host cells ( [Em460/Em530]t = 0hrs ) / [Em460/Em530]t = 3hrs ) . We further analyzed data by fitting to a cubic spline function and estimating gradients to extract maximal rate of entry ( rmax ) and rate over time ( see SI Materials and Methods ) . Experimental traces were limited to three hours , since beyond this time point the FRET signal decayed , likely due to degradation of the substrate within the host cell cytoplasm . First , we set out to verify whether ClyA-Bla fusion constructs retained ClyA’s ability to partition into vesicles , and were indeed targeted to E . coli OMVs . Following induction of probe production , OMVs were isolated from enterohemorrhagic E . coli ( EHEC ) containing empty vector , or expressing either ClyA-Bla ( C-terminal fusion , enzyme exposed on the OMV surface ) or Bla-ClyA ( N-terminal fusion , enzyme facing the OMV lumen ) enzymatic probes . Probe expression did not significantly change gross OMV morphology ( S1A Fig and S1 Text ) or charge ( mean ζ-potential -6 . 7 ± 3 . 6 mV , S1C Fig ) , but did cause a slight but significant increase in OMV size distribution ( ~ 20% increase in median diameter; S1B Fig ) . Probe expression did not appear to result in cell envelope stress , as the amount of OMVs released per cell did not change significantly compared to the untransformed strains ( approximately 41 vs 39 vesicles/cell ) . Sizing data ( mean diameter 134 nm , range 10–400 nm across all OMV preparations ) were in accordance with previously published data for E . coli OMVs [12] . Intact ClyA-Bla fusion protein was detected in samples from EHEC whole cell lysate , supernatant and OMV fractions ( Fig 1B ) , suggesting that the fusion protein was targeted to and enriched in OMVs , as previously reported for non-pathogenic E . coli [12] . The ClyA-Bla probe was oriented with Bla facing the exterior of the OMV , as the protein was gradually degraded during treatment of ClyA-Bla OMVs with papain protease , while the probe remained intact in OMVs containing Bla-ClyA , where Bla faces the vesicle lumen ( S1D Fig ) . The specific enzymatic activity was ~ 3-fold higher for ClyA-Bla OMVs than for Bla-ClyA OMVs with similar activities in whole cell lysates , and both activities were equalized by lysis of vesicles and probe solubilization , suggesting efficient expression of active β-lactamase with the anticipated orientation ( inward facing for Bla-ClyA , outward facing for ClyA-Bla ) in isolated OMVs ( Fig 1C ) . Average OMV concentration was 5 x 1012 particles per ml , and particle concentrations of all samples were normalized to give a consistent OMV concentration for subsequent experiments . Having verified the correct targeting , orientation and enzymatic activities of the Bla probes , we used them to dissect OMV entry ( i . e . , exposure of ClyA-Bla to cytoplasmic dye ) and release of OMV luminal contents ( i . e . , exposure of Bla-ClyA to cytoplasmic dye ) into epithelial cells . We used both Hela ( cervical epithelial ) and RKO ( intestinal epithelial ) cells loaded with CCF2-AM dye and exposed to OMVs at an MOI of 1000 OMVs/cell . OMV yield was approximately 27 ± 13 OMVs/bacterial cell for the different pathovars used , so this corresponds to a bacterial MOI of approximately 37 bacteria/cell , a dose commonly used in infection assays , or approximately 10 μg/ml OMV protein ( published assays use between 5–200 μg/ml OMV protein ) . EHEC ClyA-Bla OMVs caused a rapid increase in blue/green fluorescence over the course of a 3 hour experiment . OMVs lacking probe did not cause a significant change in FRET signal . ( Fig 2A–2C ) . While the rate of cargo release remains stable throughout the experiment ( S2B Fig ) , the rate of entry is initially high but gradually decreases and approaches the rate of cargo release ( S2A Fig ) . OMV entry kinetics are similar in intestinal epithelial ( RKO ) cells ( S3 Fig ) . Results of these kinetic analyses were visually confirmed by capturing FRET of samples at the onset and endpoint of the experiment ( Fig 2D ) . The rapid kinetics inferred from the FRET traces also correlated with rapid internalization and re-distribution of OMV lipid inside host cells , with a significant portion of OMV material localized to an intracellular , tubular structure surrounding the nucleus , likely the ER , even after 10 minutes , the fastest we could feasibly prepare samples for imaging ( Fig 2E ) . These results suggest that our approach is capable of capturing the rapid internalization and dismantling of OMVs , which proceeds too fast to adequately capture by imaging . As the rate limiting step for cargo release appears to be OMV entry , we further focused on analyzing potential determinants of the entry process . Next , we compared the uptake kinetics of OMVs isolated from EHEC and non-pathogenic E . coli K12 . Uptake of EHEC OMVs was faster ( Fig 3A ) , and approximately 30% more efficient ( Fig 3C ) , compared to K12 OMVs; the maximal rate was higher ( Fig 3B ) , and a high rate of uptake was sustained for longer for EHEC than for the K12 strain ( S2C Fig ) . Both rmax ( S2D Fig ) and uptake efficiency ( S2E Fig ) increased with increasing OMV concentration for both EHEC and K12 , but for K12 vesicles rmax saturated at a lower OMV concentration and a lower uptake efficiency was achieved . Taken together , these results suggest EHEC OMVs contain cargos absent from K12 OMVs that accelerate and sustain the rate and thus increase the efficiency of vesicle uptake by host cells . Since OMVs are derived from the outer membrane of Gram-negative bacteria , they contain lipopolysaccharides ( LPS ) , [23] . Whilst lipid A and the core oligosaccharide regions are well conserved , many species including EHEC contain a highly variable polysaccharide domain known as O antigen [24] . The O antigen constitutes the outermost structural region of LPS , and due to its length of up to 40 nm [24] , likely the first component in contact with host cells . These characteristics led us to hypothesize that the O antigen present on EHEC OMVs may be a structural determinant of OMV recognition and uptake by host cells . To test this hypothesis , we carried out FRET assays with Hela cells exposed to ClyA-Bla reporter OMVs harvested from three pairs of strains , reflecting different E . coli serotypes and pathovars and O antigen deficient isogenic mutants , to determine how the presence or absence of O antigen would impact OMV uptake kinetics in each case . OMVs were derived from two different pathovars of E . coli , EHEC ( serotype O157 ) and enteroaggregative E . coli ( EAEC , serotype O42 ) , and from the non-pathogenic lab strain K12 ( serotype O16 ) . For EHEC , OMVs from O157 wild type cells and an isogenic strain lacking the O157 O antigen ( gne::IS629 , [25] ) were compared ( Fig 4 ) . The O antigen deficient mutant gne::IS629 carries a 1310 bp insertion in gne , disrupting the epimerase required for synthesis of the oligosaccharide repeating unit in the O antigen [25 , 26] , leading to a ~ 10 nm decrease in median OMV diameter ( S1B Fig ) . The rmax for ClyA-Bla reporter OMVs derived from this O antigen deficient EHEC strain and the isogenic wild type O157 strain were not significantly different ( Fig 4B ) . However , OMVs derived from wild type EHEC with intact O antigen sustained a higher entry rate over a longer period ( S4A Fig ) , and thus entered host cells ~ 43% more efficiently than those derived from O antigen deficient EHEC ( Fig 4C ) . OMVs from wild type EAEC ( serotype O42 , intact O antigen ) were compared with an isogenic O antigen deficient mutant ( ΔwbaC , lacking a glycosyltransferase necessary for O antigen synthesis; [27] ) . EAEC OMVs with intact O antigen were around 20 nm larger in median diameter than EHEC OMVs , suggesting they carry a longer O antigen , and the diameter dropped in the O antigen deficient mutant , to the same size as EHEC O antigen deficient OMVs ( S1B Fig ) . EAEC OMVs with intact O antigen entered host cells ~ 66% more efficiently than OMVs without O antigen , due to a 77% higher rmax ( Fig 4D–4F ) and a higher sustained rate over time ( S4B Fig ) . The non-pathogenic E . coli K12 strain MG1655 has lost its ability to produce O antigen due to a disruption in wbbL encoding the rhamnosyltransferase required for O antigen synthesis [28] . We compared entry of OMVs from this O antigen deficient strain ( median OMV diameter decreased by ~ 10 nm , compared to O16 positive strain ) , to those from an isogenic strain ( DFB 1655 L9 ) , where wild type wbbL has been restored , allowing for expression of the strain’s original O16 O antigen [27] . Similar to O157 , the presence or absence of O antigen did not alter rmax , but the presence of O antigen allowed for a higher rate to be sustained for longer ( S4C Fig ) , leading to a ~ 22% higher efficiency overall ( Fig 4G–4I ) . A similar effect of O antigen on uptake kinetics was observed in intestinal epithelial cells ( S3 Fig ) . Taken together , these results suggest that the presence of the LPS O antigen increases the entry efficiency of OMVs into host cells , independent of the specific mutation leading to O antigen deficiency . Depending on the serotype used , this is caused by enhancing rmax and/or by sustaining a higher uptake rate over a longer period , compared to OMVs lacking O antigen . These variations may be due to differences in physicochemical features and/or other vesicle cargos between the different serotypes . Next , we evaluated the relative contribution of cellular trafficking pathways to OMV uptake and determined if this was affected by LPS structure . Inhibition of macropinocytosis following treatment of host cells with 20 uM blebbistatin enhanced both the rate and efficiency of uptake in the strains with shorter O antigen ( EHEC and K12 ) and left it unaltered for EAEC ( S5 Fig ) . These data suggest that only a small fraction of OMVs usually enters cells by micropinocytosis , and inhibition of this relatively slow uptake route either does not affect or accelerates uptake . Next , we tested if OMV uptake required dynamin , using the dynamin GTPase inhibitor dynasore . Treatment of host cells with dynasore completely abolished uptake of OMVs , independent of serotype and the presence of O antigen ( S5 Fig ) . Next , we determined whether OMV uptake was via clathrin-coated pits , or via lipid raft-mediated endocytosis , both of which require dynamin [29–31] . We inhibited clathrin-mediated endocytosis , either by proteolytic removal of all protein receptors from host cells with papain prior to OMV incubation , or by blocking pit assembly using chlorpromazine [32] . Removal of protein receptors from the host cell surface increased uptake rate ( S6 Fig ) and efficiency ( Figs 5 and S6 ) for OMVs with O antigen , but decreased or abolished uptake rate and efficiency of O antigen deficient OMVs . In general , both papain and chlorpromazine treatment decreased the uptake of O antigen negative OMVs but , although they had variable effects , they did not reduce uptake of O antigen positive OMVs ( Figs 5 and S6 ) . This suggests that OMVs lacking O antigen require protein receptors for uptake and use clathrin-mediated endocytosis as a main route of entry . In contrast , OMVs with intact O antigen do not rely on protein receptors for entry , and inhibition of clathrin-mediated endocytosis does not prevent their uptake into host cells . Since OMVs displaying O antigen on their surface accessed host cells faster in the absence of clathrin-dependent endocytosis , we investigated whether this was mediated by raft-dependent pathways . Disruption of raft-mediated endocytosis , either by sequestration of membrane cholesterol from membrane microdomains via methyl-β-cyclodextrin or by disrupting raft dynamics with filipin [33] , led to a reduced rmax ( Fig 5 ) and uptake efficiency ( S6 Fig ) . These data show that , while OMVs are able to access different uptake routes including macropinocytosis , clathrin-dependent and raft-dependent endocytosis , OMVs displaying O antigen on their surface are able to access raft-dependent endocytosis more efficiently , while OMVs lacking O antigen are more reliant on clathrin-mediated uptake ( Fig 6 ) . Shifting a larger fraction of O antigen-positive OMVs to raft-mediated endocytosis further accelerates their uptake , and we conclude the differences in uptake routes driven by LPS structure account for differences in uptake rate and efficiency we observe .
Interactions between bacterial outer membrane vesicles and epithelial cells are now recognized as an important driver of bacterial pathogenesis . Yet , our ability to study vesicle-host cell interactions has been limited by a lack of methods to capture the rapid kinetics of vesicle entry and dismantling in real-time , and without altering the physicochemical properties of the vesicle . Here we describe a novel assay that fulfils these requirements and allowed us to study the kinetics of OMV uptake with enough temporal resolution to reveal critical differences in rate and uptake efficiency of vesicles derived from different E . coli serotypes and pathovars . The method uses a genetically encoded , OMV targeted probe and a cell-permeable dye , resulting in a change in FRET upon reporter uptake and dye cleavage . Advantages of this system include its high sensitivity ( 5 μg/ml OMVs , the lowest concentration reported in the literature , produced a reproducible trace with good signal/noise ratio ) and rapid response ( signal was detected within seconds ) . A potential drawback is , that it is not known if the ClyA-Bla probe is expressed equally across the entire OMV population , but this is equally true for other markers and assays currently in use . The system’s use can be extended to a high-throughput format , allowing further study of bacterial and host factors determining OMV uptake and trafficking . Using a transwell format , the method can be applied to cell-based assays consisting of bacteria releasing OMVs , and host cells without the need for OMV isolation . Although the specific probes used here were functional across a range of E . coli isolates and different host cell types , their use in other bacterial species will require further characterization to determine if they are targeted to OMVs and retain correct orientation and enzymatic activity . We selected EHEC and EAEC OMVs for this study , since OMVs have been shown to play a crucial role in toxin stabilization and delivery for both pathovars [34 , 35] , and have been considered as a means to vaccinate and protect against hemolytic uremic syndrome , a severe complication of EHEC infection [36] . It is clear that LPS , and specifically O antigen , contributes to bacterial within-host fitness and pathogenicity , by enhancing resistance to complement , modulating phagocytosis and phage infection [37 , 38] . The O antigen of most E . coli strains has 10–18 repeats , but can exceed 80 repeats [39 , 40] . The length of the O antigen is equally variable ( ~5–50 nm ) , and is positively correlated with the ability of the bacterial cell to adhere to host cells and tissues , while loss of O antigen results in defects in colonisation , biofilm formation , and increased pathogen clearance [24 , 41–43] . Recent work showed that EHEC OMVs allow efficient delivery of LPS into the host cell cytoplasm , resulting in inflammatory responses , caspase-11 activation and cell death , but did not explore the role of LPS in uptake [44] . Our data suggest that O antigen has an additional , previously unrecognized role during bacteria-host interactions , which is to steer OMVs towards raft-mediated endocytosis , accelerating uptake and delivery of vesicle associated virulence factors such as hemolysins and Shiga-like toxins [45] to host cells and enhancing pathogenicity . It is well known that OMVs contain different cargos , depending on pathovar and serotype [46] . This means the comparison of O antigen deficient mutants with wild type OMVs as well as comparison of different pathovars has the pitfall that other vesicle cargos may be modulated and alter uptake kinetics . To dissect the effect of O antigen independent of other cargos , we attempted to deplete O antigen of wild type OMVs by treatment with a glycoside hydrolase , but found enzymatic activity was not limited to O antigen cleavage but modified the core LPS as well . However , we observed a strong correlation between O antigen and uptake kinetics across three different serotypes and pathovars , suggesting that O antigen is , if not the only factor , at least a key determinant of uptake kinetics . Since EAEC OMVs showed the most distinct change in entry kinetics upon O antigen deletion , with rmax impacted as well as rate sustenance and efficiency ( Fig 4 ) and O42 antigen seemed to be much longer than EHEC O157 or K12 O16 antigens , which seemed similar in size and displayed similar changes upon O antigen deletion ( S1B Fig ) , we speculate that O antigen length may impact maximal entry rate . We used our newly-devised assay to identify the relative contribution of cellular uptake pathways to OMV entry into host cells . Clathrin- and raft-dependent endocytosis , macropinocytosis and membrane fusion have all previously been reported as uptake pathways for bacterial OMVs , and it is likely that discrepancies between studies result , at least in part , from differences in species , strains and methodology used to study uptake [47] . Uptake of OMV cargo by fusion of vesicles with the host cell membrane can be ruled out as a major route of uptake for OMVs used in our study , since in this case ClyA-Bla would be exposed on the outer leaflet of the host cell membrane and would not account for the rapid cleavage of the cytoplasmic FRET dye . Assays using pharmacological inhibitors to block specific endocytic pathways , showed that while all OMVs use multiple uptake routes , their surface structure biases them towards different pathways . For example , O antigen deficient OMVs had a stringent requirement for surface protein receptors for their uptake , while O antigen containing OMVs were able to access protein-receptor independent pathways . Depletion of such receptors actually allowed them to access protein-receptor independent pathways more efficiently and utilize raft-mediated endocytosis , a more rapid mode of uptake , as main route of entry . While raft-mediated endocytic routes are not as well characterized as clathrin-mediated endocytosis , it is clear there are multiple pathways , including caveolin and non-caveolin dependent raft-mediated endocytosis . Our experiments suggest that the entry of O antigen containing OMVs is raft- and dynamin dependent , but protein-receptor independent , and no co-localization between OMVs and caveolin was detected . The requirement of dynamin is likely , based on complete inhibition of uptake following treatment with dynasore , however this is confounded by the dual inhibitory effect of dynasore both on dynamin as well as cholesterol containing micro domains [48] . A recent study focusing on vesicular cargo delivery of EHEC OMVs to host cells over longer time frames also concluded that OMVs enter host cells via dynamin-dependent endocytosis [45] . We therefore conclude they use a raft-mediated , and likely dynamin dependent , but protein-receptor and caveolin-independent route of uptake , and the detailed requirements regarding their uptake are subject to current studies .
The strains used in this study were the E . coli serotype O157:H7 strain Sakai 813 , a derivative of enterohaemorrhagic E . coli ( EHEC ) RIMD 0509952 , and its O antigen deficient derivative , MA6 ( Δgne , [25]; the E . coli serotype O42 wild type strain ( an enteroaggregative E . coli isolate , [49] , and its isogenic , O antigen deficient derivative strain ( ΔwbaC , [27]; the E . coli serotype O16 strain DFB 1655 L9 ( a K12 strain containing a restored wbbL gene ) , and its isogenic , O antigen deficient derivative , MG1655 [27] . All strains were transformed with plasmids pBAD ClyA-Bla , Bla-ClyA , or empty vector ( a kanR derivative of the pBAD ampR vector provided by Matthew DeLisa , Cornell University ) , [12] . Strains were grown in LB containing 50 μg/ml kanamycin , at 37°C with shaking at 200 rpm . 100 ml cultures were grown in LB at 37°C , with agitation at 200 rpm . Once the OD600 reached 0 . 5–0 . 6 , expression of ClyA-Bla was induced with 0 . 2% L-arabinose and grown for a further 16 h . Cells were then pelleted at 6000xg , and the supernatants were removed and filtered with a 0 . 45um syringe filter . Aliquots of filtered supernatants were spread on LB agar and grown overnight at 37°C to check that all viable cells had been removed by filtration . 25 ml of filtered supernatants were centrifuged in a Beckman XL90 ultracentrifuge using a 70Ti rotor at 100 , 000xg ( 30 , 000 rpm ) for 2 h at 4°C . After centrifugation , supernatants were removed , and the OMV pellets were resuspended in 1 ml colorless DMEM or sterile water ( for TEM ) and stored at -20°C . 12 μl of samples normalized for their protein content from EHEC ClyA-Bla and Bla-ClyA whole cell lysate , supernatant and OMV fractions were added to 3μl 5X SDS loading dye and boiled for 10 min . Samples were loaded onto a 15 well BioRad pre-cast stain-free SDS-PAGE gel and run at 120V , 200mA for 45 min . The gel was then transferred onto a PVDF membrane in transfer buffer containing 20% methanol for 80 minutes at 100V . After transfer , the membrane was blocked at room temperature in TBS 0 . 1% Tween-20 and 5% skim milk for 1h with agitation . The membrane was washed 3 times with TBS 0 . 1% Tween-20 ( 5 min per wash ) . After blocking , the membrane was incubated with a 1:2000 dilution of mouse anti-Bla primary antibody in TBS 0 . 1% Tween-20 and 5% skim milk overnight at 4°C with agitation . The following day , the membrane was washed 3 times as before , and incubated with a 1:5000 dilution of sheep anti-mouse secondary antibody in TBS 0 . 1% Tween-20 , 5% skim milk for 1h at room temperature with agitation . The membrane was washed again 3 times , and 2 ml BioRad ECL reagents were added to the membrane and incubated for 5 min , before visualization with a BioRad ChemiDoc imager . 50 μl of samples were added in triplicate to a 96-well plate . Nitrocefin was diluted to 0 . 5 mg/ml in PBS and 50 μl was added to each sample . The absorbance at 486 nm was measured in the FluoStar Omega plate reader for 2 h , and the change in absorbance over time was used to determine the specific activity in samples , using the protein concentration determined by the CBQCA kit . To quantify levels of protein in cell fractions , the ThermoFisher CBQCA Protein Quantitation kit was used according to the manufacturer’s instructions . Triton X-100 and SDS were added at a concentration of 1% to 20 μl OMVs for 45 min at 37°C . 5ug/ml papain was then added for 30 or 60 min at 37°C . The papain reaction was inactivated using 1 mM PMSF at room temperature for 30 min . 5 μl SDS-PAGE loading dye was added to the samples , which were then boiled for 10 min . Samples were run on a 15-well pre-cast stain free gel for 45 min at 120V , and then subjected to Western blotting with anti-β-lactamase primary antibody ( Pierce ) as described above . HeLa cells ( passage 1–7 ) were seeded in triplicate in a black-walled , clear bottom 96-well plate at a concentration of 1x105 cells per ml in Dulbecco’s modified Eagle medium ( DMEM ) supplemented with 1% L-glutamine , 1% Penicillin/Streptomycin and 10% heat inactivated fetal bovine serum . The plate was incubated at 37°C , 5% CO2 for 24 h prior to experiments . The following day , cells were loaded with 20 μl 6X CCF2-AM with 100 μl colourless unsupplemented DMEM ( cDMEM ) and incubated at room temperature for 1 h in the dark to allow dye loading . The dye was removed by washing 2x in PBS and 1x in cDMEM . Cells were treated with 5 mM methyl-ß-cyclodextrin or 1 μg/ml filipin to inhibit cholesterol mediated endocytosis , 80 uM Dynasore for dynamin inhibition , or 20 uM blebbistatin for macropinocytosis inhibition for 1h at 37°C . Cells were treated with 1 μg/ml chlorpromazine for 1h at 37°C to inhibit formation of clathrin-coated pits , or with 5 μg/ml papain for 15 min at 37°C to remove surface proteins , before inactivation of papain with 5 mM PMSF for 20 min . Reporter OMVs were diluted in cDMEM and added to the cells for a final concentration of 10 μg/ml , or 1x108 vesicles , corresponding to an MOI of 1000 . The plate was immediately placed in the PheraStar plate reader , with excitation at 405 nm and simultaneous dual emission at 530 nm and 460 nm . The wells were scanned ( bottom optic ) with orbital averaging for a total of 150 cycles , equating to a measurement every 90 seconds for 3 hours . The ratio of blue to green fluorescence intensity detected in the cells at each cycle was calculated using GraphPad Prism , and ratios for uninfected , dye-loaded cells were used as the baseline value for each cycle . All traces were normalized to 0 for their first ratio value . All experiments were performed with a minimum of three technical replicates and three independent repeats . Efficiency of uptake was calculated as the absolute change in blue:green fluorescence intensity ratio between 0 and 3 hours ( [Em460/Em530]t = 0hrs ) / [Em460/Em530]t = 3hrs ) . Analysis of variance ( ANOVA ) was used to determine statistical significance , with a Brown Forsythe test to determine equal variance ( GraphPad Prism software ) . A p-value of <0 . 05 was considered statistically significant . To estimate the gradients of the data , polynomials were fitted to each data set using the cubic spline function csaps in Matlab . Numerical estimates of the gradients of the resulting polynomials were determined using the gradient function . To ensure that the gradient estimates were as smooth as possible whilst also retaining the overall shape and trend of the data , a small smoothing parameter was used . Analysis of variance ( ANOVA ) was used to determine statistical significance , with a Brown Forsythe test to determine equal variance ( GraphPad Prism software ) . A p-value of <0 . 05 was considered statistically significant . HeLa cells ( P3-7 ) were seeded on 13mm coverslips in a 12-well plate at a concentration of 1x105 cells per ml in complete DMEM , 24 h prior to experiments . The following day , cells were washed and loaded with 100 μl 6X CCF2-AM dye with 500 μl colourless unsupplemented DMEM , and incubated in the dye solution for 1 h at room temperature in the dark . Cells were then incubated with ClyA-Bla reporter OMVs for 0–4 h . The cells were washed with PBS and then fixed with 0 . 5 ml 4% PFA . The next day , coverslips were mounted onto slides with a drop of Gold Anti-Fade mounting solution and then imaged using a Nikon A1R confocal microscope ( Birmingham Advanced Light Microscopy Facility ) , and fluorescence was observed from excitation at 409 nm and dual emissions at 450 nm and 520 nm . Z stacks were produced with gain , slice thickness , exposure and laser intensity kept the same for all slides , and images were taken for 3 representative fields of view per slide and n = 3 independent samples . The Z stacks were converted to maximum intensity projection images . For OMV localization experiments , OMVs were stained using cell mask orange ( 1:500 ) for 1 h at 22°C and gentle agitation . Following staining , samples were washed with 28 volumes of PBS and labelled OMVs pelleted by ultracentrifugation ( 100 , 000xg , 2h ) . Hela cells were exposed to labelled OMVs for 10 of 60 minutes prior to fixation in 3 . 2% formaldehyde . Slides were imaged using an Olympus IX83 inverted microscope fitted with a FV3000 confocal system and 100x Super Apochromat oil objective . Images were captured using Olympus Fluoview software and processed using the CellSens extension package . | All Gram negative species of bacteria , including those that cause significant disease , release small vesicles from their cell membrane . These vesicles deliver toxins and other virulence factors to host cells during infection . Current methods for studying host cell entry are limited due to the nanometer size and rapid uptake kinetics of vesicles . Here we developed a method to monitor the rapid vesicle entry into host cells in real-time . This method highlighted differences in kinetics and entry route of vesicles into host cells , which varied with the bacterial cell wall composition and thus , the vesicle surface . Increased understanding of vesicular entry mechanisms could identify targets which may allow us to combat infections by inhibiting delivery of vesicle-associated toxins to host cells . | [
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] | 2017 | Lipopolysaccharide structure impacts the entry kinetics of bacterial outer membrane vesicles into host cells |
Understanding the mechanisms that generate complex host-parasite interactions , and how they contribute to variation between and within hosts , is important for predicting risk of infection and transmission , and for developing more effective interventions based on parasite properties . We used the T . retortaeformis ( TR ) -rabbit system and developed a state-space mathematical framework to capture the variation in intensity of infection and egg shedding in hosts infected weekly , then treated with an anthelminthic and subsequently re-challenged following the same infection regime . Experimental infections indicate that parasite intensity accumulates more slowly in the post-anthelminthic phase but reaches similar maximum numbers . By contrast , parasite EPG ( eggs per gram of feces ) shed from rabbits in the post-treatment phase is lower and less variable through time . Inference based on EPG alone suggests a decline in parasite intensity over time . Using a state-space model and incorporating all sources of cross-sectional and longitudinal data , we show that while parasite intensity remains relatively constant in both experimental phases , shedding of eggs into the environment is increasingly limited through changes in parasite growth . We suggest that host immunity directly modulates both the accumulation and the growth of the parasite , and indirectly affects transmission by limiting parasite length and thus fecundity . This study provides a better understanding of how within-host trophic interactions influence different components of a helminth population . It also suggests that heterogeneity in parasite traits should be addressed more carefully when examining and managing helminth infections in the absence of some critical data on parasite dynamics .
The large variation in disease severity and transmission often observed among individuals infected with helminths is strongly determined by the local conditions that incoming and established parasites encounter within the host , in addition to variation in the exposure of hosts to infective stages [1–3] . These conditions are mainly determined by the current and previous immuno-physiological attributes of the host , such as the local immune profile or the level of chemical homeostasis , and ecological parasite processes mostly driven by intensity-dependent competition for resources [1 , 4–15] , where intensity represents the number of parasites in infected hosts . Host and parasite constraints have been shown to affect both the intensity and the life history of parasites with interactions that frequently lead to non-linear dynamics of infection and complex trade-offs between parasite life-history traits . The within-host regulation of gastrointestinal helminths , and the consequences for the dynamics and life history of the parasite , remains a subject of ongoing research [4 , 16–19] . The fundamental challenge is to provide convincing evidence for the mechanisms that affect the intensity of infection and the way parasite traits ( i . e . development , fecundity and shedding ) adjust to these changes . Ultimately , the understanding of these processes under ecological and immunological forces , and their relative contribution to the observed phenotype of infection , is important for explaining the often large variation in the host’s ability to control the infection . A useful approach to disentangling the contribution of these two forces is by examining how parasites adjust to external perturbations , such as anthelminthic treatments . Anthelminthics are commonly delivered over one to several days ( depending on the type of treatment ) and parasites are removed within hours or multiple days; however , in endemic areas reinfection is inevitably the norm . Because of this , by altering the number of parasites competing for host resources and/or the strength of intensity-dependent immune responses [20 , 21] treatments lead to patterns of infection and re-infection that can be fundamentally different [2 , 4 , 22] than treatment-free settings . Indeed , the transient disruption caused by the drug treatment resets the background environment by placing the initial parasite population to 0 , thus upon re-infection the new incoming parasites face a primed environment where competition for resources is minimal and/or the immunity still holds some memory from the previous history of infection . Any change in the dynamics of parasite establishment from the pre-treatment to the post-treatment phase should be due to the additional effects of memory in the immune response . In contrast , no changes in parasite dynamics between treatments should be indicative of a regulation by ecological forces driven by intensity-dependent processes in the parasite population . Irrespective of the anthelminthic treatment , following the constant exposure to infective stages and the accumulation of parasites within the host , the intensity of infection will exhibit a logistic growth with host age if regulated by intensity-dependent ecological forces , such as processes of competition for space and food [4] . A hump-shaped age-intensity profile , where older hosts carry fewer parasites , is indicative of an immune response that regulates the parasite population with a strength that increases proportionately with the force of infection [1 , 20] . Other processes can generate this convex profile [2 , 22] but here we address this shape as an immunity-generated process . Our previous observations in the Trichostrongylus retortaeformis—rabbit system have conformed to the latter pattern , indicating immune regulation [12 , 23–26] . The interpretation of the relative contribution of these two forces—ecological limitation through parasite competition for resources and host immune regulation—have conventionally relied on either cross-sectional measures of parasite intensity or longitudinal measures of egg shedding . However , both of these approaches have limitations . For instance , the common method of using age-related , cross-sectional measures of parasite intensity , from dead or drug-treated hosts , assumes that sequential cross-sectional observations in different animals approximate the temporal progression of parasite intensity in a single or average animal and is indicative for a time series of infection [1 , 10 , 21 , 22 , 27–31] . This necessarily averages out the within-host heterogeneity in parasite dynamics and traits , as it assumes that the unobservable or hidden dynamics of infection in individuals measured at later times behave similarly to those measured at earlier times . This problem can be circumvented by using time series of parasite shedding , which allows observations of individual host variation during the course of the infection . However , this method requires the assumption that the amount of eggs or larvae shed by a host into the environment is directly proportional to the actual parasite burden [32 , 33] and remains constant over time . Yet , an increasing number of studies have shown that this assumption is rarely confirmed or correct [34–43] and the view that the infection-shedding relationship remains constant over time can lead to wrong conclusions . Indeed , any feedback between parasite intensity and fecundity/shedding , or between-host immunity and parasite fecundity , would violate this assumption . The apparent limitations in the conventional measures of within-host parasite dynamics reflect two challenges in the inference for dynamical systems: the states of interest are often not directly measurable ( e . g . parasite burden prior to sacrifice ) and/or they are only indirectly measurable ( e . g . egg shedding as an indicator of adult parasite burden ) . State-space or hidden Markov models provide a statistical framework that links a dynamical model of the progression of unobservable states through time to observable measures [44–47] . This class of statistical models has increasingly been used to study the dynamics of infectious diseases at the host population level , where the true incidence of infection is only partially observable through a subset of cases reported through surveillance [48–50] . To understand the relative contribution of immunity and parasite ecological constraints to patterns of helminth intensity , fecundity and shedding , we developed a within-host state-space model of parasite dynamics using a laboratory experiment and the Trichostrongylus retortaeformis ( TR ) -rabbit system . By linking a dynamic model to an observation model and by combining cross-sectional data on TR intensity , body length and fecundity with longitudinal data on egg shedding we are able to reconstruct the unobservable dynamics of infection within each individual and make inference on the underlying dynamic processes . Specifically , to evaluate changes in TR dynamics and traits following external perturbations and provide a mechanism of regulation we compare the model performance before and after anthelminthic treatment and under constant exposure to infective stages .
The regular exposure of rabbits to a constant amount of TR infective larvae leads to a convex pattern between the intensity of infection and host age ( i . e . sampling time ) both in the pre- and post-treatment phase of the experiment ( Fig 1 ) . This is clearer for some rabbits , which is consistent with our previous work [22 , 51 , 55] . There was no significant difference between pre- and post- treatment in the mean and maximum intensity of infection , controlling for sampling date ( ANOVA p-value = 0 . 528 , for additional results see S2 Table ) . The convex age-intensity profile in both experimental phases , slower parasite accumulation , and delayed increase in egg shedding in the post-treatment phase , is suggestive of an effect of accumulated exposure on the establishment and/or clearance of TR . However , it is difficult to make a definitive conclusion from the data as they reflect averages from rabbits sampled at a single time point and variation in TR intensity among hosts is high . The time series of eggs shed in the feces ( EPG ) by every animal also exhibited a tendency to a convex pattern with host age ( Fig 2 ( a ) and 2 ( b ) ) . The peak in EPG was reached in approximately 4 weeks in both phases of the infection . The number of eggs shed was significantly lower in the post-treatment ( pre- and post-phase , mean = 1842 eggs ( range 1000–3850 ) and 890 eggs ( range 475–1325 ) , respectively; mixed effects ANOVA p-value <0 . 001 with rabbit included as a random effect , S3 Table ) . The ratio of the number of eggs in utero per female length was not significantly different between phases ( p = 0 . 35; Poisson regression , S2 Fig , S4 Table ) . However , we found that adult TR were significantly shorter in the post-treatment compared to the pre-treatment phase ( body length post- vs pre-phase 95% CI: 6 . 49 − 6 . 61 mm vs 7 . 12 − 7 . 25 mm Fig 2 ) , after accounting for the effect of sampling time ( DPI ) , the section of the intestine and their interactions ( p = 0 . 006; mixed effects ANOVA with rabbit as a random effect; S5 Table ) ; consistent with the overall lower rate of egg shedding in the post-treatment phase . Forward simulations from the fitted model ( Fig 3 ) replicated the qualitative patterns described by the experimental data . The simulated intensity of infection was comparable between pre- and post-treatment , albeit more variable in the pre-treatment . The number of TR eggs shed was also lower and less variable in the post-treatment , consistent with the original data . Additionally , the fitted model also predicted smaller parasites at 60 days following initial exposure in the post-treatment phase of the experiment , consistent with the observed data ( Fig 3 ( c ) , see further discussion below ) . A total of nine parameters were fitted from the parasite population dynamic and the individual growth models for every rabbit , both in the pre- and post-treatment phase of the experiment . In the population dynamic model , we estimated parameters corresponding to the baseline establishment of infective larval stages ( L3 ) , γ1 , adult parasite clearance , β1 , and the degree of either the intensity of adult infection ( γ3 , β3 ) or the cumulative exposure ( γ2 , β2 ) on establishment and clearance rates ( Table 1 ) . Parameter estimates based on the joint likelihood for the longitudinal and cross-sectional data ( Fig 3 ) indicate that the baseline rate of establishment was reduced in the post- , compared to the pre-treatment phase of the experiment; the posterior mean and central 95th quantiles of the fraction of L3 larvae that establish are 0 . 94 ( 0 . 84 , 0 . 98 ) and 0 . 88 ( 0 . 75 , 0 . 95 ) , respectively ( Fig 3—panel ( a ) ) . The lower establishment rate in the post-treatment phase is consistent with the later peak in the intensity of TR infection observed . Estimates of adult parasite clearance were low in both the pre- and post- treatment phases of the experiment and the baseline clearance rate was similar in both phases . Overall , parameter estimates were highly variable among rabbits ( see points below Fig 3 panels ( b ) , ( c ) , ( e ) , ( f ) ) . We note that the posterior mean for larval establishment tended to be lower ( Fig 3—panel ( a ) ) and adult clearance tended to have higher mean ( Fig 3—panel ( c ) ) in animals sampled later in the experiment . There is a weak trend for a stronger effect of TR intensity and cumulative exposure on establishment ( Fig 3—panels ( b ) , ( c ) ) in the animals sampled later in the experiment . Averaged over all rabbits in the experiment there was no consistent effect of either cumulative exposure to infective stages or current adult intensity on either larval establishment or adult clearance rate; the posterior distribution is similar to the uniform prior distribution for these four parameters when the model is fit to all rabbit simultaneously . Averaged over all rabbits , there was a strong effect of the treatment on the distribution of parasite lengths; the posterior mean and central 95th quantiles of parasite mean final length in the pre- and post-treatment phases are 9 . 1 mm ( 8 . 0 , 10 . 4 ) and 7 . 9 mm ( 7 . 4 , 8 . 8 ) , respectively ( prior mean and 95th percentiles 9 . 0 mm ( 7 . 1 , 10 . 9 ) ) . In both sub-models ( cumulative exposure submodel and current intensity submodel ( S1 Appendix ) ) , the general patterns remained consistent; specifically the larval establishment rate remained lower and mean final body length of parasites were shorter in the post-treatment phase . As in the full model , the posterior mean for the model fit to all rabbits simultaneously showed no strong evidence of an effect of cumulative exposure to infective stages ( cumulative exposure sub-model ) or TR intensity ( current intensity sub-model ) on establishment or clearance ( S5 Fig ) . Compared to the full model with the cross-sectional and longitudinal data , the model fit with longitudinal data only , results in broadly similar patterns in the parameter estimates ( S3 Fig ) . However , the estimated effect of the treatment on establishment of L3 larvae and the estimated adult parasite final length was greater than that from the fit based on the full likelihood for the longitudinal and cross-sectional data . We can directly compare the observed adult parasite length distribution to the predicted size distribution of parasites at the end of the pre- and post-treatment phases of the experiment for the models fit only to the longitudinal observations ( which include no explicit length data ) and to both longitudinal and cross-sectional observations . The mean ( and IQR ) of observed TR lengths at 60 days in the pre- and post- phases was 7 . 1 mm ( 6 . 2 , 7 . 9 ) and 6 . 17 mm ( 5 . 3 , 6 . 9 ) respectively; a mean difference of 0 . 93 mm . For the model fit to both the longitudinal and cross sectional observations , the corresponding predicted mean ( and IQR ) lengths of adult parasites at 60 days in the pre- and post- phases were 8 . 1 mm ( 7 . 5 , 8 . 7 ) and 7 . 0 mm ( 6 . 7 , 7 . 5 ) , respectively; a predicted mean difference of 1 . 1 mm . For the model fit to only the longitudinal EPG observations the corresponding mean ( and IQR ) lengths of adult parasites at 60 days in the pre- and post- phases were 8 . 8 mm ( 8 . 0 , 10 . 2 ) and 7 . 3 mm ( 7 . 0 , 7 . 7 ) respectively; a predicted mean difference of 1 . 5 mm . Thus , the model fit to only the EPG data , including no explicit length data , estimates a larger effect of anthelminthic treatment on adult parasite length than the full model that includes length data . Both the empirical analyses and model fits suggest a cumulative effect of time , which here reflects the cumulative exposure to both larvae and adult TR , on TR growth , namely body length . Based on the observed data , we found a significant fixed effect of both treatment and sample day on parasite length in a mixed model with a random effect for rabbit; AIC for the null model of no fixed effects , model with treatment only , and model with treatment and sample day is 9450 . 1 , 9437 . 5 , 9433 . 7 , respectively ( Fig 4 ( a ) . To investigate the role of host immunity on these patterns , we repeated the same analysis , including the additive effect of IgA and IgG . There was no significant effect of the Igs , suggesting that individual rabbit-level variation in antibody response could not explain variation over and above the cumulative effect over time . We note that when the antibody variables were included in models that did not include sampling day , all were significant; thus time was confounded with the increasing trend in antibody measures across all rabbits , but the host-to-host variation in antibody response did not provide additional explanatory power . We note that the level of both serum and mucosal IgA and IgG increased monotonically over time ( Fig 4 ( b ) –4 ( e ) ) as mean TR length decreased . We note further that both serum and mucosal IgA and IgG levels at the first sampling date ( day 15 ) of the post-treatment phase were similar to those on the last sampling day ( day 60 ) of the pre-treatment phase ( Fig 4 ( c ) and 4 ( e ) ) , indicating that immunity remained relatively high during the month in which rabbits were not infected .
Our modeling framework provides a novel approach to the study of helminth infections at the host level . Indeed , while offering a parsimonious explanation of the forces driving the dynamics of the parasite it also identifies the traits that are primarily constrained and the processes that generate such patterns . Moreover , the impact of external perturbations , namely anthelminthic drug treatment , is well captured in the simulated dynamics of infection . Ultimately , although this modeling approach needs information both on longitudinal and cross-sectional data it can provide quantitative predictions on changes in helminth intensity and traits under testable external disturbance .
All the animal procedures were approved by the Institutional Animal Care and Use Committee of The Pennsylvania State University ( USA ) . The intestinal helminth Trichostrongylus retortaeformis ( TR ) is a common parasite of the European rabbit ( Oryctolagus cuniculus ) . Hosts become infected by ingesting herbage contaminated with infective third stage larvae ( L3 ) . Larvae colonize the small intestine where they mature into adults that reproduce and shed eggs into the environment through the host’s feces . We previously showed that rabbits develop a strong type 1 and type 2 immune response that can clear or reduce the parasite load , although there is no life-long protection and animals are constantly re—infected under endemic exposure [12 , 51–53] . The initial type 1 response is probably a reaction to the bacterial infiltration in the intestinal wall damaged by the movements of larvae across the tissue during maturation [25 , 54] . This inflammatory response does not seem to affect the long—term ability of the rabbit to control the infection , which occurs through a type 2 immune response [12 , 25] . We also showed that there is a negative relationship between body length , or fecundity ( i . e . number of eggs in utero per body length ) , and both antibody IgA levels and intensity of infection [51 , 54] . While immunity plays an important role to the dynamics of this helminth [12 , 29 , 55] , it is still unclear if ecological density-dependent parasite processes also have any impact on parasite dynamics and traits and how they adjust to these host and parasite driven forces under anti-helminthic perturbation . Here we performed a laboratory experiment where we quantified parasite variables ( counts , body length and egg shedding through feces ) and immune response ( IgA and IgG antibodies ) before and after anthelmintic treatment and over a six-month period . Out-bred , New Zealand , 2 months old , male rabbits were housed in single cages with a 12h light cycle and a daily access to 125 g of standard rabbit pellets and water ad libitum . Animals ( n = 36 ) were orally trickle dosed every 7 days with 400 infective third stage larvae ( L3 ) suspended in 5 ml of tap water; control animals ( n = 12 ) only received tap water . In the current study we focus only on the infected rabbits; the feces of control animals were routinely monitored for TR eggs and were found to be zero at all time points and no parasites were found in the intestine of control animals at sacrifice; we take this as evidence of the independence of infections in rabbits . Groups of four infected animals were sacrificed at days 15 , 30 , 45 and 60 post initial infection . At day 60 , animals were orally treated for five consecutive days with the broad spectrum anthelminthic Fenbendazole at dosage adjusted by animal body mass ( 5mg fenbendazole/kg body weight , based on a 10% suspension ( 100 mg/ml ) ) ( Panacur , Intervet Inc . , USA ) . Infection was then suspended for 30 days , including the 5 day drug treatment , and subsequently reinstated following the same infection procedure and sampling frequency with four animals sacrificed at days 90 , 105 , 120 , 135 and 150 post initial infection . The removal of TR by the anthelminthic drug means that L3 larvae introduced in the post-treatment phase experience the same population dynamic conditions as in the pre-treatment , though may experience a novel immunological environment due to the history of prior exposure . We selected a one-month hiatus in the re-infection to allow the immune response enough time to decrease in the absence of parasites and mimic the natural condition of lack of exposure for a limited period , but one that was longer than the weekly dosing . This experimental design allows us to investigate the relative contribution of coupling between ecological and immunological forces within the host . Specifically , it provides a context for the effect of host immunity ( which necessarily depends on parasite intensity ) as well as parasite population on parasite establishment , development and reproduction of new parasites in nature . The treatment successfully removed all the parasites as no eggs were found in the feces during the one month following anthelminthic treatment also no parasites were found in the intestine of rabbits sampled prior to the start of the post-treatment phase at day 0 ( or 90 days post initial infection ) . The sampling points were selected to be compatible with the helminth life cycle ( pre-patent period about 12 days ) and to provide information on the parasite variables over the course of the infection . At every sampling point , animals were processed as described previously [25] . Briefly , the small intestine was divided into four sections ( from duodenum to ileum: SI–1 to SI–4 ) and the parasites from one half of each section were collected in a 50mL tube and then counted and sexed in 10x2 . 5 ml aliquots from that tube ( i . e . raw counts reflect 50% of worms in 50% of each section of the intestine ) . For every section , a sample of randomly selected specimens ( ≈ 50 parasites for each sex ) was stored in 10% PBS—buffered formalin and subsequently processed for biometry data ( body length ( male and female ) and number of eggs in female’s uterus ) [51 , 54] . Along with these cross—sectional data , we also collected longitudinal records on the number of parasite eggs shed in feces of every host twice a week starting from the second week post initial ( re– ) infection , following standard parasitological procedures ( eggs per gram of feces , EPG , [56] ) . In summary , for each host we collected four distinct parasite measurements: total count from a fraction of intestine , body length ( male and female ) and eggs in ( female’s ) uterus at the time of individual sampling , as well as eggs shed through host’s feces every week prior to rabbit sampling . Parasite-specific antibody responses ( IgA and IgG ) were measured in the blood serum ( every week ) and in the duodenum mucus ( at sampling points ) using indirect ELISAs . We used homogenates of adult TR as the source of antigen , as described previously [25 , 51] . The following dilutions were used: IgA mucus extracts 1: 10 , serum samples 1: 20; IgG mucus 1: 20 and sera 1: 160 . Briefly , all dilutions were prepared in 1% ( w/v ) non-fat milk diluted in Phosphate buffered saline ( ph7 . 2 ) supplemented with 0 . 05% Tween-20 ( Fisher Scientific , Hampton , NH ) . Antigen-antibody complexes were allowed to form overnight at 4°C prior to addition of anti-rabbit IgA or IgG detection antibody ( diluted 1:2000 in the same buffer that was used for diluting test samples ) . Quantification was based on spectrophotometric analysis; antibody values were then transformed and standardized into optical density ( OD ) indexes based on positive and negative control samples that were included in each assay plate , see full details reported in our previous work [25 , 51 , 57] . For simplicty we refer to the OD index as “antibody levels” . To examine the model for the within-host dynamics of infection and development of TR , we applied a state-space modelling framework that linked the quantified data of parasite intensity—a direct but cross-sectional measure of the state of interest , and time series of fecal egg counts—an indirect longitudinal measure of parasite intensity—to dynamic models that describe the time progression of the unobservable states ( parasite counts and size ) via an observation model [58] . We modeled the dynamics of parasite intensity within the host as a birth-death process ( i . e . establishment of infective L3 larvae and parasite mortality/clearance ) , where the rates of establishment and clearance are assumed to depend on both cumulative exposure to L3 larvae and current adult parasite intensity . The population of adult parasites at time t + 1 , Pt+1 is the result of the combined effect of cumulative exposure and parasite clearance P t + 1 = Λ γ 1 exp ( - γ 2 ∑ t Λ - γ 3 P t ) + P t - β 1 ( 1 - exp ( - β 2 ∑ t Λ - β 3 P t ) ) P t ( 1 ) where Λ is the known force of infection ( i . e . weekly L3 dose: assumed to be 0 on the days when larvae are not administered and 400 on the days of infection ) , γ1 is the baseline probability of parasite establishment , γ2 and γ3 give the per capita reduction in L3 establishment due to either new L3 larvae or established adults , respectively . The baseline probability of clearance is given by β1 , β2 and β3 give the per capita increase in clearance rate ( e . g . adult mortality ) due to new L3 larvae or established adults , respectively . The expected body length ( in mm ) of individual parasites at time t , xt is described by the discrete time logistic model [59] , x t + 1 = x t + α x t ( 1 - x t / L ) ( 2 ) where α is the mean developmental rate in length and L is the mean final length of an adult parasite . The distribution of parasite length at age t , is assumed to be normal with expectation μL and variance σ L 2 . Based on our lab measurements we assumed that all L3 larvae start at an initial mean length x0 of 1 . 069 mm and standard deviation of 0 . 062 ( a total of 53 L3 larvae were sampled to estimate the length distribution at the start of the experiment ) . The starting L3 size distribution is assumed to be constant at all times during the experiment . Adult male parasites are assumed to have mean final length that is 0 . 75 times that of adult female parasites which is ≈ 8mm [51 , 54]; the mean final length μL reflects the average of all male and female parasites assuming a 1:1 sex ratio . For every rabbit , and at each sampling point , parasite counts from one quarter of each of the 4 small intestine sections were combined and the observed number of parasites at time t , Ct , was thus assumed to be distributed as follows: C t ∼ Binomial ( P t , 0 . 25 ) where Pt is the observed parasite intensity in the whole small intestine at the time of sampling in that specific rabbit . The observed counts of parasites at each length , was binned into length classes of 0 . 25 mm intervals between 2 and 20 mm . The resulting vector Z , of length 72 , with elements zl is assumed to be drawn from a multinomial distribution with probability vector equal to the proportion of simulated parasites in each size class . Binning the lengths into these discrete classes allows the use of a multinomial likelihood , with expectation determined by the simulated length distribution , without assuming an explicit functional form of distribution of parasite lengths . The number of parasite eggs shed by every host ( EPG , based on the average of 2 measurements a week ) is a partial observation of the total number of parasite eggs , Et discharged by an animal on a given day . EPG counts are assumed to reflect approximately 3% of the average daily fecal production by a rabbit ( based on 30g of dry feces from laboratory rabbits of similar age and under the same feeding regime , unpublished data ) and assumed to be uniform across the different animals and constant over the day ( see S4 Fig for sensitivity of fitted parameters to the assumed observation rate ) . The observed number of eggs shed is assumed to be distributed as: EPG t ∼ Poisson ( 0 . 03 E t ) . This makes the implicit assumption that the rate of eggs shed is homogeneous through time and that the sampled feces are representative of all feces from a given animal . Parasite fecundity ( i . e . the number of eggs in utero per adult female parasite at sampling day t ) is known to be positively correlated with parasite length [51 , 54 , 60] . The observed relationship between eggs in utero and female parasite length ( S2 Fig , S4 Table ) was fit using a generalized linear mixed model ( GLMM ) , with Poisson error distribution . We also tested whether the slope of this relationship changed from the pre-treatment to post-treatment phase of the experiment . In the simulation model , the total number of eggs produced by every female at time t was a random draw from the fitted GLMM , conditional on the predicted length of that female . Only body lengths longer than 4mm , the length at which females are assumed to be sexually mature , were considered ( no eggs were observed in females smaller than 4mm throughout the experiment ( S1 Fig ) ) . The estimated fecundity of the total helminth population at time t , Et was then quantified as the sum of eggs over all adult females , predicted by the population dynamic ( Eq ( 1 ) ) and individual growth ( Eq ( 2 ) ) models . We used a Bayesian particle filter method [61] to estimate the parameters of the parasite population dynamic model ( Eq ( 1 ) ) and the individual parasite growth model ( Eq ( 2 ) ) independently for each rabbit . The posterior distribution for all parameters was estimated by sampling from a large ( 50 , 000 ) set of parameter combinations with probability proportional to the product of the joint prior probability and the approximate likelihood , where the latter is calculated via a particle filter ( explained below ) , for each parameter combination . Candidate parameters were sampled from conditionally independent uniform distributions . The upper and lower bounds on the uniform prior distributions were chosen to limit dynamics to the range of observed data . We estimated an approximate likelihood for each parameter combination using a particle filter [49]; an algorithm for estimating the likelihood for models where the exact likelihood cannot be stated analytically but realizations of the model can be generated by simulation [50] . These methods have a long history in signal processing and have been recently applied in a number of population dynamic and epidemiological scenarios with partial or imperfect observation of state variables [44–47] . The evaluation of the estimated likelihood requires two linked models: a dynamic model to generate the forward projection of the state variables—here the within-host parasite intensity and body length distribution ( Eqs ( 1 ) and ( 2 ) ) —and an observation model to evaluate the likelihood of observing the data—in our case—parasite counts , EPGs and parasite lengths , conditional on the values of the unobservable state variables . The steps below outline the approximation of the likelihood for each parameter combination , ϕk for k = 1…50 , 000 for a single rabbit . We treat all rabbits as statistically independent and thus the likelihood of parameter combination given the observations for all rabbits is the product of the individual rabbit likelihoods; below we present estimates conditional on the observations for all rabbits jointly , and each rabbit independently . The evaluation of the particle filter for one parameter combination returns an estimated likelihood , which is used in the calculation of the posterior sampling probability . To evaluate the particle filter for the longitudinal observations of EPG for rabbit r conditional on one specific parameter combination the following approach was implemented: This process was repeated for all EPG observation points; the average of the likelihood of all N particles at each time point gives an estimate of the conditional likelihood of the observed EPG at time step t . The product of these conditional likelihoods , for all observations , 1 to Tr , ( where Tr is the time of sacrifice for rabbit r in days ) is then the estimated joint likelihood of the longitudinal EPG observations from each rabbit for a single parameter combination ∏ t = 1 T r L ( E t | EPG t ) , ϕ k ) . On the final observation—the day of animal sacrifice , we evaluated the likelihood of the cross-sectional observations of parasite intensity and parasite length . The conditional likelihood of the observed parasite intensity ( O T r ) was calculated from the average of the likelihood for each N simulated parasite intensities ( P T r ) , which is given by L ( O T r | P T r , ϕ k ) , assuming O T r ∼ Binomial ( P T r , 0 . 25 ) . The conditional likelihood of the observed parasite lengths ( X T r ) was calculated given the average of the likelihood for each of the N simulated lengths distributions ( Z T r ) , is given by L ( X T r | Z T r , ϕ k ) , assuming that the observed vector of counts in length classes is a Multinomial draw , with trials equal to the number of observed adult parasites and probability vector equal to the proportion of simulated parasites at the final time step , Tr , in each size class for particle i . The product of these two likelihoods gives the estimated likelihood of the cross-sectional observations from each individual rabbit for a single parameter combination . The product of the estimated likelihoods for the longitudinal and cross-sectional observations gives the total likelihood of all observations for a single parameter combination for each rabbit . We assume that all rabbits are independent , thus the total likelihood for parameter combination ϕk is the product of the likelihoods for all , R , rabbits L R | ϕ k = ∏ r = 1 R ( ∏ t = 1 T r L ( E t | E P G t , ϕ k ) ( L ( O T r | P T r , ϕ k ) ) ( L ( X T r | Z T r , ϕ k ) ) ) . As many parameter combinations have very low likelihood , we implemented a two-step algorithm for computational convenience . We first estimated the likelihood for all parameter combinations using N = 100 particles and discarded all parameter combinations with estimated likelihood less than 1% of the maximum . We then re-estimated the likelihood for the remaining parameter combinations using N = 1000 particles , to obtain a more precise estimate of LR . After estimation of the likelihood for each parameter combination , we generate draws from the posterior distribution by re-sampling the parameter combinations , with replacement , using probabilities proportional to the likelihood for all rabbits . We compare this posterior distribution to that derived by re-sampling parameter combinations , with replacement , using probabilities proportional to the likelihood of the longitudinal observations only; e . g . assuming the cross-sectional data were not available . We also generated rabbit-specific parameter estimates by re-sampling parameter combinations , with replacement , using probabilities proportional to the likelihood for each rabbit independently . We fitted the same model framework to the pre- and post-treatment phase independently , assuming no explicit effects on the post-treatment dynamics from the pre-treatment conditions; as such any effect of the pre-drug exposure would be reflected in the estimated rates and strength of cumulative exposure on establishment and clearance . To examine the single effect of either cumulative exposure of infective stages or intensity of adult infection on explaining the dynamics observed , we fitted sub-models that set the effects of cumulative exposure ( γ2 and β2 ) or the effects of parasite intensity ( γ3 and β3 ) to 0 ( S5 Fig ) . | Host-parasite interactions frequently lead to complex dynamics of infection that can be difficult to explain when parasite data are not accurate or complete , which is often the case in natural systems . We used the helminth-rabbit study case and developed a state-space mathematical model to capture the variation in intensity of infection and egg shedding in an experimental trial where hosts were infected weekly , then treated with an anthelminthic and subsequently re-challenged following the same infection regime . Simulations indicate that hosts control the parasite proportionally to the accumulated force of infection and intensities decrease both in the pre- and post-treatment phase . The peak and mean intensity of infection is similar before and after treatment; however , the degree of egg shedding declines proportionately with parasite body length and both traits are lower in the post- than the pre-treatment phase . The intensity of infection fails to adequately explain the variation in the degree of shedding within and between hosts . Instead , this pattern is captured by changes in parasite body length . More attention should be given to the host-parasite interactions within hosts and the impact of external perturbation on the dynamics of infection and transmission . | [
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] | 2018 | Changes in parasite traits, rather than intensity, affect the dynamics of infection under external perturbation |
HIV controllers are rare individuals who spontaneously control HIV replication in the absence of antiretroviral treatment . Emerging evidence indicates that HIV control is mediated through very active cellular immune responses , though how such responses can persist over time without immune exhaustion is not yet understood . To investigate the nature of memory CD4+ T cells responsible for long-term anti-HIV responses , we characterized the growth kinetics , Vβ repertoire , and avidity for antigen of patient-derived primary CD4+ T cell lines . Specific cell lines were obtained at a high rate for both HIV controllers ( 16/17 ) and efficiently treated patients ( 19/20 ) in response to the immunodominant Gag293 peptide . However , lines from controllers showed faster growth kinetics than those of treated patients . After normalizing for growth rates , IFN-γ responses directed against the immunodominant Gag293 peptide showed higher functional avidity in HIV controllers , indicating differentiation into highly efficient effector cells . In contrast , responses to Gag161 , Gag263 , or CMV peptides did not differ between groups . Gag293-specific CD4+ T cells were characterized by a diverse Vβ repertoire , suggesting that multiple clones contributed to the high avidity CD4+ T cell population in controllers . The high functional avidity of the Gag293-specific response could be explained by a high avidity interaction between the TCR and the peptide-MHC complex , as demonstrated by MHC class II tetramer binding . Thus , HIV controllers harbor a pool of memory CD4+ T cells with the intrinsic ability to recognize minimal amounts of Gag antigen , which may explain how they maintain an active antiviral response in the face of very low viremia .
HIV controllers are rare individuals who spontaneously control HIV replication in the absence of antiretroviral treatment [1] , [2] . HIV controllers harbor plasma viral loads that remain undetectable by conventional assays and cell-associated HIV DNA loads that are in the very low range , close to one log below those detected in patients receiving efficient antiretroviral therapy [3]–[5] . HIV controllers show a very low risk of progression to AIDS [3] , emphasizing the importance of limited viral dissemination in maintaining a healthy status in the long term . Emerging evidence indicates that controllers suppress HIV replication through a very active immunological process . HIV controllers harbor effector memory CD8+ T cells capable of rapidly killing infected autologous CD4+ T cells through a cytotoxic mechanism involving the upregulation of perforin and Granzyme B [6] , [7] . Signs of immune activation are more prominent in HIV controllers than in efficiently treated patients , and include increased plasma LPS [8] , increased expression of T cell activation markers [9] , and increased propensity to secrete IFN-γ and MIP-1β upon polyclonal stimulation [10] . Longitudinal studies of efficiently treated patients who achieve undetectable viral load have shown a waning of cellular antiviral responses , which paralleled the progressive decrease in viral burden [11] . In contrast , HIV controllers maintain polyfunctional effector memory T cells with the capacity to secrete multiple cytokines [12]–[14] . How controllers maintain an active antiviral response in the long term in spite of a very low viral burden remains poorly understood . One element contributing to the persistence of an active immune response may be the quality of the HIV-specific central memory ( CM ) compartment . CM T cells are thought to be responsible for the long-term maintenance of immune memory , due to their long half-life , high proliferative potential , and capacity to replenish the pool of effector and effector memory ( EM ) T cells that directly control pathogens [15]–[17] The progressive depletion of the CM CD4+ T cell compartment parallels disease progression in a simian model of AIDS [18] . CM CD4+ T cell functions , such as proliferation and IL-2 secretion , are impaired as early as the primary infection stage in progressive HIV infection [19]–[21] , and are only partially recovered in efficiently treated patients [22] , [23] . Chronic antigenic stimulation is thought to drive an accelerated differentiation of CM into effector CD4+ T cells , and thus contribute to T cell exhaustion . Importantly , CM CD4+ T cell numbers and functions are preserved in HIV controllers , who appear protected from this accelerated differentiation process [24] , [25] . A recent study suggests that inactivation of pro-apoptotic molecules may contribute to the remarkable proliferative capacity of CM CD4+ T cells of HIV controllers , which can exceed that seen in healthy controls after non-specific stimulation [26] . We have previously shown that signs of CD4+ T cell immune activation could be detected in HIV controllers who nevertheless had an intact CM CD4+ T cell compartment , with preserved IL-2 secretion capacity and efficient proliferative responses [10] . How chronic immune activation was induced in controllers , and why it did not generally lead to accelerated CD4+ T cell differentiation and exhaustion remained unclear . To explore these issues , we tested the capacity of Gag-specific memory CD4+ T cell to differentiate in vitro , comparing primary CD4+ T cell lines derived from HIV controllers and efficiently treated patients with equivalent duration of infection . We found that HIV controller harbored a pool of memory CD4+ T cells able to differentiate into effector cells with high functional avidity for an immunodominant Gag epitope . This heightened sensitivity to Gag antigen could be explained by a high avidity interaction between the TCR and the peptide/MHC complex , as measured by class II tetramer binding . The capacity to mount a CD4 recall response in the presence of minimal amounts of Gag antigen may help explain how HIV controllers maintain a continuously activated antiviral response in spite of very low viremia .
Memory CD4+ T cell responses were compared in patients who spontaneously controlled HIV replication ( HIC group , n = 17 ) and in patients who achieved viral control following successful antiretroviral therapy ( HAART group , n = 20 ) . Patients in both groups had viral loads <40 HIV RNA copies/ml plasma . The duration of infection and the CD4+ T cell count did not differ significantly between the two groups ( Table 1 ) . We analyzed the properties of memory CD4+ T cell precursors by determining their capacity to generate CD4+ T cell lines specific for three immunodominant HIV-1 Gag peptides ( Table 2 ) . The peptides were chosen because of their broad immunodominance and their capacity to bind multiple HLA-DRB1 alleles [20] , [27]–[30] . The frequency of response was determined by measuring the percentage of patients for whom viable CD4+ T cell lines ( defined by a growth ratio >0 . 7 at day 14 ) could be obtained after stimulation with a Gag 20-mer peptide . PBMC from HIV-seronegative donors did not yield viable cell lines ( not shown ) . 89 out of 90 cell lines obtained from HIV-seropositive donors proved peptide-specific , as indicated by a positive IFN-γ response measured in ELISPOT assay . The frequency of response to the most immunodominant peptide , Gag293 , was remarkably high in both the HIC and HAART groups , with 94% and 95% of responders , respectively ( Table 3 ) . Responses to the second peptide , Gag263 , were also frequent , with 82% responders in the HIC group and 77% responders in the HAART group . These findings confirmed that several CD4 epitopes in Gag could achieve strong immunodominance in patients with controlled HIV-1 infection . Interestingly , responses to the third peptide , Gag161 , were more frequent in the HAART group than in the HIC group , with 91% versus 53% responders , respectively ( P<0 . 05 ) . We did not detect an association between the lack of response to Gag161 and particular HLA-DR genotypes . Ex vivo IFN-γ ELISPOT responses to the 3 Gag peptides were low , as expected for CD4 responses directed to single HIV peptides ( Fig . S1 ) . However , it was interesting to note that 7/13 Controllers had a detectable ex vivo response to Gag293 while only 1/13 Controller responded to Gag161 ( P<0 . 05 ) . This finding supported the notion of a higher frequency of Gag293-specific than Gag161-specific CD4+ T cells in Controller patients . Taken together , these observations suggested that the HIV controller status may be associated with a change in the immunodominance pattern of Gag CD4 epitopes . As controls , we generated CD4+ T cell lines specific for the CMV pp65 protein . Since the immunodominance pattern of CMV responses proved more variable than that observed for HIV-1 , we optimized the generation of CMV-specific CD4+ T cell lines by choosing the pp65 peptide in function of the HLA DR genotype of the patients ( Table 2 ) [31]–[33] . Using this strategy , close to half of the patients responded to pp65 peptides in both groups , which was consistent with CMV seroprevalence in the studied populations . CD4+ T cell lines typically showed an initial loss of cells due to apoptosis , followed by growth due to multiplication of HIV- or CMV-specific cells . Measurement of the growth ratio at day 7 showed that viable CD4+ T cells lines from HIV controllers had a faster growth kinetics than those from treated patients , with a significant difference in response to the 3 Gag peptides but not in response to CMV peptides ( Fig . 1A ) . Analysis of CD4+ T cell lines derived from a control group of untreated patients with HIV-1 viremia ( VIR ) showed limited growth capacity in all cases , consistent with the notion that active HIV-1 replication impaired the proliferative capacity of memory CD4+ T cells [19] . Measurement of growth ratios at day 14 ( Fig . 1B ) confirmed the rapid amplification of controller CD4+ T cell lines in response to Gag but not to CMV peptides . The clearest differences between the HIC and HAART groups were seen in response to Gag293 ( P = 0 . 003 ) , suggesting that HIV controllers harbored CD4+ T cell precursors with particularly good proliferative capacity in response to this immunodominant Gag peptide . Analysis of IFN-γ production by ELISPOT at day 8 showed a prominent response in HIV controller CD4+ T cell lines ( median SFC/106 cells = 7 , 716 ) , while most cell lines from treated patients remained negative . However , the fact that cell lines from treated patients had not yet entered the exponential growth phase could account for these differences . To compare CD4+ T cell lines at equivalent growth stages , all following measurements were made at doubling time ( mean doubling time = 8 days in the HIC group , 13 days in the HAART group , and 14 days in the VIR group ) . In these conditions , IFN-γ production remained higher in the HIC group compared to the HAART group in response to the immunodominant Gag293 peptide ( Fig . 2 , P = 0 . 006 ) . In control experiments , CD4+ T cell depletion abrogated the ELISPOT signal , confirming that IFN-γ was produced by CD4+ T cells , and not by the CD8+ T cells that may have escaped CD8 depletion ( Fig . S2 ) . IFN-γ ELISPOT responses measured at doubling time were higher in the HIC group than in the VIR group for all peptide tested ( Fig . 2 ) . However , we and others have previously shown that when responses are measured ex vivo , which gives an evaluation of ongoing effector responses , CD4+ T cells from viremic patients produce as much IFN-γ as those of HIV controllers , while CD4+ T cells from treated patients have low IFN-γ production [10] , [11] , [22] , [24] . The hierarchy of IFN-γ responses measured after proliferation of CD4+ T cell memory precursors is different , and rather reflects the capacity of these precursors to differentiate into cells with effector functions . We conclude that HIV controllers harbor memory CD4+ T cell precursors that can differentiate into efficient cytokine-secreting cells . To assess the sensitivity of memory CD4+ T cells to antigenic stimulation , we measured IFN-γ production in response to serial peptide dilutions ( Fig . 3 ) . We did not observe a significant difference in the dose of Gag293 peptide that induced a half-maximal ELISPOT response ( median EC50 = 1 . 38 10−6 M in HIC vs . 1 . 55 10−6 M in HAART , P = 0 . 09 ) . However , we observed that the shapes of the response curves differed , with a marked trailing end in the HIC group , suggesting the presence of a high avidity component within the responding CD4+ T cell population ( see representative examples in Fig . 3A and Fig . S3 ) . To extend this observation , we measured the last peptide concentration that gave a positive ELISPOT reading at least 2 fold above background . We verified that measurement of this concentration was reproducible in duplicate experiments ( Table S1 ) . Importantly , all measurements were carried out on CD4+ T cell lines at doubling time , to normalize for growth stage . This analysis revealed that the functional avidity of CD4+ T cells recognizing the immunodominant Gag293 peptide was higher in HIV controllers ( Fig . 3B ) . In the HIC group , 10 out of 15 patients tested had a positive ELISPOT reading at peptide concentrations ≤5 10−8 M , while in the HAART group only 1 out of 17 patients tested had a positive response at the same concentrations ( P = 0 . 005 ) . In contrast , functional avidities measured for Gag263 , Gag161 , and CMV peptides did not differ significantly between groups . These results pointed to a particular efficiency of CD4+ T cells specific for the immunodominant Gag293 in HIV controllers . Interestingly , the functional avidity of these cells correlated with the level of the IFN-γ response measured by ELISPOT assay at high peptide dose ( R = −0 . 68 , P<0 . 0001 ) . Thus , CD4+ T cells responses to Gag293 appeared both sensitive and potent in the group of controller patients . Patients were genotyped for the HLA DRB1 gene at 4 digit resolution . Table 4 reports the frequency of the most common HLA DRB1 alleles among the 34 HIV controllers and the 34 efficiently treated patients who were genotyped at initiation of the study . Interestingly , the frequency of the DRB1*0701 allele was 44% in the controller group and 18% in the treated patient group , which yielded a significant difference as measured by Fisher's exact test ( P = 0 . 03 ) . No other DRB1 allele showed significant differences . The frequency of the DRB1*0701 allele in the French population was reported to be 26% [34] , close to that seen in the group of treated patients . In contrast , the frequency of DRB1*0701 appeared increased among HIV controllers . To further explore the possibility that DRB7*0701 conferred an advantage in CD4+ T cell memory function , we compared functional parameters in DRB1*0701 positive versus DRB1*0701-negative individuals included in the study . We did not detect significant differences within the HIC group in terms of growth ratio , IFN-γ response , or functional avidity of CD4+ T cells following Gag293 stimulation . Within the HAART group , DRB1*0701-positive individuals showed lower IFN-γ responses ( median SFC/106 cells = 2458 , n = 6 ) than DRB1*0701-negative individuals ( median SFC/106 cells = 6116 , n = 9 , P = 0 . 02 ) . Taken together , these findings suggest that the DRB1*0701 allele may confer an increased chance to acquire a controller phenotype upon HIV infection , but that once the controller phenotype is established , the presence of the DRB1*0701 allele does not confer further benefit in terms of CD4+ T cell function . To further characterize the nature of Gag293-specific CD4+ T cells , we identified these cells through MHC class II tetramer labeling . Analysis of Gag293-specific CD4+ T cell lines revealed the presence of tetramer-positive ( Tet+ ) cells for all the patients tested , confirming the antigen specificity of the cell lines ( representative examples in Fig . 4A and 4B ) , and the capacity of the Gag293 peptide to bind multiple HLA-DR alleles [27] . At doubling time , the frequency of Tet+ cells was in the order of 1% . ( Fig . 4A ) . The population of tetramer-negative ( Tet− ) cells may correspond to Gag293-specific cells restricted by an HLA-DR , -DP , or DQ allele distinct from that used in the tetramer , or to cells amplified through bystander effect . At later time points , the population of Tet+ cells could reach up to half of the CD4+ T cells ( Fig . 4B ) , suggesting an efficient amplification of Gag293-specific cells restricted through HLA-DR . Comparison of the percentage of Tet+ cells at doubling time showed no significant difference between the HIC and HAART groups ( Fig . 4C ) , which validated our normalization strategy . Namely , CD4+ T cell lines analyzed at equivalent growth stages contained equivalent numbers of peptide-specific cells , and could thus be usefully compared . To determine the proliferative capacity of Tet+ cells , Gag293-specific CD4+ T cell lines were labeled with CFSE at doubling time and analyzed by flow cytometry 3 days later ( Text S1 , supplementary methods ) . The percentage of CD4+ Tet + cells that had divided ( CFSElo ) was comparably high for the 3 HIC and 3 HAART cell lines analyzed ( Fig . S4 ) . The proliferative index , which represents the average number of divisions undergone by the population that divided , showed a trend toward higher values in the HIC cell lines . Interestingly , the difference became more apparent when the number of cells that had undergone 5 divisions or more was computed . In HAART cell lines , 6 to 7% of Tet+ cells had undergone 5 or more divisions , while in HIC cell lines these percentages were of 40% , 35% , and 18% . These data suggested that Tet+ cells from HIV controllers comprised a population endowed with an intrinsically high proliferative capacity and a short generation time . The TCR Vβ specificities of Tet+ and Tet− cells within CD4+ T cell lines were determined at doubling time by immunostaining with a panel of anti-Vβ antibodies ( Fig . 5A and B ) . The TCR Vβ repertoire of Gag293-specific CD4+ T cells was diverse in both the HIC and HAART groups and varied depending on the individual . Table 5 lists the Vβ specificities showing an amplification within the Tet+ population , as defined by a ratio Tet+/Tet− ≥4 in a Tet+ population ≥2% of CD4+ T cells . The frequency of amplified Vβ populations is reported in supplementary Table S2 . This analysis showed that Vβ1 was amplified in 3 out of 4 cell lines in the HAART group and in 2 out of 4 cell lines in the HIC group . Vβ9 and Vβ13 . 2 were also amplified in half of cell lines tested . These observations suggest that some Vβ chains may be preferentially selected in response to the Gag293 peptide . However , we did not detect a Vβ signature characteristic of the HIC group . Taken together , these data suggest that multiple clones contribute to the high avidity memory CD4+ T cell population in HIV controllers . The high functional avidity of controller CD4+ T cells could result from increased avidity of the TCR for the pMHC complex , or from multiple factors that facilitate APC/T cell interactions or effector functions , including expression tuning of costimulatory molecules or efficiency of the IFN-γ secretion system [35] . To explore this issue , we set to directly test the avidity of the TCR/pMHC interaction . The TCR avidity was evaluated by measuring the percentage of Gag293-specific Tet+ CD4+ T cells detected as a function of decreasing class II tetramer concentrations , as described in reference [36] ( Fig . 6A ) . The concentration measured at half-binding ( EC50 ) did not show significant differences between the HIC and HAART groups . However , the shape of the binding curves differed between groups , with a persistence of detectable binding at low tetramer concentrations in the HIC group ( Fig . 6B ) . The TCR avidity was measured by the inverse of the last concentration that gave a Tet+ staining at least 2 fold higher than control CLIP-tetramer staining . Comparison of Gag293-specific CD4+ T cells at doubling time showed that the TCR avidity was significantly higher in the HIC group than in the HAART group ( Fig . 6C ) , indicating a difference in the nature of CD4+ T cell clones responding to the immunodominant Gag293 epitope . Thus , the high functional avidity of HIV controller CD4+ T cells could be explained , at least in part , by an intrinsic property of their TCR .
This study provides evidence that HIV controllers harbor a pool of high avidity memory CD4+ T cell precursors directed against an immunodominant Gag peptide . Memory CD4+ T cells specific for the Gag293 peptide were endowed with rapid growth potential and , importantly , with IFN-γ secretion capacity , suggesting that they would rapidly generate a pool of CD4+ T cells with effector function upon antigenic stimulation in controller patients . The high functional avidity of Gag293 specific cells points to their capacity of initiating recall responses in the presence of minimal amounts of HIV antigen . The high functional avidity could be explained , at least in part , by a high avidity interaction between the TCR and the cognate Gag293 peptide/MHC complex . Thus , the high sensitivity of controller CD4+ T cells to antigen appeared intrinsic , rather than dependent on the antigen presentation context or the cytokine milieu . The Vβ repertoire of tetramer-positive Gag293-specific cells proved diverse , suggesting that multiple clones contributed to the high avidity CD4 response in HIV controllers . This property may favor the long-term persistence of a high avidity response , since the presence of multiple clones reduces the probability of viral escape or of immune senescence . Taken together , these findings suggest that CD4 recall responses to Gag293 are rapid and efficient in the group of controller patients . We propose that the rapid triggering of recall responses may contribute to viral control . A rapid CD4+ T cell response upon occurrence of “viral blips” will keep the immune system in alert , provide immediate help for CD8+ T cells to exert efficient cytotoxic function , and possibly provide direct antiviral effector function [37] . This rapid recall response may help keep HIV-1 replication under a low threshold , and avoid the progressive undermining of the immune system associated with repeated viral replication episodes [38] . The presence of high avidity CD4+ T cells helps explain how HIV controllers maintain an active T cell response in the face of very low viremia . We and others have previously shown that the level of the specific CD4+ T cell responses in HIV controllers exceeds that seen in efficiently treated patients , even though both groups have very low antigenemia [10] , [11] , [22] , [24] . The triggering of recall responses at very low antigenic load in the controller group may account for this difference . Emerging evidence suggests that the CD8+ T cell response may also be of high avidity in HIV controllers . In particular , individuals harboring the protective HLA-B27 allele frequently develop a high avidity response against the immunodominant KK10 CD8 epitope , the avidity of the response correlating inversely with viral load [39] . The fact that HIV controllers maintain antiviral CD8+ T cells with high cytotoxic potential in spite of their low viral load is also suggestive of a high avidity response [6] , [7] , [40] . Thus , both the CD4+ and the CD8+ T cell compartments may contribute to the high sensitivity to antigen characterizing antiviral responses in the controller group . Studies in mouse models of chronic viral infections have shown that efficient CD8 responses do not persist in the long term without CD4 help [41] . Therefore , a high avidity CD4 response may be essential in maintaining the quality of the CD8 response in low viremia conditions . A correlate of a heightened sensitivity to HIV antigens may be a chronic level of immune activation , due to the recall of cellular responses upon each viral replication episode , however limited . Indeed , we have previously reported on signs of ongoing immune activation in the effector memory CD4+ T cell compartment of HIV controllers , as measured by the expression of HLA-DR , the downregulation of the IL-7 receptor , and the secretion of MIP-1β [10] . Other signs of activation include raised levels of LPS in plasma [8] and increased expression of HLA-DR within the HIV-specific CD8+ T cells [6] as compared to efficiently treated patients . These observations confirm the notion that viral control is achieved through an active immunological process . One should note that excessive chronic activation may be deleterious in the long term , as suggested by a trend toward CD4+ T cell decrease in controller patients with the highest degree of immune activation , even in the persistence of undetectable viral load [9] . It will be important in future studies to determine if such individuals show a decrease in T cell functional avidity , which may lead to more prolonged induction of recall responses to achieve viral control , and consequently to prolonged episodes of immune activation . On the other end of the activation spectrum , a few controllers appear to have low HIV-specific CD8 responses and a generally quiescent immune system [42] . This phenotype may result from a particularly successful viral control , with an antigenemia so low that it would not activate the high avidity memory T cell population for long periods of time . It also remains possible that non-T cell based , alternate mechanisms of viral control predominate in these rare individuals . It was intriguing that HIV controllers responded less frequently to the Gag161 peptide than efficiently treated patients , while the quality of the CD4+ T cell response appeared generally better in the former group . This observation pointed to possible changes in the immunodominance pattern associated with the controller status . Responses to Gag161 may have become subdominant in the controller group due to competition by high avidity CD4+ T cells responding to other epitopes , including that present in the Gag293 peptide . Indeed , high avidity has been shown to sharpen immunodominance in mouse models [43] . The key mechanism appears to be the increased proliferative capacity of high avidity T cells , which progressively fill the memory T cell niche , a phenomenon accounting for the apparent avidity maturation of T cell responses over time [36] , [44] . Importantly , in the present study , the duration of HIV-1 infection in the group of efficiently treated patients did not differ significantly from that in the controller group , with median of 12 ( 7–20 ) vs . 15 ( 10–21 ) years , respectively . Thus , a longer infection time was unlikely to account for the presence of high avidity CD4+ T cells in the controller group . The CFSE analysis identified a population of Gag293-specific cells with high proliferative capacity in HIV Controller cell lines , which was consistent with the presence of a pool of high avidity CD4+ T cells . The number of divisions undergone by Tet+ cells was heterogeneous , with only a fraction reaching 5 generations and above . This may reflect a range of avidities for the Gag293 antigen , with only a fraction of Tet+ cells being endowed with high avidity and thus high proliferative capacity . This notion is also supported by the shape of the functional avidity curves , which suggests the presence of both high and low avidity populations within the pool of Gag293-specific cells from HIV Controllers . However , the high avidity component was absent in the Gag293-specific CD4+ T cell population from treated patients , independent of the method of analysis ( functional avidity , tetramer avidity , or proliferation of Tet+ cells ) . One should note that the growth ratio of CD4+ T cell lines depended on the intrinsic proliferative capacity of specific cells but also on the frequency of these specific cells at the initiation of culture . We have previously shown that the frequency of p24 Gag-specific cells measured ex vivo by intracellular cytokine staining was approximately 3 fold higher in HIV controllers than in efficiently treated patients [10] . The analysis of ex vivo ELISPOT responses to the Gag293 peptide also showed a trend for higher values in the controller group . Thus , it is likely that both an increased precursor frequency and a higher proliferative capacity contributed to the efficient growth of CD4+ T cell lines from HIV controllers . Both properties may also contribute to the long term persistence of CD4 responses in these patients . The genetic background may play a role in conferring a better ability to mount high avidity CD4+ T cell responses . The increased frequency of HLA DRB1*0701 in the controller group could suggest a beneficial effect of this allele on the development of anti-HIV CD4 responses . However , since we did not detect an association between the presence of HLA DRB1*0701 and the level or avidity of the CD4 response within the controller group , we speculate that this allele may play a role in initially facilitating viral control , rather than in maintaining high avidity CD4+ T cells . Alternatively , HLA DRB1*0701 may be in linkage disequilibrium with a protective MHC class I allele associated with viral control . A beneficial effect of the HLA DRB1*13 alleles on CD4 responses has also been suggested [13] , [20] , though we did not detect a significant effect in our study . It will be important to confirm these findings in cohorts of patients powered for large scale genetic studies . An intrinsic advantage in CD4+ T cell growth capacity may also promote efficient CD4 responses in controllers . Van Grevenynghe et al . [26] have reported an increased growth capacity of controller CD4+ T cell lines in response to polyclonal stimulation , as compared to cell lines derived from efficiently treated patients or even from healthy donors . These authors demonstrated a role for the activation of the PI-3 kinase pathway , and the resultant inactivation of the downstream apoptosis inductor FOXO3a , in this particular growth phenotype . We did note a trend for higher growth ratios in controller CD4+ T cell cultures in the presence of CMV peptides , even in the absence of a positive IFN-γ ELISPOT response ( not shown ) . However , an increase in growth propensity only partially accounts for the CD4 response characteristics observed in HIV controllers . High avidity CD4+ T cells were directed against HIV but not CMV , pointing towards a selective advantage in the induction of anti-HIV responses . The selection of high avidity gag-specific CD4+ T cells may result from a lower exposure to HIV antigens during the acute infection stage , when the repertoire of responding T cells is initially shaped . The few reported cases of acute HIV-1 infection followed by spontaneous viral control support the notion of a lower viral peak in patients who acquire a controller status [45] . Mouse models indicate that low antigen exposure is associated to the development of a high avidity response , since only the high avidity T cells receive sufficient signals through the TCR to proliferate in the long term [44] , [46] . Such a scenario may predominate in patients who spontaneously control HIV replication . The presence of high avidity T cells may in turn stabilize the controller status by limiting viral replication episodes . On the other hand , we cannot rule out that high avidity Gag-specific CD4+ T cells are selected but subsequently lost in progressor patients . Since high avidity CD4+ T cells are the first to respond in the presence of low HIV antigen amounts , they may be the first to get activated in the presence of replicating HIV , and may represent the initial wave of target cells available to the virus . HIV is known to preferentially infect HIV-specific cells [47] , and among those it may well preferentially infect the most readily activated population . A recent report suggests that responses to several CD4 epitopes can be detected during the acute infection stage but are subsequently lost in progressor patients , which supports the idea of a rapid culling of the CD4 repertoire [21] . Another reason for the loss of high avidity CD4+ T cells may be senescence due to overstimulation by high antigenic loads in progressor patients . The observation that high avidity CD8 responses can be lost after acute HIV infection supports such a model [48] . An important area of future research will be to elucidate mechanisms that protect high avidity CD4+ T cells from depletion in HIV controllers . In conclusion , this study provides evidence for the presence of high avidity CD4+ T cells directed against Gag in HIV controllers . It is remarkable that the distinctive properties of HIV-specific T cells in Controllers , including high proliferative potential [14] , [24] , [49] , polyfunctionality [12] , [13] and high cytotoxic capacity per cell [6] , [7] , are all known attributes of high avidity T cells [36] , [40] , [50] , [51] . Thus , high avidity may underlie many of the characteristics of an efficient adaptive immune response against HIV . The presence of high avidity T cells has been associated with control of chronic viral infections in mice [50] , [52] , monkeys [53] , and humans [54] . Since high avidity also confers long-term memory and rapid reactivation in presence of antigen [36] , [44] , [55] , it represents a desirable property to be induced by candidate T cell vaccines against HIV .
HIV controllers ( HIC group; n = 17 ) were recruited through the French “Observatoire National des HIV Controllers” established by ANRS . HIV controllers were defined as HIV-1 infected patients who had been seropositive for >10 years , had received no antiretroviral treatment , and for whom >90% of plasma viral load measurements were <400 copies of HIV RNA/ml . All HIV controllers included in the present study had current viral loads <40 copies/ml . Control groups included: ( 1 ) HAART group ( n = 20 ) : HIV-1 infected patients successfully treated with antiretroviral therapy for more than 5 years and with a viral load <40 copies of HIV RNA/ml; ( 2 ) VIR group ( n = 10 ) : viremic patients with viral loads >10 , 000 copies HIV RNA/ml . Viremic patients had been infected with HIV-1 for more than 1 year and had not received antiretroviral therapy . Patients from the HAART and VIR groups were recruited through the SEROCO-HEMOCO cohort and the Bicêtre hospital . The study was promoted by ANRS under number EP36 and approved by the Comité de Protection des Personnes IDF VII under number 05–22 . All participants gave written informed consent prior to blood sampling . PBMC from HIV infected patients were plated at 2×106 cells per well in 24-well plates in the presence of one HIV-1 Gag or pp65 CMV peptide ( 10 µM ) in RPMI 1640 supplemented with 10% human AB serum , 2 mM L-glutamine , 10 mM HEPES , 100 ug/ml penicillin/streptomycin , 0 . 5 µM AZT , 5 nM Saquinavir and 5 ng/ml recombinant IL-7 ( Cytheris ) . The peptides used to stimulate the culture were highly purified 20-mers ( >99% purity; PolyPeptide Laboratories ) . Recombinant IL-2 was added after 2 days to a final concentration 100 U/ml . Cell lines were restimulated with IL-2 every 2 days until the end of culture . Starting from day 7 , cells were counted every day by trypan blue exclusion to determine the growth ratio ( GR: observed number of cells/number of input cells at day 0 ) . The CD8+ T cell population represented a median of 5 . 9% ( range: 0–22 . 7% ) of the CD3+ population . CD8+ T cells were depleted with magnetic beads ( IMag particles , BD Biosciences ) at doubling time ( GR = 2 ) , before performing functional assays . Less than 1% CD8+ T cells remained after CD8 depletion ( Fig . S2A ) . IFN-γ secretion by CD4+ T cell lines was evaluated by ELISPOT assay as previously described [56] . Briefly , 96-well nitrocellulose plates were coated with 1 µg/ml anti-human IFN-γ capture monoclonal antibody ( Mabtech ) . Cell lines starved off IL-2 for 16 h were plated in duplicate at 30 , 000 cells/well in coated ELISPOT plates and incubated with 4 µM peptide for 24 h at 37°C . Wells were then washed , incubated with a biotinylated anti-IFN-g detection antibody ( Mabtech ) , followed with alkaline phosphatase-labeled extravidin ( Sigma-Aldrich ) , and with a chromogenic alkaline phosphatase-conjugated substrate . IFN-γ spot-forming cells ( SFC ) were counted with a Bioreader 4000 system ( Bio-Sys ) . The ELISPOT response was expressed as SFC/106 cells after subtracting background . Wells were counted as positive if the number of SFC was at least two times above background level . Functional avidity assays were carried out on all cell lines with ELISPOT responses >1000 SFC/106 PBMC . ELISPOT responses were measured in response to serial peptide dilutions from 4×10−6 to 10−11 M , and the last dilution that gave a number of SFC at least two times above background was determined . At initiation of the study , 34 HIV controllers and 34 treated patients were genotyped for HLA-DRB1 . Patients were included in the study if their genotype matched at least one of the 6 HLA-DRB1 alleles available for MHC class II tetramer studies . This panel allowed the analysis of ≥70% of Caucasian patients [34] . PE-labeled tetramers for the DRB1*0101 , DRB1*0301 , DRB1*1501 and DRB5*0101 alleles were obtained through the NIH Tetramer Facility at Emory University . HLA-DRB1*0401 , DRB1*0701 , and DRB1*1101 biotinylated monomers were produced in insect cell cutures as previously described [57]–[59] . Monomers were loaded with 0 . 2 mg/ml peptide by incubation at 37°C for 72 h in the presence of 2 . 5 mg/ml n-octyl-b-D-glucopyranoside and 1 mM Pefabloc SC ( Sigma-Aldrich ) . Peptide-loaded monomers were tetramerized using APC- or PE-conjugated streptavidin ( eBioscience ) . To each tetramer loaded with the Gag293 peptide corresponded a control tetramer loaded with the CLIP peptide . The class II tetramer labeling protocol was adapted from [38] . CD4+ T cell lines were incubated with 4 µg/ml class II tetramer for 90 min at 4°C in PBS-1% BSA buffer . Surface marker antibodies CD4-PerCP , CD3-AF750-APC , CD14-FITC ( eBioscience ) , CD8-FITC , CD19-FITC ( BD Biosciences ) , and the Aqua Live/Dead viability dye ( Invitrogen ) were added for the last 20 min of labeling . The percentage of tetramer-positive ( Tet+ ) cells was measured in the live , CD3+ , CD4+ , CD8− , CD14− , CD19-gate . Events were acquired on a CyAn flow cytometer ( Beckman Coulter , Fullerton , CA ) and analyzed using the Flowjo software ( Tree Star ) . Negative controls were obtained by staining with HLA-DR matched tetramers loaded with the CLIP peptide . To determine the avidity of the TCR/pMHC interaction , CD4+ T cell lines were incubated with decreasing concentrations of class II tetramer from 1 to 0 . 01 µg/ml . The avidity was defined as the inverse of the last concentration that gave a percentage of Tet+ cells at least 2 fold higher than CLIP-tetramer control values . The TCR Vβ repertoire of HIV-specific CD4+ T cell lines was determined by co-staining cells with an MHC class II tetramer and a panel of Vβ-specific antibodies ( IOt-Test Beta Mark TCR Vβ repertoire kit , Beckman Coulter ) , according to the manufacturer's instructions . The kit covered approximately 70% of human Vβ specificities . The Vβ nomenclature is that of Wei et al . [60] . A Vβ specificity was considered amplified when the Vβ frequency was increased at least 4 fold in the Tet+ compared to the Tet- population . A ratio of 4 was above the range of Vβ variation observed in the ex vivo repertoire of HIV-infected patients [61] and within the range of Vβ expansions induced by superantigens in vitro [62] . Data are expressed as medians and range . Analyses were performed with the GraphPad Prism 5 . 0 software , using nonparametric statistical tests in all cases . Differences in variables between groups were analyzed with the Mann-Whitney U Test . Differences in percentages of response were analyzed with the Fisher's exact test . Correlations were analyzed with Spearman's coefficient R . All significant differences between groups ( P<0 . 05 ) were reported on data plots . | HIV infection , if left untreated , leads to the progressive disruption of the immune system , the destruction of the CD4+ T cell population , and the occurrence of multiple opportunistic infections . However , a small fraction of HIV-infected individuals ( less than 1% ) avoid these deleterious effects by spontaneously controlling HIV replication to very low levels in the absence of antiretroviral therapy . Emerging evidence indicates that these rare patients , named HIV controllers , contain HIV through a very active T cell-mediated immune response . In this study , we found that memory CD4+ T cells from HIV controllers had the capacity to respond to minimal amounts of antigen derived from the viral protein Gag . This property was intrinsic to controller CD4+ T cells , and resulted from the expression of T cell receptors ( TCRs ) with high avidity for a particular Gag peptide . The presence of high avidity CD4+ T cells may explain how HIV controllers maintain the antiviral immune response in constant alert , even though the amount of virus inducing this response is minimal . Based on this study , we propose that future candidate vaccines against HIV should induce high avidity memory CD4+ T cells , to mimic the rapid and persistent antiviral response characteristic of HIV controllers . | [
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] | 2010 | HIV Controller CD4+ T Cells Respond to Minimal Amounts of Gag Antigen Due to High TCR Avidity |
The retinal pigment epithelium ( RPE ) is a specialized monolayer of pigmented cells within the eye that is critical for maintaining visual system function . Diseases affecting the RPE have dire consequences for vision , and the most prevalent of these is atrophic ( dry ) age-related macular degeneration ( AMD ) , which is thought to result from RPE dysfunction and degeneration . An intriguing possibility for treating RPE degenerative diseases like atrophic AMD is the stimulation of endogenous RPE regeneration; however , very little is known about the mechanisms driving successful RPE regeneration in vivo . Here , we developed a zebrafish transgenic model ( rpe65a:nfsB-eGFP ) that enabled ablation of large swathes of mature RPE . RPE ablation resulted in rapid RPE degeneration , as well as degeneration of Bruch’s membrane and underlying photoreceptors . Using this model , we demonstrate for the first time that zebrafish are capable of regenerating a functional RPE monolayer after RPE ablation . Regenerated RPE cells first appear at the periphery of the RPE , and regeneration proceeds in a peripheral-to-central fashion . RPE ablation elicits a robust proliferative response in the remaining RPE . Subsequently , proliferative cells move into the injury site and differentiate into RPE . BrdU incorporation assays demonstrate that the regenerated RPE is likely derived from remaining peripheral RPE cells . Pharmacological disruption using IWR-1 , a Wnt signaling antagonist , significantly reduces cell proliferation in the RPE and impairs overall RPE recovery . These data demonstrate that the zebrafish RPE possesses a robust capacity for regeneration and highlight a potential mechanism through which endogenous RPE regenerate in vivo .
The RPE is a polarized monolayer of pigment-containing cells that separates the retina from the choroid and performs many critical functions for vision . Microvilli extend from the apical RPE surface and interdigitate with photoreceptor outer segments , enabling the RPE to support photoreceptor health [1] . The basal surface of the RPE abuts and helps to form Bruch’s membrane ( BM ) , which , along with tight junctions between RPE cells , creates the blood-retina barrier and facilitates nutrient and ion transport between the retina and choriocapillaris [2–4] . Additionally , RPE pigment prevents light scatter by absorbing stray photons . Due to its importance in maintaining retinal function , diseases affecting the RPE have dire consequences for vision . Age-related macular degeneration ( AMD ) is one such disease , and is the third leading cause of blindness in the world [5 , 6] . AMD is commonly divided into two types: atrophic ( dry ) and exudative ( wet ) . In the early stages of atrophic AMD , RPE cells in the parafovea become dysfunctional and progressively degenerate , and this is thought to result in death of parafoveal rods [7–9] . Progressively , RPE dysfunction and degeneration spread to the fovea , resulting in loss of cone photoreceptors , and ultimately , loss of high-acuity vision [10–12] . Exudative AMD occurs in a subset of atrophic AMD cases when choroidal vasculature invades the retina [11 , 13] . Transplantation of stem cell-derived RPE has emerged as a possibility for treating AMD [14–16] , and clinical trials are currently underway [17–23] . However , little is known about the fate of transplanted RPE , and whether their survival and integration can be improved . An unexplored complementary approach is the development of therapies that stimulate endogenous RPE regeneration . In mammals , RPE regeneration is limited and dependent upon the size of the injury [24]; small lesions can be repaired by the expansion of adjacent RPE [25 , 26] , but existing RPE are unable to repair large lesions [24 , 27–30] . In some injury paradigms , RPE cells proliferate but do not regenerate a morphologically normal monolayer ( e . g . [26 , 31 , 32] ) . Indeed , RPE often overproliferate after injury , such as during proliferative vitreoretinopathy ( PVR ) , where proliferative RPE invade the subretinal space and lead to blindness [33–35] . Recently , a subpopulation of quiescent human RPE stem cells was identified that can be induced to proliferate in vitro and differentiate into RPE or mesenchymal cell types [30 , 36] , suggesting that the human RPE contains a population of cells that could be induced to regenerate . Little is known about the process by which RPE cells respond to elicit a regenerative , rather than pathological , response . Indeed , no studies have demonstrated regeneration of a functional RPE monolayer following severe damage in any model system . The development of such a model is a critical first step to acquiring a deeper understanding of the molecular mechanisms underlying RPE regeneration . Zebrafish offer distinct advantages for this purpose: the development , structure and function of the zebrafish eye is similar to human , including a cone-rich larval retina; they are amenable to genetic manipulation and imaging , and they can regenerate neural tissues ( e . g . [37–39] ) . However , it is unknown whether the zebrafish RPE is capable of regeneration . Here , we demonstrate that the zebrafish RPE possesses a robust capacity for regeneration and identify cellular and molecular mechanisms through which endogenous RPE regenerate in vivo .
To develop an RPE injury model , we utilized a transgenic line in which a promoter element from rpe65a drives expression of the nfsB-eGFP fusion protein in mature RPE [40] ( rpe65a:nfsB-eGFP; Fig 1 ) . nfsB is an E . coli nitroreductase that converts the ordinarily benign prodrug metronidazole ( MTZ ) into a potent DNA crosslinking agent , leading to apoptosis in expressing cells [41–44] . rpe65a:nfsB-eGFP transgenic embryos were treated with phenylthiourea ( PTU ) [45] to suppress melanin synthesis . To ablate RPE , 5dpf larvae were removed from PTU and exposed to 10mM MTZ for 24 hours . After treatment , eGFP+ cells degenerate ( Fig 1D ) , nuclei in the outer nuclear layer ( ONL ) adjacent to ablated RPE become disorganized ( Fig 1D’ ) and photoreceptor outer segment morphology is disrupted ( Fig 1D” ) . Degeneration of eGFP+ cells was accompanied by the absence of pigmentation recovery after removal of PTU . To quantify this , eyes were enucleated from ablated and control larvae , and brightfield images were taken to provide an en face view of the RPE ( S1 Fig ) . Quantification of the mean pigment intensity showed that pigmentation in ablated eyes was significantly reduced compared to controls by 2dpi ( p<0 . 0001 ) . To characterize the temporal dynamics of RPE and photoreceptor ( PR ) degeneration following MTZ treatment , sections were taken from larvae at 3 , 6 , 12 , 18 , 24 and 48 hours post-injury ( hpi ) and stained for TUNEL ( Fig 2; S2 Fig ) . At 3hpi , TUNEL+ nuclei were detected in the RPE ( S2A and S2B Fig ) while the ONL appeared normal . At 6hpi , nuclear organization in the ONL began to deteriorate and by 12hpi , apoptosis significantly increased in the RPE ( Fig 2E , p = 0 . 016 ) and ONL nuclei became delaminated . By 18hpi , apoptosis in the ONL increased significantly ( Fig 2F , p<0 . 0001 ) and eGFP accumulated in blebs , a process which left regions of the RPE devoid of eGFP signal ( S2G and S2H Fig ) . RPE apoptosis peaked at 24hpi ( Fig 2E , p<0 . 0001 ) . Apoptosis , while remaining significantly elevated when compared to controls , began to decrease in both layers by 48hpi ( Fig 2E and 2F , p<0 . 0001 in RPE , p = 0 . 0301 in ONL ) . However , by 48hpi , all remaining eGFP signal was contained in irregular eGFP+ blebs , likely consisting of RPE cell debris . ONL nuclear lamination remained severely disrupted ( S2I–S2L Fig ) . Non-transgenic siblings treated with MTZ showed no significant increase in apoptosis ( S3 Fig ) . To characterize degeneration further , RPE-ablated larvae were stained with markers for RPE ( ZPR2 ) [46] , red/green cone arrestin ( ZPR1 ) [47] , and F-actin ( phalloidin ) ( Fig 2G–2O ) . In unablated larvae , nfsB-eGFP colocalized with ZPR2 in the central RPE , confirming fidelity of the transgene ( Fig 2G ) . rpe65a:nfsB-eGFP was expressed in mature RPE cells while ZPR2 signal extended further into the periphery , labeling both mature eGFP+ RPE as well as less-mature eGFP- RPE closer to the ciliary marginal zone ( CMZ ) ( Fig 2G ) . Between 1 and 3 days post-injury ( dpi ) , changes to ZPR2 staining recapitulated disruption of eGFP+ RPE , including degeneration of the RPE cell body . ZPR1+ cones also began degenerating at 1dpi ( Fig 2J ) , and F-actin bundles in photoreceptor outer segments became more diffuse and lost their perpendicular orientation ( Fig 2M ) . By 3dpi , both eGFP and ZPR2 signals were absent from the central RPE , confirming degeneration of RPE in the central injury site ( Fig 2I ) . PR degeneration in the central retina also peaked at this time , displaying aberrant cone morphology ( Fig 2L ) , and significant degeneration of photoreceptor outer segments throughout the injury site ( Fig 2O ) . Despite rigorous screening , some variability in ablation severity was observed , likely from variations in transgene expression and ablation efficiency . To mitigate variability , only larvae with high levels of eGFP signal disruption in the eye ( severe ablation ) were utilized in subsequent experiments . In severely ablated larvae , ablation-mediated degeneration reliably peaked between 1-2dpi ( i . e . as in Fig 2 ) . Immunohistochemical data strongly supported RPE and PR degeneration following ablation and this was confirmed by transmission electron microscopy ( TEM ) analyses ( Fig 3 ) . In unablated larvae , central RPE cells containing pigmented melanosomes were easily observable ( Fig 3A ) . The PR layer was also properly laminated and contained readily identifiable cone and rod outer segments ( Fig 3A ) . Analysis of ablated larvae at 3dpi revealed severe degeneration of the RPE , which was occupied by debris that was either distributed throughout the injury site or collected in membrane-enclosed structures that may be macrophages ( Fig 3C , arrow ) . Bruch’s membrane ( BM ) underlying the ablated RPE was also significantly thinner than in controls ( Fig 3B , 3D and 3E; p<0 . 0001 ) and contained obvious gaps ( Fig 3D ) . Consistent with defects detected by histology ( Fig 2 ) , the PR layer of ablated larvae was severely degenerated , showing reduced size and integrity of photoreceptor outer segments , and containing degenerated outer segment material and other cellular debris ( Fig 3C ) . Taken together , these data indicate that RPE ablation via the rpe65a:nfsB-eGFP transgene causes specific loss of central RPE cells in larval zebrafish , with morphological defects beginning at 3hpi and destruction of the central RPE peaking at 2dpi . Further , immunohistological analyses demonstrate that underlying photoreceptor cells also degenerate rapidly after RPE ablation . Visual function of larvae was evaluated by analyzing the optokinetic response ( OKR ) to determine whether ablation of the RPE results in vision defects [48–50] ( Fig 4 ) . A cohort of ablated and control larvae were exposed to a rotating full-field visual stimulus at 1dpi , 2dpi and 3dpi , and visual responses were recorded ( Fig 4A–4C ) . At 1dpi , ablated larvae exhibited a modest reduction in stimulus tracking gain relative to controls , and this reduction in gain became significant at 2dpi ( Fig 4D , p = 0 . 0055 ) indicating that visual function is disrupted after ablation . By 3dpi , ablated larvae demonstrated a recovery of stimulus tracking gain ( Fig 4C and 4D ) . Likely , this rapid recovery is due to new photoreceptors being generated from the continually proliferative CMZ ( see below ) . Collectively , these data demonstrate that ablation of large swathes of mature RPE cells in rpe65a:nfsB-eGFP transgenics results in the rapid degeneration of underlying PRs and BM , and a loss of visual function . As discussed above , a subset of RPE possess a latent ability to proliferate in vitro [36] and various degrees of RPE repair have been documented ( e . g . [25 , 26 , 31 , 32 , 51 , 52] ) but in none of these systems is the RPE able to recover a functional monolayer following a large injury . Zebrafish possess a remarkable ability to regenerate a multitude of tissues [37 , 38 , 53] , but it is unknown if they can regenerate RPE . Thus , we analyzed the regenerative capacity of ablated larvae at 4 , 6 , 7 , and 14dpi with ZPR2 , ZPR1 , and phalloidin ( Fig 5 ) . At 4dpi , ZPR2+ cells extended into the injury site ( Fig 5D ) and RPE pigmentation significantly increased when compared to 2dpi levels ( S1 Fig ) , suggesting that RPE cells have begun to regenerate . Although ZPR1-labeled cones and photoreceptor outer segments remained degenerated in the central ablation site , morphologically normal ZPR1-positive cones reappeared in the periphery , and these were always in direct apposition to regenerated eGFP+ RPE ( Fig 5E and 5F; S4 Fig ) . At 6dpi , morphologically normal eGFP+/ZPR2+ RPE cells populate the periphery and approach the central injury site ( Fig 5G ) , and PR morphology improves in a similar pattern ( Fig 5H and 5I; S4 Fig ) . Interestingly , ZPR2+/eGFP- cells always appeared at the advancing tip of the regenerating monolayer ( Fig 5G ) . While the rpe65a:nfsB-eGFP transgene is expressed specifically in mature RPE , ZPR2 labels less-mature RPE , suggesting that these ZPR2+/eGFP- cells are RPE that have not yet fully differentiated . By 7dpi , the injury site was populated by ZPR2+ RPE ( Fig 5J ) . Although ZPR1-labeled cones continued to possess aberrant outer segment morphologies compared to controls in the central retina at 7dpi ( Fig 5K ) , photoreceptor outer segment architecture began to improve at this time ( Fig 5L ) . By 14dpi , ZPR2+/eGFP+ cells populated the entire RPE layer , and these displayed proper RPE cell morphology ( Fig 5M and 5N ) . While most ZPR1+ cones displayed proper morphology , ONL disorganization persisted , particularly in the injury site , where cones failed to align perpendicularly to the RPE ( Fig 5N ) . Seven months post-ablation , the RPE of ablated larvae were morphologically similar to those in unablated siblings ( Fig 6 ) . Regeneration appeared to proceed in a periphery-to-center fashion in fixed samples . We utilized optical coherence tomography ( OCT ) to quantify the spatial and temporal dynamics of RPE degeneration and regeneration in individual larva over time . The RPE in OCT images presents as a bright line due to the density and pigment present in intact tissue; in ablated eyes , the intensity of the signal decreases as a result of tissue disruption ( Fig 7A and 7B ) . Intensity of RPE signal ( backscatter ) can be quantified by determining the pixel intensity at each position of the RPE; here , we quantified the intensity from the optic nerve to the dorsal periphery , and examined changes in intensity in individual larvae over time ( Fig 7 , S1–S6 Videos ) . This analysis revealed that backscatter was significantly decreased in ablated larvae compared to controls in the central-most three quintiles of the RPE at 1dpi , and that all but the central-most quintile recovered to unablated levels by 5dpi ( Fig 7C , p<0 . 0001 ) . These results further support a model in which RPE regeneration occurs in a peripheral-to-central manner . To confirm that the regenerated RPE is morphologically normal , TEM analyses were performed on 14dpi larvae ( Fig 8 ) , a time point at which eGFP+/ZPR2+ RPE cells populate the injury site ( Fig 5N ) . Both unablated and ablated RPE contained melanosomes ( Fig 8A and 8C ) . Moreover , BM thickness was restored in ablated larvae ( Fig 8B , 8D and 8E; p = 0 . 3402 ) . Despite this apparent recovery , subtle differences still existed between regenerated and unablated RPE: RPE in the regenerated region appeared to contain more melanosomes and had thicker cell bodies ( Fig 8A and 8C ) . Consistent with immunohistochemical results , at 14dpi ONL lamination was improved but not completely recovered . Taken together , these data demonstrate that larval zebrafish are capable of regenerating a functional RPE monolayer following widespread RPE ablation and that regeneration is rapid , occurring within 1–2 weeks post-ablation . In larvae , the eye undergoes significant growth , making it possible that RPE regeneration is the result of a permissive growth environment rather than an ability of the RPE to regenerate per se . Thus , we determined whether RPE regeneration also occurs in the adult eye . Transgene expression in unablated adults is restricted to mature central RPE as it is in larvae ( Fig 9A ) . At 3dpi , there were clear signs of RPE degeneration that mirrored those in RPE-ablated larvae , including disruption of cell body cohesion and deterioration of apical processes , as indicated by the aberrant expression of ZPR2 and eGFP ( Fig 9B ) . Degeneration extended from the central RPE ( Fig 9B” ) to the periphery ( Fig 9B’ ) . At 7dpi , adults showed signs of RPE regeneration in the peripheral injury site , such as recovery of contiguous eGFP+/ZPR2+ RPE ( Fig 9C , arrowheads; Fig 9C’ ) , however central RPE had not yet recovered ( Fig 9C” ) . By 14dpi ( Fig 9D ) and 35dpi ( Fig 9E ) , adult zebrafish showed restoration of peripheral eGFP+/ZPR2+ RPE ( Fig 9D’ and 9E’ ) as well as successful regeneration of central RPE with apically localized ZPR2 expression ( Fig 9D” and 9E” ) , similar to sibling controls ( Fig 9A , 9A’ and 9A” ) . Quantification of RPE recovery based on contiguous eGFP+/ZPR2+ expression showed significant degeneration occurred by 3dpi ( p = 0 . 0286 ) , and that RPE fully regenerated by 35dpi ( Fig 9F ) . Taken together , these results demonstrate that the adult zebrafish is also capable of regenerating the RPE , and in a similar periphery-to-center mechanism as occurs in larvae ( Fig 9B–9E , arrowheads ) . Given these similarities and the technical advantages of using larvae over adults ( e . g . comparatively rapid regeneration , access to a large number of samples , the ease of in vivo imaging and genetic manipulations , and utility in high-throughput drug screens ) , we focused further efforts on characterizing the mechanisms underlying RPE regeneration in larvae . The rate and periphery-to-center pattern of RPE regeneration suggest that regeneration is driven by cell proliferation , and not simply the expansion of individual RPE cells , a response noted in several systems after small RPE injuries [24 , 31 , 54] . Proliferative cells are a major component of regeneration in diverse tissues , and they often derive from a resident pool of progenitor cells , e . g . as in blood and skin [55 , 56] , or from differentiated cells that are stimulated to respond to injury , e . g . as in heart and retina [53 , 57 , 58] . Moreover , RPE cell proliferation results from the loss of BM contact in several injury contexts , and pathologically , during PVR [24 , 30 , 59 , 60] . Thus , we hypothesized that uninjured peripheral RPE cells respond to injury by dedifferentiating and proliferating to replace lost tissue . To test this hypothesis , we first performed 24-hour BrdU incorporation assays to characterize the total number of proliferative cells within the RPE layer throughout regeneration ( Fig 10 ) . Proliferative cells appeared in the RPE as early as 1dpi , largely appearing immediately adjacent to the CMZ or in the center of the ablation site ( Fig 10G and 10U , p<0 . 0001 ) . Between 2-3dpi , more proliferating cells localized to the center of the eye , within the injury site ( Fig 10I ) , and the number of proliferative cells in the RPE peaked between 3-4dpi ( Fig 10J and 10U ) . During this period , proliferative cells populated much of the central eye in ablated larvae , with many localizing adjacent to or within the injury site ( Fig 10J and 10Q–10T ) . In contrast , unablated eyes showed eGFP+ RPE throughout the central RPE ( Fig 10D and 10M–10P ) and sparse BrdU+ nuclei ( Fig 10M–10P ) . As regeneration continued , eGFP+ RPE cells appeared closer to the center of the injury site and the number of proliferative cells in the RPE layer decreased ( Fig 10L and 10U ) , with most remaining proliferative cells localizing to the injury site . As expected , BrdU+ cells were also observed in the retina , and these are likely Müller glia-derived progenitor cells ( MGPCs ) generated in response to PR degeneration [58 , 61 , 62] . Quantification of the number of BrdU+ cells in the central retina demonstrated that the kinetics of retinal regeneration largely overlapped that of the RPE ( Fig 10V ) . Next , we sought to obtain greater spatial and temporal resolution in our analysis of proliferative cells in the RPE layer . Therefore , larvae 1-7dpi were exposed to short 2-hour pulses of EdU and subsequently eyes were enucleated , stained for ZPR2/EdU , and imaged to acquire en face views of the entire RPE ( Fig 11 ) . To quantify the spatial dynamics of RPE cell ablation and regeneration , we divided the RPE of each eye into four regions based on cell location and two markers of differentiated RPE: pigmentation level and ZPR2 staining . The four RPE regions were delineated as follows: ( 1 ) peripheral RPE cells are pigmented and dimly ZPR2+ , ( 2 ) differentiated RPE cells are highly pigmented and ZPR2+ ( 3 ) transition zone RPE cells are lightly pigmented but ZPR2+ and therefore likely consist of differentiating RPE extending into the injury site , and ( 4 ) the injury site , which contains no identifiable RPE cells , and which is often filled with aggregates of what are likely GFP+ and/or ZPR2+ debris ( Fig 11K ) . Using these criteria to quantify RPE layer composition , our analysis confirmed that a large proportion of the RPE degenerates rapidly after ablation ( Fig 11F and 11L ) . Strikingly , these analyses also revealed that differentiating RPE cells form a transition zone as soon as 1dpi ( Fig 11G inset ) , and newly-formed differentiated RPE reappear in the periphery at 2dpi ( Fig 11H and 11L ) . As regeneration proceeded , ZPR2+ transition zone cells always appeared in the periphery , stretching between the region of differentiated RPE and the central injury site . Furthermore , the proportion of the RPE encompassed by the transition zone at each time point correlated with the proportion of differentiated RPE cells added at the following time point , strongly suggesting these transitional RPE cells differentiate into regenerated RPE ( Fig 11L ) . These analyses confirm earlier analysis showing that new RPE is added to the peripheral injury site , and that regeneration of a pigmented ZPR2+ RPE is completed by 7dpi . Analysis of EdU+ cells revealed that there are more EdU+/ZPR2+ cells in the peripheral RPE of ablated larvae at 0 . 5dpi and 1dpi , and though this increase did not achieve significance ( Fig 11O , p = 0 . 076 and p = 0 . 078 , respectively ) , cryosections of 1dpi eyes showed peripheral EdU+ZPR2+ cells similar to those observed after BrdU exposure ( compare Fig 11M and 11N to Fig 10G ) . During these early time points , EdU+/ZPR2+ cells were largely restricted to the peripheral retina , with only a few EdU+ cells appearing in the injury site ( Fig 11R ) . During intermediate time points , when RPE cells reappear and the transition zone extends centrally , EdU+/ZPR2+ cells were present in both . As regeneration proceeded , the density of EdU+/ZPR2+ in regenerated RPE decreased , while increasing in the transition zone ( Fig 11P and 11Q ) . By 4dpi , proliferative cells were largely restricted to the center of the eye ( Fig 11I ) , and the majority of the remaining proliferative cells were located either in the injury site or the transition zone . Interestingly , the transition zone and regenerated RPE contained an even mix of EdU+ and EdU+/ZPR2+ cells , which may suggest that some differentiated RPE cells remain proliferative in this region and continue to generate new EdU+/ZPR2- cells that later enter the transition zone and differentiate . As expected , proliferative cells were also observed in the CMZ , particularly during early time points ( e . g . Fig 11A , 11F , 11M and 11N and Fig 10A–10H ) ; however , there appeared to be fewer proliferative CMZ cells beginning at 2dpi . As part of our experimental paradigm , embryos were incubated in PTU until 5dpf , and therefore it is possible that PTU withdrawal elicits a proliferative response throughout the retina , or that CMZ proliferation may ordinarily decelerate starting at 7dpf . Since both ablated and unablated larvae have fewer proliferative CMZ cells at later time points , it is unlikely that this phenomenon is a critical factor influencing RPE regeneration . Taken together , these results strongly suggest that peripheral RPE respond to injury by proliferating , that proliferative RPE cells and/or their progeny move into the injury site , and that proliferation continues within newly-generated RPE cells adjacent to the injury site until the lesion is repopulated . We were interested in the EdU+/Zpr2+ differentiated RPE and the possibility that they continue to proliferate after injury . Thus , to determine whether early-proliferative cells enter the injury site and continue proliferating , we pulsed ablated larvae with BrdU between 0-1dpi , and with EdU at 3dpi before fixation and analysis ( Fig 12A and 12B ) . Transverse sections revealed a significant increase of BrdU+/EdU+ cells and BrdU+ cells within the RPE of ablated fish ( Fig 12C , p<0 . 0001 ) . Interestingly , BrdU+/EdU+ cells often appeared at the interface between pigmented RPE and the unpigmented injury site , and some appeared to be pigmented ( Fig 12B” ) . We next sought to determine whether early-proliferative cells ultimately integrate into the regenerated RPE . To do this , we exposed ablated larvae to BrdU between 0-1dpi and fixed them at 7dpi for analysis ( Fig 12D and 12E ) . Transverse sections revealed a significant increase of BrdU+ cells within the RPE ( Fig 12E and 12J , p<0 . 0001 ) . These data suggest that early-proliferative cells enter the injury site at the leading edge of the regenerating RPE layer and either continue proliferating or give rise to proliferative cells there . Were this the case , we hypothesized that early-proliferative cells would integrate into both the peripheral and central RPE layer , while later-proliferative cells would form RPE only within the central RPE . To assess this , we pulsed ablated larvae with BrdU at 3-4dpi or 5-6dpi before fixing at 7dpi ( Fig 12F–12I ) . BrdU+ cells were distributed throughout the RPE after a 0-1dpi pulse , but became more restricted to the central RPE after 3-4dpi and 5-6dpi pulses ( Fig 12K ) . This analysis demonstrated that proliferative cells at early time points were distributed throughout the RPE , while later-proliferative cells were restricted to the central RPE . Finally , to determine whether early-labeled proliferative cells ultimately differentiate into RPE by 7dpi , we quantified the number and centrality of BrdU+/eGFP+ cells in 7dpi larvae that had been pulsed with BrdU between 0-1dpi ( Fig 12L and 12M ) . Our analysis revealed that significantly more BrdU+ cells in the RPE were eGFP+ than eGFP- ( Fig 12L , p = 0 . 0005 ) , and that BrdU+/eGFP+ cells preferentially integrated toward the center ( Fig 12M , p<0 . 0001 ) . In summary , these data indicate that early-proliferating cells in the RPE layer ultimately differentiate into regenerated RPE , and strongly suggest that these proliferative cells are located in the periphery and that they or their progeny migrate into the injury site . Our results thus far provide the first demonstration in any model system that RPE can endogenously regenerate after widespread injury . Next , we wanted to leverage this in vivo system to begin to identify the molecular underpinnings of the regenerative response . Previous studies have identified Wnt signaling as a regulator of tissue regeneration in multiple contexts [63–69] , including the retina [70–72] , and possibly RPE [73] . Thus , we examined Wnt signaling to begin to gain mechanistic insight into the molecular mechanisms underlying RPE regeneration . To assess Wnt pathway activity after RPE ablation , we examined expression of the Wnt target gene , lef1 [69 , 74] . lef1 was upregulated in ablated larvae at 1dpi ( Fig 13B and 13B’ ) , but not in unablated siblings ( Fig 13A and 13A’ ) or in sense controls ( Fig 13C , 13C’ , 13D and 13D’ ) . Closer analysis of lef1 expression in ablated eyes revealed transcripts distributed in and adjacent to the RPE layer ( Fig 13B’ ) , suggesting the Wnt pathway is activated post-ablation . We next utilized IWR-1 , which stabilizes Axin2 and promotes destruction of ß-catenin [75] , to determine if disrupting Wnt pathway components impedes RPE regeneration . Larvae were pre-treated 24 hours prior to ablation ( 4dpf/-1dpi ) with 15μM IWR-1 or with a vehicle control ( 0 . 06% DMSO ) and kept in drug or vehicle until fixation at 4dpi ( the time at which peak proliferation is observed in the RPE layer ( Fig 10U ) ) . Quantification of BrdU+ cells/section revealed a significant decrease in proliferation in IWR-1-treated RPE when compared to controls ( Fig 13E–13G , p<0 . 0001 ) . Further , there was a noticeable lapse in recovery of a pigmented monolayer in IWR-1-treated larvae ( Fig 13I , arrowheads ) relative to DMSO controls ( Fig 13H ) . ZPR2 staining overlapped with pigmented RPE in both ablated DMSO- ( Fig 13K ) and IWR-1-treated ( Fig 13L ) larvae , indicating the lapse in pigment recovery was not simply a pigmentation deficiency , but rather a failure of the RPE to regenerate . Quantification of percent RPE recovery indeed showed a significant decrease in the IWR-1-treated larvae ( Fig 13J , p<0 . 0001 ) . These data suggest that components of the Wnt signaling pathway may be involved in RPE regeneration .
The stimulation of endogenous RPE regeneration is an appealing possibility for treating degenerative RPE diseases . However , the development of such a therapy is constrained by the paucity of data regarding the cellular and molecular underpinnings of regeneration . While the mammalian RPE possesses a latent proliferative ability , the process by which RPE cells respond to damage by proliferating and regenerating a functional monolayer , remains largely unknown . The development of an animal model of RPE regeneration following specific and widespread RPE damage is a critical first step towards elucidating the regenerative process . Here , we developed a zebrafish model to ablate mature RPE and assess its regenerative capacity . In this model , ablation of a large contiguous stretch of RPE led to apoptosis and degeneration of the majority of mature RPE , which was rapidly followed by BM and PR degeneration and loss of visual function . In comparison , most RPE injury/regeneration models create small lesions using non cell-specific injury techniques ( e . g . debridement or laser photocoagulation; [32 , 76 , 77] ) , or ablate a diffuse subpopulation of RPE cells via sodium iodate [78–82] . In mouse , a genetic RPE ablation system expressing diphtheria toxin in a subpopulation of RPE did not cause BM degradation or RPE proliferation [54] . Indeed , many RPE injury models preserve an intact BM or spare large regions of RPE ( e . g . [54 , 83] ) . In contrast , our zebrafish model creates RPE and photoreceptor degeneration , which more closely resembles defects observed in late-stage AMD , wherein RPE dysfunction and degeneration precedes PR loss [12 , 84] [85 , 86] , and thus may represent a more clinically-relevant starting point than other extant models for studying RPE regeneration . Remarkably , we found that zebrafish are capable of regenerating after such a severe injury: within 7-14dpi in larvae , and within 1 month in adults . To our knowledge , these data provide the first evidence of RPE regeneration after widespread injury in any model system . Mammals largely fail to regenerate a functional RPE monolayer following injury [25 , 26] . One exception to this is in “super healer” MRL/MpJ mice , which regenerate the RPE within ~30 days after administration of mild doses of sodium iodate that elicit degeneration of the central RPE [51] . Beyond this example , mammalian RPE are incapable of regenerating after severe injuries ( e . g . 27–29 , 31 ) . Our zebrafish RPE ablation model differs significantly from Xenopus [87] , newt [88 , 89] , and embryonic chick [90–92] retinectomy models wherein the entire retina is surgically removed and subsequently regenerates from remaining RPE tissue that that transdifferentiates , proliferates , and regenerates retinal tissue . Studies in these models have focused on the RPE-to-retina transdifferentiation process , and RPE-specific regeneration remains unexplored . We present data here demonstrating that both larval and adult zebrafish possess the capacity to regenerate their RPE . However , due to the technical advantages of using larvae in studying regeneration ( i . e . rapid regeneration , large sample sizes , feasibility of in vivo imaging , utility of the available genetic toolkit , and the ability to perform high-throughput drug screens ) we mainly focused on characterizing RPE regeneration during larval stages . In larvae , regenerated RPE appeared at the periphery of the injury site at 2dpi , and the entire lesion was repopulated with differentiated RPE cells within 1 week . Our data support the following model of larval RPE regeneration ( Fig 14 ) : injury-adjacent RPE expand into the injury site , where they encounter degraded BM and proliferate to form daughters that enter the injury site and differentiate into RPE . RPE commonly expand to fill territory vacated by lost RPE [24 , 54 , 93] , and contact with a degenerated BM induces RPE proliferation in many contexts [24 , 30 , 35 , 94–96] . Supporting this , we found that early-dividing cells ( 0-1dpi ) often appear in the RPE periphery , localize to the injury site during peak phases of regeneration , and ultimately form RPE that integrate into the regenerated RPE monolayer . Wholemount analyses indicated that proliferative cells appear in the peripheral RPE soon after injury , and proliferative cells differentiate into RPE in distinct zones: ( 1 ) newly differentiated injury-adjacent RPE , ( 2 ) a transition zone , containing actively differentiating RPE cells , and ( 3 ) the injury site , which contains cellular debris as well as some proliferative cells that do not yet express RPE markers . Further experiments are necessary to determine whether all injury-adjacent RPE are capable of proliferating in response to injury , or if proliferation occurs within a subpopulation . Several lines of evidence suggest the latter possibility , and highlight the important role played by peripheral RPE: in mouse , a subpopulation of mature RPE in the periphery remain in the cell cycle and respond to microscopic photocoagulation injuries by proliferating at a higher rate than central RPE [32 , 97] , while experiments in pig have shown that peripheral RPE respond to debridement of central RPE by proliferating [98] . Indeed , preservation of the peripheral RPE is also a prerequisite for successful RPE regeneration in the MRL/MpJ mouse model , which fails to regenerate RPE when high doses of sodium iodate cause degeneration of both central and peripheral RPE [51 , 99] . Finally , the discovery of a subpopulation of RPE stem cells [36] suggests that an endogenous regeneration-capable population of RPE could exist in the human eye , and these might be analogous to injury responsive cells in zebrafish . While we present strong evidence supporting a model in which regenerated RPE derives from injury-adjacent RPE , we cannot definitively establish the source of regenerated RPE without performing lineage tracing . In response to retinal injury , Muller glia proliferate and generate MGPCs that differentiate into new neurons [37 , 58] . MGPCs are present at identical time points and in regions adjacent to ablated RPE . While it is possible that MGPCs transdifferentiate into RPE , this ability is not supported by any published studies . Another possible source for regenerated RPE is the CMZ , which generates new neurons throughout the life of the animal [100–102] . It was recently shown that rx2+ stem cells in the CMZ generate both RPE and retinal neurons [103] . Thus , rx2+ cells in the CMZ could potentially respond to RPE injury by generating RPE in the periphery that migrate and proliferate within the injury site . Attempts at lineage tracing to date have been unsuccessful and therefore , it will be necessary to develop new genetic tools to unambiguously identify the source of regenerated RPE . We were surprised by the recovery of the OKR at 3dpi , well before the complete regeneration of the RPE layer ( Fig 4 ) . The threshold number of PRs required for a positive OKR in zebrafish is unknown . However , since the OKR is elicited by a large-field stimulus , activating PRs throughout the eye , this early recovery could be driven by CMZ-derived PRs that integrate into the peripheral retina by 3dpi . Consistent with this hypothesis , we observed CMZ-derived BrdU+ PRs in the retinal periphery at 3dpi ( Fig 12B ) . It is also possible that recovered PRs adjacent to newly regenerated RPE in the peripheral injury site contribute to the OKR before regeneration of the central injury is complete . Alternatively , it is possible that a sub-population of central photoreceptors is functionally compromised , but survives ablation of the RPE , and these recover function by 3dpi . More work will be required to elucidate which of these processes might contribute to the rapid recovery of visual function at 3dpi . Wnt signaling plays a known role in multiple regenerative contexts [63–69] , including in the eye [70–72] . We show here that the Wnt pathway is activated in a subset of cells after RPE injury and that chemical inhibition using IWR-1 impairs regeneration . While we have not identified the lef1-expressing cell type , two possible sources are: 1 ) apoptotic cells in the injury site and 2 ) cells of the immune system . First , there are notable similarities between lef1 expression and TUNEL staining in the ONL and the RPE , and we detect lef1 expression ( Fig 13B and 13B’ ) at the same time that TUNEL+ cell numbers peak in ablated larvae ( Fig 2D–2F ) . A previous study in Hydra showed that apoptotic cells are a source of Wnt3 , and that this is required for head regeneration [104] . The immune system has also been shown to play a critical role in influencing the regenerative response [105–107] and Wnt signaling regulates the inflammatory properties of immune cells . Wnt signaling is also important for RPE development in vivo [108–110] , and it is possible that Wnt activation is important at multiple time points during RPE regeneration for initiation and to stimulate progenitor proliferation and/or differentiation . Further work is required to distinguish between cells in which Wnt is activated and regenerating RPE , and ultimately , these experiments may determine how the Wnt signaling pathway modulates RPE regeneration . Finally , as noted above , we observed signs of BM degeneration in the central injury site following ablation . That RPE ablation leads to BM breakdown may provide insight into the mechanisms underlying the initiation of CNV at the onset of exudative AMD . During CNV , choroidal endothelial cells penetrate the BM and grow into the subretinal space; whether this process is initiated by the degeneration of the choriocapillaris or the RPE remains controversial [105 , 111 , 112] . Our results suggest that the RPE is required for BM maintenance . A logical next step would be to determine if choroidal vasculature invades the subretinal space following degeneration , as this would provide evidence that RPE degeneration is causative of CNV . Additionally , we show that zebrafish RPE are capable of repairing the BM during regeneration . In human , the BM undergoes a series of changes during aging that are thought to underlie AMD pathogenesis and inhibition of RPE function , changes that are thought to underlie the barrier to development of successful RPE transplant therapies [113 , 114] . The mechanisms underlying BM repair in zebrafish may provide critical insights into improving transplant survival and reintegration in humans .
Zebrafish were maintained at 28 . 5°C on a 14-hour light/10 hour dark cycle . Embryos were obtained from the natural spawning of transgenic or wild-type parents in pairwise crosses . According to established protocols [45] , embryos were collected and raised at 28 . 5°C in the dark until they reached appropriate ages for experimentation . rpe65a:nfsB-eGFP was propagated by outcrossing to AB-strain wild-type fish . All animals were treated in accordance with provisions established by the University of Texas at Austin and University of Pittsburgh School of Medicine Institutional Animal Care and Use Committees . To establish the rpe65a:nfsB-eGFP transgenic , 0 . 8KB immediately upstream of the coding sequence of rpe65a was cloned into a p5E entry vector , and this was placed upstream of nfsB-eGFP using the Multisite Gateway Cloning system ( Thermo Fisher , Waltham , MA ) within a backbone vector enabling Tol2-mediated transgenesis . This construct was then inserted into the genome using Tol2 recombinase [115] . Embryos derived from rpe65a:nfsB-eGFP x AB crosses were maintained in PTU-containing ( Sigma-Aldrich , St . Louis , MO ) fish water between 1-5dpf , and dechorionated at 3dpf . At 5dpf , larvae were exposed to 10mM metronidazole ( MTZ; Sigma-Aldrich ) , dissolved in fish water , for 24 hours in the dark . After treatment , larvae were removed from MTZ and allowed to recover in fish water for 48 hours . At this point , severely ablated embryos were selected based upon the disruption of eGFP signal in the RPE and lack RPE pigmentation . Adult zebrafish ( age 11 months ) were ablated using the same parameters as for larvae: 24-hour 10mM MTZ treatment in a light-blocking incubator . Post-MTZ treatment , adults were returned to a 14-hour light/10 hour dark cycle and allowed to recover for 48 hours . Post-recovery , all ablated adults and larvae were placed on the recirculating water system ( Aquaneering , San Diego , CA ) . Larvae were euthanized at 2 , 4 , or 7dpi and fixed in 4% paraformaldehyde ( PFA ) ( Thermo Fisher ) overnight . Eyes were enucleated and the lens was removed before mounting in 3% methylcellulose . Images were acquired at a 112X magnification on a Zeiss ( Oberkochen , Germany ) Axio Zoom . V16 microscope . MetaMorph Image Analysis Software ( Molecular Devices , San Jose , CA ) was used to normalize the background of each image , delineate a region of interest ( ROI ) around each eye , and measure the average intensity ( total intensity within the ROI divided by the area of the ROI ) . To quantify pigment ( gray intensity ) rather than white intensity , values were inverted by subtracting the generated intensity value from 65 , 536 ( the number of gray levels in a 16-bit image ) . For BrdU labeling , larvae were incubated in system water containing 10mM BrdU ( Sigma-Aldrich ) for 24 hours at various time points . After incubation , larvae were either immediately fixed for analysis or rinsed with fresh system water and then allowed to recover until 7dpi . For EdU labeling , larvae were incubated in system water containing 500μM EdU ( Thermo Fisher , #C10338 ) for 2 hours immediately prior to fixation with 4% PFA in PBS . Larvae were euthanized with Tricaine ( Spectrum Chemical , New Brunswick , NJ ) before fixation in 4% PFA overnight at 4°C , and adults were euthanized with Tricaine before removal of the eyes . Larvae and adult eyes were then sucrose-protected and embedded in tissue-freezing medium ( TBS , Inc . , Waltham , MA ) before being sectioned at 14μm ( larvae ) or 16μM ( adults ) on a Leica CM1850 cryostat . Sections were rehydrated in 1xPBS for 5 minutes , and blocked in 5% normal goat serum in PBS for 2 hours at room temperature . For BrdU imaging , sections were treated with 4N HCl for 8 minutes at 37 degrees for antigen retrieval . Sections were counterstained with 1:500 DAPI ( Life Technologies , Waltham , MA ) for 9 minutes at room temperature , washed 3X with PBS , and mounted with Vectashield ( Vector Laboratories , Burlingame , CA ) . Images were obtained with a 40X objective on an Olympus ( Tokyo , Japan ) FV1200 confocal microscope . Antibodies used in this study include BrdU ( Abcam , Cambridge UK , ab6326 ) , ZPR2 ( ZIRC , Eugene , OR ) , ZPR1 ( ZIRC ) , Phalloidin ( Thermo Fisher , A22284 , 1:33 dilution ) . TUNEL ( Roche , Mannheim Germany , #12145792910 ) and Click-It EdU staining ( Thermo Fisher , C10618 ) were performed according to manufacturer’s instructions . After fixation , larvae were prepared using a protocol adapted from [116]: larvae were rinsed in PBST and water before permeabilization using acetone ( -20°C for 12 minutes ) , rinsing in PBST , 1mg/mL Collagenase ( Sigma-Aldrich ) ( 30 minutes at RT ) , and 2mg/mL Proteinase K ( Thermo Fisher , cat#BP1700 ) ( 30 minutes at RT ) . Larvae were refixed in 4% PFA ( 20 minutes at RT ) , rinsed in PBST , blocked using PBDTX ( PBS + 1% bovine serum albumin , 1% DMSO and 0 . 1% Triton X-100 ) , and stained overnight in PBDTX at 4°C with primary antibodies listed above at 1:250 . Larvae were rinsed PBDTX on a shaker at RT ( 4X for 15 minutes ) , incubated with secondary antibodies ( 1:250 ) , rinsed with PBDTX again ( 4X for 15 minutes ) and counterstained with DAPI ( 1:200 in PBS ) . Chemically etched Tungsten needles [117] were used to dissect the eye and remove the choroid , and RPE flatmounts were mounted on Superfrost slides in PBS immediately prior to imaging . Whole mount in situ hybridization was performed as described [118] using a previously reported DIG-labeled RNA probe for lef1 [119] . Post-labeling , larvae were fixed overnight in 4% PFA at 4°C then sucrose-protected , embedded , sectioned ( 12μm ) , and mounted using DPX Mounting Medium ( Electron Microscopy Sciences , Hatfield , PA ) . Images were obtained with a 40X objective on a Zeiss Observer . Z1 inverted microscope . Embryos were fixed in fresh 4% glutaraldehyde , 2% PFA overnight at room temperature , stained in 2% osmium tetroxide ( OsO4 ) and 2% potassium ferrocyanide and 2% uranyl acetate , and microwave-embedded with a modified reduced-viscosity Spurr-Quetol-651 resin using a BDMA accelerator ( Electron Microscopy Sciences ) via a 30 , 50 , 75 , 100 , and 100% resin/acetone infiltration series [120] . Samples were sectioned using a Leica Ultracut UC7 ultramicrotome at a thickness of 70nm , and imaged on a FEI Tecnai transmission electron microscope . For quantification of BM thickness , three transverse sections of the eye including the optic nerve were taken from the central RPE injury site in n = 3 zebrafish . The central RPE was defined as the region between the optic nerve head and the intersection of the dorsal eye and brain , as this correlated both with the area of highest transgene expression in unablated larvae , and degeneration in ablated larvae . In each section , three images of the BM were collected at 20 , 500X magnification in each image , and in each image , 3 measurements of the BM were taken by drawing lines perpendicular to the retina connecting the retinal and choroidal surface of the BM . In summary , 3 lines were drawn per image , 3 images taken per section , and 3 sections taken per animal for a total of 27 BM measurements per larva . Larvae were immobilized in 3% methylcellulose , oriented dorsal up and exposed to a full field rotating stimulus projected onto a screen ( NEC , Itasca , IL ) that encompassed 180 degrees of the stimulated eye’s field of vision . Responses were captured using infrared light ( 880nm; Spectrum , Montague , MI ) through a Flea3 Camera ( Point Grey Research , Richmond , BC , Canada ) mounted on a dissecting microscope ( Leica Microsystems , Wetzlar , Germany ) . Videos were recorded by FlyCapture software ( FLIR , Richmond , BC , Canada ) and quantified using custom MATLAB scripts [50] . Larvae were immobilized dorsal up in 3% methylcellulose and imaged with Optical Coherence Tomography ( OCT ) ( ~840nm; Leica Bioptigen R2210 Spectral Domain Ophthalmic Imaging System ) . After imaging , larvae were rinsed and transferred into petri dishes to recover . OCT scans were analyzed using Bioptigen software ( InVivoVue; Bioptigen , Research Triangle Park , NC ) , FIJI , and MetaMorph . Larvae were treated with 15μM IWR-1-endo ( Sigma-Aldrich ) or 0 . 06% dimethyl sulfoxide ( DMSO; Thermo Fisher ) as a vehicle control from 4dpf/-1dpi until 9dpf/4dpi with daily replenishing of water and treatment . To assay proliferating cells , 10mM BrdU ( Sigma-Aldrich ) was added for 24 hours prior to fixation at 9dpf/4dpi . BrdU immunohistochemistry was performed as described . For quantification of apoptosis , TUNEL+ cells within the RPE layer and ONL were recorded in unablated and ablated larvae at 3 , 6 , 12 , 18 , 24 and 48hpi . For quantification of BrdU+ nuclei in the RPE layer , BrdU+ nuclei contained within the RPE monolayer or presumptive RPE space between the retina and choroid were counted . To quantify the number of BrdU+ nuclei in the central retina , a reference line was drawn between the proximal-most BrdU+ cell originating from the CMZ in the dorsal and ventral retina . All BrdU+ cells in all layers of the retina proximal to this line were then counted . To quantify the centrality of BrdU+ cells , an additional perpendicular line bisecting the line demarcating CMZ-generated BrdU+ cells was drawn . BrdU+ cells in the RPE were counted using criteria detailed above , and the angle of each cell relative to these lines ( 0° ≤ x ≤ 90° ) was calculated using FIJI software [121] . For quantification of RPE following OCT imaging , three transverse images of the retina were captured per fish at each time point ( MTZ- n = 10 , MTZ+ n = 9 ) . Using MetaMorph , a line was drawn on the RPE beginning just dorsal of the optic nerve and terminating at the dorsal periphery , and a linescan of pixel intensity was taken from this line . The average RPE pixel intensity from the optic nerve to the periphery was graphed using GraphPad Prism 7 . 04 for Windows ( Microsoft , Redmond , WA ) ( error bars = SEM ) . To quantify positional differences , each graph was divided into quintiles and the area under the curve was calculated for each quintile and compared using the Student’s t test . For quantification of adult RPE recovery , central sections from adult eyes ( MTZ- , 3dpi , 7dpi , 35dpi n = 4 , 14dpi n = 3 ) were obtained and the edges of the injury site were designated based on contiguous-expressed eGFP+/ZPR2+ ( e . g . arrowheads in Fig 9B–9E ) . Measurements for: 1 ) intact RPE ( contiguous eGFP+/ZPR2+ expression ) and 2 ) degenerated RPE were made along the dorsal and ventral retinae using the line segment tool in FIJI ( ImageJ ) . Measurements were made along the basal side of the RPE from the distal tip to the junction of the optic nerve head ( ONH ) . Width of the ONH was omitted from these measurements to avoid variability between adults . Dorsal and ventral measurements were summed and adult RPE regeneration was represented as percent eGFP+/ZPR2+ RPE . Normality of datasets was assessed by the D’Agostino-Pearson Omnibus test . When analyzing non-normal datasets , Mann-Whitney U test was utilized to determine the significance of differences between unablated and ablated larvae . In normally-distributed datasets , Student’s t-test was utilized . These analyses were performed by GraphPad Prism 7 . 0c Software for Mac OS ( La Jolla , CA , www . graphpad . com ) and Microsoft Excel 14 . 7 . 3 ( Microsoft ) . All statistical analyses are included in S1 Table . | Diseases resulting in retinal pigment epithelium ( RPE ) degeneration are among the leading causes of blindness worldwide , and no therapy exists that can replace RPE or restore lost vision . One intriguing possibility is the development of therapies focused on stimulating endogenous RPE regeneration . For this to be possible , we must first gain a deeper understanding of the mechanisms underlying RPE regeneration . Here , we develop a transgenic zebrafish system through which we ablate large swathes of mature RPE and demonstrate that zebrafish regenerate RPE after widespread injury . Injury-adjacent RPE proliferate and regenerate RPE , suggesting that they are the source of regenerated tissue . Finally , we demonstrate that Wnt signaling may be involved in RPE regeneration . These findings establish a versatile in vivo model through which the molecular and cellular underpinnings of RPE regeneration can be further characterized . | [
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] | 2019 | Regeneration of the zebrafish retinal pigment epithelium after widespread genetic ablation |
The orphan , atypical response regulators BldM and WhiI each play critical roles in Streptomyces differentiation . BldM is required for the formation of aerial hyphae , and WhiI is required for the differentiation of these reproductive structures into mature spores . To gain insight into BldM function , we defined the genome-wide BldM regulon using ChIP-Seq and transcriptional profiling . BldM target genes clustered into two groups based on their whi gene dependency . Expression of Group I genes depended on bldM but was independent of all the whi genes , and biochemical experiments showed that Group I promoters were controlled by a BldM homodimer . In contrast , Group II genes were expressed later than Group I genes and their expression depended not only on bldM but also on whiI and whiG ( encoding the sigma factor that activates whiI ) . Additional ChIP-Seq analysis showed that BldM Group II genes were also direct targets of WhiI and that in vivo binding of WhiI to these promoters depended on BldM and vice versa . We go on to demonstrate that BldM and WhiI form a functional heterodimer that controls Group II promoters , serving to integrate signals from two distinct developmental pathways . The BldM-WhiI system thus exemplifies the potential of response regulator heterodimer formation as a mechanism to expand the signaling capabilities of bacterial cells .
Two-component signal transduction systems are of central importance in regulating gene expression in bacteria . Canonically they consist of a response regulator , which functions as a homodimer , and a cognate sensor histidine kinase ( which may also function as a cognate phosphatase ) . The activity of the kinase/phosphatase is modulated in response to a perceived stimulus . The sensor kinase autophosphorylates on a conserved histidine residue , and the phosphoryl group is then transferred to a conserved aspartate in the response regulator . The addition of the phosphoryl group stabilizes a conformation of the response regulator that drives an output response , most often the activation of gene expression . However , the intrinsic modularity of these systems has allowed bacteria to evolve variations on this basic theme , including more complex multicomponent phosphorelays , and changes in the nature of the response regulator effector domain such that the output can be , for example , an enzymatic activity rather than DNA binding [1]–[3] . Recognizing the diversity of mechanisms associated with these systems is therefore critical to understanding the full potential of the signaling capabilities of bacterial cells . This study concerns the behavior of two response regulators required for morphological development in the filamentous bacteria Streptomyces . When streptomycete spores germinate , one or two germ tubes emerge and grow by tip extension and branching to form an extensive , multicellular vegetative mycelium [4]–[6] . Streptomycetes differentiate by forming specialized reproductive structures called aerial hyphae , which emerge from the colony surface into the air . The formation of aerial hyphae requires the activity of a class of developmental master regulators encoded by the bld ( bald ) genes [4]–[6] . Subsequently , in the most dramatic event of the lifecycle , each multigenomic aerial hypha arrests tip growth and undergoes a massive , synchronous septation event , giving rise to ∼50–100 unigenomic prespore compartments that ultimately develop into mature , pigmented exospores [4]–[6] . The differentiation of aerial hyphae into mature spores is coordinated by the activity of a second class of developmental master regulators encoded by the whi ( white ) genes . The focus of this work is the interaction between two of these global regulators , BldM and WhiI . BldM and WhiI are both atypical response regulators ( ARRs ) . In canonical response regulators , the aspartate residue that is subject to phosphorylation sits in a highly conserved pocket within the N-terminal receiver domain . ARRs usually lack essential residues within this phosphorylation pocket , suggesting that their activity is not controlled by phosphorylation [7]–[11] . WhiI has a degenerate phosphorylation pocket , lacking a universally conserved lysine and one of a pair of adjacent aspartate residues essential for binding Mg2+ [12] , [13] . Although BldM does have a conserved phosphorylation pocket , a bldM allele carrying a D54A substitution at the putative site of phosporylation fully complements a bldM null mutant [14] . Moreover , BldM could not be phosphorylated in vitro [14] . Finally , BldM and WhiI are both ‘orphan’ response regulators – their genes are not adjacent to a sensor kinase gene , as is most often the case for canonical response regulators . Taken together , these observations strongly suggest that BldM and WhiI are not controlled by phosphorylation as part of conventional two-component systems . BldM and WhiI both belong to the NarL/FixJ subfamily of response regulators . bldM and whiI are found in all sequenced streptomycete genomes and their chromosomal context is conserved throughout . Strikingly , the amino acid sequence of BldM is 100% identical across all sequenced streptomycetes ( the only response regulator that is 100% identical across all streptomycetes ) . WhiI is at least 93% identical , with amino acid variations found mainly in the linker region between the degenerate receiver domain and the DNA-binding domain . All sequenced WhiIs have a degenerate phosphorylation pocket . During differentiation , the whiI and bldM genes are activated by two cognate , development-specific sigma factors , σWhiG and σBldN , respectively . whiI expression is activated from a single σWhiG target promoter , and thus whiI is not expressed in a whiG mutant [12] . σBldN directs transcription of the p1 promoter of bldM ( the other promoter , bldMp2 , is σBldN-independent , and so transcription of bldM is developmentally activated from only one of its two promoters in a bldN mutant [15] , [16] ) . In addition to bldM , the other key targets of σBldN are the genes encoding the chaplins and rodlins , the major proteins of the hydrophobic sheath that coats the aerial hyphae and spores in Streptomyces [16]–[19] . Streptomyces venezuelae has recently emerged as an attractive new model system for the analysis of Streptomyces development because it sporulates in liquid culture [16] , [20] . Here we take advantage of the S . venezuelae system to apply global microarray transcriptional profiling and ChIP-Seq to characterize the BldM and WhiI regulons . Through this route we go on to show that a key stage in Streptomyces development is controlled by response regulator heterodimer formation between BldM and WhiI , and to greatly expand our understanding of the regulatory network that controls morphological differentiation in these multicellular bacteria . The BldM-WhiI system thus exemplifies the potential of response regulator heterodimer formation as a mechanism to expand the signaling capabilities of bacterial cells .
Having established conditions in which S . venezuelae sporulates abundantly in liquid culture [16] , [20] , immunoblotting of samples taken at 14 , 15 and 16 h of growth in MYM liquid sporulation medium showed that BldM was abundant at each of these time points ( Figure S1 ) . To gain greater insight into BldM function , we defined the genome-wide BldM regulon using ChIP-Seq . As described in Materials and Methods , wild-type S . venezuelae was subjected to formaldehyde cross-linking , lysis and sonication after 16 h of growth . After immunoprecipitation using a BldM-specific polyclonal antibody , the resulting DNA was subjected to deep sequencing . As a negative control , a ChIP-Seq experiment was performed on the congenic bldM null mutant . Several well-characterized developmental loci , including ssgR , rshA , smeA-sffA , whiB and whiE , were among the direct BldM targets identified . Next , in order to determine how BldM influences the expression of its target genes , wild-type S . venezuelae and the congenic ΔbldM mutant were subjected to time-resolved , genome-wide transcriptional profiling during vegetative growth and sporulation . Strains were grown under the same conditions used for the ChIP-Seq experiments . RNA samples were prepared at 2-hour intervals from 8 to 20 hours , by which time sporulation was nearing completion , and following cDNA synthesis and labeling , samples were hybridized to Affymetrix DNA microarrays . Three independent biological replicates were performed for each strain , and analysis of the resulting data showed that the expression of 131 direct BldM targets was significantly down-regulated in the ΔbldM mutant ( p<0 . 01 ) in comparison to the wild type . In contrast , only six genes were up-regulated in the ΔbldM mutant ( p<0 . 01 ) in comparison to the wild type . These results suggest that BldM functions mainly as a transcriptional activator . We next determined the time-resolved transcriptional profiles of the BldM target genes in seven constructed white mutants: ΔwhiA , ΔwhiB , ΔwhiD , ΔwhiG , ΔwhiH and ΔwhiI . Strikingly , many of the BldM target genes clustered into two well-defined groups according to their dependencies on the whi genes . Group I genes consisted of developmentally induced genes that depend on bldM , but were activated normally in all the whi mutants ( Figure 1A and Table S1 ) . Group II BldM target genes were also developmentally induced , but depended not only on bldM , but also on whiG and whiI ( Figure 1B and Table S2 ) . To gain further insight into Group I binding sites , we fed the sequences of Group I promoter regions into the MEME algorithm [21] to search for over-represented sequences , using as input the entire intergenic region in each case . This analysis revealed a well-conserved copy of a 16 bp palindromic sequence , 5′-TCACcCgnncGgGTGA-3′ , for which the sequence logo is shown in Figure 2A . The palindromic nature of this sequence would be consistent with BldM binding as a homodimer to Group I promoters . To test the validity of the MEME output , and to confirm and extend the ChIP-Seq analysis , we overexpressed and purified BldM from E . coli as a soluble , N-terminally His6-tagged protein . The resulting BldM protein was used in DNase I footprinting analysis on the intergenic regions upstream of two Group I BldM targets , sven1998 and sven4150 . In both cases , BldM protected a region containing a well-conserved copy of the palindromic , MEME-predicted binding site ( Figure 2B ) , consistent with this sequence serving as a high-affinity binding site for a BldM homodimer . Group II BldM target genes were expressed later than Group I genes ( see insets in Figure 1 ) . Further , and in contrast to Group I , the expression of Group II genes depended not only on bldM but also on whiG and whiI ( Figure 1 and Table S2 ) . It is straightforward to account for the dependence of Group II gene expression on whiG . In S . coelicolor , whiI expression is activated from a single σWhiG target promoter , and thus whiI is not expressed in a whiG mutant [12] . This σWhiG target promoter appears well conserved at the sequence level in S . venezuelae ( Figure S2A ) and whiI is not expressed in an S . venezuelae whiG mutant ( Figure S2B ) . Thus all genes that depend on whiI must necessarily also depend on whiG . Expression of whiI was not significantly affected in the ΔbldM mutant and vice versa ( p<0 . 01 ) ( Figure S2B ) , implying independent σWhiG-WhiI and σBldN-BldM regulatory pathways . The challenge then was to determine why Group II BldM target genes depend on whiI . To determine if the dependence of Group II BldM target genes on WhiI was direct or indirect , we characterized the in vivo WhiI binding sites across the S . venezuelae genome using ChIP-Seq . Wild-type S . venezuelae was harvested at 16 h of growth and treated as described for the BldM ChIP-Seq , except that a WhiI-specific polyclonal antibody was used . As a control , a ChIP-Seq experiment was performed using the congenic whiI null mutant . The data showed that all of the BldM Group II targets were also direct targets of WhiI ( Figure 3 ) . As an independent confirmation , we repeated the ChIP-Seq experiment using a FLAG-tagged WhiI protein . An N-terminally 3xFLAG-tagged allele of whiI ( TF-WhiI ) was constructed such that it was expressed from its native promoter and cloned into the single-copy vector pMS82 , which integrates site-specifically into the chromosome at the phage ΦBT1 attB site [22] . This construct fully complemented the phenotype of the whiI null mutant ( Figure S3 ) , and the complemented strain was used for the ChIP-Seq experiment , now using wild-type S . venezuelae as the negative control . The ChIP-Seq results seen using FLAG immunoprecipitation were almost identical to those obtained using WhiI polyclonal antibodies , confirming that Group II genes are directly regulated by both BldM and WhiI ( Table S2 ) . Our data showed that expression of Group II genes depends on bldM and whiI and that both BldM and WhiI bind directly to the promoters of these genes . One possible model consistent with these observations would be that BldM and WhiI co-activate Group II promoters by binding as two separate homodimers . An alternative model would be that these two proteins activate Group II promoters by binding as a functional BldM-WhiI heterodimer . To begin to differentiate between these models , we performed BldM ChIP-Seq in a ΔwhiI mutant and WhiI ChIP-Seq in a ΔbldM mutant , using the same conditions described above . In a ΔwhiI mutant BldM binding was still observed at all Group I promoters ( which depend solely on bldM ) , but no BldM binding was seen at Group II promoters ( Figure 3 ) . Equally , no WhiI binding to Group II promoters was observed in a ΔbldM mutant ( Figure 3 ) . Thus , in vivo , BldM and WhiI show mutual dependence for binding to Group II promoters . To explore the possibility of BldM-WhiI heterodimer formation , we tested BldM and WhiI for direct interaction in E . coli using a bacterial two-hybrid ( BACTH ) system [23] . bldM was fused to the gene encoding the T18 fragment of adenylate cyclase in the vector pUT18 such that BldM was at the N-terminus of the fusion protein , and was also fused to the gene encoding the T25 fragment of adenylate cyclase in the vector pKT25 such that BldM was at the C-terminus of the fusion protein . Parallel pUT18 and pKT25 constructs were made carrying fusions to WhiI . Interacting pairs of proteins were screened initially by transforming E . coli BTH101 with the appropriate plasmids and monitoring restoration of adenylate cyclase activity on X-gal indicator plates; clones of each pair were then assayed for β-galactosidase activity ( Figure 4 ) . Interaction of BldM with itself was readily observed ( ∼750 Miller units ) , but a stronger interaction ( ∼3000 Miller units ) , was observed between BldM and WhiI , regardless of which protein was fused to the T18 fragment of adenylate cyclase and which was fused to the T25 fragment ( Figure 4 ) . WhiI showed no detectable interaction with itself ( Figure 4 ) . To confirm and extend the BACTH analysis , we tested the interaction of BldM and WhiI in Streptomyces by coimmunoprecipitation . A C-terminally 3xFLAG-tagged allele of bldM ( BldM-TF ) was constructed such that it was expressed from its native promoter and cloned into the integrative vector pMS82 [22] . This construct complemented the phenotype of the ΔbldM null mutant to wild-type levels of sporulation ( Figure S3 ) . This strain was used in conjunction with the ΔwhiI mutant complemented with the N-terminally 3xFLAG-tagged allele of whiI ( TF-WhiI ) described above . The BldM-TF and TF-WhiI proteins were immunoprecipitated directly from 16 h MYM liquid cultures using the M2 anti-FLAG monoclonal antibody . WhiI coimmunoprecipitated with BldM-TF and BldM coimmunoprecipitated with TF-WhiI , but neither was detected in the negative controls when the wild-type strain was used ( Figure S4 ) . Thus BldM and WhiI interact in Streptomyces in vivo . An early frustration in the in vitro analysis of WhiI function was that it overexpressed in an insoluble form in E . coli under all conditions tested . The realization that WhiI might act as part of a functional BldM-WhiI heterodimer led us to try an alternative approach . Where proteins form a complex , it is often observed that individual components are insoluble when expressed in isolation but become soluble when expressed with their cognate partner protein . Two examples are the α and β subunits of lambda integrase [24] , and Streptomyces σBldN and its cognate anti-sigma factor , RsbN [16] . Accordingly , BldM and WhiI were co-expressed in E . coli using the pETDuet-1 system ( Novagen ) . Initially , BldM was N-terminally His6-tagged and WhiI was left untagged . Co-expression of BldM was found to solubilize WhiI completely . Further , when His6-BldM was purified on a HisTrap Ni column , WhiI copurified with His6-BldM in approximately equal amounts , despite the fact that WhiI was untagged ( Figure 5A ) ( the identity of the two proteins was confirmed by tryptic mass fingerprinting ) . Thus BldM rescues WhiI from inclusion bodies and the two proteins copurify in approximately stoichiometric amounts via a His6-tag present on BldM only . This approach was extended by coexpressing BldM and WhiI carrying two compatible affinity tags . N-terminally His6-tagged BldM and N-terminally StrepII-tagged WhiI were co-expressed from the pETDuet-1 vector . As before , both BldM and WhiI were found in the soluble fraction . The BldM-WhiI complex was then purified over consecutive HisTrap Ni and StrepTrap HP affinity columns and the BldM and WhiI proteins were found to be present in stoichiometric amounts in the resulting preparation ( Figure 5B ) . To further understand Group II binding sites , we searched for over-represented sequences in Group II promoter regions using MEME [21] , again using as input the entire intergenic region in each case . This analysis revealed a well-conserved 16 bp non-palindromic sequence , 5′-TGnnCCGnnCGGGTGA-3′ , for which the sequence logo is shown in Figure 6A . Strikingly , the 3′ half of the Group II logo was equivalent to a half-site of the Group I palindrome , but the other half was different in sequence , potentially consistent with a BldM-WhiI heterodimer binding to Group II targets . To directly test the model that Group II promoters are controlled by a functional BldM-WhiI heterodimer , and to validate the MEME-predicted binding motif for these promoters , the doubly-tagged BldM-WhiI that had been purified over consecutive HisTrap Ni and StrepTrap HP affinity columns was used in DNase I footprinting analysis on the intergenic regions upstream of two Group II targets , sven1263 and murA2 ( sven5810 ) . BldM-WhiI footprinted on both promoters and in each case the protection region contained a copy of the non-palindromic , MEME-predicted binding site ( Figure 6B ) , consistent with this sequence serving as a high-affinity binding site for a BldM-WhiI heterodimer . In contrast , neither BldM alone nor WhiI ( produced as a soluble GST fusion ) , footprinted on either promoter ( Figure 6B ) .
BldM homodimer activates several genes known to play key roles in the differentiation of aerial hyphae into spores , including whiB and ssgR . In addition to their positive regulation by BldM homodimer , these genes are also subject to repression during vegetative growth by the master regulator BldD , as is bldM itself ( Figure 7 ) [25] . SsgA and SsgB are homologous proteins directly involved in the positive control of cell division in Streptomyces [26] . Sporogenic aerial hyphae undergo a synchronous round of cell division , initiated by the polymerization of a ladder of 50 or more FtsZ rings . SsgA and SsgB function in the recruitment and accurate positioning of these FtsZ rings , and ΔssgA and ΔssgB mutants of S . coelicolor lack sporulation septa [26]–[28] . ssgR encodes an IclR-family transcriptional regulator that directly activates the expression of ssgA in S . coelicolor [29] . Here we show that ssgR is directly activated by , and is completely dependent on BldM in S . venezuelae . ssgB is developmentally induced in S . venezuelae , but despite being a direct BldM target , its expression is only weakly affected in the ΔbldM mutant , suggesting complex regulation of this gene . BldM homodimer also activates expression of whiB , which plays a vital role in developmentally controlled cell division . whiB null mutants fail to arrest aerial tip growth , the normal prelude to sporulation , and are completely blocked in the initiation of sporulation sepatation , producing abnormally long , undivided aerial hyphae [30] . WhiB is the founding member of a family of proteins confined to the actinomycetes , and several of these WhiB-like ( Wbl ) proteins have been shown to play key roles in the biology of streptomycetes and mycobacteria . Wbl proteins carry a [4Fe–4S] iron-sulfur cluster coordinated by four invariant cysteines in a C ( X29 ) C ( X2 ) C ( X5 ) C motif [31]–[35] , and although the biochemical role of these unusual proteins has been controversial [36] , it seems increasingly certain that they function as transcription factors [33] , [37]–[38] . Among the Group II targets controlled by the BldM-WhiI heterodimer are two loci with well characterized roles in sporulation: smeA-sffA and whiE . The smeA-sffA operon encodes a DNA translocase ( SffA ) involved in chromosome segregation into spores that is specifically targeted to sporulation septa by the small membrane protein SmeA [39] . Deletion of smeA-sffA in S . coelicolor results in a defect in spore chromosome segregation and has pleiotropic effects on spore maturation [39] . Like the Group I targets ssgR and whiB , expression of the smeA-sffA operon is also repressed by the master regulator BldD during vegetative growth [25] . whiE is a complex locus that specifies the spore pigment . The structure of the spore pigment has not been determined in any Streptomyces species but its polyketide nature was first predicted from the sequence of the whiE locus in S . coelicolor , because it encodes proteins that closely resemble the components of type II polyketide synthases involved in the synthesis of aromatic antibiotics [40]–[42] . Based on their coordinate regulation and proposed functions , we predict the whiE locus of S . venezuelae consists of an operon of seven genes ( sven6798-6792 ) and the divergently transcribed gene sven6799 . Two distinct ChIP-Seq peaks were seen in the intergenic region separating sven6799 and the sven6798-6792 operon , and all eight genes fail to be expressed in the bldM and whiI mutants , implying the BldM-WhiI heterodimer controls expression of the entire locus . The work presented here suggests that there is no set of genes regulated by a WhiI homodimer and that WhiI functions as an auxiliary protein to modulate BldM binding specificity through heterodimerization . With no exceptions , all the genes down regulated in a ΔwhiI mutant were also down regulated in a ΔbldM mutant . Although some promoters were exclusively enriched as peaks in the WhiI ChIP-Seq experiment , without exception the transcriptional profile of such targets was unaffected in a ΔwhiI mutant , showing that WhiI has no regulatory influence on these genes . Further , in a bldM null mutant , some WhiI peaks are seen in ChIP-Seq , but these sites , often internal to ORFs , show no correlation with the wild-type WhiI regulon and the targets lacked a consensus binding motif . These results suggest that , in the absence of BldM , any DNA binding by WhiI is aberrant and unrelated to its behavior in the wild type . In contrast , in a ΔwhiI mutant , BldM fails to bind to Group II promoters but binds normally to its Group I promoters . In a recent study , evidence was presented that the DNA-binding domain of S . coelicolor WhiI ( in the absence of the receiver domain ) can bind in vitro to the promoter of the sco3900-sco3899 operon encoding a transcriptional regulator ( InoR ) and an inositol 1-phosphate synthase ( InoA ) , respectively [43] . Our ChIP-Seq data show that WhiI does not regulate these genes in S . venezuelae . Further , in wild-type S . venezuelae both genes are actively expressed during vegetative growth but are down-regulated during development , and this expression pattern is unaffected in a whiI mutant . Transcription factor heterodimerization can coordinate responses to different cues by integrating signals from distinct regulatory pathways . Although heterodimerization is prevalent as a regulatory mechanism in eukaryotes [44] , it is rare in bacteria . Prior to the work described here , the only response regulator reported to heterodimerize with an auxiliary regulator was RcsB . In Escherichia coli , the typical response regulator RcsB plays a central role in the regulation of capsule synthesis . Once phosphorylated by the histidine kinase RcsD , RcsB directly activates target genes including rprA , osmC , osmB and ftsZ , functioning as a homodimer [45] , [46] . It also activates exopolysaccharide synthesis genes , required for capsule formation , as a heterodimer with RcsA , which is distantly related to response regulators ( like RcsB , RcsA has a typical LuxR-type C-terminal DNA-binding domain , but its N-terminal domain is not related to typical response-regulator receiver domains ) . Like WhiI , RcsA appears to lack the capacity to activate genes by itself , and therefore functions solely as a modulator of RcsB binding specificity . RcsA is actively degraded by the Lon protease and in lon mutants capsule genes are highly upregulated causing a mucoid phenotype , due to enhanced activation by the stabilized RcsB-RcsA heterodimer [45] , [46] . The capacity of RcsB to complex with other transcription factors is not limited to its interaction with RcsA , since RcsB also forms functional heterodimers with BglJ [47] and with GadE [48] . Using in vitro FRET analysis , weak hetero-pair interactions were detected between several members of the OmpR sub-family of response regulators from E . coli [49] . While these in vitro data may suggest potential crosstalk between distinct signaling pathways , their physiological significance has yet to be demonstrated . The activities of numerous bacterial promoters respond to multiple cues , and there are many examples of promoters that depend on two activators for their activity . Several different regulatory mechanisms underpinning such codependence have been identified [50] , [51] . The most widely documented is found at promoters where both activators bind independently , and both activators make independent contacts with RNA polymerase . However , there are rare examples of coactivators that exhibit cooperative binding , such as MelR and CRP at the E . coli melAB promoter [52] . There are also examples in which DNA binding by a secondary activator leads to the repositioning of the primary activator from a site where it cannot activate transcription to a site where it can , such as the repositioning of MalT by CRP at the malK promoter [53] . Response regulator heterodimer formation provides a new model for coactivation of target genes and the integration of regulatory signals at promoters . BldM-WhiI heterodimer formation serves to integrate signals from two independent pathways ( σWhiG-WhiI and σBldN-BldM ) and it may also function as a timing device , since Group II genes are activated later than Group I genes . Thus the BldM-WhiI system exemplifies the potential of response regulator heterodimer formation as a mechanism to expand the signaling capabilities of bacterial cells .
Bacterial strains and plasmids are listed in Table S3 and the oligonucleotide primers with corresponding restriction sites used in cloning are listed in Table S4 . For microarray and ChIP-Seq experiments , S . venezuelae strains were grown at 30°C in MYM liquid sporulation medium [16] made with 50% tap water and supplemented with 200 µl trace element solution [54] per 100 ml . The phenotypes of mutants and complemented strains were scored after 3–4 days growth on MYM-agar at 30°C . bldM and whiI deletion mutants were constructed by ‘Redirect’ PCR targeting [55] and their chromosomal structures were confirmed by PCR analysis and by Southern hybridization using the parental cosmids as probes . For each strain , two flasks containing 35 ml of MYM were inoculated with spores ( or mycelium in case of the ΔbldM mutant ) to give an OD600 ∼0 . 35 after 8 h of growth . The crosslinking reagent formaldehyde was added to a final concentration of 1% ( v/v ) to the cultures at 16 h of growth and incubated at 30°C with shaking for 30 min before glycine was added to a final concentration of 125 mM to quench the crosslinking reaction . The samples were incubated at room temperature for 5 min and washed twice in PBS buffer pH 7 . 4 ( Sigma ) . Mycelial pellets were resuspended in 0 . 5 ml lysis buffer ( 10 mM Tris-HCl pH 8 , 50 mM NaCl , 15 mg/ml lysozyme , 1x protease inhibitor ) and incubated at 37°C for 20 min . The lysate was resuspended in 0 . 5 ml IP buffer ( 100 mM Tris- HCl pH 8 , 250 mM NaCl , 0 . 1% Triton-X-100 , 1x protease inhibitor ) and the lysate was kept on ice for 2 min before sonication . The samples were subjected to seven cycles of sonication , 15 s each , at 10 microns , to shear the chromosome into fragments ranging in size from 300–1000 bp . The sample was then centrifuged twice at top speed , 4°C for 15 minutes to clear cell extracts . To pre-clear non-specific binding , 90 µl protein A sepharose ( Sigma ) was added to cell lysate ( about 900 µl ) and incubated for 1 h at 4°C with mixing . The beads were cleared by centrifugation at top speed for 15 min . 100 µl BldM or WhiI antibodies were added to the corresponding cell lysates overnight at 4°C with mixing . 100 µl Protein A Sepharose 1∶1 suspension was added to immunoprecipitate antibody-BldM or WhiI chromatin complexes and incubated for 4 h at 4°C with mixing . The samples were centrifuged at 3500 rpm for 30 s and the beads were washed four times with IP buffer . The pellets were eluted in 150 µl IP elution buffer ( 50 mM Tris-HCl pH 7 . 6 , 10 mM EDTA , 1% SDS ) overnight at 65°C to reverse crosslink . The samples were centrifuged at top speed for 5 min to remove the beads and the pellets were re-extracted with 50 µl TE buffer ( 10 mM Tris-HCl pH 7 . 4 , 1 mM EDTA ) . The supernatants were combined and incubated with 3 µl 10 mg/ml proteinase K ( Roche ) for 2 h at 55°C . The samples were extracted twice with phenol-chloroform to remove protein followed by chloroform extraction to remove traces of phenol and purified with Qiaquick columns ( Qiagen ) . The IP DNA was eluted in 50 µl EB buffer ( Qiagen ) . Sequencing libraries were generated and the IP DNA was sequenced as described previously [20] . The BayesPeak package was used to identify significantly enriched regions and the default parameters were applied [56] . Microarray transcriptional profiling experiments were carried out as described previously [16] , [20] . Multi-experiment viewer software ( MeV 4 . 8 ) was used for viewing and statistical analysis [57] . The non-parametric tool ‘Rank Products’ [58] was used in MeV to assign ‘down regulated’ , ‘up regulated’ and ‘not significant’ genes based on expression at 16 , 18 and 20 h of growth . Group I genes were defined as direct BldM ChIP-Seq targets that were significantly down regulated in ΔbldM and not significantly changed in all Δwhi mutants ( Table S1 ) . Group II genes were defined as direct ChIP-Seq targets of both BldM and WhiI that were significantly down regulated in ΔbldM , ΔwhiG and ΔwhiI ( Table S2 ) . The open reading frame of interest was PCR-amplified using Expand High-Fidelity DNA polymerase ( Roche ) . Plasmids containing the correct inserts were confirmed by sequencing and introduced into electrocompetent E . coli BL21 ( DE3 ) /pLysS . The transformed cells were spread on LB-carbenicillin/chloramphenicol and one colony was used for inoculation . Proteins were expressed in two 2 . 5 litre volumetric flasks each containing 400 ml LB culture and expression was induced with 0 . 25 mM IPTG . The optimised temperature for expression varied with the protein: His6-BldM was expressed at 25°C for 5 h; GST-WhiI was expressed at 15°C overnight; His6-BldM/WhiI or SII-WhiI were co-expressed at 30°C for 5 h . The pellets were lysed in a buffer containing 50 mM Tris-HCl pH 8 , 250 mM NaCl , 10% glycerol , 0 . 1% Triton X100 , protease inhibitor ( complete mini , EDTA-free , Roche ) and incubated at room temperature for 20 min . HisTrap HP Ni and StrepTrap HP affinity columns ( GE Healthcare ) were used to purify the His6- and SII-tagged proteins in a tandem manner . The Gst-WhiI was purified with 1 ml GSTrap FF column ( GE Healthcare ) . Singly 32P end-labelled probes ( Table S4 ) were generated by PCR and purified using Qiaquick columns ( Qiagen ) . Transcription factors were incubated with probe DNA ( ∼150 , 000 cpm ) for 30 min at room temperature in 40 µl reaction buffer [50 mM Tris-HCl pH 7 . 5 , 100 mM NaCl , 10% glycerol , 10 mM MgCl2 , 2 mM dithiothreitol and 1 µg/reaction poly ( dI-dC ) ] , prior to treatment with 1 U DNase I ( Promega ) for 30–50 s in the case of group-II promoters and 3 U DNase I for 15–20 s in the case of group-I promoters . Reactions were terminated with 140 µl of stop buffer ( 192 mM sodium acetate , 32 mM EDTA , 0 . 14% SDS , 70 µg/ml yeast tRNA ) and samples were extracted with phenol-chloroform prior to ethanol precipitation . Footprinting samples were loaded on 6% polyacrylamide sequencing gels , next to a G+A ladders prepared according to the Sure Track footprinting kit ( Amersham Pharmacia Biotech ) . C-terminally 3×FLAG-tagged bldM expressed from the native promoter was used to complement the ΔbldM mutant and N-terminal 3×FLAG-tagged whiI carrying native promoter was used to complement the ΔwhiI mutant . 50 µl dense spore suspension was used to inoculate 300 ml MYM in 2 litre flasks with spring baffles . After 17 h growth , cultures were harvested by centrifugation at 6000 rpm for 15 min at 4°C . Pellets were lysed in a buffer containing 50 mM Tris-HCl pH 8 , 250 mM NaCl , 10% glycerol , 0 . 1% Triton X100 , protease inhibitor ( complete mini , EDTA-free , Roche ) and sonicated for 5 cycles at 15-micron amplitude for 20 s . FLAG-tagged proteins were immunoprecipitiated using M2 beads [anti-FLAG antibodies covalently attached to agarose beads ( Sigma ) ] , the beads were washed using TBS buffer containing protease inhibitor ( Roche ) and 0 . 05% Triton X100 , and the protein was eluted using FLAG peptides as recommended by the manufacturer . S . venezuelae strains were grown in MYM medium . 10 ml samples were taken at 14 , 15 , and 16 hours of growth . Strains were harvested by centrifugation at 3000 rpm for 1 min , washed in 5 ml ice-cold washing buffer ( 20 mM Tris pH 8 . 0 , 5 mM EDTA ) and resuspended in 0 . 4 ml of ice-cold sonication buffer [20 mM Tris pH 8 . 0 , 5 mM EDTA , 1x protease inhibitor ( Roche ) ] . Samples were sonicated immediately for 5 cycles , 20 s at 10 microns with 1 min intervals of ice incubation , then centrifuged at 13000 rpm at 4°C for 15 min to remove cell debris . Protein concentrations of the supernatant crude cell extracts were measured by Bradford assay and samples ( 10 µg protein ) were separated on a 12 . 5% SDS-PA gel and blotted onto nitrocellulose membrane . The membrane was incubated in blocking solution [10% dried milk powder in TBS ( 0 . 05 M Tris , 0 . 9% NaCl , pH 7 . 6 , 0 . 1% Tween ) ] overnight and then incubated for 1 h at room temperature with the 1/2500 dilution of anti-BldM antiserum in blocking solution . The membrane was rinsed ( twice for 10 min ) in TBS and then incubated for 1 h with 1/5 , 000 dilutions of horseradish peroxidase-linked goat anti-rabbit immunoglobulin G antibody ( GE Healthcare ) . Blots were developed using the ECL enhanced chemiluminescence system from GE Healthcare and were typically exposed to X- ray film for between 30 s and 5 min . | Two-component signal transduction systems are a primary means of regulating gene expression in bacteria . Recognizing the diversity of mechanisms associated with these systems is therefore critical to understanding the full signaling potential of bacterial cells . We have analyzed the behavior of two orphan , atypical response regulators that play key roles in controlling morphological differentiation in the filamentous bacteria Streptomyces-BldM and WhiI . We demonstrate that BldM activates its Group I target promoters as a homodimer , but that it subsequently activates its Group II target promoters by forming a functional heterodimer with WhiI . BldM-WhiI heterodimer formation thus represents an unusual mechanism for the coactivation of target genes and the integration of regulatory signals at promoters , enhancing the known repertoire of signaling capabilities associated with two-component systems . | [
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] | 2014 | Response Regulator Heterodimer Formation Controls a Key Stage in Streptomyces Development |
Schistosoma flatworm parasites cause schistosomiasis , a chronic and debilitating disease of poverty in developing countries . Praziquantel is employed for treatment and disease control . However , its efficacy spectrum is incomplete ( less active or inactive against immature stages of the parasite ) and there is a concern of drug resistance . Thus , there is a need to identify new drugs and drug targets . We show that RNA interference ( RNAi ) of the Schistosoma mansoni ortholog of human polo-like kinase ( huPLK ) 1 elicits a deleterious phenotypic alteration in post-infective larvae ( schistosomula or somules ) . Phenotypic screening and analysis of schistosomula and adult S . mansoni with small molecule inhibitors of huPLK1 identified a number of potent anti-schistosomals . Among these was a GlaxoSmithKline ( GSK ) benzimidazole thiophene inhibitor that has completed Phase I clinical trials for treatment of solid tumor malignancies . We then obtained GSKs Published Kinase Inhibitor Sets ( PKIS ) 1 and 2 , and phenotypically screened an expanded series of 38 benzimidazole thiophene PLK1 inhibitors . Computational analysis of controls and PLK1 inhibitor-treated populations of somules demonstrated a distinctive phenotype distribution . Using principal component analysis ( PCA ) , the phenotypes exhibited by these populations were mapped , visualized and analyzed through projection to a low-dimensional space . The phenotype distribution was found to have a distinct shape and topology , which could be elicited using cluster analysis . A structure-activity relationship ( SAR ) was identified for the benzimidazole thiophenes that held for both somules and adult parasites . The most potent inhibitors produced marked phenotypic alterations at 1–2 μM within 1 h . Among these were compounds previously characterized as potent inhibitors of huPLK1 in cell assays . The reverse genetic and chemical SAR data support a continued investigation of SmPLK1 as a possible drug target and/or the prosecution of the benzimidazole thiophene chemotype as a source of novel anti-schistosomals .
Flatworm parasites of the Schistosoma genus are responsible for schistosomiasis , a chronic and often painful disease of poverty that affects more than 200 million people worldwide [1–3] . For over 35 years , treatment and control of this disease has relied on a single drug , praziquantel ( PZQ ) [4–6] . Apart from the concern over the possible emergence and establishment of resistance to this drug in the field [4 , 7–9] , PZQ has a number of other problems that encourage the search for alternate drugs . It is rarely curative at the single dose employed [10 , 11] in part due to its rapid metabolism [12 , 13] , and the dose used is consequently high ( 40 mg/kg ) relative to other oral anthelmintics and medications in general . Importantly , PZQ has diminished or no efficacy against developing schistosomes [14–16] . Finally , the drug has an unpalatable taste [17] . Efforts continue to identify and develop small synthetic compounds or natural products as anti-schistosomal drugs , e . g . , [18–21] . In the hope of decreasing both the time and cost associated with developing drugs , researchers have also looked to either reposition registered drugs ‘as is , ’ or use drugs or drug candidates as starting points for further chemical exploration and development [20 , 22–25] . In this context , various anti-cancer small molecules , including those targeting components of the kinome [26] have been the subject of recent interest as potential anti-schistosomal drugs [24 , 27–29] . Of these , a number of polo-like kinase ( PLK ) inhibitors , that either target the ATP-binding site [30–34] or the unique Polo-box domain [35 , 36] , have attracted our interest ( see below ) as a number of these are progressing pre-clinically or clinically as anti-cancer agents ( S1 Table ) . S . mansoni has just two PLK genes , Smplk1 and Smsak ( Smplk4 ) ( GenBank IDs AAV49163 and GU084154 , respectively ) , which is in contrast to the five found in humans [37–39] . PLKs are a family of conserved serine/threonine kinases , which , in humans , are involved in cell division , including G2/M transition , centrosome maturation , formation of bipolar spindles , cytokinesis and regulation of the spindle assembly checkpoint [40–43] . Plk1 is the best characterized member of the family and is vital to normal mitotic progression [40 , 41 , 44–46] . Its over-expression in human tumors [47–49] has identified this kinase as a selective target for anti-cancer drugs . In S . mansoni , SmPLK1 is expressed in sporocysts ( asexually dividing stages parasitizing the snail vector ) and in adult worms , particularly in their reproductive organs , suggesting a contribution by this kinase to cell division [39] . The huPlk1 inhibitors , GW843682X and BI2536 , are nanomolar inhibitors of SmPLK1 when the enzyme was expressed in Xenopus oocytes [39] . BI2536 also decreased the number of immature oocytes relative to mature oocytes in the female reproductive organs; in males , the size of testicular lobes and the number of spermatocytes were reduced [39] . Interestingly , SmSAK , which shares 37% and 13% identity in the kinase and polo-box domains , respectively , is not inhibited by BI2536 suggesting that the inhibitor is selective for SmPLK1 [39] . Using RNA interference ( RNAi ) , we show that SmPLK1 and less so , SmSAK , are important to the normal development and survival of S . mansoni schistosomula ( post-infective larvae , a . k . a . somules ) in culture . Based on this finding , we then tested 11 clinically tracked inhibitors of huPLK1 for bioactivity on somules and adult parasites in vitro . One of these , a phase I clinical candidate benzimidazole thiophene inhibitor from GlaxoSmithKline ( GSK ) , was bioactive at low micromolar concentrations . This inhibitor served as a starting point for a phenotypic screen of 38 benzimidazole thiophene analogs contained within GSKs Published Kinase Inhibitor Sets ( PKIS ) 1 and 2 [50–53] . We identify a structure-activity relationship ( SAR ) for this inhibitor set that is shared between somules and adult parasites , and we discuss our findings with a view to the possible future development of this compound class as a source of novel anti-schistosomals .
Maintenance and handling of vertebrate animals were carried out in accordance with a protocol ( AN107779 ) approved by the Institutional Animal Care and Use Committee ( IACUC ) at the University of California San Francisco . UCSF-IACUC derives its authority for these activities from the United States Public Health Service ( PHS ) Policy on Humane Care and Use of Laboratory Animals , and the Animal Welfare Act and Regulations ( AWAR ) . A Puerto Rican isolate of Schistosoma mansoni was maintained by passage through albino Biomphalaria glabrata snails and infection of 3–5 week-old , female Mesocricetus auratus Golden Syrian hamsters [54 , 55] . Cercariae ( infectious larvae ) were obtained from infected snails and mechanically transformed into somules as previously described [20 , 56 , 57] . To obtain adult schistosomes , hamsters were euthanized 42–45 days post-infection using an intra-peritoneal injection of 50 mg/kg sodium pentobarbital containing 50 U/ml heparin ( as an anti-coagulant ) in a total of 100 μL PBS . Worms were harvested by reverse perfusion of the hepatic portal system [54 , 55 , 58] in RPMI 1640 medium supplemented with 100 U/ml penicillin and 100 mg/ml streptomycin [54 , 55] . Adults were transferred into Basch medium 169 [59] supplemented with 100 U/ml penicillin and 100 mg/ml streptomycin . In this medium , parasites were washed three times , allowed to stand for 30–60 min in the presence of 2X amphotericin B ( fungizone ) and then washed another three times in medium minus fungizone prior to phenotypic screening . Two dsRNA fragments , SmPLK1RNAi ( 358 bp ) and SmPLK2RNAi ( 498 bp ) , that target the regulatory domain of the Smplk1 gene transcript were generated by PCR using gene-specific primers containing a T7 promoter sequence ( S2 Table ) and the plasmid , SmPLK1-pcDNA3 . 1 , as a template [39] . A similar strategy was employed for SmSAK-dsRNA , whereby two fragments of 722 bp and 473 bp were amplified from a SmSAK-pcDNA3 . 1 plasmid construct [37] . DsRNA synthesis employed the MEGAscript RNA Kit ( Ambion ) according to the manufacturer’s instructions . DsRNA was purified by precipitation with 3 M sodium acetate ( pH 5 . 2 ) and ethanol , resuspended in dH2O , and quantified using a Nanodrop ND-1000 spectrophotometer ( Nanodrop Technologies ) . The integrity of the dsRNA was confirmed by 1% agarose gel electrophoresis . DsRNA to the fluorescent Discosoma sp . mCherry protein was generated as a schistosome-unspecific control [20 , 57] . Co-incubation of somules with dsRNA was as described previously [20 , 57] . Briefly , 300 somules were maintained at 37°C and 5% CO2 in 24-well plates ( Corning Inc . , 3544 ) containing 1 ml ‘complete’ Basch medium 169 supplemented with 100 U/ml penicillin , 100 mg/ml streptomycin and 5% FBS . DsRNA ( 30 μg/ml in 10–20 μl water ) targeting SmPLK1 , SmSAK or mCherry was added to the parasite cultures twice weekly out to 22 days . Cultures were observed daily for the appearance of phenotypes and experiments were performed twice each in duplicate . To measure changes in gene expression as a consequence of RNAi , somules were co-incubated with the above dsRNA preparations for seven days . Experiments were performed twice each in duplicate . Parasites were then processed for reverse transcription-quantitative real time PCR ( RT-qPCR ) as described [20 , 57] . These analyses were performed as described [20 , 57 , 60] . For RT-qPCR , total RNA was extracted using TRIzol reagent ( Invitrogen ) and the High Pure RNA Isolation Kit ( Roche ) following the manufacturer’s instructions . Complementary ( c ) first-strand DNA was synthesized using the SuperScript III First-Strand Synthesis kit ( Invitrogen ) . cDNA was then used as a template for PCR amplification using the Light Cycler 480 SYBR green I Master mix ( Roche ) and a Mx3005P qPCR detection system ( Stratagene ) . Specific primers for each of the regulatory and kinase domains of SmPLK1 and SmSAK , and for mCherry were designed using the Primer Express Program ( Applied Biosystems; see S2 Table ) . Primers were experimentally validated and S . mansoni cytochrome C oxidase I ( GenBank AF216698 ) was used as the reference transcript . Reactions were carried out in a final volume of 20 μl in 96-well plates ( QPCR 96-Well Plates , Non-Skirted , Agilent Technologies ) . Experiments were performed twice each in duplicate . The 2-ΔΔCt method [61] was employed to measure transcript levels post-RNAi and these were expressed as a percentage of those following exposure to mCherry dsRNA . Statistical analysis employed the two-tailed Student’s t-test . Quantification of mRNA from cathepsin B1 . 1 ( AJ506157 ) was used as bystander control to monitor for off-target RNAi effects . Controls for genomic DNA contamination ( no reverse transcriptase ) and reagent purity ( water control ) were included for each sample . Eleven inhibitors of huPLK1 were purchased . Of these , BI2536 , BI6727 , HMN-214 and MLN0905 were sourced from MedChemExpress; GSK461364 , GW843682X , ON01910 and TAK960 were from AdooQBioScience LLC; thymoquinone was from Sigma , and poloxin and SBE13 were from Millipore . Inhibitors were dissolved in dimethyl sulfoxide ( DMSO ) at 10 or 20 mM stock concentrations which were stored at -20°C . A set of 38 benzimidazole thiophenes were also sourced as part of GSK’s Published Kinase Inhibitors Set ( PKIS ) 1 and 2 as 10 mM ( 10 μl ) stocks in DMSO . Phenotypic screens involving S . mansoni somules and adults were carried out as described [20 , 21 , 55 , 62] . For somules , approximately 300 newly transformed parasites were dispensed into flat-bottomed 96-well plates in 100 μL complete Basch medium ( Corning Inc . , cat . # 3599 ) . Compound was then added in a volume of 1 μl DMSO and the final volume brought up to 200 μL with complete Basch medium . Parasites were then incubated for up to two days at 37°C under 5% CO2 . First pass , single concentration screens at 10 μM were performed and those compounds eliciting phenotypes were then re-screened over a concentration range of 0 . 5–10 μM ( 0 . 5% DMSO final ) . For adult schistosomes , single concentration ( 10 μM ) screens were performed in 24-well plates ( Corning Inc . , cat . # 3544 ) using five male worms per well in a final volume of 2 ml complete Basch medium . Compound was added in a volume of DMSO ranging from 0 . 5 to 2 μL . Those compounds eliciting phenotypes within 48 h were then re-screened over a concentration range of 1–10 μM . In the recent past , important advances have been made in algorithmic ( automatic ) phenotype analysis of somules; analogous methods do not yet exist for the adult stage of the parasite . In particular , methods have been developed for parasite segmentation [63–65] , parasite tracking from video recordings [66] and quantitative identification of helminth phenotypes [63 , 67] , including for hit detection in high-throughput screens [68] and dose-response characterization [69] . As yet , however , no automated method exists for identification of phenotypes and simultaneous determination/scoring of their severity . Therefore , we combined automated phenotypic analysis with manual phenotype assessment in an integrated analysis process and applied it to analyze the phenotypes arising from exposure of the parasites to the commercially available inhibitors and the PKIS 1 and 2 inhibitors . We began by automatically clustering the unaffected and affected juvenile parasites based on their differential phenotypic response . The existence of such a clustering and the fact that it could be identified without manual intervention , not only provided a rigorous and objective basis for the subsequent expert analysis , but also allowed us to quantify and visualize the phenotypic response space of the parasite . For the above automated analysis , photographic images were taken at each time point and compound concentration using a Zeiss Axiovert 40 C inverted microscope and a Zeiss AxioCam MRc digital camera controlled by AxioVision 40 ( version 4 . 8 . 1 . 0 ) software , as previously described [20 , 55 , 69] . The images were segmented ( i . e . , individual parasites were differentiated from background ) using the Asarnow-Singh segmentation algorithm [64] , to yield a total of 4 , 125 parasites ( 1 , 047 control and 3 , 078 drug treated ) across the entire study . Subsequently , 11 descriptors were calculated for each segmented parasite . These descriptors included parasite area , length of the perimeter of the parasite , ratio of the major to the minor axis of the parasite body , ratio of the area of the parasite body to the area of its bounding box and a set of descriptors capturing the visual appearance of the parasite in terms of its intensity and texture . The parasite intensity was described using the mean and variance of the intensity distribution ( we used the standard deviation as the specific numeric measure ) . Texture was described using Grey-Level Co-occurrence Matrices ( GLCM ) , which capture how often two intensities occur side by side , and the following five descriptors ( described further in Table 1 ) : entropy , contrast , correlation , energy and homogeneity , which are computed on a normalized co-occurrence intensity matrix I ( i , j ) . Further details on these descriptors as applied to parasitic screening can be found in [67] . The ‘traditional’ approach to adjudicating the many phenotypic responses possible for this parasite involves microscopical observation . We use simple ‘descriptors’ to record changes in movement , shape , translucence , surface integrity and , for adults specifically , the ability of the parasite to adhere to the culture dish surface ( see S1 File and [20 , 55 , 70] ) . To convert these observations into an ordinal numeric output and thus facilitate relative comparisons of compound effects , each descriptor was awarded a ‘severity score’ of one up to a maximum score of four . When damage to the adult parasite’s tegument ( surface ) was evident , the maximum score of four was awarded on the assumption that such damage is lethal to the parasite , including in the mammalian host [14] . In the case of somules , phenotypes were recorded at 24 and 48 h; for adults , phenotypes were recorded at 1 , 5 , 24 and 48 h . To understand whether residues in the SmPLK1 ATP-binding site will accommodate the human benzimidazole thiophene PLK1 inhibitors , we built a homology model of SmPLK1 from the human ortholog’s structure ( PDB ID: 2YAC ) . The homology model was constructed using the software PRIME ( v . 3 . 9; Schrödinger Inc ) . GSK benzimidazole thiophene inhibitors , GSK461364 , GSK1030058 , GSK326090 and GSK483724 were built using Maestro’s Edit/Build panel ( v . 10 . 1; Schrödinger Inc ) . LigPrep ( v . 3 . 3; Schrödinger Inc . ) was used to minimize the ligand structures . We docked the ligands using Glide ( v . 6 . 6; Schrödinger Inc . ) with the standard-precision docking scoring function . To compare the docking mode of the ligand GSK461364 , we also docked it against the human PLK1 structure . Similarity between the docking poses was determined by evaluating the root-mean-square-distance ( RMSD ) of heavy atoms .
To evaluate whether SmPLK1 and SmSAK are important to the growth and/or survival of S . mansoni somules , we co-incubated the parasite with dsRNA targeting the respective gene transcripts . Parasites were exposed to 30 μg/ml dsRNA targeting the regulatory polo-box domain and the cultures observed every day for 22 days ( Fig 1 ) . In the presence of SmPLK1-dsRNA , rounding and darkening of the parasite were evident ( Fig 1A , panel 2 ) . Similar , but less pronounced , changes were also observed after co-incubation with SmSAK-dsRNA ( Fig 1A , panel 3 ) . After 22 days in culture , 32% and 16% of the somules exposed to SmPLK1- and SmSAK-specific RNAi , respectively , had been affected ( Fig 1B ) . Quantification of RNAi was performed by RT-qPCR analysis of transcripts in parasites after seven days of incubation with the respective dsRNA preparations . These experiments indicated that expression of Smplk1 and Smsak was decreased by 92 . 5 and 64 . 5% , respectively ( Fig 1C ) . Expression of Smcb1 , which we use as a ‘bystander’ gene to assess off-targeting by the dsRNA preparations of interest [20 , 57] was not altered in the experiment . For adult parasites , we previously attempted RNAi of SmPLK1 via electroporation of 25 μg dsRNA but observed no phenotype after 5 days in culture [37] . We investigated the distribution of the phenotypes exhibited by the somules upon exposure to the 11 commercially available huPLK1 inhibitors and the 38 benzimidazole thiophene inhibitors available in PKIS 1 and 2 . It may be noted that in phenotypic assays , it is common to observe differentiated responses of somules ( and adult parasites ) when exposed to drugs , even within a single well . This phenomenon can be due to natural variation of individuals , low drug concentration , or insufficient duration of exposure to the compound . The fact that schistosome clones do not exist , typically exacerbates phenotypic variability . The algorithmic analysis carried out in this paper highlights this issue through automated quantitative analysis . We employed cluster analysis to determine whether the unaffected and affected parasites could be automatically differentiated . For this purpose , based on the 11 computational descriptors of shape and appearance described earlier , each of the 4 , 125 parasites , identified after automatic segmentation , was represented as a point in an 11-dimensional phenotype ( feature ) space . To reduce dimensionality , we then projected the data to a lower dimensional feature space while retaining its variance , using principle component analysis ( PCA ) . We mapped the data to a 6-dimensional PCA space , in which 95% of the variance in the data was accounted for . Next , we performed automatic clustering of the data using the k-means algorithm [71] ( with the parameter k = 2 , corresponding to the intuitive notion of separating the affected and unaffected parasites ) to establish whether the phenotypes exhibited by the drug-treated parasites could be separated from those exhibited by controls . The clustering results produced two clear data groups ( Fig 2 ) . The first group formed a near compact core and corresponded to parasites from the control images ( blue points ) and also included parasites exposed to compounds that did not exhibit significant phenotypic changes , i . e . , were similar to controls ( green points ) . The second group was distributed around this core cluster and consisted of parasites that exhibited various phenotypic changes in their shape or appearance as a result of drug exposure ( red points ) . Some of the parasites from the control images were also placed in this cluster due to naturally occurring degeneracies . Examples of individual parasites belonging to the different groups are presented in S1 Fig . All of the inhibitors eliciting phenotypic changes in the parasite as determined visually did so in a concentration- and time-dependent manner ( Fig 3 for severity scores and S1 File , worksheets 1–3 for both the descriptors and severity scores ) . The ATP-competitive dihydropteridinone inhibitor , BI2536 , and its successor BI6727 , which displays improved pharmacokinetic , efficacy and safety profiles [72–74] , were similarly active against adults and somules whereby phenotypic alterations were first noted at 5 μM after 24 h . The ATP-competitive benzimidazole thiophenes , GSK461364 and GW843682X , diverged in their relative potencies . The former was much more active against both developmental stages , particularly the somules , for which the inhibitor was the most potent of 11 inhibitors tested ( multiple phenotypic changes noted at 1 μM after 24 h ) . This is perhaps not surprising , as GW843682X was an early tool molecule with modest cellular activity , whereas GSK461364 was a clinical candidate [51–53 , 75 , 76] . The natural product , thymoquinone , and its synthetically derived analog , poloxin , both of which target the unique polo-box domain of huPLK1 [77] , were also potent anti-schistosomals: thymoquinone exerted preferential activity against adults ( a severity score of 2 at 5 μM after 5 h ) . The other five inhibitors ( ON01910 , MLN0905 , HMN-214 , TAK960 and SBE13 ) displayed little to no activity ( severity scores of 1–2 only after 48 h ) . For both somules and adults , Fig 4 depicts a snapshot of the severity scores as a function of time and concentration for the 38 benzimidazole thiophenes found within GSKs PKIS 1 and 2 . The complete data set ( descriptors and severity scores ) is provided in S1 File ( worksheets 4–6 ) . For the bioactive compounds , the most common phenotypic response noted for adult parasites involved overactive uncoordinated movements whereby the parasites displayed rhythmic movements being unable to adhere to the bottom of the culture well . For some compounds , e . g . , GSK1030058A and GSK579289A , this response appeared rapidly ( within 1 h ) at 1 or 2 μM . The uncoordinated response generally progressed during the two day incubation period to include a loss of translucency ( darkening ) sometimes accompanied by worm shrinkage , each of which increased the overall severity score . For the 21 benzimidazole thiophenes present only in the PKIS 1 and screened previously against adults [29] under assay conditions described earlier [78] , 14 bioactive compounds were shared ( see S1 File worksheets 4 and 6 ) . For somules , concentration- and time-dependent changes in the parasites were also evident of which over-activity and a general darkening and rounding of the parasite were the most common . For the most potent compounds , multiple phenotypic changes were noted at 1 μM by the first time-point of 24 h . For some compounds at 48 h , internal disruption was evident by the appearance of multiple ‘vacuoles’ ( e . g . , GSK483724A; and GSK641502A; S2 Fig ) . Also evident from Fig 4 and S1 File is a clustering of active and inactive benzimidazole thiophenes for both adults and somules ( SAR detailed below ) with some exceptions . This clustering may indicate a shared target ( s ) and/or mechanism ( s ) of action between both developmental stages which would be encouraging from the point of view of developing a compound that possesses bioactivity across the entire developmental cycle of the parasite in the mammalian host . S1 File should be used to adjudicate the SAR . The predominant substituents at R1 , R2 and R3 for the 38 available benzimidazole thiophenes were a 2-trifluoromethylbenzyl or 2-chlorobenzyl ( 21/38 compounds ) , a primary amide ( 33/38 ) , and a 5 , 6-dimethoxybenzimidazole ( 18/38 ) , respectively . Of 58 thiophene benzimidazoles synthesized and reported in [51] , these particular R1-R3 substitutions yielded the lowest IC50 values against both the target huPLK1 enzyme ( 2 nM ) and the HCT116 human colon carcinoma cell line ( 699 nM ) used to assess cellular activity . Focusing first on the adult responses , the cut-off we assigned to indicate compound activity was the annotation of one or more phenotypic changes at 10 μM by 24 h . With the above stated R1 and R3 sub-structures fixed , R2 as a methyl ester ( compound ID ending in 0058A ) , methyl amide ( 0061A ) , dimethylamide ( 0062A ) or primary amide ( 3682X and 2849X ) maintained activity against the parasite whereas the methyl ketone ( 0059A ) was inactive . Of the R2 substitutions tested , the methyl ester was the most potent with activity recorded at 2 μM after 1 h compared to 5 or 10 μM after 24 h for the others . This is interesting in that GSK reported the methyl ester to be essentially inactive against the huPLK1 ( IC50 > 1 mM ) [51] . The potent activity against the parasite could be explained if , under these assay conditions , the ester is hydrolyzed to the carboxylic acid which does inhibit PLK1 , or if the ester is an inhibitor of an important , but as yet unknown , target . Apart from 0058A , SAR in this area proved to be insensitive to the presence or absence of hydrogen bond donors , however the loss of the polar terminus , in the case of 0059A , showed that the presence of a hydrogen bond acceptor alone is not sufficient to retain activity . Continuing with R2 fixed as the primary amide , the presence of a methyl group at the benzylic position of the terminal R1 ring system 7701A ( R ) and 7700A ( S ) improved bioactivity compared to 2849X which does not have a methyl group in the benzylic position . The original GSK report highlights this change as one that can enhance cellular activity [52] . Compounds such as 7314A and 7315A with mono-methoxy benzimidazoles retained activity against the parasite . In the context of the mono-methoxy benzimidazoles , both an ortho CF3 group ( 7315A ) and an ortho Cl group at R1 were active . Focusing on R1 , replacing the 2-trifluoromethylbenzyl or 2-chlorobenzyl with a 3-choro- ( thiophen-2-ylmethoxy ) ( 8459A ) retains activity at least transiently , but in the absence of the halogen , the 2-thiophen-2-ylmethoxy alone ( 2948A ) is inactive . Also inactive at R1 are a 2-furylmethoxy ( 4607A ) , cyclohexylmethoxy ( 4559A ) and a 4-pyridinylmethoxy ( 6313A ) even when the latter has an additional bromine at position 2 ( 4278X ) . Compounds with an additional one ( 4925A ) or two methylene linkages ( 5189A ) extending to the terminal R1 phenyl group are not active . Likewise , the R1 phenyl containing a 4-methyl sulfone ( 9979X ) or the same group at position 2 in the presence of an R2 cyano group ( 9347A ) is inactive . Overall , the data thus far indicate the importance of an electronegative group at R1 . Perhaps there is a columbic attraction at play in a binding pocket making this advantageous for binding . Continuing on with a 2-trifluoromethylbenzyl or 2-chlorobenzyl and the primary amide fixed at R1 and R2 , respectively , the effects of altering ( extending ) R3 were pronounced and indeed yielded the most active compounds tested . For huPLK1 , these substituents likely extend towards solvent , and can be used to modulate solubility and other chemical properties , in addition to potency for the enzyme . Thus , with a 4- ( 1-methylpiperidin-4-yl ) methyl at R3 , the 2-trifluoromethylbenzyl at R1 ( 6090A ) was as potent as the 2-chlorobenzyl ( 1989A ) , i . e . , bioactivity was discernible at 10 μM after just 1 h . Shortening the R3 to a 4- ( 1-methylpiperidin-4-yl ) moiety ( 9289A ) produced the most potent compound tested whereby activity was recorded at 1 μM after 1 h . Strong potency was retained by substituting the 4-pyridin-4-yl group but only when it originated from the 6 position ( 3724A ) . Placing the same 4-pyridin-4-yl group in the 5 position ( 9719A ) resulted in the complete loss of activity against the parasite . Compounds 0432A , 1502A , and 7232A also have substituents appended in the 5-position and they too show little or no activity against the parasite . The current clinical candidate GSK461364 containing a terminal 4-methyl piperazine was active with bioactivity detectable at 5 μM after 24 h . Not all polar substituents in the 6 position retained activity , however . The diol 8744A , for example , was inactive against the parasite . Finally , compound 6294A with a lipophilic t-butyl urea substituent in the 6 position was active . The last groups of compounds include relatively simple versions of this chemotype , with no groups in the 5 and 6 positions of the benzimidazole . They are generally inactive against the parasite . For example , when R1 is a 2-fluorobenzyl , 2-bromobenzyl or 4-methoxyphenyl group ( 4306A , 3156A and 4304A ) and there is no substitution of R3 , the compounds are inactive . In contrast , a 3-methoxyphenyl variant ( 4482X ) at R1 was active , albeit only at 10 μM . With a 2-bromophenyl ( 3609X ) or 2-methoxyphenyl group ( 3349X ) at R1 , R3 substitution at the 5 position with trifluoromethyl or chloro substituents , respectively , resulted in no activity , whereas the 6-trifluoromethyl variant ( 3606X ) possessed some activity . With some exceptions , e . g . , 2948A and 4925A , the compounds active against the adults were also active against the somules ( using the same cut-off for activity , i . e . , 10 μM by 24 h ) . Notable was the fact that the addition of the methyl group proximal to the terminal R1 group enhanced activity against somules but did not greatly influence activity against adults ( compare 2849X with both 7701A ( R ) and 7700A ( S ) ) . Lastly , neither the antinematodal benzimidazole drug , albendazole , nor its sulfoxide metabolite , was active over the concentrations tested . Based on the above analysis of 38 benzimidazole thiophenes in the PKIS 1 and 2 libraries , an optimized structure emerges for further SAR: a 2-trifluoromethylbenzyl or 2-chlorobenzyl at R1 , a primary carboxamide or methyl ester at R2 and bi-aryl rings at R3 decorated with solubilizing aliphatic amines . Continued exploration of the influence of halogen substitutions on different positions of the R1 ring would be warranted to understand whether efficacy can be improved . Also , for R2 , methyl esters present pharmacokinetic ( PK ) liabilities and are prone to hydrolysis in aqueous media , and , thus , would need to be avoided in favor of the primary amide common to most of the compounds tested here . If , indeed , the methyl ester serves as a pro-drug for an active carboxylic acid , this could be explored in more detail to optimize the release of the acid . For R3 , the bi-aryl R3 substitutions yielded a cluster of potent compounds , including two ( 9289A and 3724A ) that were active at 2 μM or less after 1 h , and that induced progressively more severe phenotypic disturbances . In order to assess the binding mode of the benzimidazole thiophenes , we docked a representative inhibitor , the clinical Phase I drug candidate , GSK461364 , in the ATP-binding site of the huPLK1 structure ( pdb id: 2yac ) and in an homology model of SmPLK1 . The predicted binding poses , shown in Fig 5A , are very similar ( 0 . 7Å heavy atom RMSD ) , as are the Glide-SP docking scores , which were -9 . 3 and -9 . 6 for the human and S . mansoni enzymes , respectively . Thus , the data from the molecular modeling calculations suggest that the current benzimidazole thiophenes bind to the SmPLK1 without undue energy penalties , as observed by their similar docking scores and predict that the binding orientation will be very similar between huPLK1 and SmPLK1 . A close-up view of the GSK461364 binding to SmPLK1 ( Fig 5B ) shows that the inhibitor makes hydrogen bonding interactions with Lys54 and Glu112 residues . Similar interactions were also observed for GSK461364 docked to huPLK1 ( not shown ) . Further , the docking poses of GSK461364 and three other GSK benzimidazole thiophene inhibitors ( GSK483724 , GSK1030058 and GSK326090 ) against SmPLK1 show that the benzimidazole thiophenes bind in the same orientation for all the inhibitors ( Fig 5C ) .
Reliance on a single drug to treat ‘continents’ of people afflicted with schistosomiasis encourages the search for new drugs and drug targets [6 , 11 , 79] . RNAi has proven to be a key research tool in this endeavor by identifying gene products that are essential to parasite survival ( e . g . , [20 , 29 , 57 , 80 , 81] ) . In this report , we demonstrate that RNAi of the single gene schistosome ortholog of huPLK1 leads to degenerative changes in the morphology of S . mansoni somules . Our findings are consistent with those very recently reported by Bickle and colleagues [29] as are our previous unsuccessful attempts to discern phenotypes consequent on RNAi of SmPLK1 in adult schistosomes [39] . Because somules undergo major transformative changes as they adapt to the mammalian host , it’s possible that they are more sensitive to perturbations in gene expression than adults . With the knowledge that SmPLK1 contributes to survival and that huPLK1 is a well-validated drug target for treatment of various cancers [30 , 32 , 48] , we obtained pre-clinically and clinically advanced small molecule inhibitors of huPLK1 that might form starting points for the development of novel anti-schistosomals . The algorithmic analysis of somule phenotypes upon exposure to the commercially available and PKIS benzimidazole thiophenes produced two significant results . First , it demonstrated an objective distinction between phenotypes of affected and unaffected somules , and that drug exposure leads to distinct phenotypic effects , which were identified and quantified without recourse to ( subjective ) human intervention and perceptual analysis . Second , we were able to quantify and visualize the phenotype space of the parasite through low-dimensional projections . In the low-dimensional space , the distribution of the phenotypes was not random , but was found to have a distinct shape and topology . Our analysis focused on somules owing to the lack of automated techniques for segmenting and phenotyping adult parasites . Part of our ongoing research involves the development of algorithms to enable a similar analysis of the phenotypic response space of adults . We first tested 11 commercially available PLK1 inhibitors , including a number that are in clinical trials , for their activity against both somules and adults . A range of phenotypic responses , from pronounced deleterious changes at low micromolar concentrations to complete inactivity , were recorded . The benzimidazole thiophene , GSK461364 , was the most potent of the inhibitors tested against somules and generated early ( within 5 h ) and sustained alterations in the adult parasite . GSK461364 is a single digit nanomolar inhibitor of huPLK1 and other PLK isoforms , and is at least 100-fold less potent against non-PLK kinases [52 , 75 , 76 , 82] . The drug candidate is also a low micromolar inhibitor of cancer cell line proliferation from multiple origins with minimal toxicity to non-dividing human cells [82] . GSK461364 has successfully completed Phase I clinical trials for treatment of specific advanced solid tumors and Non-Hodgkin’s Lymphoma [83] . The clinical progress of GSK461364 along with the recent availability of 38 structurally related benzimidazole thiophenes within GSK’s PKIS 1 and 2 libraries [50–53] prompted us to explore: ( i ) whether more effective inhibitors than GSK461364 against the parasite exist , ( ii ) whether an SAR for anti-parasitic activity could be identified , and ( iii ) whether any SAR was similar to that demonstrated for huPLK1 by previous GSK research [51–53] . Satisfying the first two conditions would be of interest in having introduced and characterized a new anti-schistosomal chemotype yet , in the absence of particular knowledge of the molecular target or mechanism of action , would create a more challenging , but not insurmountable ( e . g . , [84–87] ) , situation for chemical optimization . Meeting all three conditions , however , would support a decision to initiate a target-based SAR program centered on SmPLK1 which would include recombinant expression , purification and crystallography of SmPLK1 in order to drive the iterative chemical optimization process . Such a program would be aided by the considerable metabolism and toxicity data that are available for many of the benzimidazole thiophenes [52 , 53] . A number of the 38 benzimidazole thiophenes tested induced phenotypic changes in the parasite that increased in severity as a function of time and concentration . Compound clusters with bioactivity against both adults and somules were identified . For adults , the main initial response recorded for this chemotype was uncoordinated over-activity with an inability of the parasite to adhere to the dish , perhaps suggesting a disruption in neuromuscular homeostasis . A cluster of four R3 bi-aryl compounds ( GSK571989A , GSK326090A , GSK579289A and GSK483724A ) was especially potent , inducing uncoordinated over-activity at just 1 or 2 μM within 5 h for adults . For somules , these same compounds induced multiple and progressively more severe responses in the parasite ( e . g . , over-activity and internal degeneracy ) . The data are encouraging given the need to identify anti-schistosomal compounds that target across the spectrum of developmental stages of the parasite in the human host , which is a key failing of the current drug , PZQ [14] . These bioactive compounds are not significantly dissimilar in structure from the clinical candidate GSK461364 and are part of a sub-series of compounds designed to explore solubility and/or limit CYP-mediated metabolism [53] . For example , GSK483724A ( compound 14 in [53] ) displays IC50 inhibition values of 0 . 1 μM or less for the major drug-metabolizing CYP450 isoforms , CYP2C9 and CYP3A4 . Thus , further exploration of an expanded R3 bi-aryl substituent series , including those presented in [53] would be worthwhile . Whether or not these early uncoordinated responses in the adult parasite prove relevant for in vivo efficacy remains to be seen , however , it is pertinent to note that , for helminths , interference with neuromuscular activity ( if indeed that is occurring here ) is a well-proven anthelmintic strategy [88] . Overall , it is noteworthy that the progressive phenotypic disturbances recorded for both adults and somules occur at low micromolar , and potentially sustainable , plasma concentrations of compounds . Some correlation exists between our data for bioactivity of the PKIS 1 and 2 benzimidazole thiophenes against the parasite and those published for inhibition of the huPLK1 enzyme and growth of a human HCT116 colon carcinoma cell line [51–53] ( summarized in S1 File , worksheet 6 ) . In spite of the gaps in the published data , we note that those PLK1 inhibitors generating the lowest nanomolar IC50 values against both huPLK1 and the HCT116 cells were also those most active against the parasite irrespective of the developmental stage tested ( e . g . , compare 6090A and 9289A vs . 6313A and 3606X ) . Although not proof that SmPLK1 is the relevant target of these inhibitors , the data are suggestive . Also , from a molecular perspective , the DOCKing results with representative benzimidazole thiophenes suggest that SmPLK1 should be potently inhibited . The above observations support an attempt to heterologously express SmPLK1 in order to understand whether the relationship noted at the whole organism level can be substantiated at the level of the respective PLK enzymes . HuPLK1 has been successfully expressed in baculovirus-infected Trichoplusia ni cells [82] . We do not discount the possibility that the activities noted against the parasite are due to off-targeting , i . e . , compound interactions in addition to or apart from inhibition of SmPLK1 . Indeed , it is difficult at this juncture to reconcile the rapid onset of uncoordinated motility upon exposure to some of the benzimidazole thiophenes with the intended molecular target that has a highly constrained activity during mitosis . Again , recombinant expression of the target schistosome enzyme would be important to address whether the whole organismal effects noted here correlate with kinase inhibition . To conclude , SmPLK1 is an essential gene for the somule stage of S . mansoni . Based on the druggability of the human ortholog in anti-cancer chemotherapy , we phenotypically screened commercially available PLK1 inhibitors and a series of 38 benzimidazole thiophenes present in GSK’s PKIS 1 and 2 . An SAR across somules and adults was observed for the benzimidazole thiophenes , particularly for substitutions off the bi-aryl system at R3 which yielded fast-acting and potent compounds that merit further exploration . The apparent correlation between the present anti-parasite data and those noted previously for inhibition of PLK1 in human cancer cells suggests that SAR studies with the respective human and schistosome PLK orthologs should be considered . | Just one drug is available to treat schistosomiasis , a parasitic disease that affects hundreds of millions of people in developing countries . In the search for new drugs and drug targets , therefore , we have been interested in the schistosome version of human polo-like kinase ( huPLK ) 1 , an enzyme with critical functions in cell division . We used RNA interference to knock down messenger RNA for the SmPLK1 –the Schistosoma mansoni parasite’s version of huPLK1 . This interference caused disruptive changes in the morphology of the immature ‘somule’ stage of the parasite , indicating that SmPLK1 is an important protein for survival . We then purchased , or acquired from GlaxoSmithKline ( GSK ) , various small chemical inhibitors of huPLK1 and tested these against both the somules and adult parasites in culture . Many of these inhibitors caused severe changes in the parasite and , for somules , the differences could be computationally mapped and distinguished from unexposed parasites . For the GSK inhibitors , we observed ‘somule-adult bioactivity clustering , ’ that is , chemicals active against the adults were also active against somules . This suggests that certain chemical attributes in the inhibitors are being favoured . Interestingly , many of the GSK inhibitors most active against the parasite are also known to both potently inhibit huPLK1 and kill cancer cells . Overall , our data suggest that SmPLK1 is a possible drug target and that the GSK chemistries could form the basis for developing a new drug to treat schistosomiasis . | [
"Abstract",
"Introduction",
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"Results",
"Discussion"
] | [] | 2016 | Structure-Bioactivity Relationship for Benzimidazole Thiophene Inhibitors of Polo-Like Kinase 1 (PLK1), a Potential Drug Target in Schistosoma mansoni |
Extrachromosomal DNA amplification is frequent in the protozoan parasite Leishmania selected for drug resistance . The extrachromosomal amplified DNA is either circular or linear , and is formed at the level of direct or inverted homologous repeated sequences that abound in the Leishmania genome . The RAD51 recombinase plays an important role in circular amplicons formation , but the mechanism by which linear amplicons are formed is unknown . We hypothesized that the Leishmania infantum DNA repair protein MRE11 is required for linear amplicons following rearrangements at the level of inverted repeats . The purified LiMRE11 protein showed both DNA binding and exonuclease activities . Inactivation of the LiMRE11 gene led to parasites with enhanced sensitivity to DNA damaging agents . The MRE11−/− parasites had a reduced capacity to form linear amplicons after drug selection , and the reintroduction of an MRE11 allele led to parasites regaining their capacity to generate linear amplicons , but only when MRE11 had an active nuclease activity . These results highlight a novel MRE11-dependent pathway used by Leishmania to amplify portions of its genome to respond to a changing environment .
The protozoan parasite Leishmania is responsible for a group of diseases named leishmaniasis , affecting approximately 12 million people worldwide . No vaccine is currently available against Leishmania and treatments mainly rely on chemotherapy [1] , [2] . Pentavalent antimony is the main anti-leishmanial drug although treatment failure due to resistance has been reported in most endemic regions [3]–[7] . Locus amplification is a frequent resistance mechanism allowing the parasite to modulate gene copy number and increased gene expression . Indeed , the parasite Leishmania is an early diverging eukaryotic parasite with no control of gene expression at the level of transcription initiation [8]–[10] and amplification of DNA loci is one strategy to increase the expression of resistance genes . Resistance genes can be amplified as part of extrachromosomal circular DNAs ( circular amplicons ) or as inverted duplications ( linear amplicons ) under drug pressure [11]–[16] . Gene rearrangements leading to locus amplification always occur at the level of either homologous direct or inverted repeated ( IRs ) sequences leading respectively to circular or linear extrachromosomal amplification [14] , [15] , [17]–[19] . A model for the generation of linear amplicons is shown in Figure 1 . A recent bioinformatics screen revealed that repeated sequences are widely distributed in the Leishmania genome , which is continuously being rearranged at the level of those repeated sequences . This process is adaptive as the copy number of advantageous extrachromosomal circular or linear elements increases upon selective pressure [19] . The whole genome of Leishmania is thus stochastically rearranged at the level of repeated sequences and the selection of parasite subpopulations with changes in the copy number of specific loci is used as one strategy to respond to drug pressure . Circular or linear amplification has been observed when parasites were selected against a wide variety of drugs including the mainstay antimony [15] , [20] but one drug that has proven highly useful in deciphering gene amplification mechanisms in Leishmania is the model antifolate drug methotrexate ( MTX ) . Two loci are frequently amplified after MTX selection , one encoding the dihydrofolate reductase-thymidylate synthase ( DHFR-TS ) gene , the target of MTX , usually as part of circular elements [14] , [21]–[23] the second encoding the pteridine reductase 1 ( PTR1 ) gene , which is less sensitive to MTX but can reduce folates when DHFR-TS is blocked [24] , [25] . The PTR1 gene is amplified as part of either circular amplicons [22] , [26] , [27] or linear amplicons [13] , [14] , [19] , [28] , [29] . We have recently provided mechanistic insights into the formation of circular amplicons mediated by homologous recombination between direct repeated sequences catalyzed by the RAD51 recombinase [19] . However rearrangements at IRs leading to linear amplicons were not RAD51-dependent . Studies in other organisms have provided some evidence that a DNA break is required for palindromic amplifications formed by annealing of IRs [30]–[33] and that an exonuclease activity must be recruited to perform DNA end resection after chromosomal breakage in order to allow annealing of IRs [34]–[36] ( see also Figure 1 ) . Based on these observations , we hypothesized the involvement of the nuclease MRE11 ( Meiotic REcombination 11 ) in the generation of linear amplicons in Leishmania ( Figure 1 ) . MRE11 interacts with RAD50 and NBS1 to form the MRN complex [37] , [38] . Indeed , the nuclease MRE11 is a sensor of DNA double-strand breaks in cells and is important for the DNA double-strand break repair pathway [39] , [40] by homologous recombination ( HR ) or non-homologous end joining ( NHEJ ) [41] . Leishmania infantum encodes a putative MRE11 with conserved endo- and exonuclease domains as well as DNA-binding domains [41] . In this manuscript , we present our biochemical , cellular and molecular characterization of the L . infantum MRE11 orthologue and provide evidence that this nuclease is involved in the formation of linear amplicons in the parasite Leishmania .
Since the critical catalytic residues of MRE11 are conserved in Leishmania [41] and the replacement of the histidine ( H ) at position 217 by a tyrosine ( Y ) is known to abolish the nuclease activity of the human MRE11 but not its nucleic acid binding property [42] , we scrutinized the amino acid alignment between the human and Leishmania sequences and found the equivalent of human H217 at position 210 of LiMRE11 ( Figure 2A , upper panel and Figure S1 ) . We therefore produced a LiMRE11 mutated at the corresponding amino acid ( LiMRE11H210Y ) and used a two-step affinity purification procedure to purify LiMRE11WT and LiMRE11H210Y as described in Material and Methods ( Figure 2A , lower panel ) . We used the electrophoretic mobility shift assay to study DNA interactions with MRE11 proteins ( Figure 2B ) . We observed that the splayed arm ( SA ) and single-strand ( SS ) DNA structures were shifted in the presence the wild-type and mutated MRE11 protein in a dose-dependent manner while neither version of the protein were able to shift the double-strand ( DS ) structure in this competitive assay . The binding was quantitated and at 15 nM of either protein , 65% binding was observed with either SS and SA DNA structures while we observed only 10% binding for DS DNA ( Figure 2C ) . We next tested whether purified LiMRE11WT and LiMRE11H210Y displayed exonuclease activity ( Figure 3A ) , in comparison with human MRE11WT and hMRE11H217Y proteins ( Figure 3B ) . Our findings suggest that LiMRE11WT is enzymatically active and can perform exonucleolytic degradation with a 3′ to 5′ polarity but it is less effective than the human MRE11WT protein in cleaving DNA into smaller fragments ( Figure 3A–B , lanes 1–4 ) . As expected , LiMRE11H210Y was unable to perform DNA resection ( Figure 3A , lanes 5–7 ) , similar to its human mutated counterpart ( Figure 3B , lanes 5–7 ) . The substrate specificity was also monitored by using 25 nM of LiMRE11WT protein with DS DNA , either blunt or with 3′ or 5′ overhang DNA structures ( Figure 3C ) . The same extensive degradation was observed with DS DNA and 5′-overhang ends ( Figure 3C , lane 2 and 4 ) while the protein was blocked by 3′-overhang extremities ( Figure 3C , lane 3 ) . LiMRE11WT also exhibits endonuclease activity , as shown by the 13 bp band found at the bottom of the gel . We generated a LiMRE11-GFP fusion construct that was transfected in L . infantum cells . However , we could never achieve a high copy number of the plasmid ( overexpression of MRE11 can be toxic to the cell , see below ) and fluorescence levels were too low for analysis . We then turned to a heterologous system to study LiMRE11 in vivo . DNA constructs encoding the fusion protein LiMRE11WT-GFP and the human counterpart hMRE11WT-GFP were transfected in human ATLD cells , which are deficient for hMRE11 [43] . After laser-induced DNA damage in these cells , we detected a localized fluorescent foci representative of the recruitment of LiMRE11WT-GFP in micro-irradiated nuclear regions ( Figure 4 , upper panels ) , similar to what was observed for the human MRE11WT-GFP fusion protein ( Figure 4 , bottom panels ) . Among 24 ATLD cells micro-irradiated , we observed a recruitment of LiMRE11 to DNA damages sites in 75% of the cases , while the human homolog was recruited in 100% of the cells . No recruitment was observed for the control GFP alone . These observations confirmed the ability of the Leishmania MRE11 protein to be recruited at DNA damage sites , in a heterologous cellular model . Altogether , these results show that LiMRE11 display similar localization properties as the human enzyme . L . infantum MRE11 null mutant parasites were generated by replacing the entire ORF ( LinJ27 . 1790 ) with genes coding for the neomycin ( NEO ) and hygromycin ( HYG ) phosphotransferases . The two resistant markers were cloned between the 5′- and 3′-MRE11 flanking regions and targeting constructs were transfected independently in two rounds by electroporation . Southern blot analysis confirmed the homologous chromosomal integration of the two antibiotic markers in the MRE11 locus ( Figures 5A and 5B ) . Genomic DNAs of the WT and the HYG/NEO MRE11−/− null mutant were digested with XhoI , transferred onto membranes and hybridized . Hybridization with a probe recognizing the 5′UTR region of MRE11 yielded a 3 kb band in WT cells ( Figure 5A and 5B-lane 1 ) while hybridization with a 3′UTR probe generated a 3 , 4 kb band as expected ( Figure 5A and 5B , lane 5 ) . In the HYG/NEO MRE11−/− strain , replacement of both MRE11 wild-type alleles by NEO and HYG led , as expected , to 4 , 7 kb and 4 , 9 kb bands respectively , with either UTR probes ( Figure 5A and 5B , lanes 2 and 6 ) . It is standard practice to introduce episomal copies of the corresponding wild-type gene in a null mutant background to reverse a potential phenotype . However , we noticed that episomal overexpression of MRE11 as Psp72-α-PUR-α-MRE11WT in WT cells led to a growth defect ( Figure S2A ) . This prompted us to use an alternative to generate revertants . We replaced the NEO chromosomal integrated cassette in the MRE11 null mutant by a re-expressing cassette containing either a WT or a mutated allele ( H210Y ) of LiMRE11 along with the PUR gene in order to generate respectively the HYG/PUR-MRE11WT and HYG/PUR-MRE11H210Y re-expressing add back strains . Hybridization of the DNAs of the add-back strains with a 5′UTR probe led , as expected , to a 3 kb band in both strains corresponding to the restoration of a WT allele at the MRE11 locus , a 4 , 9 kb band corresponding to the HYG chromosomal integration , and a loss of the 4 , 7 kb-NEO containing band ( Figure 5A and 5B-lanes 3 and 4 ) which was replaced by the MRE11-α-PUR re-expressing cassette as supported by the hybridization of a 2 , 7 kb band when using a 3′UTR probe ( Figure 5A and 5B-lanes 7 and 8 ) . MRE11 expression level was assessed by quantitative real-time RT-PCR in the various cell lines generated . As expected MRE11 expression level was not detectable in the MRE11−/− null mutant while it was approximately half the level of the WT in both add back strains , consistent with one new active allele ( Figure S3 ) . A growth defect was observed in L . infantum HYG/NEO MRE11−/− parasites compared to the WT strain . Promastigotes of the WT strain had a calculated generation time of 12 hours while the MRE11 null mutant had a generation time of 26 hours . ( Figure 5C ) . Reintroduction of one intact WT or mutated ( H210Y ) allele of MRE11 into the chromosomal locus in add back strains partially rescued the growth defect with respective generation time of 15 and 22 hours . ( Figure 5C ) . Since the MRE11 complex is known to promote repair of DNA double-strand breaks ( DSBs ) [44] , [45] , we tested the impact of the LiMRE11 inactivation using the alkylating damaging agent methyl methanesulphonate ( MMS ) , a compound known to induce DSBs [46] . The HYG/NEO MRE11−/− cells were significantly more sensitive to MMS compared to both WT and MRE11 add back re-expressing cells ( Figure 5D ) . As indicated above , intriguingly , overexpressing MRE11 in WT cells led to a growth defect ( Figure S2A ) but also to a significant increase in MMS sensitivity ( Figure S2B ) . However , episomal overexpression of MRE11WT in HYG/NEO MRE11−/− cells restores the growth defect and MMS susceptibility of the mutant strain to WT levels ( Figures S2A and S2B ) . We compared the ability of the MRE11 null mutants and WT cells to generate extrachromosomal linear amplicons . We selected clones of wild-type cells and of HYG/NEO MRE11−/− for MTX resistance in a stepwise manner ( up to 1600 nM , a 16-fold increase in resistance compared to starting parent cells ) , a drug known to select for PTR1 linear DNA amplifications [13] , [18] , [28] . Leishmania chromosomes extracted from ten MTX resistant clones derived from either WT or HYG/NEO MRE11−/− parasites were separated by pulse field gel electrophoresis ( PFGE ) and hybridized with a PTR1 probe . Ethidium bromide stained gels already indicated that some linear amplicons smaller than the smallest chromosome were present in some resistant clones derived from WT but not in the MTX resistant MRE11−/− mutants ( Figure S4 ) . Hybridization data revealed that all ten MTX resistant clones derived from WT cells displayedPTR1 linear amplicons of varying size of 125 kb , 250 kb , 450 kb and 565 kb ( Figure 6A ) . Clones 6 and 7 also gave rise to PTR1 circular amplicons , as suggested from the hybridizing smears ( Figure 6A ) . The situation was drastically different in the HYG/NEO MRE11−/− parasites selected for MTX resistance . We observed only one resistant clone from the MRE11 null mutant with a faint hybridization signal corresponding to a PTR1 linear amplification ( Figure 6B , clone 1 ) , while a PTR1 circular amplification was present in clones 4 and 5 derived from the MRE11−/− mutant ( Figure 6B ) . Clone 3 displayed a hybridization signal at around 1150 kb ( Figure 6B ) which could correspond to either a very large linear amplicon or to a chromosomal translocation . The difference in formation of linear amplicons between WT and MRE11−/− null mutant was found to be significant ( p<0 , 01 ) . We also selected the add back strains HYG/PUR-MRE11WT and HYG/PUR-MRE11H210Y for MTX resistance for testing for the specificity of the phenotype and for assessing the role of the MRE11-exonuclease activity in the generation of linear amplicons . While the MRE11−/− mutants had a decreased capacity to generate linear amplicons after MTX selection ( Figure 6B ) , nine out of ten MTX resistant clones derived from the HYG/PUR-MRE11WT add back strain had PTR1 linear amplicons ( Figure 6C , clones 1 , 2 , 4–10 ) . Similar to the mutants derived from the wild-type cells ( Figure 6A ) , four different PTR1 linear amplicons of 125 , 250 , 450 and 565 kb ( Figure 6C ) were present and four clones derived from HYG/PUR-MRE11WT had additional PTR1 circular amplicons ( Figure 6C , clones 1 , 2 , 6 and 7 ) . This phenotype reversion was not observed when the MRE11−/− cells were complemented with MRE11WT as part of an episomal construct . Clones derived from the latter transfectants and selected for MTX resistance were similar to the MRE11−/− mutants with no PTR1 linear amplicons ( Figure S5 ) . The results were even more surprising with the MTX resistant clones derived from HYG/PUR-MRE11H210Y . Strikingly all mutants had circular amplifications and the PTR1 hybridization intensity was in general much higher suggesting a higher copy number of the circles . Four clones derived from this add-back revertant also had a PTR1 linear amplicon ( Figure 6D , clones 2 , 7 , 9 , 10 ) . Previous data has indicated that linear amplicons are constituted of inverted duplications rearranged at the level of IRs with the formation of a new junction that can be amplified by PCR ( see Figure 1 ) . The diversity in size of linear amplicons observed in Figure 6A and 6C would suggest that different IRs were used and we tested for the presence of IRs in the chromosome 23 that could lead to PTR1 amplicons with size ( 125 , 250 , 450 and 565 kb ) consistent with what observed in the blots . We detected a potential of 5 such IRs with size ranging from 440 to 790 bp and with a minimum of 85% identity ( Figure 7A ) , a finding consistent with our demonstration that low copy repeated sequences are widespread throughout the genomes [19] . We performed PCR assays using five different pairs of primers recognizing the five different pairs of IRs under the principle shown in Figure 1 . Amplification of the GAPDH gene was also done as a control . In the ten MTX resistant clones derived from WT cells and the HYG/PUR-MRE11WT add back strain , we detected several junctions by PCR ( Figure 7B and D ) . The junction formed following a rearrangement at the level of IRs AA′ was the most frequently observed rearrangement , but junctions BB′ , EE′ were also detected frequently while the junctions DD′ and CC′ were detected once in clones derived respectively from the WT or add back strains ( Figures 7B and 7D ) . In clones in which we detected circles by Southern blots ( Figure 6 ) , we also obtained a positive signal for junction FF′ where the repeats are in direct orientation ( Figure 7 ) . There was a general good agreement between the number of amplicons detected by southern blots and PCR although PCR was more sensitive . For example , we could not detect a linear amplicon in clone 2 in MRE11−/− ( Figure 6B ) but it had a positive PCR reaction for the junction AA′ ( Figure 7C ) . The MRE11−/− MTX resistant mutants do not have PTR1 amplification ( Figure 6B ) and these cells must resist MTX by other means . Several mechanisms of resistance have been described [47] , [48] including transport defects and gene amplification . We have carried out transport experiments in the mutants and observed no difference in MTX uptake between the MRE11−/− and the MRE11−/− MTX resistant mutants . We also hybridized PFGE blots with a DHFR-TS probe and while we failed in detecting circular DHFR-TS amplification , we observed an extra high molecular weight band hybridizing to DHFR-TS ( Figure S6 ) . The exact mechanism leading to this rearrangement is not known but it leads to a 2-fold increase in DHFR-TS expression ( Figure S7 ) and it may contribute to MTX resistance .
Gene amplification as part of linear or circular extrachromosomal elements is frequently observed in the parasite Leishmania selected for drug resistance or subjected to nutritional stresses [11] . The circles or linear elements are formed at the level of homologous direct or inverted repeats , with more than 2000 repeats of more than 200 bp representing close to 5% of the Leishmania genome [19] . The genome of Leishmania is continuously being rearranged at the level of these repeats and while each cell has a core genome , they each differ by a complement of circular or linear amplicons . Upon selection the copy number of these elements increases and in the absence of selection the copy number of these elements decreases [19] . It was shown that circular elements are formed by homologous recombination between direct repeated sequences which is catalyzed by the RAD51 recombinase , known to be involved in the homologous recombination process in kinetoplastids [49] , [50] . However , the rate of formation of linear amplicons was unchanged in a RAD51−/− mutant and linear amplicons must then be formed through another pathway [19] . We hypothesized that DNA repair proteins with nuclease activities may be involved since genomic DNA must be processed when inverted repeats are annealing for the formation of linear amplicons ( Figure 1 ) . We thus focused our efforts on the nuclease MRE11 that is part of the MRN complex [1] , [51] . The biochemical characterization of the Leishmania MRE11 protein indicated that it has properties similar to other MRE11 orthologues . Indeed , as previously reported for MRE11 homologs in other organisms [42] , [51]–[53] , it binds preferentially SS and SA DNA structures in competitive assays ( Figure 2 ) ; it is capable of DNA end resection and exhibited a 3′→5′ exonuclease activity on DS DNA structures , albeit with less effectiveness than the human enzyme ( Figure 3 ) . A H210Y mutant abolished its nuclease activity without impairing its DNA binding properties ( Figures 2 and 3A ) . Finally , LiMRE11 recruitment at DNA damage loci was demonstrated in human ATLD cells , an indication that LiMRE11 detects and binds DNA breaks in these cells ( Figure 4 ) . Having established that the Leishmania MRE11 protein bears all the hallmarks of the MRE11 family of DNA nucleases , we generated a recombinant parasite where the two alleles were inactivated ( Figure 5 ) . These parasites were viable but displayed a growth defect ( Figure 5C ) . The parasites were also more sensitive to the DNA damaging agent MMS ( Figure 5D ) as also observed for Trypanosoma brucei [54] . The growth defect and susceptibility to MMS were reverted when we re-introduced one allele of MRE11 at its original chromosomal locus ( Figures 5C and 5D ) or as part of an episomal construct ( Figure S2 ) . Surprisingly , the expression of an episomal MRE11 gene in a WT strain severely impaired its growth rate ( Figure S2A ) and led to increased sensitivity to MMS ( Figure 2 ) . This result strongly suggests that in a wild-type background , the overproduction of MRE11 is somehow affecting the parasite cell growth , a phenomenon usually not observed in other cell types . The expression level of GFP-MRE11 in WT cells was very low , a phenotype also reported in the trypanosomatid parasite Trypanosoma brucei [55] . These early divergent eukaryotes may be more sensitive to exonuclease overexpression . Alternatively , overexpression of MRE11 in Leishmania may alter more acutely the stoichiometry of interactions with partner proteins such as RAD50 [56] and this could have an impact on cell growth . Interestingly and in support of the above hypothesis , we have shown in an independent study that RAD50 is essential in Leishmania WT cells but its gene can be inactivated in a MRE11−/− background ( Laffitte et al . , unpublished data ) . Possibly that recombination pathways are changed in MRE11−/− to compensate for the loss of MRE11 , therefore altering the importance of the MRN complex and its components . Growth delay observed in WT cells overexpressing MRE11 may also relate to the known role of MRE11 in cell cycle regulation [37] . Indeed , MRE11 is involved in control of DNA replication initiation [57] and overexpression of MRE11 in Leishmania may have stronger effect on replication of Leishmania chromosomes . Selection for MTX resistance often leads to linear amplifications of PTR1 in Leishmania [13] , [14] , [28] . We selected wild-type cells , MRE11−/− null mutants and reverted lines for MTX resistance . Amplified linear PTR1-containing amplicons were observed in all the clones derived from the WT strain but in only 1 out of 10 clones derived from the MRE11−/− null mutant ( Figure 6 ) . The capacity to generate circular amplicons was similar in the two different lines ( Figure 6A and 6B ) . This strong phenotype was specific to MRE11 , as reintroduction of MRE11 at the original chromosomal locus restored the ability of the parasites to generate PTR1 linear amplicons upon MTX selection ( Figure 6C ) . Leishmania differs from yeast in this process since the MRN complex can prevent palindrome amplification in yeast . This process requires the interaction with the CtIP protein [36] , [58] which is absent in Leishmania [41] , possibly explaining the difference between the two organisms . Two others important parameters to consider are the length of the inverted repeats , which are very long in Leishmania , and the length of the sequences between these repeats . Indeed , it was previously suggested in yeast that hairpins with large loops are handled differently than hairpin with smaller loops [59] . This explanation is consistent with our study where IRs are few kb apart ( Figure 7A ) , creating large loops in the hairpin structure , while most of the experiments done in yeast presents IRs closer to each other [58]–[61] . Further experiments could be interesting to determine whether the hairpin strength and structure influences DNA processing by the MRN complex . The reversion of linear amplicons phenotype is dependent on MRE11 nuclease activity since reintegration of the mutated version MRE11H210Y led to parasites generating more efficiently circular PTR1 amplicons but not linear ones ( Figure 6D ) . The mutated MRE11 therefore appears to favor homologous recombination in rescued parasites at the level of direct repeated sequences leading to circular amplicons . It is known that MRE11 is involved in initial events of homologous recombination in many organisms [37] , [38] , [62] and can interact with a number of nucleases and helicases , several of which are encoded in the L . infantum genome [41] . We suggest that the Leishmania MRE11H210Y is still capable of binding DNA and therefore MRN complex formation is intact , as it was previously suggested in yeast [63] . However , MRE11 lack of nuclease activity possibly makes it a better bait for recruiting HR proteins including RAD51 . This facilitated recruitment could be due for example to putative longer association kinetics of the mutated MRE11 to DNA . Alternatively , the inability of LiMRE11H210Y to perform DNA resection may alter the first steps of DNA repair and possibly increase HR proteins recruitment , hence facilitating the formation of circular amplicons . This phenotype is observed in a MRE11−/− background in which we believe that recombination pathways have changed , possibly for compensating loss of MRE11 . Thus , a combination of alterations in recombination pathways along with the mutated MRE11 may be responsible for the phenotype observed . It is salient to reiterate that while the episomal expression of MRE11 in the MRE11−/− null mutants reverted the growth phenotype and sensitivity to MMS ( Figure S2 ) , it did not revert the phenotype of generating linear amplicons upon MTX selection ( Figure S5 ) . This is a further demonstration of the importance of a suitable level of expression to recover proper MRE11 functions . We have shown that gene rearrangements are continuously taking place at the level or repeated sequences , and that these rearrangements can be highlighted by PCR assays . Using PCR , we have shown that the PTR1 linear amplicons are generated at the level of 5 different inverted repeats , indicating that different rearrangements led to the linear amplicons ( Figure 7 ) . The IRs most frequently used are AA′ , BB′ and EE′ and these are relatively close to one another ( 10 kb between A and A′ as well as between E and E′ , 3 kb between B and B′ ) while IRs CC′ and DD′ are further apart ( respectively 38 and 53 kb ) and used only once . This suggests that the length of the intervening sequences between the IRs may impact the rate of annealing of the IRs and the rearrangements leading to linear amplicons . Few amplicons detected by southern blot were not observed by PCR , suggesting that either smaller inverted repeats were used or secondary rearrangements occurred . Our bioinformatics screen has revealed only one direct repeat that could entertain PTR1 circular amplification and indeed the PCR assay has revealed that in every cell in which a circular amplicon was observed in Figure 6 , we observed a positive PCR signal indicative of recombination between direct repeat sequences FF′ ( Figure 7 ) . Because of its lack of control at the level of transcription initiation , Leishmania is likely to use several mechanisms to regulate its expression . We have suggested that gene rearrangements leading to copy number variation is one such mechanism . Indeed the whole Leishmania genome is continuously and stochastically rearranged at the level of repeated sequences . We have shown that there are at least two pathways of rearrangement . One leads to circles after recombination between two direct repeated sequences and this requires RAD51 . Here we have shown that linear amplicons , formed at the annealing of two IRs , is facilitated by the presence of an active MRE11 . We proposed that double-strand breaks ( see Figure 1 ) would be necessary , although this will require experimental validation , which may be challenging in Leishmania as they are no suitable inducible systems . Gene rearrangement is one main mechanism of resistance in Leishmania and a further understanding of the proteins involved in gene rearrangements may provide a strategy to circumvent the emergence of drug resistance .
Promastigotes of Leishmania infantum ( MHOM/MA/67/ITMAP-263 ) and all recombinants were grown in SDM-79 medium at 25°C supplemented with 10% fetal bovine serum , 5 µg/ml of hemin at pH 7 . 0 . Independent clones of all cells generated in this study were selected for methotrexate ( MTX ) resistance , using a stepwise selection starting from an EC50 of 100 nM up to 1600 nM of MTX . All chemical reagents were purchased from Sigma-Aldrich unless specified and were of the highest grades . The L . infantum MRE11 gene ( LinJ . 27 . 1790 ) was amplified by PCR using primers 1 and 2 ( Table S1 ) on genomic DNA template and cloned in a modified pFASTBAC1 plasmid ( Invitrogen ) [64] encoding the glutathione-S-transferase tag ( GST ) at the N-terminus of MRE11 and a 10-histidine tag at its C-terminus for protein purification . Site-directed mutagenesis ( Stratagene , Quickchange ) was used to generate the LiMRE11 mutant H210Y using primers 15 and 16 ( Table S1 ) . The LiMRE11WT protein and the mutated version LiMRE11H210Y were purified from baculovirus-infected SF9 cells and the GST tag was removed by PreScission cleavage as described in [64] . Full-length human MRE11 cDNAs cloned in pFASTBAC were generously provided by Tanya Paull ( University of Texas , Austin ) . Primers 22 and 23 ( Table S1 ) were used for site-directed mutagenesis ( Stratagene , Quickchange ) to generate the human MRE11 mutant H217Y . Proteins hMRE11WT and hMRE11H217Y were purified as described [65] . Full-length human MRE11 cDNAs cloned in pEYFP-C1 ( Clontech ) was generously provided by John Petrini ( University of California , San Francisco ) . The fluorescence observed with pEYFP-C1 is equivalent to that from pEGFP-C1 . The L . infantum gene LiMRE11WT was cloned in pEGFP-C1 plasmid ( Clontech , encoding a GFP tag located at the N-terminus ) for FRAP analysis . DNA substrates were made by the annealing of the 32P-labelled primer 21 with either primer 17 for double-stranded DNA substrate ( DS ) or primer 20 for splayed arm ( SA ) ( Table S1 ) . Reactions ( 10 µL ) contained 25 nM of 32P-labeled DNA oligonucleotides with the indicated concentration of proteins ( see Figure 2 ) in MOPS buffer ( 25 mM MOPS ( morpholine-propanesulfonic acid ) pH 7 . 0 , 0 , 2% tween-20 , 2 mM CaCl2 and 2 mM DTT ) . After 15 minutes of incubation at 37°C , reactions were fixed at 37°C during 15 minutes with 0 . 2% glutaraldehyde . Samples were loaded onto a 8% TBE 1× acrylamide gel , run at 150 V for 1h30 , followed by autoradiography . Exonuclease assays were performed in MOPS/EXO buffer ( 25 mM MOPS ( morpholine-propanesulfonic acid ) pH 7 . 0 , 60 mM KCl , 0 . 2% tween-20 , 2 mM DTT , 2 mM ATP , 5 mM MnCl2 ) . Double-stranded DNA substrate ( DS ) was generated as stated above . The indicated concentration of proteins ( see Figure 3 ) were incubated in MOPS/EXO buffer with 200 nM of 32P-labeled DNA for 30 minutes at 37°C , followed by deproteinization in one-fifth volume of stop buffer ( 20 mM Tris-Cl pH 7 . 5 and 2 mg/mL proteinase K ) for 30 minutes at 37°C . The reactions were boiled during 5 minutes at 95°C after the addition of formamide blue ( 50% final ) then loaded on 8% acrylamide/urea gels . Gels were run at 75W for 60 minutes , dried onto DE81 filter paper , followed by autoradiography . For exonuclease assay on different DNA substrates , 32P-labeled oligonucleotide 21 ( Table S1 ) was labeled at the 5′-end ( using the terminal transferase and the New England Biolabs protocol ) and hybridized to primers 17 , 18 and 19 ( Table S1 ) . ATLD human cells ( kindly obtained from Yossi Shiloh , University of Tel Aviv , Israël ) were maintained in DMEM medium supplemented with 20% fetal bovine serum and 1% penicillin/streptomycin ( Life Technologies ) . ATLD cells were transfected by electroporation with 50 µg of LiMRE11-GFP or hMRE11-GFP DNA constructs . After 16 hours , we performed Fluorescence recovery after photobleaching ( FRAP ) analysis . Briefly , fluorescence was monitored on a Leica TCS SP5 II confocal microscope and laser-induced DNA damage was created using a 405-nm UV laser . Visualization of GFP fluorescence within the micro-irradiated nuclear region was achieved using a 488 nm excitation filter and a 63× objective . Background and photo-bleaching corrections were applied to each dataset using the Volocity-software . The L . infantum MRE11 null mutant ( MRE11−/− ) cells were obtained by targeted gene replacement . MRE11 flanking regions were amplified from L . infantum wild-type genomic DNA and fused to both neomycin phosphotransferase ( NEO ) and hygromycin phosphotransferase ( HYG ) genes using a PCR fusion based-method as described previously [66] . Briefly , 5′UTR of MRE11 was amplified using primers 3 and 4 for the NEO cassette and primers 3 and 5 for the HYG cassette . The NEO gene was amplified with primers 7 and 10 and the HYG gene with primers 8 and 11 . 3′UTR of MRE11 was amplified using primers 13 and 14 for both inactivation cassettes ( see primer sequences in Table S1 ) . At least 3 µg of the 5′UTR-NEO-3′UTR and 5′UTR-HYG-3′UTR linear fragments were successively transfected by electroporation ( as described in [67] ) into L . infantum WT to replace both MRE11 alleles . Recombinants were selected in the presence of 300 µg/ml of hygromycin B ( New England Biolabs , Beverly , MA , USA ) and 40 µg/ml of G418 ( Geneticin; Sigma-Aldrich ) . After 4–5 passages , cells resistant to the drug selection were cloned in SDM-Agar plates ( 1% ) in the presence of antibiotics at the same concentrations . Ten clones of each recombinant were picked up after 10 days and used for further analysis . A re-expression cassette , 5′UTR-MRE11-α-PUR-3′UTR was designed to reintroduce MRE11 into its original chromosomal locus in the HYG/NEO MRE11−/− null mutant . Briefly , this cassette was obtained by PCR of the PUR gene using primers 9 and 12 on the plasmid template Psp72-α-PUR-α [68] encoding the puromycin acetyltransferase marker . This fragment was fused to the 5′UTR and coding sequences of MRE11 ( amplified using primers 3 and 6 ) and 3′UTR fragments ( amplified using primers 13 and 14 in Table S1 ) . The cassette was then transfected by electroporation in the L . infantum HYG/NEO MRE11−/− parasites to replace the NEO allele and recombinants were selected with 100 µg/ml of puromycin ( Sigma–Aldrich ) and 300 µg/ml of hygromycin B ( New England Biolabs ) . The same strategy was used to introduce MRE11 containing the mutation H210Y in the HYG/NEO MRE11−/− strain . The MRE11 ORF was also cloned in the episomal plasmid Psp72-α-puro-α , transfected in L . infantum WT and HYG/NEO MRE11−/− parasites , and stable transfectants were selected with 100 µg/ml of puromycin . MRE11 allele replacement was confirmed by Southern blot analyses . Genomic DNAs from clones were isolated using DNAzol as recommended by the manufacturer ( Invitrogen ) . Digested genomic DNAs or separated chromosomes were subjected to Southern blot hybridization with [α-32P]dCTP-labeled DNA probes according to standard protocols [69] . All probes were obtained by PCR ( see primers in Table S1 ) from L . infantum genomic DNAs . RNAs were extracted using RNeasy plus mini kit ( Sigma ) according to the manufacturer recommendations . The cDNA was synthesized using Oligo dT12–18 and SuperScript II RNase H-Reverse Transcriptase ( Invitrogen ) and amplified in SYBR Green Supermix ( Bio-Rad ) using a rotator thermocycler Rotor Gene ( RG 3000 , Corbett Research ) . The expression level was derived from three technical and three biological replicates and was normalized to constitutively expressed mRNA encoding glyceraldehyde-3-phosphate dehygrogenase ( GAPDH , LinJ . 36 . 2480 ) . The sequences of the primers used in this assay are listed in Table S1 . L . infantum WT , HYG/NEO MRE11−/− , MRE11 re-expressing cells ( HYG/PUR-MRE11WT and HYG/PUR-MRE11H210Y ) , WT Psp72-α-puro-α-MRE11 and HYG/NEO MRE11−/− Psp72-α-puro-α-MRE11 were resuspended at a concentration of 5×106 cells/ml and exposed to increasing doses of MMS ( Sigma–Aldrich ) . Cells were counted after 72 h and reported in survival rate . Intact chromosomes were prepared from L . infantum promastigotes harvested from log phase cultures , washed once in 1× Hepes-NaCl buffer then lysed in situ in 1% low melting agarose plugs . Briefly , cells were resuspended in HEPES-NaCl buffer at a density of 5×107 cells/ml and mixed with an equal volume of low melting-point agarose ( Invitrogen ) . Cells were then lysed overnight at 50°C in lysis buffer ( 0 . 5M ethylenediaminetetraacetic acid ( EDTA ) pH 9 . 5 , 1% sodium dodecyl sulfate ( SDS ) , 350 ug/ml proteinase K ) . Leishmania intact chromosomes were separated in 1× TBE buffer ( from 10× TBE: 1M Tris , 1M Acid boric , 0 , 02M EDTA ) by Pulsed-Field Gel Electrophoresis ( PFGE ) using a Bio-Rad CHEF-DR III apparatus at 5 V/cm and a 120°C separation angle as described previously [70] . The range of chromosome separation was between 150 and 1500 kb . Repeated intergenic sequences were already characterized [19] . Primers ( see Table S1 ) used to detect new junctions created by amplicon formation ( Figure 1 ) were designed for all putative recombination/annealing events between repeated sequences . Primers were chosen within 150 nucleotides from the repeated sequences with their orientation shown in Figure 1 . Optimal primer length was 20 nucleotides and optimal melting temperature ( Tm ) was 64°C . Late log phase promastigotes ( 30 ml ) were pelleted at 3000 rpm for 5 minutes and pellets were washed once with 1× HEPES-NaCl , resuspended in suspension buffer ( 100 mM EDTA , 100 mM NaCl , 10 mM Tris pH 8 . 0 ) , then lysed in 1% SDS and 50 µg/ml proteinase K at 37°C for 2 hours . Genomic DNAs were extracted with 1 volume phenol , precipitated with 2 volume 99% ethanol , washed with 70% ethanol twice and dissolved in 1 ml 1× TE buffer . RNAse A ( Qiagen ) was added at 20 µg/ml and DNAs were incubated at 37°C for 30 minutes , followed by the addition of 50 µg/ml of proteinase K and 0 . 1% SDS at 37°C for 30 minutes . DNAs were extracted with 1 volume of phenol , precipitated and washed in ethanol , and dissolved in DNase free-water ( Millipore ) at 37°C overnight . PCR reaction mixtures consisted of 100 ng of genomic DNA isolated as described above , 1 µl of forward and reverse primers at 100 µM ( Table S1 ) , 0 . 5 µl dNTP mix at 10 mM , 1 . 25 U of FastStart Taq DNA polymerase ( Roche ) , 1× PCR buffer+MgCl2 and 1 . 25 µl BSA at 66 mg/ml . The total reaction mixture was made up to 25 µl by addition of the genomic DNA . For each PCR reaction , the number of cycles was optimized to prevent saturation of the amplification . Saturation of band intensities of the amplified PCR products was determined using the AlphaImager 2000 software . The housekeeping chromosomal gene GAPDH ( LinJ36 . 2480 ) was used as an internal control ( primers pair gg' in Table S1 ) to normalize the amount of DNA loaded in each reaction . | Extrachromosomal DNA amplification is frequent in the human protozoan parasite Leishmania when challenged with drug or other stressful conditions . DNA amplicons , either circular or linear , are formed by recombination between direct or inverted repeats spread throughout the genome of the parasite . The recombinase RAD51 is involved in the formation of circular amplicons , but the mechanism by which linear amplicons are formed is still unknown in this parasite . Studies in other organisms have provided some evidence that a DNA break is required for linear amplifications , and that the DNA repair protein MRE11 can be involved in this process . In this work , we present our biochemical , cellular and molecular characterization of the Leishmania infantum MRE11 orthologue and provide evidence that this nuclease is involved in the formation of linear amplicons in Leishmania . Our results highlight a novel MRE11-dependent pathway used by Leishmania to amplify portions of its genome to respond to a changing environment . | [
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] | 2014 | Formation of Linear Amplicons with Inverted Duplications in Leishmania Requires the MRE11 Nuclease |
Relative contribution of these infections on anemia in pregnancy is not certain . While measures to protect pregnant women against malaria have been scaling up , interventions against helminthes have received much less attention . In this study , we determine the relative impact of helminthes and malaria on maternal anemia . A prospective observational study was conducted in coastal Kenya among a cohort of pregnant women who were recruited at their first antenatal care ( ANC ) visit and tested for malaria , hookworm , and other parasitic infections and anemia at enrollment . All women enrolled in the study received presumptive treatment with sulfadoxine-pyrimethamine , iron and multi-vitamins and women diagnosed with helminthic infections were treated with albendazole . Women delivering a live , term birth , were also tested for maternal anemia , fetal anemia and presence of infection at delivery . Of the 706 women studied , at the first ANC visit , 27% had moderate/severe anemia and 71% of women were anemic overall . The infections with highest prevalence were hookworm ( 24% ) , urogenital schistosomiasis ( 17% ) , trichuria ( 10% ) , and malaria ( 9% ) . In adjusted and unadjusted analyses , moderate/severe anemia at first ANC visit was associated with the higher intensities of hookworm and P . falciparum microscopy-malaria infections . At delivery , 34% of women had moderate/severe anemia and 18% of infants' cord hemoglobin was consistent with fetal anemia . While none of the maternal infections were significantly associated with fetal anemia , moderate/severe maternal anemia was associated with fetal anemia . More than one quarter of women receiving standard ANC with IPTp for malaria had moderate/severe anemia in pregnancy and high rates of parasitic infection . Thus , addressing the role of co-infections , such as hookworm , as well as under-nutrition , and their contribution to anemia is needed .
Anemia affects nearly 25% of all pregnancies worldwide and more than 40% of those in Sub-Saharan Africa [1] . Associated with poor pregnancy outcomes including increased risk of fetal death and preterm birth , maternal anemia also increases risk of maternal mortality associated with obstetric hemorrhage and severe morbidities [1]–[5] . Defined as hemoglobin <11 g/dL , anemia in pregnancy contributes to maternal morbidities and increased risk for mortality associated with conditions such as post-partum hemorrhage [1] , [6] . Maternal anemia may also lead to fetal anemia , and , subsequently , to infant anemia as well as long-term childhood morbidities , including impaired neurodevelopmental outcomes [5] , [7]–[10] . Although anemia in pregnancy is multi-factorial , poor nutrition and infection are common causes . In Sub-Saharan Africa , soil-transmitted helminthes ( STH ) including hookworm , urogenital schistosomiasis , and other parasitic infections such as malaria contribute to the high anemia rates in women and young children [11]–[18] . Helminthic infection prevalence of up to 50% has been documented in some regions in Sub-Saharan Africa [19] . Estimates suggest that more than 25% of pregnant women are infected with hookworm , which causes intestinal bleeding and blood loss , and has been most commonly associated with anemia [1] , [19]–[23] . In a study of parasitic infection in pregnancy conducted in coastal Kenya from 2000 to 2005 , about 32% of women were infected with hookworm , 31% with urogenital schistosomiasis ( Schistosomiasis haematobium ) , and almost 43% with malaria ( Plasmodium falciparum ) , while more than 46% of women were co-infected [24] . Parasitic infections , including hookworm , may be evaluated by intensity of infection , as measured by the concentration of eggs in the stool [25] . While most morbidity has been seen with high intensity infections , in populations with low iron stores , even low-intensity hookworm infection has been associated with morbidities [25]–[30] . In addition to hookworm , P . falciparum malaria increases risk for moderate and severe maternal anemia [11]–[16] . While urogenital schistosomiasis causes adverse health outcomes including anemia , its association with maternal anemia has been less clearly established [27] , [28] . Finally , poor nutrition , which contributes to inadequate intake of iron , folate , and other micronutrients , is common in the geographic areas where these parasitic infections are prevalent , and may have an important role in the relationship of infections and anemia [29]–[33] . Many studies have focused on the effects of a single infectious agent on pregnancy outcome and maternal anemia , although a few studies have attempted to understand the relative effects of multiple agents with conflicting results [24] , [30] , [31] . Fetal anemia has been documented in association with maternal anemia , with rates of 10% to 23% reported in recent studies in Malawi [5] , [7] but its association with infection is less well understood . With preventative treatment during pregnancy with sulfadoxine-pyrimethamine ( IPTp-SP ) as recommended by the World Health Organization ( WHO ) in 2004 [34] , rates of malaria in pregnancy have decreased [35]–[37] . Thus , other causes of maternal anemia and poor birth outcomes have become increasingly important . Recent trials show that presumptive hookworm treatment reduces the infection rates in pregnancy , although the impact on pregnancy outcomes such as maternal anemia has varied [38]–[40] . Where hookworm infection is endemic , the WHO recommends provision of antihelminthic treatment ( e . g . , albendazole or other treatments safe during pregnancy ) in the second trimester [41] . Furthermore , safe , effective treatment is available to treat urogenital schistosomiasis during pregnancy and endorsed by the WHO [42] , [43] . However , for various reasons , the WHO recommendations for hookworm and urogenital schistosomiasis treatment during pregnancy have not been widely implemented [40] , [44] . In this study , we sought to ascertain the contributions of parasitic infection to maternal and fetal anemia among a cohort of pregnant women in coastal Kenya .
All women provided written informed consent prior to study enrollment . Institutional review board approval was received by Case Western Reserve University , Kenya Medical Research Institute , and the University of North Carolina at Chapel Hill . Blood was drawn at ANC visits to make both thick and thin blood smears at antenatal care and at delivery for maternal peripheral , cord and placental samples . The remaining blood was processed and stored at the laboratory , under temperature controlled conditions , for subsequent PCR analyses . P . falciparum malaria was determined by microscopy using the standard Giemsa stain ( thick and thin slices ) on site and post-study by PCR/Ligase Detection Reaction Fluorescent Microsphere Assay , as previously described [45] . PCR is considered to have high sensitivity to detect malaria parasitemia; however , microscopy , which is more commonly used in clinical settings , is generally considered reliable to detect malaria present in higher concentrations [46] . For this study , as exposures of interest , microscopy was defined as a proxy for high intensity malaria infection and PCR-positive malaria as any malaria infection . Maternal stool and urine samples were collected at the first ANC visit and at delivery . Study participants brought a morning stool sample to the respective visit and these stool specimens were brought to the laboratory immediately following the collection by the health worker . Stool samples were tested for hookworm infection and other STH ( Ascaris lumbricoides , Trichuris trichuria , Strongyloides stercoralis ) . Approximately one gram of fresh stool specimen was processed and examined by Richtie's concentration method [47] . STH infections were determined by the presence of ova or larva in the stool sample . Burden was also determined by count of parasites/gram . Urine samples were collected and processed by the laboratory immediately following collection . Urine was evaluated for presence of urogenital schistosomiasis ( S . haematobium ) and results expressed as number of eggs/mL . Schistosomiasis was also categorized as light ( 0–<50 eggs/mL ) or moderate ( ≥50 eggs/mL ) , according to WHO criteria [42] . Hemoglobin ( Hb ) levels were measured at the first ANC visit and at delivery by Coulter counter ( Beckman Coulter Inc . ) . Women were classified as anemic ( Hb<11 g/dL ) and then categorized as being moderately to severely anemic ( Hb<9 g/dL ) , as the primary outcome , and being mildly to non-anemic ( Hb≥9 g/dL ) according to the WHO classification of anemia [1] . Cord blood hemoglobin levels were also determined and cord ( fetal ) hemoglobin defined by hemoglobin <12 . 5 g/dL , as previously defined [7] . Maternal height and weight were taken at the first ANC visit ( generally in the second trimester ) and body mass index ( BMI ) calculated as kg/m2 . Since pre-pregnancy BMI was unavailable , to assess BMI , low BMI was defined as the lowest 10th percentile for the gestational age at measurement . The overall BMI ranged from 19 . 5 to 31 . 4 kg/m2 and the 10th percentile cut-off for GA at measurement ranged from 19 . 8 to 20 . 7 kg/m2 . Trained study nurses interviewed the women at antenatal care visit to obtain key socio-demographic and basic medical history . The study was a secondary analysis of data collected as part of a larger study to evaluate the association of in utero malaria infection on neurodevelopmental outcomes ( NCT00314899 ) . Analyses were performed in SAS version 9 . 3 ( SAS Institute , Cary , NC , USA ) . Descriptive analyses were performed . Parity , gestational age , maternal age , maternal education , and socio-economic status ( as measured by monthly household expenditures ) were evaluated as potential confounders , based on previous research [14] , [17] , [19]–[23] . The risk ratios for moderate/severe anemia associated with each of infections evaluated are presented with and without the potential confounders , using a log-binomial regression model . A backward elimination strategy was employed to estimate the adjusted RR of moderate/severe maternal anemia associated with infections and maternal BMI , accounting for the potential confounders . An a priori cut-off ( p<0 . 15 ) was defined for variable to be considered significant and retained in the final regression model . Analyses were restricted to those with complete case information .
Of the 813 women screened at ANC , 706 ( 88% ) consented women had blood and urine samples available for anemia , malaria , and schistosomiasis evaluation , respectively . Of these participants , 544 ( 71% ) provided stool samples at antenatal care for measurement of STHs and 394 had outcomes at delivery available . At enrollment at first ANC , the mean gestational age was 24 . 5 weeks ( SD 3 . 8 weeks ) . 516 ( 71% ) were anemic ( Hb<11 g/dL ) and 190 ( 27% ) had moderate to severe anemia ( Hb<9 g/dL ) . For subsequent analyses , moderate/severe anemia was evaluated as the primary outcome of interest . About 19% of the women were <20 years of age , nearly 86% were married , 21% had no formal education , and about 23% were primagravidas ( Table 1 ) . In unadjusted analyses , these factors were not associated with increased risk of anemia . Insecticide-treated bednet use , malaria treatment , and iron/folic acid received 3 months prior to the ANC visit were also not associated with moderate/severe anemia risk . Few women ( <2% ) received anti-helminth treatment prior to first ANC ( data not shown ) . The association of demographic characteristics and the prevalence at first ANC of hookworm infection , PCR-positive malaria ( P . falciparum ) , and urogenital schistosomiasis ( S . haematobium ) are summarized in Table 2 . Risks of P . falciparum PCR-positive ( RR 2 . 29 , 95% CI 1 . 38 , 3 . 79 ) , hookworm ( RR 1 . 42 , 95% CI 1 . 02 , 1 . 98 ) , and urogenital schistosomiasis infection ( RR 2 . 25 , 95% CI 1 . 66 , 3 . 07 ) were higher among those <20 years compared to women ≥20 years . Risks for infection did not differ significantly by maternal education levels . Primigravidity was associated with increased risk of P . falciparum PCR-malaria ( RR 1 . 52 , 95% CI 1 . 11 , 2 . 56 ) and urogenital schistosomiasis ( RR 1 . 51 , 95% CI 1 . 07 , 1 . 78 ) , but not hookworm infection . About one-fourth ( 25 . 7% ) of the women reported no use of insecticide-treated bednets ( ITNs ) prior to enrollment , which was associated with increased risk of malaria infection ( RR 2 . 00 , 95% CI 1 . 23 , 3 . 27 ) . Hookworm ( 23 . 7% ) , P . falciparum PCR-malaria ( 10 . 8% ) , S . haematobium ( 17 . 1% ) , and T . trichuria ( 10 . 1% ) were the most common infections at the first ANC visit . Of women positive for one of these infections , approximately 10% were co-infected , 4% with urogenital schistosomiasis and hookworm , and 2% with either malaria and hookworm or malaria and schistosomiasis , and the remaining with another combination ( data not shown ) . Hookworm intensity ranged from 1 to 1035 eggs/g; thus all were considered ‘light’ according to the WHO criteria ( light defined as <1999 eggs/g ) . To further evaluate whether relative intensity of infection was associated with outcomes , we also classified the highest intensity of infection ( ≥100 eggs/g ) among the cohort as ‘moderate’ infection . We next examined the risk for moderate/severe maternal anemia at ANC associated with these infections , in unadjusted and adjusted analyses ( Table 3 ) . In analyses adjusted for gestational age , primagravid status , and low BMI , moderate/severe anemia was associated with moderate hookworm infection ( aRR 2 . 53 , 95% CI 1 . 62 , 3 . 92 ) , P . falciparum PCR-positive and microscopy positive ( aRR 1 . 45 , 95% CI 1 . 01 , 2 . 08 and aRR 1 . 98 , 95% CI 1 . 17 , 3 . 35 , respectively ) . S . haematobium and T . trichuria , although common , were not significantly associated with moderate/severe anemia and few had moderate burden of infection . S . stercoralis and A . lumbricodes were observed in about 1% of women , using a single sample , which may be an under-representation since single-sample fecal assays have low sensitivity , especially for detecting S . stercoralis [48] . These infections were not significantly associated with moderate/severe anemia at ANC , in adjusted or unadjusted analyses . Among all infections , only moderate burden hookworm and malaria were associated with moderate/severe anemia . For those women who delivered live , term births at the study hospital , we evaluated the association between infection at the first ANC visit with maternal and fetal anemia at delivery , as well as the association of infections detected at delivery for those women and their fetuses who had stool ( n = 210 ) , or urine and blood samples ( n = 394 ) available at delivery . Women whose births were included were comparable whose births were excluded ( exclusion criteria were delivery outside the study hospital , voluntary discontinuance of study participation , lost to follow up , premature delivery , or non-collection of samples ) , on socio-demographics , maternal characteristics and infection at ANC ( malaria , hookworm ) ( data not shown ) . At delivery , 34 . 2% of the women had moderate/severe anemia and 18 . 4% of the neonates had fetal anemia ( cord Hb<12 . 5 g/dL ) . Moderate hookworm burden at the first ANC visit was associated with moderate/severe maternal anemia at delivery ( aRR 2 . 30 , 95% CI 1 . 42 , 3 . 71 ) , but other infections at first ANC visit were not significantly associated with risk of moderate/severe maternal anemia at delivery ( Table 4 ) . Fetal anemia was not significantly associated with any of the infections , in adjusted or unadjusted analyses . Of women tested for presence of hookworm , P . falciparum malaria ( PCR and microscopy ) and schistosomiasis at delivery , none of these infections were significantly associated with maternal or fetal anemia at delivery; however , there were insufficient numbers of high-burden hookworm at delivery to test for this association . Furthermore , hookworm infection at ANC was associated with risk of hookworm infection at delivery and P . falciparum malaria infection was associated with risk of malaria infection at delivery ( p<0 . 0001 for both ) . In exploratory analyses , we also examined the association of the anemia at ANC with maternal and fetal anemia at delivery among 210 women and their newborns with both measures available . Women with moderate/severe anemia at first ANC visit had increased risk of maternal anemia at delivery ( unadjusted RR 3 . 84 , 95% CI 2 . 94 , 4 . 98 ) . Fetal anemia was also associated with moderate/severe maternal anemia at first ANC visit and moderate/severe maternal anemia at delivery ( RR 1 . 58 , 95% CI 1 . 02 , 2 . 45 , p = 0 . 05; RR 2 . 75 , 95% CI 1 . 78 , 4 . 24 , p<0 . 001 , respectively ) ( data not shown ) . Finally , a multivariate regression model was developed to assess the infections identified as risk factors associated with moderate/severe maternal anemia at ANC . We found that moderate hookworm ( aRR 2 . 37 , 95% CI 1 . 44 , 3 . 91 , p = 0 . 0007 ) and P . falciparum microscopy-positive malaria infection ( and aRR 2 . 06 , 95% CI 1 . 24 , 3 . 44 , p = 0 . 005 , respectively ) remained significantly associated with moderate/severe maternal anemia at ANC , when adjusting for primigravid status and low maternal BMI ( Table 5 ) .
Few studies have examined the burden of helminthic infection and under-nutrition in pregnancy on maternal and fetal anemia in malaria-endemic regions . This is now especially important in geographic areas with a declining incidence of malaria found with widespread use of insecticide-treated bednets and IPTp-SP [49]–[51] . In this study of pregnant women at ANC , the prevalence of P . falciparum PCR-malaria had fallen to 9% from previous rates of 40% at delivery reported in a similar region in Kenya , prior to widespread IPTp-SP and ITN's [24] . Despite this decline in the malaria rate , 71% of the pregnant women studied were anemic , and overall more than 25% had moderate/severe anemia . Infection with hookworm ( 24% ) , and schistosomiasis ( 17% ) , which had less significant reductions since the previous study period [24] , were also common , although most hookworm infections were light . Both P . falciparum malaria as diagnosed by microscopy and moderate hookworm infections at ANC were associated with moderate/severe anemia at the ANC visit , while urogenital schistosomiasis and trichurisis and light infections were not . No association between PCR-detected malaria infections and anemia was found , probably because malaria detected by PCR included many low-level parasitemias . Thus , our findings are consistent with previous studies showing an association with anemia among populations with higher rather than low-intensity parasitic infection [11] . Compared to the first ANC visit , the prevalence and burden of both infections at delivery were lower and neither type of infection was significantly associated with anemia at delivery . The decrease infection rates detected at delivery may be related to restricting analyses to women with term , live births as well as the consequence of the enhanced treatment and care given to this study cohort . Socio-demographic factors assessed including age , gravidity , education , socio-economic and marital status , and low BMI were not significantly associated with moderate/severe maternal anemia in this cohort . However , the study was conducted among a relatively homogenous community , and thus these disparities may not have been large enough to be detectable . One limitation was that pre-pregnancy BMI and additional measures of under-nutrition were not available for this cohort and thus a more sophisticated assessment of the relationship of nutritional intake and anemia was not possible . Our findings are also consistent with research suggesting that in the context of low socio-demographic status , even light infections such as hookworm and malaria may be associated with anemia [27] , [52]; however , further research is needed to address these relationships . In low-resource areas where hookworm , malaria , and other parasitic infections , in addition to poor nutritional intake , are common , maternal anemia is prevalent and adversely affects the health of both women and their children . In this study , maternal anemia was associated with increased risk of fetal anemia . While we found no significant association of fetal anemia with maternal infection , these results should be interpreted with caution given the restriction to live , term births and the relatively small sample size , reducing our ability to detect differences . While fetal anemia has been less well studied , emerging research suggests that it may also be common in areas with high-burden of infection [5]–[7] , [53] . Since fetal and childhood anemia associated with maternal anemia potentially may lead to long-term impaired neurologic function , 8–10 , a better understanding of etiology and effects of fetal anemia is important . Effective , safe treatments are available to prevent and treat hookworm and malaria , both of which were associated with maternal anemia in this study . While numerous studies have evaluated preventative treatment for malaria in pregnancy , fewer have assessed antihelminthic treatment in the context of malaria treatment . Of those that have assessed hookworm , the results suggested that benefit may be most pronounced among women with higher burden of hookworm infection [22]–[24] , [54]–[56] . Additionally , few studies have evaluated the roles of multiple infections and under-nutrition in pregnancy and interventions . In a study assessing the role of malaria , hookworm , and nutrition in Uganda , malaria was significantly associated with maternal anemia while hookworm and nutrition were not . The authors speculated that this was in part due to the relatively good nutritional indicators and coverage of helminthic treatment in the region , while malaria prevention strategies were limited [54] . In contrast to the Uganda study , in our study , while all women in this study received antenatal care including IPTp-SP for malaria , treatment for hookworm as indicated , and iron/folic acid , most were not enrolled until after 20 weeks gestation . Thus , even with relatively good antenatal care , treatment was not initiated until the second trimester at which time anemia was prevalent in this cohort . Furthermore , unlike the region where this study was conducted and despite the international recommendations , uptake of treatment for hookworm , malaria , and schistosomiasis in ANC is still low in many parts of Africa [40] , [56] . In part , this may relate to perceptions that treatment has not been associated with improved pregnancy outcomes [38] , or may be harmful [40] . Given the high prevalence of anemia seen in our study and elsewhere implementation of known effective interventions prior to or early in pregnancy to reduce anemia and ultimately reduce maternal and newborn mortality is needed . | International guidelines recommend routine prevention and treatments which are safe and effective during pregnancy to reduce hookworm , malaria and other infections among pregnant women living in geographic areas where these infections are prevalent . Despite their effectiveness , programs to address common infections such as hookworm , schistosomiasis and malaria during pregnancy have not been widely adopted . Hookworm , malaria and other infections have been associated with anemia in children , but the studies on the impact of these infections on anemia in pregnancy have not been as clear . This study was undertaken to evaluate the prevalence of parasitic infections among women attending antenatal care which provided the nationally recommended malaria preventive treatment program in coastal Kenya . At the first ANC visit , more than 70% of women were anemic , nearly one-fourth had hookworm and about 10% had malaria . Women with high levels of hookworm or malaria infections were at risk of anemia . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"hookworm",
"infection",
"parasitic",
"diseases",
"helminth",
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] | 2014 | The Association of Parasitic Infections in Pregnancy and Maternal and Fetal Anemia: A Cohort Study in Coastal Kenya |
The composition of the gut microbiome in industrialized populations differs from those living traditional lifestyles . However , it has been difficult to separate the contributions of human genetic and geographic factors from lifestyle . Whether shifts away from the foraging lifestyle that characterize much of humanity’s past influence the gut microbiome , and to what degree , remains unclear . Here , we characterize the stool bacterial composition of four Himalayan populations to investigate how the gut community changes in response to shifts in traditional human lifestyles . These groups led seminomadic hunting–gathering lifestyles until transitioning to varying levels of agricultural dependence upon farming . The Tharu began farming 250–300 years ago , the Raute and Raji transitioned 30–40 years ago , and the Chepang retain many aspects of a foraging lifestyle . We assess the contributions of dietary and environmental factors on their gut-associated microbes and find that differences in the lifestyles of Himalayan foragers and farmers are strongly correlated with microbial community variation . Furthermore , the gut microbiomes of all four traditional Himalayan populations are distinct from that of the Americans , indicating that industrialization may further exacerbate differences in the gut community . The Chepang foragers harbor an elevated abundance of taxa associated with foragers around the world . Conversely , the gut microbiomes of the populations that have transitioned to farming are more similar to those of Americans , with agricultural dependence and several associated lifestyle and environmental factors correlating with the extent of microbiome divergence from the foraging population . The gut microbiomes of Raute and Raji reveal an intermediate state between the Chepang and Tharu , indicating that divergence from a stereotypical foraging microbiome can occur within a single generation . Our results also show that environmental factors such as drinking water source and solid cooking fuel are significantly associated with the gut microbiome . Despite the pronounced differences in gut bacterial composition across populations , we found little differences in alpha diversity across lifestyles . These findings in genetically similar populations living in the same geographical region establish the key role of lifestyle in determining human gut microbiome composition and point to the next challenging steps of determining how large-scale gut microbiome reconfiguration impacts human biology .
The human gut is comprised of a diverse community of bacteria , the microbiome or microbiota , that influences several aspects of human physiology , including nutrient metabolism , immune responses , and resistance to infectious pathogens [1–3] . This highly malleable microbial component of human biology exhibits rapid , and in some cases , irreversible changes in response to dietary and environmental factors [4–11] . Modern humans have experienced diverse environments since expanding out of Africa approximately 100 , 000 years ago . Over the past approximately 10 , 000 years , hunting and gathering has largely yielded to different forms of agriculturally supported lifestyles . More recently , the diet of billions of people has undergone profound changes with the advent of industrialism . Dietary changes combined with a variety of other factors associated with the industrial revolution have been credited as contributing to the alterations in the gut microbiome in industrialized populations [12] . However , interpretation of the current data is clouded by potential contributions of human genetic variation , environment , and geographical factors [5 , 7 , 13] . While current evidence is consistent with the extent of lifestyle change impacting the gut microbiome [14] , to what extent shifts in lifestyles away from a foraging lifestyle influence gut microbiomes remains poorly understood . Moreover , whether shifts in lifestyles influence gut microbiomes in preindustrial populations remains poorly understood . Previous studies of the gut microbiome have demonstrated a stark contrast between industrialized versus unindustrialized populations . Comparisons of the gut microbiomes of traditional human populations in Africa and South America with those of the industrialized Western populations from Europe and the United States of America reveal that the human gut microbiome varies across geography and corresponds to differences in lifestyles [15–30] . Microbiome differences between these populations are often large and stark . However , since these populations reside in geographically distinct regions , represent extreme modes of human subsistence , and are culturally distinct , identifying the factors responsible for microbiome differences remains a challenge , as diet , sanitation , and access to medical care are often associated with geographic and cultural features that differentiate populations being compared and confound lifestyle variables . For example , one common trend from these studies is the higher diversity of gut bacteria in unindustrialized traditional populations . However , comparison of human populations that reside in close geographical proximities but practice different types of subsistence have shown little differences in alpha diversity across lifestyles [19 , 22 , 29] . Most of the traditional societies investigated thus far live within tropical latitudes , which differ from other regions of the world in macroecological biodiversity , climate , and numerous other factors . Hence , whether difference in alpha diversity between these traditional societies and Europeans is due to contrasting lifestyles , residence in the tropics , or other factors remains unclear [31] . Additionally , it remains unknown when microbiome compositions shifted during the process of industrialization and how long it took for those transitions to occur . Hence , understanding how transitions in human lifestyles lead to changes in the gut microbiomes would be greatly aided by studying populations that cohabit similar geographic regions and have undergone recent changes in culture , lifestyle , and diet . In order to explore how a gradient of traditional lifestyles may affect the human gut microbiome , we have analyzed the gut microbiomes from four rural Himalayan populations . The Himalayan populations include the Chepang ( a foraging population ) , the Raute and Raji ( two foraging communities that are currently transitioning to subsistence farming ) , and the Tharu ( former foragers that have completely transitioned to farming within the last two centuries ) . We assessed contributions of lifestyle , diet , and environment on the gut microbial variation in the rural Himalayan populations . To further assess how the gut microbiomes of these traditional groups differ from an industrial population , we compared them to Americans with European ancestry . Our results show that gut microbiome composition mirrors the transitions from a traditional to an agrarian lifestyle in Himalaya . In addition to the dietary gradient across these populations , intra- and interpopulation variability in lifestyle elucidated additional environmental factors that may contribute to microbiota change .
Our participants included 54 individuals from four Himalayan groups , including Chepang ( N = 14 ) , Raji ( N = 9 ) , Raute ( N = 11 ) , and Tharu ( N = 20 ) , with median age of 40 years ( SD ± 14 years ) from rural villages in Nepal ( Fig 1 and S1 Table ) . These four populations are long-term residents of the Himalayan foothills ( altitude less than 1 , 000 m ) . Ethnographic , linguistic , and cultural data suggest that these populations are of East Asian ancestries , they speak closely related languages , and their cultural practices are similar to one another [32–34] . Although all four of the Himalayan populations in this study were foragers until recently [35–38] , habitat loss due to rapid deforestation , population expansions of non-native groups , establishment of new settlements , and construction of modern highways led to settlement of these groups at various time points in the last 300 years . Historical records indicate that the Tharu gradually transitioned into agrarian lifestyles beginning in the late 18th century ( 250–300 years ago ) [38] . They have fully transitioned into farming and are virtually completely disengaged from foraging practices . The population size of the Tharu is approximately 1 . 5 million , and they are distributed throughout the Terai plains in Nepal [39] . Historically , the Raute , Raji , and Chepang were seminomadic foragers , and their diets included native tubers , greens , and fruits from the jungle and wild honey , fish , and occasional game [36 , 37 , 40] . The Raute and Raji abandoned their foraging lifestyles in the 1980s [35 , 36] . While the Raute have settled in the remote hills in western Nepal , the Raji have settled in the Terai plains , which are relatively more urbanized . The current census size of Raute and Raji are approximately 650 and approximately 3 , 750 , respectively [39] . The Chepang were fully nomadic at least until 1848 [41] and began supplementing their foraging practices with subsistence agriculture less than a century ago [37] . The Chepang population size is approximately 48 , 500 [39]; however , they exist as fragmented tribes in small , geographically isolated villages of a few hundred individuals deep within the hills of lower Himalaya . The Chepang in this study currently inhabit a remote village that is devoid of modernity , with no electricity , running water , irrigation , fertilizers , modern machines , or marketplaces . They still practice slash and burn agriculture and are completely dependent on rainwater for farming . Because yields from such traditional farming are low , their daily diet consists of wild plants such as sisnu ( nettles ) that are foraged from the forests . We conducted surveys to assess the extent of lifestyle change as these seminomadic populations transitioned to farming in the last few hundred years . The survey questionnaire included questions pertaining to current dietary practices , traditional and modern medicines , and several environmental factors , including sources of drinking water , types of cooking fuel , alcohol use , and tobacco consumption ( N = 53 , S2 Table ) . We also surveyed presence of parasites in our participants microscopically . Supervised learning using a Random Forest classifier model on the survey data ( including intestinal parasite load ) assigned the individuals to their respective populations with high overall accuracy ( 94% , AUC = 0 . 997 , S1 Fig ) . The Chepang , Raute , and Tharu were classified with 100% accuracies , indicating these populations have distinct lifestyles ( S3 Table ) . 67% of the Raji individuals were classified accurately as Raji while the remainders were classified as Tharu . A correspondence analysis ( CA ) of the survey data ( including intestinal parasite load ) also revealed lifestyle differences between these populations ( Fig 2A ) . The first CA dimension ( CA1 ) explained 15 . 8% variation in the data and was strongly correlated with lifestyle gradients . Along CA1 , samples progressed from the Chepang foragers at one extreme , to the Raute and Raji transitioning populations , and then to the Tharu farmers at the opposite extreme ( Fig 2B ) . Despite the geographical distance between them , the Raji lifestyle appears to be more similar to that of the Tharu farmers , consistent with the Raji settlement occurring in a more urbanized setting compared to the Raute . Similarly , the Raute reside in geographical proximity to the Raji , although their lifestyle partitions between the Raji and the Chepang , indicating geographical proximity is not driving the lifestyle differences . A total of 10 variables contributed highly to the first two CA dimensions , and most of them are strongly associated with dietary differences and modernity ( Fig 2C ) . These differences are described in detail in S2 Fig . Briefly , foraged plants such as sisnu ( nettles ) and jaand , a slushy alcoholic beverage made from fermenting millet or corn , are staples of the Chepang diet . In contrast , sisnu and jaand consumption was minimal among the Raute , Raji , and Tharu . Also , perceived food scarcity was higher in the Chepang and Raute relative to the Raji and Tharu . Although meat consumption was low across all four populations , the Tharu consumed animal products such as yogurt more frequently than the other three populations . Furthermore , the Tharu and Raji also showed increased signs of modernity . For example , they have installed tube wells at their homes , enabling access to underground water for drinking . In contrast , the Chepang and Raute still fetch drinking water from rivers and streams . Also , use of solid biomass fuel was lower in the Tharu and Raji , while the Chepang and Raute are still completely dependent on burning firewood for cooking . Although we detected low overall levels of intestinal parasites across the participants , Ascaris , Entamoeba , Trichuris , Hymenolepis , and Coccidia were detected in some , and most of the infected were the Chepang . Together , the diet and lifestyle assessments provide unbiased support that the four populations represent a gradient from traditional to increasingly agrarian and urban lifestyles . In order to assess whether the gut microbiome varies across lifestyles , we characterized the gut bacterial composition of these populations using the Illumina MiSeq to sequence the V4 region of 16S ribosomal RNA ( rRNA ) gene obtained from a total of 79 stool samples ( including technical replicates ) , with an average of 11 , 570 ( ±4 , 653 ) high-quality reads per sample ( S3 Fig and S4 Table ) . Since flash freezing of the samples was not possible in the remote sampling areas in the Himalaya , we used commercially available DNAGenotek OMNIgene kits to collect stool samples from the four populations ( N = 54 ) . We also collected stool samples from 10 Americans of European descent using OMNIgene kits and compared them with freshly frozen samples to evaluate whether the preservation method affected the microbiome profile . The 16S rRNA profiles of the same samples stored by flash freezing or by OMNIgene were remarkably similar , with reproducible differences in minor taxa ( Euryarcheota and Cyanobacteria ) , demonstrating the reliable preservation of microbiome composition with the OMNIgene kits ( S4 Fig ) . Due to the reproducible , albeit minor , differences between the two collection methods , we used the OMNIgene data from the Americans for consistency in subsequent comparative analyses . The American samples provide a thoroughly investigated population as an industrialized reference for the Himalayan data . Comparison of the community structure in the five study populations using unweighted UniFrac distances , a measure of compositional similarity that includes the phylogenetic relatedness between microbiomes , showed that the gut microbial composition varies across populations ( P < 2 . 2 × 10−16 , Kruskal–Wallis test , S5 Table ) . Within Himalaya , the Chepang foragers were closest to the Raute , and the distance between the two was significantly smaller than the Chepang–Tharu distance and marginally smaller than the Chepang–Raji distance ( false discovery rate [FDR] adjusted P = 5 . 7 × 10−4 and 0 . 057 , respectively; Dunn’s posthoc test ) . The Raute , Raji , and Tharu were equidistant from one another ( FDR adjusted P = 0 . 99 for all pairwise comparisons , Dunn’s posthoc test ) . Similar results were also observed with weighted UniFrac and Bray–Curtis distances , both of which take the taxa abundance into account ( S5 Table ) . These results suggest that differences in traditional lifestyles as these groups transition from foraging to farming influence their gut microbiomes . Comparison of these traditional Himalayan populations with industrialized Americans showed that all four Himalayan populations exhibited much larger distances from the Americans than when compared to one another ( P < 1 . 3 × 10−5 for all pairwise comparisons , Dunn’s posthoc test , S5 Table ) . The Chepang were the most distant from the Americans , followed by the Raute , while the Raji and Tharu were equally close to the Americans . Visualization of these distances using a Principal Coordinates Analysis ( PCoA ) revealed separation of populations along the top two dimensions ( P = 1 × 10−5 , permutational multivariate analysis of variance [PERMANOVA] , Fig 3A ) . Furthermore , gradients in lifestyles were reflected by the distribution of populations along the primary axis ( PCoA1 , Fig 3B ) . These distributions remained consistent when using Bray–Curtis and weighted UniFrac distances as well ( P = 1 × 10−5 for both , PERMANOVA , S5 and S6 Figs ) . When American microbiomes were eliminated from the PCoAs , the gradient between the Himalayan populations remained pronounced ( P = 1 × 10−5 , PERMANOVA , S7 Fig ) . Among the four Himalayan populations , the strongest separation was observed between the Chepang foragers and the Tharu farmers . A random forest classifier based on the 16S rRNA-defined amplicon sequence variant ( 16S ASV ) data assigned the Chepang , Tharu , and American individuals to their respective source populations with 79% , 100% , and 100% accuracies ( overall accuracy = 66% , AUC = 0 . 9 , S8 Fig and S3 Table ) . The classification accuracy for the Raute and Raji , the two populations that recently transitioned from foraging to farming , were relatively poor ( <10% ) . While some of the individuals from these groups were classified as the Chepang , others were classified as the Tharu . However , none of the Himalayan individuals were classified as American . These results show that the gut microbiome of the Chepang foragers differs from that of the Tharu farmers , while that of the Raute and Raji reflect a transitional state in their lifestyles . They also indicate that the gut microbiome compositions of the Himalayan populations are distinct from those of the Americans . Therefore , these findings collectively indicate that the transition from foraging to farming is accompanied by noticeable shifts in gut microbiome , which may be further exacerbated in industrial populations . To formally evaluate whether variation in gut microbiota reflects lifestyle differences within Himalaya , we assessed associations between the respective primary dimensions from the lifestyle questionnaire , parasite analysis ( CA1 ) , and gut microbial composition analysis ( PCoA1 calculated using the four Himalayan populations ) ( Fig 3C and S5 Fig ) . We found that the CA1 was strongly correlated with the PCoA1 obtained from all of the three distance matrices ( Spearman’s Rho = 0 . 47 , 0 . 44 , and 0 . 28 for Bray–Curtis; unweighted UniFrac; and weighted UniFrac distances , respectively; P values = 4 . 5 × 10−4 , 1 . 1 × 10−3 , and 0 . 05; correlation test ) . The CA1 was also correlated with PCoA2 of all three distance matrices ( Spearman’s Rho = 0 . 26 , 0 . 44 , and 0 . 39; P values = 0 . 06 , 0 . 001 , and 0 . 004 for Bray–Curtis; unweighted UniFrac; and weighted UniFrac distances; correlation test ) . Conversely , no significant correlations were detected between CA2 and either of the PCoA axes from all three distances ( P value > 0 . 05 , correlation test ) . Notably , CA1 but not CA2 is associated with lifestyle gradient in Himalaya ( Fig 2 ) . Strong and consistent correlations between CA1 and PCoA axes indicate that gut microbiome compositions of the Himalayan populations mirror their lifestyles . Previous studies have suggested that elevated species diversity in the gut microbiome is a hallmark of traditional populations [19 , 24] . We assessed the alpha diversity in the five study populations using species richness and Shannon’s H at various rarefaction depths ranging from 10–6 , 500 reads ( Fig 4 ) . Species richness measures the presence and absence of taxa , whereas Shannon’s H additionally accounts for the relative abundances of each taxon within each population . We compared alpha diversity across the five populations at a rarefaction depth of 3 , 000 to include all 64 samples and at a higher rarefaction depth of 6 , 500 , which included 61 samples . Regardless of the rarefaction depth , species richness was not significantly different between any of the five populations ( P > 0 . 05 , Kruskal–Wallis test ) . We did find marginally significant differences in Shannon’s H between these populations ( P = 0 . 01 and 0 . 03 at 3 , 000 and 6 , 500 rarefaction depths , Kruskal–Wallis test ) . A posthoc pairwise comparison of all five populations showed that only the alpha diversity in the Tharu was slightly lower than that in the Americans ( FDR adjusted P = 0 . 02 and 0 . 045 at the two rarefaction depths , respectively; Dunn’s posthoc test ) . Next , we evaluated association between the 10 factors that differentiate the lifestyle of the Himalayan populations ( Fig 2 ) with the two alpha diversity measures . Neither species richness nor Shannon’s H were significantly associated with any of these factors at either rarefaction depth ( P > 0 . 05 , nonlinear mixed effects model ) . Finally , we assessed additional metrics of diversity ( Fisher’s alpha , Simpson’s D ) , which similarly fail to differentiate populations ( S9 Fig ) . These results indicate that lifestyle differences among the Himalayan populations or between these populations and Americans have little effect on the alpha diversity of their gut microbiome . Although lifestyle differences have little effect on the alpha diversity , gut microbiome compositions of the Himalayan populations reflected the gradient in their lifestyles . To identify taxa driving the differences in the gut microbiomes across lifestyles , we compared the abundance of individual phyla across the five populations using a negative binomial generalized linear model ( GLM ) , as implemented in differential expression analysis for sequence count data version 2 ( DESeq2 ) [42] . Differential abundances were detected for six out of 10 phyla ( FDR adjusted P values are shown in S6 Table ) , and four of the six phyla reflect a traditional western lifestyle gradient . The Himalayan populations were characterized by higher abundance of Proteobacteria , while abundances of Actinobacteria , Firmicutes , and Verrucomicrobia were highest in the Americans , intermediate in the farmers ( Tharu , Raji , and Raute ) , and lowest in the Chepang foragers ( Fig 5A ) . Higher levels of Proteobacteria and lower levels of Actinobacteria and Verrucomicrobia are common features of many traditional human gut microbiomes around the world [19 , 21 , 24 , 29] . To characterize the taxonomic differences between populations at a finer taxonomic level , we repeated the above analysis at the genus level and identified 52 out of 116 genera that showed significant differences in abundance across the five populations ( Fig 5B , FDR adjusted P values are shown in S7 Table ) . The majority of these genera show consistent differences along the lifestyle gradient within the Himalayan samples ( Fig 5B ) . For example , among the Himalayan populations , the Chepang foragers were enriched for Ruminobacter , Campylobacter , and Treponema relative to the Tharu farmers ( S10 Fig ) . Although we did not detect significant differences in the abundance of Bacteroidetes phylum across these populations , several members of this phylum distinguished the Himalayan and American populations . The rural Himalayan communities were enriched for Prevotella , Alloprevotella , and Anaerophaga and significantly depleted in Bacteroides , Alistipes , Butyricimonas , Odoribacter , and Barnesiella . 29 genera belonging to Firmicutes differed significantly across the five populations , and their distribution was complex across these populations ( S11 Fig ) . The Himalayan populations were enriched for Clostridium sensu stricto , Catenibacterium , Lactobacillus , Bulleidia , Sarcina , Enterococcus , Eubacterium , Oribacterium , Mogibacterium , Mitsuokella , Allisonella , Weissella , Papilbacter , and two unknown genera of Erysipelotrichaceae and Veillonellaceae families . Alternatively , abundances of several Clostridium genera , Oscillibacter , Blautia , Butyriciococcus , Anaerostipes , and Flavonifractor were elevated in the Americans . The Americans also showed highest abundances of Bifidobacterium ( Actinobacteria ) and Akkermansia ( Verrucomicrobia ) , both of which were extremely low in the Chepang foragers and intermediate in the Tharu farmers . Elevated abundances of Treponema and Prevotella with reduction of Bacteroides and Bifidobacterium is a characteristic feature of gut microbiomes of foraging communities [19 , 21 , 24 , 29] . In addition to the individual taxa that differ across subsistence strategies , we wanted to determine whether microbial networks are also associated with lifestyle differences [24 , 43 , 44] . To understand how the gut microbiome network structure varies across these populations , we calculated the correlations between all pairs of bacterial genera in the gut using Sparse Correlations for Compositional data ( SparCC ) [45] . Clustering based on these correlations revealed seven bacterial coabundance groups ( CAGs , S12 Fig ) . The dominant genera that defined these CAGs are Ruminococcus , Bacteroides , Roseburia , Escheria/Shigella , Suturella , Prevotella , and Dialister ( Fig 6A and S13 Fig ) . These seven CAGs showed two antagonistic clusters: one cluster contains CAGs defined by Ruminococcus , Bacteroides , and Roseburia , and the second cluster contains CAGs defined by Prevotella , Escheria/Shigella , and Dialister ( S13 Fig ) . Notably , the CAG dominated by Prevotella is most prominently represented in the Chepang and Raute , while members of the CAG dominated by Bacteroides are elevated in the Raji and Tharu ( Fig 6B ) . Within the Prevotella CAG , Treponema and Ruminobacter are characteristic of the Chepang foragers . Conversely , the American gut is highly depleted of the Prevotella CAG and is dominated by the Bacteroides CAG . The results suggest how these changes in the microbiome that accompany lifestyle transitions may be viewed both at the level of individual taxa as well as higher-order community structure . Transitions from foraging to farming in Himalayan populations show changes in gut microbial networks , which appear to become more profound in industrialized societies . We next assessed whether any of the 10 dietary and environmental factors that differentiate the Himalayan populations ( from Fig 2 ) correspond to the variation in gut microbiome composition . A canonical correspondence analysis ( CCA ) revealed that the 10 factors collectively explain 28% of the gut microbiome variation within Himalaya , while 72% of the variation remained unexplained . Of the 10 variables , the source of drinking water and use of solid biomass fuel were significantly associated with the gut microbiome composition in the Himalayan populations ( P value = 0 . 009 and 0 . 028 , respectively; permutation test ) . Both of these factors contributed most to the first CCA axis ( CCA1 ) , which distinguished the Chepang and Raute individuals who drink river water and exclusively burn solid biomass fuel for cooking from the Raji and Tharu who drink underground water and use biogas for cooking ( Fig 7 ) . As an alternative approach , we assessed associations between the gut microbiome composition of the Himalayan individuals ( using Bray–Curtis , unweighted UniFrac , and weighted UniFrac ) and the 10 lifestyle-associated factors by performing a PERMANOVA ( S14 Fig ) . These analyses also revealed that drinking water was strongly associated with the gut microbiome variation within Himalaya ( P = 0 . 001 for all three distances; effect sizes = 0 . 096 , 0 . 1095 , and 0 . 088 for Bray–Curtis; unweighted UniFrac; and weighted UniFrac distances , respectively ) . Individuals who drank river water had higher abundances of Treponema , and those who drank underground water had elevated levels of Fusobacterium ( FDR adjusted P value = 0 . 01 and 0 . 003 , respectively; Mann–Whitney test ) . Although cooking fuel was significantly associated with overall composition , none of the individual genera reached statistical significance after correcting for multiple testing . To assess whether the association between gut microbiome and drinking water extend beyond the Nepali populations , we reanalyzed an independent 16S rRNA amplicon data set from Hadza hunter–gatherers from the Hukamako camp ( N = 60 ) [21] . In the late dry season , the Hadza use water from two distinct sources—springs ( N = 22 ) and streams ( N = 38 ) . We used a CCA to assess the associations between the gut microbiome of the Hadza and several dietary and environmental factors , including 72-hour recall of baobab , berries , honey , maize , meat , and tuber consumption; alcohol and cigarette use; as well as differences in drinking water sources ( S8 Table ) . These variables collectively explained 16 . 5% of the gut microbiome variation in the Hadza gut microbiome . Among the variables used in the CCA , difference in drinking water source was most strongly associated with the Hadza gut microbiome composition followed by honey consumption ( P = 0 . 0001 and 0 . 03 , respectively; permutation test; S15 Fig ) . We also performed a PERMANOVA to assess associations between the gut microbiome composition of the Hadza individuals and their dietary and environmental factors using Bray–Curtis , unweighted UniFrac , and weighted UniFrac distances . All three analyses revealed the association between drinking water source and the Hadza gut microbiome ( P = 0 . 001 , 0 . 002 , and 0 . 003 , PERMANOVA; effect sizes = 0 . 051 , 0 . 051 , and 0 . 059 for Bray–Curtis , unweighted , and weighted UniFrac distances , respectively; S15 Fig ) . Therefore , our results in the Hadza and the Himalayan populations suggest that drinking water is strongly associated with the human gut microbiome and emphasize the need for additional work to elucidate the mechanisms by which drinking water may influence the gut microbiome .
Several previous reports show that gut microbiomes of traditional populations vary from those of populations living industrialized lifestyles [15 , 16 , 18–20 , 24–27 , 29 , 30] . These studies have emphasized that gut bacterial composition differs between these populations , alpha diversity is higher in traditional populations , and diet may be the primary driver of variation in the human gut microbiome . Contrary to previous studies , our work focuses on how the extent of departure from a foraging lifestyle may affect the human gut microbiome . In this study , we compared the gut microbiome from four rural Himalayan populations that led nomadic lifestyles until recently and transitioned to farming at various time points in the last 300 years . Although the individuals in our study have historically cohabited a geographically small region ( less than 150 , 000 sq . km ) in the Himalayan foothills and shared similar diets until recently , their current diets and lifestyles vary . Our results demonstrate that their gut microbiota strongly mirrors their lifestyles , indicating that the human gut microbiome can undergo pronounced changes within a short time ( decades ) of departure from foraging , as seen in the Raute and Raji . As dependences on agriculture increases , these changes become more pronounced , as seen in the Tharu . Since these populations cohabit comparable latitudinal regions , such changes in the gut microbiota are unlikely to be confounded by geography . Therefore , our findings suggest that a range of lifestyle changes more subtle than those associated with industrialization are strongly associated with alterations of the gut microbiome . The gut microbiome variation between the Himalayan populations is consistent with the general patterns observed in many traditional human populations . More importantly , our results suggest certain genera represent conserved gut microbial markers of human subsistence states ( S16 Fig ) . Previous studies of the industrialized gut community have demonstrated that microbiome composition associates with and can be driven by differences in host diet [4–6 , 8 , 15 , 17 , 21 , 27 , 46] . Several genera , including Ruminobacter and Treponema , that are associated with metabolizing uncultivated plant products and are enriched in the Chepang foragers in this study are also elevated in hunter–gatherers around the world [19 , 21 , 24 , 29] . Moreover , Prevotella and Eubacterium , which have been previously associated with vegetarian diet in the industrialized microbiome [5] , were enriched in all Himalayan populations relative to Americans . In contrast , taxa associated with animal proteins in diet such as Bacteroides and Blautia [5 , 43] were enriched in the Americans relative to Himalayan populations . Notably , dietary animal protein content is low across Nepal [47] . In addition to diet , environmental factors may also influence the human gut microbiome [7 , 28 , 48] . Consistent with these findings , we found that differences in sources of drinking water are associated with gut microbiota composition in these Himalayan populations as well as Hadza hunter–gatherers , although additional work is needed to establish causality and to understand the mechanism by which drinking water may influence the gut microbiome . Drinking water contains a plethora of minerals and chemical compounds that influence human physiology [49] . Mineral and chemical contents in drinking water may differ by water source , which may alter the gut environment , thereby influencing the gut microbes . Moreover , the microbiome community in the drinking water may also vary between different sources . Recently , we have reported that different sources of Hadza drinking water contain a diverse set of bacterial taxa including families that are also found in the gut , such as the Prevotellaceae and the Spirochaetaceae [50] . Another recent study found surface water exposed to human and animal activities may contain higher levels of human commensal bacteria [22] . Microbes within drinking water may colonize the human gut or influence the resident microbial ecology during transit . Differences in mineral and microbial content in drinking water have also been previously reported in Nepal [51–54] . Furthermore , the chemical components in drinking water may interact with components of food [49] , and the impact of such interactions on the complex gut ecosystem is currently unknown . Additionally , we found an association between gut microbiome composition in Himalayan populations and their use of solid biomass cooking fuel , which produces high levels of particulate matter . Prolonged inhalation of polluted air can influence the gut microbiome in mice [48] . In addition , intestinal parasite load has been shown to alter gut microbiota [28] . The association between gut microbiome and parasite load approached significance in our participants as well ( P = 0 . 075 , permutation test ) , although it did not reach significance likely due to lower parasite abundance in our participants . Despite noticeable differences in the gut microbiome composition , we did not observe significant differences in gut bacterial diversity ( alpha diversity ) across lifestyles in the Himalayan populations . This finding is consistent with previous studies that compared populations that reside in similar geographical areas but practice different subsistence strategies such as the BaAka hunter–gatherers and Bantu farmers [19] , Matses hunter–gatherers and Tunapuco farmers [29] , as well as Bassa farmers and urban Nigerians [22] . These results collective indicate that difference in lifestyle alone is unlikely to generate differences in alpha diversity of the gut . However , these and other traditional populations such as the Hadza [24] have elevated gut bacterial diversity relative to the industrialized populations used as comparators in the respective studies . Some of these studies have also found lower interindividual variation within traditional societies compared to Western populations [22 , 29] . Neither the interindividual variation nor the alpha diversity differed between the Himalayan populations and Americans included in this study . The lack of differences in alpha diversity could be ascribable to geography as previously hypothesized [31] . Macroecological features , roughly corresponding latitude , may be an important factor that influences gut bacterial diversity in humans . The traditional populations included in previous studies reside in the tropical climate zones , which have higher macroecological biodiversity likely affecting both diet and environmental microbial exposures . In contrast , the Americans and Nepalis in this study reside in comparable nontropical latitudes ( 37 . 44 °N for Palo Alto and 26 . 97–29 . 15 °N for Nepal ) . Integration of data from multiple studies to examine alpha diversity trends is difficult due to technical differences in sample storage/preparation and batch effects of data generation . Future studies focused on people living in both temperate and tropical climates across a range of subsistence strategies are needed to provide further insight into how lifestyle and environment influence gut microbiota alpha diversity . Our results provide key insights into how the extent of departure from a foraging lifestyle can impact the gut microbiome within the context of traditional , preindustrialization lifestyles but also reveal some limitations . Future important work includes determining how the transition from farming to industrialization may influence gut microbes . Ideally , such a study would compare the individuals living traditional lifestyles versus those from the same ethnic groups that have shifted to industrialized lifestyles , using metrics and surveys to quantify aspects of diet , lifestyle , and medical practices . Increasing sample sizes in future studies may provide more statistical power , although it can be difficult because most traditional populations across the world exist in small numbers . For example , in our study , the total population of Raute , including newborns and children , is 650 . The Raji and Chepang exist as fragmented tribes in small and extremely remote villages within Himalaya that are separated by large geographical barriers . In this study , each village consists of a few hundred individuals from which we carefully sampled individuals with different grandparents , further reducing the number of participants . Even with small sample sizes , studies such as ours that focus on traditional populations have the potential to address key gaps in the field of human microbiome science . In conclusion , our results emphasize the need to study additional traditional populations to understand how geography , climate , diet , and environment affect the gut microbiome . The global trends of bacterial taxa within the gut that undergo depletion or enrichment upon lifestyle transitions are striking . Incorporating metagenomics to characterize the gut microbial variation at finer scales , metabolomics and strain culturing to assess functional differences , and immune and metabolic profiling of these populations may reveal the functional consequence of these changes , both in terms of the intrinsic microbial ecology of the gut and the impact on human biology . Pursuit of mechanisms by which the gut microbiome interacts with human biology may reveal conserved connections with large implications for industrialized humans who lack these microbes that may have been part of our species’ evolution .
This work was approved by the Ethical Review Board of the Nepal Health Research Council ( NHRC ) as well as by the Stanford University Institutional Review Board . Stool samples were collected with informed consent from 56 genetically unrelated adult participants ( over 18 years old with different grandparents ) from four indigenous Himalayan populations from Nepal and 10 adult Americans of European descent . Indigenous populations from Nepal included Chepang ( N = 14 ) , Raji ( N = 10 ) , Raute ( N = 12 ) , and Tharu ( N = 20 ) inhabiting Chitwan , Bardia , Dadeldhura , and Sarlahi districts , respectively . The samples were collected in winter of 2016 ( March and April ) with consent from all participants . In addition to collecting the fecal samples , we also obtained ethnolinguistic , demographic , environmental , and dietary data from the Himalayan participants using a survey questionnaire specifically designed for this study . The survey questionnaire assessed participants’ age , gender , diet , health status , use of medication , and behavioral practices such as tobacco and alcohol consumption , along with several environmental variables ( S2 Table ) . In addition , we also visually inspected the stool samples of each individual under the microscope for the presence of intestinal parasites ( triplicate slides per individual ) . Participants’ responses to survey data questionnaires are included in S4 Table . Freshly produced stool samples from the Himalayan participants were collected on a clean OMNIgene gut accessory collection paper ( OM-AC1 ) . About 500 mg of the stool samples was transferred to the OMNIgene gut kit collection tube containing the stabilizing buffer using the clean spatula provided with the kit . The tubes were shaken hard in a back and forth motion until the fecal samples were completely homogenized . Tubes were transported at room temperature within 48–72 hours of collection to the Tribhuvan University Institute of Medicine , Kathmandu , Nepal , where they were transferred to −80 °C until DNA extraction . DNA was extracted using a MolBio Power Soil Kit according to the manufacturer’s protocol . Extracted DNA was shipped to Stanford University on dry ice and stored at −20 °C until sequencing . Samples from Americans were collected from volunteers at Stanford University in 15-ml centrifuge tubes and transported to the laboratory on ice . Half of each sample was immediately frozen at −80 °C . From the other half , 500 mg stool was transferred to OMNIgene collection tubes and kept at room temperature for 48–72 hours after which they were stored at −80 °C . DNA was extracted from both sets of samples simultaneously using the MolBio Power Soil Kit according to the manufacturer’s protocol and stored at −20 °C until sequencing . The V4 region of the 16S rRNA gene was PCR amplified using the primers and protocols described previously [55] . The amplified DNA fragments were multiplexed and subjected to paired-end sequencing using Illumina MiSeq . Of the 66 samples , one yielded very low levels of DNA and another failed the paired-end sequencing . After discarding these two samples , the final data set included 64 individuals ( 14 Chepang , 9 Raji , 11 Raute , 20 Tharu , and 10 Americans ) . The amplification primers and barcodes used for multiplexing are described in S4 Table . Paired-end reads were processed using DADA2 [56] and subsequently analyzed in R using phyloseq [57] . In order to identify high quality sequences , reads were trimmed to 150 bp . Sequences with N nucleotides and/or >2 expected errors were discarded ( maxN = 0 , maxEE = 2 , truncQ = 2 ) , and sequence variants were inferred by pooling reads from all samples ( pool = TRUE ) . Sequence tables were then created by merging paired-end reads . A naïve Bayesian classifier method [58] implemented in DADA2 algorithm was used to assign taxonomy using the RDP v14 training set [59] . Multiple alignment was conducted using DECIPHER [60] package in R , and a maximum likelihood phylogenetic tree was constructed using phangorn [61] , with a neighbor-joining tree as the starting point . A total of 1 , 183 , 760 merged reads passed quality control , and 1 , 630 taxa were initially identified . After removing chimeric sequences , which constituted 22% of the reads , 921 , 345 merged reads remained . Further elimination of low-abundance phyla—Synergistetes and Deferribacteres—that were observed only once across all samples resulted in 883 taxa in the data set . After quality control , mean ( ±SD ) sequencing depth per sample was 11 , 570 ( ±4 , 653 ) . We performed three technical replicates of the frozen sample for one individual and a total of five replicates for two additional individuals for the OMNI samples . Since we did not observe marked differences in the technical replicates ( S3 Fig ) , we retained the sample with highest coverage for these individuals . After removing the replicate samples , 64 individuals and 875 taxa remained in the final data set . Stool samples and dietary recall from 60 Hadza individuals were conducted in the field at the time of sampling with the aid of an interpreter . Following informed consent , each participant provided a list of the plants , animals , and animals products consumed over the previous 72 hours , including alcohol and cigarette use . Location and type of water source was also recorded . Although the Hadza consume water primarily from a single source near their camp , foraging activities often take subjects several kilometers away from camp where their water source may vary . Raw 16S reads from the Hadza were previously published [21] and were processed using DADA2 , as described above . This data set included 1 , 038 , 333 nonchimeric reads from 60 individuals that were assigned to 1 , 511 taxa . A random forest classifier with 5 , 000 trees was constructed using all 35 variables ( S3 Table ) from the survey data . The R-package randomForest [62] was used to build the trees , and its “tuneRF” function was used to assess the optimal number of variables randomly sampled as candidates at each split . ( “mtry” parameter , mtry = 6 for survey data ) . We also repeated these analyses on the 16S data using the RSVs as features and using mtry = 29 as determined by the tuneRF . The R-package “pROC” [63] was used to calculate and plot the area under the receiver operating characteristic curves ( AUC ) for each of the populations . In addition , brier skill scores ( BSSs ) in R-package “verification” [64] was used to assess the calibration of the RF model . Intestinal pathogens and all 35 variables recorded from the Himalayan populations were used to perform CA of the survey data using “FactoMineR” package in R [65] . Associations between rows and columns in the correspondence analysis were evaluated by performing a chi-squared test ( P = 3 . 9 × 10−6 ) . The contributions of each factors to the top two dimensions of CA were visualized using the “fviz_contrib” function in R-package factoextra [66] . The expected contribution to the top two dimensions under a uniform model was determined , and factors that contributed more than the expected were considered important in differentiating lifestyles . CCA was performed in the Himalayan populations using the 10 variables that differentiated lifestyles in the correspondence analysis by calling the “cca” function from vegan package [67] via phyloseq . For the Hadza , CCA was performed using nine variables including six dietary variables ( baobab , berries , maize , tubers , honey , and meat consumption in the last 72 hours ) , alcohol and cigarette consumption , as well as source of drinking water . RSV counts were used as features of gut microbiomes in both the Himalayan and the Hadza populations . Permutation tests with 10 , 000 permutations were performed to evaluate the significance of each CCA model and terms using “anova . cca” function in “vegan . ” For all CCA models , the P values from the permutation tests were less than 0 . 05 , indicating that the CCA model explained more variance of the gut microbiome in the Himalayan and the Hadza populations than expected by chance . American samples were excluded for both CA and CCA analyses . Phylogenetic diversity was computed by rarefying the samples to various depths starting from 10–6 , 500 sequences per sample . Alpha diversity was measured using species richness , Shannon’s H , Simpson’s D , and Fisher’s alpha calculated as the mean values from 100 iterations at each depth . Kruskal–Wallis tests were used to assess the significance of differences in each of the alpha diversity metrics between populations at each rarefaction depth . Dunn’s posthoc test was performed to assess pairwise differences between populations . Differences in rarefaction depth did not alter significance of the observed differences . A generalized linear mixed effect model was used to evaluate associations between the 10 dietary and environmental factors and the two metrics of alpha diversity . Four models were created , each with the four metrics of alpha diversity ( observed species , Shannon’s H , Simpson’s D , and Fisher’s alpha ) as the response variables; the 10 factors were treated as explanatory variables with fixed effects; and each individual had random effect . Beta diversity was assessed using Bray–Curtis as well as unweighted and weighted UniFrac distances calculated by log transformation of the nonrarefied 16S count data . PERMANOVA was performed using the vegan package in R [68] . For all PERMANOVA analyses , 10 , 000 randomizations were performed to assess the statistical significance . In order to identify differentially abundant taxa at the phylum and genus levels , we first agglomerated the taxa abundance ( counts ) at each taxonomic level , respectively . The differences in taxa abundance ( counts ) were then assessed using the DESeq2 package [42] . SparCC was used to assess correlations between bacterial genera , as described previously [45] . SparCC is specifically designed to measure correlations in microbiome data and computes compositionality robust correlations by averaging multiple iterations of data . The statistical significance of the inferred correlations is then assessed using a bootstrap procedure . First , a large number of simulated data sets , in which all components are uncorrelated , are generated . Then , correlations are inferred from each simulated data set with the same parameter setting as is used for the original data . Finally , for each component pair , pseudo P values are assigned to be a proportion of simulated data sets for which a correlation value is at least as extreme as the one computed for the original data . We computed bacterial correlations for all pairs of genera after removing genera with less than 2 reads in at least 5% of samples ( 3 individuals and 124 genera ) . Correlations were computed from 100 iterations of the data , and we repeated the iterative procedure 100 times to compute the P values . P values < 0 . 05 after multiple testing correction were considered significant . Bacterial networks were visualized using the Fruchterman–Reingold force–directed layout algorithm implemented in the igraph package in R [69] . All multiple testing corrections were performed by computing FDRs using the Benjamini–Hochenberg method , and adjusted P values < 0 . 05 were considered statistically significant . A phyloseq object containing the 16S data and metadata as well as the analyses protocols used in this work are included in the supplementary data . | Although much of humanity’s history has been spent foraging in the forests , the advent of agriculture approximately 10 , 000 years ago and industrialization approximately 250 years ago mark major shifts in human lifestyle . Several studies have investigated the effect of industrialization on the human gut microbiome—a collection of microbes that inhabit the human gut . However , little is known about whether the gut microbiome changed as humans shifted away from foraging . To investigate how the gut community changes in response to shifts in traditional human lifestyles , we characterized the gut microbial community from four Himalayan populations representing diverse subsistence strategies . We show that the divergence of the gut microbiome from the foraging population is strongly correlated with agricultural dependence in these populations . Many of the taxa that differ across lifestyles are known to be influenced by diet , but we also demonstrate that environmental factors , such as sources of drinking water , are strongly associated with the human gut microbiome . Our findings show that both diet and environment play key roles in shaping the human gut microbiome . | [
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] | 2018 | Gut microbiome transition across a lifestyle gradient in Himalaya |
Maintenance of cellular size is a fundamental systems level process that requires balancing of cell growth with proliferation . This is achieved via the cell division cycle , which is driven by the sequential accumulation and destruction of cyclins . The regulatory network around these cyclins , particularly in G1 , has been interpreted as a size control network in budding yeast , and cell size as being decisive for the START transition . However , it is not clear why disruptions in the G1 network may lead to altered size rather than loss of size control , or why the S-G2-M duration also depends on nutrients . With a mathematical population model comprised of individually growing cells , we show that cyclin translation would suffice to explain the observed growth rate dependence of cell volume at START . Moreover , we assess the impact of the observed bud-localisation of the G2 cyclin CLB2 mRNA , and find that localised cyclin translation could provide an efficient mechanism for measuring the biosynthetic capacity in specific compartments: The mother in G1 , and the growing bud in G2 . Hence , iteration of the same principle can ensure that the mother cell is strong enough to grow a bud , and that the bud is strong enough for independent life . Cell sizes emerge in the model , which predicts that a single CDK-cyclin pair per growth phase suffices for size control in budding yeast , despite the necessity of the cell cycle network around the cyclins to integrate other cues . Size control seems to be exerted twice , where the G2/M control affects bud size through bud-localized translation of CLB2 mRNA , explaining the dependence of the S-G2-M duration on nutrients . Taken together , our findings suggest that cell size is an emergent rather than a regulatory property of the network linking growth and proliferation .
Cell size is a fundamental systems level property of life . It emerges as a combination of the cell cycle , controlling the orderly orchestration of duplication and division , and the individual growth rate , reflecting extra- and intracellular physiological conditions . The cell cycle and the growth rate are coupled , such that proliferation and growth are balanced , avoiding abnormally large or small cells . Understanding the coupling is of particular interest for two reasons . First , the cell cycle as well as cellular growth are two fundamental properties that can be found in nearly all forms of life . Second , decoupling of the two can have disastrous consequences for an organism , e . g . deterioration of cell size . The unicellular eukaryote Saccharomyces cerevisiae can be observed to grow to a ‘critical cell size’ in the G1 phase before committing to passage through the cell cycle [1] . The commitment is called START in S . cerevisiae and constitutes the transcriptional activation of more than 200 genes by the transcription factor complexes SBF and MBF [2] . This triggers the onset of downstream events , such as budding and DNA replication . SBF/MBF activity is controlled by the G1 network , which involves the cyclin dependent kinase ( CDK ) Cdc28 , its activating subunits the G1 cyclins Cln1/2/3 and the transcriptional repressor Whi5 ( reviewed in [3] ) . The most upstream undisputed activator of START is Cln3 . Cln3 binds to and activates the CDK to phosphorylate Whi5 , which relieves the repression of SBF/MBF . The START transition is triggered when a critical activity of the CDK is reached [4] . Beyond the critical level , CDK activity stabilises through positive feedback involving Cln1/2 [5 , 6] . The core network architecture with the competition between the active CDK and the transcriptional repressor is analogous to the Restriction Point , which is the equivalent of START in mammalian cells [7] . The nature of the mechanism within the START network that ties growth and proliferation together remains unknown . Size control must be as old as the cell cycle itself . It is conserved across species over a huge range of cell sizes and shapes , and it is well established that size control can occur in cell cycle phases other than G1 [8] . Recent evidence strongly suggests that also in budding yeast size control is likely to be exerted outside of G1 [9 , 10] . The fission yeast Schizosaccharomyces pombe has a size control checkpoint at the G2/M boundary and many of its components are conserved in budding yeast [11 , 12] . The observation that the budded phase duration responds to growth media and the high degree of conservation between the two yeasts prompts the question , whether a size control mechanism guards mitotic entry in budding yeast as well [13–16] . Unfortunately , size control at the G2/M transition is less well understood in budding yeast [8 , 9 , 17–19] . It is well known , however , that S . cerevisiae can arrest its cell cycle at the G2/M boundary through activation of the so called morphogenesis checkpoint [20] . The kinase responsible for mitotic entry Clb2-Cdc28 is inhibited through phosphorylation at the Tyr19 residue by Swe1 [20 , 21] . Re-activation of Clb2-Cdc28 requires removal of the inhibitory phosphate by the mitotic inducer homolog Mih1 phosphatase [22] . The exact property that is monitored in budding yeast remains a matter of debate , but it has been argued that the checkpoint responds to perturbations of the actin cytoskeleton or even bud growth [9 , 17 , 18] . Indeed , recent work suggests that polarised exocytosis may be required to pass through the checkpoint [9] . This hypothesis directly connects membrane growth at the bud site to cell cycle progression suggesting a growth dependent size control checkpoint for mitotic entry in budding yeast [9] . In the regulation of both the START and the G2/M transitions , a master CDK is balanced against an opposing regulator that must be overcome to initiate crucial cellular events , like DNA replication or cell division [3] . The master CDK is activated in G1 and G2 by the accumulation of cyclins and this activation is antagonised by CDK inhibitors and rapid degradation of the cyclins to form elaborate molecular switches [23 , 24] . Accordingly , the transitions occur in a switch like manner , when the time ( or size ) is right . It is not exactly clear how growth can flip a transition switch , but one theory for size control proposes the level of an unstable cell cycle regulator as a gating device to measure the growth capacity of the cell [25 , 26] . The growth or biosynthetic capacity of the cell determines the growth rate and the unstable regulator is presumed to be one of the G1 cyclins , most likely Cln3 [8] . Cln3 levels are heavily influenced by the available nutrients and Cln3 translation is slowed down in conditions when fewer ribosomes are available [27–29] . The G1 cyclins and other components of the START network modulate cell size at START [15 , 30] . Additionally , many other components influence cell size , especially those with a functional link to the cellular growth machinery , like Sfp1 or Sch9 [31] . Recently , it was shown that cell size at START is set as a function of the individual growth rate of a cell and that START network components modulate the strength of this correlation , arguing for the existence of a growth rate dependent cell sizer in G1 [15] . Both in vivo and in silico analysis suggested that G1 is not the only size control phase during the cell division cycle [9 , 10 , 17 , 32] . Also , if G1 were the sole size control phase , the question would remain how budding yeast can maintain size homeostasis and control in case the mechanisms in G1 are impaired , as e . g . through CLN3 overexpression . CLN3 overexpression mutants display a substantial reduction in G1 length and a small cell size ( whi phenotype ) [33 , 34] . Since Cln3 is an upstream activator of START , its overexpression can intuitively explain the shortened G1 phase and the concomitant reduction in cell size . Counterintuitively , the generation time remains largely unchanged , arguing for the existence of another control point further downstream in the cell cycle to compensate for the reduced time in G1 [33 , 34] . The obvious candidate for this would be the G2/M transition . Here , the mitotic cyclins Clb1/2 are responsible for CDK activity to trigger mitotic entry [4] . Intriguingly , CLB2 mRNA ( mCLB2 ) accumulates in the bud , while the Clb2 protein is distributed throughout the cell [35 , 36] . If the cell localises its CLB2 transcripts to the bud it is likely that translation of the transcript is also a local phenomenon . In principle , active transport of the mRNA leading to localised translation of mCLB2 could serve to measure the biosynthetic capacity of the bud , to form a bud ( daughter ) sizer in G2 . Once the bud is strong enough for independent life , measured through production of sufficient Clb2 , the cell enters mitosis . It is tempting to speculate that if Cln3 is a cell sizer , then Clb2 might be a bud sizer . Here , we approach the elusive problem of size control from a theoretical angle , and use a number of mathematical models for rigorous testing of different control concepts . Our approach is to model single cells that are capable of growth and division , and grow them in in silico cell cultures . Through inheritance , we let the culture evolve over time , generating fully traceable cell populations for analysis [32] . In the model , growth and proliferation is integrated through production of a critical unstable cell cycle regulator as function of the biosynthetic capacity of the cell . Using this approach , we formally establish that the accumulation of cyclins to a threshold provides the necessary prerequisites for size control and homeostasis . Since it is unlikely that size control occurs in G1 alone , we use models that include additional size control at the G2/M boundary . We find that we need to take into account the intriguing , and to our knowledge unexplained , fact that mCLB2 is localised to the growing bud of yeast cells to fully explain experimental data [35] . Bud-localised translation of the major cyclin that activates the master CDK for mitotic entry can , in theory , constitute a growth rate dependent sizer for the bud . With the suggested mechanism we ( i ) offer a functional explanation for the mCLB2 transport into the bud , ( ii ) elucidate the prolongation of the budded phase in response to poor nutrients or CLN3 overexpression , and ( iii ) propose a unifying model for integration of growth and division in the G1 and G2 phases of budding yeast .
We present here an extended version of a minimal eukaryotic cell model that is capable of growth and division ( Fig 1A ) [32] . In the model , two types of biomass define growth: structural and internal biomass . Structural biomass represents all those components that are destined for the cell wall or membrane . Internal biomass are soluble agents within the cell , describing the cell’s capacity to metabolise nutrients and build new macromolecules ( biosynthetic capacity ) . Structural and internal biomass accumulate with replicative age , leading to an increase in size over generations as observed experimentally ( Fig 1B ) [37 , 38] . Budding yeast grows and divides asymmetrically and , therefore , growth of the mother and the bud is considered separately [39] . The volume trajectory for a single cell is biphasic with altered growth rates dependent on cell cycle stage as observed experimentally as well ( Fig 1B ) [15 , 40–42] . A rudimentary version of the cell cycle machinery is included , with one proxy for the G1 cyclins ( Cln ) and one proxy for the mitotic cyclins ( Clb ) that determine CDK activity ( Fig 1B ) [43] . Transcription of cyclins is considered stochastic and restricted to a distinct cell cycle phase ( CLN in G1 , CLB in G2—Fig 1B ) . Growth and division are coupled via production of Cln in G1 and Clb in G2 , as a function of the biosynthetic capacity of the cell . This means that the translation of cyclin mRNAs is dependent on the internal biomass ( see also Materials and Methods ) . At division , two cells emerge from one and all soluble components are split according to the volume ratio of the mother and the bud . Through cell growth and division , we evolve an entire asynchronously growing population from one progenitor cell , as previously described [32] . The model is a comprehensible , minimal approximation of the complex process controlling duplication and separation in living eukaroytes [44] . To test the effect of mCLB2 localization on size control , we implemented two model versions . In Model-1 , mCLB is uniformly distributed in the cell . It is translated in the entire cell . In this model , the biosynthetic capacity of the whole cell is integrated at the G2/M transition ( G2 size control of the entire cell ) . In Model-2 , the mRNA is translated exclusively in the newly forming bud mimicking the effect of mRNA localization . This model integrates only the biosynthetic capacity of the bud in G2 ( G2 bud size control ) . Regardless of the specific G2 size control mechanisms , in both models , the productive capacity of the cell is measured in G1 through translation of mCLN ( G1 size control ) , the primary phase for size control in budding yeast [1 , 45] . The two models differ in a single equation ( Table 1 ) . In silico cultures generated with Model-1 and Model-2 show size homeostasis on the population level ( Figs 2A and S1 ) . Moreover , in both models there is a strong dependence of the cell volume at START on the individual growth rate in G1 phase ( Figs 2B and S1–S3 ) , as observed experimentally [15] . Thus , in accordance with experiments , both models exert growth rate dependent size control primarily in G1 , regardless of the mCLB2 localization in G2 [1 , 15 , 45] . For the models to become reliable mathematical tools to investigate the coupling of growth and division they were fitted independently to complex experimental growth and proliferation data ( Fig 3 ) [15] . The data is available for cells grown on glucose , galactose , raffinose and ethanol [15] . In the models , simulation of cell growth on different carbon sources can be achieved by modulating the parameter growth ( Table 2 ) . The parameter growth scales the biomass formation and represents the availability of nutrients to the system ( Table 1 ) . A decrease in growth leads to a reduction of cell size and a prolongation of the cell cycle [32] . We used the data for glucose and ethanol for parameter estimation and the data for galactose and raffinose for validation of the models ( Fig 3A ) . The antagonistic trend within the data ( ‘fast growth → short cell cycle → large cells’ versus ‘slow growth → long cell cycle → small cells’ , shown in Fig 3A as a grey shadow ) enables to constrain most of the model parameters ( Fig 3B , Materials and Methods ) . Although parameters show correlations ( S4 Fig ) , within the parameters boundaries the fits converge into a global minimum ( Figs 3B , S5 and S6 ) . Both model variants predict cell size at birth , at budding , and duration of G1 and S-G2-M for all conditions with high accuracy . To test which model is more suitable to describe the given data , we ranked them using the Akaike Information Criterion ( AIC ) [46] . Ranking yields that Model-2 fits the data better than Model-1 , although both versions use the same number of parameters ( S1 Table ) . The worst fit of Model-2 is still better than the best fit of Model-1 ( Fig 3B ) . This indicates that the mechanism of compartmentalization ( bud-localised mCLB ) renders the system more capable to describe ( glucose and ethanol ) and predict ( galactose and raffinose ) the data [15] . Experimental data clearly show that S-G2-M duration is not constant between different media [13–15] . Since S and M phase are constant , a G2 regulator is needed to account for the adaptation of G2 duration in response to nutritional status in vivo [13] . Since both models reproduce the experimental data for glucose , galactose , raffinose and ethanol ( Fig 3A ) , we conclude that , regardless of compartmentalization , translation of mCLB is a good candidate . The proposed mechanism also leads to correlation between the volume of the bud at division and the individual growth rate in the budded phase as well as the S-G2-M duration ( S7–S9 Figs ) . Thus , Clb2 constitutes a growth rate dependent sizer candidate in G2 . To further distinguish the models , we analysed the effect of mRNA localization on other systems level properties beyond average size and cell cycle phase duration . It has been reported that a growth rate dependent sizer can prevent large fluctuation of G1 length to reduce the generation time on the population level [15] . We find that compartmentalization of Clb translation ( Model-2 ) reduces fluctuations of G2 length ( Fig 4A ) . Interestingly , this does not lead to a reduction in generation time on the population level ( S10 and S11 Figs ) . The higher noise at mitotic entry , inherent in Model-1 , propagates directly to cell division ratios ( Fig 4B ) , where cells that spend too much or little time in S-G2-M produce abnormally large or small buds , respectively . In comparison with experiments , Model-1 predicts a too high variability in division ratios for the first five generations of the population , i . e . for more than 95% of the cells in the culture [41] . In contrast to Model-1 , the predictions of division ratios from Model-2 are accurate for young and old cells ( Fig 4B ) . This is even more pronounced for slow growing cells ( S12 Fig ) . Thus , a bud sizer can tune mitotic entry to reduce noise and maintain division ratios over generations . It is well known that cells grown on rich media grow faster and are larger compared to cells grown on poor media [47] . Previously , we analysed cell size distributions of cells grown on rich and poor media and found that , in contrast to average cell size , the relative variability in cell size does not change [32] . A model that allowed size control exclusively in G1 ( constant S-G2-M ) could not explain this [32] . Model and data could only be reconciled when we adapted S-G2-M duration to growth conditions . Thus , we hypothesised that , for a robust setting of average cell size and variability within the culture , S-G2-M duration must show some form of adaptation to growth conditions . We have seen that Model-1 and Model-2 are able to reproduce and predict the adaptation of S-G2-M duration in response to different conditions ( Fig 3 ) . To test whether both versions stably reproduce an increase in average cell size while keeping the relative variability constant , we analysed their behavior with respect to cell size statistics ( Fig 5 ) . It is apparent that both versions show the expected increase in average cell size , but that relative variability is increased in Model-1 . Model-1 shows an increase in relative variability of roughly 25% over different conditions ( glucose ∼ 0 . 65 , ethanol ∼ 0 . 81 ) , whereas Model-2 of only 7% ( glucose ∼ 0 . 60 , ethanol ∼ 0 . 64 ) . This indicates that a growth rate dependent G2 bud sizer stabilises the relative variability in cell size observed in yeast populations [32] . Both model versions show adaptation of S-G2-M duration to growth conditions ( Fig 3 ) . Hence , according to our hypothesis ( adaptation of S-G2-M stabilises the variability—see last paragraph ) , both models should in theory be able to stabilise the variability [32] . However , only Model-2 is able to limit the size variability ( Fig 5 ) . To explain this , we analysed the apparent S-G2-M adaptation in both models . The difference between Model-1 and Model-2 in G1 and S-G2-M duration seems minor under most conditions ( Fig 3 ) . However , the difference between the models becomes striking when inspecting the time that daughter and mother cells spend in G1 and S-G2-M separately ( Fig 6A ) . Apparently , in Model-1 mother and daughter lines diverge , whereas in Model-2 they do not . In Model-1 , time in G1 is different for mothers and daughters , as expected [48] . This difference is larger for cells grown on ethanol than for cells grown on glucose , also as expected [14] . In Model-1 , the time in S-G2-M differs for mothers and daughters as well , which is in clear contrast to experimental evidence [13 , 14] . Single cell data has shown in detail that there is little difference for time in S-G2-M between daughters and mothers ( Table 3 ) [14] . Model-2 also displays the expected differential G1 duration between mother and daughter cells , which again decreases with nutrient quality , as expected ( Fig 6A ) [48] . In contrast to Model-1 , the S-G2-M duration of mothers and daughters in Model-2 is more equilibrated , very similar to experimental findings ( Table 3 ) . Yet , there is a trend in Model-2 that for slow growing cells budded phase is longer for mother than daughter cells . A consequence of the equilibrated budded phase within the population ( Model-2 ) , is that the volume of new born daughter cells increases with the age of the corresponding mother ( Fig 6B ) . As a result , daughters of old mothers are considerably larger than daughters from young mothers in Model-2 . This observation has also been made in vivo [49] , suggesting that a mechanism exists that controls the size of the bud , rather than absolute cell size , in G2 . To further compare the predictive power of the models , we simulated a scenario similar to over producing CLN3 . We enforced a doubling of CLN expression in the models ( referred to as OE-CLN ) . Both models react to the overproduction of CLN with a decrease in G1 duration and a compensatory increase of the budded phase duration ( S-G2-M; Fig 7 ) . In agreement with experimental observations , average generation times are only slightly ( Model-2 ) or not at all ( Model-1 ) reduced by the mutation ( S10 and S13 Figs ) [33 , 34] . However , the difference between cell cycle durations of mothers and daughters is reduced in the mutant ( S13 and S14 Figs ) . Yet , only Model-2 shows a the reduction in cell size in response to OE-CLN ( Fig 7 ) . Model-1 , with the whole-cell G2 sizer , fails to predict the small cell size phenotype that is typical for CLN3 overexpressing cells [33 , 34] . This shows that a bud sizer ( Model-2 ) is required to predict the CLN3 overexpressing strain’s whi mutant phenotype .
Here , we present a mechanistic single cell growth model that is able to predict cell growth and division timing in budding yeast populations . There are very few models that are able to ( i ) show and explain size homeostasis , ( ii ) offer mechanistic insight into the cellular machinery governing growth and division , and ( iii ) that are still comprehensible and manageable [32 , 50] . Our model is designed to omit all details that are not absolutely required to reproduce the coordination of growth and proliferation . Using the model we show that a G2 bud sizer mechanism is required in addition to a G1 sizer in order to ( i ) better fit and predict population size and timing data for different nutritional conditions ( Figs 3 , 4B and 5 , Table 3 ) , ( ii ) offer a functional explanation for the experimentally observed mCLB2 transport into the bud [35] , ( iii ) reduce the noise at mitotic entry ( Figs 4 and S12 ) and ( iv ) render the model capable of predicting the phenotype of a CLN3 overproducing strain ( Fig 7 ) . Thus , our results indicate that a bud sizer mechanism could operate at the G2/M boundary in vivo [10] . We use the model to show that biomass dependent accumulation of cyclins to a threshold results in size homeostasis on the population level and growth rate dependent size adaptation in G1 ( Fig 2 ) , as seen in vivo [15] . Thus , our results are in accordance with the view that G1 is the primary phase for size control in budding yeast [1 , 45] . It was shown that the START network sets the cell size as a function of the growth rate [15] . We observe a similar behaviour in our model , meaning that we implement a growth rate dependent sizer through a minimal version of the START network . The model proposes that the underlying network developed from a single CDK-cyclin pair that later differentiated between G1 and G2 phase . Consistently , cells can be driven through the cell division cycle by artificial expression of a single CDK-cyclin fusion protein [43] . While we observe a stronger correlation between the cell volume at START and the individual growth rate than seen in experiments ( Fig 2B ) [15] , this can be explained by the simplicity of the model and the lack of feedbacks and other regulatory mechanisms [23 , 26 , 51 , 52] . Taken together , the model predicts ( a ) that already a single G1 cyclin suffices for size control in G1 and ( b ) that monitoring biosynthetic capacity through production of a critical unstable cyclin is a growth rate dependent sizer . In previous work , we found that a growth model with size control operating exclusively during G1 is not able to fully reproduce data of cell sizes and proliferation times measured for different conditions [32] . Also , recent experimental evidence strongly supports the existence of a size-regulating mechanism in the budded phase [10] . Accordingly , we tested models with additional size control at the G2/M transition for the entire cell ( Model-1 ) or only for the bud ( Model-2 ) . The G2 size control is implemented analogously to the G1 sizer , through biosynthetic capacity dependent production of a critical cell cycle regulator . The models establish that a G2 size control point , but not necessarily bud size control , is required to reproduce the experimentally observed adaptation of the budded phase in poor growth media ( Fig 3 ) and in the CLN3 overexpression mutant ( Fig 7 ) [13–15 , 33 , 34] . Hence , both models argue in favor of the existence of a sizer mechanism in G2 . Yet , simply regulating G2 length in response to growth conditions does not seem to be the end of the story . Conceptually , the reduction of G1 and the compensatory adaptation of the budded phase , inherent in both models , is not sufficient to explain the small size phenotype of the CLN3 overproducer ( Figs 7 and S14 ) . The prolongation of the budded phase has different reasons in Model-1 and Model-2 . In Model-1 , cells pass START quickly with a reduced biosynthetic capacity ( and size ) as a direct consequence of the CLN overexpression . Since Model-1 is implemented to integrate the whole cell’s biosynthetic capacity at mitotic entry , cells compensate by extending S-G2 to build up sufficient productive power to enter mitosis . The requirement to enter mitosis here equals the one in the wild type so that , in Model-1 , CLN overexpressing cells are very similar in size compared with wild type cells . A reduction in growth rate , due to early passage through START is compensated late in the cell cycle when enough biosynthetic capacity has built up . Consequently , cells overexpressing CLN in Model-1 tend to have larger buds leading to larger cells at birth ( S14 Fig ) . In contrast , the prolongation of the budded phase seen in Model-2 is due to a general reduction in growth rate , which is again the consequence of the early passage through START . In Model-2 , the biosynthetic capacity of the bud is monitored , which accumulates slower because of the generally reduced growth rate . However , in Model-2 , a cell can enter mitosis as long as the bud fulfills minimal requirements , even if the cell in total is smaller and less productive . This is in accordance with the fact that entry into mitosis is correlated with the size of the bud , but not with the size of the mother cell [17] . Thus , only the bud sizer and not a whole cell sizer concept at mitotic entry is able to accommodate the small size phenotype of the CLN3 overproducer and can thus reconcile model and experimental observations [33 , 34] . We found that the budded phase is slightly longer in mothers than in daughters for slow growing cells in Model-2 ( Fig 6A ) . While such an effect might be too small to be detected in experiments , it is more likely that the model oversimplifies at this point: The assumption that 100% of the CLB2 transcript is transported to and exclusively translated in the bud in vivo could be too strong . Indeed , it is difficult to assess the exact number of bud-localised transcript ( 100% in the model and ≥ 90% as reported experimentally [35] ) . Predictions of Model-1 show the effect of non-localization , i . e . shorter budded phase in mothers . This is indicative that allowing some translation of the cyclin in the mother ( e . g . 10% ) can eradicate the difference . We predict bud-localised mCLB2 to be an essential part of the growth and division coupling . Obviously , we cannot be sure that we have implemented the true biological mechanism in our model , but we show here that our minimal mechanism displays many characteristic properties of the system . It is tempting to speculate that the G2 bud sizer is of similar design as the one operating in G1 , since it seems easier to duplicate a working mechanism than to invent two distinct , yet functionally equivalent ones . Still , we acknowledge that there are other hypotheses on how cyclin synthesis is related to growth rate [30 , 31 , 40 , 53] . Most likely , the in vivo situation is the complex result of different interacting molecular effects . Nonetheless , the mechanism proposed here suffices to explain most of the data , and—unlike many other hypotheses that rely on a specific molecular mechanism in the G1 phase—the mechanism proposed here is generic and not phase specific . In light of this , passive accumulation of CDK regulators ( Cln3 in G1 and Clb2 in G2 ) are promising candidates [8] . The old concept of the unstable regulator is seductively simple and elegant [25] . By making the underlying size control mechanism depending on the critical CDK activity induced by an exchangeable regulator ( cyclins can substitute for one another ) one could explain why none of the components , neither concerning the START network nor the morphogenesis checkpoint , are absolutely indispensable [43 , 54 , 55] . The importance of the mRNA localisation , as we highlight it here , can possibly be tested experimentally . The polarised localisation of mCLB2 could be perturbed by disruption of the sequence in mCLB2 required for transport ( if identified ) , or by deletion of the MYO4 gene that encodes the type V myosin motor responsible for bud-localisation of mRNA [56] . Such perturbations would revert the phenotype from Model-2 to Model-1 . To discriminate the two experimentally , the MYO4 deletion would need to be combined with a CLN3 overexpression . The model predicts the loss of the whi phenotype in this double mutant , which should be detectable in an experimental setting ( Fig 7 ) . However , this experiment involves two major genetic perturbations and the outcome may not be as clean as the in silico experiment . Seeing that mCLB2 is localised to the bud of the cell and during S-G2-M mainly the bud grows , it is likely that a G2 sizer actually has an effect on bud size rather than absolute cell size in vivo [35] . Also , while measuring the biosynthetic capacity of a cell in G1 works well to ensure that it is strong enough for duplication , measuring the capacity again in G2 ( cell and bud—Model-1 ) seems futile and less accurate . It seems rather more useful to measure the capacity only of the bud ( Model-2 ) , ensuring that the new descendant is strong enough for independent life . From a population’s perspective , it is reasonable to control the offspring’s size at least as strictly as individual cell size , maybe even with the same mechanism . Taken together , we propose a model where cell size in budding yeast is controlled at the cell cycle junctions G1/S and G2/M in a growth rate dependent fashion ( Fig 8 ) . Our results suggest that the simple mechanism we employ here at both transitions is sufficient . Moreover , the model works without setting of a ‘critical cell size’ . Considering the growth medium dependent nature of the ‘critical cell size’ itself , this strongly advocates an interpretation of cell size as an emergent property of the coupling between growth and division , rather than a regulatory parameter . In accordance , cell size at START would be a function of the growth rate in G1 . Strong evidence in this direction recently emerged [15] . We propose here that bud size at division is a function of the growth rate in S-G2-M , meaning that there is a common size control theme in the two growth phases of the cell division cycle . This can explain the prolongation of both phases , G1 and G2 , in response to poor nutrients and the small size phenotype seen for CLN3 overexpression [14 , 15 , 33 , 34] . In conclusion , we present a cell growth model , which unifies integration of growth and division in the G1 and G2 phases of the cell division cycle to accurately reproduce and predict cell size at birth and at budding , as well as timing of the cell cycle phases over four different nutritional conditions for budding yeast .
The model is an extension of a minimal eukaryotic cell model [32] . It is implemented with ordinary differential equations , stochastic functions and algebraic equations ( Table 1 ) . Two species were added ( mCLB & Clb ) , such that Cln drives the cell cycle in G1 phase and Clb in G2 . Hence , transcription of CLN is restricted to G1 and of CLB to G2 . By default , the model links metabolism to progression through the cell cycle via biomass dependent accumulation of the two regulatory proteins ( G1 cyclin Cln and G2 cyclin Clb ) , whereas the synthesis phase ( S-phase ) and the Mitose ( M-phase ) simply delay cell cycle progression ( see Table 2 ) . The cell cycle of the model has four transitions , corresponding to the eukaryotic phase transitions ( G1/S , S/G2 , G2/M , M/G1 ) . The model equations governing the dynamics are displayed in Table 1 . The model rests on a set of explicit assumptions: namely , that nutrient supply is defined by uptake , which is proportional to cell area; that transcription is stochastic and that nutrient incorporation into biomass relies on the biosynthetic capacity of the cell . Thus , production reactions are dependent on precursors and the internal biomass . The efficiency of nutrient incorporation is inversely scaled with volume to reflect dilution . Furthermore , that the total area of the cell is the sum of the area of the mother and the bud . Correspondingly , we calculate the total volume of the cell as the sum of mother and bud volume . Mothers and buds are approximated as separate spheres , thus V ∝ A3/2 . As a cell grows , the ratio of the area to the volume shifts , since the area expands slower than the volume . Given our above stated assumption about the influence of nutrient supply ( area ) and dilution ( volume ) , it follows that the decrease of the area-to-volume-ratio places an increasing constraint on the cellular biosynthetic capacity slowing down growth [32] . The idea that the surface area-to-volume-ratio plays an important role in connecting the cell growth to the cell division cycle was also explored by others [50 , 57] . In our model , cells may allocate their resources according to cell cycle stage , which means that resources can be used to form structural or internal biomass in different proportions in different cell cycle stages . Specifically , we assume that the increase of the biosynthetic capacity is strong in G1 ( heavier allocation of resources to internal biomass ) and less so during S-G2-M [58 , 59] . The structural biomass can furthermore be distributed to either the area of the mother cell or the area of the bud . There is no bud growth during G1 and we assume that there is only bud growth during S-G2-M [15 , 40] . Finally , we simplify phase transitions to a threshold for nuclear kinase activity , assuming zero order ultra-sensitivity [60] . The model itself is a single cell model that can grow and divide ( S1 File ) . To model entire asynchronously growing cell cultures , however , we developed an algorithm to simultaneously simulate a growing ensemble of the single cell models [32] . An executable and editable version of the algorithm implemented in python is added in the Supporting Information ( S2 File ) . During the simulation , a cell grows during G1 , then it grows a bud during S-G2-M . At division , the bud is detached from the mother cell . The mother cell starts a new cell cycle and , additionally , a new cell instance is created according to the size of the detached bud . All soluble components are split at division between the two cells according to the volume ratio of the mother and the bud . In this fashion , two distinct cells are created from one cell . The two cells differ in starting conditions , e . g . cell size or biosynthetic capacity , which lead to differential growth and proliferation properties for the individual cell , e . g . growth rate or time spent in G1 . Simulation of cell cultures in different nutrients is implemented through the parameter growth , which is used to scale the biomass formation to control the nutrient availability of the system ( Tables 1 and 2 ) . In both models we use the constraint that growthethanol = growthglucose⋅0 . 5 . Similar relations are used to simulate galactose ( growthgalactose = growthglucose⋅0 . 77 ) and raffinose ( growthraffinose = growthglucose⋅0 . 6 ) . We implemented two different versions of the model . Note that the only difference between the two is equation 4 . Model-1 uses equation 4 . 1 and Model-2 is implemented with equation 4 . 2 . Model version 2 is based on the fact that mCLB2 is actively transported into the nascent bud in S . cerevisiae [35] . However , since the model does not include spatial displacement of components , we employed a work-around to implement the consequence of the transport . Assuming that the important function of mCLB2 transport into the bud is to localise translation to this sub compartment , we allowed only the fraction of ribosomes in the bud to translate the CLB mRNA . This is why , in equation 4 . 2 , the metabolic capacity ( BR ) is scaled with the term Vd ( t ) /V ( t ) , assuming well-stirred conditions . Model-1 and Model-2 contain 14 parameters each , five of which we estimated using a maximum likelihood approach ( see Table 2 ) . Both model versions were fitted independently to experimental data from Aldea and colleagues who analysed asynchronously growing daughter cells using time-lapse microscopy [15] . We used two different though related types of their data to constrain our parameters: ( i ) time at START ( T1 ) , budding ( T2 ) and division ( T3 ) ; ( ii ) volume at birth ( V0 ) and budding ( Vbud ) . Aldea and colleagues provide the mean value ( μ ) and coefficient of variation ( cvar in % ) for both types of data for daughter cells grown on four different carbon sources ( glucose , galactose , raffinose and ethanol ) . Here , we used the data for glucose and ethanol for parameter estimation . We recalculated the standard deviation ( σ ) from the cvar and the mean , such that σ = μ⋅cvar/100 . The data and the model fits are shown in Fig 3 . In the model we do not distinguish between START transition ( T1 ) and budding ( T2 ) but consider that as soon as the threshold is crossed , the in silico cells enter S-phase and G1 is finished . As such , for this special case , we relate the model and the data as follows . Time in G1 ( TG1 ) from the model equals the experimental time at START ( T1 ) plus the time from START to budding ( T2 ) TG 1 = T 1 + T 2 . ( 1 ) Now the mean data point is the sum of T1 and T2 with two stdevs σT1 and σT2 , respectively . Neglecting correlations of T1 and T2 , the formula for propagation of uncertainties provides an approximation of the combined error σ f = ( ∂ f ∂ x 1 ) 2 σ x 1 2 + ( ∂ f ∂ x 2 ) 2 σ x 2 2 ( 2 ) where f = x1+x2 ( see Eq 1 ) [61] . This simplifies to yield the combined error for TG1 σ TG 1 = σ ( T 1 + T 2 ) = σ T 1 2 + σ T 2 2 . ( 3 ) As objective function for the fitting of parameters we chose the weighted sum of squared residuals ( wRSS ) given by the measured values x and the simulated values x ^ , such that wRSS = ∑ ( x − x ^ σ ) 2 . ( 4 ) Minimization of Eq 4 is equivalent to maximizing the log-likelihood given by l n ( L ( p ) ) = − m 2 l n ( 2 π ) − ∑ l n ( σ ) − 1 2 ∑ ( x − x ^ σ ) 2 . ( 5 ) It is important to stress at this point that the experimental data was generated analysing only daughter cells [15] . Accordingly , only in silico daughter cells we used to calculate the appropriate data x ^ . Model-1 and Model-2 were fitted independently using a custom evolutionary parameter estimation algorithm ( S1 Text ) . The algorithm is suitable for fitting population data and has been used for all parameter estimation tasks . The algorithm allows specification of parameter boundaries for the estimation ( parameter boundaries are shown in Table 2 ) . The estimation was performed for 100 uniformly distributed initial values ( in the range of the parameter boundaries ) for the parameters which enabled us to derive the parameter correlations ( S4 Fig ) . We estimate five out of 14 parameters because to a certain degree we anticipated parameter correlations , over-fitting or under-determination of parameters since the nature of the data ( population averaged ) and the parameters ( single cell ) are distinct . There are correlations in the parameters that the data cannot account for . For example , the parameters for Cln protein production kp1 , degradation kd , probability of mCLN synthesis PmCLN and the threshold value ( Table 2 ) together influence the duration of G1 phase . Their combined effect determines the final duration . Since the given data concerns the duration , we cannot hope to estimate the true value of the four influential parameters but only their combined effect . This is why we set three out of four to a fixed value and estimated the fourth , such that the global effect matches the data . We can thus not report unique kinetic parameters for protein production and degradation but values that are useful in combination to describe the global process that determines G1 duration . Accordingly , our approach enables us to describe the global effect and also distinguish different model versions . To find the model version that would best approximate reality given the data and the number of parameters we employed the Akaike Information Criterion ( AIC ) to rank the models [46] . The AIC establishes a relationship between the maximum likelihood and the Kullback-Leibler information , which is a measure for the information lost when approximating reality with a model [62] . The AIC was computed as A I C = − 2 ( l n ( L ( p ) ) ) + 2 K ( 6 ) with K being the number of estimated parameters in the model . Model statistics with respect to the objective function , the log-likelihood and the AIC are summarised in S1 Table . | The size between different organisms ranges considerably , yet , the size of the individuals and even the same types of cells within the individuals are remarkably constant . Cell size emerges from the balance between how fast the cell grows and the frequency with which it divides . This system level coordination of growth and division is universal across species and is required to ensure faithful duplication and genetically intact offspring . We have devised a computational model for the interplay of growth and division in the premier model organism , Baker’s yeast , to test the fundamental architecture of this coupling and to assess the role that cell size itself can play in it . In contrast to traditional theories that assume a yet-to-be-determined cell size sensor , our model relies on a single mechanism , effectively measuring the cell’s translational capacity , applied twice at different stages of the cell’s life-cycle to explain this coupling . In our model , a growth condition specific cell size emerges , as has been found in experiments . Our analysis shows how the nature of the two linked properties growth and proliferation can shape eukaryotic cells and explain cell size as an emergent rather than regulatory property of this process . | [
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] | [] | 2015 | Bud-Localization of CLB2 mRNA Can Constitute a Growth Rate Dependent Daughter Sizer |
Blood flukes ( Schistosoma spp . ) are parasites that can survive for years or decades in the vasculature of permissive mammalian hosts , including humans . Proteolytic enzymes ( proteases ) are crucial for successful parasitism , including aspects of invasion , maturation and reproduction . Most attention has focused on the ‘cercarial elastase’ serine proteases that facilitate skin invasion by infective schistosome larvae , and the cysteine and aspartic proteases that worms use to digest the blood meal . Apart from the cercarial elastases , information regarding other S . mansoni serine proteases ( SmSPs ) is limited . To address this , we investigated SmSPs using genomic , transcriptomic , phylogenetic and functional proteomic approaches . Genes encoding five distinct SmSPs , termed SmSP1 - SmSP5 , some of which comprise disparate protein domains , were retrieved from the S . mansoni genome database and annotated . Reverse transcription quantitative PCR ( RT- qPCR ) in various schistosome developmental stages indicated complex expression patterns for SmSPs , including their constituent protein domains . SmSP2 stood apart as being massively expressed in schistosomula and adult stages . Phylogenetic analysis segregated SmSPs into diverse clusters of family S1 proteases . SmSP1 to SmSP4 are trypsin-like proteases , whereas SmSP5 is chymotrypsin-like . In agreement , trypsin-like activities were shown to predominate in eggs , schistosomula and adults using peptidyl fluorogenic substrates . SmSP5 is particularly novel in the phylogenetics of family S1 schistosome proteases , as it is part of a cluster of sequences that fill a gap between the highly divergent cercarial elastases and other family S1 proteases . Our series of post-genomics analyses clarifies the complexity of schistosome family S1 serine proteases and highlights their interrelationships , including the cercarial elastases and , not least , the identification of a ‘missing-link’ protease cluster , represented by SmSP5 . A framework is now in place to guide the characterization of individual proteases , their stage-specific expression and their contributions to parasitism , in particular , their possible modulation of host physiology .
Schistosomiasis caused by Schistosoma blood flukes is a chronic disease with more than 200 million people infected [1] . Schistosome larvae ( cercariae ) , released into an aquatic environment from snail intermediate hosts , penetrate human skin and subsequently develop into adult worms . Adult worms reside in the host vascular system as male/female pairs , and survive for many years , if not decades [2] , producing hundreds of eggs per day . Morbidity arises from the host immune responses to eggs in tissues [3] . Treatment relies on one drug , praziquantel , and no effective vaccine has yet been developed [4] . During its complex life cycle , the parasite survives in various environments by presenting or releasing bioactive molecules that aid survival and modulate host physiology [5] , [6] . Disruption of these potential mechanisms by specific drugs/vaccines may provide therapeutic benefits . Proteolysis is a fundamental physiologic process [7] , [8] . Proteases ( proteolytic enzymes ) are crucial to parasitism , including by schistosomes , in facilitating invasion , nutrient intake , hatching , excystment , immune evasion [9] , [10] and modulation of host physiology [10]–[15] . Most schistosome research has focused either on cysteine and aspartic proteases ( MEROPS database Clans CA and AA , respectively [8] ) , which are responsible for digesting the blood meal [16] , [17] or on the serine proteases ( SPs ) , known as cercarial elastases ( CEs; Clan PA , family S1 ) that facilitate active penetration of the mammalian host [18]–[20] . Regarding the nomenclature for eukaryotic SPs , whereas members of the S1 or ‘chymotrypsin’ family of SPs share a similar tertiary structure , their substrate cleavage specificities differ [8] . Thus , substrate preferences at the P1 subsite [21] may be divided into trypsin-like ( P1 preference for basic residues ) , chymotrypsin-like ( bulky hydrophobic residues ) and elastase-like ( small aliphatic residues ) [7] . Despite their name , which was derived from their ability to cleave insoluble elastin , the S . mansoni CEs have a chymotrypsin-like P1 specificity [22] due to preferences for phenylalanine and leucine . In contrast to these well-studied CEs [18]–[20] , there are fewer descriptions of ‘non-CE’ Clan PA , family S1 serine proteases in S . mansoni ( SmSPs ) [6] , [12]–[15] , [23] , [24] . Among these , SmSP1 ( S . mansoni serine protease 1 , GenBank AJ011561 ) , has been partially described [13] , [14] . The open reading frame ( ORF ) of SmSP1 comprises two non-proteolytic domains , followed by a C-terminal trypsin protease domain . Expression of the trypsin domain ( mRNA and protein ) was noted in adult worms with a significant accumulation in the tegument ( surface ) of males [13] . Another SmSP was identified ( under TC16843 code ) by microarray analysis with a remarkably elevated expression in post-infective larvae ( schistosomula ) that had been maintained in vitro [23] . Two additional biochemical studies support a function for schistosome SPs in modulating host physiology . Specifically , a protein fraction of S . mansoni adult worm extracts was shown to possess kallikrein-like protease activity [12] . The isolated native enzyme , termed sK1 , cleaved kallikrein substrates and processed kininogen to bradykinin which induced strong vasodilatation and decreased arterial blood pressure in experimental rats; sK1 was found in higher abundance in males [12] . Both , sK1 and SmSP1 , are proposed to regulate host vascular functions [6] . In the second study , SP activity in extracts of S . mansoni eggs induced significant fibrinolytic activity and was associated with a 27 kDa protein [15] . This protease activity had a similar cleavage pattern to human plasmin and it was hypothesized that the enzyme blocks the intravascular deposition of fibrin by platelets activated by schistosome eggs [15] . In the present study , we sought to understand the gene repertoire of non-cercarial elastase SmSPs by employing a series of genomic , transcriptomic , proteolytic and phylogenetic approaches . In addition to SmSP1 , we identified and re-annotated four distinct SmSPs in the S . mansoni GeneDB genome database [25] , [26] and term them SmSP2 through SmSP5 according to a previous terminology [13] . The data reveal intriguing expression profiles and phylogenetic relationships that stimulate further study of the individual proteases involved , and their contributions to modulating host physiology .
Mice are kept in the animal facility of the Biology Center ( Academy of Sciences of the Czech Republic ) in Ceske Budejovice and all animal experiments are carried out as approved by the Animal Rights Ethics Committee under protocol no . 068/2010 issued according to the national regulation 246/1992 Sb . A Liberian isolate of S . mansoni has been maintained in the laboratory by cycling between CD-1 mice and the freshwater snail , Biomphalaria glabrata . Mice were subcutaneously injected with 200 cercariae and sacrificed 6–7 weeks post-infection by intra-peritoneal injection of thiopental ( 50 mg/kg ) . Adults , eggs and miracidia were isolated as described previously [27] . Cercariae were obtained from infected snails induced to release the parasite under a light stimulus . Cercariae were chilled on ice , collected and transformed mechanically to schistosomula [27] , [28] , which were then cultured for five days under a 5% CO2 atmosphere at 37°C in Basch Medium 169 [29] containing 5% fetal calf serum and 1% ABAM ( antibiotics/antimycotics; Sigma-Aldrich ) . Daughter sporocyst material was isolated by excision of the hepato-pancreases from two month-infected B . glabrata snails . The hepato-pancreases from uninfected snails were used as a negative control when evaluation gene expression . Adult worms , eggs , miracidia , daughter sporocysts , cercariae and schistosomula were re-suspended in 500 µl of Trizol reagent ( Life Sciences ) and processed [30] . Single-stranded cDNA was synthesized from total RNA by SuperScript II reverse transcriptase ( Life Sciences ) and an oligo dT18 primer , and then stored at −20°C . Genes encoding complete SmSPs or their specific domains were retrieved from the S . mansoni genome database ( S . mansoni GeneDB , available at http://www . genedb . org/Homepage/Smansoni ) through BLAST searches . Amino acid sequences of vertebrate family S1 SPs were used as queries . Specific PCR primers were employed to amplify each of the sequences retrieved , and the respective amplicons cloned into the TOPO TA 2 . 1 vector ( Life Technologies ) for propagation in TOP10 E . coli cells . For SmSP4 and SmSP5 , full-length sequences were obtained by 5′ and 3′ RACE ( Rapid Amplification of cDNA Ends , Life Technologies ) . Based on more recent annotations , the original sequence information for SmSP4 and SmSP5 ( GenBank XM_002572739 and XM_002574902 ) were corrected in the S . mansoni GeneDB database . All newly described SmSP sequences were deposited in GenBank under the accession numbers listed in Table 1 . For genes with multi-domain structures , PCR analysis was performed using domain-specific primers in order to detect possible differential expression . Gene expression of the SmSPs was assessed using RT-qPCR . For genes with multi-domain structures ( SmSP1 and SmSP3 ) , the expression levels of individual domains were evaluated separately . cDNA for various life stages was generated using the mRNA isolation protocol described above and previously [30] . For mRNA isolation , 3 infected B . glabrata hepatopancreases and approximately 20 adult pairs , 500 hundred eggs , cercariae and schistosomula were used . Primers for quantitative PCR analysis were designed using the Primer 3 software ( http://frodo . wi . mit . edu/ [31] , ) , in order to amplify 150–250 bp regions of the targeted genes or their domains . Primer efficiency was evaluated by serial dilutions of both the primers and the cDNA template as described [32] , [33] . Two to three primer pairs were generated per target from which one primer set with optimal efficiency and generating only a single dissociation peak was used ( see Supporting Information Table S1 ) . Reactions , containing SYBR Green I Mastermix ( Eurogentech ) , were prepared in final volumes of 25 µL in 96-well plates [30] . The amplification profile consisted of an initial hot start ( 95°C for 10 min ) , followed by 40 cycles comprising 95°C for 30 s , 55°C for 60 s and 72°C for 60 s , and ended with a single cycle of 95°C for 60 s , 55°C for 30 s and 95°C for 30 s . PCR reactions were performed in duplicate for each cDNA sample . At least one biological replicate , i . e . , samples from a different RNA isolation was performed for each gene target . Analysis of the cycle threshold ( CT ) for each target was carried out as described [30] and employed S . mansoni cytochrome C oxidase I ( SmCOX I , GenBank AF216698 , [33] ) as the sample normalizing gene transcript [27] . Finally , the resulting transcript values were calculated as a percentage of the expression of the normalizing gene ( SmCOX I ) which was set as 100% . Transcript levels were expressed as log functions and as a percentage relative to that of SmCOX I in order to compare variable expression patterns . The threshold for significance of expression was set to 0 . 01% of the expression of SmCOX I . The amino acid sequences of 96 vertebrate and invertebrate members of the S1 serine protease family were aligned in MAFFT [34] using the E-INS-i method , and gap opening ( –op ) and extension penalties ( –ep ) of 5 . 0 and 0 . 0 , respectively . The non-catalytic domains and N-terminal extensions were excluded from the resulting alignment in BioEdit ( v7 . 0 . 5 . 2; [35] ) . The bacterial trypsin from Streptomyces griseus was used as an outgroup . The list of family S1 proteases ( SPs sequences ) used for the phylogenetic analysis is in the Supplementary Table S2 . The Maximum Parsimony analysis was performed in PAUP* ( v4 . b10; [36] ) , using a heuristic search with random taxa addition , the ACCTRAN option , and the TBR swapping algorithm . All characters were treated as unordered whereas gaps were treated as missing data . Maximum Likelihood analysis was performed in RAxML under the WAG model [37] . Clade support values were calculated from 1000 bootstrap replicates with random sequence additions for both analyses . All trees were displayed using the TreeView32 program [38] . Fifty pairs of adult worms , 1 000 eggs or 1 000 schistosomula were washed five times in Basch Medium 169 containing 1% Fungizone ( Gibco ) and allowed to stand for 1 h at 37°C in 5% CO2 . Samples were washed 10 times and then incubated in the same Basch Medium overnight ( adults and eggs ) or for five days ( schistosomula ) at 37°C in 5% CO2 . Parasite material was then washed 10 times in M-199 medium ( alternative medium for schistosoma cultivation without serum and proteins , Gibco ) containing 1% ABAM and incubated in the same medium for 16 h at 37°C in 5% CO2 . Medium containing E/S products was removed and filtered using an Ultrafree-MC 0 . 22 µm filter ( Millipore ) . Filtered medium was buffer exchanged into ice-cold 1× PBS ( pH 7 . 4 ) and concentrated at 4°C to a 2 ml final volume by centrifugation at 4000 g using an Amicon 10000 Ultra-15 Centrifugal Filter Unit ( Millipore ) . The total volume of PBS used for buffer exchange was 40 ml . Samples ( 0 . 04–0 . 37 mg protein/ml ) were frozen in liquid nitrogen and stored at −80°C . Soluble protein extracts ( 1–5 mg protein/ml ) from S . mansoni adults , eggs and 5 day-old schistosomula were prepared by homogenization in 50 mM Tris-HCl buffer , pH 8 . 0 , containing 1% CHAPS , 1 mM EDTA and 10 µM of the cysteine protease inhibitor , E-64 , in an ice bath . The extracts were cleared by centrifugation ( 16 , 000 g , 10 min , 4°C ) , filtered with an Ultrafree-MC 0 . 22 µm and stored at −80°C . Proteolytic activities were measured in a kinetic continuous assay using the following peptidyl fluorogenic , 7-amino-4-methylcoumarin ( AMC ) substrates ( Bachem ) at a 50 µM final concentration: Z-F-R-AMC ( Z , Benzyloxycarbonyl ) , Bz-F-V-R-AMC ( Bz , Benzoyl ) , Z-G-P-R-AMC , P-F-R-AMC , Boc-L-R-R-AMC ( Boc , t-Butyloxycarbonyl ) , Boc-Q-A-R-AMC , Boc-V-L-K-AMC Suc-A-A-F-AMC ( Suc , Succinyl ) , Suc-A-A-P-F-AMC , Suc-L-Y-AMC , MeOSuc-A-A-P-V-AMC ( MeOSuc , 3-Methoxysuccinyl ) , Z-G-G-L-AMC and Z-V-K-M-AMC . Assays were performed at 37°C in 96-well black microplates in a total volume of 100 µl . Parasite extracts ( 1–3 µg ) or E/S products ( 0 . 05–1 µg ) were pre-incubated for 10 min in 150 mM Tris-HCl , pH 8 . 0 , containing 10 µM E64 , 1 mM EDTA in the presence or absence of 0 . 5 mM of the serine protease inhibitors , Pefabloc SC and PMSF . E64 was included routinely in extract preparations in order to inhibit Clan CA cysteine protease activity that is present in the life-stages examined [30] , [39] , [40] . Hydrolysis of substrate was measured continuously using an Infinite M1000 microplate reader ( Tecan ) at excitation and emission wavelengths of 360 and 465 nm , respectively . All measurements were performed in triplicate and results normalized to protein concentration . A spatial model of SmSP1 was constructed using the template X-ray structure of bovine trypsin in complex with the peptidyl inhibitor leupeptin ( PDB entry 1JRT ) and utilizing a pairwise sequence alignment generated by the BLAST program ( BLOSUM62 substitution matrix ) . The homology module of the MOE program was used for modeling the SmSP1 structure ( MOE: Chemical Computing Group; http://www . chemcomp . com ) . The conformation of leupeptin was refined by applying the LigX module of the MOE . The final binding mode of the inhibitor was selected by the best fit model based on the London dG scoring function and the generalized Born method [41] . Molecular images were generated with UCSF Chimera ( http://www . cgl . ucsf . edu/chimera/ ) . The electrostatic surface potential was calculated using the APBS software [42] and input data were prepared using PDB2PQR [43] .
Genes were selected in silico based on a proteolytic domain organization that matched with family S1 serine proteases: cercarial elastases were excluded because of their detailed studies previously [20] , [22] . The five remaining SmSP genes , including the previously sequenced and partially characterized SmSP1 [13] , [14] , were cloned and sequenced . The other four gene sequences named SmSP2 through SmSP5 ( Table 1 ) were significantly corrected and re-annotated in the primary database ( S . mansoni GeneDB ) due to various sequence inaccuracies . The sequences of SmSP2 through SmSP5 were deposited into the GenBank as KF510120 , KF510121 , KF510122 , KF939306 , respectively . The sequence of SmSP1 defined here was also deposited ( KF535923 ) because of sequence differences from the original description ( CAA09691 [13] ) and from the information in S . mansoni GeneDB ( Smp_030350; Figure S1 ) . A search of the Schistosoma japonicum genome [44] indicates that orthologs for each of the SmSPs are present; SjSP1 ( GeneDB Sjp_0012180 , GenBank N/A ) , SjSP2 ( Sjp_0100980 , CAX74751 ) , SjSP3 ( Sjp_0023390 , CAX73257 ) , SjSP4 ( Sjp_0047680 , N/A ) and SjSP5 ( Sjp_0114710 , CAX73292 ) . The sequence domain organization for the particular proteases is represented in Figure 1 . Based on sequence homology analysis , we describe SmSP1 as a multi-domain protein comprising a matriptase-like structure made up of Complement-Uegf-BMP-1 ( CUB ) extracellular and plasma membrane-associated domains , a LDL-binding receptor domain class A ( LDLa domain ) and a S1 family serine protease domain . However , the full gene product has been detected only in the eggs , whereas in other parasite stages , the CUB and protease domains are expressed as separate spliced products , as demonstrated by PCR and sequencing ( Figure S2 ) . Primary sequence homology analysis shows that SmSP2 to SmSP5 are distinct molecules with the same family S1 type catalytic protease domain at the C-terminus , but with different N-terminal extensions which include a potential pro-peptide , i . e . , a peptide that is removed during zymogen activation . The N-terminal extensions vary from 201 residues in SmSP2 to just a seven residues in SmSP5 ( Figure 1 ) . SmSP1 , SmSP3 and SmSP5 do not contain a predicted signal sequence for the secretory pathway as identified by the SignalP program [45] . In contrast , SmSP2 and SmSP4 are synthesized as pre-pro-proteins with a typical N-terminal signal peptide preceding an N-terminal extension region containing a putative pro-peptide ( ‘activation peptide’ ) that is then followed by the protease domain ( Figure 1 ) . The pro-peptide is separated from the protease domain of SmSPs by a basic residue , Arg or Lys ( Figure 2 ) which constitutes a potential activating cleavage site , i . e . , is hydrolyzed during protease maturation as is known for other S1 family proteases [7] . For SmSP3 , the N-terminal extension contains an incomplete CUB domain . PCR and sequencing revealed that , as found for SmSP1 , the CUB and the protease domains of SmSP3 are only co-expressed in eggs whereas they are separate spliced gene products in the other stages ( Figure S2 ) . SmSP5 contains a Thr/Asn rich C-terminal sequence extension not present in orthologous SPs from other trematodes ( Figure S4 ) . The catalytic protease domains of SmSP1 to SmSP4 share significantly greater sequence identity ( about 30% ) with each other than with SmSP5 ( about 20%; Figure S3 ) . All five SmSPs have a catalytic triad in the order of His , Asp and Ser that is typical for S1 family proteases; also , the regions surrounding the catalytic triad residues have the most notable sequence identity ( Figure 2 ) . The protease domains of SmSP1 to SmSP4 contain cysteine residues at positions 28 , 44 , 130 , 160 , 173 , 184 , 194 , and 212 ( SmSP1 protease domain numbering ) , which are conserved in other trypsin-like proteases . They form four disulfide bonds that can be predicted from the alignment with the crystal structures of bovine trypsin and bovine chymotrypsin ( Figure 2 ) . Moreover , the protease domain of SmSP2 through SmSP4 contains an additional cysteine residue , Cys112 . By comparison with bovine chymotrypsin , this residue in SmSP2 and SmSP3 is likely to form a disulfide bond with a Cys in the N-terminal extension region ( at the positions -p13 and -p9 , respectively ) , whereas in SmSP4 a similar Cys in the N-terminal extension region is lacking ( Figure 2 ) . SmSP5 diverges from the other four SPs in that it contains only six cysteine residues that likely form three disulfide bonds . The first two bonds , Cys28-Cys44 and Cys160-Cys173 , are identical to those in trypsin , chymotrypsin and other SmSPs . The remaining cysteine residues ( Cys46 and Cys72 ) are absent , but correspond to Cys46 and Cys77 in SmCE that were predicted to form a disulfide bond by homology modeling [46] ( Figure 2 ) . Moreover , both SmSP5 and SmCEs lack the disulfides Cys130-Cys194 and Cys184-Cys212 , which are conserved in SmSP1 to SmSP4 . Taken together , SmSP5 clearly differs in its disulfide pattern from the other investigated SmSPs . This close structural relationship between SmSP5 and the SmCEs is confirmed for the other analyses performed ( see below ) . In addition , two other splice variants of SmSP5 were detected . Compared to the full-length SmSP5 , both are C-terminally truncated and one is missing the crucial His residue from the catalytic triad ( Figure S4 ) . Asp182 determines the trypsin-like specificity of serine proteases for substrates with Arg/Lys in the P1 position [47] , and this residue is conserved in all of the SmSPs except SmSP5 ( Figure 2 ) , which has Gly . Therefore , it might be the case that SmSP5 displays a substrate specificity similar to that of chymotrypsin/elastase-type proteases which also contain a hydrophobic/uncharged residue in the position 182 . The calcium binding site in mammalian trypsins is formed mainly by Glu70 and Glu80 ( trypsin numbering , corresponding to Glu60 and Glu70 in SmSP1 ) [48] . This motif is not strictly conserved in the analyzed SmSP sequences; however , it might be present in a modified functional form in SmSP2 , SmSP3 and SmSP4 that contain acidic residues in the close proximity of those locations ( Figure 2 ) . Messenger RNA transcript levels for the five SmSPs were evaluated in eggs , miracidia , daughter sporocysts , cercariae , schistosomula and adults using RT- qPCR ( Figure 3 ) . For SmSP1 and SmSP3 , we determined gene expression for both the protease and non-protease domains ( Figure 4 ) . For SmSP1 , the greatest expression was recorded in eggs at 2 . 5% of the expression level of the reference gene , SmCOX I . Low expression was recorded in adult worms , five-day old schistosomula and daughter sporocysts at around 0 . 1% or below relative to SmCOX I . Expression in the other stages was below significance , i . e . , less than 0 . 01% of SmCOX I . As described above , the ORF of SmSP1 consists of 3 domains and their individual expression was evaluated by RT-qPCR and PCR ( Figure 4A; Figure S2 ) . The data show a differential expression pattern for the CUB , LDLa and protease domains of SmSP1: expression of the CUB domain is mostly in eggs and sporocysts , whereas LDLa is only expressed in eggs with an expression level about 20-fold lower than that of the protease domain ( Figure 4A ) . As stated above , only in eggs is the whole ORF amplified by PCR suggesting that some SmSP1 is expressed as the full-length multi-domain protein ( Figure S2 ) . Among the SmSPs , SmSP2 is the most abundantly expressed SmSP ( Figure 3 ) . In fact , expression in schistosomula and adults is on a similar level to that previously measured for the well-characterized S . mansoni cysteine and aspartic proteases [27] . In adults , SmSP2 expression is equivalent to that of SmCOX I , whereas in five-day old schistosomula expression is even greater - 150% that of SmCOX I . Significant expression , i . e . , 10% that of SmCOX I , is also detected in eggs . In the other stages , expression is close to or below 1% of the SmCOX I level . The expression pattern of SmSP3 across all life stages is similar to that of SmSP1 ( Figure 3 ) , with minor variations regarding expression in cercariae and schistosomula . Most expression is found in eggs at 2 . 5% of the SmCOX I expression level . Interestingly , the CUB and protease domains are only co-expressed in eggs and adults ( Figure 4B ) , whereas differential expression is seen for the other developmental stages ( Figure S2 ) . SmSP4 is expressed predominantly in eggs ( around 10% of SmCOX I level ) . For the other stages , approximately 1–2% of the SmCOX I level is detectable in cercariae , adults and five-day old schistosomula . Finally , SmSP5 is expressed predominantly in the eggs ( 2% of the level of SmCOX I ) with low expression in the other life stages ( 0 . 02–0 . 05% of SmCOX ) . The maximum likelihood analysis of a wide spectrum of vertebrate and invertebrate S1 family SPs based on amino acid sequences revealed that SmSPs clustered with related trematode proteases into five distinct and well-supported clades ( Figure 5 ) . Identical results were obtained using maximum parsimony analysis ( data not shown ) . The clades did not create a monophyletic group . Thus , SmSP1 and SmSP3 were placed as two closely related but independent clades ( trematode SP clade 1 and 3 ) and clustered with a large group of vertebrate SPs , including regulatory- and epithelial-derived effector trypsin-like proteases such as plasminogens , plasma kallikreins , tryptases , matriptases and transmembrane SPs ( Figure 5 ) . SmSP2 and SmSP4 also segregated into two separate but related trematode clades ( numbers 2 and 4 ) , which clustered with cestode SPs and a group of insect plasminogen-like and trans-membrane SPs ( Figure 5 ) . Finally , SmSP5 clustered with S . japonicum and Clonorchis sinensis ( Chinese liver fluke ) orthologs and created a sub-clade that grouped with a sub-clade of CEs within the trematode SP clade 5 . This clade also clustered with chymotrypsin-like proteases from invertebrates . Accordingly , SmSP5 and its trematode orthologs associate more with the divergent schistosome CEs [22] than with other S1 family proteases [18] . S1 family SP activities in soluble extracts of S . mansoni adults , five day-old schistosomula and eggs were profiled for proteolytic specificity using peptidyl fluorogenic substrates . Two sets of specific protease substrates were used; ( i ) substrates with a basic amino acid residue ( Arg , Lys ) in the P1 position that are cleaved by trypsin-like SPs , and ( ii ) substrates containing bulky hydrophobic ( Phe , Tyr ) or aliphatic residues ( Val , Leu , Met ) at P1 that are cleaved by chymotrypsin- or elastase-like SPs [49] . The measured activities were further authenticated as S1 family SPs by their sensitivity to the small molecule inhibitors , Pefabloc SC and PMSF . The results indicate that trypsin-like activities predominate over chymotrypsin/elastase-like activities in the analyzed extracts ( Figure 6 ) . The trypsin substrates were hydrolyzed with variable efficiencies giving distinct cleavage patterns for the individual life stages . The prominent activity in all extracts was best measured with the Boc-L-R-R-AMC substrate , hence making this substrate a useful probe to detect and measure SmSPs . Extracts of eggs displayed a particularly complex profile by cleaving an additional two substrates , Bz-F-V-R-AMC , and Z-G-P-R-AMC . This suggests that this life-stage possesses additional , possibly stage-specific , trypsin-like proteases . In contrast to the major trypsin-like activities , chymotrypsin/elastase-like activity was relatively weak being measured only in schistosomula and adults . Subsequently , we tested whether SmSPs is measurable in the E/S products from eggs , schistosomula and adults . For this purpose , we used the substrate Boc-L-R-R-AMC , which was identified as the most efficient substrate for homogenates of all the life stages ( Figure 6 ) . The specific activities of the E/S products , which were inhibited by the SP inhibitors , Pefabloc SC and PMSF , were 1 . 05±0 . 10 , 1 . 38±0 . 05 , and 0 . 11±0 . 01 RFU/µg protein for eggs , schistosomula and adults , respectively . A spatial homology model of the protease domain of SmSP1 was constructed to analyze its binding pocket and substrate specificity . The X-ray structure of bovine trypsin in complex with the small-molecule inhibitor , leupeptin ( PDB code 1jrt ) , was used as a template . We used SmSP1 as representative of SmSP1 to SmSP4 , which have substantial sequence identity , a similar disulfide pattern and homology in active site regions ( Figures 2 and S3 ) . Figure S5 shows that the SmSP1 protease domain displays the conserved architecture of S1 family proteases which consists of two six-stranded β-barrel domains packed against each other . The catalytic amino acid residues are located at the junction between the domains . The major insertion/deletion variations between SmSP1 to SmSP4 ( such as the SmSP2 insertion at residue 140 , Figure 2 ) are located at surface-exposed loops . The primary substrate specificity determinant of S1 family proteases is the S1 binding subsite . In SmSP1 , this subsite forms a deep and narrow negatively charged pocket that contains Asp182 at the bottom ( Figures 7A and 7B ) . Leupeptin , the transition state analog protease inhibitor , was docked into the active site of SmSP1 . The arginal residue of leupeptin forms a covalent linkage with the catalytic Ser188 , a salt bridge with Asp182 in the S1 subsite and hydrogen bonds with the carbonyl oxygen of Ala183 and Asp211 ( Figure 7C ) . An additional hydrogen bond is formed between the side chain nitrogen of Gln185 and the carbonyl oxygen Leu2 residue of leupeptin . The putative interaction pattern of leupeptin at the S1 subsite of SmSP1 is similar to that found in bovine trypsin [50] . This demonstrates that SmSP1 has a substrate binding preference for basic residues at the P1 position , the positive charge of which compliments the negatively charged Asp182 , i . e . , trypsin-like activity . This conclusion can be generalized to SmSP2 to SmSP4 which also contain the critical Asp182 residue .
Much has been reported on the genetic , biochemical and functional characterization of cysteine and aspartic protease activities in schistosomes [16] , [17] and flatworms in general [16] , [51] , and of the schistosome CE SPs [20] that putatively facilitate parasite invasion of the mammalian host [18]–[20] . By comparison , relatively little detail is available for non-CE SPs . There are , however , indications that non-CE S1 family SPs contribute to successful infection [6] . Thus , kallikrein-like protease activity from S . mansoni adults [12] and plasmin-like fibrinolytic activity from S . mansoni eggs [15] have been recorded previously . Both activities displayed trypsin type cleavage specificities and both may contribute to the phenomenon , whereby large occlusions of veins by schistosomes are not associated with intravascular deposition of fibrin and thrombus formation [52]–[54] . At the gene and primary sequence levels , however , only two SmSPs , namely SmSP1 [13] , [14] and another [23] , [24] , which we term SmSP2 , have been described . The S . mansoni GeneDB currently contains 16 unique sequences that belong to Clan PA family S1 SPs . This number is significantly lower than the 135 family S1 proteases found in the human genome [8] , [25] and may be due to the lack of need to regulate the more complex and expanded physiological processes found in vertebrates [55] . In our study and apart from SmSP1 [13] , [14] , we identified four additional SmSP genes encoding typical sequence features of the S1 family [7] , [8] and which we term SmSP2 through SmSP5 . Two further genes ( Smp_194090 and Smp_06530 in GeneDB ) were identified in the S . mansoni GeneDB as putative proteolytically inactive SmSPs as they lack the catalytic serine or histidine residue in the catalytic triad . The remaining nine of the 16 family S1 SPs comprise eight CEs ( encoding both putative proteolytically active and inactive products ) and a gene ( Smp_174530 ) that encodes an S1 family SP ORF fused downstream of an M01 family metallo-protease . This protease that was not known to us at the beginning of our study and because of its domain complexity and sequence size was not described further . Our phylogenetic analyses of trematode SPs displayed interesting evolutionary trends . The SmSPs segregate into five clusters of family S1 proteases . The protease domains of SmSP1 and SmSP3 , forming clades 1 and 3 , respectively , cluster with a large group of vertebrate trypsin-like SPs including regulatory and effector epithelial-derived proteases . In addition to a protease domain , the ORFs for SmSP1 and SmSP3 include non-catalytic CUB domains and SmSP1 LDLa domain . Several vertebrate matriptases that also contain CUB domains are present in our phylogenetic analysis including those belonging to the ‘suppressor of tumorigenicity’ group . As judged by the domain organization , SmSP1 resembles mammalian matriptases ( a . k . a . epithin , MT-SP ) ; however unlike conventional matriptases with multiple CUB and LDLa domains , SmSP1 has only one of each . CUB domains were first described in the complement proteins C1r and C1s and are modules of approximately 110 amino acids with four conserved cysteine residues [56] . These domains mediate protein-protein interactions and are generally associated with proteins that have diverse , usually regulatory , functions in the extracellular space and/or plasma membrane [56] . CUB domains can also interact with heparin and glycoproteins [56] and are often associated with metallo-proteases , in addition to serine proteases [8] . Based on the RT-qPCR analysis , the complete ORFs of SmSP1 and SmSP3 molecules share a similar expression profile ( quantitatively and , to a smaller degree , qualitatively ) across the developmental stages tested . However , it is also clear that the individual protease , CUB and/or LDLa domains are differentially expressed across the developmental stages tested being only co-expressed in eggs and , for SmSP3 , adults . The particular functions of these enzymes and their component domains are unknown and their importance to parasite vitality and/or survival might be tested via specific RNA interference ( RNAi ) , which has been shown to operate in schistosomes [30] , [57] , [58] . According to our phylogenetic analysis , the closest vertebrate orthologs to SmSP1 and SmSP3 are those associated with regulatory cascades such as fibrinolysis and vasodilation . This , together with the fact that SmSP1 was detected apparently on the surface area of worms and secreted into the cultivation media [13] , suggests a possible function at the host-parasite interface . The presence in the ORF of SmSP1 of an LDLa domain ( positioned between the CUB and catalytic domains ) deserves a note . Schistosomes and other flatworms do not synthesize cholesterol ( found within LDL ) and must therefore scavenge it from the environment , particularly for the energy-intensive work of producing eggs [59] , [60] . There is also a report that the presence of S . mansoni eggs is connected with decreased circulating levels of cholesterol in the host [61] , however , we can only speculate about the real function of the SmSP1 LDLa domain . SmSP2 and SmSP4 form two other separate clades and cluster with trypsin SPs from insect and other invertebrates . Both proteases are characterized by their longer but different N-terminal extensions that lack homologies to known proteins but which are shared in orthologous SPs from S . japonicum [44] and C . sinensis [62] . Functions as yet are unknown , however , it is certainly remarkable that SmSP2 is massively expressed in schistosomula and adults ( 150% and 60% of SmCOX I expression levels , respectively ) and , therefore , conceivably contributes significantly to host and/or parasite protein hydrolysis , perhaps in modulating of host physiologic processes [6] , [12] . The presence also of close orthologs of SmSP2 in Fasciola gigantica [63] and C . sinensis [62] suggests a general role for SP2 during infection in the mammalian host . The impressive expression levels for SmSP2 are consistent with high levels of SmSP2 expression from microarray [23] and transcriptome data [24] . Also , the expression levels are close to those for the gut-associated , digestive cysteine and aspartic proteases , SmCB1 and SmCD , respectively , for which expression is close to that of SmCOX I [27] . Finally , for SmSP5 , phylogenetic analysis identified its relative position in what we term clade number 5 . This clade is most closely related to chymotrypsins from invertebrates and comprises SP5 orthologs in S . japonicum [44] and C . sinensis [62] , and the CE genes in S . mansoni , S . haematobium [20] , [22] , S . japonicum [44] and Schistosomatium douthitti [20] . Clade 5 is particularly significant for phylogenetic relationship studies of schistosome proteolytic enzymes as it contains sequences that bridge the outlier CE group and other ‘more typical’ S1 family SPs . Specifically , our previous phylogenetic work [18] had highlighted that the CEs coalesce as a tight group that diverges from other family S1 protease sequences . At that time the SmSP5 sequence was incomplete and not amenable to analysis [18] . The current sequence analysis suggests that SmSP5 and its trematode orthologs are ‘a missing link’ between the outlier CE group and the common ancestor . CEs initially evolved from chymotrypsin regulatory proteases and may provide an evolutionary advantage in contributing to host invasion [22] . For the SmSP protease domains , we investigated the structure-function relationships using primary structure analysis , homology modeling and protease activity profiling with peptidyl substrates . The sequence alignment shows that all the SmSPs except SmSP5 share a conserved Asp182 residue . This residue defines the specificity for the S1 binding site and drives a strong preference for Arg and Lys residues at the P1 position of protein/peptide substrates , as demonstrated for vertebrate trypsins [47] . The homology model of SmSP1 reveals that the S1 pocket with its critical Asp182 residue has an architecture analogous to vertebrate trypsins . In contrast , the S1 binding pocket of SmSP5 has a Gly182 . Also , SmSP5 lacks the disulfide Cys184-Cys212 which is present in the other four SmSPs and known to stabilize the S1 binding site in vertebrate trypsins . Interestingly , this disulfide is also absent in schistosome CEs , which contain non-polar residues ( Ile or Leu ) at the bottom of the S1 binding pocket resulting in elastase and chymotrypsin-like activities [22] . Consistent with the number of trypsin-like sequences in all of the life-stages studied , major trypsin-like activities could also be measured using peptidyl fluorogenic substrates in eggs , schistosomula and adult extracts . Eggs , in particular , presented the most diverse and active profile compared to adults and schistosomula suggesting they express more than one highly active SP . Schistosomula , in contrast , displayed an activity profile restricted to one substrate , and one might suppose that this activity is in fact due to SmSP2 which was , expressed at higher levels than other SPs as measured by RT-qPCR ( see above ) . Finally , the finding that significant trypsin-like activity was found in the E/S products of the three life stages tested suggests that one or more SmSPs are secreted into the host environment where they may interfere with relevant proteolytic cascades such as blood coagulation , complement or blood pressure regulation [6] , [12] . In contrast to the trypsin-like activities measured , chymotrypsin/elastase-like activities were absent in eggs , and in schistosomula were at least one order of magnitude weaker . It is possible that the activity in schistosomula is , in whole or part , due to residual CE activity carried forward after mechanical transformation of cercariae and in vitro culture of schistosomula . In adults , however , this possibility seems remote and the minor activities measured may be contributed to by SP5 . To conclude , the present study provides a comprehensive phylogenetic , transcriptomic and functional framework illustrating the heretofore unknown complexity of schistosome S1 family SPs , other than the well-studied CEs [20] , [22] . The individual enzymes underlying the activities measured remain ‘undiscovered country’ both in terms of their functional characterization and , not least , their possible contributions to successful parasitism by the schistosome , including at the host-parasite interface . | Schistosomes are blood flukes that live in the blood system and cause chronic and debilitating infection in hundreds of millions of people . Proteolytic enzymes ( proteases ) produced by the parasite allow it to survive and reproduce . We focused on understanding the repertoire of trypsin- and chymotrypsin-like Schistosoma mansoni serine proteases ( SmSPs ) using a variety of genomic , bioinformatics , RNA- and protein-based techniques . We identified five SmSPs that are produced at different stages of the parasite's development . Based on bioinformatics and cleavage preferences for small peptide substrates , SmSP1 to SmSP4 are trypsin-like , whereas SmSP5 is chymotrypsin-like . Interestingly , SmSP5 forms part of a ‘missing link’ group of enzymes between the specialized chymotrypsin-like ‘cercarial elastases’ that help the parasite invade human skin and the more typical chymotrypsins and trypsins found in the nature . Our findings form a basis for further exploration of the functions of the individual enzymes , including their possible contributions to influencing host physiology . | [
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] | 2014 | Trypsin- and Chymotrypsin-Like Serine Proteases in Schistosoma mansoni – ‘The Undiscovered Country’ |
Leprosy elimination defined as a registered prevalence rate of less than 1 case per 10 , 000 persons was achieved in Kenya at the national level in 1989 . However , there are still pockets of leprosy in some counties where late diagnosis and consequent physical disability persist . The epidemiology of leprosy in Kenya for the period 2012 through to 2015 was defined using spatial methods . This was a retrospective ecological correlational study that utilized leprosy case based data extracted from the National Leprosy Control Program database . Geographic information system and demographic data were obtained from Kenya National Bureau of Statistics ( KNBS ) . Chi square tests were carried out to check for association between sociodemographic factors and disease indicators . Two Spatial Poisson Conditional Autoregressive ( CAR ) models were fitted in WinBUGS 1 . 4 software . The first model included all leprosy cases ( new , retreatment , transfers from another health facility ) and the second one included only new leprosy cases . These models were used to estimate leprosy relative risks per county as compared to the whole country i . e . the risk of presenting with leprosy given the geographical location . Children aged less than 15 years accounted for 7 . 5% of all leprosy cases indicating active leprosy transmission in Kenya . The risk of leprosy notification increased by about 5% for every 1 year increase in age , whereas a 1% increase in the proportion of MB cases increased the chances of new leprosy case notification by 4% . When compared to the whole country , counties with the highest risk of leprosy include Kwale ( relative risk of 15 ) , Kilifi ( RR;8 . 9 ) and Homabay ( RR;4 . 1 ) , whereas Turkana had the lowest relative risk of 0 . 005 . Leprosy incidence exhibits geographical variation and there is need to institute tailored local control measures in these areas to reduce the burden of disability .
In 1991 , the World Health Assembly passed a resolution to “eliminate” leprosy as a public health problem by the year 2000 . Elimination , defined as a registered prevalence rate of less than 1 case per 10 000 persons , was realized globally in the year 2000 and in most countries by 2005 [1] . This achievement was driven by the utilization of multiple drug therapy ( MDT ) as a strategy for elimination of leprosy . More than 16 million leprosy patients have been treated globally over the past 20 years and the prevalence rate of the disease has dropped by 99%: from 21 . 1 per 10 000 in1983 to 0 . 2 per 10 000 persons in 2015 [2] . All countries with a population of one million or more have achieved the elimination of leprosy as a public health problem at the national level [1] . Despite the definite gains in control of leprosy , on-going transmission continues to be documented . More than 200 , 000 new leprosy cases are detected and reported annually from 121 countries [3] . This number has been fairly stable in the past 8 years with India , Brazil and Indonesia accounting for 81% of all new cases . Kenya is in the post elimination phase of leprosy control , having achieved the WHO elimination target of less than 1 case per 10 , 000 people in 1989 . The number of new reported leprosy cases in the country declined steadily from 6 , 558 in 1986 to 131 cases in 2015 [4] . Despite the low number of reported cases , leprosy continues to cause high morbidity among those infected with 48% of new cases notified in 2013 having advanced disease with disability grade 1 and 2 . In 2014 , 133 Leprosy cases were notified , majority ( 90% ) being multibacilary ( MB ) patients . This advanced form of the disease implies localized infection which continues to be spread in the communities as individuals stay for longer periods before being diagnosed [5] . Additionally , childhood cases accounted for 11% and 2% of the cases diagnosed in the year 2014 and 2015 respectively , indicating ongoing active transmission [4 , 5] . Geographical variations are a striking feature of leprosy at every level . In Kenya , for example , most new leprosy cases have been documented in Kwale , Kilifi , Kisumu , Siaya , Homabay and Busia counties [5] , located in the south east and western parts of the country . These geographical patterns may indicate important risk factors that remain to be elucidated and whose recognition could be useful in control of the disease . In addition , the recognition of tailor made leprosy control activities e . g . active case finding which improves the cost–effectiveness of control programs , considering that a reduced disease burden in terms of the number of new cases is likely to define the nature of leprosy in the future . We set out to describe the geospatial distribution of leprosy cases and to determine factors influencing leprosy notification in the 47 counties in Kenya as a way of providing data to plan for leprosy elimination strategies .
This is a retrospective cross sectional study that covered all the 47 administrative units ( counties ) in Kenya . Kenya covers an approximate area of 591 , 971 km2 with an estimated population of 43 million people in 2014 [6] . The county is the most important unit of administration in provision of social services in Kenya’s decentralized system of governance . Leprosy care and control in Kenya is fully integrated in the national primary health care network and involves most government health facilities , faith based organization , communities and private health care units . Health care workers in these facilities are responsible for case finding , infection control and treatment of leprosy patients . At the county and sub-county levels Tuberculosis ( TB ) and leprosy coordinators are responsible for providing technical assistance and supervision to the health facilities . This cadre of healthcare workers is also responsible for aggregating data at the sub county level and updating the web-based surveillance system ( TIBU ) making leprosy case-based data available at the national level . The National Tuberculosis , Leprosy and Lung Disease Program ( NTLD-P ) designs standard data collection and reporting tools for all the levels of reporting ( national , county , sub-county and facility ) [7] . The study utilized secondary data from the TIBU system i . e . retrospective extraction of leprosy case-based notification data as well as geospatial data from Kenya National Bureau of Statistics ( KNBS ) . The key patient variables collected in the TIBU system and of relevance to this study include , sex ( male/female ) , age in years , classification of patient ( MB/PB ) and disability grade at diagnosis ( 0 , 1 , 2 ) . This disability grading is done according to the WHO disability grading scale [8] outlined in Table 1 . The case-based data was downloaded as an excel file . Considering the relatively few numbers of leprosy cases notified in the country , and the geographic distribution , counties with the highest number of notified cases were purposively sampled for data verification . These included Kwale , Killifi , Malindi , Kisumu , Siaya , Homabay , Busia and Bungoma counties . Two health facilities from among those which reported any case of leprosy within the study period were randomly selected from each county and data in the facility register ( considered to be the primary data source ) matched to data available in the online TIBU system . We included all notified cases of leprosy within a 4-year period ( 2012 to 2015 ) . Observations missing any one or more requisite variables were excluded . The variables of interest for description of the trend were sex , the number of leprosy cases reported over the four year period , annual new case detection rate ( per 100 , 000 population ) , disease classification of patient ( MB/PB ) , disability grade at diagnosis ( 0 , 1 , 2 ) and age in years . Microsoft excel was used to summarize data in graphs and frequency tables to illustrate changes in leprosy case detection over the years . Univariate analysis ( chi square tests ) to explore the relationship between types of leprosy notified and sociodemographic/ disease factors were carried out in STATA version 11 . 2 . To describe the geospatial distribution of notified leprosy cases across the 47 counties , a Bayesian approach was used [9 , 10] . Two separate spatial Poisson Conditional Autoregressive ( CAR ) models were fitted i . e . one for all cases ( new , retreatment , transfers from other health facilities ) and another including only new leprosy cases . The covariates included in both models were population density , proportion of < 15 year olds among newly diagnosed cases , proportion of newly diagnosed cases with Grade 2 Disability ( G2D ) , proportion of MB cases among new cases , median age of leprosy cases ( years ) and sex ratio per county . The relationship between notified leprosy cases and the covariates were characterized by spatial random effects . Spatially unstructured random effects were assumed to be normally distributed whereas spatially structured random effects were assigned a conditional autoregressive prior and the corresponding precision parameters given non-informative gamma distributed priors . Two counties were said to be neighbors if they shared a border . Bayesian inference was used to estimate the parameters in the model with Markov Chain Monte Carlo ( MCMC ) technique . The models were implemented using WinBUGS version 14 and MCMC convergence of all model parameters assessed by checking trace plots . The relative risks per county were then mapped . Ethical approval to carry out the study was obtained from Kenyatta National Hospital/University of Nairobi Ethics and Research Committee . The data extracted from the National Leprosy Control Program database was anonymized and no reference made to the patient names , serial numbers or address/ immediate neighborhoods or any other person identifiable variable . All data were password protected hence only authorized persons had access to it .
When all cases were considered , age proved to be the only significant predictor , with the risk of leprosy notification increasing by about 5% for every 1 year increase in age . Among newly diagnosed cases , the proportion of MB cases was a significant predictor for leprosy notification . A one percent increase in the proportion of MB cases increased the leprosy risk by approximately 4% . Table 3 summarizes the model results .
Our results suggest the higher and lower risk areas of leprosy occurrence in mainland Kenya . Areas proximal to Lake Victoria in the west ( Homa Bay and Siaya Counties ) ; and coastal area ( Kwale and Kilifi ) are the hotspots for leprosy occurrence and transmission . Leprosy transmission remains active in Kenya as evidenced by the substantial number of cases aged below 15 years . Childhood cases are usually associated with recent active foci of transmission , given leprosy’s long periods of incubation i . e . 2–5 years for PB disease and 5–10 years or sometimes longer for MB disease [11] . The large proportion of notified cases with MB disease is also concerning as it is the major risk factor for leprosy transmission [12] . We documented an increase in cases notified with age . This is a similar trend in both low and high density transmission areas where a peak in cases is highly correlated with the life expectancy [13 , 14] . While most of the incidental infection occurs in young adulthood , the long incubation period of disease and the fact that disability rather than mortality occurs in cases , combine to contribute to the high numbers in late adulthood [15] . The high proportion of cases with disability indicates late diagnosis of leprosy . The high numbers of disability have a negative socioeconomic impact on communities that harbor the patients not only because of the symptoms but also the stigma attached to the condition [16] . Community engagement in ensuring early diagnosis , instituting multidrug therapy and tackling stigma has been suggested as a way of reducing the psychosocial and economic impact of leprosy [17] . It is possible that adopting similar strategies would have an impact in reducing disability in highly endemic counties in Kenya . The annual case notification rates increased from 2012 and peaked in 2014 followed by a drop in 2015 . Leprosy control activities have not been a priority in Kenya . In the year 2014 , funds were availed to conduct an active case finding in some counties like Kwale and Kisumu generating considerably high numbers of cases . This kind of financial support is not consistent and the health system in Kenya largely relies on passive surveillance of leprosy . The low index of suspicion among health workers is detrimental to the process . The results also indicate that more males than female cases were reported across the ages . From the results , it remains unclear whether there is a significantly higher risk in males or if it is merely due to a biased case ascertainment . A similar scenario has been documented in most countries where leprosy still exists [18] . However , in most African countries , while the men make up the bigger proportion of leprosy cases , the outcomes- disability and death- seem to be poorer in women [19 , 20] . The nature of our study does not allow us to reach conclusions on gender differences related to access to health which may play a role in determining outcomes . The Global Leprosy Strategy ( 2016–2020 ) advocates for special focus on women and children . The leprosy control program should therefore ensure equity in access to health services . The regression models revealed that significant risk factors for leprosy incidence in Kenya include the age ( for all cases ) and the proportion of MB disease among newly diagnosed cases . These findings are consistent with existing literature; given MB contact is a high risk factor hence the higher the proportion , the higher the probability of contact . In addition , with increasing age , so does the probability of manifestation of an earlier infection . As depicted by the spatial maps , the coastal areas and those near to Lake Victoria have high relative risks . In other countries proximity- and especially bathing in open water bodies has been shown to catalyze transmission [21] . In addition , counties with high relative risks tend to be close to each other . This suggests geographical variation in either the risk factors , population based factors and/or health system factors . One of the fundamental strategies towards improving the surveillance system proposed in the Kenya National Strategic Plan for Leprosy ( 2015–2018 ) was to map leprosy cases to identify the hot spots . This work contributes substantially to this process and therefore guides appropriate and cost-effective programmatic interventions . These would focus the constrained resources on high risk counties herein defined as Kwale , Kilifi , Homa Bay , Siaya , Busia , Mombasa , Kisumu and Lamu . In these counties , the following approaches should be implemented: i ) sensitization of health care workers and community health volunteers on leprosy , specifically early symptoms of the disease ii ) tracing and screening household contacts of children ( below 15 years of age ) and those with newly diagnosed multibacillary leprosy and iii ) conducting active case finding ensuring no biased case ascertainment; women , children and other vulnerable populations to be included . The respective county health departments should be proactive and allocate resources for leprosy control . A well planned surveillance system will not only improve treatment outcomes , but also strengthen monitoring and evaluation by generating data that is comparable over time . This study is not without limitation . Firstly , leprosy case notification rate is used as a proxy for disease incidence and does not capture the true incidence of disease as not all leprosy cases may be registered at a health facility . However , the data generated , even if a proxy , is instrumental in planning interventions . Secondly , secondary data sources , whose collection we had no control over , usually has problems with completeness . To address this problem , we compared primary data at randomly selected health centers and we found it to be complete . Future studies could focus on the effects of various socio‐economic and environmental risk factors for the high occurrence of the disease in the clustered areas and analyses of case isolates will enable anti‐microbial and strain‐specific factors to be considered . We conclude that there is evidence of geographical variation in occurrence of leprosy cases with clustering in western and coastal areas of Kenya . There is also evidence of active leprosy transmission and significant disability among the cases . This work will guide tailored policies to address leprosy control . | Leprosy is a chronic bacterial disease that mainly affects the nerves . If untreated , it may cause progressive and permanent damage to the skin , nerves , limbs , and eyes leading to physical disability . Through use of a combination of drugs , Kenya was able to declare the disease as eliminated in the year 1989 . However , there are still pockets of leprosy in some Kenyan counties where physical disability persists , mainly due to late diagnosis . To be able to curb this disease , control measures must be intensified , especially in the counties reporting more cases . We used data of the leprosy cases reported in Kenya for the period 2012 through to 2015 in order to describe geographical variation and factors influencing this variation . More than half of the registered cases had visible physical disability . The risk of leprosy notification increased with an increase in age as well as the severity of disease . We estimated that people living in Kwale , Kilifi and Homabay counties are 15 , 9 , and 5 times respectively more at risk of leprosy as compared to the whole country . Given the limited resources , it’s therefore paramount that high risk counties be initially targeted for control , with a focus on early diagnosis . | [
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] | 2019 | The spatial epidemiology of leprosy in Kenya: A retrospective study |
The life long relationship between herpes simplex virus and its host hinges on the ability of the virus to aggressively replicate in epithelial cells at the site of infection and transport into the nervous system through axons innervating the infection site . Interaction between the virus and the sensory neuron represents a pivot point where largely unknown mechanisms lead to a latent or a lytic infection in the neuron . Regulation at this pivot point is critical for balancing two objectives , efficient widespread seeding of the nervous system and host survival . By combining genetic and in vivo in approaches , our studies reveal that the balance between latent and lytic programs is a process occurring early in the trigeminal ganglion . Unexpectedly , activation of the latent program precedes entry into the lytic program by 12 -14hrs . Importantly , at the individual neuronal level , the lytic program begins as a transition out of this acute stage latent program and this escape from the default latent program is regulated by de novo VP16 expression . Our findings support a model in which regulated de novo VP16 expression in the neuron mediates entry into the lytic cycle during the earliest stages of virus infection in vivo . These findings support the hypothesis that the loose association of VP16 with the viral tegument combined with sensory axon length and transport mechanisms serve to limit arrival of virion associated VP16 into neuronal nuclei favoring latency . Further , our findings point to specialized features of the VP16 promoter that control the de novo expression of VP16 in neurons and this regulation is a key component in setting the balance between lytic and latent infections in the nervous system .
Herpes simplex virus ( HSV ) remains a significant human pathogen associated with extensive acute and chronic disease in humans worldwide . Despite safe and effective antiviral treatment for limiting viral replication , the numbers of infected individuals continues to increase . The expansive reservoir of HSV genetic information is maintained unaffected by current antivirals within the nervous system of every infected individual . This reservoir of latent viral genomes fuels transmission , sporadically giving rise to infectious virus shed to surface sites [1] . Understanding how the virus establishes latent infections is an important goal for the development of new treatment strategies . Although progress has been made , the core mechanism ( s ) underlying the latent/lytic balance are yet to be defined . The HSV life cycle is complex , interacting in a series of distinct and temporally shifting host cellular environments . Infection of a host at a mucosal surface and pursuant replication results in viral invasion of the innervating sensory nerve termini and transport to the sensory neurons in the ganglion . Viral replication occurs in the ganglion and at the periphery but is resolved within about 10 days . This acute stage of HSV infection is followed by a lifelong latent infection , primarily marked by the absence of signs of infection . Thus lytic and latent viral programs appear to be temporally distinct features of the viral life cycle . However , following the identification of the latent program promoter ( LATp ) [2] and generation of viruses carrying a LATp reporter gene [3] , it became evident that during the acute stage of infection in the TG , both viral programs were represented in the population of infected neurons [3–5] . These findings revealed that viral program selection in the TG neuron was more complex and regulated in ways that were not understood . Several hypotheses have been proposed to explain this dichotomy , including neuronal type [6] , transition from some lytic gene expression into the latent program [7] , and alternative regulation of gene expression [8] . However , while progress has been made , the mechanism underlying this complexity remains unclear . Recent findings indicate that the virion transactivator protein , VP16 , may play a pivotal role in these processes [9–11] . VP16 is an essential viral tegument protein required for virion morphogenesis [12] . In infected cell cultures VP16 also provides an important role in initiating the viral lytic cycle at low multiplicity of infection ( moi ) , increasing plaquing efficiency 10–1000 fold in a cell type dependent manner [13–15] . VP16 has a potent carboxy-terminal acidic activation domain and an upstream core domain region that interacts with host cell proteins HCF and Oct-1 to form a VP16 induced complex ( VIC ) [16–18] ( for review see [19] ) . These host cell partners are required for binding of the VIC to the TAATGARAT motifs unique to 5 viral immediate early ( IE ) gene promoters [20–25] . The possibility that latency in neurons is linked to the regulation of VP16 function , either through its absence ( e . g . VP16 does not reach the neuronal nucleus or there is strict nonnuclear compartmentalization of essential co-activators ) , or its conversion from an activator to a repressor through altered neuronal cofactor interactions ( e . g . HCF-2 , Oct-2 or Brn3 ) have been proposed [19 , 25–33] . In an influential study , attempts to perturb the establishment of latent infections utilizing strategies to induce VP16 expression in infected TG failed [26] , and reactivation ( in an ex vivo setting ) was not dependent upon VP16 transactivation function [27 , 34–36] , limiting enthusiasm for the hypothesis that VP16 functioned as a central regulator in latency and reactivation in vivo . At this time , an alternate viral protein , IE gene ICP0 , became the focus of intense investigation as possibly initiating reactivation . The ability of ICP0 to induce modifications of the histone codes on quiescent/latent viral genomes from those associated with repression to those associated with derepression and induce the transcription of reporter promoters in tissue culture models make it an attractive candidate ( recently reviewed in [37–39] . In more recent studies , two distinct VP16 transactivation deficient mutants failed to exit latency in vivo , both blocked at a very early stage prior to viral protein synthesis [11 , 40] . In addition , evidence that de novo expression of VP16 may be required for viral replication in neurons during the acute stage of infection has also been reported [11] . In cultured neurons strong evidence that VP16 dissociates from the nucleocapsid prior to retrograde transport into the nucleus favoring quiescence ( latency ) has been published [41 , 42] although alternate mechanisms of retrograde transport of VP16 occur that can induce viral replication [43 , 44] . The design of experiments to test the functions of VP16 , ICP0 or other factors in the latent/lytic decision is not straightforward . Infection of neurons in the TG is highly asynchronous , with new infections and superinfections occurring in the TG from virus transported from the surface over multiple days . Thus studies of the temporal expression patterns in neurons are difficult to interpret . Already by 32–36 hrs pi , infectious virus is detectable in the TG [45 , 46] and when examined at 3 days pi , neurons undergoing productive lytic infection were found temporally and spatially distributed among neurons in which the viral latent program was activated [3 , 5 , 47 , 48] . At very early times pi , extremely few trigeminal ganglion ( TG ) neurons are infected . For these reasons classic single cell based measures such as immunohistochemistry and in situ hybridization on tissue sections , or bulk analyses such as quantitative reverse transcription PCR , proteomic approaches and RNAseq cannot adequately address these important questions . In this study we sought to overcome these difficulties by employing viral mutants expressing reporter proteins from viral promoters of diverse kinetic classes or viral transcriptional regulators expressed from the LAT promoter , combined with whole ganglion histochemical and immunohistochemical techniques . This permits the interrogation of the earliest events in TG neurons infected from the body surface in the context of essentially wild type virus infection at the single neuron level . In addition whether select viral proteins are sufficient to alter the balance between latent and lytic infection in neurons in vivo can be determined . Our findings reveal that the balance between latent and lytic programs in individual neurons is ( i ) observable , ( ii ) that entry into the latent program is the default ( occurs first ) , ( iii ) the lytic cycle begins as a transition out of this early latent program , ( iv ) VP16 can play a dominant role in this transition , and ( v ) the VP16 promoter contains elements dispensable in cultured cells and corneas , but required for efficient transition out of the default latent program and productive lytic infection in neurons . Importantly , these findings reveal a previously unrecognized transition occurring in individual neurons . This transition from latent into lytic programs is gated , at least in part , by de novo VP16 expression and emphasizes the importance of the novel regulation of this gene during acute infection of sensory neurons in vivo .
We exploited the properties of the basal latency associated transcript promoter ( LATp ) to express selected open reading frames from the viral genome . Prior studies demonstrated that the ( LATp ) ( driving expression of the E . coli beta galactosidase gene ( LacZ ) ) is silenced in the context of infection in cultured cells [49–51] . However , in the mouse corneal model , as early as day 3 pi , this promoter is active in a population of trigeminal ganglion ( TG ) neurons which are largely devoid of viral proteins [3 , 48 , 52 , 53] . Importantly , this promoter contains neuronal specific elements , and an ICP4 binding site that down regulates its activity very early in the lytic cycle [49–51] . These features provide for a viral platform in which an open reading frame driven by the LATp would fail to express during lytic infection ( and thus would not alter the normal lytic cycle ) but would express de novo ( in the absence of an ongoing lytic cycle ) in a population of TG neurons . Since our focus in these studies is neurons that have entered the latent program during the acute stage of infection ( in contrast to the common use of “latent” at times >30days pi ) , we will designate this as acute stage latent program to avoid confusion . The design of the viral mutants is shown schematically in Fig 1 . Importantly , placement of the expression cassettes after base pair 138047 in the intergenic region between glycoprotein J ( gJ ) and gD leaves all known wild type proteins intact and does not alter the transcription of either the gJ or gD genes [11] . Since expression from the LAT promoter is silenced during lytic infection [2 , 51] , LacZ , the second copy of VP16 , or the third copy of ICP0 driven by LATp would be unexpressed . However , it is possible that the LATp is leaky in neurons and that the effects of a second copy of VP16 expressed early in the lytic cycle could confound the interpretation of in vivo studies . Therefore , an additional control mutant containing a second copy of VP16 driven by the ICP0 promoter was constructed . The ability of the proteins expressed from the mutagenesis constructs to enhance expression of relevant promoters in reporter assays was confirmed by transient co-transfection assays ( S1A Fig ) . At least three independently derived viral mutants of each type were generated as detailed previously [11 , 55 , 56] and in methods . In some cases genomically restored isolates were also constructed and tested to confirm that the altered phenotypes displayed by the mutants were the result of the intended mutation . RFLP analysis of the mutants revealed the predicted genomic structures ( for example see S2A and S3A Figs ) and sequence analysis demonstrated correct insertion of the mutation cassette into the viral genome . Biological characterization was performed in rabbit skin cell ( RSC ) cultures to assess ( i ) replicative capacity using multistep replication kinetics , ( ii ) levels of VP16 or ICP0 mRNA and protein in the context of 17LATpVP16 and 17LATpICP0 infection , and ( iii ) levels of VP16 mRNA and protein in the context of ICP0pVP16 infection . The mutants employed are genotypically as expected , and as shown ( S1B–S1E Fig ) are replication competent , and express the inserted transgenes with the expected kinetics in RSC cultures . Our prediction was that if VP16 and/or ICP0 could drive entry into the lytic cycle from the activated LATp , a significant increase in viral titer in the TG would be observed . Three independent isolates of each virus were analyzed . One of the 17LATpVP16 mutants ( isolate 17LATpVP16 4AA1 ) was restored to wild type sequence ( see methods ) to further test that the phenotypic changes detected were not the result of some unknown second site mutation . Significantly , although independently derived , the phenotypes were consistent for a given set of mutants . Infection of mice with 1x105 pfu of 17syn+ , 17LATpICP0 , 17LATpVP16 , 17LATpVP16R , or 17LATpLacZ yielded similar infectious titers in the eyes through day 4 pi ( day 2 pi , 1 . 8x104 ± 6 . 6x103 to 2 . 8x104 ± 5 . 1x103 , p = 0 . 76 ANOVA; day 4 pi , 5 . 6x104 ± 4 . 6x103 to 6 . 4x104 ± 4 . 3x103 , p = 0 . 41 , ANOVA ) . 15-fold more infectious virus in TG of 17LATpVP16-infected mice was observed already on 2 dpi , ( 17syn+ , 17LATpICP0 , 17LATpVP16R , and 17LATpLacZ ranged from 146 . 7 ± 55 . 8 to 234 . 7 ± 76 . 4 , average 195 . 3 ± 69 . 55 vs 2 , 967 ± 1041 pfu/ml for 17LATpVP16 ) . On day 4 , when virus titers peak in the TG , the differences in titers among the viruses varied by less than 1 . 7 fold , ranging , 6x104 to 1x105 . However , on 7 dpi , when titers are normally declining in the TG , there was a 45-fold difference ( range 183 . 3 ± 104 . 1 to 329 . 94 ± 122 . 3 , average 211 . 7 ± 156 . 3 vs . 9 , 540 ±1 , 323 pfu/ml ) and >1 , 000-fold ( range 0 to 10 vs . 800 to 10 , 000 pfu/ml ) on day 9 ( Fig 2A ) . All of the mutants tested with the exception of the 17LATpVP16 mutants exhibited virulence profiles similar to that of the parental strain 17syn+ with 75% or more of the mice surviving inoculation with 1x105 pfu ( Fig 2B ) . In contrast , all of the 17LATpVP16 isolates tested were significantly more virulent than the parental strain 17syn+ ( Fig 2B and Table 1 below ) . To determine whether the increased virulence of 17LATpVP16 infection correlated with increased viral replication in the CNS viral replication in the central nervous system CNS was quantified over time . Groups of male Swiss Webster mice were infected with 1x105 pfu on the corneal surface with 17syn+ , 17LATpVP16 , or 17LATpVP16R . As early as 2 days pi , 100% ( 4/4 ) of brains from mice infected with 17LATpVP16 contained low levels of infectious virus , ranging from 13–150 pfu which increased to a mean of 2 . 5x103 ( range: 900–5000 ) by day 4 pi . In contrast , virus was not detected in the brains harvested from either 17syn+ or 17LATpVP16R infected mice on day 2 and on day 4 pi , a mean of 7 pfu ( range: 0–20 ) was detected , a difference of ~300 fold ( p<0 . 0001 , ANOVA ) . As shown in Fig 2C , infectious virus titers in all portions of the brains from 17LATpVP16 infected mice ( n = 4 ) were greater than those detected in 17syn+ ( n = 3 ) or 17LATpVP16R ( n = 4 ) infected brains by 10 to >1000 fold . At times when replication in the CNS was no longer detected in either 17syn+ or 17LATpVP16R infected brains , replication continued in 17LATpVP16 infected mice with more than 1000 pfu present in the front portions of the brain . Thus the increase in mortality of 17LATpVP16 directly correlated with earlier , increased , and prolonged viral replication in the CNS ( Fig 2C ) . Virulence was evaluated by infecting groups of male Swiss Webster mice on corneas with serial dilutions of 17syn+ , the viral mutants , 17LATpVP16R , or wild type strain Mckrae . This strain was included as a benchmark of “natural virulence” observable among unmodified HSV isolates . Virulence data is summarized in Table 1 . While the increase in virulence displayed by the 17LATpVP16 mutants was significant , these mutants did not exceed the virulence of the laboratory strain McKrae and thus remained within the range of virulence naturally occurring among human isolates . We also tested the effect LATpVP16 expression in the highly resistant C57BL/6J background . Groups ( 3–5 mice/group ) of age and sex matched adult C57BL/6J mice were infected on scarified corneas with 1x106 pfu of either 17syn+ , 17LATpLacZ , 17LATpVP16 , or McKrae . All of the mice infected with 17syn+ or 17LATpLacZ survived , in contrast , none of those infected with either 17LATpVP16 ( 0/4 ) or McKrae ( 0/5 ) survived . To test the importance of the transactivation function of VP16 to the observed phenotypes , we generated an HSV-1 mutant that expresses a second copy of a transactivation null VP16 from the LATp in the gJ locus ( 17LATpVP16/Y364A ) . This VP16 ORF contains a single amino acid change of tyrosine to alanine at position 364 that disrupts the ability of VP16 to bind to its co-activator protein HCF-1 [57 , 58] . Consistent with previous reports , the ability of the VP16 with the Y364A mutation to transactivate the ICP0 promoter in transient transfection assays was 15 fold reduced compared to WT VP16 and not different than the empty vector control in cultured cells [59 , 60] . Characterization of the genomic structure of six independently derived isolates revealed insertion in the gJ locus but no other perturbations ( S2A Fig ) . The multi-step replication of the mutants in RSC in vitro was not significantly different than the parental strain ( S2B Fig ) . Groups of mice were infected with 1x105 pfu each of the three independently derived isolates of 17LATpVP16/Y364A or wild type 17syn+ . Survival was evaluated and at various times pi tissues were collected and analyzed for infectious virus . Neither survival ( 17LATpVP16/Y364A , 8/10; 17syn+ , 7/9 ) nor replication ( 17LATpVP16/Y364A , d4pi TG , 5 . 7x104 ± 6 . 8x103; 17syn+ , d4pi TG , 7 . 7x104 ± 1 . 3x104 ) were different , p = 0 . 25 , Student’s t-test ) demonstrating that the transactivation function of VP16 was required for the 17LATpVP16 enhanced virulence phenotype . The finding that de novo expression of ICP0 from the LATp did not alter viral replication in the TG was somewhat unexpected . There are several lines of evidence supporting a role for ICP0 in either enhancing [61 , 62] or initiating reactivation from latency [61–70] . However , in studies discriminating between roles in initiation or in progression , ICP0 was not required for the efficient initiation of reactivation from latency , but essential for infectious virus production in reactivating neurons in vivo or in the setting of quiescent infection in cultured cells [54 , 71] . To test for the possibility that gross genomic rearrangements of ICP0 might occur during replication in vivo , three independently derived isolates of the 17LATpICP0 mutants were examined . Virus was recovered from the eyes and TG from mice on day 5 pi ( three mice per isolate ) . Viral DNA was examined by Southern blot for RFLPs . Rearrangements were not detected in any of the eighteen DNA samples derived from acutely infected tissues ( S3A Fig ) . Viral DNA isolated from latently infected TG following explant reactivation [72] was also examined . In this case , nine virus samples were obtained from groups of three mice latently infected with each of one of the three 17LATpICP0 isolates . No evidence of genomic instability was detected in any of the samples ( S3A Fig ) . Finally , the LATpICP0 gene cassette was cloned from the recovered viruses and employed in transient transfection assays to test the function of the recovered ICP0 genes . Without exception , the recovered LATpICP0 gene cassettes transactivated a target plasmid similarly to the original clone employed to produce the viruses ( S3B Fig ) . The LAT locus expresses virally encoded large and small non-coding RNAs with the potential to interfere with ICP0 protein expression . These include the stable LAT introns that are partially antisense to the ICP0 mRNA [73] and viral miRNAs that may target the ICP0 mRNA [74–77] . Because these RNAs could be expressed in neurons in which the LAT promoter is active and might interfere with the expression of ICP0 protein in these neurons [54 , 78] , the LATpICP0 cassette was recombined into our prototypical LAT null mutant 17AH ( in which none of these RNAs are expressed from the LAT promoter [79] [80] ) to generate 17AHLATpICP0 mutants . Groups of mice were infected with three independently derived isolates of 17AHLATpICP0 and a control virus that expresses LacZ from the LATp in the same locus in the 17AH background . Viral replication in eyes and TG were quantified on days 2 , 4 , 6 , 8 , and 10 pi . No difference in viral replication in eyes or TG was observed between the mutants and control virus . Titers on day 4 pi in 17AHLATpICP0 infected eyes ranged from 4 . 4x104 ± 3 . 2x103 to 4 . 9x104± 5 . 1x103 pfu/ml , values not different from each other or from 17AHLATpLacZ infected eyes ( 4 . 6x104± 1 . 1x103 pfu/ml ) ( p = 0 . 64 , ANOVA ) . Viral titers in TG were also not different , peaking on day 4 pi in both groups at 1 . 4x104 ± 4 . 7x103 to 3 . 3x104± 2 . 0x103 pfu/ml and 2 . 9x104 ± 4 . 1x103 pfu/ml in TG from 17AHLATpICP0 and 17AHLATpLacZ infected animals , respectively . No increase in virulence was observed in mice infected with the 17AHLATpICP0 mutants compared to 17AHLATpLacZ . Because a significant rise in viral titer was observed in the TG of mice infected with 17LATpVP16 mutants by 48 hrs pi ( Fig 2A ) we examined viral protein expression in TG neurons at 40 hrs pi , a time at which viral spread is minimal in wild type infected TG . Groups of mice were infected with 1x105 pfu of either 17syn+ , 17LATpVP16 , 17LATpVP16R , 17LATpICP0 , 17AHLATpICP0 , or 17AHLATpLacZ . At 40 hrs pi , eyes and TG were harvested and infectious virus was quantified in eye homogenates and whole ganglia were stained for HSV proteins and positive neurons counted ( Fig 3A ) . There was no significant difference in the amount of infectious virus detected in eyes with values ranging from 1 . 6x104 to 2 . 7x104 pfu/ml and only TG infected with17LATpVP16 had significantly more positive neurons ( see Fig 3A , p<0 . 0004 , ANOVA ) . Thus prior to day 2 , the expression of VP16 from LATp increases the number of TG neurons entering the lytic program by 6 fold . In addition , there is no significant difference in the number of lytic program neurons between 17LATpICP0 and 17AHLATpCP0 . A variation of this experiment using the LacZ gene in the 17LATpLacZ to determine the relationship between the number of neurons LATp positive with the number of neurons expressing viral protein was performed . Groups of mice were infected with 17LATpVP16 , 17LATpLacZ , or 17syn+ . Eyes and TG were harvested at 46 hrs pi . Viral titers in eyes were not different among the groups . TG were processed for the dual detection of b-gal activity and viral protein expression as previously described [11 , 54] . The 17LATpVP16 infected ganglia contained ~4 fold more neurons expressing viral protein compared to 17syn+ , or the 17LATpLacZ mutant ( 49 . 3 ± 6 . 3 vs . 13 . 7 ± 1 . 6 or 11 . 4 ± 1 . 8 , p<0 . 0005 , ANOVA Fig 3B ) . Importantly , in 17LATpLacZ infected TG , the number of combined LATp and lytic positive neurons was similar to the total number of neurons positive for viral protein in 17LATpVP16 infected TG ( 42 and 49 ) ( Fig 3B ) . Representative photomicrographs of TG are shown in Fig 3C . Positive neurons in all groups were distributed as isolated neurons indicating that these neurons represent primary infections and not secondary spread . These types of experiments were repeated several times with consistent outcomes . The preceding studies demonstrate that VP16 expressed de novo from the LATp in TG neurons disrupts the acute stage latent program and that the first phenotypic evidence for this occurs earlier than 48 hrs pi . However , what underlies the “decision” to enter the acute stage latent or lytic program during a wild type infection remains an open question . Mapping the relative numbers of neurons and timing pi that either the latent or lytic program becomes active in neurons could be informative . We utilized a set of viral promoter LacZ reporter mutants ( representing immediate early , late , and latent transcription ) ( Fig 1 ) to determine the number of neurons and activation order of the lytic vs . latent transcriptional programs in neurons in vivo at early times pi . As shown here for 17LATpLacZ ( Figs 2A and 3C ) and previously for 17VP16pLacZ and 17ICP0pLacZ [11 , 54 , 78] , the viruses employed replicated equivalently in vivo . A potential complicating factor is relative promoter strength since “timing” of expression might be influenced by expression if there were very large differences in promoter activity . We analyzed relative promoter strengths in cultured cells with transient assays utilizing the promoter/reporter constructs that were employed to generate promoter/reporter viruses . As previously reported , the LacZ ORF was cloned behind the immediate early ICP0 gene promoter ( ICP0p ) [54 , 78] , the leaky late UL48 gene promoter ( VP16p ) [11] or the basal LATp as detailed in methods . As shown in Fig 4A the ICP0p was slightly stronger than the VP16p promoter and both lytic phase promoters were stronger than the basal LATp . One caveat is that VP16 protein would be carried into the cell in the virion tegument . To mimic this condition an additional plasmid expressing VP16 from the human CMV IE gene promoter was added to the transfections . The order of the relative strengths of the promoters remained the same ( Fig 4A ) . The addition of VP16 increased expression from the ICP0 promoter , but had no significant effect on the VP16p or LATp . In vivo in TG neurons findings were similar in that it required more time for blue color to develop in TG infected with 17LATpLacZ than did TG infected with either 17ICP0pLacZ or 17VP16pLacZ . While only semi-quantitative this is consistent with the relative activity of the promoters in transient assays . As seen in Fig 4 , LacZ expression from the lytic promoters coincided with detection of viral proteins , suggesting the native and reporter promoters behaved similarly and that the moderate difference between the strengths of the ICP0 and VP16 promoters did not affect detection on the time scale employed here . Preliminary experiments to determine the earliest time that activity could be detected from these promoters in the TG revealed that while no viral promoter activity was detected at 10 hrs pi , by 18 hrs pi , activity exclusively from the LATp was observed in rare neurons in a subset of TG . The global view afforded by the whole ganglia approach revealed that at these early time points , all viral activity was restricted to neurons ( based on morphological criteria ) . In these experiments , mice were infected on corneas with 5x105 pfu and groups of 20 TG were examined for each virus at each time point . As shown in Fig 4C , at 22 hrs post infection all trigeminal ganglia from mice infected with the 17LATpLacZ mutants contained multiple neurons positive for b-gal activity . In contrast , none of the ganglia from mice infected with the 17ICP0pLacZ or 17VP16pLacZ mutants contained b-gal positive neurons ( p<0 . 0001 , Fisher’s exact test ) . The TG were subsequently processed for the detection of viral proteins as previously detailed [54 , 82] . Viral protein expression was not detected in any of the ganglia at this early time ( in contrast to 40–46 hrs , Fig 3C ) . Importantly , titers of virus in the eyes were not different among the groups , ranging from 1 . 2x105 to 3 . 1x105 pfu per pair of eyes at 22 hrs pi . We analyzed viral promoter and viral protein expression at times later than 22 hrs pi to determine ( i ) timing of lytic promoter activation , ( ii ) timing of viral protein expression , ( iii ) the relationship between lytic promoter activity and protein expression , and ( iv ) the relationship between acute stage latent neurons and viral protein expressing neurons . Viral protein could be detected in rare neurons as early as 32 hrs pi in a subset of TG from all infected groups . The results of analysis of TG from additional infected mice at 36 hours pi are shown in Fig 4C . The amount of virus detected in the eyes of these mice was not different between groups at the time of tissue harvest ( p = 0 . 70 , ANOVA ) . In mice infected with the 17LATpLacZ reporter mutant 20/20 ( 100% ) of ganglia tested contained numerous neurons positive for only b-gal , and far more neurons were positive for b-gal alone than for viral proteins ( Fig 4C , p<0 . 0001 Fisher’s exact test ) . A similar subset of ganglia contained neurons positive for viral proteins in all groups examined 16/20 ( 80% , 17LATpLacZ mutant ) , 14/18 ( 77% , 17VP16pLacZ mutant ) and 15/18 ( 83% , 17ICP0pLacZ mutant ) ( p>0 . 72 , Fisher’s exact test ) . Likewise there was no difference in the distribution and number of neurons positive for viral proteins in these ganglia ( Fig 4C , p = 0 . 79 , ANOVA ) . Thus , infection of TG was similar with all of the promoter/reporter mutants as determined by the number of neurons positive for viral protein expression . In every case in these TG , all neurons positive for viral proteins were also positive for b-gal . In contrast , in TG infected with the 17LATpLacZ mutants , 3 to 4 fold more neurons were positive for b-gal than those expressing viral proteins . Of particular importance , all neurons positive for viral proteins in ganglia infected with 17LATpLacZ mutants were also positive for b-gal activity at this time . No viral proteins were detected in non-blue neurons in this group of TG . These findings offer compelling support for the idea that in sensory neurons infected from the body surface HSV first enters the latent transcriptional pathway and expresses the LAT locus . About 12–14 hrs later a portion of the LAT expressing neurons transition from this acute stage latent program into the lytic transcriptional pathway ( the dual labeled neurons ) while other neurons remain b-gal positive only ( e . g . remain in the latent transcriptional program ) . These findings reveal an important feature of the early events in neuronal infection in vivo , namely that entry into the lytic cycle is gated through a transition from an early latent program . We reported previously that while the VP5 promoter could substitute for the VP16 promoter in cultured cells and on the corneal surface , replication in the TG was impaired despite equivalent viral genomes feeding into the ganglion [11] . This suggested that unique regulatory information was embedded in the VP16 promoter . While detailed mapping of functional sites in this region is ongoing , a region of interest was identified near and downstream of the VP16 TATA box and one set of mutants generated has phenotypic properties similar to those observed when the VP5 promoter was used to drive VP16 expression ( [11] and Fig 5 ) . This mutant named 17VP16pπRR ( for preIE regulatory region ) contains 13 nucleotide changes clustered within three elements predicted to bind factors known to reciprocally regulate stress responsive neuronal genes ( see methods , changed nucleotides are boxed in Fig 5A ) . Three independently derived 17VP16pπRR mutants were generated as detailed in methods . All three isolates ( i ) express wild type VP16 , ( ii ) exhibit no plaquing deficits , ( i . e . are not hexamethylene bisacetamide responsive [13] ) , and ( iii ) replicate like wild type in cultured cells and on the corneal surface ( Fig 5B ) . These outcomes confirm that VP16 is packaged into virions sufficient to support low moi infection indistinguishable from wt virus . However , these mutants are extremely defective for replication in TG ( Fig 5B ) . At 3 days pi there were >100-fold fewer neurons positive for VP16 in TG infected with the promoter mutant than found in TG infected with wild type ( Fig 5C ) . Eyes and TG infected with 17syn+ , 17VP16pπRR , or 17VP16pπRR-R were examined for VP16 expression using whole and sectioned tissue immunohistochemical methods . The intensity and distribution of VP16 protein on corneas was not different between infection with 17VP16pπRR or 17VP16pπRR-R ( Fig 5D ) or 17syn+ but VP16 was not efficiently produced in TG ( Fig 5E and 5F ) . Our hypothesis predicts that 17VP16pπRR mutants would not efficiently transition from acute stage latent program into the lytic program . To determine whether this is the case , the LATpLacZ expression cassette was recombined into the 17VP16pπRR viral mutant to generate 17VP16pπRR+LATpLacZ mutants . Three independent mutants were generated and characterized for replication in vitro and in vivo . These mutants exhibited phenotypes like 17VP16pπRR ( see Fig 5 ) . Mice were infected with 17VP16pπRR+LATpLacZ and 17LATpLacZ as above and at 44 hrs pi , TG were harvested and processed for the simultaneous detection of b-gal and viral proteins ( Fig 6 ) . At this time 12/12 TG were positive for b-gal with 17 or more neurons positive per TG . This was not different than the results for 17LATpLacZ ( p = 0 . 16 , Student’s t test ) . A small number of neurons ( <3 ) were positive for viral protein in 7/12 TG from 17VP16pπRR+LATpLacZ infected mice , and these all showed evidence of LATp activity . In contrast , 10/10 TG with an average of 12 . 6 neurons/TG were viral protein positive in the 17LATpLacZ infected group ( p<0 . 0001 , Student’s t test ) . While the total numbers of neurons evidencing expression from the viral genome ( either viral proteins or b-gal ) were similar between the two groups ( 362 and 350 , respectively ) the percentage of neurons that had transitioned from the early latent program into the lytic program , was very different , 33% for 17LATpLacZ and only 2% for 17VP16pπRR+LATpLacZ . At this time , a small number of neurons ( total of 3 ) in which blue could not be visualized were detected in the 17LATpLacZ infected TG . Whether these represent loss of blue or a separate population is not clear . These findings do indicate that because similar numbers of LATp positive neurons were observed in 17VP16πRR+LATpLacZ and 17LATpLacZ , that the failure of the 17VP16πRR promoter mutants to replicate in the TG is not a result of a defect in transport to the TG . Combined our results indicate that de novo expression of VP16 mediated through sequences in a specific region of the VP16 promoter is a major determinant of whether neurons transition into productive lytic infection during the acute stage of infection .
Together , our studies support a model in which virus infecting neurons from the periphery first enters a latent program . Entry into the lytic program occurs as a transition out of this default latent state which is gated by de novo VP16 expression . Importantly , perturbing this natural virus/host determined balance , as we did by expressing VP16 directly from the LATp , reveals the significance of the evolved strategy of VP16 regulation in the viral life cycle . Three lines of evidence underpin this model . First , activation of the latent program in TG neurons infected from the corneal surface precedes activation of the lytic program by 12–14 hrs . Importantly our studies revealed that at the individual neuronal level , the lytic program begins as a transition out of this early acute stage latent program . This temporal pattern of viral transcription in neurons is consistent with the view that the potent transactivator VP16 packaged in the virion tegument does not reach neuronal nuclei concomitantly with the viral genome , thus promoting entry into the latent program [42 , 85] . The possibilities that in the neuronal context , VP16 is sequestered in the cytoplasm [33] , or the required coactivators are absent [86] are not consistent with the robust phenotype observed when VP16 but not the VP16 transactivation function ( TF ) deficient mutant is expressed de novo ( from the LATp ) in the neuron . While we have not formally demonstrated at the single neuron level that the viral protein expression marking the transition out of the latent program reflects productive viral replication , low levels of infectious virus can be detected in the TG at this time . Since these are the only HSV protein positive cells , it is reasonable to assume that these neurons are the source of the infectious virus detected . Third , we have identified a region downstream of the VP16 TATA box that regulates exit from the acute stage latent state . A bioinformatics analysis revealed three potential combinatorial sites known to confer reciprocal regulation on stress responsive neuronal genes ( Fig 5 ) . A total of 13 nucleotide changes in three potential regulatory elements within this ~170 bp region results in viral mutants that replicate like wild type in cultured cells and in mouse eyes . However , these mutants fail to shift efficiently out of the acute stage latent state resulting in ~100 fold reduction in viral titers in TG . The timing , number , and viral expression pattern of TG neurons infected from the corneal surface ( at times prior to viral spread within the TG ) confirmed the expected asynchrony of infection of TG neurons ( Figs 4 and 6 ) . However , our analysis is a compilation of “snap shots” over time and thus a sequence of events in any particular neuron can be inferred but not proven . The fact that the LAT promoter is strongly repressed by ICP4 in cells including neurons during acute infection [3 , 48 , 49 , 51] , allows us to conclude with some confidence that the viral protein expression observed at these early times pi occurs in neurons transitioning out of the acute stage latent program into the lytic cycle . Our findings point to the regulation of de novo VP16 expression as an important component in gating the transition from the acute latent state into the lytic program . However , the temporal lag of 12–14 hrs suggests that additional factors influence this outcome . Increasing numbers of viral genomes feeding into these neurons as lytic infection progresses at the surface is a likely factor . We propose that i ) the preIE regulatory region of the VP16 promoter is skewed toward “off” and ii ) a context related threshold , potentially viral genome copy number and/or neuronal subtype related factors [6] ultimately flips the de novo regulation of VP16 to the “on” position . Mutation of the preIE region of the VP16 promoter results in the dramatic reduction in VP16 expression and viral replication selectively in the TG ( Figs 5 and 6 ) , although this is not absolute . We also know that the requirement for VP16TF is not absolute in so far as transactivation deficient mutants do replicate to some extent in TG albeit ~100 fold reduced [11 , 40 , 87] . High genome copy number could potentially override a requirement for VP16TF and this is supported by the finding that strategies increasing surface replication also increase replication in the TG [11 , 40] . Viral proteins that can shift the balance from latent to lytic infection are of great interest because their identification yields insights into the mechanisms governing the lytic/latent switch and represent targets for novel therapies . By expressing extra copies of ICP0 or VP16 from the basal LAT promoter we tested the ability of these proteins to precipitate productive lytic viral replication in neurons in which the acute latent program would be starting . A comparable experimental design has not previously been reported , however superinfection of dissociated cells from latently infected ganglia with adenovirus constructs expressing ICP0 or VP16 [62] suggests that both of these proteins can induce HSV reactivation from latency in vitro . Surprisingly , we found that the de novo expression of VP16 , but not that of ICP0 resulted in measurable phenotypic differences ( Fig 2 ) . Possible technical issues , including genomic rearrangement and/or instability of ICP0 , nonfunctional ICP0 , and ICP0 silencing by viral microRNAs were systematically ruled out . However , further experimentation is needed before the lack of effect of ICP0 in this context can be fully interpreted as at least one host microRNA can target ICP0 [74] . We recently published evidence that de novo synthesis of VP16 is required prior to detectable viral protein synthesis during exit from the latent state [10 , 11] , which is also the case in quiescently infected ( latent ) neuronal cultures [85] . Combined with the results presented here we conclude that VP16 protein expression is a dominant nexus regulating the boundary between latency and lytic infection of neurons at all stages of the complex natural history of HSV infection . We hypothesize the VP16 promoter has evolved to strike a balance between three critical tasks ( i ) to maximize the establishment of latent infections , ( ii ) to limit the involvement of the central nervous system and resulting neurovirulence , and ( iii ) to control viral reactivation frequency ( Fig 7 ) . It is likely that the probability of productive infection of neurons infected from the body surface , or during reactivation from latency is greatly enhanced by the coordinated expression of the five viral immediate early genes .
Stocks of HSV-1 strain 17syn+ ( originally obtained from John H . Subak-Sharpe at the MRC Virology Unit in Glasgow , Scotland ) and the mutants employed in this study were generated in rabbit skin cell ( RSC ) monolayers ( RSC originally obtained from Bernard Roizman , University of Chicago ) and the viral titers were determined by serial-dilution plaque assay [4 , 88] . The basal LAT promoter ( 118006 to 118858 bp ) was employed to express LacZ ( termed LATpLacZ ) , or an additional copy of VP16 ( 103442 to 105108 bp , termed LATpVP16 ) or ICP0 ( the full protein coding region including the first and second introns ( 120468 to 124107 bp , termed LATpICP0 ) in the strain 17syn+ background . The promoter/transgene cassettes ( terminated by bi-directional SV40 polyadenylation signals ) were cloned in the orientation opposite that of the viral gJ and recombined into the viral genome after BP 138 , 047 . To control for any effect of potential leakiness of LATp ( and thus earlier expression of VP16 ) an additional copy of VP16 was also expressed from the ICP0 promoter ( 124 , 818 to 124109 bp ) cloned into the same location and recombined into the viral genome as previously described [11] . All restriction enzyme sites and base pair numbering are referred to as the corresponding positions in the published HSV-1 sequence of strain 17syn+ [89 , 90] as currently present in Genbank ( JN555585 ) . The genomic structures of the mutants were analyzed by DNA ( Southern ) blot analysis and sequencing of PCR products spanning the sites of insertion or engineered mutations as previously detailed [11 , 54 , 78 , 88 , 91] . Blots were developed and analyzed on a Storm phosphoimager and quantified with GelQuantNet software . In the case of virus recovered from infected tissues this analysis had the power to detect rearranged genomes present at less than one percent of the total or greater . As part of ongoing studies we have identified a region near and downstream of the VP16 promoter TATA box ( TAAAT ) that is required for proper de novo expression of VP16 . This region spans base pairs 105125–105287 ( complementary orientation ) on the viral genome . A bioinformatic analysis ( Genomatix ) using promoter modeler suggested several potential binding sites for factors known to reciprocally regulate neuronal genes in response to stimuli ( Fig 5A ) . Whether these factors do bind and regulate the VP16 promoter is currently not known and is under investigation . As part of these studies the bases indicated by bold and underline were changed to those following in parentheses and recombined into the VP16 promoter to generate the VP16 promoter mutants named 17VP16pπRR . The VP16 open reading frame in these mutants is wild type . TAAATG ( C ) CGTGGTGGC ( AAT ) GACCACGGGCTGTCATTCCTCGGGAAC ( TTA ) GGACGGGGTTCCCGCTGCCCACTTCCCCCCATAAGGTCCGTCCGGTCCTCTAACGCGTTTGGGGGTTTTCTCTTCCCGCGCCGTCGGGCGTCCCACACTCTCTGGGCGGGCGGGGACG ( TAAT ) ATC ( AG ) . Co-transfection of 17syn+ genomic DNA and mutagenesis constructs were performed as previously described [11 , 92] . Only one viral isolate was derived from an individual transfection plate . At least three independently derived mutants were obtained by 4 rounds of limiting dilution plating on multi-well plates . The genomic structures of the mutants were analyzed by restriction length polymorphism on Southern blots with appropriate probes ( S2 and S3 Figs ) . All procedures were performed as previously detailed [11 , 54 , 56 , 92 , 93] . Three independent isolates were tested for their ability to replicate under single step ( moi = 5 pfu/cell ) and multi-step ( moi = 0 . 001 ) conditions in RSC monolayers and in mice as described below . Whole TG were first stained histochemically with x-gal to detect b-gal activity followed by whole ganglia immunohistochemistry utilizing either primary rabbit anti-HSV ( AXL237 , Accurate ) or rabbit anti-VP16 antibody ( Clonetech ) , and the secondary antibody utilized was HRP labeled goat anti-rabbit ( Vector ) . In some cases TG were stained with x-gal as above and then processed by paraffin embedding of TG , sectioning and immunohistochemical detection of viral proteins . These methods and the dilutions and characterizations of antibodies utilized have been detailed extensively in previous reports [11 , 40 , 54 , 78 , 94] . B-gal activity was quantified utilizing a CPRG assay kit according to the manufacture’s protocol ( Agilent ) . The same plasmid promoter/b-gal reporter preparations employed to produce the various genetically engineered reporter mutants were employed in the assays . At least three wells were transfected for each transfection experiment and each experiment was performed at least three times . The amount of DNA per well was kept equal , and transfection efficiency was monitored and normalized by co-transfection of p-RL-TK ( Promega ) which expresses the Renilla Luciferase . The Agilent lysis buffer is compatible with the Promega Renilla luciferase assay . Dual-Glo luciferase assays ( Promega ) were employed in co-transfection assays according to the manufacturers’ protocols . To assay for ICP0 function , the LATpICP0 construct ( flanked by sequences homologous to the gJ/gD region ) that was employed to make the mutants described above was co-transfected with the pRL-TK renilla expression plasmid ( promega ) or no promoter vector . To assay for VP16 function the LATpVP16 construct employed to make the mutants described above was co-transfected with an ICP0 promoter ( 124 , 818 to 124109 BP ) firefly luciferase construct or a CMV promoter luciferase construct . Transfection efficiency was determined by including the relevant renilla or firefly luciferase expression plasmids . Quantitative northern blots were performed as described previously [11 , 55 , 95] . RSC were infected at an moi of 5 in the presence or absence ( + or - ) of 20 ug/ml cycloheximide ( Sigma ) . At the indicated times total RNA was isolated with RNA STAT-60 using the manufacturer’s protocol ( Tel-Tec , Inc ) . RNA was glyoxylated ( Ambion ) , electrophoresed , capillary blotted onto Genescreen membranes and probed with radiolabeled probes specific for VP16 ( 104533–105105 bp ) and ICP0 ( 122709–123030 bp ) . The blots were developed on a Storm Phosphoimager and quantified with ImageQuant or GelQuantNet software . SDS PAGE and Western blot analysis were performed using standard methods ( see for recent comprehensive protocol [96] ) . RSC were infected with mutant and wild type 17Syn+ and at the times indicated ( see S1 Fig ) infected RSC were lysed and boiled in Laemmli cocktail , loaded onto a 10% polyacrylamide gels and separated by electrophoresis [97] . Following separation , proteins were transferred to nitrocellulose [98] . The uniformity of transfer was evaluated by Pounceau S staining of the membrane . Western blot was performed using standard procedures , including blocking of non-specific binding of antibodies in 5% nonfat milk ( 1 hr ) , incubation in 2% PBS-BSA solution containing primary antibody ( 1 hr ) , followed by rinsing ( PBS , 3x15 min ) , incubation in a solution containing HRP conjugated anti-rabbit antibody ( 1 hr ) ( Vector labs ) , and rinsing ( PBS , 3x15 min ) . The primary antibodies include a rabbit Pan HSV antibody 1:5 , 000 ( Accurate ) , an affinity purified rabbit anti-VP16 peptide antibody 1:1000 [11] , and HSV-1 anti-ICP0 affinity purified mouse monoclonal ( Santta Cruz: 110600 ) diluted 1:1000 [54 , 78] . The peroxidase substrate , VIP ( Vector ) was utilized according to manufacturer’s protocol . Blots were scanned and analyzed using Image J software . All procedures in mice were performed as approved by the Children’s Hospital Institutional Animal Care and Use Committee ( protocol# IACUC2013-0162 or University of Cincinnati Institutional Animal Care and Use Committee ( protocol # 13-04-04-010 and were in compliance with the Guide for the Care and Use of Laboratory Animals . Animals were housed in American Association for Laboratory Animal Care-approved quarters . Male , outbred , Swiss Webster mice ( 22–25 grams in weight ) were obtained from Harlan Laboratories . C57Bl/6J mice ( 4–5 weeks old ) were obtained from Jackson Laboratories . | Herpes simplex virus remains a significant human pathogen associated with extensive acute and chronic disease in humans worldwide . The virus invades the peripheral and central nervous systems where it replicates but also establishes life-long latent infections in neurons . Two distinct viral transcriptional programs support these distinct lifestyles , but how entry into either the lytic or latent programs is regulated in the neuron is not understood . This process is fundamentally important to a virus with the capacity to be extremely virulent , in balancing two objectives , efficient widespread seeding of the nervous system and host survival . In this report , we provide new insight into this regulation and data that support a novel model in which virus transported into the neuron from the body surface enters the latent program by default . In a subset of these , there is a transition into the lytic cycle , which requires VP16 transactivation and is gated by a region in the VP16 promoter . Thus , HSV takes advantage of the anatomy and axonal transport systems in sensory neurons so that VP16 is left behind and latency is favored , while features of the VP16 promoter insure adequate virus spread in the nervous system and maximized latent infections . | [
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] | 2016 | De Novo Herpes Simplex Virus VP16 Expression Gates a Dynamic Programmatic Transition and Sets the Latent/Lytic Balance during Acute Infection in Trigeminal Ganglia |
More than 100 years after Grigg’s influential analysis of species’ borders , the causes of limits to species’ ranges still represent a puzzle that has never been understood with clarity . The topic has become especially important recently as many scientists have become interested in the potential for species’ ranges to shift in response to climate change—and yet nearly all of those studies fail to recognise or incorporate evolutionary genetics in a way that relates to theoretical developments . I show that range margins can be understood based on just two measurable parameters: ( i ) the fitness cost of dispersal—a measure of environmental heterogeneity—and ( ii ) the strength of genetic drift , which reduces genetic diversity . Together , these two parameters define an ‘expansion threshold’: adaptation fails when genetic drift reduces genetic diversity below that required for adaptation to a heterogeneous environment . When the key parameters drop below this expansion threshold locally , a sharp range margin forms . When they drop below this threshold throughout the species’ range , adaptation collapses everywhere , resulting in either extinction or formation of a fragmented metapopulation . Because the effects of dispersal differ fundamentally with dimension , the second parameter—the strength of genetic drift—is qualitatively different compared to a linear habitat . In two-dimensional habitats , genetic drift becomes effectively independent of selection . It decreases with ‘neighbourhood size’—the number of individuals accessible by dispersal within one generation . Moreover , in contrast to earlier predictions , which neglected evolution of genetic variance and/or stochasticity in two dimensions , dispersal into small marginal populations aids adaptation . This is because the reduction of both genetic and demographic stochasticity has a stronger effect than the cost of dispersal through increased maladaptation . The expansion threshold thus provides a novel , theoretically justified , and testable prediction for formation of the range margin and collapse of the species’ range .
Species’ borders are not just determined by the limits of their ecological niche [1 , 2] . A species’ edge is typically sharper than would be implied by continuous change in the species’ environment ( reviewed in [3 , Table 2] ) . Moreover , although species’ ranges are inherently dynamic , it is puzzling that they typically expand rather slowly [4] . The usual—but tautological—explanation is that lack of genetic variation at the range margin prevents further expansion [5] . Indeed , a species’ range edge is often associated with lower neutral genetic variation [3 , 6–11] , suggesting that adaptive genetic variation may be depleted as well [12] . Yet why would selection for new variants near the edge of the range not increase adaptive genetic variance , thereby enabling it to continuously expand [5 , 13] ? Haldane [14] proposed a general explanation: even if environmental conditions vary smoothly , ‘swamping’ by gene flow from central to marginal habitats will cause more severe maladaptation in marginal habitats , further reducing their population density . This would lead to a sharp edge to a species’ range , even if genetic variance at the range margin is large . However , the consequences of dispersal and gene flow for evolution of a species’ range continue to be debated [15–18]: a number of studies suggest that intermediate dispersal may be optimal [19–23] . Gene flow across heterogeneous environments can be beneficial because the increase of genetic variance allows the population to adapt in response to selection [13] . Current theory identifies that local population dynamics , dispersal , and evolution of niche-limiting traits ( including their variance ) and both genetic and demographic stochasticity are all important for species’ range dynamics [13 , 19–21 , 24–28] . Yet these core aspects have not been incorporated into a single study that would provide testable predictions for range limits in two-dimensional habitats . As Haldane [14] previously pointed out , it is important to consider population and evolutionary dynamics across a species’ range jointly , as their effects interact . Due to maladaptation , both the carrying capacity of the habitat and the population growth rate are likely to decrease—such selection is called ‘hard’ [29] . Classic deterministic theory [24] shows that when genetic variance is fixed , there are two stable regimes of adaptation to a spatially varying optimum ( see Fig 1 ) : ( i ) a ‘limited adaptation’ , in which a population is only adapted to a single optimum or becomes a patchy conglomerate of discrete phenotypes , or ( ii ) continuous or ‘uniform’ adaptation , which is stable when the genetic variance , measured in terms of its cost in fitness ( standing genetic load ) , is large relative to the maladaptation incurred by dispersal between environments ( dispersal load ) . Under uniform adaptation , a species’ range gradually expands—a stable boundary only forms when the genetic variance is too small to allow continuous adaptation to the spatially variable environment , and hence , limited adaptation is stable . When genetic variance can evolve , such a limit no longer exists in infinitely large populations: the population maintains continuous adaptation as the environmental gradient steepens [13] . Deterministic theory thus predicts that a sharp and stable boundary to a species’ range does not form when the environment changes smoothly . Uniform adaptation is the only stable regime when genetic variance can freely evolve in the absence of genetic drift [13] , yet there is a limit to the steepness of the gradient . This limit arises because both the standing genetic load and the dispersal load increase as the gradient steepens , reducing the mean fitness ( growth rate ) of the population: when the mean fitness approaches zero , the population becomes extinct . Obviously , ignoring genetic drift is then unrealistic . In finite populations , genetic drift reduces local genetic variance [32] , potentially qualitatively changing the dynamics . Indeed , it has been shown that for one-dimensional habitats ( such as rivers ) , a sharp range margin arises when the fitness cost of dispersal across environments becomes too large relative to the efficacy of selection versus genetic drift [26] . However , most species live in two-dimensional habitats . There , allele frequencies can fluctuate over a local scale , as the correlations between them decline much faster across space than they do in linear habitats [33] , and the effect of genetic drift changes qualitatively , becoming only weakly dependent on selection [34] . Is there still an intrinsic threshold to range expansion in finite populations when dispersal and gene flow occur over two-dimensional space rather than along a line ? If so , what is its biological interpretation ?
I study the problem of intrinsic limits to adaptation in a two-dimensional habitat . Throughout , I assume that the species’ niche is limited by stabilising selection on a composite phenotypic trait . This optimum varies across one dimension of the two-dimensional habitat—such as temperature and humidity with altitude . Demography and evolution are considered together . Selection is ‘hard’: both the rate of density-dependent population growth and the attainable equilibrium density decrease with increasing maladaptation . Both trait mean and genetic variance can freely evolve via change in allele frequencies and the associations among them ( linkage disequilibria ) . The populations are finite , and both genetic and demographic stochasticity are included . The model is first outlined at a population level in terms of coupled stochastic differential equations . While it is not possible to obtain analytical solutions to this model , this formalisation allows us to identify the effective dimensionless parameters that describe the dynamics . Next , individual-based simulations are used to determine the driving relationship between the key parameters and test its robustness . The details are described in the Model section of the Methods . The dynamics of the evolution of a species’ range , as formalised by this model , are well described by three dimensionless parameters , which give a full description of the system . The first dimensionless parameter carries over from the phenotypic model [24]: the effective environmental gradient B measures the steepness of the environmental gradient in terms of maladaptation incurred by dispersal across a heterogeneous environment . The second parameter is the neighbourhood size of the population , 𝒩 , which can be understood as the number of diploid individuals within one generation’s dispersal range . Originally , neighbourhood size was defined by Wright [35] as the size of the single panmictic diploid population that would give the same probability of identity by descent in the previous generation . The inverse of neighbourhood size 1/ 𝒩 hence describes the local increase of homozygosity due to genetic drift . The third dimensionless parameter is the ratio s/r* of the strength of selection s per locus relative to the strength of density dependence , r* . Detailed description of the parameters and their rescaling can be found in the Methods sections Parameters and Continuous model: Rescaling . In order to see how the rescaled parameters capture the evolution of a species’ range , I simulated 780 evolving populations , each based on different parameterisations , adapting to a linear gradient in the optimum . Depending on the parameters , the population either expands , gradually extending its phenotypic range by consecutive sweeps of loci advantageous at the edges , or the species’ range contracts or disintegrates as adaptation fails . Fig 2 shows the results of the projection from a 10-dimensional parameter space of the individual-based model ( see Methods sections Individual-based simulations and Parameters ) into a two-dimensional space . The axes of Fig 2 represent the first two compound dimensionless parameters: ( i ) the effective environmental gradient B and ( ii ) the inverse of neighbourhood size 1/ 𝒩 , which describes the effect of genetic drift on the allele frequencies . These two dimensionless parameters B and 𝒩 give a clear separation between expanding populations , in which the neighbourhood size 𝒩 is large relative to the effective environmental gradient B ( shown in blue , Fig 2 ) , and the rest , in which adaptation is failing . The separation gives an ‘expansion threshold’ , estimated at 𝒩 ≈ 6 . 3B + 0 . 56 ( red line ) . Above the expansion threshold , populations are predicted to expand ( see Fig 3 ) ; below it , adaptation fails abruptly . If conditions change uniformly across space ( as in these simulation runs , with constant gradient and carrying capacity ) , this means that adaptation fails everywhere—a species’ range then either collapses from the margins ( Fig 2 , red hues ) and/or disintegrates ( Fig 2 , open circles ) , forming a fragmented metapopulation ( i . e . , a spatially structured population consisting of discrete locally adapted subpopulations with limited dispersal among them ) . When a metapopulation forms , it exhibits an extinction and colonisation dynamics . The subpopulations drift freely along the neutral spatial axis . Because the trait distributions of the subpopulations are unstable , the subpopulations also drift slowly along the environmental gradient . Over time , the metapopulation very slowly collapses to a virtually single trait value , with many subpopulaitons along the neutral axis . The subpopulations forming this metapopulation have only a very narrow phenotypic range and maintain locally only minimal adaptive variance . They correspond to the limited adaptation regime identified for a phenotypic model with genetic variance as a parameter [24] . In contrast to one-dimensional habitats [26] , these patchy metapopulations are stabilised by dispersal from surrounding subpopulations in the two-dimensional habitat and can thus persist for a long time . An example of such a metapopulation is given in Fig 4 . Interestingly , the third dimensionless parameter s/r* has no detectable effect on the form of the expansion threshold . In other words , whilst the expansion threshold reflects the total fitness cost of dispersal in a heterogeneous environment , it appears independent of the strength of selection per locus s: the dashed lines in Fig 2 compare the estimated expansion threshold for small and large s/r* . Increasing the strength of selection is inefficient in aiding drift-limited adaptation , in line with the expectation that the effect of genetic drift is only very weakly dependent on selection in two-dimensional habitats [27] ( see also S1 Fig ) . This suggests that genetic basis of adaptation is not important for a drift-induced limit to a species’ range . Yet it is plausible that there is another limit , in which selection per locus becomes important [27] , that arises when the optimum changes abruptly and even when the population ( neighbourhood ) size is large ( i . e . , in an entirely different regime ) . A dedicated synthesis connecting the step-limited and drift-limited regimes would be of a clear interest . Importantly , once genetic drift starts to have an effect , the habitat needs to be fairly broad to be two-dimensional [37] . In narrow habitats ( such as in [27] ) , some dependency of drift-induced expansion threshold on selection per loci would be expected [26] . Note that the apparent independence of the expansion threshold on s/r* does not imply that rate of range expansion should also be independent of the strength of selection . In nature , conditions are unlikely to change uniformly . Abiotic environment ( such as temperature , precipitation , solar radiation ) does not , in general , change in a linear and concordant manner [38] , and neither does the biotic environment , such as the pressure from competitors and predators , which affects the attainable population density and can increase the asymmetry in gene flow [39 , 40] . I now investigate whether adaptation fails near the expansion threshold as conditions change across space . For example , we can imagine that the population starts well adapted in the central part of the available habitat , and as it expands , conditions become progressively more challenging ( see S2A Fig ) ; i . e . , the effective environmental gradient B gets steeper . As the expanding population approaches the expansion threshold , adaptive genetic variance progressively decreases below the predicted value [13] , which would be maintained by gene flow in the absence of genetic drift ( Fig 5A , grey dashed line ) . This is a result of an increased frequency of demes with limited adaptation , leading to higher rates of extinctions and recolonisations , which reduce both adaptive and neutral diversity ( see Fig 5B ) . Range expansion then ceases at the expansion threshold as the genetic variance drops to the critical value at which only limited adaptation is stable [24] , assuming genetic variance is fixed ( Fig 5A , dotted line ) . This is because although populations can persist with limited adaptation ( Fig 4 ) , the transient amount of genetic variance maintained under limited adaptation is almost never consistent with range expansion ( see Fig 2 , open circles ) . On a steepening gradient , a sharp and stable range margin forms . This contrasts to uniformly changing conditions ( linear gradient , constant carrying capacity ) in which populations steadily expand or contract . In a large population , the ability to adapt to heterogeneous environments is independent of dispersal: this is because both the local genetic variance ( measured by standing genetic load ) , which enables adaptation to spatially variable environments , and the perceived steepness of the environmental gradient ( measured by dispersal load ) increase at the same rate with gene flow [13] . Yet , in small populations , dispersal is beneficial because the drift-reducing effect of dispersal overpowers its maladaptive effect . This is demonstrated in Fig 6—the neighbourhood size 𝒩 increases faster with dispersal than the effect of swamping by gene flow ( B ) does; hence , as dispersal increases , the population gets above the expansion threshold at which uniform adaptation can be sustained . Around the expansion threshold , a small change in dispersal ( connectivity ) can have an abrupt effect on adaptation across a species’ range and the species’ persistence . A small increase in dispersal can lead to recovery of uniform adaptation with an arbitrarily wide continuous range . Further increase of dispersal is merely enhancing the rate of range expansion at the expense of a slight cost to the mean fitness due to rising dispersal load and standing load and can be associated with further costs , such as Allee effect ( see , e . g . , [17] ) . Therefore , the expansion threshold provides an interpretation for optimality of an ‘intermediate’ dispersal , benefiting the species’ persistence .
Here , I have shown that adaptation fails when positive feedback between genetic drift , maladaptation , and population size reduces adaptive genetic variance to levels that are incompatible with continuous adaptation . The revealed expansion threshold differs qualitatively from the limit to adaptation identified previously [26] for a population living along a one-dimensional habitat . This is because in two dimensions , dispersal mitigates the loss of diversity due to genetic drift more effectively , such that it becomes ( almost ) independent of selection [34] . The expansion threshold implies that populations with very small neighbourhood sizes ( 𝒩 ⪅ 1/2 ) , which suffer a severe reduction in neutral heterozygosity , will be prone to collapse based on demographic stochasticity alone . However , even in the absence of demographic stochasticity , genetic drift reduces the adaptive genetic variance required to sustain adaptation to a heterogeneous environment . The expansion threshold describes when this reduction due to genetic drift is incompatible with continuous adaptation , predicting a collapse of a species’ range . If the expansion threshold is reached as the species expands through its habitat , a sharp and stable range margin forms . If there is a drop below the expansion threshold throughout the species’ range , as after a sudden drop in carrying capacity , adaptation abruptly collapses throughout a species’ range . The result is either extinction or a fragmented metapopulation consisting of a conglomerate of subpopulations , each adapted to a single phenotypic optimum . It follows that near a range margin , we expect increased range fragmentation and a decrease in adaptive genetic variance . The threshold gives a theoretical base to the controversial issue of the importance of evolution ( genetics ) and ecology ( demography ) for assessing vulnerability of a species [41 , 42] . The predicted sharp species’ range edge is in agreement with the reported lack of evidence for ‘abundant centre’ of a species’ range , which , although commonly assumed in macroecological theory , has little support in data [3 , 11 , 43 , 44] . Lack of abundant centre is consistent both with uniform adaptation and with limited adaptation in a metapopulation . The expansion threshold provides a general foundation to species-specific eco-evolutionary models of range dynamics [45] . Its components can be measured in wild populations , allowing us to test the robustness of the theory . First , the effective environmental gradient B can be measured as fitness loss associated with transplant experiments on a local scale , relative to a distance of generational dispersal along an environmental gradient . The environmental gradient can include both biotic and abiotic effects and their interactions [46]—notably , the effective environmental gradient B steepens due to increased asymmetry in gene flow when carrying capacity varies across space , e . g . , because of partial overlap with competitors [40] . Second , the neighbourhood size 𝒩 can be estimated from neutral allele frequencies [47 , 48] . Estimates of neighbourhood size are fairly robust to the distribution of dispersal distances [49] . Though near the expansion threshold , both the noisiness of the statistics and the homozygosity will increase due to local extinctions and recolonisations [50] . An alternative estimate of neighbourhood size can be also obtained from mark-recapture studies by measuring population density and dispersal ( as an approximation for gene flow ) independently [47] . Because the expansion threshold is free of any locus- or trait- specific measure , the result appears independent of genetic architecture , readily extending to multiple traits regardless of their correlations ( compare to [51–55] ) —yet the mean fitness will decline because of ‘drift load’ as the number of traits independently optimised by selection increases [56 , 57] . Hence , if the fitness landscape is highly complex , the expansion threshold constitutes a lower limit . Naturally , there can be further costs arising in a natural population that I have neglected here , such as the Allee effect [17] . In general , while the numerical constants may change when natural populations deviate in their biology from our model assumptions , the scale-free parameters identified in this study remain core drivers of the intrinsic dynamics within a species’ range . Notably , the early classic studies assuming fixed genetic variance [24] predicted that dispersal into peripheral populations is detrimental because it only inflates the effective environmental gradient B . Yet , when genetic variance can evolve , dispersal into small marginal populations also aids adaptation by increasing local genetic variance and by countering genetic drift . The net effect of dispersal into small marginal populations ( below the expansion threshold ) is then beneficial because their neighbourhood size increases faster with dispersal than the effective environmental gradient B steepens .
I model evolution of a species’ range in a two-dimensional habitat , in which both population dynamics and evolution ( in many additive loci ) are considered jointly . The coupling is via the mean fitness r¯ ( z¯ , N ) , which gives the growth rate of the population , and decreases with increasing maladaptation: r¯ ( z¯ , N ) =re ( N ) +r¯g ( z¯ ) . The ecological component of growth rate , re , can take various forms: here , the regulation is logistic so that fitness declines linearly with density N: re = rm ( 1−N/K ) , in which rm is the maximum per capita growth rate in the limit of the local population density N→0 . The carrying capacity K ( for a perfectly adapted phenotype ) is assumed uniform across space . The second term , rg ( z¯ ) ≤0 , is the reduction in growth rate due to deviation from the optimum . Selection is stabilising: the optimum θ changes smoothly with one spatial dimension ( x ) : for any individual , the drop in fitness due to maladaptation is rg ( z ) = − ( z−θ ) 2/ ( 2Vs ) . Here , Vs gives the width of stabilising selection; strength of stabilising selection is γ = −VP/ ( 2Vs ) , in which VP = VG+VE is the phenotypic variance . A population with mean phenotype z¯ has its fitness reduced by r¯g ( z¯ ) =− ( z¯−θ ) 2/ ( 2Vs ) −VP/ ( 2Vs ) . The phenotype z is determined by many di-allelic loci with allelic effects αi; the model is haploid , hence z¯=∑iαipi , in which pi is the allele frequency at locus i . Phenotypic variance is VP = VG+VE . The loss of fitness due to environmental variance VE can be included in rm*=rm−VE/ ( 2Vs ) ; VE is a redundant parameter . Selection is ‘hard’: both the mean fitness ( growth rate ) and the attainable equilibrium density N^=Kr*/rm=K ( 1−VG/ ( 2rmVs ) ) decrease with maladaptation . Expected genetic variance maintained by gene flow in the absence of genetic drift is VG=bσVs [13]; the contribution due to mutation is small , at mutation-section balance VG , μ/s=2μVsnl , in which μ gives the mutation rate per locus and nl the number of loci . Discrete-time individual-based simulations are set to correspond to the model with continuous time and space . The space is a two-dimensional lattice with spacing between demes of δx = 1 . Every generation , each individual mates with a partner drawn from the same deme , with probability proportional to its fitness , to produce a number of offspring drawn from a Poisson distribution with mean of Exp[r ( z , N ) ] ( this includes zero ) . The effective diploid population density Ne hence equals half of the haploid population density N , and 𝒩 = 4πNe σ2 = 2πNσ2 . The life cycle is selection → mutation → recombination → birth → migration . Generations are nonoverlapping , and selfing is allowed at no cost . The genome is haploid with unlinked loci ( the probability of recombination between any two loci is 1/2 ) . The allelic effects αi of the loci combine in an additive fashion; the allelic effects are uniform throughout this study , αi ≡ α . Mutation is set to μ = 10−6 , independently of the number of loci . Migration is diffusive with a Gaussian dispersal kernel . The tails of the dispersal kernel need to be truncated: truncation is set to two standard deviations of the dispersal kernel throughout , and dispersal probabilities and variance are adjusted so that the discretised dispersal kernel sums to 1 [58] . Simulations were run at the computer cluster of IST Austria using Mathematica 9 ( Wolfram ) . The code for the simulations , together with a working example , have been deposited as a single * . cdf file at Dryad Digital Repository , https://doi . org/10 . 5061/dryad . 5vv37 [36] . This file can be viewed with CDF Player , a free application developed by Wolfram Research , and also contains all the figures with their underlying data . For any given additive genetic variance VG ( assuming a Gaussian distribution of breeding values ) , the change in the trait mean z¯ over time satisfies: ∂z¯∂t=σ22 ( ∂2z¯∂x2+∂2z¯∂y2 ) +σ2 ( ∂2ln ( N ) ∂x∂z¯∂x+∂2ln ( N ) ∂y∂z¯∂y ) +VG∂r¯∂z¯+ζ . ( 1 ) The first term gives the change in the trait mean due to migration with mean displacement of σ; the second term describes the effect of the asymmetric flow from areas of higher density . The third term gives the change due to selection , given by the product of genetic variance and gradient in mean fitness [59 , Eq 2] . The last term ζ gives the fluctuations in the trait variance due to genetic drift: ζ=VG , LE/NdWζ ( x , y , t ) , in which dW* represents white noise in space and time [34 , 60] . VG , LE=∑iαi2piqi denotes genetic variance assuming linkage equilibrium . The trait mean is z¯=∑iαipi for a haploid model , in which pi is the i-th allele frequency , qi = 1−pi and αi is the effect of the allele on the trait—the change of the trait mean z¯ as frequency of locus i changes from 0 to 1 . For both haploid and diploid models , the allele frequencies pi change as: ∂pi∂t=σ22 ( ∂2pi∂x2+∂2pi∂y2 ) +σ2 ( ∂pi∂x∂ln ( N ) ∂x+∂pi∂y∂ln ( N ) ∂y ) +piqi∂r¯∂pi−μ ( pi−qi ) +ε . ( 2 ) The expected change of allele frequency due to a gradient in fitness and local heterozygosity is piqi∂r¯∂pi=sipiqi ( pi−qi−2Δi ) , in which selection at locus i is si≡αi2/ ( 2Vs ) and Δi= ( z¯−bx ) /αi [13 , Appendix 3] . Here , the fourth term describes the change due to ( symmetric ) mutation at rate μ . The last term ɛ describes genetic drift [34 , Eq 7]: ε=piqiNdWε ( x , y , t ) , in which N is the haploid population density . Population dynamics reflect diffusive migration in a two-dimensional habitat , growth due to the mean Malthusian fitness r¯ , and stochastic fluctuations . The number of offspring follows a Poisson distribution with mean and variance of N; fluctuations in population numbers are given by [61]: ξ=NdWξ ( x , y , t ) : ∂N∂t=σ22 ( ∂2N∂x2+∂2N∂y2 ) +r¯N+ξ ( 3 ) The model can be simplified by rescaling [13 , 59] time t relative to the strength of density dependence r* , distance x relative to dispersal σ , trait z relative to strength of stabilising selection 1/ ( 2Vs ) and local population size N relative to equilibrium population size with perfect adaptation: N^=Kr*/rm , T = r*t , X=x2r*σ2 , Z=zr*Vs , N~=N/N^ . Note that near the equilibrium of a well-adapted population , N~≈1 . The rescaled equations for evolution of allele frequencies and for demographic dynamics are ∂N˜∂T=∂N˜∂X2+∂N˜∂Y2+R¯N˜+2N˜N^σ2dWς˜ ( X , Y , T ) ∂pi∂T=∂2pi∂X2+∂2pi∂Y2+2 ( ∂pi∂X∂ln ( N˜ ) ∂X+∂pi∂Y∂ln ( N˜ ) ∂Y ) ++sr* ( piqi−2Z¯−BXα* ) −μr* ( pi−qi ) +piqiN˜N^σ2dWε˜ ( X , Y , T ) ( 4 ) in which R-≡r-/r*=1-N~-BX-Z2/2 . The rescaled Eqs 4 show that four parameters fully describe the system . First , the effective environmental gradient , B≡bσ/ ( r*2Vs ) . Second , the strength of genetic drift 1/N^=1/ ( 2πN^σ2 ) . The parameter N^ gives the neighbourhood size at an equilibrium with uniform adaptation . The third parameter is the strength of selection relative to the strength density dependence , s/r*; the scaled effect of a single substitution α* also scales with s/r*: α*≡α/r*Vs=2s/r* . The effect of this third parameter s/r* is expected to be small , because typically s≪r* . Therefore , assuming throughout that s is uniform across loci is a reasonably justified simplification . The fourth parameter , μ/r* , will typically be very small and will be neglected throughout . Table 1 ( top ) summarises the full set that describes the system . | The flow of genetic diversity across environments has conflicting effects . On the beneficial side , it increases the genetic variation that is necessary for adaptation and counters the loss of genetic diversity due to genetic drift . However , it may also swamp adaptation to local conditions . This interplay is crucial for the expansion of a species’ range . In this work , I develop a theory that shows how range expansion depends on two dimensionless parameters: ( i ) the fitness cost of dispersal—a measure of environmental heterogeneity—and ( ii ) the strength of genetic drift—a measure of the reduction of genetic diversity . The more heterogeneous an environment , the more challenging it is to expand into , and the lower the genetic diversity , the more limited is the scope for potential adaptation . Together , these two parameters define an ‘expansion threshold’: adaptation fails when the number of individuals accessible by dispersal within one generation is so small that genetic drift reduces genetic diversity below that required for adaptation to a heterogeneous environment . This threshold provides a novel , theoretically justified , and testable prediction for the formation of a range margin and for the collapse of a species’ range in two-dimensional habitats . | [
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] | 2018 | Is the sky the limit? On the expansion threshold of a species’ range |
Periplasmic binding proteins ( PBPs ) in association with ABC transporters select and import a wide variety of ligands into bacterial cytoplasm . They can also take up toxic molecules , as observed in the case of the phytopathogen Agrobacterium tumefaciens strain C58 . This organism contains a PBP called AccA that mediates the import of the antibiotic agrocin 84 , as well as the opine agrocinopine A that acts as both a nutrient and a signalling molecule for the dissemination of virulence genes through quorum-sensing . Here , we characterized the binding mode of AccA using purified agrocin 84 and synthetic agrocinopine A by X-ray crystallography at very high resolution and performed affinity measurements . Structural and affinity analyses revealed that AccA recognizes an uncommon and specific motif , a pyranose-2-phosphate moiety which is present in both imported molecules via the L-arabinopyranose moiety in agrocinopine A and the D-glucopyranose moiety in agrocin 84 . We hypothesized that AccA is a gateway allowing the import of any compound possessing a pyranose-2-phosphate motif at one end . This was structurally and functionally confirmed by experiments using four synthetic compounds: agrocinopine 3’-O-benzoate , L-arabinose-2-isopropylphosphate , L-arabinose-2-phosphate and D-glucose-2-phosphate . By combining affinity measurements and in vivo assays , we demonstrated that both L-arabinose-2-phosphate and D-glucose-2-phosphate , which are the AccF mediated degradation products of agrocinopine A and agrocin 84 respectively , interact with the master transcriptional regulator AccR and activate the quorum-sensing signal synthesis and Ti plasmid transfer in A . tumefaciens C58 . Our findings shed light on the role of agrocinopine and antibiotic agrocin 84 on quorum-sensing regulation in A . tumefaciens and reveal how the PBP AccA acts as vehicle for the importation of both molecules by means of a key-recognition motif . It also opens future possibilities for the rational design of antibiotic and anti-virulence compounds against A . tumefaciens or other pathogens possessing similar PBPs .
In bacteria , periplasmic binding proteins ( PBPs ) are involved in the import into the cell of a wide variety of extracellular compounds . PBPs recognize and bind chemical compounds in order to bring them to ABC transporters which transport them into cells [1] . PBPs are also potential vehicles that facilitate the penetration of antibiotics into bacterial pathogens . To our knowledge , the best known system that exemplifies this paradigm is the antibiotic agrocin 84 , which penetrates into the cytoplasm of the bacterial pathogen Agrobacterium tumefaciens strain C58 by hijacking the PBP called AccA and its cognate transporter [2 , 3] . The antibiotic agrocin 84 is produced by the non-pathogenic bacterial strain Agrobacterium radiobacter strain K84 [2 , 3] . Since the 1970’s , A . radiobacter K84 has been used as a biocontrol agent in several countries to prevent outbreaks of the crown gall disease caused by the pathogen A . tumefaciens in a wide range of plants [4] . Agrocin 84 , having gained access to the A . tumefaciens C58 cytoplasm , is maturated into a toxic moiety ( TM84 ) that inhibits agrobacterial growth [5 , 6] . TM84 acts as a tRNA-dependent inhibitor of leucyl-tRNA synthetase that traps the enzyme in a ternary inhibition complex [7] thus preventing tRNALeu aminoacylation and thereby halting protein synthesis . AccA also plays a key-role in the importation of the characteristic plant tumour-derived compounds such as the opines agrocinopines A and B discovered in 1981 in crown gall tumour tissue [8] . Agrocinopine A is composed of a sucrose linked to a L-arabinose via a phosphodiester bond . Agrocinopine B results from the cleavage of the sucrose moiety of agrocinopine A . Hence agrocinopine B is composed of a fructose linked to a L-arabinose via a phosphodiester bond . The agrocinopines A and B were purified from tumours induced by A . tumefaciens strain C58 . Upon infection , A . tumefaciens genetically engineers the plant host by transferring a piece of DNA ( the T-DNA ) from its tumour inducing ( Ti ) plasmid to the nuclear genome of plants . Proliferation of the transformed plant cells results in the formation of tumours colonized by the bacteria . In plant tumours , T-DNA genes expression redirects the metabolism towards the production of several opines which are used by the pathogen as nutrients ( C , N and P sources ) and signals to control the quorum-sensing signal expression [9–11] . Indeed , agrocinopines A and B play a crucial role in the A . tumefaciens C58 infection process by inducing the synthesis of the quorum-sensing signal 3-oxo-octanoylhomoserine lactone ( OC8HSL ) which increases aggressiveness of agrobacterium and activates the dissemination of the Ti plasmid by horizontal transfer ( by conjugation ) [12 , 13] ( Fig 1 ) . Binding of agrocinopine to the transcriptional repressor AccR , which belongs to the DeoR transcriptional factors family , is proposed to inhibit its repression [14] , releasing the expression of a second transcriptional factor TraR , which binds the quorum-sensing signal OC8HSL . In turn , the complex TraR-OC8HSL activates the expression of quorum-sensing regulated genes such as the tra and trb operons , encoding for horizontal transfer of the Ti plasmid by conjugation and the rep operon which controls the replication of the Ti plasmid . In addition to quorum-sensing , AccR also regulates the transcription of acc ( agrocinopine catabolism ) genes [15] . These genes encode an agrocinopine ABC transporter system including the PBP AccA and two putative enzymes required for catabolism of agrocinopine , the phosphodiesterase AccF and the phosphatase AccG . Agrocin 84 differs from agrocinopine A [16 , 17 , 18] . It is composed of a D-glucose-phosphoramidate linked to TM84 . The phospho-glucose moiety of agrocin 84 is required for its import into pathogenic A . tumefaciens cells [6] . Agrobacterium phosphodiesterase AccF should cleave the antibiotic agrocin 84 into a D-glucose-phosphate and TM84 , whereas the same enzyme cleaves agrocinopine A into L-arabinose-2-phosphate and sucrose . Using X-ray crystallography , we investigated at the atomic level the binding mode of AccA with the antibiotic agrocin 84 and the virulence signal agrocinopine A , which are chemically different and exhibit antagonist functions . Agrocin 84 was purified from an agrobacterial strain containing a derivative of the plasmid pAgK84 that allows agrocin 84 overproduction from cells [18] , while agrocinopine A was synthetized through a multistep protocol . Based on the structural analysis of AccA structures in complex with agrocinopine A or agrocin 84 , we then focussed on three additional potential ligands which are analogues of agrocinopine A: agrocinopine 3’-O-benzoate , L-arabinose-2-phosphate and L-arabinose-2-isopropylphosphate . To address the structural basis for AccA specificity , the carbohydrate fragment of agrocin 84 , namely D-glucose-2-phosphate was also synthetized . To understand the regulation mode of the AccR transcriptional repressor activity and the role of the opine agrocinopine A and its AccF-mediated degradation product arabinose-2-phosphate , we combined affinity measurements and a plasmid conjugation assay using the wild-type strain and a defective strain mutant accF of A . tumefaciens C58 . We found that agrocinopine A is not the effector of AccR as previously described [14] , in contrast to L-arabinose-2-phosphate and D-glucose-2-phosphate which both activate the quorum-sensing signal synthesis and the plasmid Ti conjugation . Because L-arabinose-2-phosphate ( from agrocinopine A ) and D-glucose-2-phosphate ( from agrocin 84 ) are compounds which are unique among the natural products described so far , this work reveals that A . tumefaciens evolves a pyranose-2-phosphate motif which is the key-recognition pattern by the PBP AccA for bacterial importation and by the master transcriptional regulator AccR for quorum-sensing activation and acc operon expression .
The synthesis strategy for the production of agrocinopine A ( Fig 2 ) allowed us to produce derivatives , which are L-arabinose-2-phosphate , L-arabinose-2-isopropylphosphate and agrocinopine-3’-O-benzoate ( S1 Fig ) . A D-glucose-2-phosphate was also synthetized ( Fig 2 and S1 Fig ) . The mature AccA expression plasmid was obtained by cloning the accA gene lacking the first twenty-nine signal sequence residues that serve for localization to bacterial periplasm . Because AccA shares low sequence identity ( around 20% ) compared to PBPs with known three dimensional structures , we first solved the structure of seleniated AccA in complex with agrocinopine A at 2 . 65 Å resolution by single-wavelength anomalous dispersion method . A better resolution structure of this complex at 1 . 9 Å resolution in a different space group using the native protein was determined . The two agrocinopine-complexed structures are very similar , displaying an average root mean square deviation ( rmsd ) of 0 . 35 Å over 489 Cα atoms . By the molecular replacement method , we then solved the structure of AccA in complex with agrocin 84 at 2 . 15 Å resolution ( Table 1 ) . The mature AccA structure is a monomer of 493 residues composed of two lobes , each formed by a central β-sheet flanked by α-helices . The biggest lobe ( lobe 1 ) consists of residues 29–280 and 494–521 and the smallest ( lobe 2 ) comprises the residues 285–489 . The two lobes are connected by a very short hinge region of 8 residues defining two short segments ( Fig 3 ) . AccA fold belongs to the cluster C within the PBP structural classification [1] . Overall , the ligand bound structures are very similar ( average rmsd value of 0 . 33 Å over 490 Cα atoms ) adopting a closed conformation . The major difference concerns the region comprising the residues 403–408 , which can move up to 1 . 7 Å to accommodate the ligand in the binding site . The unliganded AccA structure solved at 1 . 7 Å resolution adopts an open conformation . Indeed , superposition with the liganded structures results in an average rmsd of 1 . 5 Å for all Cα atoms and structural comparisons show that upon ligand binding , a 12° rotation around an axis defined by the two hinge residues , Met292 and Tyr493 , leads to a similar closed form of the protein . Both ligands are bound between the two closed lobes of AccA . While Agrocinopine A is fully defined in the electron density maps , the leucine-like moiety of agrocin 84 is mobile ( S1 Fig ) . Notably , the electron density maps of agrocin 84 show a D-glucopyranose connected via its C2 carbon to the N6-phosphoramidated TM84 moiety and not a D-glucofuranose connected via a C1-linkage as previously suggested using analytical chemistry approaches that were available at the end of the 1970’s [2 , 3 , 19] . Later , a possible D-glucopyranose-C2 structure for the uptake moiety of agrocin 84 has been proposed [20 , 21] . Both bound ligands , agrocinopine A and agrocin 84 , share a deeply buried pyranose ( L-arabinose or D-glucose ) -2-phosphate-like moiety which superimposes very well in the AccA binding site , and makes numerous and very similar protein contacts ( Fig 4 ) . The pyranose-2-phosphate moiety lies on residues 418–421 from the strand β16 of lobe 2 and is surrounded by the N-terminal loop region 52–54 and the side chains of Tyr145 , Trp178 , Glu504 and Glu510 from lobe 1 , that of Asn284 from the hinge region and those of Met372 , Tyr375 , Tyr376 , Thr430 , Glu434 from lobe 2 . It makes extensive protein hydrogen bonds: 8 for L-arabinose and 10 for D-glucose ( Fig 4A and 4B ) . Remarkably , the OH1 group of the pyranose is anchored by 4 hydrogen bonds involving Asn54 and Glu510 from lobe 1 , Asn284 from the hinge region and Ser419 from lobe 2 . These four side chains form a rigid template , which also maintains the lobe closure by interacting together two-by-two ( S2a Fig ) . Moreover , in addition to their pyranose interactions , two of these major residues Asn54 and Ser419 also tightly interact with the phosphate/phosphoramidate groups . Two more hydrogen bonds involving the side chains of two tyrosines 375 and 376 retain the phosphate/phosphoramidate oxygens . In contrast , the sucrose moiety of agrocinopine A and the TM84 part of agrocin 84 make only 3 and 2 polar interactions with AccA , respectively . The sucrose moiety and TM84 occupy a different position in the binding site . We determined the structures of AccA in complex with several synthetic derivatives of agrocinopine A and agrocin 84: L-arabinose-2-isopropylphosphate , L-arabinose-2-phosphate and D-glucose-2-phosphate at 2 . 1 , 2 . 3 and 1 . 75 Å resolution respectively ( Table 1 ) . L-arabinose relatives and D-glucose-2-phosphate are well defined in the electron density maps ( S1 Fig ) . The L-arabinose-2-isopropylphosphate and L-arabinose-2-phosphate alone or within agrocinopine A make the same protein interactions and superimpose well ( Fig 4C and 4E ) . A similar observation can be made for the bound D-glucose-2-phosphate ( Fig 4D and 4E ) . To summarize , AccA binds the pyranose-2-phosphate/phosphoramidate key-template through numerous polar interactions and its selectivity for this motif was validated by the structures of AccA in complex with L-arabinopyranose-2-phosphate and D-glucopyranose-2-phosphate . The synthesis process of agrocinopine A allows obtaining bulky agrocinopine A derivatives such as agrocinopine 3’-O-benzoate . We used this compound to test whether AccA could bind a synthetic ligand exhibiting the minimal pyranose-2-phosphate motif . We therefore co-crystallized AccA with this compound . The structure at high resolution shows a very well defined bound agrocinopine 3’-O-benzoate ( S1 Fig ) . Interestingly , while the L-arabinose-2-phosphate between bound agrocinopine 3’-O-benzoate and agrocinopine A superimpose , their sucrose moieties do not occupy the same position . Whereas the benzoate group of the agrocinopine 3’-O-benzoate adopts the position of the sucrose in agrocinopine , its sucrose moiety follows that of the TM84 ( S2b Fig ) . When present as the sole carbon source for A . tumefaciens C58 , agrocinopine 3’-O-benzoate is used as nutrient by the bacteria as indicated by bacterial growth ( S3 Fig ) . A similar behaviour is observed with L-arabinose-2-phosphate used as a sole carbon source meaning that agrocinopine 3’-O-benzoate is degraded in the bacterial cytoplasm once imported by AccA and its ABC transporter . This experiment confirmed that the transporter can uptake bulky synthetic molecules such as agrocinopine 3’-O-benzoate into the cell as observed for the toxin agrocin 84 , which is also a relatively large molecule . Ligand binding to the protein AccA was investigated using tryptophan fluorescence spectroscopy , a method exploiting significant conformational changes accompanying the binding . The autofluorescence intensity enhancement correlated with the ligand concentrations between 0 . 3 and 20 μM and saturated above 50 μM . Titration experiments yielded apparent KD values of 1 . 3 ± 0 . 17 μM , 5 . 88 ± 1 . 6 μM , 2 . 93 ± 0 . 66 μM , 4 . 79 ± 0 . 6 μM and 2 . 5 ± 0 . 5 μM with agrocinopine A , agrocinopine 3’-O-benzoate , L-arabinose-2-phosphate , L-arabinose-2-isopropylphosphate and D-glucose-2-phosphate , respectively ( S4 Fig and S1 Table ) . No fluorescence intensity change was detected by incubating AccA with L-arabinose , D-glucose or phosphate alone . As expected from models based on the X-ray liganded AccA structures , no autofluorescence signal changes were measured by incubating AccA with glucose-1-phosphate or glucose-6-phosphate , which both are common metabolites in all living organisms . Unexpectedly , no signal was measured with agrocin 84 , likely due to the presence of the adenosine moiety of agrocin 84 which provokes a quenching signal . Therefore , we used isothermal titration microcalorimetry to assess the binding of agrocin 84 to AccA , which yielded a mean KD of 1 . 5 ± 0 . 41 μM . The mean KD values were 0 . 3 ± 0 . 03 μM , 7 . 5 ± 2 . 2 μM , 1 . 33 ± 0 . 12 μM , 2 . 2 ± 0 . 58 μM and 1 . 16 ± 0 . 22 μM for agrocinopine A , agrocinopine 3’-O-benzoate , L-arabinose-2-phosphate , L-arabinose-2-isopropylphosphate and D-glucose-2-phosphate respectively , consistent with the values obtained from fluorescence spectroscopy . The isothermal titration microcalorimetry data also confirmed the 1:1 binding stoichiometry and demonstrate a negative enthalpy change upon each ligand binding ( S5 Fig and S2 Table ) , suggesting that the binding is enthalpy driven . The similar binding isotherms for all ligands suggest a same binding mechanism involving polar interactions , in agreement with what is observed in the complexed structures ( Fig 5 ) . Nevertheless , the benzoate group of the agrocinopine 3’-O-benzoate molecule appears to be responsible for an entropic effect leading to a 25-fold lower affinity of this ligand with AccA in comparison with agrocinopine A . We could not detect any binding of L-arabinose , D-glucose , nor adenosine monophosphate in line with the results obtained by fluorimetry . A structural comparison of AccA with all PDB entries using SSM-EBI ( http://www . ebi . ac . uk/msd-srv/ssm ) [22] indicates that the most similar overall structures are PBPs from the same cluster: the structure of Staphylococcus aureus NikA in complex with nickel and histidine ( PDB: 4XKN ) [23] , Bacillus subtilis AppA with a bound nanopeptide ( PDB: 1XOC ) [24] , Escherichia coli OppA with different bound tripeptides ( PDB: 1JET , 3TCG , 1B46 , 3TCF ) [25–27] , Escherichia coli NikA with a bound nickel and histidines ( PDB: 4I8C ) [28] , Thermatoga maritima CBP ( tmCBP ) with bound cellobiose and cellopentaose ( PDB: 2O7I and 3IO5 ) [29] and Escherichia coli DPP with a bound dipeptide ( PDB: 1DPP ) [30] . The values of rmsd range from 1 . 9 Å to 2 . 7 Å over 413 to 434 residues . Superposition with other PDB-entries results in an rmsd of over 2 . 7 Å for less than 410 Cα atoms . Therefore , the most similar PBPs bind either oligopeptides or nickel ions or oligosaccharides . The ligand binding site of these PBPs lies on a conserved β-strand ( β14 in AccA ) except that of tmCBP . A particularity of AccA which is not shared by these related PBPs is the presence of the flexible loop 402–414 located between the β-strands β15 and β16 which can accommodate agrocinopine A . This loop corresponds to a conserved rigid helix in all similar PBPs and models superposition shows that an equivalent helix in our structure would clash with the last glucose of agrocinopine A ( S6a Fig ) . Therefore the flexible loop 402–414 in AccA seems so far unique among PBPs from cluster C . The comparison with the oligopeptide binding proteins reveals that AccA resembles the E . coli DPP , which can bind short oligopeptides ( dipeptides ) only due to the presence of a loop ( residues 344–350 ) which creates a steric hindrance and reduces the size of the ligand binding site on one side . The position of the dipeptide ligand in DPP closely matches the position of the L-arabinose-phosphate group in AccA despite the large differences in their binding mode . Interestingly , two tryptophan side chains ( W178 and W423 ) from each AccA lobe , which occupy equivalent positions of Met152 and Asp408 in DPP form a gate that closes the ligand binding site preventing the binding of longer substrates linked beyond the pyranose moiety on the O6 or O5 atoms ( S6b Fig ) . Structural comparison between AccA and NikA binding sites shows that the two bound histidines in NikA overlap the L-arabinose-2-phosphate moiety in AccA with the nickel ion overlapping the oxygen atom linking the arabinose to the phosphate . However , the loop region 51–55 in AccA prevents the interaction of a histidine in the binding site of AccA . An equivalent loop in tmCBP would restrain its ligand binding site preventing the binding of long oligosaccharides . TmCBP binds oligosaccharides ranging from two rings ( cellobiose ) to five ( cellopentaose ) in a deep groove at the domain interface . The minimal cellobiose binding site is found at one extreme of this groove and provides extensive network of hydrogen bonds for the di-saccharide . In contrast , the cellopentaose binding site spans the entire interface in a large cavity lacking aromatic and polar residues for the last three sugars . Therefore , tmCBP exhibits specificity only for the cellobiose moiety of the cellopentaose . Although the position and conformation of the binding site between tmCBP and AccA differ ( S7 Fig ) , both PBPs select a molecule class rather than single species . In the A . tumefaciens C58 cytoplasm , the enzyme AccF releases the L-arabinose-2-phosphate and D-glucose-2-phosphate from agrocinopine A and agrocin 84 . The question arose about their biological role in A . tumefaciens , especially in relation to regulation of the AccR/quorum-sensing pathway that controls dissemination of the Ti plasmid . We first measured the affinity of the synthetic agrocinopine A towards purified recombinant AccR by isothermal titration microcalorimetry . An unexpected outcome was that no affinity was detected . In contrast , the isothermal titration microcalorimetry data show that AccR displays affinity to L-arabinose-2-phosphate and D-glucose-2-phosphate in two steps with KD values for L-arabinose-2-phosphate of KD1 = 0 . 11 ± 0 . 06 ( n1 = 0 . 4 ) μM for the first step and KD2 = 1 . 64 ± 0 . 8 ( n2 = 0 . 9 ) μM for the second step and for D-glucose-2-phosphate KD1* = 0 . 43 ± 0 . 1 ( n1* = 0 . 5 ) μM and KD2* = 1 . 64 ± 0 . 8 μM ( n2* = 0 . 9 ) ( Fig 6A ) . The binding stoichiometries of 2:1 ( protein:ligand ) in the first step and 1:1 in the second step are in line with the fact that AccR forms a dimer in solution as observed by native gel electrophoresis , suggesting that the binding of the effector to AccR may be described as following: first , a ligand binds to one molecule within the dimer before another binds to the second molecule . These results suggest L-arabinose-2-phosphate and D-glucose-2-phosphate are the effector molecules that regulate AccR and thus they should activate quorum-sensing synthesis in A . tumefaciens cells . In vivo , the functions of agrocinopine A , L-arabinose-2-phosphate and D-glucose-2-phosphate as quorum-sensing effectors were examined by measuring the production of quorum-sensing molecules and efficiency of Ti plasmid transfer in conjugation between A . tumefaciens donors and the recipient A . tumefaciens C58 . 00 which is cured of plasmids ( Fig 6B ) . Results indicated that all cell cultures supplemented with agrocinopine A , L-arabinose-2-phosphate and D-glucose-2-phosphate accumulate high concentrations of quorum-sensing molecules ( between 50 and 100 nM ) compared with mock cultures ( less than 1 nM ) . Consistently , Ti plasmid transconjugants could also be detected in the supplemented cultures , demonstrating that agrocinopine A , L-arabinose-2-phosphate and D-glucose-2-phosphate are activators of the quorum-sensing pathway of A . tumefaciens C58 . Finally , because the purified AccR exhibited affinity for L-arabinose-2-phosphate and not for agrocinopine A in vitro and because L-arabinose-2-phosphate activated quorum-sensing and plasmid Ti conjugation in vivo , we tested the hypothesis of the key-role of AccF in the maturation of agrocinopine A into L-arabinose-2-phosphate as the efficient activator of quorum-sensing signal . We used an accF defective mutant [14] . Remarkably , both accumulation of quorum-sensing molecules and Ti plasmid conjugation were abolished in the accF mutant supplemented with agrocinopine A , but restored by genetic complementation of accF demonstrating the key role of AccF in quorum-sensing signaling ( Fig 6B ) . Moreover , quorum-sensing signals were still observed in the presence of L-arabinose-2-phosphate in both wild type and accF mutant ( Fig 6B ) . Altogether , our findings prove that agrocinopine A needs to be degraded into L-arabinose-2-phosphate , whose interaction with the master regulator AccR induces the quorum-sensing regulation of the conjugative transfer of the Ti plasmid in A . tumefaciens C58 cells . The pyranose-2-phosphate motif with L-arabinose-2-phosphate and D-glucose-2-phosphate are the effectors of AccR .
Understanding the molecular mechanism whereby toxic or beneficial compounds are imported into bacteria is a major issue , whether it aims at to understanding their biological and ecological roles or to design antibiotics . In this work , we characterized the binding mode of agrocinopine A and agrocin 84 in the PBP AccA thanks to synthetic agrocinopine A and to agrocin 84 purified from an agrobacterial strain containing a derivative of the pAgK84 plasmid . Our structural work showed that the toxin agrocin 84 , composed of a glucose moiety linked at its position 2 to TM84 , is recognized through a D-glucopyranose-moiety , whereas only a furanose linked at position 1 form had been previously suggested for the structure of agrocin 84 [19] . Our structural analysis of AccA ligand binding site combined with spectrofluorescence and microcalorimetry studies reveal that AccA recognizes , through numerous polar interactions , only the pyranose-2-phosphate/phosphoramidate motif of agrocinopine A , agrocin 84 and other relatives such as agrocinopine 3’-O-benzoate in which the sugar is either a D-glucopyranose or a L-arabinopyranose . AccA cannot recognize a phosphate alone , nor a D-glucose , nor a L-arabinose , nor a glucose linked to a phosphate via an oxygen atom other than its O2 . The selectivity of AccA for this ligand motif was validated by the structures of AccA in complex with L-arabinose-2-phosphate and D-glucose-2-phosphate and their affinity measurement . Indeed , the KD for L-arabinose-2-phosphate is very similar to those of longer/bulkier ligands having this group , such as agrocinopine A , agrocinopine 3’-O-benzoate , and L-arabinose-2-isopropyl phosphate . Hence , structural and affinity data on AccA with different ligands revealed that AccA is sufficiently flexible to accommodate bulkier ligands as long as they possess the key motif on one end . Moreover , given that the extended ligand agrocin 84 is imported into the pathogen [15 , 31] in addition to the bulky agrocinopine 3’-O-benzoate shown in this study , and since Tate and Kerr [32] have shown that a derivative compound of agrocin 84 lacking the leucine-like portion is transported into the bacteria cell , the transporter associated with AccA does not restrict the size or length of the transported molecules . Thus , it should import any natural or synthetic molecules at least as bulky as the agrocin 84 and agrocinopine 3’-O-benzoate that could be identified or designed for improving control of the A . tumefaciens pathogen . The example of tmCBP [29] , which also exhibits limited specificity for the first two sugar rings from its carbohydrate ligand emphasizes that PBPs have promising properties that can be exploited by both natural and synthetic antibiotics that employ a “Trojan Horse” strategy . Arabinose-2-phosphate and glucose-2-phosphate are uncommon and original molecules in the living world due to the unusual phosphate linkage on the C2 atom of the pyranose . No atomic structure of these molecules has been reported so far and minimal literature described the role of these molecules . The presence of a glucose-2-phosphate has recently been reported in glycogen using NMR [33] . Phosphate incorporation ( glucose C2 and C3 phosphomonoesters ) into glycogen results from a catalytic error of the glycogen synthase leading to insoluble glycogen-like polymer in the Lafora disease . Another important biological role of the pyranose-2-phosphate motif concerns the quorum-sensing signal synthesis and dissemination of the Ti plasmid in agrobacteria which are under the control of the master transcriptional regulator AccR [12] . Once imported by AccA-ABC transporter into agrobacteria , agrocinopine A is degraded by the enzyme AccF into L-arabinose-2-phosphate and sucrose [15] . A single study based on gel-shift assays with AccR has proposed a direct binding between agrocinopine A and AccR [14] using a mixture of agrocinopines A and B purified from tomato tumours under acidic conditions ( pH 5 . 4 ) . We hypothesize that the gel shift result can be accounted for by the presence of L-arabinose-2-phosphate in the mixture . Here , we worked with synthetic agrocinopine A , L-arabinose-2-phosphate and D-glucose-2-phosphate . We show that ( 1 ) AccR interacts with L-arabinose-2-phosphate and D-glucose-2-phosphate and not with agrocinopine A; ( 2 ) agrocinopine A , L-arabinose-2-phosphate and D-glucose-2-phosphate induce the quorum-sensing signal synthesis and Ti plasmid transfer in wild-type A . tumefaciens; ( 3 ) the quorum-sensing signal synthesis and Ti plasmid transfer are abolished in an accF mutant of pTiC58 in the presence of agrocinopine A; but are still activated in the presence of L-arabinose-2-phosphate . Our work demonstrates the key-role of AccF in the maturation of L-arabinose-2-phosphate which triggers the quorum-sensing signal and acc operon expression . In the absence of L-arabinose-2-phosphate , the operon acc is not over-expressed leading to a weak expression of AccA thus to a weak import of agrocinopine . This can explain why the accF mutant failed to remove detectable amounts of a mixture of agrocinopines A and B from the media [17] . However , the same publication also showed that the accF mutant still took up agrocin 84 . Therefore , from these results and from our data , AccF is not directly required for agrocinopine A transport . Our work refreshes the knowledge on the regulatory cascade of the quorum-sensing activation by the opine agrocinopine A in A . tumefaciens . It is important to keep in mind that AccF is surely involved in the maturation of agrocin 84 into the toxic moiety TM84 and D-glucose-2-phosphate . Altogether , our work supports the hypothesis that agrocin 84 hijacks the transport process of agrocinopine A via AccA and that D-glucose-2-phosphate has the same role than L-arabinose-2-phosphate in the quorum-sensing pathway of A . tumefaciens C58 pathogen and likely in the acc operon expression . Agrocinopine A is the archetype of the agrocinopine family which encompasses the agrocinopines A , B , C and D [8] . Agrocinopine C is quite similar to agrocinopine A , as it is composed of a sucrose linked to a D-glucose ( instead of a L-arabinose in agrocinopine A ) via a phosphodiester bond . Agrocinopines B and D differ from A and C respectively , by lacking one sugar from the sucrose moiety . Agrocinopines A and B were often extracted and purified at acidic pH condition from tumours induced by A . tumefaciens strains such as C58 while the agrocinopines C and D from tumours induced by A . tumefaciens strains such as Bo542 . One study has shown that radioactive agrocinopine A was detected from C58 tumours without the presence of agrocinopine B indicating that agrocinopine A is probably the sole agrocinopine secreted in tumours [16] . Moreover , agrocinopines B and D seem to be degradation products of agrocinopines A and C respectively , especially considering the latter two opines can be easily degraded either chemically by mild acid hydrolysis or enzymatically by α-glucosidase [8] . It is likely that crown gall tumours synthesize and secrete only one agrocinopine either A or C depending on its Ti-plasmid type C58 or Bo542 . The AccF by-product of agrocinopines C and D is D-glucose-2-phosphate as for agrocin 84 . In A . tumefaciens C58 and Bo542 , AccA and AccR proteins are highly conserved with 75% and 83% sequence identity respectively , suggesting that they present the same function with a similar action mechanism . Therefore , L-arabinose-2-phosphate and D-glucose-2-phosphate must share a similar crucial role in all the agrocinopine pTi strains . In A . tumefaciens strain C58 , we show that quorum-sensing and plasmid Ti transfer is activated by either L-arabinose-2-phosphate or D-glucose-2-phosphate . Moreover , a similar affinity of these compounds for AccR suggests that , although synthesis of agrocinopines A and C are induced by different T-DNAs ( from pTiC58 and pTiTiBo542 respectively ) , they play a similar role in A . tumefaciens C58 . Our work provides evidence of the functional redundancy among opines of the agrocinopine family . A . radiobacter K84 employs a smart strategy when it produces agrocin 84 taking advantage of the functional and structural similarity with the opines utilized by the agrobacteria harboring the agrocinopine-family Ti plasmids . The D-glucose-2-phosphate moiety of agrocin 84 acts as a ‘molecular passkey’ , which is recognized by a large variety of pathogenic agrobacteria expressing the PBP AccA and enzyme AccF . This feature may explain the success story of A . radiobacter K84 as a biocontrol agent against A . tumefaciens pathogens [5 , 6] . In conclusion , the pyranose-2-phosphate found in the L-arabinopyranose moiety in agrocinopine A ( and other tested analogues and derivatives ) and the D-glucopyranose moiety in agrocin 84 and agrocinopine C , is a specific motif that possesses at least two functions ( transport and regulation ) in agrobacteria . In addition to being the mature signal that triggers quorum-sensing and further dissemination of virulence genes , it is the key-recognition motif of the PBP AccA presumably allowing the importation of toxic or non-toxic molecules as long as they possess it at one end .
Agrocinopine A ( 9 ) was prepared according to an adapted synthesis which was previously reported by Lindberg and Norberg [34] . The synthesis sequence was modified by using a different phosphoramidite for the coupling step , in order to keep the phosphate group protected as an ester until the latest stage of the synthesis , easing the purification of the intermediate products by simple silica gel chromatography . Benzyl 3 , 4-O-isopropylidene-β-L-arabinoside 1 prepared in two steps from L-arabinose [35] was phosphinylated using phosphoramidate 2 [36 , 37] giving the 2-substituted arabinose derivative 3 in 79% yield , which was coupled in the presence of tetrazole to the partially protected sucrose derivative 4 having only its 4-OH unprotected prepared from sucrose in 4 steps [38] . The coupling product 5 obtained in 64% yield was oxidized with t-butylhydroperoxide forming the fully protected agrocinopine 6 in 92% yield . Deprotection of the three isopropylidene groups in 60% aq . AcOH at 50°C afforded compound 7 in 53% yield . Compound 7 was further debenzylated at the anomeric position of the arabinose and on the phosphate group by catalytic hydrogenation ( Pd/C , H2 ) leading in 83% to compound 8 having only esters as remaining protecting groups ( two acetyl groups at position 3 of the glucose moiety and position 6 of the fructose moiety , and one benzoyl group at position 3 of the fructose moiety ) . Final ester groups cleavage was performed using potassium carbonate in MeOH leading to agrocinopine ( 9 ) in 59% yield , for which all spectroscopic data were consistent with reported ones [39] . When sodium methoxide was used and the reaction stopped before total consumption of intermediate products , 3’-O-benzoylated agrocinopine ( 10 ) could be obtained as a mixture with agrocinopine . The L-arabinose derivatives 14 and 18 substituted at position 2 with a phosphate or an isopropylphosphate , respectively , were prepared from the same starting phosphinylated L-arabinose 3 following a similar sequence . Reaction of 3 with benzyl alcohol or isopropanol followed by oxidation , hydrolysis of the isopropylidene group and catalytic hydrogenation of the benzyl groups led to the desired L-arabinose derivatives 14 and 18 respectively ( Fig 2 ) . Details on the procedures and spectroscopic data are given in S1 Text . The synthesis of glucose-2-phosphate was based on a strategy involving the selective deprotection in position 2 of perbenzylated glucose and then phosphorylation . This synthesis was achieved starting from D-glucose in five steps . First , the benzyl glucoside 19 was obtained using sulfamic acid in benzyl alcohol to get the α-isomer as the major one [40] . Compound 19 was then fully benzylated to obtain compound 20 which was selectively deprotected in position 2 using TIBAL . This method described by Sinaÿ et al . can only debenzylate the α-isomer of 20 and led to compound 21 [41] which was then phosphorylated using the dibenzyl phosphoramidite and subsequently oxidized to compound 22 . Glucose-2-phosphate was finally obtained by deprotection of benzyl groups using palladium catalyzed hydrogenation , as a mixture of α/β isomers , the α isomer being the major one . The structure of glucose-2-phosphate was ascertained by mass spectrometry and 2D-NMR experiments; in particular , the position 2 of the phosphate could be confirmed by 31P-1H correlations . A 3 L starter culture of A . tumefaciens NT1 ( pAgK84::Tn5 A1-B5 ) agrocin over producing strain was inoculated into 297 L of D-glucose minimal media ( modified from Richaud et al . ) [18] containing kanamycin at 50 μg/mL and incubated in a large scale fermenter at 28°C for 24 h , at a low aeration rate and moderate agitation ( ~250 rpm ) . The final culture was centrifuged at 9600 g and the supernatant containing agrocin 84 was retained . Activated Nuchar SN-20 charcoal ( MeadeWestvaco Corporation ) was added to the supernatant at 10 g per L and stirred at 4°C for 15 min . The agrocin 84 bound charcoal was allowed to settle , the supernatant removed and the charcoal slurry was vacuum filtered using a large Buchner Funnel and Whatman filter paper No . 3 . 60 g portions of charcoal were then washed with 6 L of ddH2O at 4°C for 15 minutes and then re-filtered . Agrocin 84 was then eluted from the charcoal using 600 mL of 70% ethanol ( per 60 g of charcoal ) or reagent alcohol and stirring the solution overnight at 4°C . The agrocin 84 containing ethanol was vacuum , then filtered using Whatman paper No . 3 and then Millipore White Nylon 0 . 20 μm filters and the filtrate collected . The presence of agrocin 84 in samples was confirmed through use of an agrocin 84 bioassay [6] . Rotary evaporation was then used to remove ethanol and H2O and to concentrate the sample to a smaller volume . Methanol was added to each sample to a final concentration >70% and the resulting precipitate ( containing insoluble contaminants ) was removed by centrifugation of samples for 10 min at 17000 g and decanting the agrocin 84 containing supernatant . Remaining alcohol was removed by speed-vac or freeze dryer and samples stored at -80°C . Agrocin 84 was then purified using Reverse phase HPLC using a Clipeus C18 column ( 5 μm , 250 x 10 mm , Higgins Analytical , Inc ) and the following typical conditions: buffer A ( 25 mM TEAA pH 7 . 5 , in 100% H2O ) ; buffer B ( 25 mM TEAA pH 7 . 5 , in 50% H2O/ 50% acetonitrile; gradient 9–19% mobile phase buffer B over 33 . 5 min at a flow rate of 4 . 7 mL/min; and column temperature of 30°C . HPLC runs were monitored by UV absorbance at 264 nm and collected peaks showing activity in agrocin 84 bioassays were pooled . If necessary , samples were re-purified by HPLC , desalted , and the agrocin 84 concentration determined using agrocin 84 extinction coefficient [32] . Samples were stored at -80°C until use . The mature AccA expression plasmid was obtained by cloning accA gene , without the 29 residues signal sequence , of A . tumefaciens C58 by PCR and adding a C-terminal hexahistidine tag into the plasmid pET-9aSN1 ( a gift from S . Chéruel , I2BC , University Paris Sud , Orsay , France ) between the NdeI and NotI sites using 5’GGATTCCATATGCAAGAACGCCGGGCGCTT3’ as forward primer and 5’TTTGCGGCCGCTCAATGGAGAGTGATGGTGATGGTGGCCGAAGCTGAGATTGTT3’ as reverse primer . E . coli BL21 competent cells transformed with pET9aSN1-AccA were grown in 2TY media at 37°C until OD600 of 0 . 6 . 0 . 5 mM of isopropyl β-D-1-thiogalactopyranoside ( IPTG ) was added to the culture for 3 h of expression . The cells were pelleted by centrifugation at 8000 g for 20 min at 4°C and stored at -20°C . For protein purification , the cells are resuspended in 50 mM Tris-HCl pH 8 , 150 mM NaCl and 20 mM imidazole and disrupted by sonication . After centrifugation at 25000 g for 45 minutes , the filtered supernatant is injected on a nickel affinity column ( HiTrap 5 ml , GE Healthcare ) . After a washing step of 6% of 50 mM Tris-HCl pH 8 , 150 mM NaCl and 300 mM imidazole ( Buffer B ) , the protein is eluted with 100% of Buffer B and injected on a gel filtration Superdex 200 26/60 ( GE Healthcare ) using 50 mM Tris-HCl pH 8 and 150 mM NaCl . The protein fractions are pooled , concentrated and stored at -80°C . The E . coli BL21 cell transformed with the plasmid pET9aSN1-AccA were grown overnight at 28°C in M9 media supplemented with 0 . 4% Glucose , 2 mM MgSO4 , 1 μM CaCl2 , 100 mg/L of lysine , threonine , and phenylalanine , 50 mg/L of leucine , valine , isoleucine and methionine . The pelleted cells are resuspended in fresh M9 media ( same as above ) with 100 mg/L of selenomethionine instead of methionine for 1 h at 37°C before inducing the expression with 0 . 5 mM IPTG for 5 hours . The cells are are centrifuged at 8000 g for 20 min at 4°C and stored at -20°C . The purification protocol was the same as described above . The native AccR sequence was chemically synthesized ( Genscript , Piscataway , NJ ) with the addition of a 6-His tag at the C terminus and of two restriction sites NdeI and NotI at the N and C terminus respectively . The open reading frames were inserted into the pET-9aSN1 expression plasmid as for accA ORF . E . coli C41 competent cells transformed with pET9aSN1-AccR were grown in LB media at 37°C until OD600 of 0 . 6 . 1 mM of IPTG was added to the culture for 20 h of expression at 20°C . The cells were pelleted by centrifugation at 8000 g for 20 min at 4°C and stored at -20°C . For protein purification , the cells were resuspended in 20 mM Bis Tris propane pH 9 , 150 mM NaCl , 10% glycerol ( Buffer C ) and disrupted by sonication . After centrifugation at 25000 g for 45 minutes , the filtered supernatant was injected on a nickel affinity column ( cOmplete His-Tag Purification Column , 5ml , Roche Lif Sciences ) . After a washing step using buffer C with 5 mM imidazole , the protein was eluted with 100% of buffer C with 100 mM imidazole before a dialysis against buffer C . The protein was then concentrated and stored at -80°C . Crystallization conditions for AccA at 12 mg/mL in presence and absence of ligands ( between 50 μM and 10 mM ) were screened using Qiagen kits ( Valencia , CA , USA ) with a Cartesian nanodrop robot ( Genomic solutions ) . The crystals were manually reproduced in hanging drops experiments by mixing equal volumes of protein solution and precipitant solution mentioned in Table 1 . Crystals were transferred to a cryoprotectant solution ( paraffin oil or mother liquor supplemented with 25% PEG 400 ) and flash-frozen in liquid nitrogen . X-ray diffraction data sets were collected at 100 K on the Proxima 1 beamline ( SOLEIL synchrotron , Saint-Aubin , France ) . Data processing was performed using the XDS package [42] ( Table 1 ) . The crystal structure of the AccA-Agrocinopine complex was determined by SAD method from selenomethionine-labelled protein and refined at 2 . 65 Å resolution . Solvent content analysis using CCP4 ( Collaborative Computational Project , Number 4 ) indicated the presence of one monomer in the asymmetric unit . The positions of 17 over 18 selenium atoms were found using SHELX suite program [43] . The phases were calculated using PHASER [44] and density modification was performed by PARROT ( CCP4 suite ) . An iterative process of manual building in COOT combined with phase calculation using PHASER [44] , where a partial model was used as input , allowed the modelling of the complete polypeptide chain . The structure of the free-liganded AccA was solved by molecular replacement with PHASER [44] using the coordinates of lobe 1 and lobe 2 of SeMet-AccA monomer as separated search models whereas all the complexed structures were solved using the SeMet-AccA monomer as a search model . Refinement of each structure was performed with BUSTER-2 . 10 [45] with NCS restraints when the asymmetric unit contains more than one protein molecule . One TLS group was assigned for each structure . Inspection of the density maps and manual rebuilding were performed using COOT [46] . The three dimensional models of agrocinopine , agrocin 84 and agrocinopine 3’-O-benzoate , L-arabinose-2-phosphate and L-arabinose-2-isopropylphosphate were generated using the ProDRG webserver [47] . Refinement details of each structure are shown in Table 1 . Molecular graphics images were generated using PyMOL ( http://www . pymol . org ) . Each ligand bound to AccA was monitored by autofluorescence by excitating the protein at a wavelength of 295 nm and monitoring the quenching of fluorescence emission of tryptophans at 335 nm . All experiments were performed at 22°C in 3 x 15 mm quartz cuvettes using Cary Eclypse spectrofluorometer ( Varian ) in 25 mM Tris-HCl pH 8 . 0 and 150 mM NaCl with a fixed amount of proteins ( 2 μM ) and increasing concentrations of ligand . Each ligand has no emission signal at 335 nm . The data were analysed using Origin 7 software and fitted to the equation f = ΔFluorescencemax*abs ( x ) / ( KD+abs ( x ) ) . Isothermal titration microcalorimetry experiments were performed with an ITC200 isothermal titration calorimeter from MicroCal ( GE Healthcare ) . The experiments were carried out at 20°C for AccA and 25°C for AccR . Protein concentration in the microcalorimeter cell ( 0 . 2 mL ) varied from 10 to 25 μM for AccA and 40 μM for AccR . 19 injections of 2 μl of ligand solution ( agrocinopine A , agrocinopine 3’-O-benzoate , L-arabinose-2-isopropylphosphate and L-arabinose-2-phosphate ) concentration from 150 to 240 μM for AccR and from 300 to 500 μM for AccR were performed at intervals of 180 s while stirring at 500 rpm . For agrocin 84 experiment , we inverted the protein and ligand positions: the ligand was in the cell and the protein in the syringe . This strategy was chosen to overcome the small amount of agrocin 84 . The experimental data were fitted to theoretical titration curves with software supplied by MicroCal ( ORIGIN ) . This software uses the relationship between the heat generated by each injection and ΔH ( enthalpy change in Kcal . Mol-1 ) , Ka ( the association binding constant in M-1 ) , n ( the number of binding sites ) , total protein concentration and free and total ligand concentrations [48] . A single colony of Agrobacterium tumefaciens C58 with pTiC58 derivative pTi::Gm [49] was grown overnight at 28°C in AB media supplemented with mannitol ( 2 g/L ) and gentamicin 25 μg . ml-1 . 20 ml of AB media in presence of a carbon source ( agrocinopine 3’-O-benzoate or L-arabinose-2-phosphate at a final concentration of 1 mM ) or in absence of any carbon source and supplemented with gentamicin ( 25 μg . ml-1 ) were inoculated at an initial OD600 of 0 . 03 and the OD was monitored for 48 hours . The A . tumefaciens C58 control is the pTiC58 derivative pTi::Gm [49] and the defective accF mutant ( A . tumefaciens C58 accF ) is a kind gift from S . Farrand [14] . In complementation assay , the accF wild-type gene was cloned into p6000 [50] plasmid and electroporated into the accF mutant . Rifampicin-resistant recipient strain C58 . 00 was derived from A . tumefaciens C58 cured of the At and Ti plasmids[51] . A . tumefaciens was cultivated at 28°C in AB minimal medium containing ammonium chloride ( 1 g/L ) and mannitol ( 2 g/L ) , or in Luria-Bertani modified medium ( LBm; NaCl 5 g/L ) . The antibiotics gentamycin and rifampicin were added at 25 μg/mL and 100 μg/mL , respectively . Overnight LBm cultures of recipient ( C58 . 00 ) and donor ( C58 control ) cells were mixed in an equal ratio ( 1:1 ) . 10 μl of this mix were transferred into 140 μl of AB medium supplemented with mock or 1 mM of agrocinopine A , L-arabinose-2-phosphate and glucose-2-P for static liquid cultures of 24 , 48 , 72 and 96 hours . Suspension dilutions of these cultures were spotted onto selective agar media to enumerate the different bacterial populations ( donor , recipient and Ti plasmid transconjugants ) . In parallel , to quantify OC8HSLs , aliquots of cell cultures were spotted onto TLC plates ( RP-18/UV254 , Macherey-Nagel ) and incubated with the OC8HSL-bioindicator strain A . tumefaciens NT1 ( pZLR4 ) as previously described [52] . Tested samples were compared with a calibration curve obtained with pure OC8HSL ( Sigma-Aldrich ) . Coordinates and structure factors have been deposited at the Protein Data Bank ( PDB ) under accession codes 4ZE9 ( seleniated AccA with agrocinopine ) , 4ZE8 ( free-liganded AccA ) , 4ZEB ( AccA with agrocinopine ) , 4ZEC ( AccA with agrocin 84 ) , 4ZED ( AccA with agrocinopine 3’-O-benzoate ) , 4ZEK ( AccA with L-arabinose-2-isopropylphosphate ) , 4ZEI ( AccA with L-arabinose-2-phosphate ) and 4RA1 ( AccA with D-glucose-2-phosphate ) | We succeeded in understanding how the periplasmic protein AccA from the pathogen A . tumefaciens can bind both the plant compound agrocinopine and the antibiotic agrocin 84 . Whereas agrocinopine acts as a nutrient and regulatory signal in A . tumefaciens , agrocin 84 is lethal once degraded by the enzyme AccF into a toxic moiety . We identified the pyranose-2-phosphate-like moiety shared by these two ligands as the key recognition template for AccA . We hypothesized that agrocin 84 would kill all agrobacteria possessing AccA and AccF and that AccA would be a gateway allowing the importation of any compound possessing a pyranose-2-phosphate motif . We experimentally confirmed this , using synthetic derivative compounds of agrocinopine . Furthermore , using affinity and in vivo assays , we showed that arabinose-2-phosphate , resulting from the cleavage of agrocinopine by AccF , is the effector of the transcriptional repressor AccR , that controls quorum-sensing and virulence plasmid propagation . Therefore , we have identified an original and specific key molecular motif ( pyranose-2-phosphate ) allowing a selective passage of active compounds into the pathogen cells and acting as signals once the active compounds are cleaved into this key motif . Our work opens up new opportunities to rationally design novel antibiotics . | [
"Abstract",
"Introduction",
"Results",
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"Methods"
] | [] | 2015 | A Pyranose-2-Phosphate Motif Is Responsible for Both Antibiotic Import and Quorum-Sensing Regulation in Agrobacterium tumefaciens |
Quiescent CD4+ T cells restrict human immunodeficiency virus type 1 ( HIV-1 ) infection at early steps of virus replication . Low levels of both deoxyribonucleotide triphosphates ( dNTPs ) and the biosynthetic enzymes required for their de novo synthesis provide one barrier to infection . CD4+ T cell activation induces metabolic reprogramming that reverses this block and facilitates HIV-1 replication . Here , we show that phospholipase D1 ( PLD1 ) links T cell activation signals to increased HIV-1 permissivity by triggering a c-Myc-dependent transcriptional program that coordinates glucose uptake and nucleotide biosynthesis . Decreasing PLD1 activity pharmacologically or by RNA interference diminished c-Myc-dependent expression during T cell activation at the RNA and protein levels . PLD1 inhibition of HIV-1 infection was partially rescued by adding exogenous deoxyribonucleosides that bypass the need for de novo dNTP synthesis . Moreover , the data indicate that low dNTP levels that impact HIV-1 restriction involve decreased synthesis , and not only increased catabolism of these nucleotides . These findings uncover a unique mechanism of action for PLD1 inhibitors and support their further development as part of a therapeutic combination for HIV-1 and other viral infections dependent on host nucleotide biosynthesis .
HIV-1 replication in resting CD4+ T cells is restricted post-entry , but prior to integration [1] . Several groups have reported that suboptimal dNTP pools in these metabolically quiescent cells support only inefficient reverse transcription and subsequent integration [2 , 3] . Cellular activation , or addition of exogenous deoxyribonucleosides , relieves the post-entry block to HIV-1 infection in resting CD4+ T cells [2 , 3] . Decreasing dNTP pools in activated T cells with hydroxyurea ( HU ) , a ribonucleotide reductase inhibitor , was also shown to suppress HIV-1 replication in vitro [4 , 5] , although clinical trials were limited by serious toxicities [6] . More recently , glucose metabolism has been identified to play a fundamental role in providing a carbon source for both T cell function and HIV-1 replication [7] . Notably , glucose uptake and its metabolism via the pentose phosphate pathway produces ribose intermediates that are critical for the synthesis of all nucleotides [8] . Expression of Glut1 , a glucose transporter , is also essential for HIV-1 infection of activated CD4+ T cells [9] . Finally , catabolism of dNTPs is one of the mechanisms implicated in the anti-HIV activity of sterile alpha motif—histidine-aspartic domain-containing protein 1 ( SAMHD1 ) in resting , but not activated , CD4+ T cells [1] . Recent reports have supported a prominent role of the c-Myc oncogene as a “master regulator” of transcriptional regulation of genes needed for nucleotide biosynthesis and glucose metabolism essential for both cellular and viral processes [10 , 11] . In an elegant study utilizing acute conditional deletion of c-Myc in murine T cells , Wang and colleagues demonstrated that c-Myc is essential for metabolic reprogramming and nucleotide precursor accumulation in activated T cells [11] . Consistently , c-Myc was also found to be highly induced upon T cell activation and required for cell growth and proliferation [11] . Further , pharmacologic inhibition of the Ras/ERK pathway was found to abrogate expression of c-Myc after T cell activation [11] . Inhibition of either the Ras/ERK signaling module or c-Myc activity has been reported to suppress early steps of HIV-1 replication in activated T cells [12 , 13 , 14] . However , the mechanism by which T cell activation induces c-Myc expression to initiate this cascade remains undefined . Interestingly , one pathway potentially involved in coupling T cell activation to c-Myc expression , phospholipase D ( PLD ) -mediated hydrolysis of phosphatidylcholine to choline and phosphatidic acid ( PA ) [15] , is activated whether T cells are stimulated by the mitogenic lectin phytohaemagglutinin ( PHA ) or via antibody-mediated crosslinking of the T cell receptor ( TCR ) . In humans , PLD exists as two isoforms derived from separate genes , PLD1 and PLD2 [16] . The two PLD isoforms have been implicated in a plethora of signaling pathways that influence numerous essential cellular functions , such as vesicular trafficking , exocytosis , autophagy , regulation of cellular metabolism , and tumorigenesis [16] . Furthermore , PA upregulates Ras/ERK [17] , and increases expression of c-fos and c-Myc [18] . This occurs if PA is supplied either exogenously or endogenously through PLD1 or 2 activity [18] . Since PLD1 has been shown to mediate responses downstream of the T cell receptor [19] , we tested the hypothesis that the PLD1 signaling pathway couples T cell activation to cellular processes essential for HIV-1 replication . Experiments undertaken here using pharmacologic and genetic inhibition of PLD1 provide evidence that PLD1 activity links T cell activation signals to the Ras/ERK/c-Myc signaling cascade required for metabolic reprogramming that expands dNTP pools . We also report that PLD1 inhibition blocks HIV-1 reverse transcription and replication .
Human resting CD4+ T cells were stimulated with PHA in the presence and absence of a PLD1-selective small molecule inhibitor , VU0359595 ( PLD1i ) [20] . Here , inhibition of PLD1 reduced ERK1/2 phosphorylation after T cell activation with PHA , in a similar manner as direct suppression of ERK activity with the selective MEK/ERK inhibitor U0126 ( p-ERK in Fig 1A ) . Ras/ERK signaling also promotes site-specific phosphorylation of ribosomal protein S6 ( S6 ) at Ser235/236 ( p-S6 , in Fig 1A ) [21] . Notably , resting CD4+ T cells activated with PHA in the presence of either PLD1i or U0126 had a marked reduction in S6 phosphorylation ( p-S6 , Fig 1 ) . Since PLD1 has been shown to activate the parallel pathway of mechanistic target of rapamycin ( mTOR ) / S6 kinase 1 ( S6K1 ) , perturbation of mTOR by PLD1i to decrease its activity could also contribute to a diminution in p-S6 . To test this possibility , the levels of phosphorylation of specific targets of the mTOR pathway , carbamoyl-phosphate synthetase 2 , aspartate transcarbamoylase , dihydroorotase ( CAD ) at Ser1859 ( p-CAD ) and translation repressor protein 4E-BP1at Ser65 ( p-4E-BP1 ) were also determined . PHA stimulation increased the abundance of p-S6 , p-CAD and p-4E-BP1 , while rapamycin , an allosteric mTORC1 inhibitor known to suppress c-Myc expression , blocked these phosphorylation events ( Fig 1A ) [11] . Inhibition of either ERK or PLD1 also reduced the levels of p-S6 , p-CAD , and p-4E-BP1 , suggesting that PLD1i does indeed suppress mTOR activity in stimulated CD4+ T cells . PLD1i also abrogated induction of the total level of these proteins following T cell activation ( S6 , CAD , 4E-BP1; Fig 1A ) . Since depletion of c-Myc decreased levels of total S6 , CAD , and 4E-BP1 [11 , 22] , we hypothesized that PLD1 inhibition diminished the overall levels of these proteins by decreasing c-Myc expression . Therefore , we assessed expression levels of c-Myc in cells that were stimulated with PHA in the presence of PLD1 and ERK inhibitors . Levels of the c-Myc-dependent nucleotide biosynthetic enzymes thymidylate synthase ( TS ) , large subunit of ribonucleotide reductase ( RRM1 ) , and small catalytic subunit of ribonucleotide reductase ( RRM2 ) were also studied . Blocking PLD1 , ERK , or mTOR impaired activation-dependent induction of c-Myc , TS and RRM2 ( Fig 1A ) , as well as RRM1 , proteins ( S1 Fig ) . These results phenocopied the effects of a specific c-Myc inhibitor ( 10058-F4 ) ( Myci in Fig 1A ) [23] . PLD1i and Myci also inhibited activation-induced expression of RRM2 ( Fig 1B ) and RRM1 ( S1 Fig ) in primary CD4+ T cells . To confirm genetically that PLD1 is required for optimal c-Myc expression , ERK and mTOR activity in activated CD4+ T cells , we depleted PLD1 by siRNA-mediated silencing . We observed reduced expression of c-Myc , as well as reduced phosphorylation of ERK and mTOR target 4E-BP1 with two independent PLD1 siRNAs ( Fig 1C ) . c-Myc depletion by siRNA also reduced expression of PLD1 , as well as known c-Myc dependent targets ( Fig 1C ) . These results suggest that PLD1 catalytic activity couples T cell activation signals to de novo nucleotide biosynthesis by augmenting ERK and mTOR-dependent c-Myc expression . These results are consistent with the effects of PLD1i here and those previously reported [24] . Taken together , these results suggest a positive correlation between the level of c-Myc and PLD1expression in activated CD4+ T cells consistent with a positive feedback loop ( Fig 1D ) . We hypothesize that PLD1 activity increases expression of both c-Myc and previously described c-Myc-dependent genes , and that increases in PLD1 and c-Myc amplify each other’s expression ( Fig 1D ) . Stimulation of CD4+ T cells with PHA/IL2 for only 30 min was sufficient to induce a 2-fold increase in PLD activity , relative to the unstimulated control ( 2 . 07 ± 0 . 33 vs . 1 ± 0 . 49 relative fluorescence , P = 0 . 023 ) . Importantly , this rapid burst of activity after T cell activation was reduced by PLD1i pretreatment , when compared to PHA/IL-2 stimulated cells ( 1 . 27 ± 0 . 35 , P = 0 . 044 ) , but was not significantly different when compared to unstimulated cells ( P = 0 . 51 ) . Others have documented that c-Myc expression is also rapidly induced , within 2 hours of T cell activation [11] . This may help explain why effects of PLD1i on c-Myc ( Fig 1C ) appeared more robust than those of siRNAs against PLD1 ( Fig 1A ) . Maximal effects of siRNA on protein expression are not observed before 24 hours after transfection . Therefore , the greater decrease of c-Myc observed here with PLD1i than siRNA against PLD1 is consistent with more acute inhibition of PLD1 by the inhibitor than genetic silencing [11 , 25] . c-Myc also drives the expression of key nutrient transporters needed for cell growth and proliferation after T cell activation: Glut1 ( for glucose ) ; SNAT1 and SNAT2 ( for glutamine ) ; Slc7a5/LAT1 ( for large neutral amino acids ) [11] . Since CD28 stimulation is essential for optimal surface expression of Glut1 [26] , resting CD4+ T cells were activated with anti-CD3/anti-CD28 . Cells were then surface-stained for Glut1 and the activation marker CD25 . Consistent with previous reports , inhibition of PLD1 suppressed expression of CD25 [27] ( Fig 2A ) ; however , PLD1i had little observable effect on expression of activation markers CD71 or CD98 , suggesting that PLD1i does not lead to global inhibition of T cell activation ( S2 Fig ) . PLD1i treatment prevented the upregulation of Glut1 surface expression on a sub-population of activated T cells , similarly to prior observations ( Fig 2A ) [28] . Additionally , we found that inhibition of PLD1 or c-Myc in activated CD4+ T cells reduced total cellular expression of nutrient transporters Glut1 , SNAT1 , and SNAT2 . Importantly , inhibition of upstream mediators of c-Myc expression ( ERK and mTORC1 ) also impaired expression of these nutrient transporters ( Fig 2B ) . Quantitative PCR ( qPCR ) on RNA from CD4+ T cells transfected with siRNAs targeting c-Myc or PLD1 confirmed reduced mRNA expression of RRM2 , SNAT1 , SNAT2 , and Slc7a5/LAT1 ( Fig 2C ) . Slc7a5/LAT1 is required for both mTORC1 activity and c-Myc expression in activated T cells ( Fig 2C ) [29] . Knockdown of c-Myc and PLD1 again resulted in reduced expression of the alternate RNA ( Fig 2C ) , consistent with data in Fig 1C that suggested a positive feedback loop as illustrated in Fig 1D . The observations of inhibition of PLD1 activity resulting in the impairment of coordinated expression of c-Myc , nucleotide biosynthetic genes , and nutrient transporters are consistent with PLD1 signaling being upstream of induction of c-Myc in activated T cells . Activation of T cells leads to increased synthesis of biosynthetic precursors that enable cell proliferation [11] . To this end , c-Myc coordinates increased uptake of glucose and glutamine with nucleotide biosynthesis to facilitate metabolic reprogramming of activated CD4+ T cells . Furthermore , like genetic ablation of c-Myc activity , glucose or glutamine starvation severely compromises activation-induced proliferation of T cells [11] . Since inhibition of PLD1 activity also reduces both c-Myc ( Fig 1 ) and c-Myc-dependent nutrient transporter expression ( Fig 2 ) , we investigated the effects of PLD1i on cell cycle distribution and proliferation of activated CD4+ T cells . First , CD4+ T cells were pretreated with indicated inhibitor and then stimulated for 72 h in the continued presence of inhibitor . Cell-cycle progression was determined by simultaneously staining for RNA ( Pyronin Y ) and DNA ( 7-AAD ) followed by flow cytometry . Fig 3A shows the distribution of cell-cycle phases identified by this technique . Resting CD4+ T cells remain in G0 , but increase their RNA content after stimulation and progress into G1a then G1b . Activated CD4+ T cells then initiate DNA synthesis and enter S phase followed by G2/M phase completion . We found that PLD1i-treated cells progressed to all stages of the cell-cycle; however , when compared to control cells , more PLD1i-treated cells were in G1b ( 24 . 5% versus 16 . 8% ) ( Fig 3B ) . This suggested that inhibition of PLD1 activity delayed the initiation of DNA synthesis at the G1b/S boundary . Consistent with this hypothesis , genetic ablation of RRM2 , a c-Myc target gene suppressed by PLD1i ( Fig 1A ) , was previously found to induce G1/S phase cell-cycle arrest [30] . We also directly assessed proliferation of PLD1i-treated CD4+ T cells by determining the dilution of CellTRACE Violet stain by flow cytometry 72 h after stimulation ( Fig 3C ) . PLD1i suppressed activation-induced T cell proliferation in a concentration-dependent manner , albeit less so than did the c-Myc inhibitor ( 10058-F4 ) . We also observed a delay of activation-induced proliferation by both U0126 and rapamycin ( Fig 3C ) , as previously reported [31 , 32] . Cytotoxicity was not detected with PLD1i ( S2 Fig , bottom panel ) . To directly assess dNTP pool expansion , resting CD4+ T cells were stimulated with PHA/IL2 in the presence and absence of PLD1i , and dNTP levels were quantified by mass spectrometry . PHA stimulation of CD4+ T cells resulted in 3 . 66- , 1 . 6- , and 9-fold increase in dATP , dCTP , and dTTP , respectively . Inhibition of PLD1 activity potently restricted the expansion of the dNTP pools . Increases in both dATP and dCTP were nearly completely inhibited and dTTP levels only increased 2-fold in the presence of PLD1 inhibitor ( Fig 4A–4C ) . Hydroxyurea ( HU ) treatment decreased only dATP levels ( Fig 4A ) , as previously reported [33] . Since inhibition of PLD1 activity in activated CD4+ T cells limits dNTP pool expansion , we hypothesized that HIV-1 replication would be impaired . To test this hypothesis , resting primary CD4+ T cells were pretreated with PLD1i or vehicle and then stimulated with PHA/IL2 . Cells were then infected with a single-round CXCR4-tropic envelope-pseudotyped GFP-expressing HIV-1 . CXCR4-tropic virus , rather than CCR5-tropic virus , was used to limit assessment to effects of PLD1i on post-entry steps of HIV-1 replication . This is because PLD1i-mediated decreases in mTOR activity that diminish CCR5 surface expression and HIV-1 entry could confound analyses of CCR5-tropic virus [34] . PLD1i inhibited CXCR4-tropic HIV-1 infection by nearly 75% in CD4+ T cells from four independent donors ( Fig 5A ) . The effects of PLD1i on HIV infection were rescued by adding exogenous deoxyribonucleosides ( dN ) , which bypassed the need for ribonucleotide synthesis and reduction; degree of rescue varied in cells from different donors ( Fig 5A ) . Exogenous dN had little effect on HIV-1 infection in control cells ( Fig 5A ) . Quantitative PCR was used to measure viral early reverse transcripts ( ERT ) , late reverse transcripts ( LRT ) , and 2-LTR circles at 24 hours after infection ( Fig 5B ) ; the latter is an indicator of nuclear import of full-length viral cDNA . PLD1i had little effect on the level of ERT cDNA , consistent with normal levels of HIV-1 cell entry and initiation of reverse transcription ( Fig 5B ) . Assessment of CD4 and CXCR4 surface expression on PLD1i-treated cells also confirmed lack of receptor or co-receptor down-regulation by PLD1i that could affect entry ( S4 Fig ) . PLD1i suppressed the accumulation of LRT cDNA after HIV-1 infection ( Fig 5B ) , consistent with a previous study of ERK inhibitors [13] . PLD1i also reduced the levels of 2-LTR circles more markedly than LRT cDNA . Treatment of cells with HU , known to limit HIV-1 reverse transcription and dNTP pools by inhibiting ribonucleotide reductase RRM2-dependent activity , also reduced the levels of LRT and 2-LTR circles in HIV-1 infected cells , with a similarly greater effect on 2-LTR circles ( Fig 5B ) [5] . To confirm and further define the requirement of PLD1-dependent processes for HIV-1 replication , in a separate experiment we determined the effects of PLD1i on the accumulation of viral cDNA products at 8 , 16 , and 24 h after infection ( Fig 5C ) . Consistent with PLD1i-dependent effects on dNTP pools , the kinetics of reverse transcription was markedly delayed when compared to DMSO vehicle-treated cells . Reduced levels of LRT , and 2-LTR cDNA were again detected in PLD1i-treated cells at each time point . ERT cDNA levels were decreased at 8 and 16 hours , but only minimally decreased at 24 hours . Furthermore , inhibition of the PLD1 target c-Myc recapitulated these effects at the 24 h time point ( other time points not studied with Myci ) ( Fig 5C ) . This effect on the kinetics of reverse transcription can cause a “bottle-neck” upstream of HIV-1 nuclear import and can explain , at least in part , the reduction in 2-LTR circle levels we observed in PLD1i-treated cells based on a delay in availability of completed reverse transcripts in the cytoplasm . Since PLD1i-treated cells have reduced dNTP pools and exogenous dN have been shown to increase the kinetics of reverse transcription in resting CD4+ T cells [3] , we determined the effects of dN addition on HIV-1 cDNA products in PLD1i-treated cells ( Fig 5D ) . Exogenous dN increased the levels of LRT cDNA in PLD1i-treated cells , consistent with PLD1i’s mechanism of RT inhibition being due to its effects on dNTP pool expansion . Interestingly , dN addition did not reverse PLD1i-decreased 2-LTR formation ( Fig 5D ) . This observation suggests that inhibition of PLD1 activity has an additional effect , not reversed by exogenous dN that diminishes HIV-1 cDNA nuclear import and/or 2-LTR formation in the nucleus .
This study demonstrates that PLD1 is required to couple activation of primary CD4+ T cells to the c-Myc-dependent coordinated upregulation of nutrient transporters , dNTP biosynthesis , and other biosynthetic pathways that we and others have previously reported to support HIV-1 replication [35] . Our results now also suggest a positive feedback loop between c-Myc and PLD1 not previously appreciated ( Fig 1D ) . Loss of PLD1-mediated metabolic reprogramming caused dNTP-dependent delay in the accumulation of HIV-1 late reverse transcripts and other anti-HIV effects . PLD1-dependent downstream effects are also likely to be critical for replication of cells and other viruses , since c-Myc overexpression increases accumulation of nucleotides critical for DNA replication and cell division of cancer cells and adenovirus-infected cells [36 , 37] . Addition of exogenous dN rescues the PLD1i-mediated decreases in HIV-1 replication and accumulation of late reverse transcripts ( Fig 5 ) . This is evidence that PLD1i acts against HIV-1 through a specific effect that limits dNTP pool expansion following T cell activation , rather than via an off-target effect . It is of note that siRNA against PLD1 did not block HIV-1 reverse transcription in our hands . However , the demonstration here , and elsewhere , of rapid onset of PLD activity following T cell activation ( in 30 minutes ) indicates a technical limitation in using genetic silencing to confirm the specificity of the potent and very rapid effects of PLD1i , given that siRNAs do not decrease protein expression until 24 hours after T cell activation [25] . Results also show that limited dNTP pool expansion is not the only mechanism by which PLD1i decreases HIV-1 replication . The greater decrease in 2-LTR circles than LRT ( Fig 5B–5D ) and lack of reversal of the reduction in 2-LTR circles by added dN suggest that PLD1i has additional effects on nuclear import and/or 2-LTR circle formation that are independent of its effect on dNTP pools . In line with this postulate , it has been hypothesized that slowing reverse transcription may enhance the action of host cell restriction factors [38] . Toxicity profiles have been characterized and previously reported for the PLD2 preferring inhibitors [38] , but to date detailed toxicological characterization has not been performed on the compound series showing preference for the PLD1 isoenzymes . Importantly though , compounds using the same chemical scaffold that were shown to be dual isoenzyme inhibitors , and similar to those used in this report , have been tested in human clinical trials and no overt toxicity was observed [39] . Given serious adverse events seen in clinical trials of HU-based regimens , it is important to further exclude potential toxicity of PLD1 inhibitors . Glut1 expression in CD4+ cells is essential for HIV-1 replication in target CD4+ T cells , since knockdown of Glut1 inhibited early HIV-1 replication [9] . However , the mechanism through which Glut1 knockdown inhibited HIV-1 replication has not yet been delineated . The current results , and those previously reported , suggest the hypothesis that limiting both Glut1 and glutamine transporter expression may also indirectly decrease HIV-1 replication via host cell dNTP depletion . Moreover , increased Glut1 expression is observed in CD4+ T cells in HIV-infected patients and the magnitude of increase is directly associated with the pace of T cell depletion and disease progression , thus suggesting an additional rationale for targeting this pathway for therapeutic intervention [28] . The importance of expanded dNTP pools for HIV-1 replication is well established , and recent studies of SAMHD1 have added the suggestion that enhanced catabolism of dNTPs may also contribute to anti-HIV effects [39 , 40] . Earlier reports have clearly shown that inhibiting ribonucleotide reductase activity with HU following PHA activation suppresses early steps of HIV-1 replication , established limiting host CD4+ T cell dNTP synthesis as an antiretroviral strategy [4 , 5] . However , data shown in Fig 4 demonstrates that inhibition of PLD1-dependent biosynthetic pathways has a more robust effect on dNTP biosynthesis than RRM2 inhibition , the mechanistic target of HU . Taken together with results depicted in Fig 5 , the data strongly support a mechanism where PLD-regulated nucleotide biosynthesis and other processes play a major role in supporting HIV-1 replication . We found that inhibition of PLD1 also limits CD4+ T cell activation-induced proliferation ( Fig 3C ) . Limiting proliferation of T cells may also benefit anti-HIV strategies in ways that medications targeting viral processes cannot . Abnormal T cell activation/proliferation is hypothesized to contribute to “non-AIDS” adverse outcomes , as it persists even among patients with prolonged suppression of HIV replication by current medications . Indeed , this excessive activation/proliferation may not be ablated even when antiretrovirals are started in the earliest stages of acute infection ( Utay NS , et al . Abstract 47 , CROI 2015 , presented February 24 , 2015 ) . In addition , recent reports indicate that latently infected resting memory CD4+ T cells may persist during antiretroviral therapy at least in part , because of HIV integrant-driven cellular proliferation [41 , 42] . If PLD1 inhibition is found to be safe in the future , it could be used to test if these pathogenic processes can be ameliorated . Importantly , certain PLD inhibitors also have demonstrated ability to traverse the blood brain barrier to target HIV-1 replication in myeloid-derived cells in the CNS , unlike some current anti-HIV drugs [43 , 44] . Furthermore , perturbation of nucleotide pools may be an additional factor beyond recently described effects on innate and adaptive immune responses contributing to PLD inhibitor-mediated blockade of influenza virus replication [45] . It is also provocative to speculate that short-term blockade of host cell synthesis of ribonucleotides and deoxyribonucleotides may provide a strategy for broad-spectrum activity against diverse RNA and DNA viruses . When compared to the anti-HIV activity of FDA-approved antiretrovirals , the effects of PLD1i are modest; however , this inhibitor constitutes only an early candidate in a search for more effective compounds for advancing to clinical development . Further development of PLD inhibitors holds promise as a potential therapeutic for viral infections that require host nucleotide pools for replication as well as cancers , although the roles of PA production , whether biophysical , transcriptional , or as a signaling molecule , in these therapeutic interventions have yet to be fully elucidated .
Peripheral blood mononuclear cells ( PBMCs ) were purified from healthy blood donor specimens obtained from Lifesource ( Rosemont , IL ) by Ficoll-Hypaque PREMIUM ( GE Healthcare ) gradient centrifugation . Resting CD4+ T cells were isolated from negatively-selected total CD4+ T cells ( CD4+ T Cell Isolation Kit , Miltenyi Biotec ) using CD25+ and HLA-DR+ microbeads ( Miltenyi Biotec ) and cultured in RPMI-1640 medium ( Invitrogen ) supplemented with 10% fetal bovine serum ( Hyclone ) , glutamine ( 2 mM ) and antibiotics ( 100 U/ml penicillin , 100 mg/ml streptomycin ) . Cells were activated with PHA-L ( 5 μg/ml ) ( Roche ) and IL-2 ( 20U/ml ) ( Roche ) or anti-CD3/anti-CD28 beads ( Invitrogen ) ( 1 bead/5 cells ) . For transfections , nontargeting or siRNAs targeting c-Myc ( Santa Cruz Biotech ) or PLD1 ( Santa Cruz Biotech ( PLD1 siRNA#1 ) and Dharmacon/ThermoFisher ( PLD1 siRNA#2 ) ) were nucleofected into resting CD4+ T cells using an AMAXA nucleofector apparatus . Transfection was performed with human T-cell Nucleofector kit ( LONZA ) , following the manufacturer's instructions . Briefly , 240 pmol ( ~3μg ) of siRNA was added to 1 x 107 cells resuspended in 100 μl of Nucleofector solution for each . Nucleofector program U-14 was used . Nucleofected cells were transferred into 2 ml of medium and incubated at 37°C for 24 h before medium was changed and cells resuspended in 1 ml of medium . Cells were then stimulated by adding anti-CD3/anti-CD28 beads ( 1:5; bead:cell ratio ) , cells and beads were pelleted for 5 minutes at 1200 x g in 96-U-well plates , and incubated at 37°C for 48 h before cells were harvested for analysis . Cell cycle subcompartment determination by staining with 7-aminoactinomycin D ( Invitrogen ) and pyronin Y ( Sigma ) was performed as previously described [46] . For analysis of surface markers , cells were stained at 4°C for 30 min with antibodies against Glut1 ( R&D Systems ) , CD25 ( BD Bioscience ) , CD71 ( BD Bioscience ) , and CD98 ( BD Bioscience ) in PBS containing 1% BSA . Flow cytometric data was obtained on a LSRFortessa ( Becton Dickinson ) and analyzed with FlowJo software ( TreeStar ) . To follow cell division , cells ( 107/ml ) were pulsed with CellTRACE Violet ( 5μM ) in PBS for 30 min at 37°C . Cells were then washed with PBS , resuspended in growth medium , and treated as indicated before stimulation by adding anti-CD3/anti-CD28 beads ( 1:5; bead:cell ratio ) . Cells and beads were pelleted for 5 minutes at 1200 x g in 96-U-well plates and incubated at 37°C for 72 h before cells were harvested for analysis by flow cytometry . HIV-1 stocks were prepared by transfecting 293T cells as previously described [35] . All virus stocks were treated with TURBO DNase ( Lifetechnologies ) ( 100 U/ml ) for 30 min at 37°C followed by 30 min at RT . CD4+ T cells were infected with virus ( 50 ng of p24 per 2 x 105 cells ) by spinoculation ( 1 , 200 x g , 2 h ) , followed by incubation at 37°C . Where indicated , cells were pretreated with PLD1i ( VU0359595 ) , 1mM HU , or 50 μM deoxyribonucleosides before infection . Cells were washed with ice-cold phosphate-buffered saline , harvested and whole lysates were prepared using RIPA buffer [50mM Tris-HCl pH 8 . 0 , 150mM NaCl , 1% Nonidet P-40 , 0 . 5% sodium deoxycholate , 0 . 1% SDS , and 1mM EDTA] with Protease Inhibitor Cocktail ( Roche ) . Whole cell lysates were clarified ( 10 , 000 x g for 20 min at 4°C ) and resolved by SDS-PAGE on 4–12% gradient Bis-Tris or 3–8% Tris-acetate polyacrylamide gel and transferred to a nitrocellulose membrane . The membrane was blocked with SuperBlock Blocking Buffer ( Thermo Scientific ) and incubated with indicated antibodies overnight at 4°C in SuperBlock . Blots were then incubated with anti-mouse or anti-rabbit antibody conjugated with horseradish peroxidase ( Thermo Scientific ) before detection ( SuperSignal West Dura Chemiluminescent Substrate , Thermo Scientific ) . Cells were treated as indicated and whole lysates were prepared using NP-40 lysis buffer [50mM Tris-HCl pH 7 . 5 , 150mM NaCl , 1% Nonidet P-40 , and 1mM EDTA] with Protease Inhibitor Cocktail ( Roche ) . Whole cell lysates were clarified ( 10 , 000 x g for 20 min at 4°C ) and equal cell equivalents of whole cell lysates were used to determine total PLD activity with the Amplex Red PLD Assay kit ( Lifetechnologies ) , according to the manufacturer’s protocol . Total RNA was isolated using RNeasy Plus Mini Kit ( QIAGEN ) . Briefly , cDNA for qPCR was generated from total RNA using oligo dT primers ( Promega ) and M-MLV Reverse Transcriptase ( Promega ) . Quantitative real-time PCR was performed on an iCycler ( Bio-Rad ) using iQSYBR Green ( Bio-Rad ) detection . Samples were analyzed in triplicate and normalized to actin RNA ( ΔΔCt method ) . Primer pairs were: Actin ( GGACTTCGAGCAAGAGATGG , GGACTTCGAGCAAGAGATGG ) , RRM2 ( CAAGCGATGGCATAGTAA , TGTAAGTGTCAATAAGAAGACT ) , SNAT2 ( AAGACCGCAGCCGTAGAAG , CAGCCATTAACACAGCCAGAC ) , LAT1 ( GTGCCGTCCCTCGTGTTC , GCAGAGCCAGTTGAAGAAGC ) , PLD1 ( TGTCGTGATACCACTTCTGCCA , AGCATTTCGAGCTGCTGTTGAA ) , c-Myc ( TCCAGCTTGTACCTGCAGGATCTGA , CCTCCAGCAGAAGGTGATCCAGACT ) , ASCT2 ( ATCGTGGAGATGGAGGA , AAGAGGTCCCAAAGGCAG ) , SNAT1 ( GGCAGTGGGATTTTGGGACT , TGACCAAGGAGAACAACACCC ) . Total cellular DNA was isolated from HIV-1 infected cells ( DNeasy DNA isolation kit , Qiagen ) . Real time PCR was performed using iQSYBR Green ( Bio-Rad ) detection ( Bio Rad CFX96 ) . Reaction mixtures contained 250 nM of each primer and 100 to 300 ng template DNA in a final volume of 25 μl . The sequence of the primers used for real time PCR for early reverse transcription ( ERT ) , late reverse transcription ( LRT ) , two LTR circle DNA ( 2LTR ) and glyceraldehyde 3-phosphate dehydrogenase ( GAPDH ) were: ERT ( TTA GAC CAG ATC TGC GCC TGG GAG , GGG TCT GAG GGA TCT CTA GTT ACC ) , LRT ( TGT GTG CCC GTC TGT TGT GTG A , GAG TCC TGC GTC GAG AGA TCT ) , 2LTR ( AAC TAG GGA ACC CAC TGC TTA AG , TCC ACA GAT CAA GGA TCT CTT GTC ) , GAPDH ( GAA GGT GAA GGT CGG AGT , GAA GAT GGT GAT GGG ATT TC ) . Samples were analyzed in triplicate and normalized to GAPDH ( ΔΔCt method ) . Cellular analysis of dNTPs was performed as previously reported [47 , 48] . Briefly , after indicated treatments , cells were pelleted at 1000 × g , the supernatant aspirated and pellet resuspended in 500 μl of 70% methanol/water mixture at -20°C . Suspensions agitated for 4 minutes at 4°C , and then incubated at -20°C for one hour . Internal standards were then added; for dNTPs , 4 nmols of aminoallyl-UTP; for carbamoyl aspartate , 5 nmols of citrate-d4 . Suspensions were agitated again at 4°C for one minute and centrifuged ( 18 , 000 × g , 10 minutes , 4°C ) . The supernatant was collected , transferred to a bullet tube and solvent evaporated under vacuum . Immediately prior to analysis , extracts were reconstituted in 100 μl of a 2 mM ammonium acetate , 3 mM hexylamine solution in water ( pH 9 . 2 ) . dNTPs were quantified ( adapted from [49] ) by chromatography on Acquity I-class UPLC ( Waters , Milford , MA ) with detection by MDS SCIEX 4000QTRAP hybrid triple quadrupole/linear ion trap mass spectrometer ( Applied Biosystems ) . Acquity BEH C18 column ( 2 . 1 x 50 mm , 1 . 7 μ ) with a 10 μl sample injection was used for metabolite delivery and chromatographic resolution . Solvent A consisted of 2 mM ammonium acetate and 3 mM hexylamine in water ( pH 9 . 2 ) ; solvent B was 100% acetonitrile . Flow rate of 0 . 6 ml/min was maintained with a linear gradient as follows: 0 minutes , 9% B; 2 minutes 16% B; 5 minutes , 16% B; 5 . 5 minutes , 100% B; 6 . 5 minutes , 100% B; 7 minutes , 9% B; 8 minutes , 9% B . For dNTP analysis , the mass spectrometer was operated in negative MRM mode; the following mass transitions were monitored: dATP , 490/159; dCTP , 466/159; TTP , 481/159 . dGTP could not be reliably quantified with this method since its molecular fragmentation pattern and retention time were identical to ATP . P values were calculated with Student’s t test . P values<0 . 05 were considered significant . | Replication of all human viruses depends on building blocks derived from the metabolic pathways of the infected host cell . The production of progeny virions requires synthesis of viral nucleic acids from deoxyribonucleotide triphosphates ( dNTPs ) . HIV-1 infection in resting T cells is limited , at least in part , because the levels of critical nucleotides are low . However , stimulation of T cells turns on their metabolic machinery to increase c-Myc expression and subsequent synthesis of these key components of RNA and DNA , which augments HIV-1 replication . We have identified PLD1 as a key molecular switch that couples stimulatory T cell signals to c-Myc-dependent nucleotide biosynthesis . We also found that a small molecule that inhibits PLD1 suppresses HIV-1 replication by limiting c-Myc-dependent effects of T cell activation that support efficient HIV reverse transcription . Our study provides insight into a novel way of targeting T cell activation-induced processes such as nucleotide biosynthesis that has potential to augment current therapeutics for HIV-1 . | [
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"Methods"
] | [] | 2015 | Phospholipase D1 Couples CD4+ T Cell Activation to c-Myc-Dependent Deoxyribonucleotide Pool Expansion and HIV-1 Replication |
Robust methods for identifying patterns of expression in genome-wide data are important for generating hypotheses regarding gene function . To this end , several analytic methods have been developed for detecting periodic patterns . We improve one such method , JTK_CYCLE , by explicitly calculating the null distribution such that it accounts for multiple hypothesis testing and by including non-sinusoidal reference waveforms . We term this method empirical JTK_CYCLE with asymmetry search , and we compare its performance to JTK_CYCLE with Bonferroni and Benjamini-Hochberg multiple hypothesis testing correction , as well as to five other methods: cyclohedron test , address reduction , stable persistence , ANOVA , and F24 . We find that ANOVA , F24 , and JTK_CYCLE consistently outperform the other three methods when data are limited and noisy; empirical JTK_CYCLE with asymmetry search gives the greatest sensitivity while controlling for the false discovery rate . Our analysis also provides insight into experimental design and we find that , for a fixed number of samples , better sensitivity and specificity are achieved with higher numbers of replicates than with higher sampling density . Application of the methods to detecting circadian rhythms in a metadataset of microarrays that quantify time-dependent gene expression in whole heads of Drosophila melanogaster reveals annotations that are enriched among genes with highly asymmetric waveforms . These include a wide range of oxidation reduction and metabolic genes , as well as genes with transcripts that have multiple splice forms .
Rhythmic behavior is ubiquitous across the spectrum of life [1–4] . Diverse fundamental biological functions such as cell division , energy metabolism , and sleep are periodic , and a growing body of evidence implicates temporal dysregulation as a contributing factor to depression , neurodegeneration , cardiovascular disease , and metabolic disorders in higher organisms [5–9] . Arguably the most well-studied periodic patterns are circadian rhythms: oscillatory changes in gene expression , metabolism , physiology , and behavior with approximately 24-hour ( 24 h ) periods that enable organisms to anticipate and respond to daily changes in their environment , such as nutrient accessibility , temperature , and light [10–13] . Circadian rhythms arise from innate clocks . The components of the core clock are well characterized and are strongly conserved across a wide range of species [14 , 15] . However , it remains to be determined how this clock couples to other molecular processes . Moreover , these interactions are likely to depend on tissue type and environmental conditions [2 , 7 , 11 , 16 , 17] . There is thus a need to identify molecular profiles that cycle and to characterize them as a function of conditions . The advent of high throughput methods for measuring gene expression now makes transcriptome-wide studies of this nature possible . Previous work suggests that hundreds , possibly thousands , of genes are regulated by circadian clocks [10 , 15 , 18] . Despite the decreasing cost of measuring transcript levels , profiling time series genome-wide continues to present formidable challenges: tissue-specific samples are difficult to collect , and , in contrast to imaging , measuring transcript levels is destructive in nature , requiring separate samples for each time point . As a result , gene expression time series are typically sparsely sampled ( e . g . , every 2–4 hours ( h ) in circadian studies ) , often without multiple measurements per time point , which we refer to here as “replicates” . These experimental limitations result in low signal-to-noise ratios that prevent straightforward identification of cycling gene expression . Quantitative methods are thus needed to identify rhythmic time series from minimal data with statistical confidence . These methods can aid researchers in assessing the tradeoffs between the amount of data acquired , statistical precision , and breadth of biological discovery . While a number of different methods have been proposed for identifying cycling time series [19–30] , further analysis is needed to guide selection of the best method ( s ) for a given situation and to aid in design of improved computational methods and further experiments . In this paper , we improve on the JTK_CYCLE method [26] . The original method uses a conservative estimation for its p-values and a cosine as its only reference waveform . Here , we introduce a procedure , empirical JTK_CYCLE with asymmetry search , that provides accurate empirically-calculated p-values for arbitrary waveforms . We test its performance for detecting rhythms in simulated data and a circadian metadataset [27] against other algorithms: cyclohedron test [20 , 21] , address reduction [22 , 23] , stable persistence [24 , 25] , F24 [31 , 32] , and one-way analysis of variance ( ANOVA ) [27] . The simulated data allow us to examine how performance varies with sampling density , number of replicates and/or periods , noise level , and waveform . Most methods provide accurate rhythm detection when sampling density is high and noise is low . However , we find that the choice of method significantly affects rhythm detection when data are limited and/or noisy . In particular , JTK_CYCLE , F24 , and ANOVA consistently outperform the other methods and offer distinct advantages for certain types of data . Our improved method , empirical JTK_CYCLE with asymmetry search , performs best of all for data that include asymmetric waveforms . Application of our improved method , empirical JTK_CYCLE with asymmetry , to a metadataset of whole head D . melanogaster circadian microarrays [27] reveals a strong lights-on peak in expression for genes involved in glutathione metabolism , high enrichment for genes involved in oxidation reduction , many more metabolic genes cycling than previously appreciated , and rhythmic genes with transcripts that have alternative splicings .
The methods that we consider are general and can be applied to detecting periodic behavior in any context , but we describe them here in terms of searching for circadian rhythms in gene expression for clarity . The methods that we test are cyclohedron test [20 , 21] , address reduction [22 , 23] , stable persistence [24 , 25] , F24 [31 , 32] , one-way analysis of variance ( ANOVA ) [27] , and JTK_CYCLE [26] . We describe each briefly below and note specific features; additional details can be found in the references introducing the methods . The methods can be broadly categorized as tests with and without reference waveforms . Cyclohedron test , address reduction , stable persistence , and ANOVA seek to identify patterns without specifying the waveform a priori . Address reduction , cyclohedron test , and stable persistence test for monotonicity . ANOVA compares the means of different time points with their variances to determine if differences are significant . In contrast , F24 and JTK_CYCLE compare the time series in question to a reference waveform , which is typically sinusoidal . These methods also test for a specific period . As mentioned above , here we assume a period of 24 h , but the period of the reference can be varied , in the same manner that the phase can be varied , to search for rhythms on other time scales . It is important to note that τ ( Eq . 1 ) is calculated for a specific reference time series , and thus JTK_CYCLE typically tests against a family of curves ( e . g . , to consider the possible phases of a waveform , as illustrated in Fig . 1B ) . It is thus necessary to account for multiple hypothesis testing across reference waveforms in assessing the significance of the results . Hughes et al . [26] employed the Bonferroni correction [34] in their original formulation and implementation of the method . This method is known to be conservative [34] , and we illustrate this fact here explicitly for JTK_CYCLE ( Fig . 2 ) . These considerations motivate a new procedure for estimating the significance of the results , which we describe . We end this section by discussing the comparison of time series to reference waveforms ( Fig . 1C ) other than the cosine waveform that was used originally . Together , our improvements allow for the JTK_CYCLE method to include additional reference waveforms in its rhythm detection without compromising sensitivity and specificity .
To assess the performance of our empirical form of JTK_CYCLE against the original form as well as other methods , we utilize two simulated datasets . We employ the first simulated dataset to understand the sensitivity of each method to different shapes of time series . It comprises four types of waveforms: sine , ramp ( a triangle with maximum asymmetry ) , impulse , and step , as well as an equal number of time series consisting of Gaussian noise . We compare all the precision-recall curves for all the methods on these data via the area under the receiver operating characteristic ( AUROC ) , a measure of the sensitivity and specificity of the rhythm detection methods that does not depend on the proportions of positives and negatives in the dataset . The second simulated dataset contains 10% rhythmic time series of triangle waveform with uniformly distributed phases and asymmetries and 90% time series consisting solely of Gaussian noise . We use it to further assess the importance of considering asymmetric waveforms , and we explore how multiple hypothesis correction impacts the results when the true positives represent a relatively small fraction of the simulated time series , as we expect to be the case in genome-wide studies . Keegan et al . [27] previously assembled a metadataset comprised of data from four DNA microarray studies of Drosophila melanogaster under light-dark ( LD ) conditions ( from Ceriani [40] , Claridge-Chang [18] , Lin [41] , and Ueda [42] ) . We do not include a fifth dataset from that study [15] , because it was limited to dark-dark ( DD ) conditions . Here , we discuss issues that arise from merging data from different laboratories and use the resulting metadataset to test the methods . We find that empirical JTK_CYCLE with asymmetry search identifies a larger number of rhythmic genes and , in turn , enriched annotations among those genes , such as oxidation reduction , glutathione metabolism , and alternative splicing .
In this paper , we compare methods for detecting rhythmic time series in genome-wide expression data . With regard to experimental design , we find that increasing the number of replicates is more important than increasing the sampling density for achieving greater sensitivity . A key aspect of our study is that we improve the estimation of p-values in JTK_CYCLE . This enables control of the false discovery rate and testing waveforms beyond sinusoidal ones . For both simulated data and a circadian metadataset [27] the resulting empirical JTK_CYCLE with asymmetry search exhibits the greatest sensitivity among the methods that we evaluated . The annotation terms that are enriched among the genes that we identify as cycling include rhythm/light/circadian , glutathione/drug metabolism , oxidation-reduction , iron metabolism , and alternative splicing . These findings are consistent with known circadian biology but also suggest new investigations . | Much biomedical research focuses on how the expression of genes changes over time . Many genes’ activities vary periodically . For example , circadian rhythms repeat daily with the light-dark cycle . Understanding how such rhythms couple to biological processes requires statistical methods that can identify cycling time series in typical genome-wide data . In this paper , we improve on a method used to identify cycling time series by better estimating the statistical significance of periodic patterns and , in turn , by searching for a wider range of patterns than traditionally investigated . We apply these methods to a compilation of data on gene expression in fruit flies , an important model organism . We find that our method allows us to discover rhythmic biological activities that the other methods tested are unable to reveal . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Improved Statistical Methods Enable Greater Sensitivity in Rhythm Detection for Genome-Wide Data |
Arenaviruses are one of the largest families of human hemorrhagic fever viruses and are known to infect both mammals and snakes . Arenaviruses package a large ( L ) and small ( S ) genome segment in their virions . For segmented RNA viruses like these , novel genotypes can be generated through mutation , recombination , and reassortment . Although it is believed that an ancient recombination event led to the emergence of a new lineage of mammalian arenaviruses , neither recombination nor reassortment has been definitively documented in natural arenavirus infections . Here , we used metagenomic sequencing to survey the viral diversity present in captive arenavirus-infected snakes . From 48 infected animals , we determined the complete or near complete sequence of 210 genome segments that grouped into 23 L and 11 S genotypes . The majority of snakes were multiply infected , with up to 4 distinct S and 11 distinct L segment genotypes in individual animals . This S/L imbalance was typical: in all cases intrahost L segment genotypes outnumbered S genotypes , and a particular S segment genotype dominated in individual animals and at a population level . We corroborated sequencing results by qRT-PCR and virus isolation , and isolates replicated as ensembles in culture . Numerous instances of recombination and reassortment were detected , including recombinant segments with unusual organizations featuring 2 intergenic regions and superfluous content , which were capable of stable replication and transmission despite their atypical structures . Overall , this represents intrahost diversity of an extent and form that goes well beyond what has been observed for arenaviruses or for viruses in general . This diversity can be plausibly attributed to the captive intermingling of sub-clinically infected wild-caught snakes . Thus , beyond providing a unique opportunity to study arenavirus evolution and adaptation , these findings allow the investigation of unintended anthropogenic impacts on viral ecology , diversity , and disease potential .
Several mechanisms generate viral genetic diversity [1–3] . All work by producing populations of viral genomes , a small fraction of which may exhibit an adaptive advantage in a new host species , different tissue , or in the face of drug or immune pressure . Replication of RNA viral genomes by error-prone polymerases results in relatively high mutation frequencies , and RNA viruses replicate as collections of closely related variant genotypes [2 , 4–7] . Viral genomes can shed content or acquire new loci from their hosts by horizontal gene transfer . In coinfected cells , recombination between different viral strains or species can produce chimeric progeny [8–11] . And in cells coinfected by segmented viruses , reassortment generates virions containing shuffled mixtures of segments from the parental genotypes [11 , 12] . Understanding basic mechanisms of viral adaptation is essential in order to better combat , prevent , and predict viral diseases . For example , the ability of pandemic influenza virus strains to efficiently replicate in and be transmitted between humans frequently results from de novo mutation and reassortment [13] . The continuous emergence of drug-resistant genotypes is a major hindrance to the effective treatment of human immunodeficiency virus-1 and other pathogens [14 , 15] . And , recombination between individually attenuated strains present in the oral poliovirus vaccine results in neuropathic progeny strains , and complicates eradication efforts [16] . Viruses in the family Arenaviridae have bi-segmented single-stranded RNA genomes with a characteristic organization and gene repertoire [17–21] . The larger genome segment ( L ) is about 7 kb in length and encodes the viral RNA-dependent RNA polymerase ( L ) and a small zinc-binding RING domain protein ( Z ) . The smaller segment ( S ) is about half as long and encodes the glycoprotein precursor ( GPC ) and nucleoprotein ( NP ) . On each segment , the two viral genes are in opposite coding orientations and are separated by an intergenic region ( IGR ) that is predicted to form stable hairpin structures . Two major lineages of arenaviruses have been described: those that primarily infect rodents and those that infect snakes . The rodent arenaviruses ( proposed genus Mammarenavirus ) typically establish chronic mild infections in their natural hosts but can be transmitted to humans and other mammals [22] . Severe disease such as Lassa hemorrhagic fever can result from these zoonotic infections . The snake arenaviruses ( proposed genus Reptarenavirus ) were first identified in US cases of inclusion body disease , a progressive and sometimes fatal disease best described in members of the Boidae and Pythonidae families ( boas and pythons ) [23 , 24] . The identification and study of snake arenaviruses in captive snakes in Europe corroborated and extended this finding [25–27] . One major difference between the snake and mammalian arenaviruses is the provenance of their GPC genes , with the snake virus gene being more closely related to the glycoprotein gene of filoviruses and some avian retroviruses [23 , 28] . Several mechanisms of arenavirus evolution have been described [17 , 29–31] . Like all RNA viruses , arenavirus genome replication is relatively error prone , and arenaviruses replicate as collections of related variants in vivo [32] . Recombination is thought to have given rise to the ancestral S segment of a clade of the New World rodent arenaviruses [33–35] . Recombination and reassortment between co-infecting arenaviruses has been observed in the laboratory [36–38] . And , it has been suggested that arenaviruses detected in snakes in Europe might have undergone recombination [39 , 40] . However , arenavirus recombination and reassortment have not been confirmed in natural infections involving extant species . In this study we document and investigate viral genetic complexity of an unanticipated extent and form in naturally infected captive snakes . We determined the complete or near complete sequences of 210 viral genome segments using metagenomic sequencing . Sequencing results were corroborated and extended by discriminating quantitative reverse transcriptase PCR ( qRT-PCR ) and by tissue culture isolation experiments . We detected widespread recombination and reassortment . We also observed an unbalanced accumulation of multiple distinct viral genotypes in individual infections . These findings provide an opportunity to study basic mechanisms of virus evolution and fitness through the identification of genetic determinants underpinning their action .
In order to further characterize the genetic diversity of the snake arenaviruses , we gathered 123 frozen case and control tissue samples from around the U . S . A . that were collected between 1997 and 2014 ( S1 Table ) . We screened these samples for snake arenavirus RNA using qRT-PCR with degenerate primers targeting the glycoprotein gene . A total of 56 samples tested positive by qRT-PCR for viral RNA . Clinical data of varying detail was available for samples . Histopathology was available for 58 of the 123 samples and detection of viral RNA was well correlated with histopathology-based IBD diagnosis ( Table 1 ) . However , many infected snakes displayed no overt clinical signs ( S1 Table ) . Thus , detection of viral RNA was correlated with detection of inclusions in tissue sections , but not with obvious clinical measures in a straightforward fashion , consistent with previous reports [23 , 25 , 26 , 41] . We performed metagenomic sequencing to determine complete viral genome sequences . Samples from 98 animals , including the PCR-positive samples , were sequenced . Samples from 48 of the arenavirus-positive snakes were sequenced to a depth sufficient for assembly of complete or near complete viral genome segment sequences . In many cases , the assemblies included predicted terminal sequences ( S1 Fig , [23 , 42] ) . In total , just over 1 million reads contributed to the assembly of 210 genome segment sequences ( 148 L and 62 S sequences ) totaling 1 . 24 Mb . Genome segment assemblies were validated by re-mapping paired-end reads [43] . Assemblies were well supported , with 99-fold median coverage ( S1 Fig ) . Coverage levels were generally lowest in the intergenic regions ( IGR ) , likely as a result of the predicted hairpin-forming sequences in these regions ( S1 Fig ) . In cases where coverage levels in IGRs fell below 2 reads , PCR was used to confirm assembly continuity . Genome segments with less than 80% global pairwise nucleotide identity were classified into distinct genotypes ( Figs 1 and 2 and S2 Fig ) [44] . A total of 11 S and 23 L genotypes were delineated by this criterion , and were designated S1-S11 and L1-L23 . Within genotypes , L segment sequences shared a mean value of 96% pairwise nucleotide identity , and between genotypes , sequences shared 65% identity ( S2A Fig ) . For S segments , these values were 96% and 64% ( S2B Fig ) . Multiple alignments of the four coding sequences were used to create Bayesian phylogenies to visualize the inferred evolutionary relationship of these genotypes ( Figs 1–3 and S3 Fig ) . In 21 of 48 infected snakes , viral genotypes consisted of a single S and a single L segment genotype ( Fig 4 and S3 Table , snakes #1–21 ) . For example , snakes #1–3 , the annulated tree boas from the California Academy of Sciences described in an earlier report , harbored S genotype 1 and L genotype 1 . This S1/L1 genotype corresponds to the virus we referred to as “CAS virus” ( CASV; [23] ) . In snakes #4–6 , segments of genotype S2 and L2 were detected , a genotype corresponding to “Golden Gate virus” [23] . Numerous reassortant genotypes were evident among the singly infected snakes ( Fig 4 ) . For example , the L2 segments present in snakes #7 and 8 were nearly identical at 98 . 6% pairwise identity , but their S segments shared only 73 . 5% pairwise identity ( genotypes S3 and S6 ) , indicating a more distant common ancestor . Similarly , the S6 segments in snakes #15 and 16 were 97 . 8% identical but the L3 and L21 segments in these snakes were only 62 . 2% identical . It was not possible in most cases to determine which S/L pairs were ancestral , nor when precisely reassortment had occurred , but it was clear that reassortment had produced these permuted pairings . However , in the majority of animals ( 27 of 48 snakes ) , we observed viral genotypes that were substantially more complex than single S/L pairs . These snakes harbored multiple S and L genome segment sequences ( Fig 4; snakes #22–48 ) . The sets of viral genotypes ranged from that which would be expected for a straightforward co-infection by 2 virus strains in snake #45 , to more complex combinations such as that in snake #33 , which contained the sequences of 1 S and 10 distinct L segments . The animal with the most viral genotypes was snake #34 , which contained 15 genotypes ( 4 S and 11 L ) that could combine to produce virions with 44 unique S/L pairs ( assuming coinfection of individual cells ) . The distinct L and S segment sequences within individual snakes shared on average only 65% and 70% pairwise nucleotide identity respectively , a level of variation not consistent with sequencing error or intrahost diversification . Snakes that were housed together often shared similar combinations of genotypes , and in these cases the sequences of these segments were closely related ( Figs 1 and 2 and Fig 4 ) . The accumulation of viral genotypes within individual animals was not balanced . In all cases there were more L than S segment genotypes ( Fig 4 and S4 Fig ) . On average , there were more than twice as many L segment genotypes as S genotypes per animal ( mean values of 4 . 7 L and 2 . 4 S genotypes per multiply infected animal; S4 Fig ) . In fact , 18 of 27 animals with multiple L sequences harbored only a single S genotype . The S6 genotype was dominant in individual animals and at a population level ( Fig 1 and Fig 4 ) . This genotype was first detected in a snake from Collierville , TN , and a partial S6 sequence was reported previously [23] . S segments of this genotype were detected in 37 of 48 snakes ( 77% ) . These sequences shared 96% average global pairwise nucleotide identity . Remarkably , the S6 segment genotype was found in combination with 21 of the 23 L segment genotypes described in this study . In addition to reassortant genotypes , we also identified 6 recombinant S segments and 8 recombinant L segments ( Figs 1 and 2 , Figs 5 and 6 , S1 Fig , and S2 Table ) . We used the RDP4 software to detect and statistically evaluate support for recombination events ( Table 2; [45] ) . Recombination events were well supported by RDP4 analysis and by read coverage levels over recombination junctions ( S1 Fig and Table 2 ) . We also confirmed segment continuity by PCR amplification across junctions . While this analysis provided clear evidence of recombination , it was not always possible to determine which genotypes were parental and which were recombinant . However , it appeared that many of the recombinant segments coexisted in snakes with one of their parental genotypes . For example , in snake #35 , 2 S segment sequences were evident . One of these was a canonical S6 genotype . The other segment , designated S8 , shared 97% average pairwise nucleotide identity with the S6 segment’s GPC gene , but only 66% identity in the NP gene ( Fig 5A ) . The S8 NP appeared to derive from a segment similar to the S11 NP found in snake #34 . Similar patterns were observed for S segments in snakes #22 , 26 , 27 , 34 , and 35 , and for L segments in snakes #22 , 23 , 28 , 30 , 31 , 33 , 46 , 47 , and 48 ( Figs 1 and 2 and Fig 5B ) . These may be situations where the parental and progeny genotypes persistently replicated together following a recombination event . Alternatively , the genotypes could have been acquired in independent transmission events . Some recombination events resulted in unusual genome organizations . For example , the L10 genotype found in snakes #22 and #23 consisted of a full Z coding sequence and a partial L coding sequence from one parental segment concatenated to a partial Z and full L from a second segment ( Fig 6 ) . This segment was predicted to contain 2 intergenic hairpin-containing regions ( 2xIGR ) separating the 2 L/Z pairs . Similar double-IGR segments and partial CDS were observed in recombinant S segments as well ( Fig 6 ) . An offset template switching event during genome replication may have generated these 2xIGR segments ( S5 Fig; [10] ) . We used several independent approaches to corroborate the original metagenomic sequencing . First , we completely re-sequenced 41 samples , using independent library preparations , and derived essentially identical results . Second , we developed a panel of PCR primer pairs that discriminated between distinct viral genotypes , and performed qRT-PCR on a subset of samples and genotypes . In all cases , qPCR-based genotyping mirrored sequencing results ( S6 Fig ) . Third , we used tissue culture isolation as another means of determining viral genotype and to confirm that sequences corresponded to infectious virus ( see below ) . The introduction of an already infected snake ( #35 ) into the proximity of an uninfected snake ( #36 ) in a private collection enabled us to monitor viral transmission ( Fig 7 ) . A 2011 blood sample from snake #36 tested negative for snake arenavirus RNA by qRT-PCR and deep sequencing . Snake #36 was then not exposed to other snakes until September 2012 , when its owners obtained a second snake , #35 . Snake #35 arrived with stomatitis and was anorexic . Nevertheless , after a 4-week quarantine , snakes #35 and #36 were placed in the same enclosure . Snake #35 continued to refuse to feed and died two weeks later . We obtained the body of snake #35 and a blood sample from snake #36 taken in November 2012 , and an additional blood sample from snake #36 from January 2013 . We determined that multiple genotypes were transmitted from snake #35 to snake #36 during their cohabitation ( Fig 7 ) . Snake #35’s viral genotype consisted of 2 S and 6 L genotypes ( S6 , 8 / L3 , 8 , 11 , 17 , 18 , 21; Fig 7B ) . The November 2012 snake #36 blood sample was arenavirus positive , but the only genotypes detected by sequencing were S6 and L3 . The January 2013 snake #36 sample was still positive , but now L genotypes 11 , 17 , and 21 were also detectable in the blood . Analysis of the viral sequences recovered from the two snakes revealed that they were closely related ( 98 . 5–100% identity ) . This data supports the transmissibility of compound unbalanced snake arenavirus genotypes in the context of cohabitation . We performed tissue culture isolation and passaging experiments to investigate whether compound viral genotypes were competent to initiate productive infections . We applied homogenates from samples to cultures of boa constrictor-derived JK cells and monitored levels of virus RNA by qRT-PCR using genotype-discriminating primers . In all cases , we detected replication of all of the viral genotypes identified by our metagenomic sequencing ( Fig 8 ) . For example , snake #38 contained viral sequences of genotype S6/L3 , 18 . When a liver homogenate from this snake was applied to a JK culture , replication of all 3 of these segments was detected ( Fig 8A ) . Similarly , when a homogenate from snake #47 was used as inoculum , replication of all 6 expected viral genotypes was observed ( S6 and L3 , 4 , 5 , 6 , 7; Fig 8B ) . The distinct L segments exhibited approximately equal replication efficiencies in these experiments , and the populations could be passaged to uninfected cell cultures . Thus , sequences of multiple viral genotypes corresponded to replication competent virus , and multiple viral genome segments replicated as stable ensembles in culture . We also investigated whether an L segment with an unusual 2x IGR organization was competent for replication . Snake #47 L4 contains a partial Z region and 2 predicted IGR hairpins ( Fig 6 ) . To track this segment during infection , we performed qRT-PCR using 2 primer pairs: one pair that targeted the L gene of this segment and one pair that spanned the recombination junction ( Fig 8C ) . Throughout the experiment , near equivalent amounts of template were detected using these 2 primer pairs , suggesting that most copies of this segment maintained their unusual structure . We performed endpoint dilution experiments to determine the genotypes of individual virus particles . We prepared dilution series from liver homogenates from snakes #37 and #47 and inoculated JK cells in 96 well plates . After 7–10 days , we transferred supernatants to new plates and stained cells with anti-NP Ab to determine wells positive for the presence of virus . Positive wells were then genotyped using discriminating qRT-PCR . In most cases , RNA from a single S and a single L genotype were detected in individual wells infected with the most dilute inocula ( Fig 9 ) . In 3 of 14 ( 21% ) wells at these highest dilutions , more than one L genotype was detected . This could be the result of stochastic co-infection , clumped virus particles , or virus particles packaging more than one L segment . These results were consistent with the model that most virus particles packaged a single L segment , although we could not exclude the possibility that a minority of particles may package additional segments . Intrahost variation for individual genotypes was also evident . For most genome segments , multiple sites with minor allele variants were detected ( S7 Fig ) . The frequency of variants in most of these cases was less than 10 variant sites per kb ( i . e . , ≤1%; S7 Fig ) . In several cases , a higher frequency of variant sites was observed , for example the S6 segment of snake #41 , which averaged 17 variant sites per kb ( 1 . 7% variants sites ) . This could have resulted from a greater degree of intrahost variation and divergence or from infection by viruses with closely related genotypes whose sequences were too similar to separate using short read assembly .
In this study , we surveyed the genetic diversity of arenaviruses infecting captive snakes in the USA . We used metagenomic sequencing and de novo assembly to determine genome sequences of viruses infecting 48 snakes . We found that most snakes were multiply infected by unbalanced ensembles of S and L segment genotypes . In total , we assembled 148 L and 62 S segment sequences that grouped into 23 L and 11 S genotypes . This expands the known diversity within this group of viruses by several fold . The high level of multiple infection has apparently given rise to numerous recombinant and reassortant genotypes , altogether compromising hundreds of unique viral combinations . We also discovered recombinant genotypes with non-canonical genome organizations , including those harboring apparently superfluous content . Metagenomic sequencing results were corroborated by PCR-based approaches , and extended by tissue culture isolation experiments . These findings highlight the utility of performing unbiased whole genome sequencing to determine pathogen genotypes . Indeed , our initial PCR-based screening correctly identified infected animals , but completely failed to uncover the genetic complexity present in the infections . Although natural infection by multiple arenaviruses has not been previously documented , this phenomenon has been reported for other viruses . For example , infection involving up to 3 influenza viruses has been documented in humans and wild birds [46 , 47] . And , up to 7 or 9 distinct genotypes of torque teno virus or papillomavirus have been identified in individual human samples [48–50] . In plants , a virus isolate from citrus trees persistently infected by citrus tristeza virus was found to include several genotypes [51] . Shared characteristics of host-pathogen interaction may enable such highly multiple infections . These include persistent , sub-clinical viral replication , the absence of barriers to superinfection , the lack of an immune response capable of clearing infection , and a high prevalence of infection . Although we detected many instances of snake tissues containing multiple viral genotypes , our results do not prove that individual cells in these animals were multiply infected . However , the detection of recombinant and reassortant genotypes suggests that at least in some cases cells are multiply infected . Although multiple infection per se is not unprecedented , several aspects of these findings are . One is the apparent disconnect between the dynamics of the two viral genome segments , both in individual animals and at a population level . In individual animals , the accumulation of S and L segments was unbalanced: in all multiply infected animals , there were more L than S segments . In the most extreme case ( snake #33 ) , a single S genotype was paired with an ensemble of 10 L genotypes . It is possible that within animals the S and L segments inhabit different fitness landscapes . Another , not mutually exclusive , possibility is that differential replication kinetics or packaging efficiencies of the two segments may underlie the observed imbalance . Additional experiments in vitro and in animals will clarify this issue . The population level dominance of the S6 genotype was also unexpected and is worthy of additional investigation . Genotype S6 segments were present in 37/48 infections ( 77% ) and in 29 of these , no other S segment was detected . One possible explanation is that the S6 genotype replicates more efficiently within animals , or is more efficiently packaged and transmitted than other competing S segments . Alternatively , the high frequency of this genotype may be a stochastic effect , or may be proportional to viral genotypes in natural virus populations , from which these viruses in captive snakes presumably originate . Alternatively , it is possible that the 23 L genotypes observed here were originally paired with 23 S genotypes in free-ranging hosts . If this were the case , then 12 S genotypes are unaccounted for . Testing of wild-caught snakes could reveal the “missing” S genotypes and original S-L pairings and would reveal whether the S6 genotype has indeed risen to dominance in the context of captive animals . Whether a similar degree of multiple infection is possible in mammalian arenaviruses is an open question , and one that may be relevant to the possible emergence of new mammalian arenavirus strains with pathogenic potential . It may be that there are larger species barriers for mammalian arenaviruses than there are for snake arenaviruses . Another possibility is that an ecological situation analogous to captive snake breeding has never been created for rodents . Alternatively , characteristics of the mammalian arenavirus host-pathogen interaction may prevent multiple infection . Indeed , superinfection exclusion has been documented in mammalian arenavirus tissue culture experiments [52–57] . And , cross-protection between mammalian arenaviruses has been documented in vivo [58–60] . Assuming that superinfection accounts for at least some of the genotype accumulation observed here , then no such mechanisms are operating in these snakes . Laboratory experiments with mammalian arenaviruses and other segmented viruses could test the generality of this phenomenon . The discovery of “2xIGR” genome segment configurations was also unanticipated . It would be reasonable to predict that these segments would exhibit decreased fitness or be unstable during replication , given that they carry superfluous content . However , two lines of evidence suggested that these 2xIGR segments are capable of transmission and are stable over multiple rounds of replication . First , several of these segments were detected in co-housed snakes ( L10 in snakes #22 and 23; L22 in #28 , 30 , and 31; L4 in #46–48 ) . Presumably , each of these segments was initially generated via recombination in a single infected snake and then transmitted . Second , in tissue culture these segments replicated stably and could be passaged and isolated ( Figs 8C and 9B ) . More extensive passage experiments in animals and culture will reveal whether maintenance of the 2xIGR configuration is disfavored over the long term . These novel segment configurations also raise the possibility for the creation of payload-containing arenavirus genome segments . For example , the L10 segment found in snakes #22 and #23 contain 2 intergenic regions and 559 bases of extraneous incomplete coding sequence . If a suitable reverse genetic system were developed , this regions could be replaced with an internal ribosomal entry site and the 516 base NanoLuc luciferase gene or other payloads [61] . Such a tagged virus could be used for example in in vivo pathogenesis studies as an alternative to tri-segmented recombinant arenaviruses [62] . The nomenclature and taxonomy of the snake arenaviruses will likely have to be reconsidered in light of these findings [22] . We propose a nomenclature like that used for influenza A virus ( IAV ) subtypes , where new S and L genotypes are simply enumerated [63] . We would also propose following the taxonomic scheme for IAV subtypes , which belong to a single species , Influenza A virus . In this case , snake arenavirus genotypes could be grouped into one or possibly more species . Recombinant genotypes were not limited to those described in this study . Discordance between GPC- and NP-based phylogenies including sequences from viruses detected in snakes in Europe suggested possible S-segment recombination [39 , 40] . The increased phylogenetic resolution enabled by this study confirmed the recombinant nature of the S and L segments of Boa3 AV NL and the L segment of UHV-1 [25 , 26] ( Table 2 , Figs 1 and 2 ) . It is possible that snake importation and husbandry practices have inadvertently created an ecological context that has enabled this phenomenon . Boa constrictors with different colorations ( “color morphs” ) are highly valued by collectors and breeders . Such colorations arise in nature as local adaptations and wild-caught snakes are commonly imported for breeding purposes . An estimated 98 , 500 boa constrictors per year were imported into the USA alone between 2005–2010 [64] . Mammalian arenavirus species have co-evolved with their distinct , geographically isolated rodent hosts , and it may be that snake arenavirus strains have co-evolved similarly in the wild . It is plausible that apparently healthy snakes persistently infected by various arenavirus species have been imported and intermingled in high-density breeding operations . This possible anthropogenic disruption of pathogen ecology is reminiscent of the influenza virus diversity generated in live animal markets [65] . Sampling of viral diversity in free-ranging snake populations are needed to clarify the impact of human activities on the evolution of these viruses and to further assess the disease potential of the resulting recombinant and reassortant genotypes . In the absence of barriers to superinfection , an incalculable number of novel viral genotype configurations , made even more numerous by frequent intra-segment recombination and an error-prone polymerase , could rapidly evolve and accumulate within individual animals and in breeding facilities . In theory , this situation could be exacerbated by the introduction of mammalian arenavirus-infected rodents as feedstock [66] , although it is unknown whether recombinant or reassortant mammalian/reptile arenaviruses are possible or viable . Regardless , further investigation of high-density reptile breeding and feed rodent facilities should be considered .
Samples were collected between 1997 and 2014 from California , Washington , Arkansas , Tennessee , Louisiana , Georgia , and Florida . Veterinarians in private practice or at university teaching hospitals collected samples . Samples were submitted to the University of Florida or the University of California San Francisco for further processing and storage . Blood samples were collected by cardiac or tail vein puncture and frozen until further processing . Tissue samples were collected during necropsy and frozen until further analysis or placed in formalin for histopathology . For histopathology , samples were preserved in 10% formalin , paraffin embedded , sectioned , and stained with hematoxylin and eosin . Board-certified pathologists blinded to infection status of samples examined H&E stained sections . RNA extracted from tissues ( 500 ng ) or tissue culture supernatant was denatured for 5 min at 65°C then cooled on ice and added to 10 μl RT reactions containing 100 pmoles random hexamer oligonucleotide ( MDS-286 ) , 1× reaction buffer , 5 mM dithiothreitol , 1 . 25 mM ( each ) deoxynucleoside triphosphates ( dNTPs ) , and 100 U SuperScript III ( Life Technologies ) . Reaction mixtures were incubated for 5 min at 25°C then for 60 min at 42°C and then for 15 min at 70°C . cDNA was diluted 1:10 in 10mM Tris pH8 , 0 . 1 mM EDTA . qPCR reactions contained 5 μl diluted cDNA , 0 . 5 μM each primer , 10 mM Tris pH 8 . 8 , 50 mM KCl , 1 . 5 mM MgCl2 , 0 . 2 mM each dNTP , 5% glycerol , 0 . 08% NP-40 , 0 . 05% Tween-20 , 0 . 15 μl Taq DNA polymerase , and 1x Sybr Green ( Life Technologies ) in each 15 μl reaction . Primer sequences are listed in S2 Table . Degenerate primers targeting the glycoprotein gene ( MDS-400 and -402 ) were used to screen for infection . qRT-PCR to screen for individual S and L genotypes was performed using panels of primers that were designed to discriminate between the different sequences . Primer pair efficiencies were determined using template dilution series [67 , 68] . RNA was extracted from tissues as previously described [23] . Sequencing libraries were prepared as previously described [69] . Paired-end 2x135 bp sequencing was performed on an Illumina HiSeq 2500 in the Center for Advanced Technology at UCSF . , producing an average of 2 . 0x106 read pairs per sample . A stepwise pipeline was used to process sequencing data . First , data was demultiplexed . Then , 5 bases were trimmed of the 5′ end of reads and 1 base off the 3′ end . Next , low quality read pairs with any 10 base window with an average quality score below 30 were discarded . Then , reads sharing >98% global nucleotide identity ( likely PCR duplicates ) were collapsed using the cd-hit-est software version 4 . 6 [44] . Adapter sequences were trimmed from the ends of reads . Then , host-derived sequences were filtered as previously described [23] . Viral genome sequences were assembled from the remaining reads An iterative strategy was used to assemble genome segment sequences . First , from each dataset , post-filtering reads were aligned using Bowtie2 to a database composed of all already-described snake arenavirus genome segments sequences [43] . Alignment parameters were set stringently ( minimum alignment score of 150 in local mode alignment ) so that only reads closely matching already described sequences aligned . Alignments were converted into BAM format using SAMTools software and inspected in Geneious software [70 , 71] . Sites differing from reference sequences were corrected to generate new draft genome sequences . Remaining virus-derived reads ( determined by BLASTx , as described below ) that didn’t align to an existing virus sequence were used to seed assemblies using the PRICE targeted de novo assembler [72] . PRICE contigs were added to the set of genome segment sequences and the process was reiterated until all reads were accounted for as described below . Once a complete set of genome segment sequences was assembled , we used Bowtie2 to remap all reads from each dataset to the set of genome segment sequences derived from that dataset . These alignments were manually inspected and were used to generate coverage metrics . For each aligned base , coverage was only counted if the preceding and succeeding 3 bases also aligned . This “continuous coverage” metric is more conservative than a simple coverage metric and was implemented to identify possible incorrect assemblies . BAM files from these alignments , as well as FASTQ files of raw sequencing data for all snakes , have been deposited in the NCBI Short Read Archive ( SRA; accession SRP057522 ) . Genome segment sequences have been deposited in GenBank with accessions KP071471-KP071680 . We employed the following analysis strategy to confirm that we were accounting for the full viral genetic complexity in our datasets , i . e . that we were not overlooking any viral genome segments . We used the BLASTx tool to align translated reads from each dataset to a database containing all available snake arenavirus protein sequences , including the ones from this study . Because BLASTx alignments are based on protein sequence similarity , it is possible to use them to detect sequences with relatively distant homology . Thus , the number of reads with BLASTx alignments to arenavirus protein sequences ( with E-value ≤ 10–8 ) determined a minimum expected number of virus sequences in each dataset . Then , we used the Bowtie2 aligner to stringently map reads from each dataset to the coding sequences of the assemblies generated from that dataset as described above . This allowed us to confirm that the assemblies accounted for all of the arenavirus-derived sequences in each dataset . To calculate the fractional abundance of individual genotypes , we divided the number of read pairs mapping to that genotype by the total number of arenavirus-mapping reads from that dataset . We performed phylogenetic analyses to infer evolutionary relationships between viral genotypes . We created multiple sequence alignments of the coding sequences for each of the 4 viral gene coding sequences , using MAFFT version v7 . 017 with default parameters [73] . These alignments were trimmed using the Gblocks software version 0 . 91b using default parameters except allowing up to half gaps in columns ( parameters:-t = d-b5 = h [74] ) . We used these trimmed alignments and the JModelTest software v2 . 1 . 6 to identify a best-fit nucleotide substitution model ( GTR; [75 , 76] ) . We ran this software with parameters:-s 11-f-i-g 4-AIC-BIC-AICc-DT-p-a-w . We used MrBayes 3 . 2 . 2 to create Bayesian phylogenies from these alignments , using commands lset nst = 6 rates = propinv and mcmc ngen = 2000000 [77] . These phylogenies were visualized using FigTree software ( http://tree . bio . ed . ac . uk/software/figtree/ ) . To create phylogenies including representative snake and mammalian arenavirus sequences , we first downloaded all sequences from the NCBI nucleotide database w/ query: “txid11617[Organism:exp]” , i . e . all sequences annotated as being of arenavirus origin . We removed sequences that were not complete or not coding-complete . We extracted NP and L CDS from these sequences . We used cd-hit-est to create a set of representative sequences , with sequences sharing >80% pairwise nucleotide identity collapsed ( -c 0 . 8 ) [44] . We then created and trimmed multiple alignments and phylogenies as described above . Global multiple sequence alignments of all S and all L segments were created using MAFFT software version 7 . 017 with default parameters [73] . Full genome sequences for described European snake arenavirus isolates were included in these alignments ( University of Helsinki virus ( UHV-1 ) and Boa arenavirus NL; NCBI accessions NC_023766 . 1 , NC_023765 . 1 , NC_023761 . 1 , and NC_023762 . 1 ) . Alignments were analyzed with the RDP4 recombination detection program version 4 . 39 using default parameters except to specify linear molecule topology [45] . We required that recombination events be detected by at least 2 of the methods implemented in the software . Putative recombinant segments were validated by examination of phylogenetic discordance and pairwise sequence alignments . Genome segments sequences were divided into 140 nt sliding windows ( the approximate size of intergenic regions ) offset by 5 nt . CentroidFold v0 . 0 . 9 was used to calculate the minimum free energy of folding for each window using parameters-g 4-e CONTRAfold [78] . Variant sites were called using SAMTools version 0 . 1 . 19 , using command mpileup—I . We required that variant sites be supported by at least 4 reads in the context of a minimum coverage level of 20 total reads . The number of variant sites per genome segment was calculated and normalized to the length of each segment . RIC For inoculation experiments , frozen tissue samples were thawed on ice and homogenized in MEM + 25mM HEPES ( SF-MEM ) using a Dounce homogenizer . Homogenates were clarified by centrifugation at 10 , 000g for 2 minutes then passed through a 0 . 45 μm filter . Filtrates were diluted 1:10 in SF-MEM then added to cultures of near confluent JK cells . Culture medium was replaced periodically and supernatants were stored at -80°C until further analysis . We used qRT-PCR to measure viral RNA levels in culture supernatant . We extracted RNA from 180 μl supernatant using the Zymo viral RNA kit ( Zymo Research ) . 5 μl RNA ( 25% of eluate ) was used as template in RT reactions as above . Resulting cDNA was diluted and used in qPCR reactions as above , with primers listed in S2 Table . Primer pair efficiencies were calculated as above and used to determine quantities of viral RNA relative to the amount of S segment RNA present in the first time point sample . JK cells were grown as described above and were plated at a density of 5000 cells per well in 96-well plates . One day later diluted virus stocks were added to cells . Cells were incubated for 7–10 days and then supernatants were transferred to new 96 well plates . Then wells were stained with anti-GGV-NP antibody , which cross-reacts with the NPs of the viruses used in these experiments . Staining and washing was performed as previously described [23] . Stained plates were scanned on an Odyssey Licor instrument to identify infected wells . Supernatant from NP-positive wells were transferred to 24-well plate wells plated the day prior with 75 , 000 JK cells per well . One day later cell culture supernatant was replaced . After an additional 3 days of incubation , culture supernatant was collected and clarified by centrifugation at 10 , 000g for 1 minute . RNA was isolated from these supernatants using the ZR Viral RNA kit according to the manufacturer’s protocol ( Zymo Research ) . RNA was used as template in qRT-PCR as described above to measure levels of viral RNA of various genotypes . This study did not include experiments involving live animals . In some cases , samples ( typically blood ) were collected from live animals by attending veterinarians . In other cases , tissues were collected during necropsy . All samples were taken and used with owners consent . Some samples were collected in the context of other , related studies: The acquisition of tissue samples at the University of Florida was authorized under University of Florida Institutional Animal Care and Use Committee Protocol A116 . The acquisition of samples at the University of California Davis was authorized under IACUC protocol 17450 . | The facility with which viruses evolve underlies many of the problems they cause . Virus evolution is the reason we need a new flu vaccine each year . It’s how HIV and other viruses develop drug resistance . And it enables viruses to occasionally jump from animals to humans and cause new diseases . It is therefore important to understand how and under what circumstances viruses evolve . The arenaviruses are a group of viruses that infect mammals and snakes . Mammalian arenaviruses normally infect rodents but they can also infect humans , and , when they do severe and sometimes fatal disease can result . In this study , we studied genetic diversity in arenaviruses infecting captive snakes . We discovered an astonishing amount of diversity . Most snakes are infected by more than one virus strain , and these strains are merging and shuffling their genes ( they are undergoing recombination and reassortment ) . The extent to which this is happening is exceptional , and has likely been caused by the importation and mixing in captivity of infected snakes from the wild . This provides an excellent opportunity to study the processes of virus evolution and may be an example of human activity altering its course . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Widespread Recombination, Reassortment, and Transmission of Unbalanced Compound Viral Genotypes in Natural Arenavirus Infections |
Amyloids are ordered protein aggregates that are typically associated with neurodegenerative diseases and cognitive impairment . By contrast , the amyloid-like state of the neuronal RNA binding protein Orb2 in Drosophila was recently implicated in memory consolidation , but it remains unclear what features of this functional amyloid-like protein give rise to such diametrically opposed behaviour . Here , using an array of biophysical , cell biological and behavioural assays we have characterized the structural features of Orb2 from the monomer to the amyloid state . Surprisingly , we find that Orb2 shares many structural traits with pathological amyloids , including the intermediate toxic oligomeric species , which can be sequestered in vivo in hetero-oligomers by pathological amyloids . However , unlike pathological amyloids , Orb2 rapidly forms amyloids and its toxic intermediates are extremely transient , indicating that kinetic parameters differentiate this functional amyloid from pathological amyloids . We also observed that a well-known anti-amyloidogenic peptide interferes with long-term memory in Drosophila . These results provide structural insights into how the amyloid-like state of the Orb2 protein can stabilize memory and be nontoxic . They also provide insight into how amyloid-based diseases may affect memory processes .
Amyloids , whose cross-β fold has been postulated as the ancestral protein fold [1 , 2] , were initially associated with fatal neurodegenerative disorders [3 , 4] . However , more than a century after the identification of these “pathological amyloids , ” a growing list of so-called “functional amyloids” that fulfils a wide variety of physiological functions has recently emerged [5] . The discovery of functional amyloids raises the question of what causes such a strikingly distinct behaviour to that observed in pathological amyloids . Indeed , it remains unclear what features are shared by functional and pathological amyloids and what determines whether a particular amyloid is functional rather than toxic . One functional amyloid-like protein of particular interest is the cytoplasmic polyadenylation element binding protein ( CPEB ) . The CPEB family of proteins regulates the translation of dormant mRNAs [6 , 7] , and some members of this family are involved in synaptic plasticity and long-lasting memory [8 , 9] . The CPEB isoforms share a common C-terminal catalytic region ( RNA-binding domain ) , but they differ in the N-terminal region . Surprisingly , the N-terminus of some CPEB isoforms in Aplysia , Drosophila , and mouse have features characteristic of self-sustaining amyloidogenic ( prion-like ) proteins [9–13] . For example , the neuronal specific isoform of Aplysia CPEB ( ApCPEB ) has a glutamine-asparagine ( Q/N ) -rich N-terminal domain , which resembles a yeast prion-like domain ( PLD ) [14] , and it is predicted to have conformational flexibility [10] . Indeed , ApCPEB undergoes a conformational transition to a β-sheet-rich state similar to that undertaken by other prion-like proteins [15] . In sensory neurons , the neurotransmitter serotonin controls the prion-like switch from the monomeric form to the self-sustaining oligomeric state , which is important for the serotonin-induced increase in synaptic strength [16] . Accordingly , it has been postulated that the switch to the oligomeric and self-perpetuating state contributes to the long-term maintenance of synapse-specific changes [16] , providing a molecular mechanism for the persistence of memory [10] . The Drosophila melanogaster orthologue of CPEB , Orb2 , has two isoforms that are structurally similar to the ApCPEB isoform: Orb2A and Orb2B [17 , 18] . Both forms are expressed in the fly brain and they are required for long-term memory [11 , 17 , 19] . Orb2A , a synaptic protein , is scarce , and its availability is controlled by phosphorylation [19 , 20] . This isoform has eight amino acid residues ( YNKFVNFI ) preceding the PLD , which are critical for both the efficiency as well as the nature of the amyloid-like oligomers being formed [11] . The longer Orb2B isoform , with a region of 162 residues preceding the PLD , appears to act as a canonical CPEB [21] , regulating translation via its RNA-binding domain [18 , 21] . The PLD of Orb2A has a low Q/N content compared to ApCPEB ( 23 . 5% versus 48 . 1% ) , it acts in long-term memory independently of its RNA-binding domain [21] , and its mutations prevent memory consolidation [11 , 17] . The nervous system is particularly susceptible to amyloid-driven diseases , some of which lead to severe cognitive deficits [22] . Currently , it remains unclear why some amyloids in general are detrimental for neurons , while a seemingly similar amyloid state of some neuronal CPEB proteins is critical for memory stabilization [9 , 11] . As with other biological conundrums , a structural/functional analysis of CPEB/Orb2 proteins may shed light on the features that distinguish functional from pathological amyloids , and it may also help us to understand the molecular basis of memory consolidation . Here , we have employed an array of in vitro ( bulk and single-molecule biophysics as well as cell culture ) and in vivo ( Drosophila and yeast ) techniques to characterize Orb2 amyloid both structurally and functionally . In addition to dissecting out the characteristics of the Q/N-rich PLD-containing region of Orb2 , we have found that a known anti-amyloidogenic peptide inhibits some forms of memory consolidation in Drosophila . Furthermore , comparing with pathological amyloids , we found that although in solution Orb2 can form toxic oligomers , these toxic species rapidly progress to innocuous species . These transient Orb2 species are structurally similar to toxic Huntingtin aggregates and , when abundant , these two proteins form a heteromeric complex . These findings indicate that there are intrinsic structural constraints that prevent functional amyloids to dwell in the toxic conformation . These observations also provide clues as to how pathological amyloids may interfere with the function of those beneficial amyloids .
Both the endogenous and the recombinant Orb2 protein form sodium dodecyl sulphate ( SDS ) and urea-resistant oligomers that are characteristic of amyloids [11] . However , it remains unclear to what extent the Orb2 amyloid behaves as a typical pathological amyloid . Both recombinant Orb2A and Orb2B bind to thioflavin T ( ThT ) and Congo red ( CR ) dyes ( Fig 1A–1C and S1A Fig ) , and this binding is inhibited by amyloid destabilizing reagents such as rottlerin [23] or the polyphenol ( - ) -epigallocatechin gallate ( EGCG: S1B Fig ) [24 , 25] . The soluble form of both full-length isoforms has a helix-rich secondary structure monitored by Circular Dichroism ( CD ) spectroscopy ( Fig 1D ) . However , over time both isoforms become rich in amyloid-specific β-sheets , as evidenced by Fourier transform infrared ( FTIR ) spectroscopy ( Fig 1E ) , which coincides with aggregation of the protein , as monitored by turbidimetry ( Fig 1F ) . Furthermore , transmission electron microscopy ( TEM ) showed Orb2A to form spherical oligomers and typical unbranched amyloid fibers ( Fig 1G ) . Interestingly , Orb2A adopted an amyloid structure more efficiently ( without the typical lag phase ) than other amyloids like the Aβ42 peptide or the yeast prion Sup35NM , with a t½ of ~15 min and at a protein concentration 8–10-fold lower ( S1C Fig ) . Based on these observations , we conclude that , in isolation , Orb2A not only forms seemingly canonical amyloid structures but it also does so in an extremely efficient manner . In order to determine whether Orb2A amyloid possesses the self-sustaining properties of prion-like proteins , we took advantage of the well-characterized yeast prion Sup35 , a translation termination factor that can exist in two states: as an active monomeric state ( nonprion ) and as a less active amyloid state ( prion ) [26] . The conversion between those two states can be readily assessed by non-sense suppression of the mutant ade1-14 allele . In the nonprion state of Sup35 , yeast colonies appear red in rich media , and they cannot grow in media containing a limiting amount of Adenine ( -ade media ) , while in the prion-state , read-through translation turns the colonies white and cells can grow in -ade media . We substituted the NM prion domain of the Sup35 protein with the N-terminal domain of Orb2A , Orb2B or a paralogue of Orb2 in Drosophila , Orb1 ( Fig 1H , left ) , and these chimeric constructs were expressed under the control of Sup35 promoter , representing the sole source of the Sup35 protein [27] . While all these chimeric proteins substituted the essential function of the Sup35 protein , only Sup35 carrying the N-terminal domain of Orb2A produced two readily distinguishable phenotypes: red Ade- [prion-] and white Ade+ [PRION+] colonies ( Fig 1H , right ) . The Orb2A N-terminal domain produced the prion-like state with an unusually high frequency and perpetuated stably through hundreds of generations . Random selection of 100 colonies revealed that almost 50% of them spontaneously gave rise to white-pink Ade+ colonies for Orb2A , compared to ~1% for Orb2B and none for Orb1 ( Fig 1H , right ) . Furthermore , the nonprion and prion-like states correlated with the monomeric and SDS-urea resistant amyloidogenic oligomeric states of Orb2 , respectively ( S1D Fig ) . These results indicate Orb2A is very efficient in adopting a self-sustaining amyloid-like state . A group of the prion-like proteins characterized to date contain a PLD that is disordered and frequently composed of a Q/N rich sequence with a low content of charged residues [28–30] . In Orb2A , the entire PLD is composed of an N-terminal Q/N-rich domain ( 88 residues with 35 . 2%Q+N , Fig 1A ) , which is followed by a 74-residue region containing few charged residues ( 6 . 8% compared to 21 . 6% in the rest of the Orb2A protein ) . PLDs often adopt distinct conformational states , such as coiled-coil rich [31] or stacked β-sheet rich structures [32–36] . The conformational switch can be gauged through their susceptibility to proteases [37] . To determine the region of Orb2A PLD that adopts distinct conformational states , we inserted the tobacco etch virus ( TEV ) protease recognition motif ( ENLYFQG ) at the N-terminus of EGFP-tagged Orb2 protein ( Fig 2A ) , and we measured the accessibility of the TEV protease to these sites [38] . Insertion of the TEV protease sites at the positions indicated in Fig 2A did not alter the ability of Orb2 to oligomerize ( S2A and S2B Fig ) or bind to mRNA ( S2C Fig ) . We found that the TEV protease site located outside the N-terminal of Orb2A or Orb2B ( Orb2A162TEV , Orb2A216TEV and Orb2B370TEV ) was cleaved efficiently and equivalently in both the oligomeric and the monomeric forms ( Fig 2B ) . However , when the TEV site was located within the first 162 residues of Orb2A ( Orb2A88TEV ) , or within the first 242 residues of Orb2B ( Orb2B242TEV ) , most of the oligomers and a fraction of the monomers were not cleaved by TEV after 24h incubation with TEV protease ( Fig 2B ) . This differential cleavage of monomers implies that: i ) the 88 N-terminal residues are not inherently inaccessible; and ii ) there is a conformational variability even among monomeric forms . To further explore the conformational variability in the monomer population and to rule out the possibility that resistant monomers were not simply oligomers that have dissociated , we obtained protein fractions containing primarily monomeric Orb2 by differential centrifugation . When the TEV protease site is within the first 162 residues of Orb2A , only a fraction of the monomeric protein is accessible to TEV ( S3 Fig ) . These results suggest that the N-terminal Q/N rich region that ends at residue 162 is most likely the boundary of the PLD and that this domain can adopt at least two distinct conformational states: a protease-accessible monomeric state and a protease inaccessible state in both monomeric and oligomeric forms ( Fig 2B and S3 Fig ) . PLDs are often organized such that two distinct regions mediate the initiation of aggregation and recruitment to the aggregate for self-perpetuation . To determine whether the Orb2A PLD has this organization , we studied the recruitment of red-labelled proteins into EGFP-labelled preformed aggregates using a cell fusion-based assay [39] . Three distinct Orb2AΔPLD-Cherry fluorescent protein constructs that lacked specific regions of the PLD were generated: Orb2AΔ1-88-Cherry , Orb2AΔ88-162-Cherry , or Orb2AΔ1-162-Cherry . We fused S2 cells carrying Orb2A-EGFP to Orb2AΔPLD-Cherry expressing cells by inducing the expression of the Caenorabditis elegans epithelial fusion failure 1 ( EFF-1 ) protein [39] , which causes cell fusion via a homotypic interaction and the mixing of cytoplasmic but not nuclear components ( Fig 2C ) . While Orb2AΔ1-88-Cherry expression appeared mostly diffuse by itself , in the fused cells this protein variant formed large puncta ( Fig 2D ) . Orb2AΔ88-162-Cherry formed few puncta by itself but co-aggregated efficiently with full-length Orb2A-EGFP to form large fluorescence puncta . By contrast , Orb2AΔ1-162-Cherry , which lacks the entire PLD , failed to form any such aggregates ( Fig 2D ) . Since only Orb2A-EGFP efficiently induced the formation of Orb2AΔ88-Cherry puncta but not EGFP , Orb2B-EGFP , or Orb1-EGFP , these results indicate that the intramolecular interaction is highly specific ( Fig 2E ) . Surprisingly , the Orb2A protein from D . willistoni , which is ~81% identical to the D . melanogaster protein , forms puncta when it is expressed in S2 cells . However , Orb2A from D . willistoni failed to induce the aggregation of Orb2AΔ88-Cherry into puncta ( Fig 2D ) , indicating that this is a species-specific process . These data suggest that the organization of Orb2A’s PLD resembles that of other prion-like proteins , whereby the first 88 residues are important for initiating aggregation , and residues 88–162 are important for the recruitment into preformed aggregates . One surprising feature of PLDs is that although the domains can substitute each other , the amino acid sequence of the various PLDs are distinct . Therefore , it remains unclear to what extent individual amino acids at various positions contribute to the prion-like properties . Intriguingly , previously we found that a F to Y substitution at the 5th position of the Orb2A PLD—representing the addition of a single hydroxyl group—dramatically reduced Orb2 oligomerization and impaired memory consolidation [11] . This prompted us to determine the role of the 5th residue in amyloid formation and what aspects of amyloid formation ( e . g . , recruitment ) and/or prion-like behaviour ( i . e . , self-sustaining properties ) are affected by this mutation . To address these issues , the F5 residue was substituted for 18 different residues ( Fig 3A ) , and Orb2A puncta stability was quantified using fluorescence recovery after photobleaching ( FRAP ) . Remarkably , substitution of F5 with any residue except the highly hydrophobic ones ( V , I , L , or W , only exception being E ) strongly destabilized Orb2A oligomers ( Fig 3B ) . ThT binding to Orb2A variants correlated with FRAP results , and the Orb2AF5Y mutant showed the weakest enhancement in ThT fluorescence ( Fig 3C ) . To determine whether this 5th residue might also play an important role in oligomer packing , in addition to affecting the rate of Orb2A oligomerization , we performed fluorescence resonance energy transfer ( FRET ) assay . FRET efficiency depends on the distance between the fluorophores , which in turn depends on the orientation and packing of the protein subunits in the homo-oligomer . Surprisingly , only the F5L , F5W , F5S , F5Q and F5P ( One-way ANOVA , p > 0 . 05 ) substitutions produced an average FRET efficiency similar to the efficiency observed in the wild-type protein . Moreover , the F5Y substitution produced aggregates with very variable FRET efficiency , whereas all other substitutions decreased the average FRET efficiency ( *p < 0 . 05 ) , suggesting that the nature of the 5th residue is important in oligomer packing ( Fig 3D ) . Thus , the 5th residue of Orb2A seems to mediate a key intramolecular interaction important for the oligomerization process [40] . We then asked whether this perturbation in oligomer packing and stability affected the prion-like behaviour of Orb2A ( see Fig 1H ) . Remarkably , this single residue substitution dramatically interfered with the capacity of the Orb2A’s PLD to adopt a stable prion-like state , as concluded from a chimeric yeast prion assay ( Fig 3E ) , coinciding with the reduction in the SDS-urea resistant amyloidogenic oligomeric states ( Fig 3F ) . The Orb2AF5Y-EGFP mutant also impaired the formation of Orb2AΔ1-88-Cherry puncta ( Fig 3G ) , and this PLD variant exhibited impaired capability to aggregate , as measured by turbidimetry and far-UV CD spectroscopy ( S4A and S4B Fig ) . This mutant also has decreased ability to form amyloid components , according to a CR binding assay ( S4C Fig ) and oligomers/fibers , as assessed by TEM ( S4D Fig ) . Taken together , these results corroborate the critical role of the F5 residue of Orb2A for its self-sustaining recruitment and amyloidogenic properties reported to be necessary for memory consolidation [11] . These results also suggest that there are inherent structural features determined by specific amino acid residues that control the Orb2A amyloid formation . The possible existence of intrinsic differences ( related to protein structure or/and dynamics ) between functional and pathological amyloidogenic pathways prompted us to carry out a comprehensive analysis on the various stages of amyloid formation . The monomeric species of amyloidogenic proteins represent the starting point and a key determinant of the amyloidogenic cascade . The conformational changes that occur in the monomeric protein are difficult to study with bulk structural techniques because of the coexistence of many conformational states in a dynamic equilibrium . However , single-molecule techniques , like atomic force microscopy-based single molecule force spectroscopy ( AFM-SMFS ) , allow the determination of the conformational diversity of these monomers [41] . We focused on the disordered region comprising the first 162 amino acid residues of Orb2A-PLD ( S5A Fig ) as the sequence that is necessary and sufficient for prion-like behaviour and amyloid formation , as well as for memory consolidation [21] . We performed AFM-SMFS in the length-clamp mode ( at a constant pulling speed of 0 . 4 nm/ms ) in order to compare the amyloidogenic properties of Orb2A PLD with those of pathological amyloids at the monomer level [41] . As a single-molecule reporter , we constructed a fusion protein whereby the Orb2A PLD was mechanically protected inside the fold of a reference protein ( carrier ) , rendering it amenable to unequivocal single-molecule analysis ( S5B Fig ) . Prior to AFM-SMFS analysis , to verify that both the PLD region and the carrier in the fusion protein preserve their structural integrity , we performed 1D 1H and 2D 1H nuclear Overhauser effect spectroscopy ( NOESY ) nuclear magnetic resonance ( NMR ) and far-UV CD spectroscopies ( S5C–S5E Fig ) . Furthermore , turbidimetry analysis , CR binding , and negative-staining TEM experiments corroborate that the PLD region maintains its amyloidogenic properties when protected inside the fold of the carrier ( S6 Fig ) . For SMFS analysis , after fitting the force-extension recordings to the worm-like chain ( WLC ) model of polymer elasticity [42 , 43] , we found that Orb2A PLD exhibited a rich mechanical conformational polymorphism that could be classified into two general classes of conformers depending on their mechanical resistance to unfolding ( F ) : nonmechanically resistant , or NM , ( F ≤ 20 pN , 62 . 3% NM ) and mechanically resistant , or M , ( F > 20 pN , 37 . 7% M ) conformers ( Fig 4A , S7 Fig and S1 Table ) . Within the M class , a variety of monomeric conformers with different mechanical stabilities were observed . These conformers showed resistance barriers ( often more than one per molecule ) located at a number of positions , as measured by the length released upon unfolding ( increase in contour length , ΔLc ) , indicating the high conformational diversity of Orb2A PLD ( Fig 4A , S7 and S8A–S8C Fig ) . Like in other pathological amyloids [41] , we observed extremely stable M conformers whose unfolding forces approached the values needed to break a covalent bond [44] . Intriguingly , it must be noted that some of these M conformers might be related to the prion conformers stabilized by strong noncovalent forces thought to account for prion inheritance and transmission [37] . Interestingly , the F5Y mutation showed a significantly reduced conformational polymorphism , as measured by SMFS ( 10 . 1% of M conformers; Fig 4A and S8D–S8F Fig ) . This reduction is in agreement with the decreased ability of the F5Y variant to form self-perpetuating amyloids ( Fig 3 and S4 Fig ) and the failure to stabilize memory beyond 48 h [11] . Furthermore , Orb2 shares additional features with pathological amyloids at later stages . This includes formation of toxic oligomeric species ( Fig 4B ) recognized by the conformational antibody A11 [45] as well as the formation of unbranched fibers recognized by the conformational antibody OC ( Figs 4C and 1G ) , which also recognizes fibrillar oligomers and mature fibers in the Aβ assembly [46] . Taken together , these data suggest that the Orb2A PLD monomer samples a wide conformational space , consistent with our TEV protease analysis ( Fig 2B and S3 Fig ) , and that Orb2A follows an amyloidogenic pathway reminiscent of pathological amyloids [41] . The similarities in the amyloidogenic cascade between Orb2A and pathological amyloids led us to wonder why , despite these common features , Orb2A is functional rather than toxic to the cell . One possibility is that the lifetime of the toxic oligomers formed by Orb2A and pathological amyloids is different . To test this possibility , we first used the two aforementioned conformational specific antibodies raised against Aβ: A11 [45] and OC [46] . We found that Orb2 initially formed A11-reactive SDS-sensitive oligomers that rapidly ( minutes time scale ) evolved to form at least two different OC-reactive species: a SDS-sensitive species and a more mature , SDS-resistant species ( Fig 5A ) . By contrast , A11-reactive species formed by Aβ42 were stable in solution for days ( Fig 5B ) . In the light of these data , and to determine whether the transient Orb2 oligomers are indeed cytotoxic , we used conformational trapping with two small inhibitors ( Fig 5C ) : EGCG [24 , 47] and Amphotericin B ( AmB ) [48] . We found that AmB slowed down Orb2A assembly and arrested it in the A11-reactive conformation , while EGCG seemed to direct the protein to the fibril state through a pathway that does not involve the formation of toxic A11-reactive oligomers ( Fig 5D ) . To test cell viability , the different samples were microinjected into cultured cells [49]: Orb2A and the Orb2A:EGCG complex , in which Orb2A is stabilized in the OC-reactive conformation , were not cytotoxic , similar to the negative controls ( Fig 5E and 5F , S9 Fig ) . By contrast , the Orb2A:AmB complex , which is stabilized in the A11-reactive oligomeric state , caused extensive and acute cell death 24 h after microinjection ( Fig 5E and 5F , S9 Fig ) . Notably , incubation of the Orb2A:AmB complex with a very low concentration of the conformational A11 antibody ( complex:A11 , 100:1 ) attenuated this toxicity , indicating that toxicity was indeed due to the A11 specific conformation acquired by Orb2A ( Fig 5E and 5F , S9 Fig ) . To determine whether these differences in kinetic parameters are inherent features of nontoxic and toxic amyloids , we performed a domain swapping analysis between Orb2A and a disease-associated version of Huntingtin Htt exon 1 ( ex1HttQ72 ) . We compared ex1HttQ72 with two chimeric proteins in which the PLD and the Q-rich region have been swapped ( S10A Fig ) . When the Q-rich region of ex1Htt was substituted with Orb2A PLD , it formed nontoxic , short-lived species reactive to the A11 antibody , similar to Orb2A ( see Fig 5A ) . Conversely , the substitution of the PLD of Orb2A by the Q-rich region of the expanded ex1Htt resulted in the formation of stable A11-reactive species that exhibited a high cytotoxicity , similar to that found for ex1HttQ72 ( S10B–S10D Fig ) . Taken together , these results suggest that Orb2A can form a toxic conformer , which structurally resembles that of toxic amyloids . However , intrinsic structural features in Orb2A-PLD renders this toxic conformation rare and transient; furthermore , it appears that Orb2A may have evolved to form mature amyloidogenic oligomers much more efficiently than pathological amyloids . Our observations suggest that in solution , Orb2 and polypeptides that form pathological amyloids sample similar conformational ensembles . We wondered whether in spite of being transient and rare , because of their structural similarity , these Orb2 transient toxic conformations could be hijacked by similar , yet longer-lived conformers from neurotoxic amyloids . To test this hypothesis , we coexpressed EGFP-tagged Orb2A protein with the Huntingtin protein-containing expanded polyQ repeats ( HttQ128 ) in the larval motor neuron . HttQ128 , unlike Orb2A , is neurotoxic , and its chronic expression produces lethality [50] . In contrast to when it is expressed alone , Orb2A formed larger fluorescence puncta in the presence of HttQ128 , indicative of its aggregation , and in some cases these Orb2A puncta were surrounded by HttQ128 protein ( Fig 5G and S11A Fig ) . The effect of HttQ128 on Orb2 appears to be specific , given that it had no obvious effect on other EGFP-tagged proteins , such as the synaptic protein synaptotagmin or the Golgi-associated protein GRASP-65 , used as negative controls ( Fig 5G and S11B Fig ) . Similarly , removal of the PLD from Orb2A abrogated its HttQ128-enhanced aggregation ( Fig 5G ) . Since the structural properties of Orb2A are similar to those of the NM PLD of yeast prion Sup35 , we also studied a chimeric protein in which the Orb2A PLD was substituted with the Sup35 PLD . Consistent with the structural studies , this chimeric construct was also recruited into the HttQ128 aggregates ( Fig 5G and S11C Fig ) . These results suggest that pathological amyloids , when present in excess , can nucleate Orb2A aggregation , presumably by capturing the transient toxic conformers of Orb2 proteins . Polyglutamine-binding peptide 1 ( QBP1 ) is a known inhibitor of the amyloidogenesis of HttQ expansions . This hydrophobic peptide binds monomeric expanded polyQ proteins and blocks the initial critical β-conformational transition of these species , suppressing their subsequent oligomerization and fibrillogenesis , and consequently , the associated cytotoxicity and neurodegeneration [41 , 49 , 51–53] . QBP1 acts as a polyvalent anti-amyloidogenic agent on several amyloids [41] , and thus , we examined the potential inhibitory effect of the minimal active core of QBP1 ( Ac-WKWWPGIF-NH2 ) [54] on Orb2A amyloid formation . The conformational similarity at the monomer level between Orb2 and HttQ prompted us to further examine whether QBP1 could inhibit Orb2A amyloid formation . Isothermal titration calorimetry ( ITC ) revealed that QBP1 physically interacts with Orb2A , and that complex formation , a slow event , was exothermic in nature ( Fig 6A ) . Under similar conditions , a scrambled version of QBP1 ( SCR , Ac-WPIWKGWF-NH2 ) [54] interacted poorly , serving as a negative control in the subsequent experiments ( Fig 6A ) . Unlike the SCR , incubation with QBP1 inhibited Orb2A PLD aggregation and amyloidogenesis , as monitored by turbidimetry and CR binding ( Fig 6B and 6C ) . Far-UV CD spectroscopy revealed that QBP1 , but not SCR , suppressed Orb2A PLD signal loss due to protein precipitation ( Fig 6D ) . Furthermore , TEM showed a significant reduction in the formation of both oligomers and fibers in the presence of QBP1 but not the SCR ( Fig 6E ) compared to Orb2A PLD alone ( see Fig 4C ) . Consistent with these results , the SMFS analysis revealed that Orb2A PLD treated with QBP1 formed fewer M conformers ( from 37 . 7% to 16 . 1% ) , shifting the NM/M proportion towards an increased population of NM conformers , while SCR had no effect on this initial conformational transition ( Fig 6F and S12 Fig ) . Together , these results indicate that QBP1 interferes with Orb2 amyloid formation in vitro and suggest that early in amyloidogenesis , Orb2 and some pathological amyloid-forming polypeptides adopt similar conformations that are recognized and blocked by QBP1 . Based on the fact that QBP1 inhibits the transition from the monomeric Orb2A state to conformations that lead to amyloid formation , we tested in D . melanogaster the physiological consequences of QBP1 expression on memory consolidation . To this end , we used the male courtship suppression memory paradigm , in which a virgin male repeatedly exposed to unreceptive females learns to suppress courtship towards them , and this learned suppression persisted for days . Using the Gal4-UAS system , the QBP1-cyan fluorescent protein ( CFP ) or SCR-CFP peptides were expressed panneuronally [53] . QBP1 expression in the nervous system , or that of the control SCR , did not affect fly development , although normal courtship behaviour was slightly dampened ( S13 Fig ) . Following training , both experimental flies ( Elav-Gal4:UAS-QBP1-CFP or SCR-CFP ) and the genetic controls ( Elav-Gal4 and UAS-QBP1 or UAS-SCR ) displayed a similar suppression of courtship immediately after training , suggesting that the expression of these peptides does not interfere with learning or short-term memory . However , when measured after 24 h , the Elav-Gal4:UAS-QBP1 males displayed elevated courtship compared to the Elav-Gal4:UAS-SCR control flies , indicative of a loss of long-term memory ( Fig 7 ) . To determine whether QBP1-mediated memory loss is independent of Orb2 , we expressed QBP1 in Δ80QOrb2 flies that lack N-terminal Q-rich 80 amino acid residues of Orb2 ( Fig 7 ) . The Δ80QOrb2 flies form short-term memory but no long-term memory [11 , 17] . Expression of QBP1 in Δ80QOrb2 had no additive effect in the loss of long-term memory ( Fig 7 ) . Since QBP1 is a low affinity peptide that can only block early stages of oligomerization but not already formed oligomer , we also investigated the consequence of transient expression of QBP1 in courtship suppression memory . To this end , we used RU486-inducible GeneSwitch Elav-Gal4 system and induced expression of the QBP1 peptide in the adult flies 24 h before training [55] . Transient expression of QBP1 had no effect on the long-term courtship suppression memory ( S14 Fig ) . Finally , to determine whether chronic expression of QBP1 results in general disruption of the nervous system , we trained the ElavGal4-UASQBP1 flies in a heat-box paradigm ( S15 Fig ) . In this operant conditioning paradigm , flies learn to avoid one side of an otherwise symmetrical chamber [56] . Intriguingly , the heat box paradigm produces robust short-term memory , but the memory does not persist beyond an hour or two . The memory curve of QBP1 was similar to that of wild type flies , suggesting that QBP1 expression does not interfere with the animal’s ability to form short-lived memories ( S15 Fig ) . Taken together , these results suggest that chronic QBP1 expression can interfere with some form of long-term memory and the effects of QBP1 in memory may be partly mediated through Orb2 .
Here we have analyzed the structural states of Orb2A , an amyloidogenic protein that is important for memory consolidation . We find that Orb2A not only forms self-perpetuating amyloids but it does so extremely quickly and efficiently . Surprisingly , Orb2A has several structural features and properties that are similar to those of pathological amyloids ( Fig 8 ) . First , the Orb2A monomer shows a rich conformational polymorphism with mechanical stabilities similar to those of the well-characterized pathological amyloids [41] . Second , Orb2 binds to the conformational antibody A11 , which detects the toxic oligomeric species of pathological amyloids [45] . Third , like pathological amyloids , Orb2A can form unbranched amyloid fibers and pre-fibrillar oligomers that react against the conformational antibody OC [46 , 57–60] . Finally , Orb2A amyloid formation is inhibited by QBP1 , a peptide capable of inhibiting amyloid formation in expanded polyQ tracts [41 , 51] . What makes Orb2A PLD beneficial despite sharing all these features with pathological amyloids ? Orb2 amyloid-like oligomers are formed in response to neuronal stimulation to support memory , which indicates the existence of a highly regulated mechanism in the cell to drive Orb2 amyloid formation and most likely to avoid the toxicity of its A11-reactive oligomers . One such regulation is the tight control of the Orb2A protein level via phosphorylation [20] and possibly other mechanisms . Here we show the existence of additional intrinsic kinetic factors in Orb2A , i . e . , the lifespan of A11-reactive oligomers , which is probably related to its efficiency to form a functional amyloid state . We postulate that although the early events in Orb2 amyloid formation are similar to those of pathological amyloids , in subsequent steps it acquires structural features to rapidly evolve to a nontoxic amyloid-like state ( Fig 8 ) . For Orb2 proteostasis , these intrinsic structural features could be further enhanced in the cell by association with other components or by the direct modification of proteins , such as phosphorylation [20] . Both the presence of A11-reactive oligomers and the ability to form amyloids efficiently have been also reported for the intracellular nonpathological amyloids of the yeast Sup35 prion [48] , which suggests that this may be a general mechanism for some amyloids to avoid cytotoxicity . Still , it must be noted that neuronal CPEB/Orb2 is the only known functional amyloid-like protein in the nervous system . This distinction is fundamental for two reasons: first , the nervous system is particularly susceptible to amyloid-based disease and; second , amyloids are known to interfere with cognitive abilities including memory . The persistence of memories over months and years raises the fundamental question of how memory is protected against molecular turnover [61] . Structural changes in individual proteins and in supramolecular protein assemblies have been proposed to play an important role in long-term memory [62 , 63] . The prion features of the specific neuronal CPEB isoform , such as Orb2 in Drosophila [11] , or CPEB3 in mouse [9 , 12] that can adopt a self-sustaining state at synapses very rapidly , provide a plausible solution as to how memories can remain stable in the face of constant molecular turnover [10 , 16 , 64] . Although exactly to what extent the self-sustaining amyloidogenic properties observed in vitro reflect the in vivo properties remains unclear , accumulating evidence reinforces the hypothesis that an amyloid-like state of neuronal CPEB is involved in long-lasting memory . In this regard , the present study provides evidence that mutations in Orb2 , such as the 5th residue in Orb2A , that are known to interfere with memory consolidation also impair the protein’s ability to attain a self-sustaining amyloidogenic state as monitored by in vitro measurements . Similarly , the expression of QBP1 , an amyloid-blocking peptide , impairs memory consolidation ( Fig 8 ) . However , we are not sure whether the memory-disrupting effect of QBP1 is applicable for all forms of long-term memory or whether the effects of QBP1 in memory are mediated exclusively through the Orb2 protein . Furthermore , in the absence of atomic-level 3D structural analysis of endogenous CPEB protein in the amyloid-like state , it remains unclear exactly to what extent they are similar to toxic amyloids . Our observations also highlight certain features of functional amyloids that may be relevant for neuropathologies of amyloid-based diseases . First , the short life span of Orb2 toxic oligomers supports a therapeutic approximation based on speeding up the conversion of toxic oligomers into stable amyloids [65] . Second , Orb2’s capacity to form toxic conformers resembling those of pathological amyloids , and its sequestration into HttQ128 aggregates ( Fig 8 ) , suggests that pathological amyloids might hijack Orb2 into a nonfunctional state , impairing its physiological activity . Third , anti-amyloidogenic compounds that target early stages of amyloidogenic pathways might inadvertently interfere with functional amyloid formation , impairing their associated function . Finally , the polyvalent amyloid blocker QBP1 , or improved analogues based on its recently determined structure [66] , could be used in the future to block newly formed traumatic memories ( Fig 8 ) [67] . However , we are cognisant of the inherent limitations of our ideas since none of our studies are carried out with human proteins in the human nervous system . These anticipated connections of our in vitro findings with memory need to be explored in detail .
The ThT binding assay was performed essentially as described previously [68] . Briefly , 400 picomoles of protein/100 μl aliquot were taken at the time indicated and mixed with 700 μl of 25 μM ThT ( Sigma ) in 50 mM Glycine buffer ( pH 8 . 5 ) . The reaction mix was excited at 442 nm , and the emission at 482 nm was measured using a Fluoromax-4 spectrofluorimeter , and three independent measurements were taken for each sample . To obtain the ThT enhancement fluorescence due to Orb2 alone , the 6 M GdnHCl-PBS buffer was dialyzed similarly , and for each time point , the fluorescence of ThT in this buffer was subtracted . FTIR spectroscopy was carried out using the same His-tagged recombinant proteins used in ThT binding assay . FTIR was performed with a Nicolet 6700 spectrometer equipped with a ZnSe multibounce ATR accessory . The instrument was corrected for ATR penetration . Spectra were collected at 2 cm-1 resolution and averaged over 64 scans . Samples were precipitated by addition of methanol to a final concentration of 50% and incubated overnight at 4°C . Pellets were spread on the ATR and dried with a stream of air before each measurement . Spectra were corrected for water vapor by subtracting the background collected immediately after each scan scaled to fit the local shape of the spectrum in the 1 , 600–1 , 700 cm-1 region . The liquid water spectrum was subtracted by matching the signal at 2 , 200 cm-1 to a liquid water spectrum via linear least squares . A linear background between 1 , 580 and 1 , 720 cm-1 was subtracted , and the spectrum in this region was normalized to obtain the final spectrum . The resulting spectra were fit to four Gauasian peaks with nonlinear least squares . All subtractions and fittings were performed using custom plugins written in ImageJ ( NIH , Bethesda , MD ) and are available at http://research . stowers . org/imagejplugins . The 1 , 620 cm-1 band with a width of ~13 cm-1 was assigned to extended cross-β sheet structures only found in amyloid proteins . The 1 , 637 cm-1 band with a width fixed at 13 cm-1 was assigned to typical β-sheet structures such as those found in concanavalin-A . The 1 , 655 cm-1 band with a width fixed at 13 cm-1 was assigned to random coil and α-helical structure . Finally , the 1 , 678 cm-1 band with a width fixed at 13 cm-1 was assigned to turns . In addition , FTIR was performed after incubation of the precipitated pellets in D2O for 1 h , and a minimal change in the α-helical content was observed , suggesting the absence of pervasive random coil structure . For the chimeric yeast prion assay , we have used the methods developed previously [69] . Briefly , the N-terminal domains of Orb2A , Orb2B , and Orb1 were optimized for expression in yeast and cloned into TopoDonor vector ( Invitrogen ) . Using pAG414SUP35-ccdB-SUP35C ( LEU , CEN plasmid , Sup35 promoter , SUP35C domain ) Gateway vector and LR clonase ( Invitrogen ) , the N-terminal domains , were fused in frame to Sup35C domain to create the chimeric construct [69] . The Orb2-Sup35C ( LEU selectable marker ) construct was introduced into W303aΔsup35 strain [MATa; leu2-3 , 112; his3-11 , -15; trp1-1; ura3-1; ade1-4; can1-100; SUP35::HygB; [psi-];[PIN+]] via plasmid shuffling . The yeast were grown in YPD media and plated on either YPD-agar or SC-agar lacking adenine and the [PSI+] colonies were selected 2–3 d after plating . Selected colonies were grown in YP-glycerol plates to avoid petites . To discard that phenotypes are not due to mutation in the Orb2-Sup35C , the plasmid was isolated from both cell types and sequenced , and white phenotypes were rescued by transforming the cells with plasmid expressing just the C-terminal domain of Sup35 protein . To determine the heritability of the prion-like state , individual red or white colonies were streaked for multiple times . S2 cells expressing the Orb2A-EGFP , Orb2B-EGFP , Orb2A88TEV-EGFP , Orb2AA216TEV-EGFP , Orb2B242TEV-EGFP , or Orb2B370TEV-EGFP constructs were lysed in PBS buffer containing 0 . 1% NP-40 and protease inhibitors ( Roche ) . The lysate was centrifuged at 10 , 000 × g for 15 min to remove the cell debris and nuclei , and the supernatant was then centrifuged at 174 , 000 × g in a TLA120 . 2 rotor for 2 h . The supernatant was collected , and the pellet fraction was resuspended in PBS + 0 . 1% NP40 buffer in a volume equal to the supernatant . For western blotting , 7 . 5 μl of the lysate , and 15 μl of the supernatant and pellet fractions , were analyzed by SDS-PAGE . For TEV protease digestion , approximately four times the volume of the pellet fraction was used compared to the supernatant fraction to normalize for the amount of Orb2A and Orb2B present . TEV digestion was performed overnight at 4°C with 1 μg of recombinant purified TEV protease . To analyze Orb2A-EGFP and Orb2B-EGFP from fly heads , the ultracentrifugation protocol was modified by using 1% Triton X-100 lysis buffer instead of the NP-40/PBS buffer . All anti-Orb2 antibodies were raised against recombinant six histidine-tagged full-length Orb2A proteins . The antibody 273 was raised in rabbits ( Covance ) , and antibody 2233 was raised in guinea pigs ( Pocono ) . All antibodies were affinity purified against Orb2A protein . For western blots , the antibodies were used at the following dilutions: Ab273 , 1:1 , 000 dilution; Ab2233 , 1:2 , 000 dilution; and Ab402 , 1:2 , 000 dilution . The chicken anti-EGFP antibody ( abCAM ) was used at 1:5 , 000 , and all secondary antibodies ( Cell Signalling ) were diluted 1:10 , 000 . For all biochemical analyses , freshly prepared head extracts were used unless mentioned otherwise . For western blotting , fly heads were homogenized ( 2–4 μl of buffer/head ) in PBS buffer I , containing: 150 mM NaCl , 3 mM MgCl2 , 0 . 1 mM CaCl2 , 5% glycerol , 1% Triton X-100 , and protease inhibitors ( Roche ) . Where applicable , the extracts were also treated with 50 ng/ml of purified RNaseA ( Qiagen ) . The total homogenate was incubated at 4°C for 30 min with rotation , centrifuged at 10 , 000 × g for 10 min , and the cleared supernatant was collected . To detect Orb2 protein , extracts from approximately five head equivalents were resolved on a 4%–12% gradient SDS-PAGE ( Invitrogen ) or on 8% SDS-PAGE and electroblotted onto a PVDF membrane for 16 to 18 h at 30 mV in a cold room . The membranes were blocked with 5% nonfat dry milk in Tris-buffered saline ( TBS ) -Tween-20 buffer and incubated with the affinity-purified antibodies indicated for 12 to 16 h at 4°C with constant agitation . The antibody-antigen interaction was visualized by chemiluminescence using HRP-coupled anti-rabbit ( for Ab273 or anti-guinea pig ( Ab2233 ) secondary antibodies ( Pierce ) . For immunoprecipitation , fly heads were homogenized in PBS buffer I , and the lysates were clarified twice by centrifugation at 12 , 000 × g for 10 min at 4°C . Then , ~ 2 mg of total protein from unstimulated brain extracts and ~ 0 . 5 mg of total protein from stimulated brain extracts were incubated with 0 . 5 to 1 μg of the purified antibodies for 2 h at 4°C . After 2 h , the lysates were incubated with protein A beads ( Repligen ) for a further 2 h , and the beads were washed five times with the PBS buffer I . The protein bound to the beads was analyzed in western blots ( as described above ) . The head extracts were prepared in a buffer containing: 20 mM Tris-HCl , ( pH 8 . 0 ) , 150 mM NaCl , 1 mM EDTA , 5% glycerol , 0 . 1% Triton X-100 , 1 . 5 mM DTT , 0 . 2 mg/ml heparin , 0 . 2 mg/ml tRNA , 0 . 25% BSA , complete mini protein inhibitor ( Roche ) , 40 units/ml RNase plus inhibitor ( Promega ) . The RNA and protein ( ~2 mg of total protein ) were incubated in the buffer with rotation for 40 min at RT . Pre-equilibrated Streptavidin Paramagnetic Beads ( Roche ) were added to each binding reaction , and the mixture was incubated for a further 40 min . The beads were then captured with a magnet , washed five times for 10 min with extraction buffer , and boiled in Laemmli SDS-PAGE sample buffer . The supernatant and pellet fractions were prepared as described above . To obtain biotin-labelled RNA , the 3’ UTR of the Oscar , Tequila , Murashka , and Actin88F genes were cloned into the TopoII vector ( Invitrogen ) , and biotin-labelled RNA was prepared using Megascript RNA synthesis kit ( Ambion ) in the presence of Bio-17-ATP and Bio-11-CTP . The RNA was purified , and ~ 2 μg of biotin-labelled RNA was incubated with the supernatant and pellet protein extracts . The protein/RNA complexes were recovered with streptavidin magnetic beads ( Dynal ) . The amount of supernatant and lysate used in the control lanes is 5% the amount used for RNA binding . S2 cells were transfected with the constructs indicated using Effectene ( Qiagen ) according to the manufacturer’s instructions . The cotransfected cells were plated the next day on concanavalin-A-coated glass-bottom dishes ( Mattek ) and analysed by confocal microscopy . For EFF-1 fusion experiments , we cloned the ORF of C . elegans eff-1 gene into the copper inducible vector pMTV5HisB ( Invitrogen ) . The Orb2 constructs were cloned into the Pac5 . 1HisB vector ( Invitrogen ) under the constitutively active actin promoter . Cells were cotransfected with pMT EFF1 and pAc5 . 1EGFP-tagged ORF’s or pMT EFF-1 and pAc5 Orb2AΔ1-88-Cherry as indicated . One day later , the cotransfected cells were mixed , and fusion was initiated by adding CuSO4 to the media , thereby inducing EFF-1 expression . After 16 to 18 h , the cells were plated on concanavalin-A ( Sigma ) -coated mattek glass-bottom dishes , and the cells containing both GFP and Cherry were detected manually by confocal microscopy . Order/disorder prediction for Orb2A protein was made using a consensus artificial neural network prediction method , Predictor Of Naturally Disordered Regions PONDR-FIT ( Molecular Kinetics ) . This metapredictor was developed by combining the outputs of several individual disorder predictors ( PONDR-VLXT , PONDR-VSL2 , PONDR-VL3 , FoldIndex , IUPred , and TopIDP ) and significantly improved the prediction accuracy when compared to its individual component predictors [70] . Orb2A PLD sequence was also analyzed by applying the Rosetta-Profile algorithm , which identifies sequence segments that form the steric-zipper spines of amyloid fibrils [71] . Orb2 PLD samples and Orb2A/ex1Htt chimeric proteins ( 20 μM ) were incubated at 37°C without stirring in PBS ( pH 7 . 0 ) , and 2 μl of each sample obtained at selected intervals was spotted onto a nitrocellulose membrane . The Orb2A/ex1Htt chimeric proteins were constructed by overlapping PCR cloning , using as template a clone that contains the full sequence for HttQ72 [72] . For EGCG and AmB immunodot blot analysis , the Orb2A monomer was prepared by protein denaturation in 6M GdmCl ( as 0 min reference time ) and then dialyzing in 5 mM potassium phosphate ( pH 7 . 4 ) + 150 mM NaCl to a final concentration of 2 . 5 to 5 μM . DMSO ( AmB vehicle ) , or a 4:1 molar excess of AmB and EGCG was added . After blocking for 1 h at RT with 10% nonfat milk in TBS containing 0 . 01% Tween 20 ( TBS-T ) , the membrane was incubated for another 1 h at RT with the polyclonal specific anti-oligomer A11 antibody ( Life Technologies ) or the fiber-specific monoclonal antibody OC ( Millipore ) , diluted 1:1 , 000 in 3% BSA TBS-T . The membranes were then washed three times for 5 min each with TBS-T before being incubated for 1 h with anti-rabbit HRP conjugated anti-rabbit IgG ( GE Healthcare ) diluted 1:5 , 000 in 3% BSA/TBS-T at RT . After washing the membranes three times in TBS-T buffer , the blots were developed with the ECL Plus chemiluminescence kit from Amersham-Pharmacia ( GE Healthcare ) . Prefibrillar oligomers and fibrillar species of Aβ42 were used as positive controls for A11 and OC reactivity , respectively . In the case of full-length Drosophila Orb2A and Orb2B , recombinant proteins were overexpressed in E . coli BL21 ( DE3 ) cells and purified under denaturing conditions using Ni-NTA agarose ( Qiagen ) , according to the manufacturer’s instructions . The proteins were dialyzed against 1 M Urea , 100 mM KCl , 10 mM Na-HEPES ( pH 7 . 6 ) , 1 mM DTT , 0 . 1 mM CaCl2 , 1 mM MgCl2 and 5% Glycerol containing buffer at RT . Dialysis was performed in a Slide-A-Lyzer mini Dialysis unit with a 7 , 000 MWCO ( Pierce ) . For the A11 antibody ( Biosource ) immunoreactivity assay , 2 μl of the dialysed proteins at the time indicated were spotted on Protran BA3 nitrocellulose membrane ( Schleicher & Schhuell ) . The membranes were blocked overnight in TBS + 5% milk and then incubated with a 1:1 , 000 dilution of the A11 antibody for 1 h at RT . The binding of the A11 antibody was visualized with a HRP-tagged secondary antibody using Supersignal West Femto substrate ( Pierce ) . The COS-7 cell line was grown and maintained in supplemented DMEM with 10% ( v/v ) FBS ( Life Technologies ) . The day before performing microinjections , cells were plated on a 35 mm dish at a density of 1 x 105 cells per dish . Just before single-cell microinjection , Orb2A protein ( 2 . 5 μM in PBS [pH 7 . 4] ) was incubated in the presence or absence of a 4-fold molar excess ( 10 μM ) of AmB and EGCG for 100 min at 37 . 0°C to allow complex formation . The Orb2A:AmB:A11 ternary complex was formed by incubating the previously formed Orb2A:AmB binary complex for 3h at RT with the A11 antibody ( 100:1 , binary complex:A11 ) . Using a micromanipulator ( Narishige ) , the samples were microinjected into the cytoplasm of single COS-7 cells double-blind ( n = 100 to 200 cells per sample and repeated three times for each sample ) , along with fluorescein-labelled dextran ( 10 , 000 MW , Life Technologies ) , and fluorescence microscopy images were acquired using a CCD camera model C4742-95-12ER ( Hamamatsu Photonics ) . Dextran , 1% DMSO , Vamp2Cyt and AmB were also microinjected as negative controls . Cell viability was monitored under an IX70 fluorescence microscope ( Olympus ) by counting the number of fluorescein-positive cells displaying a healthy morphology 3 h after microinjection , and this value was assigned as 100% cell viability . Over the following 3 d , the number of fluorescein-positive cells was counted every 24 h to calculate the cell survival rate . The Orb2A/ex1Htt chimeric proteins were microinjected in the same conditions . The data are represented as the mean ± SEM , using two-way ANOVA and a Bonferroni post-test , and one-way ANOVA and a Tukey post-test for statistical analysis of the time course survival curves and survival rates after 24 h , respectively . Statistical analyses were performed using GraphPad Prism 5 ( GraphPad Software ) . ITC experiments to examine the interaction of full-length Orb2A with the minimal active core of the QBP1 and SCR peptides [54] were carried out in a VP-ITC microcalorimeter at 25°C . The proteins were equilibrated in PBS ( pH 7 . 4 ) by gel-filtration chromatography , and the equilibration buffer was used to prepare the peptide solutions . Protein/peptide binding was tested by successive injections of the protein ( 10 to 25 μl each ) into the reaction cell loaded with peptide at a high final ( peptide ) / ( protein ) molar ratio . The apparent heat of reaction for each injection was obtained by integration of the peak area . The heat developed with the protein or peptide dilutions was determined in separate runs , loading the sample cell or the injection syringe with buffer in the conditions used for the binding experiments . The complexity of the system and the lack of precise information on the distribution of the Orb2A conformations before and after complex formation precluded the quantitative analysis of the titration curves . The protein ( Orb2A , 185 μM ) and ligand ( QBP1 and SCR , 300 μM and 51 μM , respectively ) concentrations in the loading solutions were measured spectrophotometrically using their respective extinction coefficients . We used the following Drosophila strains obtained from the Bloomington Stock Center: Elav-Gal4 ( stock no . 458 ) , Drl-Gal4 ( stock no . 4669 ) , and UAS-GFP ( stock no . 1522 ) . The following transgenic lines were generated by us for the research described here: UAS-Orb2A-EGFP , UAS-Orb2B-EGFP , UAS-Orb2Δ88-EGFP , UAS-Orb2Δ162-EGFP , and UAS-Sup35NM-Orb2A-EGFP . Various genetic combinations were made by standard genetic crosses . We also used the UAS-QBP1 and UAS-SCR lines in which QPB1 and SCR were expressed at a similar level in fly heads when they were expressed under the gmr-Gal4 driver [53] . Flies expressing HttQ128 were previously described [50] . For the behavioural assays , the flies were maintained using standard fly husbandry . Briefly , flies were raised on standard cornmeal food at 25°C and 60% relative humidity on a 12 h/12 h light:dark cycle . All flies ( 1 d old ) were collected and placed in a vial with fresh food for 24 h at 25°C prior to behavioural testing , and all transgenic lines were backcrossed at least six times with white-eyed Canton-S flies . All controls and transgenic lines carried one copy of the mini white gene known to influence fly behaviour . The male courtship conditioning assay was modified from that described previously [73] . A five-d-old male virgin was paired with a freshly mated female for three sessions of 2 h each , with a 30 min rest period in between . Memory performance was tested with a fresh-mated female 5 min and 24 h after the three sessions of spaced training . A courtship index ( CI ) was measured as the fraction of time the tested male spent chasing the female in a 10 min interval . The Memory Index was calculated as: CI¯Naive−CI¯TrainedCI¯Naive×100 , where CI Naive and CI Trained are the mean courtship indices for independent samples of naive and trained males , respectively . GraphPad Prism version 4 . 0 was used for statistical analysis . We assumed statistical significance at *p < 0 . 05 . One-way ANOVA was used for comparing memory index between each genotype . The heat box apparatus was built in the workshop of Konrad Ochsner at the Universitiy of Wuerzburg and is a modified version of the one used by [74] . In brief , the heat chamber system consists of an array of 16 chambers ( length , 29 mm; width , 4 mm; height , 2 mm ) operated in parallel . Upper and counter ceilings are equipped with Peltier elements that control the temperature in the chamber . Glass side walls allow the transmission and detection of infrared light from an LED source ( invisible to the flies ) . When a fly walks along the length of the chamber , it casts a shadow on a bar code reader ( light gate array ) , and this signal is sent to a computer . The fly position signal from the bar code reader is sent to the computer with a frequency of 10 Hz . All experiments were conducted in complete darkness . Measurements are performed on at least three different days to avoid effects of daily variability . The experiment consists of a preference test , training and memory test . A computer controls the experiment in all three phases by continuously monitoring the time and direction of transition at the light gate . One half of the chamber is defined as the punished side and the other half as the unpunished side . These designations are switched for every experiment to avoid aftereffects of previous experiments ( e . g . , pheromones or stress signal ) . During the preference test ( 30 sec ) , the fly can explore the chamber at a constant temperature of 24°C , providing a measure of experience-independent spatial preference . In the training phase ( 4 min ) the whole chamber is heated at 37° , whenever the fly enters the punished side and cools down to 24°C when it enters the unpunished side . In the following 3 min test phase , the temperature is kept constantly at 24°C . The performance index ( PI ) is calculated as the difference between the time the fly spent in the unpunished versus that in the punished side of the chamber divided by the total time . Thus , the PI can range from −1 to 1 , with a PI of 0 indicating no side preference . The PI is a measure of heat avoidance and memory score during the training and memory test , respectively . To yield a measure of general activity , the sum of position changes over time is calculated . The thermosensitivity assay is recorded adjusting the temperature in the front versus back half of the chamber independently and also independent of the flies’ actions . It is stepped from 24°C on both sides to 24°C/37°C and 24°C/41°C for 1 min intervals , sequentially alternating the side with the higher temperature . The time spent on a given side is measured , and heat avoidance indices are calculated as above ( PI ) . All animals that do not show substantial motor activity or do not experience punishment were excluded from the study . Complete and detailed information on the cloning process for all the constructs used in this work , for the protein/polyprotein ( pFS-2 ) sample preparation , or for protein expression and purification , can be found in [41] . A complete list of the oligonucleotides used here can be found in S2 Table . The details of the experimental procedure and the analysis performed for the CR binding assay , far-UV CD spectroscopy , TEM imaging , 1-D 1H and 2-D 1H NOESY NMR spectroscopy , turbidimetry , and AFM-SMFS can be found elsewhere [41] . Additional information regarding FRAP and FRET can be found in [11] . The buffers , incubation times , and storage conditions , and the protein/peptide concentrations used in the aforementioned techniques , are detailed below . | Amyloids are ordered protein aggregates typically associated with neurodegenerative diseases , such as Alzheimer disease and Parkinson disease , which usually result in cognitive impairment . However , the amyloid state of the neuronal RNA binding protein Orb2 shows a quite opposite behaviour: instead of impairment , it is associated with memory consolidation . Here , we have characterized the structural features of Orb2 from the monomer to the amyloid state in order to determine why it shows this unexplained puzzling behaviour . Surprisingly , we find that Orb2 shares many structural traits with pathological amyloids . However , Orb2 rapidly forms amyloids , and its toxic intermediates are transient . Furthermore , we find that an anti-amyloidogenic peptide that blocks the initial structural transitions in pathological amyloids also blocks Orb2 amyloid formation and memory consolidation . Our results provide structural insights into how Orb2 amyloids can mediate stable memory while being nontoxic and how amyloid-based diseases may affect memory processes . They also suggest a new pharmacological approach for blocking memory consolidation . | [
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] | 2016 | Molecular Basis of Orb2 Amyloidogenesis and Blockade of Memory Consolidation |
Small RNAs called PIWI -interacting RNAs ( piRNAs ) are essential for transposon control and fertility in animals . Primary processing is the small RNA biogenesis pathway that uses long single-stranded RNA precursors to generate millions of individual piRNAs , but the molecular mechanisms that identify a transcript as a precursor are poorly understood . Here we demonstrate that artificial tethering of the piRNA biogenesis factor , Armi , to a transcript is sufficient to direct it into primary processing in Drosophila ovaries and in an ovarian cell culture model . In the fly ovarian somatic follicle cells , the transcript becomes cleaved in a stepwise manner , with a 5′→3′ directionality , liberating U1-containing ~24 nt piRNAs that are loaded into Piwi . Although uridines are preferred for generation of piRNA 5′ ends , processing takes place even in their absence , albeit at a lower efficiency . We show that recombinant Armi has 5′→3′ helicase activity , and mutations that abolish this activity also reduce piRNA processing in vivo . Another somatic piRNA pathway factor Yb , an interactor of Armi , is also able to trigger piRNA biogenesis when tethered to a transcript . Tethering-mediated primary piRNA biogenesis is also functional in the fly ovarian germline and loads all the three PIWI proteins present in this environment . Our study finds a broad correlation between piRNA processing and localization of the tethered factors to the cytoplasmic perinuclear ribonucleoprotein granules called germline nuage or somatic Yb bodies . We conclude that transcripts bound by Armi and Yb are identified as piRNA precursors , resulting in localization to cytoplasmic processing granules and their subsequent engagement by the resident piRNA biogenesis machinery .
Bulk of the eukaryotic genomes are composed of genetic material derived from mobile genetic elements called transposons . Their mobility within the genome can cause mutations or deletions , impacting genome integrity [1] . Given the diversity of transposable elements within any genome , small RNAs are used to sequence-specifically identify and repress transposons in organisms ranging from plants to animals . In animals , this task is entrusted with a set of gonad-specific 24–30 nucleotide ( nt ) small RNAs called PIWI-interacting RNAs ( piRNAs ) that associate with the PIWI clade Argonaute proteins [2] . The basic functional unit of the pathway consists of a small RNA bound to a PIWI protein , with the piRNA acting as a guide for the protein [3] . Some PIWI proteins function as small RNA-guided endonucleases ( slicers ) , while others recruit histone or DNA methylation machineries to mediate transcriptional repression of target genomic loci [2] . Any impairment of the piRNA pathway results in de-repression of transposons and failure of germ cell development , causing infertility . Biogenesis of piRNAs is a cytoplasmic event . Single-stranded transcripts that arise from large ( 50–100 kilobases ) genomic regions called piRNA clusters [4] or transposon transcripts and some genic mRNAs are substrates for piRNA production . They are transcribed by RNA polymerase II [5] and exported [6] to the cytoplasm where piRNA biogenesis factors are enriched in cytoplasmic perinuclear granules called nuage [7 , 8] . Primary processing is the default pathway that converts the piRNA precursors into thousands of piRNAs having a preference for a uridine at the 5′ end ( U1-bias ) . Since piRNA precursors resemble other cellular transcripts in having features like a 5′ cap and a 3′ poly A tail [5] , how they are specifically targeted to the primary processing pathway is largely unknown . Studies conducted in the Drosophila ovarian system indicate a role for sequences within the precursor transcripts in the recruitment process . In the fly ovarian germline , presence of complementary binding sites for piRNAs in transcripts , and the resultant slicing by PIWI proteins Aubergine and Ago3 identifies it as a precursor . This enables the entry of one of the cleavage fragments into piRNA biogenesis that generates a series of phased/non-overlapping piRNAs [9–11] . In contrast , Piwi slicing is demonstrated to be not essential for piRNA biogenesis in the fly ovarian soma [12] , and primary processing has to create piRNAs de novo from the precursors . Studies reveal that sequences termed piRNA-trigger sequences ( PTSs ) present at the 5′ end of piRNA-producing regions are necessary and sufficient for recruiting a transcript into the somatic piRNA biogenesis machinery [9 , 13] . Deletion of such sequences from an endogenous precursor transcript impacts piRNA biogenesis from the locus [13] . Precise nature of these poorly conserved sequences , and how they work are currently not understood , but they are assumed to provide binding sites for piRNA biogenesis factor ( s ) to initiate primary piRNA processing . In this study , we recruited piRNA biogenesis factors to a reporter transcript by artificial tethering and demonstrate its entry into primary processing using transgenic fly lines and an ovarian somatic cell culture model . Tethering of the conserved piRNA biogenesis factor Armitage ( Armi ) [14 , 15] or the somatic piRNA biogenesis factor Yb [16 , 17] to a transcript results in its identification as a piRNA precursor in the fly ovarian soma and a somatic cell culture model . This results in non-overlapping/phased conversion of the transcript into ~24 nt U1-containing primary piRNAs . A similar effect is seen when Armi is tethered to a transcript in the fly ovarian germline , with generated piRNAs entering all the three PIWI proteins present in this environment . We find that this ability to induce piRNA generation is broadly correlated with localization of the factors to cytoplasmic processing granules called nuage in germ cells [7] or Yb bodies in the soma [17 , 18] . Our study reveals a strategy for generating artificial piRNAs capable of targeting any germline gene , and provides a useful tool for dissecting the molecular mechanisms of primary piRNA biogenesis .
The Drosophila female germline is a widely used model for piRNA research . The fly ovaries are organized into a series of egg chambers , each of which is composed of a single layer of somatic follicle cells that enclose the germline ( nurse cells and the developing egg ) ( Fig 1A ) . While the germline expresses all three PIWI proteins ( Piwi , Aubergine and Ago3 ) , the soma is a simple system operating a primary piRNA pathway that loads Piwi . The flamenco cluster is the largest source of piRNAs in this environment , and a fragment consisting of the 1st exon of the flamenco , termed piRNA-trigger sequence ( PTS ) , when fused to any heterologous transcript is capable of initiating piRNA biogenesis [9 , 13] . We reproduced these results using a reporter consisting of the flamenco PTS placed between luciferase and LacZ sequences ( Fig 1B ) . Expression of the reporter in the ovarian somatic cell ( OSC ) culture model [19] results in the directional production of piRNAs from the downstream LacZ sequences , which are loaded into Piwi ( Fig 1B and S1A Fig ) . Negligible levels of piRNAs are produced in the absence of the PTS element . The same reporter background ( two independent constructs ) , but carrying a perfectly complementary binding site for abundant Piwi-loaded piRNAs ( instead of the PTS element ) did not result in piRNA production in the OSC system ( S1B Fig ) . Thus we can rule out any role for Piwi slicing in somatic piRNA biogenesis , as already demonstrated [12] . So we hypothesize that the PTS recruits piRNA biogenesis factors to initiate primary processing . We tested this possibility by directly recruiting piRNA biogenesis factors to a transcript in the ovaries of transgenic flies . To this end , we replaced the PTS sequence in the above reporter with five BoxB ( 5BoxB ) hairpins ( Fig 1C ) . When co-expressed with λ-N peptide-fusion proteins , the BoxB/N-peptide interaction [20] will artificially tether the protein at a central location within the transcript . We first tested Armitage ( Armi ) which is a highly conserved RNA helicase that is essential for production of all piRNAs in flies [15 , 21] . Its orthologue MOV10L1 has a similar role in mice [22–25] . Transgenic fly lines co-expressing both the BoxB reporter and NHA-Armi ( with the N-peptide and an HA-tag ) , specifically in the fly ovarian soma [under control of the traffic jam-GAL4 driver ( tj-GAL4 ) ] were generated . Entry of the reporter into the piRNA pathway was monitored by Piwi immunoprecipitations with fly ovaries and deep sequencing analysis of associated small RNAs ( Fig 1A ) . Tethering of NHA-Armi triggers piRNA production from the reporter , with most of the reads originating from the BoxB site and the LacZ region downstream ( Fig 1C ) . When HA-Armi ( that is unable to tether to the reporter ) is expressed , the BoxB reporter produces only low background levels of piRNAs ( Fig 1C ) . Although the reporter sequence has no particular nucleotide bias , the generated artificial piRNAs display a strong bias for having a uridine at the 5′ end ( U1-bias ) , a primary piRNA feature ( Fig 1D ) . Production of piRNAs from the upstream luciferase region is also increased upon the tethering ( S1D Fig ) but absolute levels remain low . This is not due to any particular features of the sequence , as the same stretch is used for piRNA production in other contexts [9] and as shown below ( S5A Fig ) . However , due to the very low levels of luciferase piRNAs triggered by Armi tethering , we limit the analysis only on piRNAs produced from LacZ region . To study how the transcript becomes cleaved during primary processing , we calculated the distances between neighbouring piRNAs . When 5′-to-5′ end distances were plotted , we observed peaks at positions ~25 , 50 and 75 nt , which correspond to multiples of the approximate length ( ~24 nt ) of a Piwi-bound piRNA ( Fig 1E ) . Measurement of 3′-to-5′ end distances reveals a major peak at position 1 and another one at ~25 nt ( Fig 1E ) . These likely correspond to the distance between 3′ end of a piRNA and the 5′ end of the one immediately downstream ( distance of 1 nt ) or to the piRNA even further downstream ( distance of ~25 nt ) . These observations indicate a phased/non-overlapping primary piRNA biogenesis mechanism [9–11] , where the primary processing machinery moves along the transcript in a stepwise/phased manner to introduce cleavages that simultaneously create the 5′ end of a piRNA and the 3′ end of the preceding one , liberating individual ~24 nt piRNAs ( Fig 1F ) . These phased cleavages are not always precise , but closely spaced ( 1 nt ) , giving rise to the additional 5′-to-5′ end distance peak at position 1 ( Fig 1E ) . Note that even in the absence of tethered Armi ( when co-expressing HA-Armi ) , the residual levels of reporter-derived piRNAs generated have a phasing signature ( S1C Fig ) . Taken together , direct binding of Armi to a transcript in the fly ovarian somatic follicle cells identifies it as a primary piRNA precursor , leading to phased piRNA production . Armi is a putative RNA helicase that has conserved sequence motifs essential for ATP binding and ATP hydrolysis ( Fig 2A ) . We directly tested this activity using recombinant Drosophila Armi ( Fig 2A and S2A Fig ) . We annealed a 5′-end labelled short single-stranded RNA ( ssRNA ) with a longer unlabelled complementary sequence to prepare double-stranded RNAs ( dsRNAs ) with either 5′ or 3′ single-strand overhangs . These RNAs were then incubated with Armi , either in the presence or absence of ATP , and reactions were resolved by 15% native polyacrylamide gel electrophoresis ( PAGE ) . Incubations with Armi , in the presence of ATP , resulted in the appearance of a fast-migrating short ssRNA band , indicative of RNA unwinding activity ( Fig 2B and S2B Fig ) . Interestingly , only the dsRNA with a 5′ single-stranded overhang was used by Armi as a substrate . RNA helicase activity was not observed in the absence of ATP or when the dsRNA has a 3′ single-stranded overhang . Importantly , this activity was abolished when a single amino acid mutation ( E863Q ) was introduced into the catalytic motif ( DEAG→DQAG ) of Armi ( Fig 2B and S2A Fig ) . This indicates that Armi is a 5′→3′ RNA helicase and is consistent with the known 5′→3′ RNA helicase activity of its mouse orthologue MOV10L1 [24] . Next , we wished to examine whether the RNA helicase activity is required for tethering-driven piRNA biogenesis . We created transgenic flies co-expressing the NHA-tagged catalytic-dead ArmiDQAG mutant protein and the BoxB reporter transcript in the somatic follicle cells of fly ovaries . When tethered to the reporter , overall piRNA production was reduced ( 2 . 5-fold ) compared to that driven by NHA-Armi ( Fig 2C ) . Examination of piRNA generation across the reporter transcript indicates a dramatic reduction in piRNA levels from transcript , except for those arising from the site of tethering ( BoxB sequences ) ( Fig 2D ) . A similar reduction ( 4-fold ) in overall piRNA levels is noted when we tethered a second Armi mutant ( NHA-ArmiGNT ) that carries a point mutation ( K729N ) in the ATP binding motif ( GKT→GNT ) ( Fig 2C ) . Again , piRNA levels decreased across the transcript , except from the site of tethering ( Fig 2D ) . Albeit at reduced levels , piRNAs initiated by Armi helicase mutants display a dominant bias for having a 5′ uridine , indicating genuine primary processing ( Fig 2E ) . In conclusion , helicase activity of Armi is essential for robust piRNA production from the tethered transcript . In addition to Armi , other factors are shown to be essential for piRNA biogenesis in the fly ovarian soma . These include the putative RNA helicase Yb [17 , 18 , 26] and the Hsp90 co-chaperon Shutdown ( Shu ) [27–29] , both of which we tested in our tethering assay using transgenic fly lines . When tethered to the reporter in the fly ovarian somatic follicle cells , Yb led to robust piRNA production from the reporter ( Fig 2C ) . The features of the generated piRNAs mirror that produced by Armi tethering . The sequences have a prominent U1-bias ( Fig 2E ) , and arise in absolute terms mostly from the BoxB sequences and the downstream LacZ region ( Fig 2D ) . Furthermore , measurement of piRNA-end distances reveals that Yb binding triggers phased primary piRNA processing of the transcript ( Fig 2F ) , as demonstrated above for Armi . In contrast , flies co-expressing NHA-Shu with the reporter revealed only background levels of piRNAs ( Fig 2C and 2D ) . We confirmed by Western analysis that NHA-Shutdown is indeed expressed in fly ovary lysates ( S2C Fig ) . These results indicate that Armi and Yb , but not Shu , when individually tethered to a transcript have the ability to identify it as a primary piRNA precursor in the fly ovarian somatic follicle cells . Most piRNA biogenesis factors are cytoplasmic , where they accumulate in perinuclear granules called nuage [7] . In the fly ovarian somatic follicle cells , this is represented by the Yb body [18 , 26] . To examine the localization of the various tethered factors , we carried out anti-HA staining of fly ovaries expressing fusion proteins under control of the soma-specific tj-GAL4 driver ( Fig 3 ) . Both HA- and NHA-tagged Armi were found in 1–2 granules/cell , which also contain endogenous Yb , identifying their presence within the Yb body ( Fig 3A and 3B ) . This was also true for NHA-Yb , which was co-localized with endogenous Armi ( Fig 3C and S2D Fig ) . In contrast , the ArmiDQAG and ArmiGNT mutant proteins were more dispersed and accumulated in numerous ( up to 10 ) cytoplasmic granules ( Fig 3A and 3B ) . Although most are non-overlapping with the Yb body , some do overlap ( Fig 3B ) . This mislocalization is not due to any impact on structural integrity of the protein , as the point mutant behaves similar to the wildtype during size-exclusion chromatography ( S2A Fig ) . Thus , loss of RNA helicase activity is directly responsible for failure of the mutants in accumulating in the Yb bodies . Interestingly , NHA-Shu is diffusely present throughout the cytoplasm of ovarian follicle cells , with no co-localization with the Yb body ( Fig 3C and S2D Fig ) . Thus , Armi mutants and Shu that fail to support robust tethering-initiated primary processing are found not to be co-localizing with the Yb body in the fly ovarian follicle cells . It is expected that localization in the Yb body might facilitate association with other piRNA processing factors , for example , like the biochemical association of Armi-Piwi that we demonstrate here ( Fig 2D ) . Taken together , we propose that tethering-induced piRNA production from the reporter transcripts is likely a consequence of the reporter transcript accumulating in the Yb bodies , where it is engaged by the resident piRNA biogenesis machinery . In the above studies , we demonstrated that recruitment of Armi or Yb to a transcript identifies it as a substrate for primary piRNA processing in the fly ovarian somatic follicle cells . To further dissect the requirements from the tethered protein and the reporter RNA for efficient piRNA processing , we made use of the OSC culture system [19] , which is a model for the ovarian somatic environment . OSC cultures were co-transfected with plasmids expressing the BoxB reporter and different NHA- or HA-fusion proteins . Cells were harvested 48-hours post-transfection and libraries were prepared with small RNAs isolated from Piwi immunoprecipitations ( Fig 4A and S3A Fig ) . These experiments largely confirm the findings with the transgenic flies: robust piRNA production when Armi or Yb is tethered to the reporter , but not when tethered with Shu ( Fig 4B and 4C and S3B and S3C Fig ) . Structural integrity of Armi is essential for this functionality , as tethering of the helicase domain alone is unable to trigger piRNA production ( Fig 4B and S3C Fig ) . As shown in fly ovaries , tethering of the ArmiGNT mutant resulted in reduced levels of piRNAs , while surprisingly , the catalytic-dead mutant ArmiDQAG induced piRNA levels comparable to that seen with the wildtype Armi protein . Interestingly , tethering of Piwi itself did not result in any piRNA production , and behaved similar to tethering of LacZ , a protein unrelated to the piRNA pathway ( Fig 4B and S3C Fig ) . Introduction of a point mutation ( D537A ) in Yb that is shown to abolish its RNA binding property [26] , did not affect its ability to induce piRNA generation ( Fig 4B and S3C Fig ) . This is expected , as artificial tethering to the transcript via N/BoxB system likely negates the requirement for this RNA-binding activity . Finally , we report that we do not see the phasing pattern of piRNA generation in the OSC system ( S3D Fig ) . We have no reason to believe that processing in the OSC proceeds differently than in the fly ovarian somatic follicle cells , so it is likely that technical aspects like transfections and small RNA library quality might have influenced our ability to detect it . Next , we probed the requirement of uridines in the reporter for tethering-driven piRNA biogenesis , as the generated primary piRNAs display a strong U1-bias ( ~75% ) . We modified part of the reporter sequence by creating two patches lacking any Us; no-U#1 and no-U#2 to prepare a U-less reporter and also prepared a U-interval reporter [11] having Us distributed at specific intervals ( Fig 4D and S1 Protocols ) . Lack of uridines in the no-U patches resulted in reduced levels of piRNAs ( Fig 4E and 4F and S4 Fig ) , indicating that Us are preferred , but in the absence of Us any available nucleotide is used for creating 5′ ends of piRNAs . Dramatically , while the U-interval reporter had an overall uridine composition of only ~ 4% , majority of the piRNAs generated displayed a prominent U1-bias ( ~52% ) ( Fig 4G and S4E Fig ) . These results align with the proposed uridine specificity ( S4D Fig ) of the nuclease Zucchini that generates the piRNA 5′ ends [10 , 11] . It is also possible that an additional enrichment of U1-containing piRNAs could be achieved by the nucleotide preference of the MID domain of PIWI proteins [3 , 30] . The above studies demonstrate that recruitment of Armi or Yb to a transcript triggers primary piRNA biogenesis that loads Piwi , which is the only PIWI clade member in the fly ovarian somatic follicle cells and the OSC culture system . In contrast , all the three PIWI proteins ( Piwi , Aubergine and Ago3 ) are expressed in the fly germline where there is a dominant dependence on PIWI slicing to initiate piRNA biogenesis . Slicing by Aubergine ( Aub ) and Ago3 reciprocally loads each other with secondary piRNAs whose 5′ ends are generated by direct slicer action [4 , 31] . Additionally , slicer cleavage of a target transcript by Ago3/Aub is also required to load Piwi with a series of phased primary piRNAs [9–11] . So we wished to examine whether our tethering-driven primary piRNA biogenesis might work in the fly germline . We created transgenic flies co-expressing the reporter and different fusion proteins in the fly ovarian germline using the NGT-GAL4 driver ( Fig 5A ) . Deep sequencing libraries were prepared with small RNAs present in isolated PIWI complexes ( Piwi , Aub and Ago3 ) . When tethered to the reporter , Armi is able to induce piRNA biogenesis that loads all three PIWI proteins , with much more sequences being loaded into Piwi than Aub or Ago3 ( Fig 5B and S5 Fig ) . However , since different polyclonal antibodies are used for PIWI immunoprecipitations , it would be difficult to definitively conclude preferential loading into any protein . The piRNAs associating with the three PIWI proteins display the phasing pattern ( Fig 5C ) and strong U1-bias ( Fig 5D ) , confirming their generation by primary processing . Interestingly , the ArmiDQAG mutant and Shu are also able to trigger piRNA generation . In contrast , the ArmiGNT mutant and the soma-specific piRNA factor Yb were inactive in the germline tethering assay ( Fig 5B and S5 Fig ) . Shu tethering induced piRNAs only to a low level compared to that initiated by Armi , but generated piRNAs from the upstream luciferase region also ( S5 Fig ) . Finally , we find a broad correlation between sub-cellular localization of the tethered proteins in the perinuclear nuage ( labelled with endogenous Ago3 ) of the germline nurse cells and their ability to initiate piRNA biogenesis on reporter RNA , with the exception of ectopic Yb , which was also localized in the nuage ( Fig 5E ) . Armi is shown to associate with Ago3 , and both proteins accumulate in the nuage along with other piRNA pathway factors [32] , allowing entry of the tethered transcripts into piRNA processing machinery . In conclusion , we demonstrate that nuage-localizing factors are able to channel a transcript into primary processing pathway in the fly ovarian germline .
Primary processing is the default pathway that generates piRNAs in all animal germlines . Since precursors are not unlike other cellular mRNAs or non-coding transcripts , there should be mechanisms in place to specify their entry into the processing machinery . Much is known about the secondary processing pathway operating in the fly ovarian germline , where PIWI slicing of a target transcript results in its entry into piRNA processing [9–11] . However , this depends on pre-existing piRNAs , which are suggested to be provided by maternal deposition in the egg . In contrast , primary processing has to kick-start piRNA production in the absence of pre-existing piRNAs ( as in fly ovarian soma ) , and without the use of PIWI slicing [12] . How this is achieved is poorly understood . Previous work implicated a role for sequences at the 5′ end of precursors termed piRNA-trigger sequences ( PTSs ) in recruiting the primary processing machinery in the OSC culture model [9 , 13] . PTS elements are poorly defined and lack conservation , preventing their detailed study , but our work provides strong support to the hypothesis that they might provide landing sites for specific piRNA biogenesis factors . In this study , we demonstrate that presence of a perfectly complementary site for abundant piRNAs within a reporter did not trigger piRNA biogenesis in the OSC system ( S1B Fig ) . Instead , we show that artificial recruitment of primary biogenesis factors , Armi and Yb , to a reporter transcript is sufficient to identify it as a primary piRNA precursor ( Figs 1 and 2 ) . Among these , Armi is highly conserved and works in all the systems tested: fly ovarian soma and germline , and in the OSC cultures . Armi [14 , 15 , 21] and its mouse orthologue MOV10L1 [22–25] are absolutely essential for biogenesis of all piRNAs in flies and mice . In contrast , Yb is restricted to Drosophila , pointing to a non-conserved role for the protein in the fly somatic follicle cells [16] . The known interaction between Yb and Armi [16 , 17] might ensure that Yb-tethered transcripts enter primary processing in the fly soma and in the OSC system ( Figs 2 and 4 ) , while lack of functionality of ectopically expressed Yb in the germline ( Fig 5 ) could be due to competition from its germ cell specific homologues BoYb and SoYb [16] . Armi- or Yb-mediated primary processing of the tethered transcript strongly resembles that initiated by PIWI slicing in the fly germline [9–11] or in the mouse male germline [33 , 34] . In both situations the transcript undergoes phased processing to generate piRNAs with a strong U1-bias , and predominantly proceeds in a 5′→3′ direction . This points to different modes of precursor identification that eventually channels the transcript into a common piRNA biogenesis machinery . We propose that tethering by nuage- or Yb body-localizing factors results in a fast-track access for the transcript to the resident piRNA biogenesis machinery in these cytoplasmic processing sites . RNA helicases are shown to recognize target RNAs in a sequence-independent manner [35] , and this raises the possibility that any spurious association of piRNA biogenesis factors with other cellular RNAs can lead to their entry into the piRNA pathway , a situation that germ cells must actively prevent from happening . We believe that our tethering-mediated piRNA biogenesis strategy provides a valuable tool for further exploring the molecular mechanisms of primary piRNA processing and may even be harnessed for creation of designer small RNAs that can target any germline gene for epigenetic silencing .
Antibodies to all three Drosophila PIWI proteins used in this study were previously described [9] . These include rabbit polyclonal antibodies ( two rabbits: GJKO and GJLD ) to Drosophila Piwi that were generated ( EMBL Protein expression and purification core facility ) against an insoluble antigen ( Piwi antigen: 42–178 aa ) produced in E . coli as an inclusion body . Single rabbits were used to generate the antibodies to Drosophila Aub and Ago3 ( Aub antigen: 1–200 aa; Ago3 antigen:1–200 aa ) . Immunized rabbit sera were directly used for immunoprecipitation . For expression in the Drosophila ovarian somatic cell ( OSC ) cultures [19] , we used the pAC5 . 1 vector ( Life Technologies ) driving expression from the fly actin promoter [9] . For expression of either HA-tag ( pAC-HA ) or N-HA-tag fusions ( pAC-NHA ) , the pAC5 . 1 vector was further modified to add the necessary coding sequences . The HA tag is for detection of the expressed protein and the λN-peptide is for artificially tethering the fusion protein to a transcript containing BoxB sequences [20] . For creating transgenic fly lines , the coding sequences for NHA- or HA-tagged fusions of Armi , Yb or Shu , and the point mutant versions were inserted into the pUASp_attB_delK10 plasmid containing the white+ gene marker . These were used for site-specific integration ( BestGene , Inc ) in the Drosophila genome using the PhiC31 ( ΦC31 ) integrase-mediated transgenesis system . Details of crosses are given in S1 Protocols . Drosophila ovarian somatic cell ( OSC; gift of Dr . M . Siomi , University of Tokyo ) culture system is representative of the fly ovarian somatic follicle cells [19] . OSCs were cultured in 75 cm flasks and grown to 80% confluence . Approximately 3 . 5x106 cells were used for each electroporation reaction using Cell Line Nucleofector Kit V ( Lonza , Cat No . VCA-1003 ) and were plated in 6-well plate . Further details in S1 Protocols . For production of recombinant proteins in the insect cells the following ovary-derived cells were used: Sf21 or Sf9 from Fall Army worm Spodoptera frugiperda or High Five ( Hi5 ) from the cabbage looper , Trichoplusia ni . Expression of desired coding sequences was carried out with the use of recombinant Baculoviruses . Either single or multiple coding sequences were integrated into the Baculovirus genome using the MultiBac protein expression system [36] . The coding sequence for Drosophila Armitage ( Armi ) was isolated by RT-PCR from fly ovarian total RNA , while the codon-optimized DNA sequence was commercially synthesized ( Shanghai ShineGene Molecular Biotech , Inc . ) . Detailed purification steps in S1 Protocols . RNA unwinding reaction was performed as described [37 , 38] with some modifications . Single stranded RNA oligos were chemically synthesized ( Microsynth , CH ) and sequences are given in S1 Protocols . Substrates for RNA unwinding assay were prepared by annealing a 5′-endlabelled strand that was annealed with its unlabelled complementary partner . For details see S1 Protocols . Reads were sorted into individual libraries based on the barcodes and the 3′ adapter sequences were clipped using cutadapt ( DOI:http://dx . doi . org/10 . 14806/ej . 17 . 1 . 200 ) . Reads which are at least 15 nucleotides in length were used for subsequent analysis and the independent replicated libraries were merged together . Reads were then aligned to the desired reporter sequence using bowtie [39] allowing no mismatches . Analyses were performed as previously described [9] . See S1 Protocols for details . | PIWI-interacting RNAs ( piRNAs ) are 24–30 nucleotide ( nt ) small RNAs that are exclusively expressed in animal germlines and are essential for suppression of transposable elements or ‘jumping genes’ . Millions of piRNAs are produced from single-stranded transcripts that arise from large RNA polymerase II transcription units called piRNA clusters . Since they resemble other RNA pol II products like protein-coding mRNAs or non-coding RNAs , how piRNA precursors are selectively recruited to the biogenesis machinery is a big mystery . Here we demonstrate that artificial tethering of specific piRNA biogenesis factors to a reporter transcript is sufficient to identify it as a piRNA precursor in Drosophila ovaries and in an ovarian cell culture model . This results in fragmentation of the transcript into thousands of piRNAs , which are generated as a series of non-overlapping ( phased ) fragments that are loaded into PIWI proteins . Our study indicates that localization of the tethered factors to cytoplasmic perinuclear granules called nuage/Yb bodies is necessary for processing . Any mutations in the tethered factors that disrupt this sub-cellular localization , reduce processing . We believe that our strategy will allow generation of designer small RNAs to target almost any germline gene , and also provides a valuable tool to dissect the molecular mechanism of piRNA biogenesis . | [
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] | 2017 | Recruitment of Armitage and Yb to a transcript triggers its phased processing into primary piRNAs in Drosophila ovaries |
Linear mixed effect models are powerful tools used to account for population structure in genome-wide association studies ( GWASs ) and estimate the genetic architecture of complex traits . However , fully-specified models are computationally demanding and common simplifications often lead to reduced power or biased inference . We describe Grid-LMM ( https://github . com/deruncie/GridLMM ) , an extendable algorithm for repeatedly fitting complex linear models that account for multiple sources of heterogeneity , such as additive and non-additive genetic variance , spatial heterogeneity , and genotype-environment interactions . Grid-LMM can compute approximate ( yet highly accurate ) frequentist test statistics or Bayesian posterior summaries at a genome-wide scale in a fraction of the time compared to existing general-purpose methods . We apply Grid-LMM to two types of quantitative genetic analyses . The first is focused on accounting for spatial variability and non-additive genetic variance while scanning for QTL; and the second aims to identify gene expression traits affected by non-additive genetic variation . In both cases , modeling multiple sources of heterogeneity leads to new discoveries .
Population stratification , genetic relatedness , ascertainment , and other sources of heterogeneity lead to spurious signals and reduced power in genetic association studies [1–5] . When not properly taken into account , non-additive genetic effects and environmental variation can also bias estimates of heritability , polygenic adaptation , and genetic values in breeding programs [5–8] . Both issues are caused by departures from a key assumption underlying linear models that observations are independent . Non-independent samples lead to a form of pseudo-replication , effectively reducing the true sample size . Linear mixed effect models ( LMMs ) are widely used to account for non-independent samples in quantitative genetics [9] . The flexibility and interpretability of LMMs make them a dominant statistical tool in much of biological research [9–18] . Random effect terms are used in LMMs to account for specific correlations among observations . Fitting an LMM requires estimating the importance of each random effect , called its variance component . General-purpose tools for this are too slow to be practical for genome-scale datasets with thousands of observations and millions of genetic markers [19] . This lack of scalability is caused primarily by two factors: ( i ) closed-form solutions of maximum-likelihood ( ML or REML ) or posterior estimates of the variance components are not available and numerical optimization routines require repeatedly evaluating the likelihood function many times , and ( ii ) each evaluation of the likelihood requires inverting the covariance matrix of random effects , an operation that scales cubically with the number of observations . Repeating this whole process millions of times quickly becomes infeasible . To this end , several specialized approaches have been developed to improve the speed of LMMs , including the use of sparse matrix operations [20 , 21] , spectral decomposition of the random effect covariance matrix [22–26] , and Monte Carlo REML [27] . These algorithms are particularly useful when the same random effect structure is used many times . For example , in genome-wide association studies ( GWAS ) , each marker is tested with the same LMM . Similarly , in population-level transcriptomics , eQTLs or variance components are estimated for each of tens-of-thousands of genes expression traits . Fast and exact algorithms for fitting LMMs are limited to the case of only a single ( full-rank ) random effect term , besides the residuals [22–24] . Recently , approximate learning algorithms have been developed for the scalable extension to multiple random effects [28 , 29] , but few of these ensure guarantees in terms of estimation accuracy . One strategy applicable to studies with multiple random effects is to estimate the variance components only once , in a model without additional marker effects , and then test each marker either using a score test [30] ( which does not produce an effect size estimate ) , or with a conditional F-test assuming the variance component estimates are fixed [31–34] . Given the “known” variance components , closed-form solutions of all other parameters of an LMM can be found using a rotated version of the simple linear model . Unfortunately , both approximations suffer from reduced power when marker effects are large , intractable posterior inference in a Bayesian analysis , and the inability to be applied to parallel analyses over many traits ( like gene expression ) . Table 1 summarizes these different methods , details their respective computational complexities , and provides relevant references . Grid-LMM takes a different approach to fitting LMMs: rather than directly optimizing the variance components separately for each test , we define a grid spanning all valid values of the variance components and fit simple linear models at each grid location . Each evaluation involves a single Cholesky decomposition of the random effect covariance matrix , which is then reused to calculate closed-form ML solutions or Bayesian posterior summaries ( under a specific conjugate prior; see S1 Text ) for all separate tests . This results in dramatic time-savings in typical GWAS settings ( see S1 Fig ) . After repeating these calculations across the whole grid , we select the highest ML ( or REML ) score for each marker to compute approximate likelihood ratio and Wald tests [32 , 38] , or analogously derive posterior distributions and Bayes factors by summing appropriate statistics across the grid . The Grid-LMM approach relies on a re-parameterization of the typical LMM framework from individual variance components σ l 2 to variance component proportions h l 2 = σ l 2 / σ 2 , where σ2 without the subscript denotes the total sum of all variance components ( including the residual ) . Since the variance components must be non-negative , their proportions are restricted to the unit interval [0 , 1] and sum to 1 , forming a simplex . Therefore , a finite grid can span all valid parameter values . While the size of the grid increases rapidly with the number of random effects , for a small number of random effects ( ∼1-8 ) and a moderate grid resolution , the size of the grid remains tiny relative to the number of models in a typical GWAS . As we show below , highly accurate test statistics are achieved even with a coarse grid in most reasonable situations , and further improvements to efficiency are possible by using heuristics to adaptively sample the grid or reduce the number of grid locations computed for the majority of tests . This strategy of conditioning on variance components over a grid and then combining solutions can be applied to many other tools in quantitative genetics including set tests for rare variants [39 , 40] , whole-genome regression models such as LASSO and elastic net [41 , 42] , and QTL mapping in controlled crosses [43] . In the following sections , we demonstrate the accuracy and advantages of the Grid-LMM approach using a simulation study and two real genome-wide quantitative genetics examples . The first is for GWAS , where tens-to-hundreds of thousands of markers are individually tested for associations with a single phenotype . The second is for gene expression , where thousands of traits are each tested for non-additive genetic variance . In both cases , the same random effect structure is used for each model . While approximate , the test-statistics and posterior quantities calculated by Grid-LMM are accurate and improve power relative to other approximation methods—all while maintaining dramatically reduced computational burdens than the direct approach . Full derivations of the model and useful heuristics are described in detail in the Methods .
As a first case-study , we used Grid-LMM to perform two types of genome-wide association studies ( GWAS ) that benefit from modeling multiple random effects: ( 1 ) the study of gene-environment interactions , and ( 2 ) detecting associations for phenotypes driven by non-additive genetic variation or spatial variation . In both cases , there are multiple sources of covariation among observations that can inflate test statistics and bias estimates of heritability if not appropriately accounted for by using mixed models . As an example of a GWAS with gene-environment interactions , we analyzed data on flowering times for Arabidopsis thaliana [44] . First , we benchmarked results from standard LMM methodologies to confirm that Grid-LMM association tests are accurate . We ran Grid-LMM on both a fine-grained grid with a small step size of 0 . 01 h2-units , and a larger step size of 0 . 1 h2-units , to test for associations between 216 , 130 single nucleotide polymorphisms ( SNPs ) and flowering times of 194 accessions measured at 10C ( i . e . in a constant environment ) . We compared the Wald-test p-values computed by both Grid-LMM models to p-values computed using the exact method GEMMA [24] , and the approximate method EMMAX [32] . Each method was applied as an LMM with only the additive relationship matrix as its single random effect . Grid-LMM p-values computed using the finer grid size ( i . e . 0 . 01 h2-units ) were almost identical to those of GEMMA , and even p-values computed with the larger step size ( i . e . 0 . 1 h2-units ) were more accurate than those resulting from EMMAX . There were particularly noticeable differences in performance for markers with larger scores , which were strongly underestimated by EMMAX since its approximations of h2 are made strictly under the null model ( Fig 1a ) . This pattern held true across all of the available 107 A . thaliana phenotypes ( see S2 Fig ) . However , despite these results , we do not advocate Grid-LMM in this setting; GEMMA ( and similar methods ) provides an exact test and is more computationally efficient . The real advantage of Grid-LMM is its ability to effectively model two ( or more ) random effects . To demonstrate this advantage , we tested for gene-environment ( G×E ) interaction effects on flowering time . Here , we combined flowering time data from two conditions: constant 10C ( as described above ) and in the field . We limited our analysis to the 175 accessions that were grown under both conditions , yielding a total of n = 350 observations . When observations come from different environments , we might expect phenotypes to cluster for at least two reasons: ( i ) the sharing of alleles with constant effects across environments due to relatedness and population structure , commonly modeled with the additive genomic relationship matrix ( A ) as a random effect , and ( ii ) the sharing of alleles with effects that differ among environments ( or are specific to one environment ) . Previous work has shown that , when testing for G×E effects with GWAS , modeling the second source of covariance by using a second random effect can prevent spurious signals and increase power [34] . The same result is replicated in this setting using simulations ( see S3 and S4 Figs ) . Here , we calculated G×E p-values for each genetic marker using Grid-LMM ( grid step size = 0 . 01 h2 units ) on the full dataset using both the A and the G×E relationship matrices as two random effects . These are compared to p-values from ( i ) an LMM which ignores the G×E covariance and only considers genetic similarity , and ( ii ) a model similar to pylmm [34] which does consider both random effects but estimates variances components from the null model ( this is referred to as null-LMM-G×E below ) . For each model , we included an additional random effect to account for the repetition of accessions which could induce additional covariance among observations . In this dataset , a simpler approach to testing for G×E was also available: test for associations between markers and the plasticity ( i . e . the difference in flowering time between the field and 10C ) of each accession , which requires only a single random effect and can be fit with GEMMA . This simple approach is only possible because every genotype was measured in both environments . Nonetheless , it is expected to produce identical tests to the full three-random-effect model and , therefore , serves as a viable benchmark for the Grid-LMM results . We used the plasticity GWAS approach as a baseline to compare the three models that use the raw data directly ( Fig 1b ) . As expected , ignoring the G×E covariance leads to greatly inflated tests for the majority of markers . Grid-LMM replicated GEMMA’s plasticity p-values almost exactly when run with three random effects; alternatively , a portion of the null-LMM-G×E’s tests were deflated , implying lower power . The full analysis using Grid-LMM with three random effects took 26 minutes . Fitting the same model for all 216 , 130 markers directly using an exact method ( e . g . LDAK [7] ) would take approximately 6 hours ( about 14× longer ) . Note that LDAK is not designed for re-estimating variance components for each SNP in multiple-random-effect models and so , to conduct time comparisons , we simply ran the program multiple times . This requires LDAK to re-load the covariance matrices for each marker . However , by controlling the maximum number of iterations in the LDAK optimization engine , we estimate that a maximum ≈ 33% of the running time for these models is due to data import , with the remainder being due to the numerical calculations . Even when all individuals are measured in the same environment , the typical additive relationship matrix may not account for all sources of covariation . In particular , spatial variation and the sharing of non-additive alleles may also induce covariance , lead to reduced power , and/or result in inflated tests in GWAS [45] . As an illustrative example , we tested for genetic association between 10 , 075 bi-allelic autosomal markers and body mass among 1 , 814 heterogenous stock mice from the Wellcome Trust Centre for Human Genetics ( WTCHG ) [46] . We first computed additive and pairwise-epistatic relationship matrices , as well as a spatial-environmental covariance matrix based on the 523 different cages used in the experiment . Next , we compared p-values derived from an LMM considering only the additive relationship matrix ( as in a typical GWAS ) to those calculated by Grid-LMM using all three relationship matrices ( Fig 2a–2d ) . Using the three-random effect model , we identified associations on two chromosomes ( numbers 4 and 11 ) that were not apparent when just using the additive relationship matrix as a random effect . Both loci have been previously identified as size-associated QTL ( see Table S1 Table ) [47 , 48] . Genomic control values for the two models were both close to one ( A-only = 0 . 975 , A+E+Cage = 0 . 974 ) ( Fig 2b and 2d ) . The three-random effect model took 8 . 5 minutes to fit using Grid-LMM , while a full analysis on all 10 , 346 markers would have taken approximately 10 hours with LDAK ( more than 100× longer , of which we estimate a maximum of ≈ 10% is spent on reading in data ) . Extrapolating to a consortium sized genome-wide analysis with 1 million markers would take ≈ 14 hours using Grid-LMM , as opposed to 40 days using LDAK . We see larger performance gains in the mouse dataset compared to the Arabidopsis dataset because of the larger sample size ( n = 1 , 814 vs . 350 ) . LDAK ( and other exact general-purpose REML methods ) is dominated by matrix inversions which scale with n3 , while Grid-LMM is dominated by matrix-vector multiplications which scale with n2 ( again see Table 1 ) . The performance advantage of Grid-LMM will increase even more for datasets with more individuals . To demonstrate the flexibility of Grid-LMM for GWAS , we also ran an analysis on the mice body weight trait using Bayesian inference and calculated Bayes Factors by comparing the models for each marker to the null model assuming no genetic effects . Here , we used a uniform prior over the grid of variance component proportions and assumed a standard normal prior for the marker effect sizes . In this setting , the Bayes Factors were highly correlated with the frequentist p-values—they also highlight the same associations on chromosomes 4 and 11 ( Fig 2e ) . In general , Bayes Factors provide additional flexibility for incorporating prior information on individual markers or even combining results among multiple studies [49 , 50] . The full Bayesian analysis for Grid-LMM took 15 . 5 minutes , just 7 minutes longer than the frequentist analysis , making it practical for genome-wide studies . As a third case-study , we used Grid-LMM to estimate the additive and pairwise-epistatic variance components for 20 , 178 gene expression traits measured on 681 Arabidopsis accessions from the 1001 Genomes Project [51] . Using a grid-size of 0 . 05 h2 units , we estimated the magnitude of each variance component by REML ( Fig 3a ) . The whole analysis took ≈ 6 minutes . Finding REML solutions for the same two-random effect model , on each of the traits separately , using the exact method LDAK took ≈ 90 minutes ( of which ≈ 20% was due to data import ) . Grid-LMM variance component estimates replicated those of LDAK accurately , but with less precision due to the coarse grid size ( see S5 Fig ) . Notably , for many genes , the proportion of variance in expression attributed to additive variance dropped considerably when the epistatic variance was also modeled ( see S6 Fig ) . Therefore , including multiple random effects can have significant impact on downstream conclusions for many traits . Even with this relatively large sample size for a population-level gene expression dataset , considerable uncertainty remains in the estimated variance components . The point-estimates calculated by REML do not capture this uncertainty and estimating confidence intervals for the variance components using REML is difficult . The full Grid-LMM approach can be used to calculate posterior distributions for each variance component with little additional cost—note that MCMC sampling is not needed because a reasonably-sized grid can span all valid values of each variance component proportion ( see Methods ) . Using this approach , we identify 8 , 585 genes with a posterior probability of non-zero epistatic variance greater than 90% , and 28 more genes with a posterior mean epistatic variance component greater than 80% . For two example genes , we show that the fitted posterior distributions are similar to those estimated via MCMC using rstan [52 , 53] ( see Fig 3b and 3c ) . The rstan analyses took ≈ 20 hours per gene to generate an effective sample size of ≈ 200−400 for the variance component parameters . Therefore , posterior inference by MCMC for all 20 , 178 genes would take about 50 years of computational time . Now that we have demonstrated in real data examples that Grid-LMM is accurate , fast , and expands the range of genetic analyses that are computationally feasible , we turn to the question of scalability . Specifically , we assess whether Grid-LMM is sufficiently accurate for the much larger sample sizes commonly used in modern human GWAS’s . There are no conceptual hurdles to implementing Grid-LMM for studies with tens-to-hundreds of thousands of samples and the improvement in time , relative to a direct mixed modeling approach , should increase dramatically ( see S1a Fig ) . Unfortunately , total computational time and memory requirements will grow significantly as well ( see S1b Fig ) . For example , storing a single Cholesky decomposition of the random effect covariance matrix for 100 , 000 samples would require approximately 80 Gb RAM and would take approximately two days to compute . This contrasts with BOLT-LMM which can run a GWAS analysis of this size in less than an hour , while using less than 10 Gb RAM [27] . However , BOLT-LMM is restricted to a single random effect and uses a “null-LMM” approach for estimating variance component parameters as part of a two-step analysis . To test if Grid-LMM’s accuracy changes with larger sample sizes , we artificially increased and decreased the sample size of the WTCHG mouse dataset by a factor of 5 , simulated phenotypic data based on a randomly selected marker and the same three random effects used in the WTCHG analysis above . We then compared the marker’s p-values calculated with the exact mixed model ( Exact-LMM ) to those calculated using Grid-LMM with two resolutions: 0 . 1 and 0 . 01 h2 units . As a baseline , we also calculated p-values with the two-step method that estimates variance components under the null ( null-LMM ) . See Methods for details on the simulations . For the Grid-LMM tests , we assumed that the nearest grid vertices were exactly centered around the variance component estimate from the exact mixed model . This represented a “worse-case scenario” for Grid-LMM . As a function of the variance contributed by the tested marker , the mean relative difference in p-values among the four methods was approximately constant across the three sample sizes ( Fig 4a ) . There were large differences when the marker effect was large , diminishing to no difference for small effects . Grid-LMM ( 0 . 01 ) was barely distinguishable from the Exact-LMM across all sample sizes and marker effect sizes . Mean ( -log10 ) p-values from Grid-LMM ( 0 . 1 ) and null-LMM were similar to Exact-LMM for small effect sizes , but Grid-LMM ( 0 . 1 ) was closer to Exact-LMM for large effect sizes . This is expected because most randomly selected markers are correlated with the dominant eigenvectors of the additive relationship matrix; hence , large effect markers will affect the variance attributed to the random effect , but small effect markers will not . While the relative change in ( -log10 ) p-values is approximately constant , the range of effect sizes where the approximate methods have a negative impact on power changes across sample sizes . Assuming a genome-wide significance threshold of -log10 ( p ) = 8 in this dataset , even the null-LMM method will consistently declare any marker with an effect size >≈ 0 . 02% of the total variance as significant for sample sizes ≈ 10 , 000 . If we focus specifically on the range of effect sizes where the difference among methods may have an impact on power Fig 4b , the relative performance of the approximate methods do change . At the smallest sample size ( i . e . n = 362 ) , mean -log10 ( p ) -values of Grid-LMM ( 0 . 1 ) were closer to those of Exact-LMM than those of null-LMM . At the medium and large sample sizes ( n = 1814 and n = 9070 ) , the mean -log10 ( p ) -values from null-LMM were more accurate than those from Grid-LMM ( 0 . 1 ) . However , the results of the finer Grid-LMM ( 0 . 01 ) model remained nearly indistinguishable from those of Exact-LMM , irregardless of the number of samples . Note that when using the Grid-LMM-fast heuristic , Grid-LMM will never perform worse than null-LMM because we peg the grid to the variance component estimate under the null model . Fig 4c compares the estimated running times of GWAS analyses with different numbers of markers for each of the three sample sizes . With > 100 markers , running times for the Grid-LMM methods were intermediate between Exact-LMM and null-LMM , with the advantage over Exact-LMM increasing for large sample sizes . At all sample sizes , Grid-LMM ( 0 . 1 ) is linearly slower than null-LMM since it effectively requires running null-LMM at each grid vertex . At small and intermediate sample sizes this speed penalty is justified by increased power . At large sample sizes null-LMM is just as accurate for effect sizes relevant to power , so Grid-LMM is not needed ( Fig 4b ) .
Grid-LMM addresses a central obstacle to the practical use of linear mixed models: the computational time needed to find optimal solutions for variance components . Our key observation is that for many common quantitative genetics analyses , optimizing variance component parameters to high precision is not necessary . When sample sizes are large , statistical power will be sufficient to detect associations even under the approximate null-LMM methods such as EMMAX [32] , pylmm [34] , or BOLT-LMM [27] . However , when sample sizes are more limited , as in the examples we have shown here , the closer approximations achieved by Grid-LMM can increase power without greatly increasing computational requirements . Such sample sizes are common in model systems genetics , evolutionary biology , agricultural biology , as well as in eQTL studies . From a Bayesian perspective , the posterior distribution of variance components tends to be broad even with large sample sizes , and coarse approximations can be sufficient given the uncertainty in their true values [55] . In GWAS applications , magnitudes of variance components are of less interest than the accuracy of test statistics for the ( fixed ) SNP effects , and we show that these are sufficiently accurate even with approximate variance component proportions . The advantage to relaxing the need for perfect variance component solutions is a vast reduction in both computational time and algorithmic complexity . This reduces the time required for a typical GWAS sized dataset with two-or-more random effects from days to hours , and provides a framework for applying LMMs to even more powerful statistical tools [56–58] . We optimize variance component estimation with a grid search , the simplest type of optimization algorithms . At each grid vertex , after conditioning on ( relative ) variance component values , the LMM simplifies to a simple linear model; therefore , general purpose solutions to normal linear models are available . This means that the simple LMMs we explored in this paper can easily be generalized to more complex methods used in other GWAS-type applications that currently cannot easily be extended to experiments with heterogeneous samples ( e . g . set-tests and multi-marker regressions ) . We demonstrated Grid-LMM using three examples that are broadly representative of many experimental settings in quantitative genetics . The first was a study of gene-environment interactions , while the second and third focused on the partitioning of genetic variance among additive and non-additive components . Recent studies have shown how neglecting gene-environment covariance when estimating heritability [8] or testing genetic associations [34] can lead to biased estimates . There is little reason to expect these results to be limited to controlled environmental settings , as we have studied here . Previous work has found that incorporating spatial covariance through a Gaussian radial basis function improved estimates of trait heritability within a sample of individuals from Uganda [8] . Spatial variability also exists in agricultural field trials , and two-step approaches are frequently used where spatial variation is removed first and then genetic associations are tested on the residuals . This two-step procedure may be underpowered when true effect sizes are large . Similarly , epistatic and other non-linear genetic variation are known to be large for many traits [59–61] and accounting for this variation may improve our ability to detect both the main effects of markers ( as we demonstrated above ) , as well as possibly interacting loci [17] . The slight differences in posterior means between the Grid-LMM and MCMC-based posterior estimates in Fig 3b and 3c are due to differences in the priors . MCMC-based LMM implementations classically use inverse-Gamma priors for variance components because of conjugacy [20] . However , others have recommended uniform or half-t-family priors for the standard-deviation parameters of hierarchical models [62] , which are easily implemented in Stan [52] . We used a half-Student-t ( 3 , 0 , 10 ) distribution for each variance component in our rstan model to produce Fig 3b and 3c . This is easy to approximate in Grid-LMM; relative prior weights can simply be applied to each grid-vertex , resulting in much closer agreement of posterior summaries between the two methods ( see S7 Fig ) . As we show in S8 Fig , supposedly “uniformative” versions of both the inverse-Gamma and half-Cauchy-type priors are actually highly informative for variance component proportions . In our experience , it is more natural to elicit priors on variance component proportions than variance components themselves , particularly when the phenotypes are on very different scales , because these can be interpreted as the relative importance of the various factors . This is an additional advantage of the LMM parameterization that we utilize in Grid-LMM . The Grid-LMM approach does have limitations . First , the size of the grid spanning the variance components increases nearly exponentially with the number of random effects . Since each grid vertex requires a separate Cholesky decomposition of the observation-level covariance matrix , a grid search quickly becomes prohibitively expensive with more than ∼6-8 variance components . This is a general problem for mixed model algorithms , and it may be possible to adapt efficient derivative-based algorithms to the grid space . Our fast-heuristic search method converges on the correct answer in most cases we have examined; however , likelihood surfaces of linear mixed models are not always convex and this algorithm may converge onto a local maximum in such cases . We note that most general-purpose algorithms for LMMs with multiple random effects are also sensitive to this issue . Second , for REML or posterior inference of variance component proportions , Grid-LMM estimates are accurate but not precise; specifically , they are limited by the resolution of the grid . We show that this has little impact on hypothesis testing for fixed effects , for example in GWAS settings . However , boundaries of posterior intervals in particular may not be reliable . Nevertheless , summaries like the posterior mean or estimates of the joint posterior density are highly accurate ( e . g . Fig 3 ) . Third , the Grid-LMM approach is limited to Gaussian linear mixed models . Generalized linear mixed model algorithms rely on iteratively re-weighting the observations , a function that changes the covariance matrix in a way that cannot be discretized . Finally , we have not explored LMMs with correlated random effects , although these are commonly used in quantitative genetics . Since correlation parameters are restricted to the interval ( −1 , 1 ) , discretizing correlations in parallel with the variance component proportions may be feasible and is an avenue that is worth future study .
We consider the following parameterization of the standard linear mixed model: y = W α + X β + ∑ l = 1 L Z l u l + e , u l ~ N ( 0 , σ 2 h l 2 K l ) , e ~ N ( 0 , σ 2 h e 2 I ) , ( 1 ) where n is the number of observations , L is the number of random effect terms ( not including the residuals ) , y is an n × 1 vector of quantitative traits , and W and X are n × c and n × p design matrices for covariates and marker effects , respectively , with α and β corresponding c × 1 and p × 1 vectors of coefficients . Similarly , Zl are n × rl design matrices with corresponding random effects ul , which are normally distributed around zero and have covariance matrices proportional to the known positive semi-definite rl × rl matrices Kl . Lastly , e is a n × 1 vector of uncorrelated normally distributed errors , and N ( • , • ) denotes the multivariate normal distribution . The common variance of all random effects are denoted by σ2 , and the vector h 2 = ( h 1 2 , … , h L 2 , h e 2 ) represents the proportion of variance attributed to each random effect term or the residual error . Elements of h2 are all non-negative and sum to one , forming an L-dimensional simplex . In GWAS applications , we assume that W is constant across markers and the value of α is not of central interest; meanwhile , X varies for each test and we aim to perform statistical inference on a subset of β . In heritability estimation applications , we focus on inferring the vector h2 . In both cases , the vectors ul and e are nuisance parameters and we can integrate them out resulting in the following equivalent model: y ∼ N ( W α + X β , σ 2 V ) , V = ∑ l = 1 L h l 2 Z l K l Z l ⊤ + h e 2 I . ( 2 ) If the matrix V is full-rank ( which is guaranteed if h e 2 > 0 ) , we can use the inverse of the Cholesky decomposition V = LL⊤ to transform Eq 2 to the following: y * ~ N ( W * α + X * β , σ 2 I ) ( 3 ) where y* = L−1 y , W* = L−1 W and X* = L−1 X . Eq 3 is a simple linear model for y* , with the likelihood function: l F ( y * ; α , β , σ 2 | h 2 ) = 1 2 [ − n log ( 2 π σ 2 ) − 1 σ 2 ( y * − W * α − X * β ) ⊤ ( y * − W * α − X * β ) ] , where efficient methods for inference of [α , β] and σ2 are well known . We derive maximum-likelihood and restricted-maximum likelihood solutions for these parameters here , as well as posterior distributions under the conditional normal-inverse-gamma prior below . The log-likelihood and restricted-likelihood functions ( respectively ) for Eq 2 are: l F ( y ; α , β , σ 2 , h 2 ) = l F ( y * ; α , β , σ 2 | h 2 ) − log | L | ( 4 ) and l R ( y ; α , β , σ 2 , h 2 ) = l F ( y ; α , β , σ 2 , h 2 ) + 1 2 [ ( c + p ) log ( 2 π σ 2 ) + log | X ˜ ⊤ X ˜ | − log | X ˜ * ⊤ X ˜ * | ] ( 5 ) which are simple ( and computationally inexpensive ) updates to the likelihood function of Eq 3 . Here , we denote |•| as the matrix determinant and let X ˜ = [ W ; X ] and X ˜ * = [ W * ; X * ] , respectively . For ML and REML applications , we can calculate the profile likelihoods lF ( y; h2 ) and lR ( y; h2 ) as functions of the profile likelihood of the rotated data , which is formed by maximizing lF ( y*; α , β , σ2 | h2 ) with respect to α , β , and σ2 . Namely: lF ( y;h2 ) =n2[log ( n2π ) −1−log ( RSSy* ) ]−log| L | ( 6 ) where RSS y * = y * ⊤ [ I − P ˜ ] y * is the residual sum of squares , and P ˜ = X ˜ * ( X ˜ * ⊤ X ˜ * ) − 1 X ˜ * ⊤ is a projection ( hat ) matrix . We now outline the Grid-LMM approach for calculating approximate Wald-test statistics , and then show extensions for computing Bayesian posterior distributions of variance components and marker specific Bayes Factors . A Wald test for the null hypothesis Mθ = 0 for θ = [α⊤ , β⊤]⊤ and an arbitrary q × ( c + p ) matrix M uses the general F-statistic: F Wald = θ ^ ⊤ M ⊤ ( M ( X ˜ ⊤ V − 1 X ˜ ) − 1 M ⊤ ) − 1 M θ ^ q ( 7 ) with q and ( n − c − p ) degrees of freedom , where θ ^ is the estimate of θ using the REML estimate of V [63] . To calculate genome-wide Wald statistics , we must estimate h ^ 2 for each of the p markers tested . There are no closed-form ML ( or REML ) solutions for h2; therefore , iterative algorithms are required . A naive approach involves repeatedly inverting V , which scales cubically in the number of observations . Since the estimates for h ^ 2 differ for each test , the total computational complexity of in a GWAS setting is O ( t p n 3 ) assuming an average of ≈ t inversions per run of the optimization algorithm ( generally ≈ 3 − 100 , increasing with the number of random effect parameters ) . The fast algorithm used in GEMMA [24] and FaST-LMM [23] reduces this to O ( n 3 + p n 2 + p t c 2 n ) by utilizing the eigenvalue decomposition of the similarity matrix K . However , this only works for a single random effect . We are unaware of any exact algorithm with lower computational complexity for models with multiple random effects ( and full-rank covariance matrices ) . With Grid-LMM , rather than optimizing h2 separately for each marker , we instead define a grid of candidate values for h2 and calculate the restricted profile-likelihood lR ( h2 | y* ) at every grid vertex for each marker . At each grid vertex , we must invert V once , but we can re-use this calculation for every marker . This has a computational complexity of approximately O ( g ( n 3 + p n 2 ) ) for a grid with g vertices . For all analyses reported in the main text , we use a rectangular grid with a resolution of 0 . 1 or 0 . 01 h2-units , with the restriction that all h l 2 ≥ 0 and ∑ l = 1 L h l 2 < 1 . As described below , we either peg this grid to the origin ( i . e . h l 2 = 0 , ∀l ) , or to the REML estimate h ^ 0 2 derived from the null model with no marker effects . This grid search generates a vector of g profiled Restricted Likelihood scores for each marker . We select the values of h ^ 2 that correspond to the highest such score for each marker and use Eq 7 to calculate the approximate Wald statistics . To calculate approximate likelihood ratio test statistics , we use the Grid-LMM approach to calculate the full profile-likelihoods for models under both the null and alternative hypothesis . For Bayesian inference , rather than working with the profile likelihoods , we instead use a conditional normal-inverse-gamma prior ( [α⊤ , β⊤]⊤ , σ2 ) ~ NIG ( 0 , Ψ , a0 , b0 ) , and then integrate over the prior to calculate the marginal likelihood of the data given h2 . This results in the following: p ( y | h 2 ) = b 0 a 0 ( 2 π ) n / 2 + ( c + p ) / 2 | V | 1 / 2 | Ψ | 1 / 2 Γ ( a ) × ( 2 π ) ( p + c ) / 2 | Ψ * | 1 / 2 Γ ( a * ) ( b * ) a * , ( 8 ) where Γ ( • ) is the gamma function , Ψ*= ( Ψ−1+X˜⊤V−1X˜ ) −1 , a* = a0 + n/2 , and b* = b0 + RSSy* , Ψ/2 with RSSy* , Ψ having the same form as RSSy* in Eq 6—except with P˜=X˜*Ψ*X˜*⊤X˜*Ψ*X˜*⊤ . See S1 Text for more detail on the specific derivations . We calculate the marginal likelihood p ( y | h2 ) at each vertex of the grid as described above . Assuming a discrete-valued prior p ( h2 ) , we can then compute the posterior distribution of h2 as: p ( h 2 | y ) = | V h 2 | − 1 / 2 | Ψ h 2 * | 1 / 2 ( b h 2 * ) − a * p ( h 2 ) Σ h 2 | V h 2 | − 1 / 2 | Ψ h 2 * | 1 / 2 ( b h 2 * ) − a * p ( h 2 ) ( 9 ) where , for clarity , parameters that are a function of h2 are denoted with a subscript . Continuous target densities π ( h2 ) can be approximated as p ( h2 ) by assigning each grid vertex a probability equal to the integral of π ( h2 ) over the L-dimensional rectangle centered at the corresponding value h2 . We assume a uniform prior over our grid for all analyses presented in the main text . Bayes factors are computed by comparing models under the alternative and null hypotheses as the ratios in Eq 9 . All analytical calculations—including the summation in Eq 9—can be performed on the log-scale to prevent numerical underflows . Terms common to both models drop out of the ratio; therefore , limiting improper priors on α and σ2 can be used , which results in scale-independence [49] . The full grid search described above is dramatically faster than the naive algorithm for mixed-model GWAS analyses , as long as the vertices of the grid is less than the number of genetic markers ( i . e . g < p ) and can easily be parallelized across multiple computers . However , g grows rapidly as the grid resolution and number of random effects increases: g = ∑ k = 1 L ( m k ) ( L − 1 k − 1 ) , ( 10 ) for a grid with m divisions per h l 2 and , therefore , can still be slow . If we make two assumptions which are commonly true , we can develop heuristics for both the ML/REML and Bayesian algorithms that prevent the need to evaluate every grid vertex: To search for the ML or REML solutions , we first find h ^ 0 2 under the null model with no marker effects . We calculate the profile ( restricted ) -likelihood scores for each test at h ^ 0 2 , and then form a grid centered at this value by adding or subtracting 1/m to each h l 2 in all combinations . For two random effects , this grid will be a ring around h ^ 0 2 with g1 ≤ 8 vertices ( depending on if h ^ 0 2 is within 1/m of a boundary of the simplex ) . We calculate the scores for each test at each vertex of the grid , and then compare the maximum scores to the scores at h ^ 0 2 . For every test , when no greater value is found , we choose h ^ 0 2 as the maximum and skip the corresponding marker in all future calculations . For the remaining p2 markers , we select the set { h ^ 0 2 , h 1 2 , … , h j 2 } of grid vertices that maximized the scores for 1+ tests , and form a new grid of size g2 around all of them , dropping vertices already tested . This procedure is repeated t7 times until the new grid no-longer increases scores for any test and we are confident that all ( approximate ) maximums have been found . This accelerated search has total complexity O ( t 6 n 3 + p n 2 + Σ i = 1 t 7 g i ( n 3 + p i n 2 ) ) , with t6 the number of iterations needed to optimize variance components under the null model , and p1 = p ( see Table 1 ) . Analogously , to accelerate evaluations of posterior distributions , we evaluate p ( y | h2 ) over an initial grid of resolution 1/m1 with discrete prior p m 1 ( h 2 ) and estimate the posterior distribution as in Eq 9 . Summary statistics , such as the posterior mean and variance , will be more accurate if the posterior mass is distributed across multiple vertices of the grid . Therefore , we identify the set H = { h l 2 } of vertices that together explain 99% of the posterior mass . If the size of this set is below a threshold ( say | H | = 10 ) , we then form a new grid with double the resolution m2 = 2m1 and a new prior p m 2 ( h 2 ) . Vertices that overlap between the grids can be filled in directly . We then begin filling in the new grid by evaluating vertices within 1/m2 distance from any h l 2 corresponding to the vertices in H . After each iteration , we re-calculate the set H , and continue evaluating the neighboring vertices as long as H continues to grow . If the size of H remains smaller than the threshold after whole grid is evaluated , or no new vertices are added to H at the end of an iteration , we double the resolution again: mi+1 = 2mi and repeat the grid-filling steps again . We note that this procedure is only appropriate if the posterior is convex and , therefore , is limited to the case of uniform priors on h2 . A similar procedure was proposed for Bayesian inference in Gaussian process models in the GPstuff toolbox , although it is not optimized for parallel inference in GWAS [55] . To accelerate evaluations of GWAS Bayes Factors , we combine the two previously described algorithms . In particular , we define a grid centered on h ^ 0 2 with a resolution 1/m that is sufficiently fine such that we expect each posterior to be distributed across multiple vertices . We then calculate p ( y | h2 ) for each test , starting from h ^ 0 2 and moving out in concentric rings on the grid . After each iteration , if the new ring contributes < 0 . 01% to the total posterior mass ( among evaluated vertices ) for that test , we assume the posterior is well characterized and stop evaluating p ( y | h2 ) for that marker . As for the ML and REML solutions above , markers with little to no association with the phenotype will lead to posteriors of h2 that are concentrated close to h ^ 0 2; hence , only markers with large effects will shift p ( h2 | y ) strongly to new regions of the grid . Unless otherwise specified , we used accelerated grid searches for all Grid-LMM analyses presented here with grid step sizes of 0:01 h l 2 units . Genotype and phenotype data on 107 Arabidopsis thaliana traits and 216 , 130 genetic markers were downloaded from https://github . com/Gregor-Mendel-Institute/atpolydb/wiki . We follow practices suggested by the original authors of these data and log-transformed a subset of the phenotypes prior to analyses ( except for traits that had values less-than or equal to zero ) [44] . For the analysis of gene-environment interactions in flowering time , we ( 1 ) extracted the trait identifiers “7 FT10” ( i . e . growth chamber at 10C ) and “57 FT Field” ( i . e . field setting ) , ( 2 ) selected data from the 175 accessions that were measured in both environments , and ( 3 ) concatenated the two datasets into a single vector of 350 observations . The two traits were individually standardized to have mean zero and standard deviation one prior to analysis . We used the sommer package in R to calculate an additive relationship matrix from all 216 , 130 markers [69] , and then created a G×E kinship matrix as DZKZ⊤D where K is the 175 × 175 additive genomic relationship matrix , Z is a 350 × 175 incidence matrix linking observations to accessions , and D is a 350 × 350 diagonal matrix with elements equal to −1 or 1 corresponding to observations measured under “7_FT10” and “57_FT Field” , respectively . Plasticities for each accession were calculated as the difference between “57_FT Field” and “7_FT10” . We ran GEMMA ( version 0 . 97 ) with the “-lmm1” option for Wald tests using the K matrix described above . We emulated EMMAX and pylmm functionality by estimating variance components using a null model with no marker effects , and either a single K matrix ( for single-trait analyses ) or 3 random effects ( for G×E analysis ) . Wald test statistics were computed for each marker using our GridLMM R function with a grid consisting of only a single vertex . For G×E analyses , we fit a model with a main effect of the environment , a main effect for each marker of interest , and an interaction between the marker and the environment—and then calculated the Wald-F statistic only for the interaction effect . The heterogeneous stock of mice data from the Wellcome Trust Centre for Human Genetics ( http://mtweb . cs . ucl . ac . uk/mus/www/mouse/index . shtml ) consists of 1 , 814 individuals from 85 families , all descending from eight inbred progenitor strains [46] . We used the marker and phenotype data provided in the BGLR R package [70] from which we extracted the “EndNormalBW” trait and information on the gender and cage of each mouse . Gender was used as a covariate in all analyses , and cage assignment was treated as a random effect in the three-random-effect models . Additive and epistatic kinship matrices were calculated using sommer . Wald test statistics and Bayes Factors were calculated using the heuristic ( accelerated ) grid search of Grid-LMM . Bayes Factors were calculated assuming flat priors on the intercept and residual variance term , a standard normal prior N ( 0 , 1 ) on the marker effects—similar to the previously proposed D2 prior [49]—as well as a uniform prior over the grid of variance component proportions . Computational timings for GWAS analyses are reported for a MacBookPro14 , 3 with a 2 . 9Ghz Intel Core i7 processor and using only a single CPU core . Further speedups are possible by parallelizing the grid search . For accelerated grid searches , REML estimates of variance components under the null model ( i . e . the starting points of the grid search ) were calculated with LDAK and are included in the computational time estimates . Gene expression data on 24 , 175 genes from 728 Arabidopsis accessions was downloaded from http://signal . salk . edu/1001 . php and subsetted to genes with average counts ≥ 10 . A genomic relationship matrix ( KA ) for 1 , 135 accessions was downloaded from http://1001genomes . org/data/GMI-MPI/releases/v3 . 1/SNP_matrix_imputed_hdf5/ . Both sets of data were subsetted to an overlapping set of 665 accessions . KA was then centered by projecting out the intercept and normalized to have mean diagonal elements equal to one . KA , and then also normalized to have mean diagonal elements equal to one . The gene expression matrix was normalized and variance-stabilized using the varianceStabilizingTransformation function of the DEseq2 package [71] . Grid-LMM REML estimates were compared to exact REML estimates from the LDAK program with variance components constrained to be non-negative . Grid-LMM posterior distributions estimates were compared to those estimated using Stan [52] with the rstan R package [53] . To speed computation , we diagonalized the KA covariance matrix by calculating the singular value decomposition KA = USU⊤ , and pre-multiplying both sides of the LMM by U⊤ . We used a half-Student-t prior distribution with 3 degrees of freedom and a scale of 10 for the three standard deviation parameters . We ran four MCMC chains each of length 10 , 000 , with the first 5 , 000 as warmup and a thinning rate of 20 . Because of poor mixing , this resulted in an “neff” of approximately 200-400 per variance component parameter for each trait . We compared the power of one and two-random effect models for detecting G×E markers based on the Arabidopsis flowering time data . We calculated additive genetic and G×E relationship matrices as described above using the actual genotypes . We then simulated phenotypes by summing together a G×E effect of a particular marker , ( co ) variance from the two random effects , and normally distributed error . For each combination of marker effect sizes ( {0 , 0 . 025 , 0 . 05 , 0 . 1 , 0 . 15 , 0 . 2}% of the total phenotypic variance ) , and random effect proportions ( h l 2 ∈ { 0 , 0 . 4 , 0 . 8 } for each random effect ) , we ran 10 , 000 simulations with different randomly selected markers from the Arabidopsis genotype data . We then fit five models to each dataset and calculated Wald-tests for marker G×E effects using each . The five methods consisted of three two-random effect approaches including: ( 1 ) an exact two-random effect model fit with LDAK , ( 2 ) the approximate two-random effect model fit with Grid-LMM with a grid size of 0 . 1 h2-units , and ( 3 ) the approximate two-random effect model pylmm that conditions on variance components estimated under the null model [34] . We also consider two one-random effect models that could be fit with GEMMA: ( 4 ) a model that only included the additive genetic relationships and ignored the G×E covariance , and ( 5 ) a model that only included the G×E covariance and ignored the additive genetic relationships . For each method and for each simulation scenario , we first calculated a genomic control inflation factor [1] for the GxE marker tests as the ratio between the median value of the the F-statistics returned by each model and the median value of a F1 , 312 distribution since n = 316 and each model included p = 4 predictors ( overall intercept , environmental effect , the main effect of the marker , and the G × E interaction between the environment and the marker ) . We then “corrected” all F-statistics by dividing by the appropriate inflation factor , and calculated statistical power for each GWAS method as the proportion of corrected Wald test p-values exceeding the genome-wide significance level at the conventional Bonferroni corrected threshold P = 2 × 10−7 ( see S4 Fig ) . The results show that Grid-LMM maintains nearly identical power to the exact method in each simulation , all while also maintaining well-calibrated p-values under the null-distribution . The approximate method based on pylmm has uniformly lower power , but maintains an accurate null-distribution . The one-random effect methods show opposite results: when including only the additive relationship matrix , the null-distribution of -log10 ( p ) -values is greatly inflated when G×E-variation is large . On the other hand , when only G×E variance is modeled and yet the additive genetic variance is large , -log10 ( p ) -values are greatly deflated . These results all confirm our expectations that modeling covariance accurately , but not necessarily precisely , is important for accurate association mapping in GWAS . We developed a simulation strategy to evaluate the effect of sample size on the accuracy of Grid-LMM in a reasonable amount of time without the confounding issue of changes in population structure across different populations . These simulations are based on the WTCHG body weight data described above , creating simulations with similar levels of structure and random effect covariance as in the real analysis , and comparing the accuracy of marker tests using the Grid-LMM and null-LMM methods to the Exact-LMM method . We began with our largest dataset ( i . e . the n = 1814 WTCHG heterogeneous stock mice ) , randomly selected 300 markers , and calculated the three covariance matrices for additive , addtive-additive epistasis , and cage random effects . Next , we created a larger dataset of 5× the original size by repeating the original marker data five times and similarly created three block-diagonal covariance matrices by repeating each original covariance matrix five times . While not completely realistic , this created a population similar to what we would expect if the heterogeneous stock population was created five separate times from five independent sets of progenitors; therefore , it has a similar level of structure relative to its size as the original n = 1 , 814 population . Finally , we created a smaller dataset of 1/5× the size by subsampling the first 362 individuals from this dataset , their corresponding marker data , and the corresponding partitioned subsets of the three covariance matrices . For each simulation , we selected a single marker and assigned it an effect size between 0 and 0 . 15 in 16 steps . We then added four random vectors corresponding to the three random effects and “iid” error . To be realistic , we used the variance component proportions 0 . 23 , 0 . 29 , and 0 . 25 , respectively , as weights for the random vectors ( with the sum scaled so that the total phenotypic variance equaled one ) . This choice was based on the observed variance components in the real bodyweight data . We repeated this simulation strategy for each of the 300 markers in the three populations and each of the 16 effect sizes . Within each simulation , we calculated marker p-values using the Exact-LMM and null-LMM methods . We then simulated Grid-LMM results by perturbing each of the three variance component proportions from Exact-LMM: ±0 . 05 for Grid-LMM ( 0 . 1 ) and ±0 . 005 Grid-LMM ( 0 . 01 ) , respectively . Lastly , we selected the p-value from the model with the highest REML score . This represented a “worst-case scenario” for Grid-LMM where the optimal variance components were maximally far from the grid vertices . To estimate the time for a GWAS under each population size , we measured the length of time to fit Exact-LMM for a single marker using LDAK ( T1 ) , the time to perform a single Cholesky decomposition ( T2 ) , and the time to calculate p-values for a set of p markers given a pre-calculated Cholesky decomposition pT3 . We then calculated the total time for a GWAS with p markers as: Exact-LMM: pT1 null-LMM: T1 + T2 + pT3 Grid-LMM ( 0 . 1 ) : g1 ( T2 + pT3 ) Grid-LMM-Fast ( 0 . 01 ) : T1 + g2 ( T2 + pT3 ) + g3 ( T2 + T3 ) where g1 = 220 is the size of a complete grid for three random effects with resolution h2 = 0 . 1 , g2 = 27 is the size of a ball around the null-LMM variance component estimates in the Grid-LMM-fast heuristic , and g3 is the number of additional grid vertices that must be traversed by the Grid-LMM-fast algorithm . For the Grid-LMM-fast calculations , we assumed that only a single marker had a non-zero effect ( and so only this marker would need to be taken through multiple iterations of the heuristic search ) , and that the effect size of this marker was approximately at the power threshold given the sample size ( ≈ 0 . 1 for n = 362 , ≈ 0 . 035 for n = 1814 , ≈ 0 . 006 for n = 9070; see Fig 4b ) . Software for computing the Grid-LMM is carried out in R code , which is freely available at https://github . com/deruncie/GridLMM . Scripts for running the analyses reported in the manuscript are available at https://github . com/deruncie/GridLMM_scripts . | The goal of quantitative genetics is to characterize the relationship between genetic variation and variation in quantitative traits such as height , productivity , or disease susceptibility . A statistical method known as the linear mixed effect model has been critical to the development of quantitative genetics . First applied to animal breeding , this model now forms the basis of a wide-range of modern genomic analyses including genome-wide associations , polygenic modeling , and genomic prediction . The same model is also widely used in ecology , evolutionary genetics , social sciences , and many other fields . Mixed models are frequently multi-faceted , which is necessary for accurately modeling data that is generated from complex experimental designs . However , most genomic applications use only the simplest form of linear mixed methods because the computational demands for model fitting can be too great . We develop a flexible approach for fitting linear mixed models to genome scale data that greatly reduces their computational burden and provides flexibility for users to choose the best statistical paradigm for their data analysis . We demonstrate improved accuracy for genetic association tests , increased power to discover causal genetic variants , and the ability to provide accurate summaries of model uncertainty using both simulated and real data examples . | [
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] | 2019 | Fast and flexible linear mixed models for genome-wide genetics |
The global prevalence of malaria has decreased over the past fifteen years , but similar gains have not been realized against Plasmodium vivax because this species is less responsive to conventional malaria control interventions aimed principally at P . falciparum . Approximately half of all malaria cases outside of Africa are caused by P . vivax . This species places dormant forms in human liver that cause repeated clinical attacks without involving another mosquito bite . The diagnosis of acute patent P . vivax malaria relies primarily on light microscopy . Specific rapid diagnostic tests exist but typically perform relatively poorly compared to those for P . falciparum . Better diagnostic tests are needed for P . vivax . To guide their development , FIND , in collaboration with P . vivax experts , identified the specific diagnostic needs associated with this species and defined a series of three distinct target product profiles , each aimed at a particular diagnostic application: ( i ) point-of-care of acutely ill patients for clinical care purposes; ( ii ) point-of-care asymptomatic and otherwise sub-patent residents for public health purposes , e . g . , mass screen and treat campaigns; and ( iii ) ultra-sensitive not point-of-care diagnosis for epidemiological research/surveillance purposes . This report presents and discusses the rationale for these P . vivax-specific diagnostic target product profiles . These contribute to the rational development of fit-for-purpose diagnostic tests suitable for the clinical management , control and elimination of P . vivax malaria .
The concerted international efforts initiated near the turn of this century to move from malaria control to malaria elimination and ultimately eradication show remarkable progress during the last decade [1] . Financial , political and scientific commitment to solve the malaria problem led so far to an overall 37% decrease in global incidence between the years 2000 and 2015 and an estimated 60% decrease in mortality during this period [1] . As a result , in sub-Saharan Africa , where most known cases of malaria occur , malaria is no longer the prime cause of death for children below the age of 5 years old . These gains have been driven by the cumulative impacts of multiple entomological and antimalarial interventions implemented via improved policies . Since 2010 the World Health Organization ( WHO ) , for example , recommended confirmation of suspected malaria cases using rapid diagnostic test ( RDT ) or by the examination of a stained blood smear by light microscopy ( LM ) . The practice of presumptive treatment without confirmation was thus discouraged . Since then , the estimated number of malaria diagnostic tests performed globally on suspected cases has risen significantly , especially in the African WHO Region where the proportion of tested cases has increased from 41% in 2010 to 65% in 2014 . This increase in testing is largely due to the availability of quality-assured RDTs . The number of RDT distributed by National Malaria Control Programs rose from less than 25 millions in 2008 to more than 125 millions in 2014 in the African WHO Region , whereas it remained relatively constant in other areas where P . vivax is present [1] . This highlights the importance of high quality , affordable and easy to use point-of-care tests to facilitate the effective diagnosis and prompt treatment of malaria . This encouraging portrait of progress does not fully extend to the malaria caused by P . vivax . In fact , P . vivax prevalence appear to decrease slower than that of P . falciparum , which results in a significant shift to P . vivax predominance almost everywhere outside sub-Saharan Africa . P . vivax is now the sole or main cause of malaria in approximately three quarters ( 26/34 ) of the endemic countries currently in the elimination phase , suggesting that this species will be much more difficult to eliminate than P . falciparum . Although approximately one half ( 47% ) of all malaria cases outside sub-Saharan Africa are caused by P . vivax and about 2 . 8 billion people live at risk of infection [2] , it has been neglected in science , clinical medicine and public health until very recently [3–5] . This has resulted in strategies and tools for malaria control and elimination suited to P . falciparum but not P . vivax . Specific biological traits of P . vivax explain that poor fit . First , a single infectious bite of a mosquito leads to a primary attack within about 2 weeks , but then goes on to cause multiple clinical attacks at intervals of about 2 months for as long as 4 years ( but typically 2 years or less ) . Those later attacks , called relapses , derive from dormant liver stages of P . vivax called hypnozoites . These forms have been shown to cause at least 80% of all P . vivax blood-stage infections in Papua New Guinea [6] , and 96% of attacks at the Thai-Myanmar border [7] . Second , during the course of a blood stage infection , gametocytes appear simultaneously with the same 48 hour developmental cycle as asexual parasites and , often , before the onset of symptoms and , while they do not seem to persist as long as P . falciparum gametocytes , their infectiousness to mosquitoes may be relatively higher [8–11] . Third , P . vivax merozoites invade only the most immature reticulocytes that most often occur in bone marrow rather than in circulation [12] . The bulk of P . vivax biomass may occur in extravascular tissues of the marrow and spleen rather than in circulating blood , whereas P . falciparum is largely impounded within vascular sinuses [13] . Those observations help explain why parasitaemias by P . vivax are naturally and consistently much lower compared to P . falciparum . Parasite densities of P . vivax at clinical presentation are typically in the range of 4 000 +/- 3 000 parasite per μL of blood ( p/μL ) , which is three to four-times lower than for P . falciparum , and peak parasitaemias rarely exceeds 100 000 p/μL in P . vivax but is quite common in P . falciparum [14–18] . In fact , a large proportion of all P . vivax infections , up to 70% in certain areas , have been found to be below the limit of detection ( LOD ) of microscopy [19] . Although present in all settings , submicroscopic infections appear to be clearly of higher relative importance in low prevalence areas , representing an additional challenge to elimination efforts [19] . The control and elimination of P . vivax is thus more complex than with P . falciparum as it requires the rapid diagnosis of infection at lower parasite densities , but also initiating radical cure treatment for hypnozoites in conjunction with acute treatment for blood stage parasites . Problematically , the only currently available hypnozoitocidal therapy , the 8-aminoquinoline primaquine regimen , is typically 14-days in duration and exposes glucose-6-phosphate dehydrogenase ( G6PD ) deficient patients–a widespread genetic disorder impacting 8% of residents of malaria endemic nations [20]–to potentially life-threatening acute haemolytic anaemia . Finally , some of the natural polymorphisms in the P450 cytochrome type 2D6 ( CYP2D6 ) result in null or impaired metabolism of primaquine to its active metabolite and cause therapeutic failure against relapse [21–23] . The increasing use of RDTs for the diagnosis of malaria resulted in significant progress in the past ten years but less so for P . vivax specifically . Relatively poor diagnostic performance of the most widely used RDTs for non-falciparum malaria may help explain continuing reliance upon microscopy for the diagnosis of P . vivax in endemic areas . RDTs for P . vivax are generally considered of lower accuracy , with performance , stability and false positivity issues being commonly reported in the literature [24–27] . The actual diagnostic coverage and the analytical performances of P . vivax RDTs are poorly documented . These deficiencies have been clearly recognised in the Malaria Eradication Research Agenda ( malERA ) initiative , which has expressed the high priority need for “more sensitive tests for P . vivax for case management” [28] . This initiative also highlighted that RDTs for P . vivax “lack consistency in sensitivity and stability” [28] . While a recent review has indicated that the quality of P . vivax RDTs is improving , only 59% ( 17/29 ) of P . vivax RDTs displayed an acceptable panel detection score ( PDS ) at 200 parasites per μl of blood as compared to 93% ( 38/41 ) for P . falciparum RDTs during the latest WHO-FIND product testing of malaria RDTs [25 , 29] . While P . vivax infections can be identified via the detection of Plasmodium specific aldolase or plasmodial lactate dehydrogenase ( pLDH ) enzymes , the univocal identification of P . vivax requires the specific detection of the pLDH isoform of this species ( Pv-pLDH ) . As a proxy of the typical performances of this type of test , the average PDS of Pv-pLDH based RDTs appear significantly lower than that of the RDTs detecting P . falciparum specific histidine rich protein 2 ( HRP2 ) when considering the cumulative results of the WHO-FIND Product Testing Programme ( Table 1 ) . This illustrates the shortcomings associated with current P . vivax specific RDTs . Performance of light microscopy is directly dependent on operator proficiency and sample preparation , and species determination in areas of P . falciparum and P . vivax co-endemicity may be challenging [31 , 32] . Microscopy , like RDTs , also suffers limited sensitivity and often fails to identify a substantial fraction of P . vivax infections of blood [19] . Alternative diagnostic methods , based on nucleic acid amplification techniques ( NAATs ) and serological markers exist or are emerging . While microscopy and RDTs are recommended by WHO as “the primary diagnostic tools for the confirmation and management of suspected clinical malaria in all epidemiological situations including areas of low transmission as well as for routine malaria surveillance” , a potential role for NAAT- and serology-based approaches is considered relevant in areas of low endemicity and near elimination for epidemiological research and surveys aimed at mapping submicroscopic infections to guide intervention measures specific to these settings [33] . While the research and laboratory applications of these tests is clear , their value for P . vivax infection detection and their optimal application are , however , currently unclear . Currently available diagnostic tests for P . vivax are not optimal to address the full range of infection detection needs , from clinical case management to surveillance and elimination-oriented interventions through “surveillance-response” . Currently , poor diagnostic effectiveness contributes to the resilience of P . vivax to global and national malaria intervention strategies . In order to facilitate the development of improved P . vivax tests , a set of target product profiles ( TPPs ) addressing the specific needs associated with this species were developed through expert consensus . These TPPs are intended to guide the efforts of test developers , donors and other stakeholders in the global health community to address the P . vivax challenge . A limited number of TPPs for malaria diagnostic tests have been developed in the past few years ( S1 Table ) . The malERA initiative published two generic TPPs in 2011 , one for the diagnosis of malaria clinical cases and one for screening and surveillance activities , with parameters that could be applied to both , P . falciparum and P . vivax [28] . In 2014 , the 10th session of the WHO Malaria Policy Advisory Committee Meeting released recommendations for malaria diagnosis in low transmission settings and described the ideal characteristics of future tests for this application , without defining species-specific needs [34] . A malaria diagnostic TPP was also developed by PATH for the Diagnostics for Malaria Elimination Toward Eradication ( DIAMETER ) project that supports the development and implementation of diagnostic solutions for malaria elimination [35] . The format and target of the test described in this profile are restricted to that of lateral flow immunoassay detecting HRP2 for P . falciparum infections . Finally , while it did not include a full TPP , the 2015 WHO technical brief about the control and elimination of P . vivax malaria highlighted the need for research to develop tests that can detect P . vivax at a minimum of 25 p/μL of blood as well as tests that “can detect submicroscopic , asymptomatic infections in elimination settings , where it is critical to detect all infections” [3] . While the very specific biological and clinical nature of P . vivax infections requires adapted tools , none of these TPPs addressed the needs of P . vivax infection detection . To fill this gap , FIND , a not-for-profit organization supporting the development and implementation of diagnostic solutions for diseases of poverty , consulted with P . vivax experts , all co-authors of this publication , to define the diagnostic needs for P . vivax and established consensus-based TPPs , with the goal to guide product development efforts toward optimized diagnostic solutions and to ultimately accelerate elimination of this malaria species .
TTPs were developed in an iterative and consensus-decision-making process involving a large number of experts from academic research institutions , national malaria control programmes , the WHO Global Malaria Programme , and the WHO Americas Regional Office ( AMRO ) /Pan American Health Organization ( PAHO ) . An initial expert meeting took place in October 2015 to review current practices and topics of interest for the diagnosis of P . vivax malaria . Three TPPs were defined based on specific intended uses , a list of forty-three TPP characteristics to be informed was established based on an initial list proposed by FIND , and preliminary values for each of these characteristics were discussed . TPPs were gradually refined through five rounds of drafts review and update through online communication ( draft versions 0 . 1 to 0 . 6 ) . Finally , an online survey was conducted to collect comments from each contributor on the remaining debated characteristics and establish a majority vote to issue final TPPs ( versions 1 . 0 ) reported here . For each TPP , the intended use , target populations and users , the implementation level as well as expected performance , operational and financial characteristics were defined . For most of these characteristics , minimal and optimal values have been defined , providing a range of values from the minimally acceptable value to the ideal one . The minimal values have been typically set to provide a distinguishing advantage over existing diagnostic solutions for P . vivax while the optimal ones were defined as the value that could provide optimal diagnostic effectiveness .
The malaria diagnostic needs are wide and primarily defined by the type of infection to be detected , either restricted to clinical cases or including the largest possible number of infections , regardless of symptoms . Additional important factors are the test outcome , which can be to guide treatment or only to inform surveillance systems , and the implementation level , which will determine how simple to implement a given test needs to be . Three TPPs were defined to cover three distinct intended uses across this spectrum ( Fig 1 ) . TPP PvA ( Pv stands for P . vivax ) is addressing the diagnosis of P . vivax clinical symptomatic infection for confirmation of suspected cases ( passive case detection ) . The two other TPPs ( TPP PvB1 and PvB2 ) are geared toward elimination settings and address the diagnosis of all infections , symptomatic or not . TPP PvB1 addresses the need for point-of-care diagnosis of P . vivax infections regardless of the presence of symptoms including sub-microscopic parasitaemia , enabling proactive and reactive infection detection interventions , while TPP PvB2 specifically addresses the requirements for a population screening test for P . vivax infection surveillance and epidemiological surveys , independent of individual infection treatment . The specific intended uses and test outcomes as well as key distinguishing characteristics for these three TPPs are summarized in Table 2 . TPP PvA addresses the need for better diagnostic for the parasitological confirmation of clinical cases in passive case detection scenarios ( S2 Table ) . This TPP is therefore designed for a point-of-care test that is simple to implement ( requiring ideally half-a-day of training and three steps or less ) and rapid ( time-to-results ≤ 30 min . ) to guide prompt clinical management of P . vivax malaria patient: blood-stage treatment of acute P . vivax infections as well as radical cure for populations to which 8-aminoquinolines can be administrated safely . Current tests for this intended use are RDTs and microscopy and the characteristics of this TPP were established with the objective to overcome the limitations of these tests . A key performance characteristic for this TPP is the analytical sensitivity . Expert microscopy is considered to provide a LOD of 50 p/μL but this value is typically assumed to be significantly higher in many endemic areas [37 , 38] . A recent analysis of the analytical performances of the best-in-class Pv-pLDH RDTs indicated these would fail to detect a majority of samples containing 200 p/μL ( Jimenez et al . , submitted elsewhere ) . A minimal target LOD of 25 p/μL would therefore represent at least a two-fold improvement over the practical microscopy LOD and be a significant improvement over current RDTs . However , an optimal LOD should be equal or inferior to 5 p/μL , corresponding to one order of magnitude below the typical lowest peripheral parasitaemia at presentation for uncomplicated P . vivax malaria , ensuring that no clinical cases would be missed because of inadequate LOD [15 , 39] . Regarding diagnostic specificity , the univocal identification of P . vivax as the Plasmodium infecting species is essential as only this species and the relatively rare P . ovale require radical cure for liver-stage parasite removal . For areas of co-endemicity between P . vivax and P . falciparum ( 39 out 98 malaria endemic countries [1] ) , a distinguishing advantage would be the capacity to identify and discriminate between these two major species . Regarding both the diagnostic sensitivity and specificity , the minimal values have been set to at least match that of current P . falciparum RDTs and the optimal ones to provide a distinguishing advantage at 95% and 99% , respectively [25] . In terms of operational characteristics and beyond the required simplicity and rapidness of the test , stability during transport , storage and usage is important . An analysis of the typical RDT supply chain revealed that these are frequently exposed to temperature above 30°C and sometimes up to more than 40°C , hence a test destined to the same intended use needs to withstand such harsh conditions , being ideally stable for up to 12 months at 45°C and 90% relative humidity and usable at temperatures as low as 5°C and as high as 45°C . Another crucial element often difficult to resolve is that of cost . The cost of diagnosis ( including sample collection , processing , and transmission of the results to the patient ) for RDT and light microscopy were evaluated to be between 1 . 0 and 2 . 0 USD in 2011 in Uganda , a P . falciparum endemic country [40] . It is somewhat complex to define at what end-user price and overall diagnosis cost a PvA test might become cost-effective as the cost of misdiagnosed and relapsing P . vivax infections is difficult to evaluate but it was assumed that the end user price should ideally not be superior to the current price of RDT ( ~0 . 5 USD ) and that the overall cost of diagnosis should not be superior to the values mentioned here above . TPP PvB1 extends the scope of PvA to address the detection of all blood-stage infections , regardless of the presence of symptoms , to enable reactive and proactive case detection and treatment ( S3 Table ) . This TPP is defining the characteristics of tests that could be deployed in elimination settings to identify the asymptomatic reservoir known to contribute to residual transmission and guide blood-stage and , if appropriate , liver-stage treatments for the asymptomatic carriers . Similar to PvA , PvB1 tests need to be deployable in a point-of-care manner ( or “point-of-contact” since it would not necessarily be used in a medical care context ) and therefore require very similar characteristics in terms of ease-of-training , ease-of-use , short time-to-results and operational robustness . There is no such test currently in use and while NAATs might meet many of the required characteristics , they are not easily deployable as a point-of-care diagnostic solution . The main distinguishing feature of PvB1 as compared to PvA is the lowered analytical sensitivity needed to ( i ) detect a substantial fraction of the asymptomatic and low parasitaemia infections , and thus ( ii ) support elimination interventions by providing crucial information about these parasite populations . A modelling study investigating the case of a P . falciparum diagnostic test used to trigger focal mass drug administration ( focal MDA , i . e . village-based mass drug administration in case of a local prevalence identified above a certain threshold ) suggested that in such low prevalence settings , an analytical sensitivity of 20 p/μL might suffice to ultimately reduce the parasite prevalence to zero within a ten-year time frame [41] . It is however not clear how such a model could apply to P . vivax , for which a majority of infections are relapses from liver stage parasites . A recent study evaluating the parasitaemia distribution in more than 1 , 500 P . vivax infected asymptomatic individuals at the Thai-Myanmar border revealed a geometric mean parasitaemia of 5 . 6 p/μL and a unimodal log normal distribution of parasitaemia in this population [42] . The minimally acceptable and ideal analytical sensitivity values for PvB1 tests were established around these estimates at 20 p/μL and 1 p/μL , respectively . The optimal value would allow to detect up to 58% of all asymptomatic infections according to the modelling of the data from Imwong et al . [42] . The other performance and operational characteristics are essentially identical between PvA and PvB1 , with the notable exception that the test format of PvB1 should ideally be amenable to batch testing of up to 100 individuals relatively easily . This is in consideration of the reactive or proactive case detection scenarios for which this type of test would be used , requiring the rapid diagnosis and treatment of a potentially large number of individuals as opposed to passive case detection , where testing is normally performed on demand and as suspected cases present at health posts and medical centers . Another key distinguishing feature of PvB1 is the cost . As mentioned above , the cost elements of diagnoses are difficult to factor in the absence of comprehensive costing analyses and cost-effectiveness studies of existing solutions . In the case of PvB1 , it was assumed that the increase in analytical sensitivity requirement would translate in an increased end-user test price ( minimal: 2 . 0 USD , optimal: 1 . 0 USD ) and cost of diagnosis ( minimal: 5 . 0 USD , optimal: 2 . 0 USD ) but that these values should ideally be lower than current NAAT tests , estimated between 2 to 4 USD per reaction , excluding capital cost [43–45] . Another element typically weighted against the cost of a diagnostic test is that of a treatment course , especially when considering mass interventions . If a diagnostic test is not significantly cheaper than a treatment course , typically targeted to 1 USD or less [46] , it is , from a pure economic point-of-view , cheaper to treat in the absence of testing than screen-and-treat . We dismiss this argument as too simplistic and are of the opinion that the true financial and societal costs of MDA or mass screen-and-treat cannot be distilled down to the only cost of the commodities associated with these interventions . We would not recommend for a test to be cheaper than a treatment course in order to be an adequate PvB1 diagnostic solution . This is especially true in the case of P . vivax , which requires not only blood-stage detection , but also potentially G6PD testing for radical cure . The TPP PvB2 is designed to answer the needs for high quality tests for epidemiological surveillance activities ( S4 Table ) . This TPP is similar to PvB1 in the sense that it aims to detect all infections , including asymptomatic and low parasitaemia typically not seen by RDTs or microscopy , but it differs from PvB1 in that the diagnostic outcome is not directly linked with treatment interventions at the individual level . A PvB2 test is designed to inform surveillance system and to support epidemiological surveys . Because of this nature , such a test would not need to be deployed in a point-of-care manner but would be restricted to district hospitals and national reference laboratories , and would need to provide a very high analytical sensitivity . Current tests in this category include highly complex and specialized NAAT protocols , such as high volume quantitative PCR , reverse-transcription quantitative PCR , or PCR targeting highly repetitive elements , which all reach analytical sensitivity of approximately 0 . 02 p/μL [47–49] . The PvB2 minimal and optimal analytical sensitivity values were set at 0 . 1 p/μL ( one order of magnitude lower than optimal PvB1 test ) and 0 . 01 p/μL ( two-fold lower than current state-of-the art technologies ) , respectively . Such values would in principle allow to detect up to 80% and 93% all of P . vivax asymptomatic infection as modelled by Imwong et al . [42] . When defining the optimal test sensitivity , it is pivotal to take into account the sampling procedures and blood sample volume . The blood volume equivalent that is added to a molecular assay critically determines the detection of low parasitaemias . All tests that target asymptomatic individuals should thus aim at maximizing the input material . For surveillance , collecting finger prick blood samples is considered feasible , whereas larger venous samples may be collected for research purposes only . In remote settings , sampling on filter paper will be required for storage and transport . This will compromise the detection of low parasitaemia in a significant way , as filter paper punches ( directly added to the reaction or extracted ) can only hold a limited blood volume . Ideally , 200 μL whole blood should be used for preparation of nucleic acids , and the final DNA solution should be concentrated as much as possible . Multi-copy target genes or reverse transcription reactions can help to detect a single parasite in the large blood volume sampled [48 , 49] . As a surveillance tool , PvB2 also includes tests that might not necessarily detect currently occurring infection but also recent past infections , such as serological tests , as a most effective way of estimating the residual transmission in an area of interest and potentially detect hypnozoite carriers . Obviously in such cases , an analytical sensitivity expressed in parasites per μL of blood becomes irrelevant and test-specific values would have to be defined ( e . g . antibody level detected by a serology test ) . Ideally , the analytical specificity would also be expected to be greater than that required for PvA and PvB1 tests , and the optimal specificity would be a detection and discrimination of all five Plasmodium spp . infecting humans . Regarding the diagnostic sensitivity and specificity , these values were defined as similar to PvA and PvB1 , however the actual reference test against which they would be determined might not easily be defined since in terms of pure analytical sensitivity , an optimal PvB2 index test is likely to be the new best standard of truth . The operational characteristics for the PvB2 TPP allow for less environment-resistant tests compared to PvA and PvB1: transport , storage and operation conditions allow for cold transportation and storage and are generally set to correspond to the typical conditions found in air-conditioned reference laboratories . The assay format is at a minimum a 96-well format and ideally a 384-well format to enable high throughput and the characterization of a high number of samples concurrently . This is linked with less stringent requirement in terms of equipment , with a tolerance for electricity requirement , up to 20 kg of equipment , and sample processing , with up to 20 steps for the assay procedure being acceptable . Similarly , the time-to-result is not of the essence in this case with an optimal time-to-results being less than 7 days , but at a minimum requirement of up to one month . These characteristics might become more critical if the test is used to target focal MDA , in which the optimal value ( one week ) would be a requirement . The cost associated with such a test is arguably expected to be high , since it is likely to involve complex laboratory procedures , reagents , and equipment as well as specialized and highly trained laboratory officers . Yet , the high throughput and high volume of testing required for population surveillance interventions is expected to drive the cost of such test down to an acceptable 1 . 0 USD end-user price per sample ( minimal requirement ) . The optimal value was set one order of magnitude below this , at 0 . 1 USD , which might be achievable for a test relying on simpler procedure and limiting the sample processing steps .
The views and opinions expressed in this article are consensus views and opinions from all individual authors and do not necessarily reflect the official policy or position of any of the authors institutions . | Plasmodium vivax is the second most prevalent Plasmodium species amongst the five that can infect humans and cause malaria . The control and elimination of P . vivax is complicated by its specific biology , such as hard-to-detect low densities of blood-circulating parasites in infected individuals , the existence of persistent liver forms causing relapse , or the early appearance of sexual stages of the parasite during the course of an infection , which facilitates its transmission . These difficulties are reinforced by the fact that most antimalarial tools have been developed primarily for P . falciparum , the most prevalent malaria species , and are not always as effective for P . vivax . Current tools for the diagnosis of P . vivax are of limited effectiveness . Rapid diagnostic tests exist but show , in average , lower performance than similar test for P . falciparum . P . vivax diagnosis often relies on light microscopy which is challenging to maintain at a high quality and not sensitive enough to detect a large fraction of all infections . Recognizing that better diagnostic tools for P . vivax are needed , we report in this study the development of new target product profiles to define the specific characteristics of such tests . The establishment of these consensus-based documents is an important first step to guide research and development efforts toward better diagnostic solutions for P . vivax malaria and to accelerate the elimination of this species alongside P . falciparum . | [
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] | 2017 | Defining the next generation of Plasmodium vivax diagnostic tests for control and elimination: Target product profiles |
Differentiation of hyphae into specialized infection structures , known as appressoria , is a common feature of plant pathogenic fungi that penetrate the plant cuticle . Appressorium formation in U . maydis is triggered by environmental signals but the molecular mechanism of this hyphal differentiation is largely unknown . Infectious hyphae grow on the leaf surface by inserting regularly spaced retraction septa at the distal end of the tip cell leaving empty sections of collapsed hyphae behind . Here we show that formation of retraction septa is critical for appressorium formation and virulence in U . maydis . We demonstrate that the diaphanous-related formin Drf1 is necessary for actomyosin ring formation during septation of infectious hyphae . Drf1 acts as an effector of a Cdc42 GTPase signaling module , which also consists of the Cdc42-specific guanine nucleotide exchange factor Don1 and the Ste20-like kinase Don3 . Deletion of drf1 , don1 or don3 abolished formation of retraction septa resulting in reduced virulence . Appressorium formation in these mutants was not completely blocked but infection structures were found only at the tip of short filaments indicating that retraction septa are necessary for appressorium formation in extended infectious hyphae . In addition , appressoria of drf1 mutants penetrated the plant tissue less frequently .
Penetration of the plant cuticle is a prerequisite for the establishment of many plant-fungal interactions and involves the formation of specialized infection structures , called appressoria . These structures mediate plant penetration in parasitic as well as in symbiotic fungi [1] , [2] . Appressoria are often melanized and contain thickened cell walls , which are necessary to generate the mechanical force to break through the cuticle of the plant epidermis . This process is driven by turgor-derived osmotic pressure and often involves targeted secretion of lytic enzymes [2]–[4] . Induction of appressorium formation is intricately regulated and triggered by chemical signals , hydrophobicity and surface texture [2] , [5] . These external stimuli are transmitted through mitogen activated protein kinases and cAMP signaling [6] , [7] . The massive reorganisation of the fungal cell observed during differentiation of infection structures is also coupled to cell cycle regulation [2] . The basidiomycetous fungus Ustilago maydis infects maize plants and causes smut disease [8] , [9] . To infect its host , compatible haploid sporidia fuse and form dikaryotic filaments spreading on the plant surface . These hyphae grow unipolar , do not branch and are arrested in the G2 phase of the cell cycle [8] , [10] . At the distal end of the growing filament , regularly spaced retraction septa are inserted , which delimit the cytoplasm-filled tip compartment from empty hyphal sections . Retraction septa are found in many filamentous fungi and ensure high-speed movement without mitosis and de novo generation of cytoplasm ( Figure 1 ) [11] . In U . maydis appressorium formation at the tip of filaments is triggered by surface hydrophobicity and cutin monomers [12] . These appressoria are unmelanized and are thought to penetrate the cuticle predominantly by secretion of lytic enzymes , rather than by mechanical force [13] . After penetration cell cycle arrest is released , the fungus proliferates within the plant and induces the formation of tumours , in which diploid teliospores are generated [14] . In U . maydis cell fusion is controlled by a pheromone/receptor system encoded by the a locus , while subsequent establishment of infectious hyphae depends on an active heterodimer of the homeodomain transcription factors expressed from the multiallelic b locus [15]–[17] . It has been shown that b is the master regulator of pathogenic development . An effector of b , the transcription factor Clp1 is important for the release of the cell cycle arrest when infectious hyphae have invaded the host and mitotic proliferation of hyphae is initiated [18] . For successful plant infection the Rho family GTPase Cdc42 is required [19] . In addition , Cdc42 , the Cdc42 specific guanine nucleotide exchange factor ( GEF ) Don1 and the Ste20-like protein kinase Don3 are required for cell separation of haploid sporidia during budding growth [19]–[22] . Morphogenesis and differentiation of eukaryotic cells involve reorganisation of the actin cytoskeleton . Formin proteins catalyze polymerization of monomeric actin into linear filaments [23] , [24] and thus play an important role in the organisation of the actin cytoskeleton [25] . Formins are large multidomain proteins and contain the highly conserved formin homology 2 ( FH2 ) domain at the C-terminus , which is responsible for actin nucleation [26] . Incorporation of monomeric actin is stimulated by the profilin-binding formin homology 1 ( FH1 ) domain [27] . The diaphanous formin was first described in Drosophila melanogaster where it is required for cytokinesis [28] . Formins of the diaphanous family are characterized by the presence of an N-terminal Rho family GTPase binding domain and a C-terminal autoregulatory domain [29] . Binding of GTPases of the Rho/Rac-family relieves the autoinhibition between the diaphanous autoregulatory domain ( DAD ) at the C-terminus and the diaphanous inhibitory domain ( DID ) resulting in stimulation of actin polymerization [28] , [30] , [31] . Here we report that in U . maydis the diaphanous-related formin Drf1 acts as an effector of Cdc42 . Interestingly , drf1 as well as don1 and don3 mutants were unable to form septa in cell cycle arrested infectious hyphae . We observed that these mutants develop infection structures only in short filaments while longer filaments lack appressoria . Therefore , we conclude that in U . maydis hyphal septation is critical for appressorium development and plant infection .
U . maydis contains two members of the formin family , the SepA-related Srf1 and the diaphanous-related formin Drf1 ( Figure 2A ) . Both proteins contain the characteristic FH1 , FH2 and DID domains . Interestingly , in Drf1 the GTPase binding domain ( GBD ) is split and the protein lacks the conserved DAD domain ( Figure 2A ) . Genome wide expression analysis revealed that expression of drf1 but not of srf1 is significantly induced during b-dependent filament formation [18] . This suggests a role for this formin during pathogenic development , which in U . maydis is controlled by the heterodimeric bE/bW homeodomain transcription factor . We confirmed the filament-specific induction of drf1 expression using qPCR ( Figure 2B ) . For this purpose we used the strain AB31 , in which filamentation can be induced by arabinose dependent transcription of bW/bE [32] . To investigate the cellular function of Drf1 we deleted drf1 in the U . maydis wild type strain Bub8 . Cells lacking drf1 were viable and exhibited normal cell shape . However , Δdrf1 cells formed clusters of up to 20 cells ( Figure 2C ) , indicating that cell separation between mother and daughter cells is affected in the absence of Drf1 . Normally budding cells form two distinct septa at the site of cell separation . The primary septum is sufficient to separate the cytoplasm of mother and daughter cells and is formed at the mother side of the bud neck . The secondary septum is needed to completely separate cells from each other and is formed at the daughter side of the bud neck [22] . The secondary septum was not formed in drf1 mutants , which could account for the cell separation defect . Interestingly , drf1 mutants deposited massive amounts of cell wall material at the site where the secondary septum is usually formed and this most likely leads to a delayed cell separation ( Figure 2C ) . We also quantified these accumulations and found that their intensity is about six times increased in comparison with secondary septa of wild type budding cells ( Figure 2D ) . These accumulations of cell wall material were not detected in cdc42 mutants , which also display a cell separation defect ( Figure 2C ) . This indicates additional functions of Cdc42 during cell separation , maybe in chitin deposition [19] . To investigate the role of Drf1 during mating and filament formation we performed mating assays on charcoal-containing agar plates . In this assay , fusion of compatible haploid cells and the subsequent switch to hyphal growth results in the formation of a white mycelium [33] . If compared to wild type cells , drf1 mutants exhibited significantly reduced mycelium formation on charcoal agar ( Figure 3A ) . Interestingly , this difference became obvious only after two days while after 24 hours a comparable number of short hyphae was visible on the surface of colonies ( Figure 3A ) . This suggests that drf1 mutants are not affected in cell fusion during mating but exhibit a defect in hyphal development . Microscopic analysis of calcofluor white stained filaments revealed that drf1 mutants lack hyphal septa in all filament-inducing conditions tested during this study ( Figures 3B , S1 ) . By contrast wild type filaments display regularly spaced retraction septa at the rear end of the growing tip compartment ( Figures 3B , S1 ) [11] . In wild type cells , the sections between these hyphal septa were empty and the cytoplasm of the dikaryotic cell was confined to the apical compartment ( Figures 1 , 3B ) [34] . In drf1 mutants , the lack of hyphal septation resulted in a marked elongation of the apical cytoplasmic compartment . Since drf1 mutants displayed reduced mycelium formation in the plate-mating assay ( Figure 3A ) we asked whether deletion of drf1 affects the growth rate of filaments . Two days after fusion , filaments of wild type strains and drf1 mutants were indistinguishable in length and reached approximately 400 µm . After three days Δdrf1 filaments had stopped elongation while wild type filaments continued to elongate ( Figure 3C ) . This observation suggests that U . maydis hyphae require distal retraction septa formation to cover long distances . We wondered whether depletion of Drf1 influences polar exocytosis as it has been shown for AgBni1 in Ashbya gossypii [35] . This could possibly cause the growth defect of b-induced filaments in absence of Drf1 . Therefore we fused the Rab GTPase Sec4 to GFP and analyzed the localization of the fusion protein in AB31 and AB31Δdrf1 filaments . Remarkably , in U . maydis Sec4 did not show predominant tip localization as it has been observed in A . gossypii [35] , but was found on moving vesicles throughout the filament ( Figure S2 ) . Longer filaments derived from drf1 mutants showed a reduced density of these vesicles presumably due to the enlarged cytoplasmic volume in the absence of retraction septa ( Figure S2 ) . drf1 mutants not only lack hyphal septa but also display a pronounced cell separation defect during budding ( see above ) , which resembles the phenotype of U . maydis strains deleted for don1 , don3 and cdc42 . These genes constitute a Cdc42 GTPase signaling network that regulates secondary septum formation during cytokinesis [19] , [22] , [36] . don1 , don3 and cdc42 mutants are unable to separate daughter cells after mitosis , but in contrast to drf1 mutants accumulations of cell wall material are not detectable [19] , [22] . We tested whether Cdc42 signaling is also required for hyphal septation . In plate-mating assays on charcoal plates , Δdon1 and Δdon3 mutants formed colonies with reduced mycelium comparable to Δdrf1 mutant colonies ( Figure 4A ) . Since Δcdc42 mutants are already affected in cell fusion [19] , colonies on charcoal plates showed only scattered mycelia ( Figure 4A ) . Microscopic analysis of Δdon1 and Δdon3 filaments revealed that these mutants were also defective in hyphal septation ( Figure S3 ) suggesting that this process involves the same regulatory network as secondary septum formation during budding . The genetic interaction between Cdc42 signaling and Drf1 during hyphal septation suggests that the N-terminal GTPase binding domain ( GBD ) of Drf1 ( Figure 2A ) may interact with Cdc42 . We used GST-Cdc42 to perform pull-down experiments with U . maydis whole cell extract . GFP-GBDDrf1 was able to interact with Cdc42 in its active GTP-bound form but not in its inactive GDP-bound form ( Figure 4B ) . Deletion of the GBD can result in constitutive active formin variants [37] . When drf1ΔGBD was introduced into a Δdrf1 strain , transformants displayed normal cell separation and retraction septum formation during filamentous growth . This observation shows that Drf1ΔGBD is fully functional ( Figures S4B , C ) . Furthermore , Drf1ΔGBD partially suppressed the cell separation defect of don1 mutants ( Figure 4C ) . We were able to detect secondary septa in don1 mutants , which expressed Drf1ΔGBD . These septa were never detected in Δdon1 cell clusters ( Figure 4C ) . Moreover , the colony morphology of don1 mutants appeared to be similar to wild type colony morphology when Drf1ΔGBD was expressed ( Figure 4C ) . This indicates that the truncated protein is constitutively active . Drf1ΔGBD was unable to rescue the cell separation defect of cdc42 mutants ( Figure S5 ) , implying additional functions of Cdc42 during cell separation . These functions might be regulated by other Cdc42-specific GEFs , which already have been identified in U . maydis ( Britta Tillmann , unpublished data ) . Altogether we assume that in U . maydis the diaphanous-related formin Drf1 acts as an effector of Cdc42 during cell separation and during hyphal septation on the plant surface . During budding growth U . maydis forms two septa to facilitate separation of mother and daughter cells . Both , the primary and secondary septation event in U . maydis involve the formation of a contractile actomyosin ring ( CAR ) [36] , [38] . It has been shown that the Cdc42-GEF Don1 and the Ste20-like protein kinase Don3 are required to trigger CAR formation specifically during the secondary septation event but not during primary septation [38] . To visualize CAR formation during hyphal septation we expressed the F-BAR domain protein Cdc15-GFP in strain AB31 . Cdc15 is an integral part of the CAR during both primary and secondary septum formation in U . maydis [36] . When filament formation of AB31 was induced with arabinose a ring-like accumulation of Cdc15-GFP fluorescence was detected during retraction septa formation ( Figure 5A ) . In AB31Δdrf1 no ring-like accumulation of Cdc15-GFP could be observed ( Figure 5B ) . CAR formation was also abolished in hyphae of don1 and don3 mutants ( Figure S6 , protocol S1 ) . Furthermore drf1 mutants failed to assemble the CAR at the site of secondary septation in haploid cells ( Figure S7 ) . Together , these data suggest that hyphal septation involves assembly of a CAR , which requires beside the formin Drf1 also the Cdc42/Don1/Don3 signaling network . To further analyze the role of Drf1 during CAR-formation we created GFP fusion proteins of Drf1 . Unfortunately , neither N-terminal nor C-terminal GFP-tagged Drf1 was able to rescue the Δdrf1 phenotype ( data not shown ) . Thus , we fused gfp to the C-terminus of the putative dominant active drf1ΔGBD , expressed this fusion protein in drf1 mutants and investigated the localization of Drf1ΔGBD-GFP in budding cells and in b-induced filaments , respectively . We found Drf1ΔGBD-GFP to be able to complement the phenotype of drf1 deletions , indicating that the protein is functional . In budding cells , Drf1ΔGBD-GFP localized at punctuated structures . Furthermore , during separation of budding cells Drf1ΔGBD-GFP clearly co-localized with the secondary septum at the daughter site ( Figure S4D ) . In b-induced filaments Drf1ΔGBD-GFP localized at the site where retraction septa form ( Figure 5C ) . These results confirm the specific function of Drf1 during both , secondary septa formation in budding cells and retraction septa formation during filamentous growth . It has been shown previously that formation of the secondary septum in U . maydis involves the formation of a septin collar at the site of secondary septum formation [38] . This collar is subsequently disassembled followed by CAR-formation . After CAR formation a septin ring assembles de novo and cell separation can be carried out [38] . We wondered whether septin organistion is impaired in drf1 mutants and consequently performed localization studies with a RFP-tagged septin ( Cdc10-RFP ) . We found that in budding Δdrf1 cells a septin collar structure formed at the expected site of the secondary septum ( Figure S8B ) . We never detected the transition to septin rings ( Figures S8A , B ) . The assembly of septin rings seems to depend on Drf1 . We also studied the localization of Cdc10-RFP in b-induced filaments . Cdc10-RFP was found to co-localize with calcofluor white stained retraction septa in wild type cells while in drf1 mutants no ring structures were observed ( Figure S8C , D ) . Moreover , Cdc10-RFP localized in dispersed patches throughout the filament as described previously [39] . This type of localization was not changed in drf1 mutants . To examine , if the inability to form retraction septa affects pathogenic development of U . maydis , we determined the virulence of drf1 mutants . Wild type and drf1 mutant cells were used to infect seven-day-old maize seedlings . While injection of wild type cells triggered tumour formation in 96% of infected plants ( 14 days post infection , dpi ) , plants inoculated with drf1 mutant cells displayed significant attenuated symptoms and induced tumours in only 16% of infected plants ( Table 1 ) . Virulence was completely restored when the open reading frame of drf1 under control of the constitutive otef promoter was reintroduced into the mutants ( Table 1 ) [40] . In the complemented strains , normal cell separation and hyphal septation have also been restored ( Figures S4A , C ) . Next we asked whether the hyphal septation defect of drf1 mutants is responsible for reduced virulence . Since virulence defects may result from reduced penetration or from reduced growth within the plant , we analyzed the infection process . The first step during infection is the formation of appressoria . Because appressoria in U . maydis are difficult to detect , we used the solopathogenic strain SG200AM1 , which expresses an appressorium specific GFP reporter [9] , [12] . drf1 , don1 or don3 were deleted in SG200AM1 and appressorium formation was investigated on the plant surface . We found that all three mutants differentiate appressorial structures ( Figure S9 ) . However , quantification of these infection structures revealed a significant reduction of appressorium development in Δdrf1 , Δdon1 and Δdon3 strains ( Figure 6A ) . Furthermore , we determined virulence of the SG200AM1 derivative don1 , don3 and drf1 mutants . The progenitor strain SG200AM1 induced tumours in 95% of the plants . SG200AM1Δdon1 , SG200AM1Δdon3 and SG200AM1Δdrf1 showed significantly reduced virulence ( Figure 6B ) . In particular the severest symptoms , i . e . dead plants were observed in only 2% of SG200AM1Δdrf1 infected plants , while 19% dead plants were scored for SG200AM1 infections ( Figure 6B ) . This reduction of virulence supports our notion that Drf1 , Don1 and Don3 play an important role in the initial phase of pathogenic development . Appressorium formation can also be induced on an artificial hydrophobic surface in the presence of long-chain hydroxy fatty acids [12] . Under these in vitro conditions appressorium formation of drf1 mutants was again significantly reduced if compared to the progenitor strain SG200AM1 ( Figure 7A ) . The appressorial structures of SG200AM1 and SG200AM1Δdrf1 were indistinguishable in morphology and had a similar diameter ( Figure S11 ) . However , we noticed that appressorium formation in Δdrf1 mutants occurred predominantly at the tip of short filaments ( 60–90 µm ) while in wild type filaments appressoria were formed also at the tip of longer filaments ( Figures 7B , C and S10 ) . Remarkably , filaments of drf1 mutants , which exceeded 120 µm were unable to form appressoria ( Figure 7B ) . Together these data suggest that formation of retraction septa in U . maydis is not required for appressorium formation in short infectious hyphae while appressorium differentiation in longer filaments ( >120 µm ) requires hyphal septation . We conclude that a limited , cytoplasm-filled tip compartment is critical for appressorisum formation . So far we have analyzed the connection between appressorium differentiation and retraction septum formation . We also wondered if the ability to penetrate the plant cuticle was impaired in drf1 mutants . We therefore compared the penetration frequencies between appressoria derived by SG200AM1 and SG200AM1Δdrf1 . Using confocal microscopy 18 h after inoculation of maize plants , appressoria were found either to penetrate the plant surface , to continue growth on the leaf surface or to be in-between these decisions ( Figure 8A ) . Appressoria derived by SG200AM1Δdrf1 penetrated the plant cuticle significantly less if compared to appressoria derived by SG200AM1 ( Figure 8B ) . Only about 25% of appressoria successfully invaded the host in drf1 mutants while about 50% of SG200AM1 appressoria reached the host tissue ( Figure 8B ) . This indicates that the penetration ability of differentiated appressoria is also affected in the absence of retraction septa . Interestingly , drf1 mutants were found to be capable of forming septa after having penetrated the plant surface ( Figure 8A ) , indicating that septation events inside the plant are independent of Drf1 . After penetration G2 arrest is released and hyphae grow intracellularly . Thereby mitotic septa are formed to separate nuclei [41] . Therefore we investigated the role of the Don1/Cdc42/Drf1/Don3 signaling network during the biotrophic phase of U . maydis . We observed that hyphal growth of drf1 , don1 and don3 mutants in planta was indistinguishable from wild type hyphae with respect to septum formation ( Figure 9 ) . Overall , our results demonstrate that U . maydis produces two distinct classes of septa . The primary septum of haploid budding cells , as well as septa formed during growth in the plant are coupled to mitosis and do not require the Cdc42 signaling module . By contrast , the secondary septum of haploid budding cells and septa of infectious hyphae are independent of mitosis and require the Cdc42 signaling module .
In this study we could show that formation of retraction septa in U . maydis infectious hyphae depends on the diaphanous-related formin Drf1 . Drf1 acts as an effector of the small GTPase Cdc42 and is together with the Cdc42-GEF Don1 and the Ste20-like kinase Don3 required for the formation of a contractile actomyosin ring during septation . Remarkably , non-septated infectious hyphae , that have exceeded a certain length , are unable to form appressoria , while short hyphae lacking retraction septa form appressoria . This led to a significant reduction in the formation of functional infection structures and thus explains the attenuated virulence of drf1 , don1 and don3 mutants . Formins are major players in actin organisation . The genome of U . maydis encodes along with the diaphanous-related formin Drf1 , a second formin , Srf1 , which belongs to the subfamily of SepA-related formins ( MIPS U . maydis data base ) [29] . While srf1 is an essential gene ( B . Sandrock , unpublished data ) , we found that drf1 deletion mutants in U . maydis display delayed cell separation during budding growth as well as a complete block in hyphal septation . In fungi , diaphanous-related formins have been characterized so far only in ascomycetes , where they regulate actin polymerisation during polar growth and cytokinesis [42]–[44] . S . cerevisiae and A . gossypii contain two diaphanous-related formins , which are functionally redundant and synthetically lethal [35] , [45] , [46] . We observed that U . maydis drf1 mutants were unable to form a contractile actomyosin ring ( CAR ) during growth of G2 arrested infectious hyphae . We have previously shown that in budding cells Cdc42 , its activator Don1 and the protein kinase Don3 are required for CAR formation specifically during secondary septum formation but not during the primary septation event [36] . Here we found that in budding cells Drf1 is essential for CAR formation during secondary septation and also for the subsequent assembly of septin rings . This confirmed that CAR formation is a prerequisite for septin ring assembly during secondary septum formation in budding U . maydis cells as it has been suggested previously [38] . Drf1 might fulfil similar functions during CAR formation in U . maydis as the formin Cdc12p in fission yeast [47] . It is well established that Cdc12p in S . pombe is necessary for CAR formation and also for the regulation of cytokinesis [48]–[50] . In U . maydis don1/cdc42/don3 mutants showed a defect in hyphal CAR formation and thus lack retraction septa . Therefore we assume that the same cellular machinery operates both in formation of the secondary septum and the retraction septum . The fact that drf1 mutants are not affected in CAR formation during cytokinesis suggests that Srf1 could be responsible for CAR formation during the primary septation event in budding cells . In higher eukaryotes , diaphanous-related formins interact with diverse GTPases such as RhoA , RhoD and Cdc42 and regulate formation of actin stress fibres , endosome dynamics and cytokinesis [51] . In Dictyostelium discoideum dDia2 binds to Rac1 and controls dynamics of filopodia [52] . We demonstrate here that Drf1 binds Cdc42 in its active GTP-bound form , but not in its inactive status . Together with the finding that a constitutive active variant of Drf1 is able to suppress the cell separation defect of don1 mutants we conclude that in U . maydis Drf1 acts as an effector of Cdc42 . Retraction septa are also found in other fungi where similar signaling events and mechanisms might operate [53] , [54] . Prior to plant invasion U . maydis forms infectious hyphae . During this developmental stage the cell cycle is arrested and the filaments grow only at their tips . At the distal end of the cytoplasm-filled tip cell regularly spaced septa are laid down , leaving empty compartments of the collapsed hyphae behind [11] , [34] . By this mechanism , the cytoplasm-filled tip compartment is maintained at a constant length of about 150 µm [11] . In drf1 mutants the cytoplasmic tip compartment was significantly stretched reaching up to 400 µm . Since these mutants stopped hyphal elongation , the super-sized length of the cytoplasmic compartment appears to be limiting for further growth . Hence , the ability to form retraction septa is a prerequisite for the rapid elongation of infectious hyphae , which continue to grow over several days and can reach up to 2000 µm within 24 h [11] . We found that appressorium development in SG200Δdrf1 strains was restricted to short hyphae up to 120 µm . By contrast , corresponding wild type filaments were able to differentiate appressoria independently of filament length . Consequently , drf1 mutants were reduced in appressorium formation and virulence . We observed that virulence of mixtures of compatible haploid Δdrf1 strains was more attenuated than virulence of solopathogenic drf1 mutants . Since compatible haploid cells have to form conjugation tubes and to fuse prior to infection , this suggests that the additional length of conjugation tubes may further restrict the ability of Δdrf1 strains to form infection structures . All mutants affected in formation of retraction septa characterized in this study were reduced in appressorium formation . This raises the question how appressorium formation is affected in these mutants . The exact mechanism , by which U . maydis form appressoria is currently unknown [13] . The rice pathogen M . grisea forms a highly melanized appressorium , which accumulates massive amounts of glycerol to generate enormous turgor pressure to penetrate the leaf cuticle mainly by mechanical force [2] , [3] , [55] , [56] . Our results open the possibility that for appressoria formation of U . maydis turgor pressure might be of importance . The distal septum could act as mechanical support to generate sufficient turgor pressure during appressorium formation . Alternatively , the extended volume of the cytoplasmic tip cell results in dilution of osmotically active substances and hence insufficient turgor pressure is generated to differentiate appressoria . This is supported by the finding that appressoria derived from drf1 mutants frequently fail to penetrate the plant surface ( Figure 8A ) . It has been suggested that secretion of lytic enzymes is necessary for plant penetration by U . maydis [13] , [57] . Because we observed a reduction in the density of exocytotic vesicles in drf1 mutants it is possible that secretion of lytic enzymes is reduced . In M . grisea nuclear migration into the appressorium and subsequent autophagic programmed cell death of the conidial mother cell is a prerequisite for successful penetration [58] , [59] . Interestingly , M . oryzae sep1 mutants form irregular septa during germ tube and appressorium formation and are also unable to differentiate functional appressoria [60] . While M . grisea appressorium formation displays hallmarks of a strict developmental program and occurs only in the close vicinity of the conidium , U . maydis is able to cover long distances before initiation of infection structures . Under natural conditions efficient infection might require growth or movement over long distances to reach young meristematic tissue , which is prerequisite for U . maydis to enter the plant [61] . The fact that non-septated filaments of U . maydis stopped extension upon a length of approximately 400 µm and were unable to differentiate appressoria when exceeded a length of 120 µm emphasizes the importance of septa for virulence of U . maydis . In wild type hyphae the cytoplasm-filled tip compartment can reach up to 150 µm and appressorium differentiation still remains unaffected . Interestingly , drf1 , don1 or don3 deletion strains fail to form appressoria when exceeded this particular length . Thus , 150 µm seems to be the critical length for U . maydis filaments to differentiate appressoria . In this study we investigated four different events of septum formation in the life cycle of U . maydis . During budding growth formation of the primary septum is coupled to mitosis , while the secondary septum permits cell separation ( Figure 10 ) [22] . We have shown previously that secondary septum formation requires Cdc42 , Don1 and Don3 [19] , [22] . G2 arrested infectious hyphae form septa , which are not coupled to mitosis . Here we could demonstrate that the Don1/Cdc42/Drf1/Don3 signaling network is not only required for formation of the secondary septum in budding cells , but also during septation of infectious hyphae ( Figure 10 ) . Finally , we investigated septum formation of proliferating hyphae inside the plant , a septation event that is coupled to mitosis [41] . We found that the Cdc42 signaling network is not required during this developmental stage and the ability to form septa returns immediately after plant penetration when cell-cycle arrest is released . Consequently , U . maydis uses at least two different mechanisms to produce septa , a mitosis uncoupled mechanism , which requires Cdc42 and a mitosis coupled mechanism , which does not require Cdc42 ( Figure 10 ) . The specific importance of the described Cdc42 signaling module during non-mitotic septation is shown by the distinct phenotypes of drf1 mutants . Drf1 is only needed for the formation of the actomyosin ring during formation of the secondary septum and during hyphal septation albeit the contractile actomyosin ring is already formed during primary septation coupled to mitosis of haploid budding cells . It is well established that the GTPase RhoA defines the division site of eukaryotic cells and requires spindle microtubules [62] . How the Don1/Cdc42/Drf1 module coordinates the positioning of hyphal septa in G2 arrested filaments and secondary septa during budding growth is yet unclear but remains an exciting topic to be elucidated . Especially how the regularity of septa in filaments is established would be interesting to understand [11] . Is the position of the nucleus important or could changes in cytoplasmic volume account for this regularity ? Taken together , our data provide new insights into the role of septation during pathogenic development of U . maydis . Cell cycle independent septation is unusual among eukaroytes [63] , which makes infectious hyphae of U . maydis a valuable model system to study this kind of septation .
Escherichia coli strain DH5a and Top10 were used for cloning and amplification of plasmid DNA . U . maydis strains FB1 , Bub8 , AB31 [32] and SG200AM1 [12] were utilized as wild type backgrounds for all strains created for this manuscript . FB1Δdon3 , Bub8Δdon3 , FB1Δdon1 , Bub8Δdon1 , FB1Δcdc42 and Bub8Δcdc42 have been described elsewhere [19] , [22] , [35] . All strains used are listed in table S1 . According to the NCBI database these reference sequences were used: M . musculus: MmDrf1: NP_031884; U . maydis: Srf1 ( um12254 ) : XP_760288 , UmDrf1 ( um01141 ) : XP_757288 . In general , transformation of U . maydis was performed as described [64] . For expression studies constructs expressing drf1 or drf1ΔGBD under control of the etef-promoter were integrated into the cbx-locus by homologous recombination [65] . Deletion mutants and the endogenous gfp-fusion of cdc15 were generated as described [66] . For septin localization in the AB31 and the AB31Δdrf1 strain the Cdc10-RFP construct was used as described previously [38] . For overexpression in U . maydis the open reading frame of drf1 ( 6588 bp ) was cloned into the SmaI- and NotI-sites of pETEF-MXN-GFP , which is derived from p123 and resulted in pETEF-drf1 [67] . The GBD ( aa 534–886 ) was deleted from this plasmid to generate pETEF-drf1ΔGBD . For the C-terminal fusion of GFP the ORF of Drf1ΔGBD was cloned in the NcoI-site of pETEF-MXN-GFP to generate pETEF-drf1ΔGBD-GFP . For exocyst localization in U . maydis the ORF of Sec4 ( um03865 ) was amplified and cloned into p123 to generate p123-GFP-Sec4 . All constructs have been confirmed by sequencing . Primer sequences will be supplied by the corresponding author ( B . S . ) on request . Deletion constructs for drf1 and don1 were generated using the SfiI-technique as described previously [66] . An excess of bacterial expressed GST or GST-Cdc42 proteins was immobilized on glutathione resin according the manufacturer's instructions ( Macherey-Nagel ) and pretreated with either 1 mM GDP or 1 mM GTPgS in the presence of 2 mM EDTA for 1 h at 4°C . The GTP/GDP loading was stopped with 40 mM MgCl2 and washed three-times with TBS . U . maydis protein extract was made as described [68] and directly used for the pull-down for 16 h at 4°C . GFP-fusion proteins were detected using a GFP-monoclonal antibody from Santa Cruz Biotechnology ( sc-9996 ) . The in vitro system for inducing filaments and appressoria in U . maydis was applied as described previously [12] with minor modifications . Briefly , SG200AM1 and derivative strains were grown in YEPSL medium ( 0 . 4% yeast extract , 0 . 4% peptone , 2% sucrose ) at 28°C to an OD600 of 0 . 8 . The cells were resuspended in 2% YEPSL to an OD600 of 0 . 2 and supplemented with 100 µM ( f . c . ) 16-hydroxyhexadecanoic acid ( Sigma-Aldrich ) . Cells were sprayed ( EcoSpray Labo Chimie , France ) on Parafilm M and incubated by 100% humidity at 28°C for 20 h . The samples were stained by calcofluor white to visualize fungal cells and the ratio of filamentous cells expressing the AM1 marker ( indicative for appressorium formation ) to the total amount of filaments was determined using fluorescence microscopy . The experiment was done in three biological replicates . The diameter of appressoria was measured using the ImageJ software ( NIH , USA ) . For examination of appressoria on the leaf surface , AM1 derivatives were inoculated into seven-day-old maize seedlings 1 cm above ground . After 20 hours the third oldest leaf was prepared , washed in water and incubated for 30 s with calcofluor white . Appressorium formation was quantified as described above . Solopathogenic strains or compatible haploid strains were grown in YEPSL to an OD600 of 0 . 8 and concentrated in H2O to a final OD600 of 1 . 0 . This suspension was used to infect seven-day-old seedlings of Early Golden Bantam ( Olds Seeds , Madison ) by injecting 0 . 5 ml into each seedling . 12 days after infection disease symptoms were evaluated according to the disease rating criteria reported by Kämper et al . [9] . The experiment was done in three biological replicates . Plate-mating assays were performed by placing mixtures of compatible strains on PD-plates containing 1% activated charcoal followed by incubation at room temperature [33] . For filament formation analysis a drop of such mixture was covered by a small glass platelet allowing filamentous growth onto the glass surface . After two and three days the platelets were analyzed on a microscope slide . U . maydis cells from logarithmically growing cultures were placed on agarose cushions . Cells were visualized by differential interference contrast ( DIC ) and epifluorescence microscopy using a Zeiss Axiophot 200 microscope ( Göttingen , Germany ) . Calcofluor white staining was performed as described previously [22] . Briefly , to stain fungal material , samples were incubated in Calcofluor Fluorescent Brightner 28 ( 100 µg/ml in 0 . 2 M Tris/HCl , pH 8; Sigma-Aldrich ) for 30 s . Images were taken using a cooled CCD camera ( Hamamatsu Orca-ER , Herrsching , Germany ) with an exposure time of 50–300 ms . Image acquisition and deconvolution were performed using Improvision Volocity software ( Perkin-Elmer , Rodgau , Germany ) and processing was carried out with Photoshop ( Adobe ) and ImageJ ( NIH , USA ) . The intensity of calcofluor white staineing of secondary septa was analyzed using ImageJ ( NIH , USA ) . To investigate septum formation of fungal hyphae inside the plant , three days after infection the third oldest leaf was destained in ethanol , transferred to 10% KOH , incubated at 85°C for 4 hours , washed three times with PBS buffer ( 140 mM NaCl , 16 mM Na2HPO4 , 2 mM KH2PO4 , 3 . 5 mM KCl , and 1 mM Na2-EDTA , pH 7 . 4 ) , and incubated under vacuum in staining solution ( 10 µg/ml propidium iodide and 10 µg/ml WGA-AF 488 in PBS , pH 7 . 4 ) according to Doehlemann et al . [57] . WGA-AF 488 was purchased from Invitrogen , propidium iodide from Sigma-Aldrich . Images were taken and processed as described above . Confocal microscopy was performed using a TCS-SP5 confocal microscope ( Leica Microsystems ) . For GFP fluorescence , an excitation of 488 nm and detection at 495–530 nm was used . Propidium iodide fluorescence was excited with 561 nm and detected at 580–630 nm . To visualize WGA-AF 488 , an excitation of 488 nm and detection at 500–540 nm was used . Calcofluor white was excited with a 405 nm laser and detected at 415–460 nm . Images were processed using LAS-AF software ( Leica Microsystems ) . For RNA isolation the U . maydis strain AB31 was grown 7 hours in 2% glucose or arabinose containing YNB-liquid media . 25 ml culture was harvested and ground in liquid nitrogen . RNA was extracted with Trizol ( Invitrogen , Karlsruhe , Germany ) and purified using RNeasy kit ( Qiagen , Hilden , Germany ) . For qRT-PCR cDNA was synthesised using the First Strand cDNA kit ( Fermentas , Mannheim , Germany ) employing 1 µg total RNA . qRT-PCR was performed using the MAXIMA SYBR-Green qPCR Master-Mix ( Fermentas , Mannheim , Germany ) on a Biorad iCycler . Reaction conditions were as follows: 5 min 95°C followed by 45 cycles of 15 sec 95°C/15 sec 60°C/30 sec 72°C . Primers used for amplification were for the control gene: TCGACATCGTCAAGGCTATC ( 5′ppi RT ) and CGATGGTGATCTTGGACTTG ( 3′ppi RT ) . To amplify a drf1-fragment we used: TCAGGGTCAAGCAAAGTCAG ( 5′drf1 RT ) and GTCAGTCATTCGAGATCGT ( 3′drf1 RT ) . Ratios of drf1/ppi were calculated for each timepoint and the glucose value was set to 1 . Data are expressed as means ±SD of triplicate samples . Statistical significance was assessed using Statistical Calculators ( www . danielsoper . com ) and considered significant if p values were <0 . 05 . We performed a Wilcoxon rank-sum test to compare the distributions of disease symptoms induced by U . maydis strains . For this purpose we used a web-calculator ( http://faculty . vassar . edu/lowry/wilcoxon . html ) . UM accession numbers are from MUMDB ( http://mips . helmholtz-muenchen . de/genre/proj/ustilago/ ) and XP/XM/NP accession numbers are from NCBI ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) : Drf1 ( um01141 ) XP_757288 , Don1 ( um10152 ) XP_758565 , Don3 ( um05543 ) XP_761690 , Cdc42 ( um00295 ) XP_756442 , Cdc15 ( um00168 ) XP_756315 , Cdc10 ( um10644 ) XP_759364 , Sec4 ( um03865 ) XP_760012 , Ppi ( um03726 ) XM_754780 , Srf1 ( um12254 ) XP_760288 , mmDrf1 NP_031884 . | Pathogens exhibit various developmental stages during the process of infection and proliferation . The basidiomycete Ustilago maydis is a model organism for plant pathogenic fungi . On the plant surface U . maydis grows as a cell-cycle arrested filament . Growth of infectious hyphae involves regular formation of retraction septa leaving empty sections behind . The tip cell forms an appressorium and penetrates the cuticle . In this study we identified for the first time a signaling module regulating formation of retraction septa in fungal hyphae . The module consists of the highly conserved small GTPase Cdc42 , its activator Don1 and the actin-organizing formin Drf1 . After penetration of the plant , cell cycle arrest is released and hyphal septation is resumed in planta but was found to be independent of Cdc42 and Drf1 . Thus , during infection Cdc42 signaling and Drf1 coordinate hyphal septation events specifically in infectious hyphae in U . maydis . The inability to form retraction septa affects filament elongation and appressorium formation resulting in significantly reduced virulence . We observed a threshold size of the cytoplasm filled tip compartment above which appressorium formation is blocked . These findings highlight that formation of retraction septa , a common feature of filamentous fungi , is an important virulence determinant of U . maydis . | [
"Abstract",
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] | [
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] | 2011 | Septation of Infectious Hyphae Is Critical for Appressoria Formation and Virulence in the Smut Fungus Ustilago Maydis |
Genetic generalised epilepsy ( GGE ) is the most common form of genetic epilepsy , accounting for 20% of all epilepsies . Genomic copy number variations ( CNVs ) constitute important genetic risk factors of common GGE syndromes . In our present genome-wide burden analysis , large ( ≥ 400 kb ) and rare ( < 1% ) autosomal microdeletions with high calling confidence ( ≥ 200 markers ) were assessed by the Affymetrix SNP 6 . 0 array in European case-control cohorts of 1 , 366 GGE patients and 5 , 234 ancestry-matched controls . We aimed to: 1 ) assess the microdeletion burden in common GGE syndromes , 2 ) estimate the relative contribution of recurrent microdeletions at genomic rearrangement hotspots and non-recurrent microdeletions , and 3 ) identify potential candidate genes for GGE . We found a significant excess of microdeletions in 7 . 3% of GGE patients compared to 4 . 0% in controls ( P = 1 . 8 x 10-7; OR = 1 . 9 ) . Recurrent microdeletions at seven known genomic hotspots accounted for 36 . 9% of all microdeletions identified in the GGE cohort and showed a 7 . 5-fold increased burden ( P = 2 . 6 x 10-17 ) relative to controls . Microdeletions affecting either a gene previously implicated in neurodevelopmental disorders ( P = 8 . 0 x 10-18 , OR = 4 . 6 ) or an evolutionarily conserved brain-expressed gene related to autism spectrum disorder ( P = 1 . 3 x 10-12 , OR = 4 . 1 ) were significantly enriched in the GGE patients . Microdeletions found only in GGE patients harboured a high proportion of genes previously associated with epilepsy and neuropsychiatric disorders ( NRXN1 , RBFOX1 , PCDH7 , KCNA2 , EPM2A , RORB , PLCB1 ) . Our results demonstrate that the significantly increased burden of large and rare microdeletions in GGE patients is largely confined to recurrent hotspot microdeletions and microdeletions affecting neurodevelopmental genes , suggesting a strong impact of fundamental neurodevelopmental processes in the pathogenesis of common GGE syndromes .
The epilepsies comprise a clinically heterogeneous group of neurological disorders defined by recurrent spontaneous seizures due to paroxysmal excessive and synchronous neuronal activity in the brain [1] . Epilepsy affects about 4% of the general population during their lifetime [2] and about 40% of all epilepsies are thought to have a strong genetic contribution . The genetic generalised epilepsies ( GGEs ) represent the most common group of epilepsies with predominant genetic aetiology , accounting for 20% of all epilepsies [3] . Their clinical features are characterised by unprovoked generalised seizures with age-related onset , generalised spike and wave discharges on the electroencephalogram and no evidence for an acquired cause [4 , 5] . Despite their strong familial aggregation and heritability [6–9] , the genetic architecture of common GGE syndromes is likely to display a biological spectrum , in which a small fraction ( 1–2% ) follows monogenic inheritance , whereas the majority of GGE patients presumably display an oligo-/polygenic predisposition with extensive genetic heterogeneity [10] . Although causative mutations for rare GGE with monogenic inheritance have been identified in genes primarily affecting neuronal excitability , synaptic transmission , and neurodevelopmental processes [11 , 12] , the genetic basis of the majority of patients with GGE remains largely unsolved . Genomic copy number variations ( CNVs ) constitute a significant source of genetic risk factors for common focal and generalised epilepsies [13–20] . By targeted screening of rearrangements at genomic hotspots associated with neurodevelopmental disorders [21] , we previously identified recurrent microdeletions at 15q11 . 2 , 15q13 . 3 and 16p13 . 11 as important genetic risk factors of common GGE syndromes [14 , 16 , 17 , 22–24] . The microdeletions at 15q13 . 3 and 16p13 . 11 represent the most prevalent genetic determinants of GGE identified so far [14 , 16] . In addition , we were able to show that non-hotspot exonic microdeletions in three brain-expressed genes encoding gephyrin ( GPHN ) [25] , neurexin 1 ( NRXN1 ) [26] and the RNA-binding protein FOX1 ( RBFOX1 ) [27] confer susceptibility of GGE . Although the GGE-associated microdeletions identified to date are individually rare ( <1% ) , they cumulatively account for a significant fraction of the genetic burden in more than 3% of patients with common GGE syndromes [14–16 , 22] . In the present genome-wide burden analysis , we used the Affymetrix SNP 6 . 0 array to screen large ( ≥ 400 kb ) and rare ( < 1% ) autosomal microdeletions with high calling confidence ( ≥ 200 markers ) in European case-control cohorts of 1 , 366 GGE patients and 5 , 234 population controls . We aimed to: 1 ) assess the genetic burden of large and rare microdeletions in common GGE syndromes , 2 ) evaluate the contribution of recurrent hotspot and unique microdeletions to the genetic burden of GGE , and 3 ) identify novel candidate genes for GGE . Specifically , we tested the hypothesis whether microdeletions affecting genes involved in neurodevelopmental processes account for a significant fraction of the genetic risk of GGE syndromes .
We identified 103 microdeletions in 100 out of 1 , 366 GGE patients compared to 214 microdeletions in 208 out of 5 , 234 controls ( S1 Table ) . Overall , 7 . 3% of patients with GGE carried at least one microdeletion compared to 4 . 0% in controls ( P = 1 . 77 x 10–7; OR = 1 . 91 , 95%-CI: 1 . 48–2 . 46 ) ( Table 1 ) . We observed a marginal increase in microdeletion frequency in the GGE patients when we considered only microdeletions affecting either at least one protein-coding RefSeq gene ( n = 18 , 299; P = 5 . 86 x 10–7; OR = 1 . 95 , 95%-CI: 1 . 48–2 . 57 ) or at least one brain-expressed gene ( n = 8 , 878; P = 1 . 38 x 10–7; OR = 2 . 19 , 95%-CI: 1 . 61–2 . 98 ) ( Table 1 ) . Likewise , the median size of microdeletions was larger in the GGE patients ( 713 kb; interquartile range ( IQR ) = 523 kb—1 , 537 kb ) compared to controls ( 589 kb; IQR = 488–930 kb; P = 3 . 99 x 10–3; Wilcoxon-Mann-Whitney-Test ) . The number of individuals carrying at least two microdeletions did not differ significantly between the GGE patients ( n = 3 ) and controls ( n = 6; P = 0 . 40 , Fisher´s exact test ) . The microdeletion burden was similar for males ( 7 . 2% ) and females ( 7 . 4% ) affected by GGE ( P = 0 . 91; OR = 0 . 97 , 95%-CI: 0 . 63–1 . 52 ) . The distribution of GGE subsyndromes did not differ between 100 GGE patients carrying a microdeletion ( 33 JME , 50 CAE/JAE , 17 EGTCS/EGMA ) and the group of 1 , 266 GGE patients without a large and rare microdeletion ( 507 JME , 548 CAE/JAE , 211 EGTCS/EGMA; P > 0 . 15 ) . The spectrum of 103 microdeletions identified in 100 GGE patients comprised: 1 ) 38 ( 36 . 9% ) recurrent microdeletions at seven known genomic rearrangement hotspots previously associated with a wide range of neurodevelopmental disorders [29] , 2 ) 27 ( 26 . 2% ) genic microdeletions that were detected only in the GGE patients , 3 ) 16 ( 15 . 5% ) microdeletions without a protein-coding RefSeq gene and that were not present in the controls , and 4 ) 22 ( 21 . 4% ) non-hotspot microdeletions which overlap with the microdeletions identified in the controls ( S1 Table ) . Most prominent was the 7 . 5-fold excess of recurrent hotspot microdeletions in the GGE patients compared to the controls ( P = 2 . 61 x 10–17; OR = 7 . 46 , 95%-CI: 4 . 20–13 . 33; χ2-test , df = 1 ) ( Table 2 ) . Overall , 2 . 8% ( n = 38 ) of 1 , 366 GGE patients carried one of the known recurrent microdeletions at 1q21 . 1 ( n = 1 ) , 15q11 . 2 ( n = 13 ) , 15q13 . 3 ( n = 11 ) , 16p11 . 2 ( n = 1 ) , 16p12 ( n = 3 ) , 16p13 . 11 ( n = 6 ) and 22q11 . 2 ( n = 3 ) , whereas these hotspot microdeletions were observed only in 0 . 4% ( n = 20 ) of 5 , 234 population controls ( S1 and S2 Figs ) . Significant associations with GGE patients were found for single hotspot microdeletions at 15q11 . 2 ( P = 4 . 18 x 10–4; OR = 3 . 58; 95%-CI: 1 . 58–8 . 09 , χ2-test , df = 1 ) , 15q13 . 3 ( P = 2 . 89 x 10–8 , Fisher´s exact test ) , 16p13 . 11 ( P = 1 . 48 x 10–3; OR = 11 . 48 , 95%-CI: 2 . 05–116 . 5 , Fisher´s exact test ) , and 22q11 . 2 ( P = 8 . 85 x 10–3 , Fisher´s exact test ) . All hotspot microdeletions in GGE patients identified by SNP arrays were validated by TaqMan qPCR . Altogether , the present findings highlight the cumulative impact of the recurrent microdeletions at 15q11 . 2 , 15q13 . 3 , 16p13 . 11 and 22q11 . 2 on the genetic risk of common GGE syndromes . Besides the recurrent hotspot microdeletions , we identified 27 GGE patients carrying a genic microdeletion that was not observed in the controls ( Table 3 and S1 Table and S3 Fig ) . These microdeletions affected 158 protein-coding RefSeq genes and exhibited an enrichment of genes previously associated with epilepsy ( NRXN1 , RBFOX1 , PCDH7 , KCNA2 , EPM2A , RORB , PLCB1 ) and neuropsychiatric disorders ( DPYD , CADM2 , PARK2 , GRM8 , TSNARE1 , TPH2 , MACROD2 ) ( Table 3 ) . Microdeletions involving NRXN1 exons 1–2 were observed in two GGE patients with genetic absence epilepsies [26] . In addition , two partially overlapping microdeletions were identified in the chromosomal region 8q24 . 3 encompassing the genes encoding the t-SNARE domain containing 1 protein ( TSNARE1; chr8: 143 , 293 , 441–143 , 484 , 601 ) and the brain-specific angiogenesis inhibitor 1 ( BAI1; chr8: 143 , 545 , 376–143 , 626 , 368 ) . All other unique microdeletions occurred only once . The microdeletions affecting the neuronal genes , NRXN1 and RBFOX1 , have been reported in two previous publications [26 , 27] . To explore the hypothesis whether neurodevelopmental genes affected by the microdeletions have an impact on the genetic risk of common GGE syndromes , we performed enrichment analyses of the deleted genes , using two previously published sets of genes implicated in neurodevelopmental disorders ( ND ) : 1 ) ND-related genes ( n = 1 , 547 ) compiled by literature and database queries [30] , and 2 ) genes implicated in autism spectrum disorder ( ASD-related genes ) comprising 1 , 669 brain-expressed genes with an enrichment of deleterious exonic de novo mutations in ASD [31] . Microdeletions carrying at least one ND-related gene were 4 . 6-fold enriched in the GGE patients as compared to the controls ( P = 8 . 02 x 10–18; OR = 4 . 58 , 95%-CI: 3 . 09–6 . 82 ) ( Table 1 ) . Likewise , microdeletions encompassing at least one ASD-related gene showed a 4 . 1-fold excess in the GGE patients relative to the controls ( P = 1 . 29 x 10–12; OR = 4 . 11 , 95%-CI: 2 . 64–6 . 40 ) ( Table 1 ) . To explore the impact of neurodevelopmental genes that are not covered by the recurrent hotspot microdeletions , we combined the ND- and ASD-related gene lists [30 , 31] and removed all genes affected by observed recurrent hotspot microdeletions . Non-recurrent microdeletions carrying at least one of the 2 , 495 selected ND/ASD-related genes showed a 2 . 3-fold excess in GGE patients ( n = 1 , 328 ) compared to control subjects ( n = 5 , 214 ) , when individuals with recurrent hotspot microdeletions were excluded ( P = 4 . 56 x 10–4; OR = 2 . 48 , 95%-CI: 1 . 42–4 . 30 ) . To rule out an artificial enrichment of microdeletions in the GGE patients , we compiled two control gene assemblies comprising: 1 ) 3 , 256 randomly selected autosomal protein-coding RefSeq genes , and 2 ) 3 , 837 autosomal protein-coding RefSeq genes not expressed in the brain [28] . Both control gene assemblies did not show evidence for an increase of the microdeletion burden in GGE patients compared to controls ( P > 0 . 40 ) ( Table 1 ) . The Disease Association Protein-Protein Link Evaluator ( DAPPLE v2 . 0 ) tool [85] was applied to identify significant physical connectivity among proteins encoded by genes affected by microdeletions . Therefore , we separately tested the gene assemblies for the GGE patients and the control subjects . Based on an initial regional query we extracted 191 seed genes from 103 microdeletions found in the GGE patients and 221 seed genes from 214 microdeletions observed in controls . There was an overlap of 61 genes between the two assemblies . DAPPLE network analyses revealed a significant enrichment for direct connections between the seed genes ( P = 0 . 01 ) in the GGE microdeletion carriers , while the control gene network did not show evidence for an enrichment ( P = 0 . 40 ) . Finally , in GGE we found eleven genes with significant connectivity: PLCB1 ( P = 0 . 002 ) , GRM1 ( P = 0 . 002 ) , ARC ( P = 0 . 002 ) , CNTN6 ( P = 0 . 015 ) , CHL1 ( P = 0 . 033 ) , BAI1 ( P = 0 . 033 ) , CYFIP1 ( P = 0 . 040 ) , TRIP13 ( P = 0 . 042 ) , MAPK3 ( P = 0 . 044 ) , GJ8 ( P = 0 . 048 ) , and KCNA2 ( P = 0 . 050 ) ( S4 Fig ) . Utilising the Enrichr tool [86] , functional enrichment analysis of the gene assembly affected by the microdeletions in the GGE patients revealed a significant enrichment of the MGI Mammalian Phenotype term "abnormal emotion/affect behaviour" ( MP:0002572; Padj = 1 . 30 x 10–3 ) and the GO biological process term “cognition” ( GO:0050890; Padj = 0 . 012 ) ( Table 4 ) . Enrichr network analysis identified one significant PPI Hub in the GGE patients based on an enrichment of nine deleted genes ( ARC , TJP1 , MAPK3 , MYH11 , EXOC3 , NRXN1 , PARK2 , PLCB1 , GRM1 ) among 219 network genes ( Padj = 0 . 018 ) , for which GRIN2B encodes the shared interacting protein .
The present burden analysis applied a screening strategy that focused on both large ( ≥ 400 kb , ≥ 200 markers ) and rare ( < 1% ) autosomal microdeletions to ensure a high calling accuracy [87] and to enrich pathogenic microdeletions among confounding benign copy number polymorphisms [88–90] . We found a significant 1 . 9-fold excess of microdeletions in the patients with GGE compared to the controls ( Table 1 ) . Overall , 7 . 3% of the 1 , 366 GGE patients carried at least one microdeletion compared to 4 . 0% in 5 , 234 controls . These findings highlight the important impact of microdeletions on the genetic susceptibility of common GGE syndromes with an attributable risk of about 3 . 3% . The spectrum of 103 microdeletions identified in the GGE patients contained a high proportion ( 36 . 9% ) of recurrent microdeletions at genomic rearrangement hotspots , known to play a pathogenic role in a wide range of neuropsychiatric disorders including epilepsy [13 , 91 , 92] . In total , 2 . 8% of the GGE patients carried one of the known pathogenic hotspot microdeletions at 1q21 . 1 , 15q11 . 2 , 15q13 . 3 , 16p11 . 2 , 16p12 , 16p13 . 11 and 22q11 . 2 ( Table 2 ) , whereas these hotspot microdeletions were found only in 0 . 4% of the population controls ( S1 and S2 Figs ) . Although these hotspot microdeletions are individually rare ( < 1% ) , they collectively result in a 7 . 5-fold increased burden in the GGE patients and a population-attributable risk of about 2 . 4% . A previous genome-wide CNV search in epilepsies observed a similar cumulative prevalence of recurrent hotspot microdeletions in 3 . 5% out of 399 GGE patients [16] . Likewise , a targeted screening of the microdeletions at 15q11 . 2 , 15q13 . 3 and 16p13 . 11 showed a cumulative frequency of 3 . 1% in 359 GGE patients and an even higher frequency of 10% in 60 GGE patients with intellectual disability [17] . Several other CNV studies targeting these genomic rearrangement hotspots also emphasised a substantial impact of recurrent microdeletions at 15q11 . 2 , 15q13 . 3 and 16p13 . 11 in the pathogenesis of GGE and other epilepsies [14–20 , 22–24 , 93 , 94] . To our knowledge , this is the first study demonstrating a significant association of the recurrent microdeletion at 22q11 . 2 with GGE . Re-evaluation of the clinical records of three GGE patients carrying a 22q11 . 2 microdeletion revealed additional congenital and developmental features fitting to known conditions of the 22q11 . 2 deletion syndrome ( OMIN 188400/192430 ) . GGE patient ( EC-EGMA094 ) had a moderate psychomotoric retardation , patient ( EC-EGTCS145 ) was affected by a cleft palate and an atrial septal defect , and patient ( EC-EGTCS044 ) had a mild impairment of his motoric coordination during childhood , moderate learning disabilities and hypocalcaemia , highlighting the 22q11 . 2 deletion syndrome as a multisystem disorder with high penetrance and variable phenotypic spectrum [95] . According to our ascertainment scheme [96] , the present GGE patients with recurrent microdeletions did not exhibit severe intellectual disability or severe psychiatric comorbidities at the age of exploration but may evolve psychiatric disorders at later age . Considering the published CNV studies of epilepsies [14–20 , 24] , meta-analyses may demonstrate an association of the less frequent recurrent hotspot microdeletions at 16p11 . 2 and 16p12 with GGE . Haploinsufficiency of CYFIP1 at 15q11 . 2 [97] , CHRNA7 at 15q13 . 3 [98] , NDE1 at 16p13 . 11 [99] and PRRT2 at 16p11 . 2 [100] has been implicated as risk-conferring mechanism for epilepsy and other neurodevelopmental phenotypes [88 , 89 , 91] . Functional-enrichment , pathway and network analyses showed significant connectivity of genes affected by microdeletions in GGE patients ( S4 Fig ) and a significant enrichment for the MGI Molecular Function category "abnormal emotion/affect behaviour" ( MP:0002572 ) as well as the GO biological process term “cognition” ( GO:0050890 ) . The protein-protein interaction analyses highlight several genes that have been implicated in epileptogenesis ( CYFIP1 , GRIN2B , KCNA2 , NRXN1 , PLCB1 ) [14 , 16 , 26 , 39 , 74 , 75 , 97] and neurodevelopmental processes ( ARC , GRM1 , PARK2 ) [51 , 52 , 55 , 57–59] . In line with our neurodevelopmental hypothesis , we found a significant 4 . 6-fold excess of microdeletions carrying at least one ND-related gene [30] and a 4 . 1-fold enrichment of microdeletions affecting at least one ASD-related gene [31] in the GGE patients compared to the control subjects . In contrast , the two control gene assemblies did not show an increase of the microdeletion burden in GGE patients compared to controls ( P > 0 . 40 ) . Accordingly , the intriguing enrichment of ND- and ASD-related genes demonstrates that genes involved in neurodevelopmental processes play an important role in the epileptogenesis of common GGE syndromes . Notably , the moderate overlap of the previously published assemblies of ND- and ASD-related genes implicates a large number of neurodevelopmental genes contributing to the risk of common GGE syndromes and extensive genetic heterogeneity . The emerging overlap of gene-disrupting microdeletions and the rapidly evolving landscape of loss-of-function gene mutations in rare and common epilepsy syndromes will facilitate the prioritisation of causal epilepsy genes and the elucidation of the leading molecular pathways of epileptogenesis [101 , 102] . We identified 27 gene-covering microdeletions in non-hotspot genomic regions that were present only in GGE patients ( Table 3 and S3 Fig ) . These autosomal microdeletions involved several genes previously implicated in epilepsy and neurodevelopmental disorders . Although it remains challenging to distinguish benign and pathogenic microdeletions , several of these contain plausible candidate genes for epilepsy . Of particular interest were seven genes at seven microdeletion loci that have been associated with epilepsy . Three of the epilepsy-associated microdeletions have been reported in two previous publications demonstrating an association of microdeletions affecting the 5´-terminal exons of the neuronal genes encoding the adhesion molecule neurexin 1 ( NRXN1; 2p16 . 3 , chr2: 50 , 145 , 642–51 , 259 , 673 , hg19 ) and the splicing regulator RNA-binding protein fox-1 homolog ( RBFOX1; 16p13 . 3 , chr16: 5 , 289 , 468–7 , 763 , 341 , hg19 ) [26 , 27] . The microdeletions involving NRXN1 exons 1–2 were observed in two female GGE patients with genetic absence epilepsies [26] . The 5´-terminal untranslated RBFOX1 exons 1–2 were deleted in a female patient with childhood absence epilepsy [27] . Deleterious mutations and microdeletions of the genes , NRXN1 and RBFOX1 , have been reported in a large number of patients with a broad range of neuropsychiatric disorders , who were frequently also affected by epilepsy [40 , 41 , 54 , 72 , 81] . A recent study demonstrated that the splicing regulator Rbfox1 controls neuronal excitation in the mammalian brain and the Rbfox1 knockout in mice results in an increased susceptibility to spontaneous and kainic acid-induced seizures [71] . Furthermore , molecular , cellular , and clinical evidence supports a pivotal role of RBFOX1 in human neurodevelopmental disorders [73 , 103] . A 3 . 45 Mb microdeletion harbouring the protocadherin PCDH7 gene ( chromosomal location: 4p15 . 1 , chr4: 30 , 721 , 950–31 , 148 , 422 , hg19 ) was found in a female GGE subject with juvenile myoclonic epilepsy . An international GWAS meta-analysis including 8 , 696 epilepsy patients and 26 , 157 controls highlights PCDH7 as susceptibility gene for epilepsy in general and GGE syndromes in particular [45] . The PCHD7 gene encodes a calcium-dependent adhesion protein that is expressed in neurons of thalamocortical circuits and the hippocampus [46] . PCDH7 has been implicated as neuronal target gene of MECP2 [47] , the gene for Rett syndrome ( OMIM #312750 ) , which manifests as a progressive neurodevelopmental disorder with recurrent seizures . Moreover , mutations in the X-chromosomal protocadherin gene PCDH19 cause epilepsy and intellectual disability in females [48] . These lines of evidence suggest an involvement of PCDH7 in epileptogenesis . A 788 kb microdeletion involving the Shaker-like voltage-gated potassium channel gene KCNA2 ( 1p13 , chr1: 111 , 136 , 002–111 , 174 , 096 , hg19 ) was identified in a male GGE patient with generalised tonic-clonic seizures starting at the age of 14 . The Kv1 subfamily plays an essential role in the initiation and shaping of action potentials , influencing action potential firing patterns and controlling neuronal excitability as well as seizure susceptibility [36 , 38 , 39] . De novo loss- or gain-of-function mutations in KCNA2 have been identified to cause human epileptic encephalopathy [39] . Furthermore , the Kcna2 knockout mice exhibit spontaneous seizures and have a reduced life span [35 , 37] . One female GGE patient with childhood absence epilepsy carried a 2 . 4 Mb microdeletion in the chromosomal region 6q24 . 6 encompassing two neuronally expressed genes encoding the metabotropic glutamate receptor type 1 ( GRM1; chr6: 146 , 348 , 917–146 , 758 , 734 , NM_001278065 , hg19 ) and laforin ( EPM2A; chr6: 145 , 946 , 439–146 , 056 , 991 , NM_005670 , hg19 ) . Deleterious mutations in the GRM1 gene have been found in patients with schizophrenia [52] . Also , familial segregation analysis of deleterious non-synonymous sequence variants revealed a co-segregation with multiple neuropsychiatric conditions including epilepsy in some families . Recessive mutations/microdeletions of EPM2A cause progressive myoclonic epilepsy type 2A ( Lafora disease , OMIM #254780 ) [53] . A 582 kb microdeletion encompassing exon 1 of the gene encoding the RAR-related orphan receptor B ( RORB; 9q21 . 13 , chr9: 77 , 112 , 251–77 , 303 , 533 , NM_006914 , hg19 ) was found in a male patient with childhood absence epilepsy , overlapping with the critical region of a novel microdeletion syndrome at 9q21 . 13 characterised by intellectual disability , speech delay , facial dysmorphisms and epilepsy [63] . The RORB gene is a strong candidate for the neurological phenotype because RORB was deleted in all affected individuals [63] , it is expressed in the cerebral cortex and thalamus , and genetic associations of RORB with bipolar disorder [64] and verbal intelligence [65] have been reported . The gene encoding the enzyme phospholipase C-beta 1 ( PLCB1; 20p12 . 3 , chr20: 8 , 112 , 911–8 , 865 , 546 , hg19 ) was partially deleted ( exons 1–3 , NM_015192 , hg19 ) in a male GGE patient with childhood absence epilepsy . PLCB1 catalyses the generation of inositol 1 , 4 , 5-trisphoshate and diacylglycerol from phosphatidylinositol 4 , 5-bisphosphate , a key step in the intracellular transduction of many extracellular signals . Homozygous microdeletions of chromosome 20p12 . 3 , disrupting the promoter region and first three coding exons of PLCB1 , have previously been reported in two consanguineous families with early infantile epileptic encephalopathy [74] . Mutation analysis of a family with severe intractable epilepsy and neurodevelopmental delay revealed compound heterozygous mutations in PLCB1 composed of a 476 kb microdeletion encompassing PLCB1 and a deleterious PLCB1 splice site mutation [75] . Girirajan et al . [54] found an enrichment of microdeletions and duplications involving the PLCB1 gene in individuals with autism . Together , these findings implicate that the PLCB1 gene contributes to the genetic risk of neurodevelopmental disorders including epilepsy . In addition to the epilepsy-associated microdeletions , nine deleted genes have been previously implicated as genetic risk factors in a broad range of neuropsychiatric disorders . Unique hemizygous microdeletions in GGE patients involved DPYD/1p13 . 3 [32–34] , CADM2/3p12 . 1 [43] , BCHE/3q26 . 1 [44] , PARK2/6q24 [54 , 55 , 57 , 58] , GRM8/7q31 . 33 [59–61] . TSNARE1/8q24 . 3 [62] , MPP7-ARMC4-MKX/10p12 . 1 [66] , TPH2/12q21 . 1 [67–69] , MACROD2/20p12 . 1 [78–81] , and ADARB1/21q22 . 3 [83 , 84] . Notably , overlapping microdeletions encompassing TSNARE1 at chromosome 8q24 . 3 in two GGE patients indicate its potential role in epileptogenesis . A recent GWAS meta-analysis of psychiatric disorders identified TSNARE1 as susceptibility gene for schizophrenia , schizoaffective and bipolar disorders [62] . While the function of TSNARE1 remains elusive , bioinformatic predictions suggest a vertebrate-specific function in synaptic vesicle exocytosis [104] . Further studies will be necessary to disentangle the pathogenic genes and to elucidate their molecular pathways in neurodevelopmental disorders and epileptogenesis . Our burden analysis of large and rare autosomal microdeletions ( size ≥ 400 kb , frequency < 1% ) revealed: 1 ) a nearly 2-fold excess of microdeletions in GGE patients relative to the population controls , 2 ) a 7-fold increased burden for known hotspot microdeletions previously associated with neurodevelopmental disorders , and 3 ) a more than 4-fold enrichment of microdeletions carrying a gene implicated in neurodevelopmental disorders . Recurrent microdeletions at seven genomic rearrangement hotspots accounted for 37% of all microdeletions identified in the GGE patients and predominantly contributed to the excess of microdeletions in GGE patients . Comorbidity of GGE with other neurodevelopmental disorders , such as intellectual disability , ASD and schizophrenia , may result in even higher prevalence of recurrent hotspot microdeletions [17] and emphasises a valuable diagnostic contribution to the clinical management of these severely affected comorbid patients with GGE . The remarkable phenotypic variability observed for the recurrent hotspot microdeletions suggests a shared susceptibility of a wide range of neuropsychiatric disorders and GGE [105] . Several genes affected by microdeletions that were found only in GGE patients highlight novel candidate genes for GGE . Altogether , the present findings reinforce converging lines of evidence that genes affected by microdeletions in GGE patients reside in fundamental neurodevelopmental processes .
The study protocol was approved by the local institutional review boards of the contributing clinical centres . All study participants provided written informed consent . Genomic DNA samples of all study participants were processed by the Affymetrix SNP 6 . 0 array . For the genome-wide CNV burden analysis , we did not include individuals with excessive CNV counts ( > 50 autosomal deletions per individual for deletions spanning > 40 kb in size and covering > 20 markers ) . In addition , we excluded all Affymetrix SNP 6 . 0 array data derived from lymphoblastoid cell lines because of the clonal source of the DNA which is prone to CNV artefacts compared to genomic DNA samples derived from blood cells [21] . All study participants were of self-reported North-Western European origin . Unrelated GGE patients of European descent were ascertained through the primary diagnosis of a common GGE syndrome according to the classification of the International League Against Epilepsy [1 , 4] . The standardised protocols for phenotyping of GGE syndromes as well as inclusion and exclusion criteria are available online at: http://portal . ccg . uni-koeln . de/ccg/research/epilepsy-genetics/sampling-procedure/ [96] . GGE patients with a history of severe major psychiatric disorders ( autism spectrum disorder , schizophrenia , affective disorder: recurrent episodes requiring pharmacotherapy or treatment in a hospital ) , or severe intellectual disability ( no basic education , permanently requiring professional support in their daily life ) were excluded . The GGE cohort comprised 1 , 366 patients ( 853 females , 513 males ) with the following age-related GGE syndromes: childhood absence epilepsy ( CAE , n = 398 ) , juvenile absence epilepsy ( JAE , n = 191 ) , unspecified genetic absence epilepsy ( GAE , n = 9 ) , juvenile myoclonic epilepsy ( JME , n = 540 ) , epilepsies with generalised tonic-clonic seizures ( GTCS ) alone predominantly on awakening ( EGMA , n = 94 ) , and epilepsies with recurrent unprovoked GTCS alone starting before the age of 26 ( EGTCS , n = 134 ) . These 1 , 366 GGE patients were collected from Austria ( n = 142 ) , Belgium ( n = 39 ) , Denmark ( n = 97 ) , Germany ( n = 801 ) and the Netherlands ( n = 287 ) . Notably , 1 , 052 of the GGE patients and 3 , 022 population controls investigated in the present study were part of a previous study that investigated six target microdeletions at genomic rearrangement hotspots [14] . Affymetrix SNP 6 . 0 data from 5 , 234 German population controls ( 2 , 559 females , 2 , 675 males ) were obtained from three epidemiologically based cohorts: 1 ) KORA cohort from South Germany ( n = 1 , 507 ) [106] , 2 ) PopGen cohort from North Germany ( n = 1 , 143 ) [107] , and 3 ) SHIP cohort from East Germany ( n = 2 , 584 ) [108] . The population controls were unscreened for epilepsy or major neuropsychiatric disorders . EIGENSTRAT principal component analysis [109] was applied to remove ancestry outliers and to match for European ancestry of the case-control cohorts [96] . Genomic DNA samples were investigated by the Affymetrix Genome-Wide Human SNP Array 6 . 0 ( Affymetrix , Santa Clara , CA , USA ) . CNV analysis was performed as previously described [14 , 22] , using the Birdsuit algorithm implemented in the Affymetrix Genotyping Console version 4 . 1 . 1 . All annotations refer to the genome build GRCh37/hg19 . The present genome-wide burden analysis focused on rare and large autosomal microdeletions to ensure a high reliability of the microdeletion calls [87] and to enrich pathogenic microdeletions [88–90] . Therefore , we filtered out autosomal microdeletions with high calling confidence according to the following criteria: a ) size ≥ 400 kb , b ) coverage of ≥ 200 probe sets , and c ) microdeletion frequency < 1% in the entire study sample . The microdeletion size of at least 400 kb was selected because all known pathogenic hotspot microdeletions identified in neurodevelopmental disorders exceed this size in CNV scans with the Affymetrix SNP Array 6 . 0 [29 , 88–90] . We did not include microduplications in the present burden analysis because the accuracy of CNV detection is lower for microduplications compared to microdeletions [110] . In particular , genomic DNA samples with substantial degradation are prone to spurious microduplication calls . Moreover , microduplications seem to exert pathogenic effects less frequently compared to microdeletions [88] . We excluded microdeletions with an overlap of > 10% with 12 chromosomal regions prone to artificial CNV calls according to a recently published "artefact list" [111] . For all QC-filtered microdeletions identified by SNP array screening , the segmental log2 ratios of the signal intensities and the SNP heterozygosity state were visually inspected by the Chromosome Analysis Suite v1 . 2 . 2 ( Affymetrix , Santa Clara , CA , USA ) to exclude spurious microdeletion calls . Validation of all 38 recurrent hotspot microdeletions and four GGE-associated microdeletions identified by SNP arrays in the GGE patients was carried out by real-time quantitative PCR ( qPCR ) according to the manufacturer´s instructions ( Life Technologies , Carlsbad , CA , USA ) . Overall burden analyses were carried out for three assemblies of autosomal microdeletions: 1 ) any microdeletion , 2 ) genic microdeletions encompassing at least one protein-coding RefSeq gene , defined by the largest NM gene transcript ( n = 18 , 299 , hg19 ) , and 3 ) microdeletions affecting a brain-expressed gene ( n = 8 , 878 ) , specified by a log ( RPKM ) > 3 . 32 of the BrainSpan RNA-Seq transcriptome dataset ( http://www . brainspan . org/ ) [28] . Specifically , we tested the hypothesis whether microdeletions affecting genes involved in neurodevelopmental processes account for a significant fraction of genetic risk of GGE syndromes . Therefore , we investigated two recently published assemblies of genes associated with neurodevelopmental disorders ( ND ) : 1 ) ND-related genes compiling 1 , 547 genes that were associated with neuropsychiatric disorders , autism candidate genes and genes of known genomic disorders based on literature and database queries [30] , and 2 ) ASD-related genes comprising 1 , 669 brain-expressed genes that were selectively enriched for deleterious exonic de novo mutations in ASD individuals relative to their healthy siblings [31] . To evaluate a spurious enrichment of microdeletions in the GGE patients relative to the population controls , we tested two control gene assemblies comprising: 1 ) 3 , 256 randomly selected autosomal genes , and 2 ) 3 , 837 autosomal genes not expressed in the brain [28] , defined by the BrainSpan RNA-Seq transcriptome dataset . ND- and ASD-related genes , genes located in genomic rearrangement hotspots , or the artefact list were removed from the compiled control gene assemblies . Functional-enrichment tests , pathway and network analyses were performed with the Disease Association Protein–Protein Link Evaluator version 2 . 0 program ( DAPPLE v2 . 0; http://www . broadinstitute . org/mpg/dapple/dappleTMP . php; [85] ) and the gene-set enrichment tool Enrichr ( http://amp . pharm . mssm . edu/Enrichr/index . html; [86] ) . Therefore , we compiled two lists of genes affected by microdeletions in either the GGE patients ( number of genes; n = 329; n = 191 regional seed genes ) or the controls ( n = 428 genes; n = 221 regional seed genes ) . There was an overlap of 103 genes ( n = 61 seed genes ) in both gene lists . To explore potential physical interactions among proteins encoded by deleted genes , DAPPLE uses experimentally validated , protein-protein interaction ( PPI ) databases to identify network and protein connectivity . Empirically , 1 , 000 random networks were generated by permutation to determine whether the connectivity of each seed protein with the PPI reference network was greater than that expected by chance . The gene-set enrichment tool Enrichr was applied separately to explore patient and control lists of genes affected by microdeletions for an overlap with pathway gene-set libraries , specifically the database PPI Hub Proteins [112] , and gene-set libraries created from Gene Ontology [113] as well as MGI Mammalian Phenotype terms [114] . A pathway or ontology term was considered as significantly enriched if the false discovery rate ( FDR , Benjamini-Hochberg ) was lower than 5% for an assembly of more than two genes and occurred only in the GGE patients but not in the controls . Burden analysis was performed by comparisons of the frequency of autosomal microdeletions in GGE patients and controls . The P-values and corresponding odds ratios ( ORs ) with the 95%-confidence intervals were calculated with a two-sided χ2-test or Fisher´s exact test if appropriate . The Wilcoxon-Mann-Whitney-Test was applied to compare differences in the genomic size of microdeletions . In addition , the individual burden of microdeletions was assessed for comparisons of microdeletion size . Nominal two-sided P-values < 0 . 05 were considered significant . | Epilepsy affects about 4% of the general population during lifetime . The genetic generalised epilepsies ( GGEs ) represent the most common group of epilepsies with predominant genetic aetiology , accounting for 20% of all epilepsies . Despite their strong heritability , the genetic basis of the majority of patients with GGE remains elusive . Genomic microdeletions constitute a significant source of genetic risk factors for epilepsies . The present genome-wide burden analysis in 1 , 366 European patients with GGE and 5 , 234 ancestry-matched controls explored the role of large and rare microdeletions ( size ≥ 400 kb , frequency < 1% ) in the complex genetic architecture of common GGE syndromes . Our results revealed a 2-fold excess of microdeletions in GGE patients relative to the population controls , 2 ) a 7-fold increased burden for known hotspot microdeletions ( 15q11 . 2 , 15q13 . 3 , 16p13 . 11 , 22q11 . 2 ) previously associated with a wide range of neurodevelopmental disorders , and 3 ) a more than 4-fold enrichment of microdeletions carrying a gene implicated in neurodevelopmental disorders . Our findings reinforce emerging evidence that genes affected by microdeletions in GGE patients have a strong impact in fundamental neurodevelopmental processes and dissect novel candidate genes involved in epileptogenesis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Burden Analysis of Rare Microdeletions Suggests a Strong Impact of Neurodevelopmental Genes in Genetic Generalised Epilepsies |
African trypanosome procyclic forms multiply in the midgut of tsetse flies , and are routinely cultured at 27°C . Heat shocks of 37°C and above result in general inhibition of translation , and severe heat shock ( 41°C ) results in sequestration of mRNA in granules . The mRNAs that are bound by the zinc-finger protein ZC3H11 , including those encoding refolding chaperones , escape heat-induced translation inhibition . At 27°C , ZC3H11 mRNA is predominantly present as an untranslated cytosolic messenger ribonucleoprotein particle , but after heat shocks of 37°C—41°C , the ZC3H11 mRNA moves into the polysomal fraction . To investigate the scope and specificities of heat-shock translational regulation and granule formation , we analysed the distributions of mRNAs on polysomes at 27°C and after 1 hour at 39°C , and the mRNA content of 41°C heat shock granules . We found that mRNAs that bind to ZC3H11 remained in polysomes at 39°C and were protected from sequestration in granules at 41°C . As previously seen for starvation stress granules , the mRNAs that encode ribosomal proteins were excluded from heat-shock granules . 70 mRNAs moved towards the polysomal fraction after the 39°C heat shock , and 260 increased in relative abundance . Surprisingly , many of these mRNAs are also increased when trypanosomes migrate to the tsetse salivary glands . It therefore seems possible that in the wild , temperature changes due to diurnal variations and periodic intake of warm blood might influence the efficiency with which procyclic forms develop into mammalian-infective forms .
African trypanosomes , like all other organisms investigated so far , respond to heat shock by repressing general protein synthesis , while enhancing or retaining synthesis of proteins that are required to survive or recover from heat stress [1] . Unlike other organisms , however , trypanosomes lack the ability to control the transcription of individual protein-coding genes [2–4] . Polymerase II transcription is polycistronic , and monocistronic mRNAs are created by 5' trans splicing of a capped spliced leader ( SL ) and polyadenylation [5] . The selectivity of the heat shock response , like other changes in gene expression , therefore relies on post-transcriptional mechanisms . Trypanosoma brucei procyclic forms are the forms that grow inside the tsetse fly midgut . In natural infections , these forms migrate to the proventriculus , developing into epimastigotes , and from there to the salivary glands where they become metacyclic forms which are infective for mammals [6] . After a tsetse fly bites a mammal , long slender bloodstream forms proliferate in the new host's blood and tissue fluids . Upon reaching high density , the parasites differentiate into non-dividing short stumpy forms [7] , which are pre-adapted for differentiation into procyclic forms upon uptake by tsetse [8] . Nearly all previous work on heat shock in T . brucei has concentrated on cultured procyclic forms subjected to a one-hour heat shock at 41°C [1] . This is on the upper edge of temperatures that can be tolerated by most tsetse species in the wild [9] , since tsetse prefer to rest in the shade and to feed on parts of animals that are not exposed to full sunlight [10] . Nevertheless , after the 41°C treatment trypanosomes recover quite rapidly upon return to the normal culture temperature of 27°C [1] . Heating to 41°C inhibits trypanosome transcription initiation [11 , 12] and stimulates overall mRNA degradation [1] , resulting in gradual loss of total mRNA [1] . In addition , translation of most mRNAs is suppressed . After an hour at 41°C , there is almost no mRNA in polysomes , while three types of messenger ribonucleoproten ( mRNP ) granules appear . These granules contain most of the mRNA [13] and various combinations of translation factors , the two poly ( A ) binding proteins PABP1 and PABP2 , the helicase DHH1 , the aggregation-prone protein SCD6 , and the 5'-3' exoribonuclease XRN1 [1 , 14 , 15] . Cycloheximide treatment causes retention of mRNAs in polysomes at 41°C , inhibiting both mRNA degradation and granule formation [1] . Thus , as in other organisms , granules are locations for storage and/or degradation of non-translated mRNAs . Despite the general shut-down in gene expression after heat shock , synthesis of proteins that are required for survival during , and recovery after , heat shock—such as protein refolding chaperones—continues . We previously showed that the zinc-finger protein ZC3H11 binds to the 3'-UTRs of chaperone mRNAs , and is required both for target mRNA retention and for cellular survival after heat shock [16] . ZC3H11 binds to MKT1 and to PBP1 , which in turn recruits LSM12 and poly ( A ) binding proteins PABP1 and PABP2[17] . MKT1 and PBP1 remain distributed throughout the cytosol after heat shock . Starvation also causes the formation of mRNP granules , but in this case MKT1 and PBP1 colocalise with SCD6 in the granules [17] . Recently , we investigated how ZC3H11 itself is regulated [13] . ZC3H11 is barely detectable in both bloodstream and procyclic forms grown at their normal culture temperatures of 37°C and 27°C respectively [16] . When procyclic forms are incubated at 37–41°C , the level of ZC3H11 protein progressively increases . This is partly caused by a loss of protein degradation , but more prominently by translational control . At 27°C , the ZC3H11 mRNA migrates in sucrose gradients as a messenger ribonucleoprotein particle at or just above the small ribosomal subunits , but after a 1h heat shock at 37°C , 39°C or 41°C , nearly all of the ZC3H11 mRNA is in the polysomal fractions and ZC3H11 mRNA does not colocalise with heat shock granules [13] . In this paper we have examined whether other mRNAs show similar translation regulation after a 39°C heat shock , and identified additional mRNAs that escape sequestration into stress granules after a 41°C heat shock .
Trypanosome culture conditions were as described in [18] . Procyclic trypanosomes were grown in MEM-Pros medium at 27°C ( unless stated otherwise ) at densities lower than 6×106 cells/ml . All experiments were done with Lister 427 monomorphic procyclic form parasites expressing the Tet repressor . 3–5×108 procyclic cells were treated with cycloheximide ( 100μg/ml ) for 5 minutes , harvested at room temperature by centrifugation ( 850g , 8min , 20°C ) , washed once in 1ml of ice-cold PBS and lysed in 300μl of lysis buffer ( 20mM Tris pH7 . 5 , 20mM KCl , 2mM MgCl2 , 1mM DTT , 1200u RNasin ( Promega ) , 10μg/ml leupeptin , 100μg/ml cycloheximide , 0 . 2% ( vol/vol ) IGEPAL ) by passing 20–30 times through a 21G needle . After pelleting insoluble debris by centrifugation ( 17000g , 10min , 4°C ) and adjusting to 120mM KCl , the clarified lysate was layered onto a 17 . 5–50% sucrose gradient ( 4ml ) and centrifuged at 4°C for 2 hours at 40000 rpm in Beckman SW60 rotor . Monitoring of absorbance profiles at 254nm and gradients fractionation was done with a Teledyne Isco Foxy Jr . system . RNAs from pooled fractions were purified using TriFast . To control for the efficiency of RNA isolation , equal amounts of a human β-globin in vitro transcript were sometimes added to each of the collected fractions before RNA purification . Proteins were detected by Western blotting according to standard protocols . For detection of the endogenous ZC3H11 protein only cytoskeleton-free extracts were used . Antibodies used were to the ZC3H11 ( rabbit , 1:10000 , [13] ) , RBP6 [19] and PTP1 [20] . Detection was done using ECL solutions ( GE Healthcare ) . Granules from normal and heat-shocked procyclic cells were enriched as described previously [21] . 5×108 control or heat-shocked ( 1 hour at 41°C ) procyclic cells were harvested at room temperature by centrifugation ( 1500g , 10min ) , washed in 1ml of PBS and lysed in 200μl of ice-cold buffer A ( 20mM Tris-HCl pH 7 . 6 , 2mM MgCl2; 0 . 25M sucrose , 1mM DTT , 10% glycerol , 1% Triton X-100 , 800u RNasin ( Promega ) , 1 tablet Complete Protease Inhibitor Cocktail EDTA free ( Roche ) /10ml buffer ) by pipetting . Lysis was confirmed microscopically . The lysate was clarified ( 20000g , 10min ) and the supernatant ( SN1 ) was transferred to fresh tube with 750μl of peqGOLD TriFast FL ( Peqlab ) . All remaining supernatant was removed after one short centrifugation ( 3min , 20000g ) . The pellet was resuspended again in 200μl of buffer A by passing 30–40 times through a 21G syringe , vortexed and centrifuged ( 20000g , 5min ) . The supernatant ( SN2 ) was taken and the pellet was resuspended in 200μl buffer A as above . The whole procedure was repeated one more time to obtain the supernatant SN3 . Then the pellet was resuspended one more time in 200μl buffer A as above and microtubules were disrupted by the addition of 12 μl 5M NaCl ( 283mM final conc . ) , the samples were passed through 21G syringe , incubated on ice for 30 minutes with vortexing every 5 minutes , then centrifuged ( 20000g , 10min ) . The supernatant ( SG ) was removed and used to prepare the "small granule" RNA ( SG ) . The pellet was washed once in 200μl of buffer A without resuspension ( 20000g , 10min ) and finally resuspended in 750μl of TriFast FL to make the "large granule" ( LG ) RNA . Another 5×107 control or heat-shocked procyclic cells were taken to obtain total RNA . Total RNA was incubated with oligonucleotides complementary to trypanosome rRNA and RNAse H , and mRNA integrity was checked by Northern blotting with a probe that detects the beta-tubulin mRNA . The samples were then subjected to high throughput sequencing such that most samples gave about 30 million aligned reads . Sequences were aligned to the latest available T . brucei TREU927 genome sequence using Bowtie [22] , allowing for up to 20 sequence matches . Reads that aligned to open reading frames were then aligned using a custom script , again allowing for each read to align up to 20 times . To extract the reads for individual open frames , we used a modified version of the "unique open reading frame" list of Siegel et al . [23] . Reads per million and other routine calculations were done in Microsoft Excel . Differences in RNA abundance between conditions or fractions were assessed using DESeq [24] . Untranslated region sequences were downloaded from TriTrypDB and sequence motifs searched using DREME and MEME [25] . Other statistical analyses were done in R . Functional gene classes were assigned manually using a combination of automated annotations and publications . All raw sequence data are available at Array Express with accession numbers E-MTAB-4555 ( polysomes ) and E-MTAB-4557 ( granules ) . The polysome gradient data are available under submission numbers E-MTAB-4555 and E-MTAB-4575 . The heat shock granule results are available under submission number E-MTAB-4557 . No ethical approval was reqiuired for this work , which did not involve either animals or human subjects .
The first part of our study concerned the movement of mRNAs into , and out of , the polysomal fraction after a one-hour heat shock at 39°C . We were particularly interested in knowing which mRNAs show regulation similar to that of ZC3H11 , since we hoped in that way to identify conserved sequence motifs . We chose 39°C because preliminary results showed that the treatment was sufficient to move ZC3H11 mRNA into the polysomal fraction , while only partially inhibiting overall translation . It is also a treatment that could be tolerated by tsetse flies [9] . Lysates from procyclic-form trypanosomes with or without heat shock were fractionated on sucrose gradients , which were then divided into free ( F ) , subunit ( S ) , monosome plus light polysome ( L ) and heavy polysome ( H ) fractions ( Fig 1A ) . The 39°C heat shock caused a shift of the ribosomes from the polysomal towards the free subunit and monosome fractions ( Fig 1A ) . To find the proportion of mRNA that was in each fraction , we analysed samples by Northern blotting , using the spliced leader as probe ( Fig 1B ) and including inputs ( non-fractionated samples ) as controls . The total amount of mRNA from the 39°C-treated cells was 57% of that from the non-shocked samples , but the sucrose gradient distribution of mRNA was similar to that of the non-shocked parasites ( Fig 1C and S1 Table , sheet 2 ) . This suggests that loss of translation is associated with mRNA degradation . We could not tell from our results which effect happened first: decreased translation might cause mRNA decay , but conversely initial decay events such as decapping would prevent translation initiation . All samples were subjected to RNASeq ( S1 Table , sheet 3 , and S1 Fig ) . To find out the proportions of each mRNA in the sucrose gradient fractions , we normalised the read counts / million reads ( S1 Table , sheet 3 ) according to the spliced leader signals ( S1 Table , sheets 4 and 5 ) . We then calculated the percentage of each mRNA that was in the different sucrose gradient fractions ( S1 Table , sheet 6 ) . ( A summary of the control results at 27°C was included in [4] . ) The correlation coefficients for percentage in polysomes between replicates ranged from 87% to 99% ( S1 Fig ) . In the following discussion we will assume that mRNAs that migrate in the denser part of the gradient are being actively translated . However , there are two caveats to this . First , binding of ribosomes to an mRNA does not necessarily mean that the ribosomes are active in translation elongation . Second , although there are no microscopically visible granules at 39°C [13] , some association of mRNAs with smaller aggregates cannot be ruled out . The percentages in polysomes ranged from 50–70% for most mRNAs ( Fig 2A , 'All’ ) , and there was a statistically significant ( but very small ) increase in these percentages after heat shock . There were 80 transcripts for which the percentage in polysomes decreased by a factor of 1 . 25 or more after heat shock ( S1 Table , sheet 8 , S2 Fig ) . For this group , the percentage on polysomes was overall higher than average at 27°C , and lower than average after an hour at 39°C . A subset of these mRNAs was distinguished by poor translation even before heat shock ( S2 Fig , subset A ) : it includes RBP33 , cis-spliced poly ( A ) polymerase , PAG2 and PAG4 mRNAs . We placed these mRNAs into functional classes based on their encoded proteins . Transcripts encoding ribosomal proteins were notably enriched in the set of mRNAs with decreased translation ( Fig 2A–2C , S1 Table , sheet 9 ) , and there was a slight over-representation of mRNAs encoding RNA-binding proteins ( Fig 2C ) . We next looked at mRNAs with a two-fold higher proportion in polysomal fraction after heat shock ( S1 Table , sheet 7 ) . 77 mRNAs , including that encoding ZC3H11 , fell into this category . This group had almost universally been in the lighter fractions at 27°C and rather oddly , it was enriched in mRNAs encoding proteins of no known function ( Fig 2D , S1 Table sheet 9 ) . More detailed analysis of these mRNAs placed them into three categories ( S3 Fig ) . At 27°C most of these mRNAs migrated in the free fraction , lighter than the subunits , and moved into the light polysomes after heat shock . Group ( B ) mRNAs started in the free fraction , but moved to both the subunit and light polysomal fractions after heat shock ( S3 Fig ) . Group ( A ) mRNAs , which included ZC3H11 , were distinguished from the others by the fact that they migrated mainly with the subunit fraction at 27°C . We have shown for ZC3H11 that this is not due to association with a small ribosomal subunit [13] and the reason for the different behaviour is unknown . The mRNAs that can bind to ZC3H11 showed slightly higher than average polysome loading at both 27°C and 39°C ( Fig 2A ) . To find changes in overall abundances of total and polysomal mRNAs , we compared the read counts from total RNA samples ( S2 Table , sheets 1 and 2 ) . 260 mRNAs were significantly ( >2x , Padj<0 . 01 ) increased in relative abundance after heat shock ( S2 Table , sheet 3 ) . However , comparison of the mRNA yields ( measured by spliced leader hybridisation as in Fig 1B ) revealed that the total amount of mRNA had decreased by about 40% after heat shock . As a consequence , the numbers of copies per cell of most mRNAs were reduced ( Fig 2B ) . Those mRNAs for which polysomal association increased tended to show less severe decreases after heat shock ( Fig 2B ) . As noted above , it is not possible to assign cause and effect since translation could influence degradation and vice-versa . The mRNAs that bind to ZC3H11 encode proteins that are always needed in high amounts , even at 27°C . These mRNAs were correspondingly strongly polysome associated at 27°C ( Fig 2A ) . This is probably ZC3H11-independent because ZC3H11 is barely detectable at 27°C and RNAi has no effect on cell proliferation or morphology [16] . Association of ZC3H11 target mRNAs with polysomes was more marked at 39°C , when ZC3H11 is expressed ( Fig 2A ) , but the relative increases were not significantly different from those of the bulk mRNA population ( Fig 2A and 2B ) . We now looked at the proteins encoded by mRNAs whose relative abundances increased at least 2-fold after one hour at 39°C , or which moved from non-translated to polysomal fractions . As expected , these included mRNAs encoding several chaperones , including two ZC3H11 targets ( Tb927 . 10 . 16100 and Tb927 . 2 . 5980 ) ( Table 1 ) . There was also a moderate increase in the mRNA encoding the major cytosolic HSP70 . The surprise came when we compared this group of mRNAs with transcriptomes from various developmental stages . We found a very significant overlap with mRNAs that are increased during the differentiation of procyclic-form trypanosomes to epimastigotes , metacyclic forms , and bloodstream forms ( Fig 3A & 3B and S2 Table , Sheet 3 ) . Even three of the chaperones were in this category . Notable among the epimastigote- or salivary-gland-specific genes were several that are associated with meiosis , MND1 , HOP1 , SPO11 and MSH5 ( Table 1 ) . The MND1 homologue ( Tb927 . 11 . 5670 ) mRNA was not only increased in the total RNA , but also moved towards polysomes ( 45% in polysomes at 27°C , 74% at 39°C ) . The HOP1 homologue ( Tb927 . 10 . 5490 ) mRNA showed a similar shift towards the polysomal fraction , but no RNA abundance change . YFP-tagged versions of both proteins are restricted to the nuclei of epimastigote-like cells in salivary glands [26] . SPO11 is probably also meiosis specific . MSH5 is annotated as a putative meiosis mismatch repair protein but there is no experimental evidence for this . Apart from these , mRNAs encoding several putative cell cycle regulators and a telomere-binding protein were increased in abundance or polysome association ( Table 1 ) . Examination of mRNAs that decreased in abundance showed that they were spread over numerous functional categories . These mRNAs significantly overlapped with mRNAs that decrease during differentiation of procyclic forms to epimastigotes or bloodstream forms ( Fig 3C ) . There was , in contrast , no significant overlap with mRNAs that decrease in stumpy bloodstream forms [27] . The numerous changes in developmentally regulated mRNAs after an hour at 39°C suggested that some regulatory proteins might also have been affected . Indeed , mRNAs encoding 9 potential RNA-binding proteins were increased after heat shock ( Table 1 ) . Of these , two mRNAs—DRBD6 and RBP6 –peak in salivary gland parasites [28] . Only 55% of the RBP6 mRNA was in polysomes at 27°C , but 87% was in the fraction after heat shock . Induced expression of RBP6 in procyclic forms is known to trigger the procyclic-epimastigote-metacyclic differentiation cascade [19] . RBP10 mRNA , which also increased after heat shock , is most abundant in growing bloodstream forms [30] . Expression of ZC3H14 and ZC3H45 proteins has not yet been detected but the ZC3H45 mRNA is preferentially translated in bloodstream forms [31]; and ZC3H46 protein is more abundant in bloodstream forms than procyclics [32] . RBP7 protein is present in slender bloodstream forms and increased in stumpy forms [33 , 34] . RNAi targeting RBP7 inhibits cAMP-induced stumpy-form differentiation , while overexpression of RBP7 causes G1/G0 cell cycle arrest and causes initial gene expression changes associated with procyclic differentiation [35] . In addition to these RNA-binding proteins , heat shock induced mRNAs encoding three protein phosphatases . Two of these have no known function , but PIP39 mRNA is higher in stumpy and procyclic forms than in bloodstream forms , and PIP39 becomes phosphorylated during stumpy-to procyclic differentiation [36] . Movement of PIP39 , RBP6 and the Tb927 . 11 . 4990 kinetoplastid-specific phosphatase mRNA to polysomes , as well as some others with a similar pattern , was confirmed by Northern blotting ( Fig 4A and 4B ) . Finally , to see whether a more moderate heat shock might also trigger changes in differentiation regulators , we grew procyclic forms at 37°C overnight . Indeed , the levels of both RBP6 and PIP39 proteins were increased ( Fig 4C ) . Our second series of experiments addressed mRNA targeting to heat shock granules , which form only at temperatures of at least 40°C [1] . Lysis of trypanosomes in the presence of 1% Triton X-100 results in trapping of structures with a diameter of more than 24nm within the microtubule corset [21] . ( For comparison , a ribosome is just under 30nm across . ) The trapped material can then be released with high salt , so that a further centrifugation yields a small granule ( SG ) supernatant and a large granule ( LG ) pellet . First , we examined cells growing at 27°C . The SG fraction contained about 3 . 2% of the mRNA , and the LG pellet just 0 . 8% , as judged by hybridisation with a spliced leader probe [13] ( S4 Table , sheet 2 ) . We subjected duplicate fractions to RNASeq ( S4 Table , sheets3 and 4 ) . To work out the proportion of each mRNA within the SG and LG fractions , we compared those results with those for total RNA ( S4 Table , sheet 3 ) . The replicates for total RNA of cells growing at 27°C did not correlate very well ( S4A Fig ) , perhaps because the cells had somewhat different cell densities at the time of harvest ( about 3 . 7 x106 and 5 x 106/ml ) . For the 27°C samples we therefore also compared the granule results for individual replicates ( S4 Table , sheet 4 ) with those for the input in the polysome experiments ( S4 Table , sheet 5 , S4A Fig ) . Independent of the way the calculation was done , the proportion of mRNA that was trapped inside the microtubule corset in normally growing cells was determined mainly by the length of either the open reading frame or the complete mRNA ( Fig 5A and S5A and S5B Fig ) . This suggests that the trapping was due simply to the size of the polysome and had nothing to do with regulation or granule formation . There was no significant correlation between the percentage in granule fractions and the percentage in polysomes at 27°C ( Fig 5B ) . It was however notable that mRNAs encoding ribosomal proteins were not trapped in granule fractions at all . Even allowing for the short lengths of most ribosomal protein mRNAs ( Fig 5A ) , their behaviour was anomalous ( S5 Table ) . We next examined the effect of a 41°C heat shock on the distribution of mRNAs in granule and non-granule fractions . First , we compared results for total mRNAs with those obtained at 39°C , and also with previously published results ( S1C–S1E Fig ) . The variability in the 27°C dataset ( S4A Fig ) meant that P-values for the total RNAs were high ( S5 Table ) and the overall correlation between different experiments was poor . This probably reflects differences in cell density as well as temperature . However , a core set of mRNAs was increased in at least 2 , and often all three , datasets ( Table 1 and S2 Table , sheet 11 ) . In addition to a few chaperone mRNAs , these once again included mRNAs indicative of developmental regulation . They encoded CYC7 , CYC11 and CYC10; SPO11 and MND1; bloodstream-specific alternative oxidase , pyruvate kinase and GPI-PLC; 6 protein kinases; 3 protein phosphatases including PTP1; and 8 RNA-binding proteins including both RBP10 and RBP6 . After one hour at 41°C , 6% of the total mRNA was in the small granule fraction , and 19% in large granules ( S4 Table , Sheet 2 ) . At the level of mRNAs from individual genes , however , the distribution looked very different . This is because half of the sequence reads were contributed by the most abundant 10% of the transcripts . For most coding sequences , 20–60% of the mRNA was in one of the granule fractions , usually with the large granule fraction predominating ( Fig 5C and 5D ) . In contrast , a subset of very abundant mRNAs was not associated with granules ( Fig 5D ) . These included those encoding ribosomal proteins , procyclin , the major cytosolic HSP70 , mitochondrial HSP60 and a DNAj ( Figs 5D and S4C and S4D . ) . Other mRNAs that showed less than 20% association with heat shock granules were those encoding histones , alpha and beta tubulin , 10 additional chaperones , the cytochrome oxidase complex and a few proteins involved in ribosome assembly . An ANOVA test showed that the mRNAs encoding ribosomal proteins were the only functional category that showed unique behaviour with regard to heat shock granule association ( P = . 00015 with Bonferoni correction ) . In subsequent analyses we therefore treated this group separately . We previously showed that ZC3H11 prevents degradation of bound mRNAs after a 41°C heat shock [16] . Correspondingly , mRNAs that co-purify with ZC3H11 [16] tend to escape granule association . For the 23 mRNAs that showed the strongest enrichment in the ZC3H11-bound fraction [16] , a median of 20% was associated with total granules , whereas for unbound mRNAs the median was 40% ( Figs 5D and 6A ) . A similar result was obtained if large granules alone were analysed ( S4E Fig ) . As previously noted , the ability to bind ZC3H11 also correlated with higher association with polysomes ( Figs 5E and 6B ) . These results suggest that ZC3H11-bound mRNAs are protected against mRNA degradation , translational inactivation , and incorporation into granules . To check this hypothesis , we prepared granule fractions from cells with and without heat shock and / or ZC3H11 RNAi . Without RNAi , two target mRNAs encoding HSP70 and an FKBP remained largely in the soluble fractions despite heat shock: as seen from the RNASeq results , only a tiny proportion was detected in the large granule fraction ( Fig 7A ) . ZC3H11 depletion had very little effect on this distribution without heat shock ( Fig 7B and 7C ) . After heat shock , however , granule-free HSP70 and FKBP mRNA disappeared but neither mRNA accumulated in the large granule fraction either: instead , the mRNAs were simply destroyed . After heat shock , there was little overall correlation between the coding region length and association with either total granules ( S4C Fig ) or small granules alone ( S4D Fig ) , but DeSeq analysis showed that granules were enriched in long mRNAs ( median length 4 kb ) including several encoding large cytoskeletal proteins . ( S5 Table , sheets 3 and 5 ) . There was no overall correlation between loading onto polysomes at 39°C and the percentage in granules at 41°C . Some potential regulators that showed reproducible mRNA abundance increases—CYC7 , DRBD5 , DRBD6 and RBP6—showed less than 30% granule incorporation , suggesting that they might in some way be implicated in recovery from heat shock .
The results from this study have confirmed that the ability of an mRNA to bind ZC3H11 correlates not only with stabilisation at high temperature , but also with continued translation and exclusion from heat shock granules . The first conclusion generalises results that were already seen for reporters with the HSP70 3'-UTR , while the second is consistent with previous published data indicating that mRNAs in stress granules are not translated [37–40] . The mRNAs that are bound by ZC3H11 are already quite well translated at 27°C , and become even more so after heat shock: it is possible that this high translation protects them from incorporation into heat shock granules; alternatively ZC3H11 and its associated proteins [17] might prevent sequestration of bound mRNAs into granules . Our results show that heat shock granules are not identical to starvation granules , despite sharing some of the same proteins and mRNAs . Some mRNAs that were excluded ( <20% ) from heat shock granules were also similarly absent from starvation granules [21] . Presumably these encode products that are required to recover from both starvation and heat shock . Apart from ribosomal protein mRNAs , which are probably a special case and are discussed below , several chaperone mRNAs were in this category . In contrast , ZC3H11 is not implicated in the starvation response , and its mRNA was 25% in heat shock granules but 79% in starvation granules . Other mRNAs that showed a similar pattern encoded RBP3 , ZC3H30 , ZFP1 , RBP6 and a histone H3 variant [21] . PABP1 may be important in protecting ZC3H11 target mRNAs , since it is recruited by the ZC3H11-MKT1-PBP1 complex [17] . The level of ZC3H11 protein is not increased after starvation , which explains why some ZC3H11 target mRNAs are incorporated into starvation granules ( S4 Table , sheet 7 ) . The mRNAs encoding ribosomal proteins were almost completely excluded from both heat shock granules and starvation granules [21] . Only two annotated "ribosomal protein" mRNAs , Tb927 . 10 . 10010 and Tb927 . 11 . 6360 , did not follow this pattern , but neither is a structural component of the mature ribosome . The extraordinary behaviour is therefore a universal characteristic of mRNAs that encode components of the mature ribosome . These mRNAs are also outliers in other ways: the mRNA levels are higher that would be predicted based on their half lives and gene copy numbers [4] , and the average ribosome densities are relatively low ( mostly less than 4 ribosomes/kb ) [4 , 31] although the majority of the mRNAs are loaded onto polysomes [4] . Association with polysomes is also notably decreased after heat shock , without much loss of the mRNAs ( Fig 2A and 2B ) : it looks as if the mRNAs are being conserved in some way other than granule sequestration . The ribosomal protein mRNAs are co-regulated during trypanosome differentiation , being decreased in stationary phase trypanosomes and increasing only 1h after addition of the differentiation stimulator cis-aconitate [41]; this is consistent with the fact that they mostly peak in the G1 phase of the cell cycle [42] . We examined the untranslated regions of these mRNAs for specific enriched motifs and found none . The only notable feature is that the 5'-UTRs are very short , with a median length of 22nt , as opposed to 108 for other mRNAs ( mean±SDs are 33±34 as opposed to 203±274 ) . Given the lack of conserved linear motifs , it is possible that secondary structures are important; or , more unusually , ribosomal protein mRNAs might be characterised by a lack of motifs required for recruitment of SCD6 [14] or other granule proteins . Alternatively they might be regulated via recognition of the nascent polypeptides . Our investigation of polysome loading revealed interesting sets of mRNAs that were retained in polysomes and/or increased in abundance at 39° . Some , like ZC3H11 , were rather poorly translated at the normal temperature; these migrated either near 40S , or somewhat above 40S . The reason for the difference is unknown but binding to the small subunit is unlikely [13] . The 7 mRNAs with patterns most similar to that of ZC3H11 encoded a protein kinase , a protein phosphatase , a DNAj-like protein , and 4 other proteins of unknown function . There is no evidence of any link between these proteins and ZC3H11 function: although ZC3H11 is phosphorylated , the most likely culprit is a different kinase , casein kinase 1 . 2 [13] . Perhaps the most interesting observation was that the mRNAs that showed increased translation or abundance at 39°C included mRNAs that are up-regulated in salivary gland trypanosomes ( Fig 2 ) . The mammalian body environment has a temperature of 37°C ( possibly higher in organs ) and a 10°C temperature decrease is known to be an important factor in the switch from bloodstream to procyclic forms . However , the mRNAs that increased were not necessarily bloodstream-form specific . Indeed , the mRNAs encoding three chaperones , two cyclins , the meiotic mRNA MND1 , and the RNA-binding proteins DRBD6 and RBP6 , are elevated in salivary-gland parasites but not bloodstream forms ( Table 1 ) . Induced expression of RBP6 in procyclic trypanosome cultures ( at 27°C ) causes differentiation to epimastigotes , and then to metacyclic forms: after 24h of RBP6 expression , about 10% of cells are epimastigotes , while metacyclics begin to appear after 5–6 days [19] . It is therefore formally possible that all of the polysomal RNA changes that we saw upon heat shock are caused by RBP6 . However , this seems unlikely since the 1-h time frame is extremely short . For example , the trypanosome alternative oxidase protein appears after 2 days of RBP6 expression , but the mRNA ( Tb927 . 10 . 9760 ) moves towards the polysomes after only an hour at 39°C ( to 72% from 54% ) . Differentiation of bloodstream forms to procyclic forms includes an intermediate called the short stumpy form . Stumpy forms are arrested in G1 , and express some proteins of procyclic form metabolism . Further differentiation to procyclic forms is induced by addition of cis-aconitate and a decrease in temperature from 37°C to 27°C . Heat shock of procyclic forms resulted in increased polysomal levels of two mRNAs implicated in this process . The first was the protein phosphatase PIP39 , which is essential for differentiation of stumpy forms to procyclic forms [36] , and which was increased at the protein level by incubating the procyclic forms at 37°C . RBP7 , a potential RNA-binding protein , is required for differentiation of bloodstream forms to stumpy forms [35] , and this mRNA moved towards polysomes after heat shock of procyclics . Importantly , we showed that PIP39 and RBP6 proteins increased in procyclics incubated at 37°C , which is a temperature that is quite likely to occur in the wild . It is possible that both PIP39 and RBP7 have functions–perhaps linked to growth arrest—in both the stumpy->procyclic and in the procyclic->epimastigote transitions . There are several indications that stress responses can promote trypanosome differentiation , but it is not always clear whether differentiation is a direct or indirect effect [43] . If cell cycle arrest is needed for alterations in signalling and re-programming of gene expression , and a stress causes cell cycle arrest , differentiation might be enhanced although the stress does not induce differentiation directly . For example , the differentiation of stumpy forms to procyclic forms can be enhanced or promoted by a variety of stressful treatments , including mild cold shock [44] , glucose deprivation [45] , mild acid [46] , and protease treatment [47] , as well as by cis-aconitate . It is not known which of these stresses is physiologically relevant in tsetse . We know even less—in fact , nothing—about the stimuli within the fly digestive tract that initiate the development of procyclic forms to epimastigotes and metacyclic forms . A heat shock is definitely not required since development happens in laboratory tsetse colonies in which temperatures are controlled below 30°C . In Africa , however , tsetse are very likely to experience higher environmental temperatures , and the developing trypanosomes are exposed to warm blood meals every 3–5 days [48] . It is therefore possible that in the wild , temperature fluctuations inside tsetse , or other stresses , could play a role in trypanosome life-cycle progression . | When trypanosomes are inside tsetse flies , they have to cope with temperature variations from below 20°C up to 37°C , due to diurnal variations and periodic intake of warm blood . In the laboratory , procyclic forms ( the form that multiplies in the midgut ) , are routinely cultured at 27°C . When procyclic forms are heated to temperatures of 37°C and above , they decrease protein production , and at 41°C , mRNAs aggregate into granules . We show here that quite a large number of mRNAs are not included in granules and continue to be used for making proteins . Some of the proteins that continue to be made are needed in order to defend the cells against the effects of heat shock . Interestingly , however , a moderate heat shock stimulates expression of genes needed for the parasites to develop further into forms that can colonise the salivary glands . It thus seems possible that in the field , temperature variations might influence the efficiency with which of trypanosomes in tsetse flies become infective for mammals . | [
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] | 2016 | Translation Regulation and RNA Granule Formation after Heat Shock of Procyclic Form Trypanosoma brucei: Many Heat-Induced mRNAs Are also Increased during Differentiation to Mammalian-Infective Forms |
Lytic gammaherpesvirus ( GHV ) replication facilitates the establishment of lifelong latent infection , which places the infected host at risk for numerous cancers . As obligate intracellular parasites , GHVs must control and usurp cellular signaling pathways in order to successfully replicate , disseminate to stable latency reservoirs in the host , and prevent immune-mediated clearance . To facilitate a systems-level understanding of phosphorylation-dependent signaling events directed by GHVs during lytic replication , we utilized label-free quantitative mass spectrometry to interrogate the lytic replication cycle of murine gammaherpesvirus-68 ( MHV68 ) . Compared to controls , MHV68 infection regulated by 2-fold or greater ca . 86% of identified phosphopeptides – a regulatory scale not previously observed in phosphoproteomic evaluations of discrete signal-inducing stimuli . Network analyses demonstrated that the infection-associated induction or repression of specific cellular proteins globally altered the flow of information through the host phosphoprotein network , yielding major changes to functional protein clusters and ontologically associated proteins . A series of orthogonal bioinformatics analyses revealed that MAPK and CDK-related signaling events were overrepresented in the infection-associated phosphoproteome and identified 155 host proteins , such as the transcription factor c-Jun , as putative downstream targets . Importantly , functional tests of bioinformatics-based predictions confirmed ERK1/2 and CDK1/2 as kinases that facilitate MHV68 replication and also demonstrated the importance of c-Jun . Finally , a transposon-mutant virus screen identified the MHV68 cyclin D ortholog as a viral protein that contributes to the prominent MAPK/CDK signature of the infection-associated phosphoproteome . Together , these analyses enhance an understanding of how GHVs reorganize and usurp intracellular signaling networks to facilitate infection and replication .
Post-translational modification of proteins by phosphorylation and dephosphorylation regulates numerous functional properties , including activation status [1] , stability [2] , protein-protein interactions [3] , and subcellular localization [4] . Such signals regulate the majority of cellular processes ranging from cell-cycle progression [5] , [6] to terminal differentiation of specific cell types [7] to activation of intracellular signals that trigger both local and organismal antimicrobial responses [8] . Following infection of host cells , viruses and intracellular bacteria manipulate cellular signaling to facilitate replication . Pathogen-directed signaling may mobilize enzymatic pathways to provide nutrients or energy necessary for the large increase in macromolecular biosynthesis [9] or reorganize host components to direct packaging , envelopment , or egress [10] . In defense , host-cell sensing of microbial infection may trigger signaling cascades aimed at hindering pathogen replication and alerting neighboring cells to the present danger [8] . Pathogens also may encode factors to deregulate anti-microbial signaling pathways in order to prevent detection or elimination by host immune responses [11] . Recent innovations coupling affinity-based phosphopeptide enrichment with high-resolution mass spectrometry followed by systematic bioinformatics analyses have enabled systems-level evaluations of phosphorylation-dependent signaling cascades in cells or tissues responding to discrete stimuli , such as epidermal growth factor receptor stimulation or DNA damage responses ( DDR ) [12] , [13] . Such analyses revealed that >90% of detectable phosphorylation sites on cellular phosphoproteins were not previously identified [13] and that critical regulatory phospho-motifs and phosphorylated effector proteins remain to be identified , even for extensively studied signaling cascades [12] , [13] . Currently , systems-level phosphoproteomic analyses to define infection-associated alterations in protein phosphorylation status during viral infection are lacking . Thus , while hypothesis-driven and intuition-based studies have identified many phosphorylation-dependent signaling events that regulate viral replication and host responses to infection , it is likely that the vast majority of infection-associated changes in host protein phosphorylation status are not yet known . This highlights a critical gap in our current understanding of virus-host interactions . Importantly , the identification of unappreciated signaling pathways and/or effector proteins usurped or inhibited by pathogens in infectious disease states may reveal new targets for pharmacologic intervention . Gammaherpesviruses ( GHVs ) are members of the Herpesviridae family of large double-strand DNA viruses [14] . GHVs include the human pathogens Epstein-Barr virus ( EBV ) and Kaposi sarcoma-associated herpesvirus ( KSHV or HHV-8 ) ; non-human primate viruses herpesvirus Saimiri ( HVS ) , rhesus rhadinovirus ( RRV ) , and rhesus lymphocryptovirus ( rhLCV ) ; and rodent pathogens wood mouse herpesvirus ( WMHV ) , rodent herpesvirus Peru ( RHVP ) , and murine gammaherpesvirus-68 ( γHV68 or MHV68 ) . Like all herpesviruses , GHVs exhibit two distinct phases of their infectious cycles . The productive replication phase ( also termed lytic replication ) is characterized by robust viral gene expression leading to viral DNA replication and the production of infectious progeny virions . In contrast , latent infections are characterized by restricted viral gene expression and indefinite maintenance of the viral genome as episomal DNA . GHVs characteristically establish lifelong latent infections of lymphocytes , thereby placing the host at risk for lymphoid and other cancers , especially in settings of immunocompromise such as HIV infection or immunosuppression for organ transplants [15] , [16] , [17] . In contrast to EBV and KSHV , MHV68 – which provides a tractable small animal model for evaluating GHV pathogenesis – undergoes robust productive replication in cultured cells . Further , given the ease of generating MHV68 mutants and the capacity to perform controlled experimental infections of various wild-type ( WT ) , knockout , and transgenic mice , MHV68 offers an attractive system for understanding the virus-host dynamic during productive viral replication [18] , [19] , [20] . Akin to what is hypothesized to occur during primary EBV infection of humans , acute MHV68 infection of mucosal epithelia following intranasal inoculation is necessary for viral dissemination and latency establishment in distal reservoirs [21] , [22] , [23] . While the importance of DDR , mitogen-activated protein kinase ( MAPK ) , and inhibitor of kappa-B kinase ( IKK ) signaling in MHV68 replication recently was demonstrated [24] , [25] , [26] , [27] , an understanding of how the kinases involved in these responses influence the overall phosphoprotein milieu within the host cell is not known . The breadth of host or viral proteins targeted by specific viral signaling proteins with critical roles in pathogenesis , such as the conserved herpesvirus protein kinase ( CHPK ) ORF36 [28] or the viral cyclin D ortholog , encoded by gamma-2-herpesviruses such as KSHV [29] and MHV68 [30] , also has not been globally evaluated . Hence , our understanding of GHV-regulated signaling events – as well as host cell responses to infection – in facilitating productive viral replication and ultimately pathogenesis is incomplete . To this point , a better appreciation of phosphorylation-dependent signaling during GHV replication may illuminate novel targets for the treatment of infectious mononucleosis , the acute phase malady of primary EBV infection [31] , or Kaposi sarcoma , a KSHV-related cancer for which lytic viral replication is thought to be a driver of disease [32] , [33] . In this study , we use a comparative , quantitative phosphoproteomic analysis to define phosphorylation-related signaling events regulated during productive MHV68 infection . We identified more than 400 host phosphoproteins that are induced or repressed in infected cells , as well as 17 viral phosphoproteins . Quantitative analyses indicated that a vast majority of definable phosphopeptides was regulated during GHV infection . Unbiased bioinformatics analyses and complementary biochemical approaches predicted the importance of extracellular-signal related and cyclin-dependent kinases ( ERK and CDK , respectively ) in facilitating viral replication , and pharmacologic inhibition and shRNA knockdown experiments confirmed the predictions . Finally , we identified the MHV68 cyclin ortholog as a key contributor to many of the virus-associated signaling changes observed . Together , these data and analyses provide a novel systems-level resource to better inform and enable studies of pathogen-host interactions .
To gain a more complete understanding of GHV-induced changes in host cell signaling networks , we devised an experimental approach integrating high-resolution mass spectrometry , predictions based on orthogonal bioinformatics analyses , and hypothesis-driven functional tests ( Fig . 1 ) . We first performed time-course immunoblot analyses using phospho-residue-specific antibodies to determine timepoints at which MHV68 elicited robust changes in protein phosphorylation patterns during productive infection of mouse fibroblasts . These experiments revealed the greatest qualitative differences in phosphoprotein detection between control and infected samples at 18 h post-infection ( Fig . 2A and S1 ) . At this timepoint , phosphopeptides were selectively enriched from mock-infected and infected cells using titanium dioxide ( TiO2 ) chromatography . Three separate enrichments were performed for each of two independent experiments , and peptides were sequenced by HPLC-coupled high-resolution MS/MS analysis using collision-induced dissociation on an LTQ Orbitrap hybrid mass spectrometer . The analysis identified 13 , 801 peptides , ca . 80% of which contained detectable phosphorylated residues . As only 30% of the cellular proteome is estimated to be phosphorylated [34] , this indicates the enrichment procedure was robust . The processed and sorted data sets are available as Table S1 . Despite identifying peptides from 14% fewer distinct proteins in infected samples , the absolute numbers of phosphopeptides identified for control and infected samples was essentially equivalent ( 5632 vs . 5472 , respectively ) , which indicates that the enrichment and analysis procedures did not introduce bias . Notably , approximately 15% of phosphopeptides identified from infected cells are specific to the infected state ( i . e . , only detected in infected analytes ) , 4% of which derive from viral proteins , whereas 11% of control phosphopeptides were not detected in infected cells ( Fig . 2B ) . Extended to the protein level , we identified a total of 989 individual proteins representing 845 proteins from control cells and 728 proteins from infected cells ( Fig . 2B ) . 584 of the identified proteins were present in both control and infected samples , while 261 or 144 proteins were unique to either control or infected cells , respectively ( Fig . 2B ) . 22 of the 144 infection-specific proteins ( ca . 15% ) were derived from MHV68 . For quantitative analyses , the relative intensity ( a quantitative measure of protein abundance ) for each identified peptide was determined using MaxQuant [13] . Of the 2428 unique peptides identified , 86% exhibited greater than 2-fold change in peptide intensity between control and infected cells . A dot plot of summed peptide intensity vs . intensity ratio demonstrates the extent to which common phosphopeptides – those identified in both control and infected samples – are up or down-regulated during MHV68 infection ( Fig . 2C ) . By comparison , recent studies to define phosphoproteomic changes induced by discrete stimuli , such as growth factor stimulation or DNA damage , found that less than 15% of phosphoproteins were regulated ( enhanced or decreased phosphorylation ) in response to stimulus [12] , [13] . Similarly , Hela cell challenge with Salmonella also elicited a more modest change in host-cell phosphoprotein status than MHV68 infection , with ca . 24% of phosphopeptides being regulated [35] . The breadth of differential phospho-protein regulation induced by MHV68 is further highlighted by gene ontology ( GO ) analyses , which revealed that infection-associated changes in protein phosphorylation status were evident in essentially all ontologically-grouped protein classes , rather than effecting specific types of proteins ( Fig . 2D ) . Interestingly , all MS-identified proteins annotated to the “kinase” protein class exhibited phosphorylation status changes during MHV68 infection ( Fig . 2D ) . This also was true for all proteins annotated to “protease” and “oxidoreductase” protein classes ( Fig . 2D ) . Thus , MHV68 infection effects dramatic changes in the host-cell phosphoproteome on a scale not previously observed in comparable phosphoproteomic analyses . Several host proteins are notable among those specifically induced in or lost from infected cells . The promyelocytic leukemia protein ( PML ) is the single most abundant phosphoprotein detected in control cells , yet absent from infected cell analytes ( Fig . 2E ) . This finding is consistent with PML being targeted for degradation by MHV68 tegument protein , ORF75C [36] , [37] . Infected samples , on the other hand , displayed highest levels of phosphorylated linker histone H1 variant 1 , MAPK1/ERK2 , and TAR DNA binding protein ( TARDBP ) , a protein originally identified as a host factor that enhances HIV transcription [24] . MAPK3/ERK1 and MAPK substrate c-Jun also were exclusive to infected cells . Although roles for ERK1/2 in MHV68 replication have not been described , ERK1/2 were previously implicated in facilitating KSHV replication [38] , [39] , [40] . Further , we recently demonstrated c-Jun phosphorylation and related AP-1 transcription factor activation during MHV68 replication [24] . Thus , several MS-defined phosphoproteins identified in our study are consistent with previously published findings , thereby providing confidence in the phosphoproteomic data sets obtained . We also identified 18 viral phosphoproteins and non-phosphorylated peptides derived from 5 viral proteins ( Table 1 ) . Of the viral phosphoproteins identified , to our knowledge only tyrosine phosphorylation of ORF21/thymidine kinase ( TK ) was previously reported for MHV68 [41] . In addition to defining the specific phospho-tyrosine residues previously reported , we also identified numerous phosphorylation events on serine and threonine residues within ORF21/TK ( Table 1 ) . Six of the MHV68 phosphoproteins we identified [ORF8/gB , ORF21/TK , ORF25/major capsid protein ( MCP ) , ORF27/gp48 , ORF39/gM , and ORF45] correspond to homologous phosphoproteins present in purified EBV virions [42] . Phosphorylation of ORF21/TK [41] , ORF45 [43] and viral DNA polymerase processivity factor ORF59 [44] also were demonstrated during KSHV infection . Interestingly , phosphorylation of pUL44 , the HCMV homolog of ORF59 [45] , [46] , [47] , [48] , and glycoprotein B ( gB ) phosphorylation , during both HCMV and HSV1 infection [49] , [50] suggest that some of these phosphorylation events may be functionally conserved among all herpesvirus subclasses . Also identified were phosphorylated peptides for ORF36 , a conserved herpesvirus protein kinase which by analogy to HCMV and HSV should be capable of autophosphorylation [28] . The identification of discretely phosphorylated residues in viral proteins , especially those conserved for other herpesviruses , provides a resource for future functional studies to define roles for specific phospho-motifs and related kinases in the processes of infection , persistence , and pathogenesis . We next performed a series of comparative immunoblot analyses to validate the MS data . In agreement with MS determinations , ERK1/2 was potently phosphorylated following MHV68 infection of murine fibroblasts , while total ERK1/2 levels remained unchanged ( Fig . 3A ) . Infection-associated c-Jun phosphorylation on Ser73 also was readily detected . As for differentially regulated phosphoproteins ( i . e . , those present at differing abundance in both control and infected MS data sets ) , quantitative MS analyses indicated that MHV68 infection enhanced phosphorylation of the myristoylated alanine-rich C-kinase substrate ( MARCKS ) , a classic protein kinase C ( PKC ) targeted scaffold protein ( highlighted in Fig . 2C ) . By immunoblot , infection-related enhancement of MARCKS phosphorylation manifested as a retarding of MARCKS mobility during SDS-PAGE ( Fig . 3A ) . In contrast to proteins for which phosphorylation was enhanced during infection , PML protein only was detected in control cell lysates ( Fig . 3A ) , which is consistent with the presence of PML phosphopeptides in control , but not infected , MS analyses . As stated above , this finding is consistent with PML protein being targeted for degradation by the MHV68 tegument protein , ORF75C [36] , [37] . To validate viral phosphoproteins , we evaluated whether ORF21/TK and ORF59 , the two most abundant viral phosphoproteins identified ( Table 1 ) , were phosphorylated during MHV68 infection . For ORF21 , phosphoproteins were captured by immunoprecipitation with p-Thr , p-Ser , and p-Tyr-specific antibodies . Immunoblot analyses readily detected ORF21 in phospho-specific immunoprecipitates ( Fig . 3B ) . Interestingly , ORF21 mobility in SDS-PAGE corresponded to the abundant ca . 80 kDa phosphoprotein detected in infected cell lysates ( see Fig . 2A ) . Likewise , p-Thr and p-Ser-specific antibodies also recognized immunoprecipitated ORF59 ( Fig . S2 ) . These data provide complementary biochemical evidence that supports the identification of ORF21 and ORF59 as phosphorylated viral proteins by MS . Taken together , the results of these experiments biochemically validate the presence or absence of several viral and host phosphoproteins identified through global phosphoproteomics analyses . Thus , these data provide additional confidence in the robustness of the MS data sets . Because our MS analyses focused on timepoints associated with robust infection-induced alterations in total phosphoprotein profiles , we next sought to correlate infection-associated phosphoproteomic changes with specific stages in the viral replication cycle . Cells were mock-infected or infected with WT MHV68 , UV-inactivated ( UVI ) MHV68 , ORF50-null ( 50 . Stop ) MHV68 , or WT MHV68 in the presence of cidofovir , a nucleoside analog that blocks viral DNA replication and hinders progression into the late phase of the viral replication cycle [51] . Due to disruption of the gene encoding the viral transactivator protein , RTA , ORF50-null MHV68 arrests at the immediate-early ( IE ) gene expression stage [52] . Consistent with time-course analyses suggesting that the majority of infection-associated phosphoproteomic changes occur during early-to-late stages of infection ( Fig . S1 ) , ERK1/2 phosphorylation , c-Jun phosphorylation , and the retardation of MARCKS mobility only were evident in cells infected with WT MHV68 or WT MHV68 in the presence of cidofovir ( Fig . 3C ) . The findings that UVI and ORF50-null MHV68 did not elicit changes in the phosphorylation status of the proteins tested indicate that active viral gene expression and progression through the replication cycle , not simple internalization of the virus or presumed initiation of IE transcription , respectively , are important for infection-associated induction of host phosphoproteins . Additionally , while cidofovir reduced viral protein production as evidenced by immunoblot analyses with MHV68 antiserum ( Fig . 3C ) and ca . 20-fold reduction in viral titers ( not shown ) , cidofovir did not inhibit ERK1/2 , c-Jun , and MARCKS phosphorylation ( Fig . 3C ) . These data suggest that viral DNA replication , and most likely late viral gene expression , do not play major roles in the infection-related signaling events evaluated . These data agree with a previous study in which JNK1/2 and c-Jun phosphorylation occurred during MHV68 infection despite inhibition of viral DNA synthesis with phosphono-acetic acid [24] . Further , while virus-related signaling undoubtedly occurs coordinate to other steps in the infection process , results of these experiments support the notion that our phosphoproteomic analyses offer an accurate representation of signaling events corresponding to the early-to-late phases of the viral replication cycle . Finally , in an effort to link cell culture observations to the in vivo setting , we evaluated c-Jun phosphorylation in infected cells during acute MHV68 replication . Mice were intraperitoneally ( IP ) inoculated with recombinant MHV68 expressing a histone H2B-YFP fusion protein as a fluorescent marker to enable detection of infected cells by flow cytometry [53] . Four days after IP inoculation with MHV68 , a timepoint at which robust productive viral replication is occurring in the spleen [54] , splenocytes were harvested and processed for flow cytometry to detect H2B-YFP and c-Jun phosphorylated on Ser73 . The H2B-YFP gating strategy and an analogous proof-of-principle experiment in productively infected fibroblasts are provided in Figure S3 . Compared to splenocytes from mock-infected animals and H2B-YFP-negative cells from infected animals , H2B-YFP-positive cells exhibited a significant increase in the detection of phosphorylated c-Jun , with an approximate 2-fold increase in mean fluorescence intensity ( Figs . 3D and 3E ) . This approximated the induction of c-Jun phosphorylation observed in productively-infected fibroblasts in culture ( Fig . S3 ) . The finding that H2B-YFP-negative cells from infected animals exhibit phospho-c-Jun signals approximating those of mock-infected animals is important , because it demonstrates that c-Jun phosphorylation during acute MHV68 infection occurs specifically in infected cells , rather than through a non-specific bystander process such as immune activation [55] , [56] . These findings provide an important experimental link suggesting that signaling parallels exist between productive replication in culture and during MHV68 pathogenesis in vivo . Having validated the initial MS data sets , we next performed a battery of bioinformatics analyses to determine the functional consequences of phosphoproteomic changes during MHV68 infection and potential signaling pathways involved . Given the extent to which infection altered the phosphorylation status of host proteins , we first sought to determine whether infection elicited a rearrangement of the global cellular phosphoprotein network . A central tenet of systems biology analyses is the assertion that most if not all components of a complex network exert some influence over other components of the system [57] , [58] . As such , network analyses predict that changes in the “interactions” ( i . e . , functional association as defined by many disparate properties ) between specific molecules , especially those positioned downstream of multiple stimuli or between functional protein modules , can have dramatic influences on the propagation of signals through , and ultimately the response of , a particular system [57] , [58] . While numerous linear analyses of signaling pathways that regulate GHV replication have been performed , how infection by GHVs modulates the host phosphoprotein network is not known . We therefore performed comparative network analyses to provisionally identify critical host molecules and protein complexes that were manipulated and reorganized during MHV68 infection . We first performed STRING ( Search Tool for the Analysis of Interacting Genes/Proteins ) analyses [59] to establish global phosphoprotein interaction networks for all proteins detected in either control or infected cells ( Figs . S4 and S5 , respectively ) . The STRING algorithm links genes or proteins into networks based on published functional or informatics-predicted interactions [59] . Phosphoprotein networks were then processed in Cytoscape [60] , and interconnected proteins ( referred to as nodes ) were color coded based on MaxQuant intensities to denote detection and/or relative abundance . Node size correlates to betweenness centrality ( BC ) , a measure of a protein's capacity to connect disparate protein modules in the network [61] , thus allowing a visualization of nodes that likely have a strong influence on signal propagation through the networks . The width of connecting arrows between nodes ( known as edges ) represents the STRING-defined confidence value of the predicted interaction . Disconnected nodes , those that did not “interact” with any other proteins evaluated ( see Fig . S4 and S5 ) , are not shown . Upon gross inspection , it was immediately evident that control and infected protein networks were topologically unique ( compare Figs . 4 and 5 , note the repositioning of highlighted common nodes during infection relative to control ) . Interestingly , several proteins specifically induced ( c-Jun , MAPK1/ERK2 , MAPK3/ERK1 , and others ) or lost ( AKT1 , PCNA , Smad2 , and others ) from the infected phosphoproteome have high BC values indicated by relatively large node size ( Figs . 4 and 5 ) . These findings suggest that the induction or repression of specific proteins during infection alters the flow of information from protein to protein within the cell [57] , [58] . MCODE analyses to identify functional protein clusters within the networks [62] identified either unique clusters or clusters in which the composition of proteins represented varied according to infection ( Figs . 4 and 5 ) . Indeed , none of the highest scoring clusters were identical , which further indicates reorganization of functional protein modules in the network during MHV68 lytic replication . Finally , GO analyses utilizing the Cytoscape plugin BiNGO [63] indicate that induction or repression of specific proteins during MHV68 infection alters many functionally grouped biological processes represented in the phosphoprotein network ( Table S2 ) . Of note , GO IDs associated with chromatin organization , RNA production and localization , and negative regulation of cell death were unique or overrepresented during MHV68 infection , whereas biological processes associated with hormone-related signaling pathways , ubiquitylation , and signal transduction were absent in comparison to control networks ( Table S2 ) . These findings suggest that infection-directed changes in the phosphorylation status of specific proteins redirects the flow of information through the cellular phosphoprotein network to effect broad functional changes to specific biological processes . We next performed a comparative analysis of Kyoto encyclopedia of genes and genomes ( KEGG ) pathways [64] that were represented in control and infected phosphoprotein networks ( Fig . 6A ) . This analysis revealed a high degree of overlap between mock and infected data sets for thirteen KEGG-defined pathways , although the majority of redundant pathways do contain several proteins whose presence or abundance was influenced by infection . This point is illustrated by extracting proteins represented in the “spliceosome” KEGG pathway from the global phosphoprotein network . Five spliceosome proteins were absent from infected cells ( solid blue ) , two were only detected in infected cells ( solid red ) , and thirteen exhibited differing abundance between control and infected cells ( blue or red outlines , respectively ) ( Fig . 6B ) . The phosphorylation status of only five spliceosome proteins remained unchanged . KEGG analyses also predicted several functional protein modules exclusively lost or induced during infection . For instance , control cells contained a high number of phosphoproteins annotated to the ubiquitin-mediated proteolysis pathway . Indeed , 5 of the 11 proteins represented were absent from infected cells , while the remaining 6 were less abundant during infection ( Fig . 6C ) . Conversely , the MAPK signaling pathway was exclusively represented by infected phosphoproteins , including MAPK3/ERK1 , MAPK1/ERK2 , MAP4K4 , c-Jun , and epidermal growth factor receptor ( Fig . 6D ) . These data indicate that MHV68 infection modulates the phosphorylation status of specific functional protein modules despite a seemingly global redistribution of ontologically grouped protein phosphorylation . Distinct protein kinases phosphorylate specific amino-acid motifs present on target proteins . To determine if distinct phospho-motifs were overrepresented in the infection-associated data set relative to control identities , we segregated all individual phosphopeptides according to their presence or absence within control or infected cells . Sequence logos generated for each data set using the ICE-LOGO resource ( http://iomics . ugent . be/icelogoserver/logo . html ) , a weighted representational analysis , demonstrate general differences in phosphorylated sequences between control and infected systems ( Figs . 7A and 7B , respectively ) . Distinct phospho-motifs in each data set were identified using the Motif-X algorithm [65] ( Fig . 7C ) . Interestingly , only two shared phospho-motifs were overrepresented in both control and infected phosphopeptides , S*XXE and XS*PX ( Fig . 7C ) , where the asterisk designates the phosphorylated residue , relative to the background Mus musculus phosphoproteome . Control phosphopeptides exhibited more promiscuity in motif representation , with eight specific phospho-motifs identified as being enriched . A number of motifs present in control peptides were characterized by acidic residues downstream and an Arg upstream of the phospho-acceptor , including an AKT-related RXXS* motif ( Fig . 7C ) . In contrast to control peptides , only three unique phospho-motifs were enriched during MHV68 infection ( Fig . 7C ) . Each of these exhibited Pro-directed phospho-acceptors reminiscent of CDK and MAPK target sequences , including a classic CDK motif characterized by a basic residue at the -2 amino acid position relative to the phosphorylated residue [66] . Of note , detection of phosphorylated CDK1 and CDK2 was reduced during infection compared to control samples ( Table S1 ) . However , the CDK1/2 phosphorylations detected in our MS analyses correspond to deactivating the post-translational modification [67] , [68] . Hence , a relative decrease in abundance within infected cells corresponds to CDK1/2 activation during infection , which was previously documented during productive MHV68 infection [69] . Additionally , the ERK1/2 phosphopeptides detected are indicative of activation ( Table S1 ) [70] . Thus , the infection-associated phosphoproteome exhibits a strong CDK/MAPK phosphorylation signature , which differs from that of uninfected cells . This finding is in agreement with the identification of activated ERK1/2 and CDK1/2 by MS during infection and KEGG biological pathway predictions . To directly test these predictions , we performed comparative immunoblot analyses using phospho-motif-specific antibodies directed against the AKT RXXS* target motif or MAPK/CDK XS*P/S*PXK motifs following mock-infection or infection with MHV68 ( Fig . 7D ) . Although two unique RXXS*-containing proteins were prominent in infected cells , infection resulted in a general reduction in the number of detectable AKT-phosphorylated proteins . In contrast , infection resulted in enhanced detection of MAPK/CDK motif-containing proteins ( Fig . 7D ) . As was the case with general phosphoprotein immunoblotting ( Fig . S1 ) , detection of MAPK/CDK phospho-motifs increased over time during infection ( Fig . S6 ) , which is consistent with the observation that ERK1/2 activation occurs during the early-to-late phase of the MHV68 replication cycle ( See Fig . 3 ) . These data independently verify the motif-based bioinformatics prediction that infection substantially alters the cellular signaling network , revealing that MAPK/CDK-related phosphorylation events are predominant in the infected system , while other signaling pathways apparently are repressed . These findings strongly suggest prominent roles for CDK and/or ERK signaling during MHV68 infection . To gain insight into specific host proteins potentially regulated by ERK1/2 and/or CDK1/2 activity during infection , we performed high-confidence group-based phosphorylation scoring ( GPS ) analyses [71] to define infection-specific phosphopeptides that contain ERK1/2 and CDK1/2 motifs . The GPS analysis data table includes predictions for all available kinases for infection-specific phosphopeptides ( Table S3 ) , although other kinase-motif predictions are not discussed here . GPS analysis predicted 220 unique phosphopeptides derived from 171 host proteins that contain CDK1 and/or CDK2 motifs and 150 unique phosphopeptides derived from 103 host proteins for ERK1 and/or ERK2 ( Fig . 7E and Table S3 ) . 98 of the 253 predicted targets contained high-confidence motifs for both CDKs and ERKs , while 155 proteins were distinct targets – 150 CDK and 5 ERK ( Fig . 7E ) . STRING analyses identified only 11 of these proteins as CDK or ERK interactors ( Fig . 7E ) . Remarkably , of the 23 most abundant infection-specific host phosphoproteins illustrated in Figure 2D ( not including ERK1 or ERK2 ) , 18 contain GPS-predicted phosphorylation events on CDK and/or ERK target motifs . Of these , only c-Jun was a STRING-defined interaction partner for both CDKs and ERKs , which highlights it as a prioritized candidate for functional studies . These data strongly support the hypothesis that CDK and ERK-related signaling are predominant during productive MHV68 infection and further suggest that this serves to regulate a core set of proteins in the cellular phosphoprotein signaling network . Three independent bioinformatics analyses strongly predict the importance of CDK and/or MAPK activity in productive MHV68 replication . First , GO and KEGG pathways analyses reveal an over-representation of proteins annotated to MAPK signaling present in the infected cell phosphoproteome . Second , infected cells exhibit a MAPK/CDK kinase motif signature . Finally , a very high percentage of infection-specific host phosphoproteins contain strong CDK and/or ERK phospho-acceptor motifs . To directly test whether CDK and ERK activity control MHV68 replication , we evaluated the capacity of pharmacologic inhibitors of CDK and ERK activity to block MHV68 replication in single-step growth curves . Target cells were pretreated with the CDK inhibitor roscovitine [72] , [73] , MEK inhibitor U-0126 [74] , or ERK inhibitor 5-iodotubercidin [75] , [76] prior to infection with MHV68 , and viral titers were evaluated by plaque assay at 24 h post-infection . Compared to vehicle and untreated control infections , both roscovitine and 5-iodotubercidin treatments led to greater than 60-fold reduction in output titers . However , U-0126 , which acts on MEK kinases 1 and 2 upstream of ERK activation [74] , only minimally affected virus production ( Fig . 8A ) . This suggests that MHV68-induced ERK activation occurs independent of MEK1/2 . These data provide biological evidence that MHV68 usurps host CDK and ERK kinases for productive replication . While roscovitine is a highly specific CDK inhibitor [72] , [73] , 5-iodotubercidin is a more promiscuous kinase inhibitor also capable of inhibiting adenosine monophosphate kinase and haspin [77] , [78] . Highly specific chemical inhibitors of ERK activity are not currently available [79] . To more definitively evaluate roles in MHV68 replication , we utilized shRNAs targeting either ERK1 or ERK2 to knockdown ERK expression in 3T3 fibroblasts . At the same time , we also knocked down c-Jun expression in an effort to establish a possible downstream target of CDKs and/or ERKs required for viral replication . Compared to control shRNA knockdown cells , all of the shRNAs tested influenced the efficiency of MHV68 replication on some level ( Fig . 8B ) . Interestingly , shRNAs targeting ERK1 slightly delayed the onset of viral replication ( see 24 h and 48 h timepoints ) , but did not significantly reduce titers at later timepoints . In contrast , knockdown of c-Jun and ERK2 expression led to an overall reduction in output titers from 48–96 h post-infection , approximating 10-fold less efficient viral yield by 96 h post-infection ( Fig . 8B ) . It is notable , however , that inhibition of viral replication was not absolute , but rather manifested as a relative deficit over time . This result may be a consequence of incomplete knockdown of the targeted proteins , or it also is possible that c-Jun , ERK1 , and/or ERK2 function to enhance the efficiency of MHV68 replication , but are not absolutely required . It is worth noting that each of 4 unique shRNA constructs targeting either ERK1 or ERK2 reduced the expression of both ERK isoforms recognized by the antiserum used to evaluate knockdown efficiency ( not shown ) . We reason this effect stems from the presence of a long stretch of highly homologous nucleotide sequence present in both isoforms . Thus , as immunoblot data suggest , it is likely that both ERK isoforms were depleted in these experiments , which complicates direct functional interpretations for particular ERK isoforms . Nonetheless , in conjunction with pharmacologic inhibition data , these findings provide strong evidence in support of the bioinformatically-predicted hypothesis that CDK and MAPK signaling promote MHV68 replication . While the bioinformatics approaches employed above facilitated the identification of host molecules involved in GHV replication , it was not yet clear whether viral signaling proteins also contributed to infection-associated changes in the cellular phosphoprotein network . MHV68 encodes two kinases ( ORF21 and ORF36 ) and an ortholog of cellular D-type cyclins ( v-cyclin ) capable of stimulating CDK activity ( ORF72 ) [30] , [69] . We therefore evaluated the capacities of WT MHV68 , and transposon mutant viruses in which ORF21 , ORF36 , and ORF72 had been disrupted [80] to elicit phosphorylation of ERK1/2 , c-Jun , and MAPK/CDK-motif containing proteins during infection . In comparison to mock-infected cells , all of the viruses tested potently induced ERK1/2 and c-Jun phosphorylation ( Fig . 9A ) . However , the ORF72 transposon mutant did not elicit robust phosphorylation of MAPK/CDK-motif-containing proteins , while the other viruses did ( Fig . 9A ) , thus provisionally identifying v-cyclin as a viral molecule that contributes to the infection-associated phosphorylation signature . As a more discrete test of this hypothesis , we infected cells with a recombinant virus containing a targeted disruption of ORF72 ( ORF72-null ) and its genetically repaired WT control , ORF72-MR [81] . While ORF72-MR infected cells exhibited enhanced MAPK/CDK-motif phosphorylation relative to mock infection , the ORF72-null virus did not elicit robust MAPK/CDK-motif phosphorylation ( Fig . 9B ) . Thus , these data indicate that v-cyclin is a pathogen-encoded molecule that plays a prominent role in directing infection-related phosphorylation events .
Our study identified a total of 405 proteins that were only detected in either control ( 266 ) or infected cells ( 144 – 22 viral and 122 host ) . One of the proteins absent from infected cells , PML , is subject to ubiquitin-mediated degradation during MHV68 infection [36] , [37] . This is also true for EBV , HSV and HCMV [82] . For HSV and HCMV , PML degradation limits an intrinsic host response to infection that represses viral gene expression [83] , [84] . It is intriguing to speculate that other phosphoproteins not detected during infection also are degraded in order to limit inhibitory host-cell responses to MHV68 infection . Of course , the lack of detection in our phosphoproteomic analyses may also reflect a simple loss of phosphorylation , perhaps through phosphatase activity or viral inhibition of an upstream kinase . Indeed , phosphorylated FOXK1 was differentially detected between control and infected cells , yet expression of FOXK1 protein actually remains unchanged during MHV68 infection ( J . A . S . and J . C . F . , unpublished result ) . Likewise , infection-specific detection of ERK1/2 and c-Jun was a result of induced phosphorylation , rather than enhanced expression . Having established the framework here , in next generation experiments we envision pairing global differential proteomics techniques , such as stable-isotopic labeling of amino acids in cell culture ( SILAC ) , with phosphoprotein enrichment to simultaneously determine changes in protein expression levels with induction or repression of phosphorylation . Kinetic analyses that combine these approaches would enable a dynamic assessment of how GHVs manipulate host protein expression levels and phosphorylation-dependent signaling events to gain control of the host cell . Moreover , such combined approaches would readily lend themselves to comparative studies aimed at determining specific contributions of viral signaling proteins , like v-cyclin , or host kinases , such as CDKs and ERKs . The extent to which infection alters the phosphorylation status of specific proteins is remarkable and is dramatically illustrated in the GO protein class and global network analyses presented in Figures 2D and Figures 4 and 5 respectively . Indeed , 86% of proteins we identified exhibited intensity changes of greater than two-fold . By comparison , analogous phosphoproteomic analyses suggest that fewer than 15% of phosphoproteins are differentially regulated during cellular responses to DNA damage or growth factor receptor signaling [12] , [13] , 24% in response to Salmonella infection of cultured cells [35] , and 14% induced by HIV binding to cells [85] . We hypothesize that these comparative differences in phosphoprotein regulation reflect the extent to which an intracellular pathogen must usurp multiple host cell biosynthetic systems and evade innate immune detection during viral replication . As an obligate intracellular parasite , a herpesvirus must commandeer host cell machinery involved in transcription , DNA replication , nuclear import and export , translation , and vesicle transport , while limiting or redirecting cell death , antiviral , and immunomodulatory host-cell responses to infection . The finding that infection alters the composition of protein clusters within the host phosphoprotein network may provide insight into mechanisms by which GHVs , and perhaps intracellular pathogens in general , gain control of functional modules within the host cell to facilitate viral replication . We hypothesize that GO analyses further illustrate this point , revealing how infection-related phosphorylation appears directed toward proteins involved in distinct biological processes , such as nucleosome organization and ribosome or rRNA regulatory processes ( Table S2 ) . One might also hypothesize that the absence of phosphoproteins in specific GO classes or KEGG pathways during MHV68 infection , such as those involved in ubiquitination , signal transduction , and cell death ( Table S2 ) , illuminate host processes shut down by virus-directed events . Along these same lines it is important to consider the possibility that some of the signaling events we observed reflect the host cell response to infection . Roles for kinases in propagating and enforcing antiviral responses have been extensively studied [55] , [86] , [87] , [88] , and recent systems-level analyses demonstrate broad reorganization of host cell transcription and signaling networks following exposure to immuno-stimulatory microbial products [89] . From this study and numerous others , functions of MAPKs clearly influence the host cell response to infection . Given the strong MAPK signature present during MHV68 infection , it is possible that , beyond ERK , JNK [24] , or Tpl2/Cot1 [26] , other MAPKs that do not overtly facilitate viral replication influence the phosphoprotein network as part of the innate host-cell response to infection . Thus , it will be of interest to elucidate if and how other MAPKs or unrelated innate-immune kinases influence the infection-associated phosphoproteome . A key feature of the MHV68 phosphoproteome is that it offers direct insight into specific host signaling pathways usurped by MHV68 to facilitate infection . The data also highlight several interesting parallels with other herpesviruses . The concurrence of several independent bioinformatics analyses in highlighting the prominence of ERK/MAPK and CDK-related phosphorylation during MHV68 infection was impressively emphasized by the detection of ERK/CDK motif phosphorylation on 55% of infection-specific host phosphoproteins ( Table S3 , compare to Table S1 ) . Functional tests using pharmacologic inhibitors and shRNA knockdown confirmed the importance of CDK and ERK signaling in MHV68 replication ( Fig . 8 ) . With regard to ERK , a number of previous studies have demonstrated presumably biphasic roles for ERK in the KSHV lytic replication cycle . In the first phase , ras/raf-MEK-ERK signaling pathways are capable of promoting reactivation from latent infection by promoting immediate-early viral gene expression [38] , [39] , [40] . As one would expect given the involvement of MEK in this canonical pathway , this phase of ERK activation – and consequently KSHV reactivation downstream of ras/raf , MEK , or chemical induction with TPA – is inhibited by treatment with the pharmacologic MEK inhibitor U-0126 [38] , [39] , [40] . In contrast , a second phase of ERK activation during the KSHV lytic cycle is mediated by ORF45 , a multifunctional tegument protein that stabilizes a ternary complex composed of ORF45 , ERK , and p90 ribosomal S6 kinase [43] , [90] . In agreement with the finding that MHV68 replication ( Fig . 8 ) and ERK activation ( not shown ) were not inhibited by U-0126 treatment , ORF45-directed ERK activation also is insensitive to MEK inhibition [43] , leading us to hypothesize that MHV68 ORF45 may similarly promote ERK activation . Indeed , MHV68 and KSHV ORF45 proteins are functionally interchangeable in facilitating viral replication [91] . Hence , in addition to enhancing a general understanding of ERK functions in GHV infection , the identification of previously unknown putative ERK-phosphorylated proteins within infected cells may provide new insight pertaining to ORF45-directed enhancement of viral gene expression , translation , and viral egress [92] , [93] , [94] . It will also be of interest to determine if predicted ERK phosphorylation sites we identified on ORF45 ( Table 1 and Table S3 ) are bona fide ERK targets , and whether they exert functional control over ORF45 complex formation during viral replication . As a group , herpesviruses are thought to usurp CDK signaling in order to provide an S-phase-like environment amenable to replicating the viral DNA genome . For instance , reactivating EBV drives high S-phase cyclin expression and CDK activity , while at the same time inhibiting host DNA replication [95] , possibly through induction of a DNA damage response ( DDR ) [96] and/or inactivation of the MCM4-6-7 helicase complex [97] . Further and in agreement with our data , pharmacologic inhibition of CDK activity with roscovitine also inhibits lytic replication of EBV [98] , HCMV [99] , [100] , and HSV1 [101] , [102] , which strongly suggests that usurping cyclin/CDK activity is a universal requirement of herpesviruses . However , roles for CDK activity in promoting GHV replication have been minimally explored . And , although a few EBV targets of cyclin B/CDK1 are known [103] , information as to host substrates of cyclin/CDK activity during lytic GHV replication are lacking . In this regard , identification of potential CDK-phosphorylated proteins during MHV68 infection may reveal how GHVs direct CDK activity to foster efficient viral replication . A related question asks which viral factors contribute to the CDK phosphorylation signature during lytic GHV replication . As a homeostatic cellular process , cyclin/CDK activity is tightly controlled on several levels . This includes transcriptional regulation of cyclin genes , phosphorylation-dependent activation and inactivation of CDKs , and direct inhibitory interactions of cyclin/CDK holoenzyme complexes with CDK inhibitors ( CKIs ) , such as p21 and p27 [67] . At the host transcriptional level , both MHV68 and KSHV LANA proteins induce transcription of cellular cyclin genes [104] , [105] . Although the functional significance of LANA-mediated induction of host cyclins during productive gamma-2-herpesvirus replication has not been specifically tested , it is interesting to note that LANA-null MHV68 exhibits attenuated replication both in culture and in vivo [106] , [107] that is dependent on its transcriptional regulatory capacity [108] . Additionally , conserved herpesvirus protein kinases ( CHPKs ) in GHVs and beta-herpesviruses , which are required for efficient viral replication [11] , [27] , [109] , [110] , exhibit CDK-like functions , most notably pRb phosphorylation and the capacity to complement temperature sensitive yeast CDC28 ( S . cerevisiae CDK ortholog ) mutants for growth [111] , [112] . Further , BGLF4 , the EBV CHPK , exhibits partially overlapping substrate specificity with cyclin B/CDK1 in vitro [103] . Finally , gamma-2-herpesviruses , including MHV68 and KSHV , encode an ortholog of cellular D-type cyclins [29] , [30] . Viral cyclins exhibit an expanded capacity to interact with host CDKs [69] , [113] , [114] and are resistant to inhibition by CKIs [115] . Indicative of their capacity to stimulate cell-cycle progression [116] , [117] , v-cyclins are oncogenic when expressed in mice as a transgene [116] , [118] , [119] , and MHV68 v-cyclin is singularly required for pRb phosphorylation during lytic MHV68 infection [69] . Further , the KSHV cyclin ortholog is thought to play initiating and sustaining roles in KSHV-related cellular transformation [33] , [120] , [121] . The finding that cells infected with v-cyclin-null MHV68 exhibit reduced MAPK/CDK phosphorylation ( Fig . 9 ) strongly suggests that v-cyclin is a major contributor to the MAPK/CDK signature of lytic MHV68 infection . Although v-cyclin is not absolutely required for viral replication in cell culture , v-cyclin-null MHV68 exhibits attenuated acute replication , delayed latency establishment , and a severe reactivation defect in vivo [81] , [122] . An elegant study using recombinant MHV68 viruses in which v-cyclin was exchanged with cellular cyclins A , D , or E demonstrates overlapping or redundant functions for host and v-cyclins in some , but not all , aspects of MHV68 pathogenesis [123] , which may explain why v-cyclin is not necessary for MHV68 replication in culture [81] , but roscovitine potently blocks viral replication ( Figs . 8 and 9 ) . Moreover , v-cyclin expression is necessary for MHV68 transformation of primary B cells in culture [124] and lymphoproliferative disease and lethal pneumonia in vivo [125] , [126] . While further validation clearly is necessary , it is tempting to speculate that the CDK-motif containing proteins identified in this report are critical host-cell targets of v-cyclin that influence GHV pathogenesis . Together , the data presented herein enhance our understanding of the GHV-host interaction . In addition to defining new proteins and hypotheses for experiments to foster a more complete understanding of basic mechanisms of GHV replication and pathogenesis , the identified ERK and CDK-predicted phosphoproteins may encompass new host targets for therapeutic interventions . Our data strongly support the further evaluation of ERK and CDK inhibitors as treatments for lytic cycle-associated GHV diseases , such as IM or KS . Toward defining the pathogen-host interaction in general , it will also be of interest to determine whether global reorganization of the host phosphoprotein network is a phenotype shared with unrelated intracellular pathogens , such as RNA viruses or bacteria . If so , are common signaling pathways or macromolecular complexes targeted ? And , could these common pathogen-exploited host proteins serve as novel candidates for new generalized treatments ? The approaches we describe should be readily adaptable to other systems . Thus , our studies lay the foundation for future comparative analyses of this sort , as well as for defining differences and commonalities between de novo lytic GHV replication and reactivation , or comparative studies with alpha and beta-herpesviruses .
Mouse experiments performed for this study were carried out in accordance with NIH , USDA , and UAMS Division of Laboratory Animal Medicine and IACUC guidelines . The protocol supporting this study was approved by the UAMS Institutional Animal Care and Use Committee ( Animal Use Protocol 3270 ) . Mice were anesthetized prior to inoculations and sacrifice to minimize pain and distress . Swiss-albino 3T3 fibroblasts ( referred to as 3T3 fibroblasts throughout ) were purchased from ATCC . All cells were cultured in Dulbecco's modified eagle medium supplemented with 10% fetal calf serum ( FCS ) , 100 units/ml penicillin , 100 µg/ml streptomycin , and 2 mM L-glutamine ( cMEM ) . Serum starvation involved culturing of cells in media containing 0 . 5–1% FCS for 18–24 hours prior to infection or treatment . Cells were cultured at 37°C with 5% CO2 and ∼99% humidity . Wild-type MHV68 was strain WUMS ( ATCC VR1465 ) , WT BAC-derived MHV68 [127] , or BAC-derived MHV68-YFP [128] . ORF50-null MHV68 ( 50 . STOP ) was previously described [52] . UV-inactivation of WT MHV68 was accomplished by diluting virus stock to 1×107 PFU/ml and autocrosslinking in 60 mm plates using a Stratalinker prior to infection . Disruption of viral gene expression was confirmed by immunoblot analyses to detect viral proteins . Cells were infected by low-volume adsorption of viruses to the monolayer . The time of adsorption for all experiments was considered t = 0 . Inocula were removed after 1 h , and cells were cultured in a normal volume of serum starvation medium . 1×107 mock-infected or infected cells were harvested 18 h post-infection by scraping in cold phosphate-buffered saline ( PBS ) . Cells were pelleted at 700 g for 5 min , and snap frozen in liquid N2 . Cell pellets were lysed on ice in 500 µL buffer containing 50 mM Tris ( pH 7 . 5 ) , 50 mM NaCl , 0 . 05% surfactant ( Promega ) supplemented with protease and phosphatase inhibitor cocktails ( Thermo Scientific ) with vortexing every 5 to 10 min for 30 min . Insoluble debris was removed by centrifugation at 11000 g for 11 min . Protein concentration in the resulting solution was determined by BCA assay . 1 mg of protein in solution was concentrated by centrifugation through a 3 kDa filter ( Amicon ) and rinsed with 500 µL buffer containing 0 . 025% surfactant and 25 mM ammonium bicarbonate ( ABC ) . The concentrated protein mixture was then diluted to 900 µL total volume in 25 mM ABC . Proteins were reduced in 5 mM DTT for 20 min at 60°C , followed by alkylating in the dark with 25 mM iodoacetamide for 30 min at 25°C . Buffer exchange to 25 mM ABC was performed by centrifugation through 3 kDa filters and the resulting concentrate was diluted to 900 µL total volume in 25 mM ABC . Trypsin diluted in 0 . 01% trifluoroacetic acid ( TFA ) was added to the protein mixture ( 1∶50 w/w ) and incubated overnight at 37°C . Digests were quenched with 0 . 1% TFA . A 20 µL aliquot of the quenched trypsin digest was set aside for analysis . Phosphopeptide enrichment was based on a previously described method [129] . Digested peptide samples ( from 1 mg total protein ) were desalted using Sep-Pak columns . Sep-Pak columns were primed with a 75/25 mixture of buffers B/A ( Buffer A - 2% acetonitrile ( ACN ) , 0 . 1% formic acid; Buffer B - 75% ACN , 0 . 1% formic acid ) and rinsed with 2 mL Buffer A . Peptide samples were passed through Sep-Pak columns , followed by rinsing with 2 ml Buffer A . Peptide were eluted with 75/25 Buffer B/A mixture and desiccated in a speed vac . TiO2 beads were pre-incubated in Loading Buffer 1 ( LB1 – 65% ACN , 2% TFA , saturated glutamic acid ) at a ratio of 1 mg beads to 20 µl LB1 . 10 µl of TiO2 bead slurry was added to each desalted peptide sample and agitated for 10 min . Beads were collected by centrifugation at 3000 rpm for 30 sec , and the enrichment was repeated twice more with a fresh aliquot of TiO2 beads for each peptide solution . Thus , three successive enrichments were performed for each sample . Beads and phosphopeptides were washed three times for 10 min with agitation using 800 µl Wash Buffer 1 ( 65% ACN , 0 . 1% TFA ) , followed by 3 identical washes with 800 µl Wash Buffer 2 ( 65% ACN , 0 . 5% TFA ) . Phosphopeptides were eluted by incubation in Elution Buffer 1 ( 300 mM NH4OH , 50% ACN ) for 10 min with agitation , followed by identical treatment with Elution Buffer 2 ( 500 mM NH4OH , 60% ACN ) . Eluted phosphopeptides were desiccated in a speed vac . Peptide samples acidified to 0 . 1% formic acid final concentration were analyzed by nano-LC/MS/MS technique on an ion trap tandem mass spectrometer ( MS ) . An auto-sampler was used for automatic injection of tryptic peptides from a 96 well plate to the NanoLC 2D system ( Eksigent ) . Peptides were separated by reverse phase HPLC using a 10 cm long analytical column ( C12 resin , Phenomenex ) . HPLC eluate was ionized by ESI ( Electrospray ionization ) , followed by MS/MS analysis using collision induced dissociation on an LTQ Orbitrap hybrid MS ( Thermo Finnigan , San Jose , CA ) with two mass analyzers - Linear ion trap ( LTQ ) , and Orbitrap . One MS scan by Orbitrap was followed by 7 MS/MS scans by LTQ . Other relevant parameters include - spray voltage 2 . 0 kV; m/z range of 350–1500; isolation width ( m/z ) of 2 . 5; and normalized collision energy 35% . MS spectrum data were acquired using XCalibur 2 . 0 software . MS technical information provided in Table S4 . Raw data files are available at https://chorusproject . org/anonymous/download/experiment/-7729244682105805562 . Data analysis was performed using MaxQuant 1 . 0 . 12 . 31 [130] . Experiment design consisted of two sample types , ‘mock’ ( Expt1 ) and ‘infected’ ( Expt2 ) . Each sample type had two biological duplicates ( A and B ) and three technical replicates ( 1 , 2 and 3 ) . Therefore we had 12 MS data files , one for each MS run , namely: M1A , M1B , M2A , M2B , M3A , and M3B for mock samples and corresponding Mr1A , Mr1B , Mr2A , Mr2B , Mr3A , and Mr3B for infected samples . MS/MS peaks were searched against a concatenated forward and reversed version of IPI_mouse_v3 . 82 [131] database using the Mascot 2 . 2 search engine [132] via MaxQuant . False discovery rate for identification was less than 1% as estimated by the number of hits to the reversed sequences in the decoy database . Additional technical parameters are provided in Table S4 . Thus , we identified a total of 986 proteins at 1% FDR . These included 791 phosphoproteins with 1101/2271 unique phospho ( ST ) and 38/39 unique phospho ( Y ) site positions . Proteins differentially enriched between mock and infected samples were identified by ( 1 ) present-absent call based on peptide intensity ( zero intensity was considered as absent call ) and ( 2 ) 1 . 5-fold increase or decrease in intensity ratio of infected/mock . This analysis was performed using the protein intensities from the MaxQuant output file proteinGroups . txt . The complete quantitated data set is provided in Table S1 . Version 9 . 05 of the STRING resource [59] was used to generate protein interaction networks for MS-identified proteins . STRING networks are provided in Figures S4 and S5 . All interactions are predicted with medium confidence threshold of 0 . 400 , and all active predictive methods were allowed . Interaction networks were processed in Cytoscape 2 . 8 [60] to assign integer values and color coding to visually depict presence , absence , increase or decrease in protein intensity during infection . Disconnected nodes are not included in the Cytoscape output . Biological Process gene ontology analyses were performed using the BiNGO cytoscape plugin [63] . Overrepresented categories were identified relative to the Mus musculus background gene set using a hypergeometric test with Benjamini and Hochberg false discovery rate correction to define significance . Sorted data provided in Table S2 . Clustered proteins in phosphoprotein networks were identified using the MCODE cytoscape plugin [62] . Protein Class gene ontology analyses were performed using PANTHER 7 . 2 [133] against the Mus musculus background gene set . Kyoto encyclopedia of genes and genomes ( KEGG ) pathways enrichment was defined through DAVID [134] , [135] . For Motif-X [65] analyses , unique phosphopeptides for either mock or infected samples were identified from the global phosphopeptide sequence list . Unique phosphopeptides for either data set were identified using the IPI mouse proteome as background with a minimum of 20 occurrences per motif and a significance threshold of 0 . 000001 . 13 amino acid long motifs were defined where the phosphorylated residue is at position 7 . Shorter peptides were extended from mouse IPI database . Prealigned Motif-X output text files were used to generate global unique sequence logos in ICE-LOGO ( http://iomics . ugent . be/icelogoserver/logo . html ) . Group-based Prediction Systems ( GPS ) 2 . 1 software [71] was utilized at highest-threshold setting to perform batch identifications of phosphopeptides containing specific kinase target motifs . Sorted data provided in Table S3 . Serum-starved 3T3 fibroblasts were untreated or pretreated with either DMSO ( vehicle ) , U0126 ( LC Laboratories ) , roscovitine ( Cayman Chemical ) , or 5-iodotubercidin ( Cayman Chemical ) at 10 µM final concentration for 1 h prior to low-volume adsorption with MHV68 at MOI = 5 PFU/cell . Inocula were removed , and cells were cultured in a normal volume of medium . Cells were harvested 24 h post-infection , and progeny virions were liberated by freeze-thaw lysis . Viral titers were determined by MHV68 plaque assay as described [136] . A separate plate was harvested immediately after adsorption and subsequently titered to ensure that drug treatment did not inhibit viral attachment and to determine the 0 h titers for viral yield calculations . Lentiviral pLKO . 1-based shRNA vectors were purchased from Sigma . The following shRNA constructs were used in this study: TRCN0000229528 ( c-Jun-1 , NM_010591 . 2-2974s21c1 ) , TRCN0000042693 ( c-Jun-2 , NM_010591 . 1-994s1c1 ) , TRCN0000360511 ( c-Jun-3 , NM_010591 . 2-2270s21c1 ) , TRCN0000023160 ( MAPK1-1 , NM_011949 . 2-490s1c1 ) , TRCN0000054730 ( MAPK1-2 , NM_011949 . 2-921s1c1 ) , TRCN0000023186 ( MAPK3-1 , NM_011952 . 1-305s1c1 ) , TRCN0000023187 ( MAPK3-2 , NM_011952 . 1-662s1c1 ) . Lentiviruses were produced by transfecting 293T cells with shRNA vector plasmid and packaging vectors pSPAX2 and pHCMV-G . Lentiviral supernatants were harvested at 48 and 72 hours post-transfection . Due to inefficient knockdown using single vectors , 3T3 fibroblasts were transduced in 24 hour succession with two distinct lentiviruses each targeting the specified protein . c-Jun ( 1 ) stable knockdown cells were transduced with shRNA vectors 1 and 3 . c-Jun ( 2 ) stable knockdown cells were transduced with shRNA vectors 2 and 3 . Transduced cells were selected with puromycin ( 4 µg/ml ) and expanded . Stable knockdown cells were plated and infected with MHV68 at MOI = 0 . 05 PFU/cell . Cells were harvested at the indicated times and viral titers were determined by plaque assay [136] . Viral yields were determined by dividing output titers at the indicated timepoint by 0 h titers which represent input virus inoculum . Cells were lysed with alternative RIPA buffer ( 150 mM NaCl , 20 mM Tris , 2 mM EDTA , 1% NP-40 , 0 . 25% DOC , supplemented with complete mini-EDTA free protease inhibitors ( Thermo ) and phosphatase inhibitor cocktail 2 ( Thermo ) and quantified using the Bio-Rad DC or Thermo BCA protein assay prior to resuspending in Laemmli sample buffer , or equivalent numbers of cells ( 1–2×105 ) were directly lysed with 100 µl Laemmli sample buffer . Samples were heated to 100°C for 10 min and resolved by SDS-PAGE . Resolved proteins were transferred to nitrocellulose and identified with the indicated antibodies . ORF59 , v-cyclin , and MHV68 antisera were previously described [69] , [116] . ORF21 mAb was a gift from P . G . Stevenson . MHV68 antiserum was generated as previously describe [137] . P-Ser ( AB1603 ) , p-Thr ( 05-1923 ) , and p-Tyr ( 05-321 ) antibodies were purchased from Millipore . P-c-Jun ( S73-#9164 ) , c-Jun ( #9615 ) , p-ERK1/2 ( #4370 ) , ERK1/2 ( #4695 ) , p-MARCKS ( #2741 ) , and S*PXK/PXS*P motif ( #2325 ) RXXS/T* motif ( #9614 ) antibodies were purchased from Cell Signaling Technology . β-actin mouse monoclonal antibody was purchased from Sigma ( A2228 ) . Immobilized antigen and antibody were detected with HRP-conjugated secondary antibodies and SuperSignal Pico West ECL reagent ( Thermo Scientific ) or Clarity ECL reagent ( BioRad ) and exposed to film or imaged on a BioRad ChemiDoc MP digital imaging system . Female C57BL/6 mice 6 to 8 weeks of age were purchased from the Jackson Laboratory . Mice were sterile housed in the animal facility at the University of Arkansas for Medical Sciences in accordance with all federal and university DLAM guidelines . Mice were mock-infected with intraperitoneal injection of 0 . 2 ml of DMEM or infected intraperitoneally with 106 PFU of H2B-YFP virus diluted into 0 . 2 ml of DMEM . Four days post-infection mice were sacrificed by isoflurane overexposure and cervical dislocation . Spleens were harvested , homogenized into single-cell suspensions , and erythrocytes were lysed with red blood cell lysis buffer ( Sigma ) according to manufacturers instructions . For flow cytometry , splenocytes were fixed and permeabilized with Foxp3/Transcription Factor Staining Buffer Set according to the manufacturers instructions ( eBioscience , #00-5523-00 ) . Fixed and permeabilized cells were washed twice with FACS buffer ( 3 µM BSA , 1 mM EDTA in PBS ) and stained with PE-conjugated rabbit anti-c-Jun pS73 ( Cell Signaling , #8752 ) and goat anti-GFP ( Rockland , #600-101-215 ) antibodies diluted in FoxP3 wash buffer ( eBioscience ) for 30 minutes at room temperature . Stained cells were washed twice with FoxP3 wash buffer and incubated with donkey anti-goat secondary antibody conjugated to Alexa fluor 488 nm ( Invitrogen , #A-11055 ) diluted in FoxP3 wash buffer for 30 minutes at room temperature . Stained splenocytes were washed twice with FoxP3 wash buffer and resuspended in FACS buffer . Antibody-stained cells were analyzed by flow cytometry using a Fortessa ( Becton Dickinson ) to quantify cellular YFP and c-Jun ( p-S73 ) levels . Splenocytes from mock-infected animals were used to gate for YFP− and YFP+ cell populations , as infected ( YFP+ ) splenocytes are absent from these animals . This gating strategy allowed for detection of YFP+ splenocytes , which expressed YFP at levels exceeding the prior gate . | Systems-level evaluations of infection-related changes to host phosphoprotein networks are not currently available for any gammaherpesvirus ( GHV ) . Here we describe a quantitative phosphoproteomic analysis of productive GHV replication that demonstrates alterations in the phosphorylation status of more than 80% of host phosphoproteins and identifies 18 viral phosphoproteins . Systematic bioinformatics analyses reveal a predominance of MAPK and CDK signaling events within infected cells and suggest a virus-induced reorganization of signal-transduction pathways within the host phosphoprotein network . Functional experiments confirmed that CDKs and ERK MAPKs facilitate efficient viral replication and identify transcription factor c-Jun as a potential downstream target contributing to MHV68 replication . Finally , we identify the viral cyclin D ortholog as a major pathogen-encoded factor contributing to the MAPK/CDK signature of the infected cell phosphoproteome . These data provide new insight into both viral and host factors that regulate phosphorylation-dependent signaling during lytic GHV replication and offer a new resource for better defining host-pathogen interactions in general . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Phosphoproteomic Analyses Reveal Signaling Pathways That Facilitate Lytic Gammaherpesvirus Replication |
Chikungunya is an emerging public health problem in tropical and subtropical regions , due to ongoing transmission and its incapacitating acute disease phase , and chronic sequelae . The disease is responsible for a major impact on Health Related Quality of Life ( HRQoL ) , which may last several years . To our knowledge , this study is the first qualitative examination of HRQoL and coping strategies of chikungunya-infected individuals . Qualitative research methods consisted of 20 in-depth interviews and seven Focus Group Discussions ( FGDs ) , n = 50 . Analysis was based on the principles of the grounded theory . Different impacts on HRQoL were reported . The physical and emotional domains of the HRQoL were mainly affected by chikungunya , while social and individual financial consequences were limited . Individual financial impact was limited through the universal health care program of Curaçao . Long-term lingering musculoskeletal and other manifestations caused significant pain and limited mobility . Hence , participants experienced dependency , impairment of normal daily life activities , moodiness , hopelessness , a change of identity , and insecurity about their future . The unpredictable nature and consequences of chikungunya gave rise to various coping strategies . Problem-focused coping styles led to higher uptake of medical care and were linked to more negative impact of HRQoL , whereas emotional coping strategies focusing on acceptance of the situation were linked to less uptake of medical care and more positive impact on HRQoL . This study provides an in-depth understanding of acute and long-term HRQoL impact of chikungunya . The results can better inform health promotion policies and interventions . Messages to the public should focus on promoting healthy and efficient coping strategies , in order to prevent additional stress in affected individuals .
Chikungunya is an arboviral disease mainly transmitted by the mosquito species Aedes aegypti and Aedes albopictus [1] . Chikungunya has ( re- ) emerged in the Americas in 2013 [2] , when it rapidly spread from the islands of the Caribbean to the Latin-American mainland [2–4] . Since then , the Pan American Health Organization reported an approximated one million chikungunya cases per annum in the Americas [5] . However , during this introduction of chikungunya virus ( CHIKV ) in the Americas , more people might have been infected , since studies describing attack rates have reported that 35–90% of the population in these regions were infected by CHIKV [6 , 7] . Curaçao witnessed a chikungunya outbreak in 2014–2015 , which culminated in October and November 2014 . The epidemic ended early in 2015 , when up to approximately 50 , 000–75 , 000 ( attack rate: 33–50% ) individuals had been infected in Curaçao [6] . Chikungunya typically consists of an acute phase and a ( sub- ) chronic phase [8] . The acute phase commonly presents with a sudden onset of fever and severe musculoskeletal pain lasting up to twelve days [8] . Other manifestations during acute disease may include , rash , fatigue , joint swelling and nausea [8 , 9] . Acute disease signs and symptoms eventually subside , but in the majority of cases linger on for months or years . Persistent joint or musculoskeletal pain and weakness are characteristic in its chronic presentation , and may be accompanied by fatigue , loss of vitality and neurologic manifestations [10] . This chronic disease course has major impact on the quality of life ( QoL ) of affected people [10] . Health Related Quality of Life ( HRQoL ) describes QoL in its relation with health conditions . The World Health Organisation ( WHO ) defines health as ‘a state of complete physical , mental , and social well-being not merely the absence of disease…’ [11] . The latter description is widely considered as a definition of QoL [12] . Hence , HRQoL is a multidimensional concept typically referring to physical , emotional and social well-being and may often include a measure of socio-economic well-being [12–14] . Studies addressing the impact of chikungunya on the HRQoL of an individual have been performed using standardized questionnaires for HRQoL as the SF-36 questionnaires . Most of these studies associate chronic chikungunya with reduced HRQoL-scores on both physical and mental components [6 , 15–17] . The impact on HRQoL varies with severity of disease , showing in particular substantial drops in HRQoL when patients are more severely affected by chikungunya [6] . The decrease of HRQoL-scores in both physical and mental components is remarkable and alarming , given the emerging spread of CHIKV . HRQoL is influenced by individual subjective perceptions , expectations and coping strategies towards the health condition . Individuals showing the same clinical presentation of disease may exhibit different coping strategies , which can either positively or negatively impact the HRQoL [18] . Lazarus’s model of coping describes that emotional ( e . g . acceptance , positive reappraisal ) and problem-focused ( targeting the cause of stress , e . g . treatment , physician visit ) coping strategies are interdependent responses towards perceived stress . Hence together , these coping strategies account for an overall coping response [19 , 20] . Previous research on malaria and HIV have revealed that both emotional and problem-focused coping strategies can be linked to HRQoL outcomes [21–23] . Generally , problem-focused coping strategies are considered to improve HRQoL . However , a shift towards emotional coping strategies might be more appropriate when there is no treatment available [24] . Therefore , with their objective to improve HRQoL , health interventions should be grounded in knowledge on coping strategies that are employed by the targeted study population for various diseases . Previous studies on HRQoL of chikungunya have linked decreased HRQoL scores to chikungunya manifestations , but have not attempted to obtain in-depth understanding of the different concepts of HRQoL; nor have they assessed coping strategies influencing the HRQoL [6 , 15–17] . This study is part of a larger mixed methods project , which was set up to investigate how chikungungya impacts on HRQoL . This qualitative study was designed to obtain an in-depth understanding of this topic , while a simultaneously performed survey provided a representative quantitative assessment of the impact of chikungunya on HRQoL in Curaçao [6] . Therefore , the two main research questions of the study presented here are ( a ) how is the impact of chikungunya on HRQoL concepts ( physical , emotional , social life and economic well-being ) experienced by people in Curaçao ? and ( b ) what coping strategies are employed by those affected by chikungunya to reduce HRQoL impact ?
The study was approved by the Medical Ethical Board of the Sint Elisabeth Hospital Curaçao ( METC SEHOS; reference number: 2015–002 ) . All subjects consented in writing to study participation . Curaçao is an island in the Caribbean Sea , formerly part of the Dutch Antilles but since 2010 an autonomous country within the Kingdom of the Netherlands [25] . Curaçao is located close to the Venezuelan coast and consists of an area of 444 square kilometres . The approximately 150 thousand inhabitants live mainly in Willemstad , the capital of Curaçao . The GDP per capita is 22 , 600 dollar ( 2012 ) , which renders Curaçao a comparably affluent Caribbean island . Willemstad covers a big part of the South-Eastern part of Curaçao and constitutes the most important economic area of the island [26] . Urbanisation and Curaçao’s semi-arid climate with a rainy season from October to December [27] , provide favourable living conditions for Aedes spp . The population consists of an Afro-Caribbean majority and diverse minorities being Latin American , Dutch , Portuguese , French , Levantine and South- or East-Asian people [27] . Generally , all inhabitants of Curaçao are insured for most medical expenses through a state sponsored health insurance scheme ( Social Insurance Bank ) , through their employer , or through a private health insurance . The health system consists of a network of general practitioners for primary care . For hospitalization and specialized care patients are referred to the Sint Elisabeth Hospital , which is the main hospital of the island . This study was set up as an exploratory , qualitative study using in-depth interviews ( IDIs ) and focus group discussions ( FGDs ) . The consolidated criteria for reporting qualitative studies ( COREQ ) [28] were followed and presented in S1 Table . FGDs and IDIs were performed simultaneously , which allowed us to use FGDs to verify and contextualise topics which had emerged in IDIs . On the other hand , themes arising in FGDs could be more profoundly understood in IDIs . Interviews were performed in June and July 2015 up to the point of data saturation , which was evaluated and agreed upon within the research group . A total of 75 people participated in this study ( age range: 18–97 ) , of which 50 participated in one of the seven FGDs ( 4–10 participants per FGD ) and 20 in IDIs ( Table 1 ) . To understand the caretakers’ perspectives , we conducted key-informant interviews with family members of chikungunya patients ( n = 5 ) . Potential participants of the IDIs were contacted via key informants ( general practitioners ) and snowballing ( performed by the research group ) . Eligible participants were then recruited by the research group via telephone . Inclusion criteria for IDIs were adults with either ( 1 ) a serologically confirmed CHIKV infection during the chikungunya epidemic of 2014–2015 based on a positive IgM/IgG , using an ELISA test or ( 2 ) a history of an acute disease with chronic musculoskeletal pain during the chikungunya epidemic of 2014–2015 , plus a positive IgM/IgG performed on screening for inclusion in this study . Participants were selected from different socio-economic strata , age categories and genders . IDIs were performed to provide deeper insights on HRQoL concepts and coping strategies , the interplay of coping strategies and HRQoL . Seven FGDs were performed amongst adults ( regardless of having ( had ) chikungunya or not ) , which were invited for the FGDs via volunteers of neighbourhood centres and key informants ( of social groups ) . Individuals from various representative socio-economic groups ( ranging from low , middle , and high socio-economic statuses ) of Curaçao were included , i . e . 1 ) residents born in the Netherlands , 2 ) local youth , 3–6 ) people from the neighbourhoods Koraalspecht , Seru Fortuna , Rooi Santu and Souax . Local survey-investigators had additional contextual information , which they received during the simultaneously performed quantitative survey on HRQoL of chikungunya , which has been published elsewhere [6] . Hence , a FGD was conducted among the survey-investigators to cross-check information that we received from IDIs , surveys and FGDs . Prior to data-collection study procedures and confidentiality were discussed , after which all participants consented in writing to study participation . Two local social workers with a wide experience with group discussions performed the FGDs and the IDIs , together with Jelte Elsinga . Interviews were conducted in neighbourhood centres , at the homes of participants , or in another place which was chosen by the participants . Only interviewers and participants were present during the IDIs and FGDs , which were held in Dutch , Papiamentu or Spanish . The concepts of the HRQoL ( physical , emotional , social life and financial well-being ) and coping strategies served as theoretical framework ( Fig 1 ) for the interview and focus group guides ( S1 and S2 Text ) . As reflected in the theoretical framework , we expected that chikungunya disease manifestations could induce stress to all concepts of the HRQoL . The ability to cope with the consequences of this stress determined the impact on HRQoL of an individual concept . The impact on all four concepts ( i . e . physical , emotional , social life and financial well-being ) of the HRQoL resulted in the final/overall impact on HRQoL of chikungunya ( Fig 1 ) . Interview and focus group guides were prepared and used as a guide in the FGDs and IDIs ( S1 and S2 Text ) . These interview guides were evaluated and adapted after pilot interviews in Curaçao . The pilot interviews were included in the analyses of this study . Interviews were recorded , translated into Dutch and transcribed in verbatim . Field notes were made during and after interviews . When information started to repeat itself ( data-saturation ) the researcher discussed with the team the themes covered and decided to stop data collection . Data analysis was based on principles of the Grounded Theory [29] , which helped to visualize the analyses and to move from data to theoretical explanations . Analyses were performed using Atlas . ti ( version 7 . 5 . 4 ) software . Two cycles of inductive and/or deductive coding were employed . The first cycle of inductive and deductive coding resulted in 31 codes . In the second cycle , only deductive coding was employed . In the second cycle , the codes of the first cycle were categorized as per the concepts of the theoretical frame HRQoL and coping mechanisms , resulting in five ( deductive ) code families: ( 1 ) physical impact ( codes: e . g . acute/chronic symptoms , lingering symptoms , daily life problems ) ; ( 2 ) emotional impact ( codes: e . g . emotions , dependency on others , confusion related to chikungunya symptoms ) ; ( 3 ) impact on social life ( codes: e . g . impact on social life , general impact of chikungunya , impact on society ) ; ( 4 ) financial impact ( codes: e . g . impact on work , insurance , financial costs of chikungunya ) ; and ( 5 ) coping strategies ( codes: e . g . doctor visit , treatment , ( emotional ) coping style ) .
The community ( as documented in FGDs ) recognized chikungunya as being a debilitating disease with major acute and chronic impact on physical health . Musculoskeletal pains and stiffness were most prominently reported and perceived to cause physical impairment . However , the common symptoms that were described by the community included a wide range of symptoms for both the acute and chronic phase of chikungunya . Before onset of the disease , some individuals reported to have perceived a prodromal period in which they had ‘felt strange’ for days or weeks . Individuals , in the IDI , narrated how acute chikungunya had started with a sudden onset of symptoms with varying intensity . The reported symptoms included fever , swollen joints , musculoskeletal pains , weakness , stiffness , fainting , difficult breathing , headache , dizziness , change in sense of taste , itch , rash , incontinence , anorexia , numbness , tingling , diarrhoea and constipation . The sudden nature of acute chikungunya was reflected in situations where people could not get out of bed , a chair or car , or fell down when trying , thus increasing the risk of fall-related injuries . Participants had different experiences with chikungunya symptoms per case , as described above . Some had not perceived long-term complaints . Others described a second ‘phase’ of disease , which lingered on , or started after acute symptoms had ceased . This second ‘phase’ referred mainly to the long-lasting complaints , typically characterized by musculoskeletal pain and weakness . Furthermore , additional chronic symptoms were reported , such as cramps , fatigue , ‘trigger fingers’ and numbness ( paraesthesia ) . These long-lasting symptoms could be constant , but could also have a lingering nature , affecting different places alternatingly . According to some participants , their weak spots ( for example old injuries ) were the places where complaints of chikungunya particularly manifested . As was narrated by many individuals , the consequences of chikungunya resulted in challenging situations . On disease onset , when generally the most intense symptoms were perceived , people were commonly forced to stay in bed , sometimes without the ability to leave bed without help . The latter inconvenience was commonly described together with the difficulties participants faced in reaching the toilet . The activities of daily living were affected for almost all the participants of the IDIs . Individuals reported other long-term daily life inconveniences , which could vary from crucial daily life activities to minor problems . Amongst these inconveniences where the inability to dress themselves because of impairment of movement , to wear shoes because of their swollen feet , to drive a car , to cook , to sport , to sleep well , and other inconveniences . It also affected the participants’ interpersonal relations with some mentioning that it was difficult for them to have sexual intercourse . A few of these were described as follows . As a consequence of chronic chikungunya , people continued perceiving musculoskeletal impairment and other sequelae , leading to long-term dependency on others , loss of mobility and emotional concerns . Within the community , different ways of how chikungunya influenced emotions were expressed . Depending on duration of disease and clinical presentation , people perceived minimal to major emotional impact caused by chikungunya . More specifically , individuals reported feelings of moodiness , anxiety , frustration , anger , and feeling left out , desperate , ashamed , confused or in some case perceived them self to be a different person . Emotional distress in the acute and chronic phase was related to lower knowledge on chikungunya . A distinction could be observed between emotions individuals perceived in the acute phase of disease and those referring to the long-lasting sequelae of chikungunya . Whereas initial emotional reactions were mainly triggered by the abrupt nature of the acute symptoms , the unpredictable and long-lasting nature of the chronic sequelae was responsible for an emotional impact on the longer term . Among the community , chikungunya was held responsible for varying consequences regarding social life of patients . These consequences varied depending on manifestation , duration and severity of disease and concerned inabilities to join social activities or to visit friends and being avoided by family or friends . However , some patients had not experienced any impact on their social life . This could be the case when people did not have a social life of notice , or if disease manifestations were so mild that no impact on social activities was perceived . Individuals narrated how they , due to their physical impairments and loss of vitality , could not join social activities such as going to the cinema , compete in sport matches , drinking with friends , play golf , do physical exercise or walk with friends . These created a situation where participants could perceive weakening of social bounds . Others explained that even if the activity only concerned visiting friends , they would not be able to go , because they were not able to drive a car , due to the symptoms of chikungunya . Furthermore , people stated not being in the mood of acting social , as was also reflected before in the section ‘emotional impact of chikungunya’ . Limited knowledge on transmission routes of disease could cause an impact on social life . Namely the belief that people with chikungunya could infect others left some participants without having their family or friends visiting them . Also , some participants did not want to visit others , since they were afraid to infect them . The belief that chikungunya was contagious by patient contact was not the only reason to avoid visiting others . Participants described how family living abroad cancelled their planned stay , or did not come because they were afraid of getting chikungunya . Thus , social impact of chikungunya was not perceived as substantial , but could still lead to social isolation . Participants reacted to the consequences of disease described above by performing different coping strategies , which we describe in the following sections . Within the community , problem-focused coping was commonly performed by using ( natural ) medicines and by visiting a physician to cope with the physical impact of chikungunya . In general , chikungunya patients stayed in bed during the acute phase of chikungunya and depended on the care of family or friends . Their physical condition permitting , most of them went to see a physician , normally accompanied by someone to transfer them . Otherwise , a physician was visited one or two days later . Some of the participants did not visit a physician . These participants did not recognize visiting a physician as a problem-focused coping strategy , because they would not be treated with more than the regular symptom relief-providing medications , i . e . paracetamol and nonsteroidal anti-inflammatory drugs . Furthermore , some participants found the symptoms obvious and consequently did not want to waste time to seek the opinion of a physician . At some point of time during the epidemic , participants reported that people were not being laboratory tested any more , and this also discouraged people from visiting a physician . Although some decided not to go to see a physician , others decided to visit a physician many times ( up to approximately 20 times in some cases ) for the complaints caused by chikungunya . Frequent physician visits were linked to desperation , a desire for solace , persistence of symptoms , and a desire to try new treatment . Frequent physician visits were also performed because people needed to prove disease persistence in order to be reimbursed for the absence from their work . Many participants had used medicines to treat their physical complaints . Those included the medicines bought over the counter or prescribed by the physician , amongst which were mainly paracetamol or NSAID’s . Furthermore , natural or other alternative medicines were very popular since they had been prominently presented in advertisements and media by alternative healers . Hence , people recommended these medicines to each other . Some individuals with persistent complaints reported to have used more than ten different medicines . Amongst the most popular medicines were mango and papaya leaves , lemon grass , aloe and vitamin B . Using frequent physician visits and different medical treatments was a pattern of problem-focused coping , which was illustrative for people who perceived severe impairment from chikungunya , which they could hardly accept . Frequent visits of health care providers as coping strategy led to unmet expectations for cure or treatment , resulting in stress ( negative impact on QoL ) . In contrast , people with lingering complaints while reporting less impact of chikungunya also showed different coping strategies , i . e . emotional coping strategies . The participants using emotional coping strategies went less often to a physician , used less medications and reported trying to do as many physical/social activities as possible . For example , they tried to mobilize by performing the physical exercises or movements they could do . With this in mind , people were more willing to accept that symptoms would eventually subside . Participants believed that chikungunya stayed in the system ( the body ) , which made them susceptible for lingering symptoms during that time . This period of having ‘chikungunya in the system’ could range from 6 months up to 2 years . During the chikungunya epidemic , people believed that a ‘high resistance’ [immune system] was a vital way of preventing chikungunya , or to make sure that chikungunya would not cause major symptoms or linger on during infection . Some participants combined the latter two perceptions , and consequently coped with chronic chikungunya sequelae by living healthy during the time that they thought that chikungunya would be in their system , in order to diminish the lingering of chikungunya symptoms . Actions participants performed to ‘strengthen the immune system’ were: eating healthy , physical activity , not visiting sex workers and using herbs or vitamins . Individuals coped with social consequences by calling their family and friends instead of visiting them . Social HRQoL impact was also diminished by emotional coping strategies relying on social support . People passed by to inform about the situation of the ill people and tried to help each other by offering advices and remedies , or made fun of each other . Moreover , people started helping each other by bringing people to the physician or to the Social Insurance Bank . Others obtained emotional support from others in order to deal emotionally with chikungunya sequelae . The financial impact due to chikungunya on participant’s personal situation was generally perceived as limited . In Curaçao , chikungunya patients could rely on an insurance system , which covered health care costs , which in turn minimized out-of-pocket expenditures . These insurances covered also parts of the income loss of chikungunya patients who could not work . However , participants pointed to financial impact on society level . This was attributed to the fact that chikungunya had left many people at home instead of going to their work , and because the epidemic might have prevented tourist from visiting Curaçao .
Chikungunya constitutes an emerging public health problem , mainly due to its debilitating acute and long-term musculoskeletal manifestations . Though the disease can cause major impairment of Quality of Life ( QoL ) , this is the first study conducted to obtain an in-depth understanding of the impact of chikungunya on Health Related ( HR ) QoL of its patients using qualitative research methods . HRQoL of participants were mainly affected on physical and emotional domains , and to a lesser extent on social and financial domains . Problem-focused coping strategies aimed at physical healing induced a high uptake of ( alternative ) medicines and consultation of physicians , whereas emotional coping strategies focused on acceptance of the situation were linked to lower perceived impacts on HRQoL and less uptake of medical care . Chikungunya patients reported an acute and chronic disease presentation in agreement with the existing literature [30–35] . Additionally , a prodromal period before disease onset and acute symptoms like ‘a change in sense of taste’ and incontinence were reported . The latter findings are interesting but should be cautiously interpreted since participant expressed their confusion regarding chikungunya sequelae , and no comprehensive clinical assessment of the participants of this study was performed . This confusion and wide variety of symptoms that participants linked to acute and chronic chikungunya are in agreement with the current situation of knowledge on chikungunya . Clinical studies are showing a wide variety of disease manifestations [10] , yet an all-encompassing consensus on the clinical picture of chikungunya is lacking . The acute and chronic disease presentation of chikungunya was directly responsible for impairment of physical HRQoL , and demonstrated indirect consequences for emotional HRQoL . The acute disease was described as highly debilitating in which patients perceived strong anxious emotions , sometimes to the extent that they thought they might be dying . This could have partly been prevented if people had been timely informed of the sudden and tough situation they might face when acquiring chikungunya . Providing timely information is important and a key to reduce health-related distress [36 , 37] . During chikungunya outbreaks , it is important that health authorities proactively provide messages on the symptoms and normally non-lethal nature of chikungunya . The unpredictable lingering nature of mainly musculoskeletal manifestations and its subsequent difficulties in normal daily life activities were linked to several consequences for emotional HRQoL . Patients narrated how these sequelae influenced their mood and ability to perform their daily-life activities . Hence , chikungunya had turned participants into ‘other persons’ . Young men linked their physical strength to a perceived resistance to diseases , as was also described in a study in Cambodia [38] . When these young men were hit by chikungunya and perceived long-lasting impairment of their physical strength , loss of part of their identity caused emotional bearing . The same was observed when women could not wear high heels , dance or go out , thus impacting negatively on their identity . The notion that chikungunya affected patients’ identity was also reflected in the perceived accelerated aging of the body . Being independent is an important value for elderly [39 , 40] . Therefore , by rendering elderly individuals dependent of care , chikungunya could have major emotional consequences for these people . Knowledge of the aforementioned emotional concerns is valuable to all health professionals directly working with chikungunya patients , and advocate for the need to target emotional concerns in health promotion policies . Health related impact on social life and the personal financial situation of participants were limited , since ways of coping diminished the significance of these concerns . The universal health care program ( via the Social Insurance Bank or other ) covered generally the health care expenses . In the countries where people cannot rely on a universal health insurance scheme such as the one available in Curaçao , it is expected that chikungunya patients will face a higher financial burden . Consequently , chikungunya has been shown to cause major financial burdens on society level across Latin America [41] . The perception that chikungunya is transmissible from patient to patient should be targeted in health campaigns by promoting transmission methods . Improved knowledge on transmission routes of chikungunya could hereby take away the barrier to engage in social activities when having chikungunya . Enhancing social activities may additionally improve coping with the impairment of emotional HRQoL [36] , as social support is an important mediator between life stress and health status [24] . This study describes several ways of how participants coped with the effects of chikungunya . A notable coping strategy was high uptake of ( natural ) medicines or frequent physician visits , which was typically performed by subjects with major impacts on HRQoL and difficulties to accept their situation . Since effective long-term treatments are not available yet for chronic chikungunya , the latter coping strategy will normally not lead to success . The consequent dissatisfaction may only give rise to further emotional stress [42 , 43] . Hence , health professionals should identify patients performing this coping strategy to promote healthier coping strategies . For example , participants showing coping strategies focused on acceptation of circumstances or on social support showed better HRQoL , which is in line with literature on coping strategies [24 , 36] . Some people solely relied on ( natural ) medicine or alternative healers; instead of attending physicians , as it was also reported from other febrile diseases and populations [e . g . 44 , 45] . Promotion of healthy coping strategies amongst these people might be a challenge , but may be achieved via media or community centres . Another coping strategy included a ‘healthy lifestyle as long as chikungunya is in the system’ . ‘Living healthy’ has also been described as a coping strategy with a possible dengue infection [38] . Currently , there is no scientific substantiation for this coping strategy . Nevertheless , because no effective long-term treatment for chikungunya sequelae is present , this way of coping will under normal conditions only improve health . This study used different qualitative methods . Since the participants of the IDIs ( except for the family members of chikungunya patients ) had their chikungunya infection serological confirmed , we focused our analysis mainly on the laboratory-confirmed chikungunya patients of the IDIs . The FGDs were used to understand perceptions of the community . This study was limited by its qualitative study design . As is normal for qualitative research , participants were not randomly selected . Therefore , generalization of these findings is limited to this specific study site and study population . Furthermore , HRQoL is a broad concept . Although this study was explorative and comprehensive in nature , it might not have captured all aspects of the HRQoL . A researcher bias may be present in this study . We minimized this by involving the local research group and the research group in the Netherlands in the different stages of the study ( i . e . study methods , data interpretation and data presentation ) . Additionally , a contamination bias due to sharing of chikungunya knowledge and study procedures between investigated communities might be present . We assume that this was no major issue , since this study mainly focused on personal experiences of chikungunya episodes . Furthermore , the issue of chikungunya was already widely discussed within Curaçao both in the community and in the media . Because it is vital to understand the impact of a disease in order to develop applicable and effective health intervention , these interventions should be grounded in research on HRQoL . This study is the first applying qualitative research methods on this topic and providing valuable new in-depth insights on HRQoL and coping strategies concerning acute and chronic chikungunya sequelae . These insights help health professionals to understand the impact of chikungunya on its patients . The results reveal that health promotions strategies could be improved , by ( 1 ) providing timely messages to the public about the devastating but normally non-lethal nature of chikungunya , to ease chikungunya patients during acute disease , ( 2a ) promoting emotional coping strategies and ( b ) targeting ‘unhealthy’ problem-focused coping strategies among chronically affected chikungunya patients , ( 3 ) focusing on providing ( emotional ) support to older chikungunya patients . Further research could include health professionals working in chikungunya endemic areas . This would allow to assess clinical management ( consultation , treatment recommendations ) of chikungunya , and to provide insight on how this was put in practice by the patients . Similar qualitative studies should be performed in different settings , as these may serve as basis for randomized surveys on HRQoL and coping strategies of chikungunya . | Chikungunya is a disease caused by a virus , which is transmitted by mosquitoes . During the past years , major outbreaks of chikungunya have occurred in the Americas . Normally , chikungunya presents with an acute , fever-like disease . After acute disease , many people develop chronic disease manifestations , which are mainly characterized by joint pain . In this study , we examined the impact of chikungunya on quality of life and how people coped with these consequences . The results of this study show that people infected with chikungunya reported long-term pain and limitation of mobility . Consequently , impairment of normal daily life activities and several emotions were experienced; for example dependency , moodiness , and insecurity about their livelihood and their future . People narrated visiting a doctor and using treatment to cope with these consequences . However , the participants who accepted their chronic ‘diseased’ condition could better cope with the consequences of chikungunya than the participants who could not accept their chronic condition . The latter group kept searching for new treatments and frequently consulted doctors . It is important that doctors and health authorities are aware of these results , because this will aid them to improve care and promote better coping strategies of people affected by chikungunya . | [
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] | 2017 | Health-related impact on quality of life and coping strategies for chikungunya: A qualitative study in Curaçao |
The identification of functionally important residues is an important challenge for understanding the molecular mechanisms of proteins . Membrane protein transporters operate two-state allosteric conformational changes using functionally important cooperative residues that mediate long-range communication from the substrate binding site to the translocation pathway . In this study , we identified functionally important cooperative residues of membrane protein transporters by integrating sequence conservation and co-evolutionary information . A newly derived evolutionary feature , the co-evolutionary coupling number , was introduced to measure the connectivity of co-evolving residue pairs and was integrated with the sequence conservation score . We tested this method on three Major Facilitator Superfamily ( MFS ) transporters , LacY , GlpT , and EmrD . MFS transporters are an important family of membrane protein transporters , which utilize diverse substrates , catalyze different modes of transport using unique combinations of functional residues , and have enough characterized functional residues to validate the performance of our method . We found that the conserved cores of evolutionarily coupled residues are involved in specific substrate recognition and translocation of MFS transporters . Furthermore , a subset of the residues forms an interaction network connecting functional sites in the protein structure . We also confirmed that our method is effective on other membrane protein transporters . Our results provide insight into the location of functional residues important for the molecular mechanisms of membrane protein transporters .
The identification of functionally important cooperative residue is important for understanding the allosteric pathways of proteins . Cooperative residues are responsible for long-range allosteric communication from the substrate binding sites to the translocation pathways of membrane protein transporters [1] . A number of methods have been proposed for the identification of functionally important residues in proteins . Based on the notion that functionally important residues tend to be conserved within a protein family [2] , [3] , sequence conservation analyses have been applied to identify specific functional sites , such as substrate/ligand binding residues [4] , protein-protein interfaces [5] , active sites of enzymes [6] , and residues responsible for functional specificity [7] . Meanwhile , co-evolutionary analyses , which were introduced by the observation that functionally important residues are likely to co-evolve with other functional residues to reduce the effects of mutations [8] , have been applied to identify energetically and/or evolutionarily coupled interactions between the domains of complex proteins [9] , the interaction sites of protein complexes [10] , and the allosteric pathways of proteins [11] , [12] . One drawback of these approaches is that residues may be conserved or co-evolved due to several underlying causes , such as the maintenance of protein structure , interaction , and folding , as well as functional constraint [13] , [14] . Therefore , a method that can quantify and detect functional constraints from the evolutionary information in protein sequences would greatly aid the identification of functionally important residues in proteins [15] . Membrane protein transporters are involved in two-state allosteric communication , which mediates the propagation of regulatory information from the substrate binding site to the translocation pathway through large conformational changes [1] . These conformational changes could be brought about through cooperative residues [16] . Recent studies have suggested that cooperative residues are conserved [17] or evolutionary coupled [18] to maintain allosteric communication . Furthermore , it has been proposed that co-evolved pairs of moderately conserved residues are important for protein function [19] . Thus , it may be possible to combine sequence conservation and co-evolutionary analyses to identify the cooperative residues of membrane protein transporters . To do this , we derived a new method for identifying the cooperative residues of membrane protein transporters by integrating two different evolutionary features . We extracted functional information from multiple evolutionary constraints based on the following deduction: we took advantage of the fact that clusters of cooperative residues might be co-evolutionary connected not only by proximal but also distal residues in order to mediate allosteric communication [18] . When we considered a protein as a co-evolving network of residues , high connectivity described the functional essentiality of a single residue . Based on these , we hypothesized that cooperative residues lining the substrate binding and translocation pathway are likely to be conserved and have more co-evolutionarily coupled partners than non-functional residues , showing high connectivity in a co-evolution network . To test our hypothesis , we introduced a co-evolutionary coupling number ( CN ) to measure the connectivity of co-evolving residue pairs in a co-evolution network . We then integrated CN with sequence conservation score and investigated the functional roles and structural positions of the conserved cores of co-evolutionarily coupled residues . We initially applied our method to the MFS transporters , LacY , GlpT , and EmrD , for which crystal structures have been solved and whose functional residues have been characterized well enough to evaluate the performance of our method . MFS transporters represent one of the largest and most diverse superfamily of membrane protein transporters and are ubiquitous to all three kingdoms [20] . The identification of cooperative residues of MFS transporters may be helpful in inferring their allosteric mechanisms , including substrate recognition and translocation . MFS transporters move various substrates ( e . g . , sugar , drug , metabolites , and anions ) in different directions across cell membranes using a unique combination of residues in their transmembrane regions [21] . One MFS transporter , lactose permease ( LacY ) , is a symporter that catalyzes the coupled translocation of lactose and H+ [22] . Another , glycerol-3-phosphate transporter ( GlpT ) , mediates the exchange of glycerol-3-phosphate and inorganic phosphate in an antiport manner [23] . Multi-drug transporter , EmrD , is an antiporter that exports a diverse group of chemically unrelated drugs [24] . Using our method , we found that conserved cores of evolutionarily coupled residues comprise residue interaction networks connecting the specific substrate recognition site and translocation pathway of MFS transporters . We also tested our method on other proteins and confirmed that it is effective in identifying the cooperative residues of membrane protein transporters .
We devised a new evolutionary feature , co-evolutionary coupling number ( CN ) , and integrated it with the sequence conservation score to select functionally important cooperative residues from protein sequences . Figure 1 diagrams the proposed method . First , we measured co-evolution and sequence conservation scores from homologue sequences . Second , we formulated the CN by counting the number of co-evolving residue pairs per residue . Finally , we calculated a quantitative integration score ( IS ) of each residue by multiplying sequence conservation score and CN ( see Materials and Methods for details ) . To examine whether functionally important cooperative residues tend to be conserved and have many co-evolved partners , we compared average IS , CN , and sequence conservation scores between central cavity residues and non-cavity residues . The central cavity of an MFS transporter is mainly composed of functionally important residues that are involved in substrate recognition and are located in the pathway of substrate transport [25] . We found that central cavity residues were significantly more conserved and had many more co-evolved partners than non-cavity residues , resulting in a high IS ( Table S1 ) . The average IS of central cavity residues was 3 . 1 times higher than that of the non-cavity residues ( p-value = 2 . 31×10−11 ) . Statistical significance was determined by Student's t-test comparing IS distributions between central cavity and non-cavity residues . We further examined the sequence conservation scores of central cavity residues to confirm our initial assumption that central cavity residues are conserved and evolutionary coupled . From the sliding-window analysis of conservation scores , we found that central cavity residues slowly evolved rather than being completely conserved ( Figure S1 ) . Central cavity residues were enriched between the 75th and 90th percentile of sequence conservation scores . The fraction of central cavity residues was sharply reduced after the 90th percentile of sequence conservation . These results suggest that a slow evolution rate allows central cavity residues to be conserved and co-evolutionarily coupled with other residues [26] . Therefore , the integration of sequence conservation and CN can be used to identify central cavity residues . To measure the sensitivity of the integrated evolutionary information , we compared our ability to detect central cavity residues by IS , CN , co-evolution , and sequence conservation scores . We examined the fraction of central cavity residues using various percentile cutoffs for IS , CN , co-evolution , and sequence conservation scores . In comparison to the conventional evolutionary approaches , we found IS to be a more effective way to select central cavity residues . As shown in Figure 2A , IS detected 1 . 1 to 2 . 2 times more central cavity residues than CN , co-evolution , or sequence conservation score . We also observed that CN had a higher sensitivity for detecting central cavity residues than co-evolution and sequence conservation . This suggests that central cavity residues tend to be co-evolutionarily coupled with many residues rather than being highly conserved . We compared the precision-recall characteristics of IS , CN , co-evolution , and sequence conservation for a more comprehensive evaluation ( i . e . how well each of the four approaches do in identifying the central cavity residues ) . We found that IS was best in the detection of central cavity residues ( Figure 2B ) . Specifically , IS achieved an average precision of 71% , whereas the other evolutionary approaches achieved an average precision of 64% ( CN ) , 58% ( co-evolution ) , and 49% ( sequence conservation ) at 30% recall . Also , the precision of IS was 3 . 2-fold higher than a randomly generated set at the same recall . Furthermore , the likelihood ratio of IS was the highest among all four evolutionary approaches ( Figure S2 ) . These results indicate that IS can capture the maximum evolutionary property of central cavity residues that would not be apparent by co-evolution or sequence conservation alone . For the sensitive detection of functional residues , we optimized the percentile cutoff of IS by examining the false-positive rate , which is the fraction of non-cavity residues selected at the given percentile cutoff . We found that , in all three MFS transporters , the 90th percentile of IS discriminated central cavity residues from non-cavity residues with an acceptable false- positive rate of 5% ( Figure 2C ) . Therefore , we used the 90th percentile of IS as a cutoff value to select functional residues for further analyses . LacY facilitates the transport of lactose through the inner membrane [22] . LacY is an intensively studied protein of the MFS transporters and its functional residues have been well characterized through mutagenesis [27] . To investigate whether the high-IS residues are involved in substrate binding and translocation , we identified 25 residues within the 90th percentile of IS ( Figure 3A ) and found that most residues detected at this cutoff have known functional roles ( Table 1 ) . The detected residues were mostly positioned within the substrate translocation pathway of the central cavity ( Figure 3B ) . When we mapped the 25 detected residues on the LacY structure , we found that 17 residues ( 68% of detected residues ) were located in the central cavity ( Figure 3C and Table S2 ) . It has been experimentally confirmed that six residues , E126 , R144 , E269 , R302 , H322 , and E325 , are irreplaceable and necessary for LacY operation [27] , [28] , and we detected five of these residues ( Figure 3C , shown in bold ) . We were able to detect E126 , R144 , R302 , H322 , and E325 , but missed E269 in the 90th percentile of IS . Meanwhile , the missed residue E269 was found in the 70th percentile of IS . Proteins use residue-residue interactions to propagate regulatory information from one functional site to another [29] . We constructed an interaction network by examining the interatomic connectivity among the detected residues . Different types of interactions , such as hydrogen bonds , salt bridges , and van der Waals interactions were assessed by measuring solvent-accessible surface and interatomic distances from the structures of MFS transporters ( see Materials and Methods for details ) . We observed that 23 of the 25 detected residues form an interaction network and 18 of these comprise a main network in the LacY structure ( PDB ID: 2CFQ ) ( Table S3 ) . Of the 18 residues , 15 are central cavity residues known to be essential for LacY operation and 5 of the 18 are irreplaceable ( Figure 4A ) . Hydrogen bonds and salt bridges formed between the residues of Y236 , D240 , R302 , K319 , H322 , and E325 ( bold line in Figure 4A ) are known to play important roles in the transduction of the substrate binding signal through the LacY structure [30] , [31] . Two irreplaceable residues , E126 and R144 , found interact through a hydrogen bond , are involved in substrate binding and release [32] . The functional implications of the interaction network are in accordance with the lactose transport mechanism proposed from LacY mutation experiments [28] . Our main network could be divided into two sub-networks based on orientation: network 1 is located on the periplasmic side and network 2 on the cytoplasmic side ( Figure 4A ) . There is evidence that the residues of both sub-networks simultaneously mediate substrate translocation from opposite sides of the membrane ( Figure 4B ) . Residue E325 detects protonation states and transports H+ with R302 and H322 on the periplasmic side , and P327 on the cytoplasmic side [33] . Substrate translocation is mediated by residues Y236 , D240 , F261 , G262 , and M299 of network 1 and residues A273 and M276 of network 2 [34]–[36] . Residues K319 in network 1 and G147 in network 2 are involved in substrate accumulation [28] . Among the residues of network 2 , E126 and R144 are essential for substrate binding [27] . Residue M299 of network 1 and A273 of network 2 connect two sub-networks and are essential for substrate transport [37] . The functional residues located on both the periplasmic and cytoplasmic sides suggest that the cooperative residues of both networks allow efficient allosteric communication for LacY operation by alternating between two major conformations , inward-facing and outward-facing conformation , respectively [22] . The residues outside the main network , L84 , Y350 , and L351 , lie close to the irreplaceable residue E126 ( average Cα distance; 16 . 5Å ) and mediate substrate translocation ( Table 1 ) . The integration of evolutionary features worked well for the identification of functional residues of other family members of MFS transporters . We applied our method to the GlpT and EmrD proteins , the functional residues of which are less well characterized than those of LacY . We found that , similar to LacY , a few residues of GlpT and EmrD have high IS ( Figure S3 ) and they use unique residue combinations for specific substrate binding and translocation . In GlpT , we chose 25 residues within the 90th percentile of IS . When we mapped the residues onto the GlpT structure , we found 18 of 25 residues located along the central cavity ( Figure 5A and Table S4 ) . Twenty-two of the detected residues form an interaction network ( Figure 5B and Table S5 ) , of which several residues have experimentally confirmed functional roles in substrate binding and translocation ( Table S6 ) . For example , residues K80 , R269 , and H165 have a critical role in substrate binding and residues E299 , Y362 , and Y393 participate in substrate translocation [23] , [38] . In particular , the formation and breakage of salt bridges between residues H165 , R269 , and E299 are known to involve conformational changes during the transport of glycerol-3-phosphate [39] . Meanwhile , in EmrD , 13 of 21 detected residues are located in the central cavity ( Figure 5C and Table S7 ) . Among them , 10 residues comprise the main interaction network associated with H+ translocation ( Figure 5D and Tables S8 , S9 ) . It has been shown that residues Q21 , Q24 , T25 , and I28 are involved in facilitating H+ translocation [24] . Compared to LacY and GlpT , little is known about the functional mechanism of EmrD . Our analysis may serve as a guide for future experimental verification of EmrD functional residue location . To ensure that our method works for transporters outside of the MFS superfamily , we tested it on other membrane protein transporters , whose allosteric conformational changes were characterized and whose cavity residues could be selected from crystal structures [40]–[42] . We investigated the positions and annotated functional roles of high-IS residues in 15 membrane protein transporters , such as KvAP and Kv1 . 2 voltage-gated K+ channels , rhodopsin , the chloride pump halorhodopsin , bacteriorhodopsin , sensory rhodopsin , archaerhodopsin , Na+/K+ ATPase , P-type Ca2+ ATPase , plasma membrane ATPase , and the sulfate/molybdate ABC transporter . Membrane protein transporters mediate the movement of ions , solutes , and metabolites across a membrane [43] . We found that , on average , IS selected 2 . 3 times more cavity residues than random selection ( Table 2 ) . Also , we discovered that many high-IS residues were located along the cavity region involved in substrate translocation pathways ( Table S10 ) and comprised interaction networks in the protein structures ( Figure S4 ) . For example , in the chloride pump halorhodopsin , 10 of 15 residues were found from the chloride translocation pathway using the 90th percentile of IS ( Figure 6A , shown in red spears ) [44] and formed an interaction network . In sulfate/molydbate ABC transporter , 9 out of 12 detected residues were located in the substrate translocation pathway ( Figure 6B , shown in red spears ) [45] and 6 residues comprised an interaction network . In addition , 64% and 55% of the detected residues in the KvAP channel and P-type Ca2+ ATPase were located in the ion conduction pathway and formed an interaction network , respectively ( Figures 6C and 6D ) [46] , [47] . These results showed IS to be an effective way to locate the cavity residues in the tested transporters . Also , in the precision-recall curves of four evolutionary approaches , IS had the highest precision at all levels of recall ( Figure S5 ) .
In this study , we attempted to identify the functionally important cooperative residues of membrane protein transporters from amino acid sequences by integrating two different evolutionary features . We demonstrated that the conserved cores of evolutionarily coupled residues of MFS transporters were mainly located in the substrate translocation pathway . One may question why functionally important residues are conserved and have evolved in a co-dependent manner . It has been suggested that protein sequences may have been robust to environmental and mutational perturbations in the course of evolution in order to preserve protein function [48] . These residues have evolved at a rate that was slow enough to avoid the loss of function [49] . Indeed , we observed that central cavity residues of MFS transporters are moderately conserved and enriched between the 75th and 90th percentile of sequence conservation scores ( Figure S1 ) . This slow evolution rate allows correlative substitutions among functional residues , resulting in high co-evolutionary coupling numbers [26] . The presence of an interaction network of cooperative residues is strongly correlated with the pathway of substrate translocation described in other studies [27] , [50] . We found that the cluster of cooperative residues comprised an interaction network that may constitute an allosteric pathway connecting the substrate binding site and translocation pathway of MFS transporters . Yifrach and colleagues found that allosteric pathway-lining residues are energetically coupled over long distances and showed that these residues are important for the sequential conformational transition of the Kv channel using electrophysiology recordings techniques [1] , [51] . In addition , other researchers have shown that perturbations of conserved residues impair the allosteric communication of protein residues [52] , [53] . These results suggest that cooperative residues are evolutionarily coupled and conserved to mediate long-range allosteric communication from the substrate binding site to the translocation pathway of membrane protein transporters . The efficient regulation of allosteric communication is achieved through the interaction of cooperative residues . Recent network-based structural analyses by Nussinov and colleagues have shown that centrally positioned residues in protein structures maintain the robustness of allosteric pathways through residue-residue interactions [29] , [54] . By mapping the detected residues onto the ligand-free ( PDB ID: 2CFQ ) and ligand-bound ( PDB ID: 1PV7 ) structures , we observed the rearrangement of residue-residue interactions . In particular , irreplaceable substrate binding residues , E126 and R144 , had different interatomic contacts between ligand-free and ligand-bound structures ( Figure S6 ) . In the ligand-free structure , the guanidine group of R144 forms a salt bridge with the carboxyl group of E126; whereas , in the ligand-bound structure , the two atomic groups directly interact with the substrate by breaking the salt bridge [55] , [56] . Also , the rearrangements of hydrogen bonds and salt bridges between residues Y236 , D240 , R302 , K319 , H322 , and E325 are known to involve conformational changes in LacY [27] . Taken together , we reasoned that the connectivity of the detected residues was changed because efficient conformational changes for substrate transport are regulated by the formation and breakage of interactions between cooperative residues . We found that some of the high-IS residues in MFS transporters are non-cavity residues , while most of them are positioned in the central cavity to control substrate transport . It may be possible that some of the detected non-cavity residues are also involved in the transport mechanism . For example , it has been reported that a non-cavity residue , R302 , of LacY is irreplaceable for substrate transport [27] and connected with central cavity residues , K319 , Y236 , D240 , and H322 ( Figure 4B and Table 1 ) . Furthermore , we noticed that some non-cavity residues that have high-IS were found from the residue interaction networks of other membrane protein transporters ( Figures S4 ) . The detected non-cavity residues that surround the cavity region may have functional roles in membrane protein transporters . Different MFS transporters may have diverse interaction networks of cooperative residues . We believe that the diversity of the networks occurs because evolution likely favors functional diversification of MFS transporters . Interestingly , we found that the interaction network of the detected residues in EmrD were found from only one symmetric half ( where H+ translocation occurs ) ; whereas , the networks of LacY and GlpT covered both symmetric halves . In EmrD , proton translocation and drug transport may occur at different sites in the central cavity [24] . EmrD has a large and flexible substrate recognition pocket that transports various chemically unrelated drug compounds; therefore , different drugs may interact with different sites of the pocket [57] . We suspect that the substrate recognition pocket of EmrD is not conserved so that functional residue detection is limited . In summary , our integrative evolutionary analysis effectively shows that the conserved cores of evolutionarily coupled residues arose from functional constraints , providing information to characterize specific functional residues of MFS transporters . We believe this method can be applied to other proteins to narrow down the potential candidates of functional residues and to save time and reduce the cost incurred by molecular biology , biochemical , and biophysical approaches . We provide downloadable source code at our website ( http://sbi . postech . ac . kr/IS/ ) for wide application of this method .
We obtained homologous sequences for LacY , GlpT , and EmrD of Escherichia coli and other membrane protein transporters from Swiss-Prot/TrEMBL . We used sequences 0 . 7∼1 . 4 times the query sequence length and <90% similarity to other sequences . We aligned the sequences using ClustalW [58] . We omitted columns with a gap ≥20% and completely conserved region . To calculate the sequence conservation score of each residue in LacY , GlpT , EmrD , and other membrane protein transporters , we used ConSeq [59] . We compared McBASC [60] , SCA [11] , and ELSC [61] algorithms for co-evolutionary analysis . The precision-recall curves showed a comparable performance in the identification of cavity residues among the different algorithms ( Figure S7 ) . Among them , the McBASC algorithm performed slightly better than other algorithms , so we used the McBASC algorithm to calculate co-evolution scores . We derived the co-evolutionary coupling number ( CN ) through the following steps . First , we selected significant co-evolving residue pairs using a length-dependent threshold [62] . The number of co-evolving residue pairs is set equal to twice the protein length . Then , we counted the number of co-evolving residue pairs per residue and defined it as the CN . To correct the different score distributions , we normalized the sequence conservation score and CN by converting their scores into the corresponding percentile rank scores ranging from 0 to 1 . Finally , we multiplied the normalized sequence conservation score by the CN to obtain the quantitative integration score ( IS ) . We used a set of cavity residues ( positive set ) and a set of non-cavity residues ( negative set ) to evaluate the performances of IS , co-evolution , and sequence conservation scores . The central cavity residues of transporters are composed of the residues involved in substrate recognition , which are located in the pathway of substrate transport; whereas , non-cavity residues include the rest of the central cavity residues [25] . To select central cavity residues , we measured the solvent accessible surface of translocation pathways of the three MFS transporter structures using VOIDOO with a 1 . 2 Å probe radius and default manner [63] . We also manually inspected the selected residues to eliminate residues from other small cavities that can occur in the structure . In LacY , 49 of 417 residues , 53 of 452 residues in GlpT , and 52 of 394 residues in EmrD are in the central cavity and are tabulated in Table S2 , S4 , and S7 , respectively . We investigated the functional implications of residues within the 90th percentile of IS . At the 90th percentile of IS , we can identify cavity residues with 5% false-positive rate , the fraction of non-cavity residues selected from the given percent cutoff . A 5% false-positive rate represents the acceptable level of selecting functionally important residues [64] . Based on the observation that most of the detected residues were positioned in the transmembrane region ( Figure S8 ) , we considered the residues of the transmembrane region for further analysis where important functions of MFS transporters occur . We designated transmembrane boundaries for the three MFS transporters using the Protein Data Bank of Transmembrane Proteins ( PDBTM ) [65] . We assessed the interatomic connectivity among the detected residues based on the crystal structures of MFS transporters in the Protein Data Bank ( http://www . rcsb . org ) ; PDB ID: 2CFQ for LacY , PDB ID: 1PW4 for GlpT , and PDB ID: 2GFP for EmrD . To measure interactions between residues , we used the contacts of structural units ( CSU ) software ( http://www . weizmann . ac . il/sgedg/csu/ ) . In a given protein structure , the CSU software provides a list of interatomic interactions and their distances by measuring the solvent-accessible surface of every atoms of two residues [66] . A van der Waals interaction was identified if the distance between any two atoms of the residues is less than the sum of their van der Waals radii plus the diameter of a solvent molecule ( 2 . 8Å ) . A salt bridge was identified when the distance between the donor atoms ( Nζ of Lys , Nζ , Nη1 , Nη2 of Arg , Nδ1 , Nε2 of His ) and the acceptor atoms ( Oε1 , Oε2 of Glu , Oδ1 , Oδ2 of Asp ) was less than 4 . 0 Å [67] . A hydrogen bond was assessed by HBPLUS [68] , which measures the angle and distance of each donor-acceptor pair to find out its fitness to the geometric criteria defined by Baker and Hubbard [69] . We used likelihood ratios to statistically evaluate how well different evolutionary features ( IS , CN , co-evolution , and sequence conservation scores ) could discriminate central cavity residues from non-cavity residues for each of the following percentile groups: 80% , 82% , 84% , 86% , 88% , 90% , 92% , 94% , and 96% . We obtained likelihood ratios for different evolutionary features with: ( 1 ) X1 and X0 represent the number of central cavity and non-cavity residues selected from the given percent cutoff , respectively . H1 indicates the total number of central cavity residues . H0 is the total number of non-cavity residues . A likelihood ratio >1 indicates a reliable probability . An increasing likelihood ratio signifies the detection of more central cavity residues . We tested our method on other membrane protein transporters . We collected the membrane protein transporters whose allosteric conformational changes were characterized and cavity residues can be selected from the crystal structures . We chose 15 protein structures from the five largest families of membrane protein transporters , which include KvAP and Kv1 . 2 voltage-gated K+ channels , rhodopsin , chloride pump halorhodopsin , bacteriorhodopsin , sensory rhodopsin , archaerhodopsin , Na+/K+ ATPase , P-type Ca2+ ATPase , plasma membrane ATPase , and sulfate/molybdate ABC transporter . Cavity residues were selected , as described in the procedure for selecting central cavity residues in MFS transporters . | Major Facilitator Superfamily ( MFS ) transporters are one of the largest families of membrane protein transporters and are ubiquitous to all three kingdoms of life . Structural studies of MFS transporters have revealed that the members of this superfamily share structural homology; however , due to weak sequence similarity , their structural similarity has only been found after structural determination . Even after the structures were solved , painstaking efforts were needed to detect functionally important residues . The identification of functionally important cooperative residues from sequences may provide an alternative way to understanding the function of this important class of proteins . Here , we show that it is possible to identify functionally important residues of MFS transporters by integrating two different evolutionary features , sequence conservation and co-evolutionary information . Our results suggest that the conserved cores of evolutionarily coupled residues are involved in specific substrate recognition and translocation of membrane protein transporters . Also , a subset of the identified residues comprises an interaction network connecting functional sites in the protein structure . The ability to identify functional residues from protein sequences may be helpful for locating potential mutagenesis targets in mechanistic studies of membrane protein transporters . | [
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] | 2009 | Integration of Evolutionary Features for the Identification of Functionally Important Residues in Major Facilitator Superfamily Transporters |
The principal objective of this study was to assess a modeling approach to Lu . longipalpis distribution in an urban scenario , discriminating micro-scale landscape variables at microhabitat and macrohabitat scales and the presence from the abundance of the vector . For this objective , we studied vectors and domestic reservoirs and evaluated different environmental variables simultaneously , so we constructed a set of 13 models to account for micro-habitats , macro-habitats and mixed-habitats . We captured a total of 853 sandflies , of which 98 . 35% were Lu . longipalpis . We sampled a total of 197 dogs; 177 of which were associated with households where insects were sampled . Positive rK39 dogs represented 16 . 75% of the total , of which 47% were asymptomatic . Distance to the border of the city and high to medium density vegetation cover ended to be the explanatory variables , all positive , for the presence of sandflies in the city . All variables in the abundance model ended to be explanatory , trees around the trap , distance to the stream and its quadratic , being the last one the only one with negative coefficient indicating that the maximum abundance was associated with medium values of distance to the stream . The spatial distribution of dogs infected with L . infantum showed a heterogeneous pattern throughout the city; however , we could not confirm an association of the distribution with the variables assessed . In relation to Lu . longipalpis distribution , the strategy to discriminate the micro-spatial scales at which the environmental variables were recorded allowed us to associate presence with macrohabitat variables and abundance with microhabitat and macrohabitat variables . Based on the variables associated with Lu . longipalpis , the model will be validated in other cities and environmental surveillance , and control interventions will be proposed and evaluated in the microscale level and integrated with socio-cultural approaches and programmatic and village ( mesoscale ) strategies .
Visceral leishmaniasis ( VL ) in America is caused by Leishmania infantum ( syn . chagasi ) . The sandfly Lutzomyia longipalpis was incriminated as the most important vector [1] and the domestic dog was involved as the main reservoir , both in urban areas [2–5] . Although Lu . longipalpis was recorded in Argentina at forest-rural sites in 1951 and 2000 with very few individuals per capture , since 2006 this species has been found in VL urban foci in captures with more than 100 insects per trap in the first focus at the city of Posadas , Province of Misiones , and also present in other cities of northeastern Argentina ( provinces of Formosa and Chaco ) , [5–9] . Salomón et al . [10 , 11] studied the presence and distribution of Lu . longipalpis in the province of Corrientes ( contiguous to Misiones where Posadas is close to the border between both provinces ) to assess the possibility of autochthonous transmission of L . infantum . This province has an active transmission scenario with canine leishmaniasis cases and vector presence since 2008 [10] , even in Santo Tomé , resulting in 16 human cases that have been diagnosed since 2010 till the 20th epidemiological week of 2015 ( 9 of which were recorded at Santo Tomé , with 3 deaths ) . Despite canine leishmaniasis was diagnosed in numerous dogs , no systematic rate of infected dogs was performed until this study . Dynamic epidemiological patterns of transmission are the result of the simultaneous and multi-scale interaction of biotic factors that coexist in heterogeneous epidemiological landscapes [12 , 13] . In this sense , Real and Biek [14] hypothesize that the spatial context and the geographic landscape contribute to the initial establishment of the disease . It should be noted that the scales from microfocal to regional , although they are inclusive to each other in increasing order , require questions , resolution , data quality , and different analytical tools to support the conclusions appropriate to each scale [13 , 15] . At a coarse resolution the micro-scale heterogeneity may not be detected , as well as general macro-scale patterns may be overlooked at a fine spatial resolution [16] . Previous studies on leishmaniasis associated Lu . longipalpis abundance in urban scenarios with the presence of chickens , dogs and/or fruit trees , or Normalized Difference Vegetation Index ( NDVI ) ranges , which can offer suitable conditions for reproductive success of the vector [17–22] . A study carried out in the city of Posadas , identified microhabitat variables such as surface of bare soil or covered with grass , distance from house to watercourse , number of plant-pots , and number of tree species as possible contributors to the abundance of vectors in an urban environment [23] . Despite these results , factors associated with the increase in presence and abundance of Lu . longipalpis in urban environments are only partially understood [24] , and the modeling at micro-scale usually explain up to 30% of the variability [25] . The micro-scale is defined by the characteristics of the house and surrounding area , and is the operational scale for focal interventions [15 , 26] . But when modeling Lu . longipalpis abundance in Posadas city at this scale , the vector showed different associations between variables recorded at micro-habitat ( trap site ) and macro-habitat variables ( theoretically the smallest homogeneous patch of the variable , instrumentally a buffer area that includes relatively homogeneous surroundings ) . Further , in this urban setting more than 30%-40% of the sites sampled had Lu . longipalpis presence while less than 5% had high abundance of the vector , suggesting that the presence and the abundance are modulated by different variables [25] . Therefore , the principal objective of this study was to assess a modeling approach to Lu . longipalpis distribution in an urban scenario different from Posadas , discriminating micro-scale landscape variables at microhabitat and macrohabitat scales , and the presence from the abundance of the vector , in order to try to improve the explanatory power of the model , and so to contribute to the design of integrated intervention strategies based on the associated variables . The visceral canine leishmaniasis distribution was also analyzed as it was proposed as indicator of transmission or human risk [27–29] .
This study was carried out in Santo Tomé City , Corrientes , Argentina ( 28°33'5 . 79"S , 56° 2'44 . 11"W ) . This city belongs to the ‘Espinal’ ecoregion , Neotropical ecozone [30] , and it is situated on the coast of the Uruguay River which determines the border between Argentina and Brazil . Santo Tomé has a stable population of 23 , 299 inhabitants [31] distributed in approximately 8 km2 . The study was conducted from 25 to 27th February 2013 . We studied vectors and domestic reservoirs simultaneously . In order to sample the entire urban area , the city was divided into a grid of 600 m2 squares ( patch ) , except for the neighborhood ‘Estación’ on the West , where high vector abundance had been reported by a previous study [11] , and was divided into 200 m2 squares . One domestic unit was selected within each patch using the ‘worst scenario’ criterion [32] . The ‘worst scenario’ is a functional definition to denote a site within the study patch with the greatest probability of sandfly presence due to habitat conditions . ‘Worst scenarios’ are distinguished by the presence of dense vegetation which provides shadow , humidity and detritus; soil rich in organic material and access to blood ingestion without the interference of external light . In the 600 m2 patches , minimum and maximum distances between traps settled in different patches were 145 and 472 m respectively; whereas in the 200 m2 patches , minimum and maximum distances between traps were 110 and 270 m respectively . The geographic coordinates of all the sites sampled were registered with a Global Positioning System ( Garmin eTrex10 ) . Sandflies were captured with automatic CDC-like light traps , used for the sampling of Phlebotominae in peridomestic environments . Traps were active from approximately 5:30 p . m . to 7:30 a . m . , for 3 consecutive rainless nights . Traps were placed 1 . 5 m above the ground . All Phlebotominae sandflies were dried and preserved prior to processing . The specimens were cleared with lacto-phenol and identified according to [33] under a microscope ( Zeiss , 400x ) . Evandromyia cortelezzii and Ev . sallesi females cannot be distinguished by their morphology , so specimens collected were included within the Ev . cortelezzii-sallesi complex . According to previous studies in urban areas where traps with more than 30 Lu . longipalpis individuals summed up to the 10–15 percentile , we operatively classified the domestic units into low ( <30 ) and high abundance ( >30 ) [20] . Maximum ( max ) and minimum ( min ) temperatures ( T ) and relative humidity ( RH ) were registered during sampling in the trap active period with digital thermo-hygrometers ( TFA , Germany ) in 17 randomly selected domestic units . During the capture period mean climatic variables were: Tmin mean: 15 . 42 , SE: 1 . 75; Tmax mean: 31 . 43 , SE: 1 . 02; RH min mean: 39 . 46 , SE: 5 . 59; RH max mean: 92 . 28 , SE: 6 . 55 . Dogs from the houses with sandfly traps were blood-sampled by veterinarians , Dogs house . We also sampled all dogs in neighboring houses within a 25 m radius , Dogs neighbours . The presence of antibodies against L . infantum by means of the immunochromatographic rK39 technique was done in situ ( Kalazar Detect Canine Rapid Test; InBios ) . For each dog , 11 variables were gathered: breed ( yes/no ) , gender , age ( years ) , size ( small , medium , large ) , sterilization ( yes/no ) , night resting place ( interior/exterior ) , unleashed ( allowed to wander around , yes/no ) , moving history ( yes/no ) , repellent use ( yes/no ) , repellent periodicity ( months ) , symptoms ( yes/no ) . The study was conducted according to the ethical regulations for research established by the World Organization for Animal Health ( OIE ) [34] and with the approval of the ethics committee ‘Comité de Ética de Investigación Clínica’ ( CEIC , Office for Human Research Protection , IRB Registration 00001678 –USA; Res . N° 1108–26 ) . All the neighbours that collaborated in the study were informed about the practices and signed an informed consent form . Satellite information to generate the environmental stratification of the city was obtained from a Spot 5 HRG1 J image ( spatial resolution , 10 m; March 2013 , facilitated by a CONAE-Argentina and CNES-France agreement ) . The synthetic image was digitally processed in order to convert digital values into reflectance values for each of the pixels of the cropped image . Land cover spectral responses were determined by band math in the Red and Near-Infrared spectra , giving a normalized difference vegetation index ( NDVI ) raster image as a result . The NDVI image was subjected to an unsupervised classification by the Isodata method so as to obtain the different classes resulting from the spectral responses of the land cover present in the area of study [35 , 36] . The classification ended in 20 classes with 98% of convergence . By cluster analysis , pixels were grouped in 6 categories: Water , Uruguay River , Bare Soil , Urban Cover ( includes non-paved streets ) , Low Density Vegetation , and Medium to High Density Vegetation . For each trap , a circular buffer area of 50 m was defined in order to avoid superposition , and the percentage of each class of land cover was calculated . At each domestic unit , a set of 6 variables were recorded at the same time of the entomological sampling ( Trees , Fruit trees , Plant pots , Dogs , Hens and UnMat ) ( Table 1 ) . Variables as Stream and Border , were obtained from the satellite image and its posterior analysis by GIS . The ‘Altitude’ was recorded from the GPS at each trap position . We constructed a set of 13 models to account for micro- ( 2 ) , macro- ( 2 ) and mixed-habitats effects ( 9 ) ( Table 2 ) . Two models took into account all the measured variables after checking for collinearity ( NB full , Hurdle full ) . Ten models set aside the ‘animal’ variables ( Dogs , Hens ) , because of its moving nature in contrast with the other ‘sessile’ things measured . As it was stated in the introduction , according to a conceptual framework that discriminates instrumentally spatial scales , conceptually the presence from the abundance phenomena , and allow to introduce the expert knowledge in the final models , 2 Hurdle models were constructed as an abundance part with 6–4 variables , and a presence part with 6 variables ( Hurdle micro/macro , Hurdle micro sessile/macro , respectively ) . Two models took into account a possible quadratic relationship of Stream with sandfly abundance , and the number of trees at the trap to represent shade and humidity at the microscale . Two other hurdle models were constructed only with biotic variables , excluding Unused materials , Altitude , Border and Stream ( Hurdle biotic 1 , both parts; Hurdle biotic 2 , only count part ) . Bare soil was not considered in Hurdle Biotic 2 since Bare Soil class had very low cover values in the entire city and could have a low influence in vector abundance/presence . The final set of candidate models was selected by means of the AICc criterion and taking into account the Akaike weights ( wi , model probabilities ) and ΔAICc [44–47] . Models with the lowest AICc and highest wi were considered the best models in the set . Spatial autocorrelation in the raw variable and models residuals were checked by Moran’s I and semivariograms with SAM software [48] . Parameter estimates and BCa intervals ( bias-corrected and accelerated bootstrap ) of the final model ( s ) where calculated by bootstrap based on 1000 replications with package boot for R [49] . To evaluate the predictive ability of the final model for the “presence part” we calculated: Kappa index , proportion of correct classifications ( PCC ) , area under the curve ( AUC ) , sensitivity and specificity with package PresenceAbsence for R [50] . As a threshold probability must be selected to distinguish positive from negative ( sandfly presence and absence , respectively ) all possible cut-off points from 0 . 01 to 0 . 99 were assessed to select an optimum cut-off point which maximized the Kappa index that assesses the improvement of classification of the model over chance . In first place , we analyzed the variable rk39 positivity ( dichotomic , = 1 if dog had a positive rk39 ) by means of a generalized linear mixed model taking into account the clusters ( random factor ) made up of Dogs house plus Dogs neighbors . We constructed 5 models with binomial family and logit link using package lme4 for R [51] . Model 1 took into account individual dog characteristics such as: breed , gender , age , size , and sterilization; Model 2 accounted for dogs habits: night resting place , unleashed , moving history , repellent use , repellent periodicity . Model 3 included all the variables . Model 4 was similar to model 1 but incorporating two interactions: gender*sterilization , and breed*sterilization . Models were compared by AICc . In second place , we analyzed the association between the proportion of dog positivity in each trapped house and its neighbours ( Proportion of Positives ) and the centered environmental variables , including also the accumulated abundance of phlebotomines . Due to over dispersion , we constructed 5 GLM models with negative binomial family and log link ( variable: number of positive dogs , offset: number of dogs analyzed ) using the same variables as the ones listed as NB models in Table 2 and incorporating the accumulated abundance of Lu . longipalpis at each house , using package MASS for R [39] .
We captured a total of 853 sandflies belonging to six species: Lu . longipalpis , Migonemyia migonei , Nyssomyia whitmani , Brumptomyia sp . , Ny . neivai and Ev . cortelezzii-sallesi ( Table 3 ) . The 98 . 35% of the sandflies captured were Lu . longipalpis . The capture effort was 53 traps/night ( total: 159 traps ) , of which 51% were positive for Lu . longipalpis ( Fig 1 ) . Of this percentage , 85% were sites with abundances between 1 to 29 specimens , and 15% showed abundances higher than 30 individuals . We sampled a total of 197 dogs , 177 of which were associated to households were insects were sampled ( Fig 1 ) . The rest of the dogs belonged to houses that could not be included in the insect sampling due to logistical issues . Positive rK39 dogs represented 16 . 75% of the total , of which 47% were asymptomatic . We did not find evidences of association between rK39 Positivity and the explanatory variables . The models showed no improvement compared to the null model . As for the Proportion of positives , it seems to be associated with microscale variables such as Trees near the trap ( p = 0 . 005 ) and Stream2 ( p = 0 . 008 ) but the effect could not be confirmed due to computational issues during bootstrapping . After model selection , one NB model and three hurdle count regression models were responsible for 99% of the collective model weight ( S1 Table ) . But the best model of the set was the Hurdle shade/Macro model that differed in almost 10 units ( or more ) of AICc from the others . After removing two non-significant terms from the presence part ( BCa intervals contained the 0 value ) , Altitude and Stream2 , AICc diminished 4 units and this model was retained . The Kappa index calculates the agreement between model predicted values and observed data , indicating how much better from a random classification the model is . The reduced final model had an intermediate Kappa value of 0 . 43 ( SD = 0 . 12; optimum cutoff = 0 . 54 ) . The reduced model improved the sensitivity from 0 . 55 ( SD = 0 . 09 ) to 0 . 71 ( SD = 0 . 08 ) but reduced the specificity from 0 . 91 ( SD = 0 . 06 ) to 0 . 73 ( SD = 0 . 1 ) . The reduced final model correctly classified 72% ( SD = 0 . 06 ) of the data ( AUC = 0 . 75 ( SD = 0 . 07 ) ) ( S1 Fig ) . After calculating BCa confidence intervals for each estimate , only distance to the border of the city ( Border ) and high to medium density vegetation cover ( HMDenVegC ) ended to be explanatory , all positive , of the presence of sandflies in the city ( Table 4 ) . All variables in the abundance model ended to be explanatory , trees around the trap ( Trees ) , distance to the stream and its quadratic ( Stream , Stream2 ) , being the last one the only one with negative coefficient indicating that the maximum abundance was associated to medium values of distance to the stream .
In Santo Tomé , the spatial distribution of dogs infected with L . infantum show a heterogeneous pattern throughout the city . We could not confirm an association of the distribution of infected dogs with the variables assessed . Although both dog’s positivity and vector abundance were found related to microhabitat variables we could not link them in this study . Besides environmental factors related to vector distribution , positive dog′s spatial pattern could be due to social factors , as networks of breeding or selling puppies ( horizontal and vertical transmission ) , transit or traffic within the locality or with other endemic locations [13 , 52] . Indeed , similar results were reported in studies performed in different cities of Brazil , where higher concentrations of VL canine cases incidence were associated just with VL human cases or altitude [53–56] . However , a meta-analysis of the factors associated with canine VL in Brazil reported evidence of statistical association with one environmental variable ( presence of green areas adjacent to the house ) , individual variables such as short hair and pure breed , and individual management variables ( peri-domestic/domestic restricted dogs ) , but the authors also highlighted design and analysis limitations of the reviewed articles [57] . Also , besides the individual determinants and individual dog-management variables , other animal management variables related to attractiveness or dilution effect of blood sources for vectors were associated with dog seropositivity ( positive association with the number of cats in the households , protective presence of chickens and pigs ) [29] . This lack of strong or consistent associations in the literature could be related mainly to: a ) design limitations due to work with: reported cases vs . actual incidence of infection , prevalence of past transmission vs . current environmental variables , individual factors of susceptibility-vulnerability-exposition mixed with environmental variables , dogs with different roaming area; b ) inconsistencies between the spatial scales of dependent and explanatory variables; c ) diagnosis limitations , in our study the majority of the rK39+ dogs were clinically asymptomatic , and it is known the relative low sensitivity of rK39 test in asymptomatic dogs [58 , 59]; and d ) dog management practices , as the dog spatial distribution could be more associated with dog transit and puppies adopting ( social/commercial networks of pets ) than to the actual distribution of the probability of transmission [13 , 52] . The last point is even further important when at higher time-space scales the data from dogs in rural-periurban and urban landscapes are analyzed together . We report Ny . whitmani for the first time in the study area . This species has been incriminated in the cutaneous leishmaniasis outbreaks due to Leishmania braziliensis of the Argentinean northeastern border both by natural infection and environment-time-space association with human cases , though observed abundances in the study area are still far from epidemic risk and this species has usually been associated to primary vegetation in Argentina [60 , 61] . However , it has been related to more urbanized environments in recent studies in the northeastern region [5] . In relation to Lu . longipalpis distribution , the strategy to discriminate the micro-spatial scales at which the environmental variables were recorded allowed us to associate presence with macrohabitat variables , and abundance with microhabitat and macrohabitat variables . The presence of Lu . longipalpis was positively affected by the variables Distance to the city border and High density vegetation cover . As the distance to the city border increased , the probability of Lu . longipalpis presence tend to be higher . The variable High density vegetation cover showed also a positive relation with the vector presence . It can be explained by the generation of enabling environments for the presence of Lu . longipalpis . Though these variables seem to be contradictory , the city under study has a not uniform physiognomy presenting centric areas with high proportion of green surface , offering small breeding and resting conditions for the vector ( Fig 2 ) . The preference of Lu . longipalpis for complex urban environments [62] with green patches ( between ruralized periurban and downtown ) were reported in the literature [5 , 20 , 23 , 25 , 63 , 64] . Further , in cities as Rio de Janeiro , Brazil , Lu . longipalpis was found in Caju Cementery surrounded by highly urbanized blocks [65] . On higher spatial scales it was also observed the association of Lu . longipalpis and its sibling species Lu . cruzi with highly urbanized areas and low NDVI indexes , but with transitional and vegetation-patched landscapes [66–68] . The abundance of Lu . longipalpis showed association with variables at both types of scale . At the microhabitat level , the number of trees around the trap was positively related with the vector accumulated abundance . Trees offer a micro environment where Lu . longipalpis can find appropriate refuge; suitable breeding places [21] by means of physical properties ( trunk structure , shadow size and quality ) ; semiochemicals ( the involved species could also be important ) [23]; and tree coverage ( 100 m buffer ) that showed an association with the abundance of this vector [25] . Other two variables that positively accounted for the differences in the vector’s abundance in the city were distance to the water course and its quadratic , both at the macrohabitat scale . Those areas placed at medium distances , between 470 and 710 m from the water course , showed an association with high abundances of Lu . longipalpis . On the other hand , houses outside this range had lower abundances . This result might indicate that water courses provide an optimum ‘window’ of humidity for the vector reproduction/survival , or for sandlfly predators ( i . e . Scenopinidae larvae [69] ) , or might be also associated with the intermediate environmental heterogeneity between highly urbanized and rural landscapes . Santini et al . [23] found association of Lu . longipalpis abundance in urban scenarios with this variable also at microhabitat scale . On the other hand , in a study that used NDWI ( Normalized Difference Water Index ) and NDVI no correlation was observed with Lu . longipalpis abundance [22] , showing again the importance about the consistency between the spatial scales of the hypothesis-sampling design and the conclusions . Other variables once reported as associated with Lu . longipalpis presence or abundance did not show association in our study . The attractiveness of mammals and birds , mainly chickens , and its capacity to enhance breeding sites ( moisture , manure , shadowed dwellings ) was proposed [17 , 69] , while the presence of chickens , but not its quantity , was associated with the abundance of Lu . longipalpis in urban settings [20] . The hen houses are usually a preferred site , selected by researchers and control programs to locate traps , as it is reported in the Materials and Methods section of many articles about Lu . longipalpis even this; therefore the homogeneity of this variable between trapping points at micro-scale could have masked the results , and the effect at the macrohabitat level was not measured . Low socio-economic level and poor sanitation ( sewage system and rubbish collection ) were associated with VL incidence and these associations were explained by vector suitability [18] , although the facts beneath the increased vector exposition could be indicators of a more complex social determination of the disease distribution . Considering the low temperatures registered during the sampling nights , sites with high trap positivity could reveal stable vector hot spots as the ones described at the city of Posadas in the 2007 and 2009 [20 , 23 , 25 , 64] , while null sandfly traps could also be false negatives . The authors suggested that this stable sites with high abundance of Lu . longipalpis could act as source populations in a metapopulation structure within a ‘city network’ of connected patches . Therefore , to identify the sites in each scale and the variables associated with presence and abundance could contribute to assess the significance of particular habitat patches [70] , with implications in vector control-surveillance integrated strategies [71 , 72] . At microscale , the operational questions to be answered will be for example , which households/areas within the city require specific interventions/recommendations at a given point of time ? In this sense , to develop a model that explains more than 70% of the Lu . longipalpis distribution could contribute to propose environmental management control interventions . From individual practices to county planning ( microhabitat to macrohabitat ) the recommendations on density and species of trees , and potential breeding sites could be assessed experimentally . On the other hand , finding areas more suitable for Lu . longipalpis ( hosting the populations with highest abundances in the village ( distance to stream ) by itself or as surrogate of socio-economic conditions or related practices ( chicken breeding ) ) , may be used to focus the allocation of resources , or to select the sites to evaluate the interventions . In conclusion , discriminating environmental spatial based variables recorded at mesohabitat and microhabitat buffers and modeling Lu . longipalpis presence and abundance as different components , allowed to explain 70% of the vector presence . Based on the variables associated with Lu . longipalpis , the model will be validated in other cities and environmental surveillance and control interventions will be proposed and evaluated in the microscale level . In this sense , programmatic and village strategies integrated with socio-cultural approaches could be incorporated in city , neighborhood and individual environmental management , according to each mesoscale and microscale scenarios , based on participatory action methodologies , so the actual intervention will be defined together with community [73] . | Visceral leishmaniasis in America is caused by an unicellular organism , Leishmania infantum ( syn . chagasi ) that is transmitted by insects belonging to Diptera:Phlebotominae , Lutzomyia longipalpis being the principal vector in urban areas . Therefore , the prevention and control of this vector is a sound objective , so as to reduce the probability of contact human-vector and reducing the probability of infection . Therefore , knowing the variables that have an impact and the spatial scale at which these act will allow us to approach an understanding of the dynamic population of the vector and allow us to develop more appropriate strategies of control . Thus , the aim of this study was to assess a modeling approach to Lu . longipalpis distribution in an urban scenario , discriminating micro-scale landscape variables at microhabitat and macrohabitat scales . For this , we worked in Santo Tomé , Corrientes , Argentina . We observed that the presence of Lu . longipalpis is defined only by the macrohabitat variables tested , but the abundance is defined by variables of both scales , microhabitat and macrohabitat . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Lutzomyia longipalpis Presence and Abundance Distribution at Different Micro-spatial Scales in an Urban Scenario |
The adipocyte-derived protein adiponectin is highly heritable and inversely associated with risk of type 2 diabetes mellitus ( T2D ) and coronary heart disease ( CHD ) . We meta-analyzed 3 genome-wide association studies for circulating adiponectin levels ( n = 8 , 531 ) and sought validation of the lead single nucleotide polymorphisms ( SNPs ) in 5 additional cohorts ( n = 6 , 202 ) . Five SNPs were genome-wide significant in their relationship with adiponectin ( P≤5×10−8 ) . We then tested whether these 5 SNPs were associated with risk of T2D and CHD using a Bonferroni-corrected threshold of P≤0 . 011 to declare statistical significance for these disease associations . SNPs at the adiponectin-encoding ADIPOQ locus demonstrated the strongest associations with adiponectin levels ( P-combined = 9 . 2×10−19 for lead SNP , rs266717 , n = 14 , 733 ) . A novel variant in the ARL15 ( ADP-ribosylation factor-like 15 ) gene was associated with lower circulating levels of adiponectin ( rs4311394-G , P-combined = 2 . 9×10−8 , n = 14 , 733 ) . This same risk allele at ARL15 was also associated with a higher risk of CHD ( odds ratio [OR] = 1 . 12 , P = 8 . 5×10−6 , n = 22 , 421 ) more nominally , an increased risk of T2D ( OR = 1 . 11 , P = 3 . 2×10−3 , n = 10 , 128 ) , and several metabolic traits . Expression studies in humans indicated that ARL15 is well-expressed in skeletal muscle . These findings identify a novel protein , ARL15 , which influences circulating adiponectin levels and may impact upon CHD risk .
Adiponectin is an adipocyte-secreted protein that increases insulin sensitivity [1] , [2] , [3] , and has anti-diabetic [4] , [5] , [6] and anti-atherogenic effects [7] . Several features render adiponectin an attractive and tractable biomarker for large epidemiologic studies , such as its long half-life , high ex vivo stability , and minimal diurnal variability [8] , [9] . While adiponectin levels are highly heritable ( 30–70% ) [10] , [11] , [12] , several well-designed studies have shown variable association between common polymorphisms in the adiponectin gene ( ADIPOQ ) , possibly due to small sample sizes and different panels of single nucleotide polymorphisms ( SNPs ) , ethnicities and clinical outcomes [12] , [13] , [14] . This has lead some observers to call for a more complete and systematic characterization of the genetic determinants of adiponectin levels [12] . Our study therefore sought to address 2 questions: first , what are the common genetic determinants of adiponectin levels both at ADIPOQ and elsewhere ? And second , do the variants robustly associated with adiponectin levels influence metabolic traits and risk of metabolic disease ? To comprehensively assess the influence of common genetic variation on circulating adiponectin levels , we undertook a large-scale meta-analysis of 3 genome-wide association studies ( GWAS ) for circulating adiponectin levels from population-based cohorts ( n = 8 , 531 participants ) . From this first stage , we chose SNPs most strongly associated with adiponectin levels ( P<10−4 , n = 250 ) , and tested these for their association with adiponectin in 5 additional population-based cohorts ( n = 6 , 202 ) . The 5 SNPs which achieved genome-wide significance in the combined stage were then tested for their association with: type 2 diabetes mellitus ( T2D ) in the Diabetes Genetics Replication And Meta-analysis ( DIAGRAM ) consortium [15] ( n = 10 , 128 ) ; indices of insulin resistance in the Meta-Analysis of Glucose and Insulin-related traits Consortium ( MAGIC ) [16] ( n = 24 , 188 ) ; risk of coronary heart disease ( CHD ) in a consortium of 8 cohorts with available genome-wide association data ( n = 22 , 421 ) ; and body mass index ( BMI ) in the Genetic Investigation of Anthropometric Traits ( GIANT ) consortium ( Text S1 ) [17] , [18] ( n = 32 , 527 ) ( Figure 1 ) .
To identify genetic variants influencing adiponectin levels , we performed a GWAS utilizing information from population-based cohorts including , in total , 14 , 733 subjects of European descent ( Table 1 ) . We identified 5 variants at 2 loci that achieved genome-wide significance ( P≤5×10−8 ) for their relationship with circulating adiponectin levels ( Table 2 ) . The SNP most strongly associated with circulating adiponectin levels lies 30 kb upstream of the ADIPOQ locus ( rs266717; P-combined = 9 . 2×10−19 ) ( Table 2 , Figure S1 , Figure S2 ) . In total , 4 SNPs at the ADIPOQ locus demonstrated genome-wide significant associations with circulating adiponectin . All 8 studies contributed to these genome-wide significant associations , with the exception of rs6444175 , which demonstrated some heterogeneity across cohorts ( Table 2 ) . Our results also identified a novel intronic SNP ( rs4311394 ) located in the ARL15 ( ADP-ribosylation factor-like 15 ) gene whose G allele was robustly associated with decreased adiponectin levels ( P = 2 . 9×10−8 ) ( Table 2 , Table S3 , Figure 2 ) . ARL15 is an ADP-ribosylation factor-like GTP-binding protein , whose function is unknown , yet belongs to a family of proteins involved in intracellular vesicle trafficking [19] . Since glycemia , T2D and CHD have been correlated with adiponectin levels , we tested whether genome-wide significant SNPs for adiponectin levels were associated with glycemia , indices of insulin resistance , and risk of T2D and CHD . Since 5 SNPs ( which , due to linkage disequilibrium , represented 4 . 59 independent statistical tests [see Methods] ) were tested for their association with T2D , CHD and metabolic traits , we employed a conservative Bonferroni-corrected threshold of α = 0 . 011 ( where 0 . 011 = 0 . 05/4 . 59 ) to declare statistical significance for these metabolic diseases and traits . None of the SNPs at the ADIPOQ locus demonstrated a robust relationship with T2D , CHD , homeostasis model assessment insulin resistance ( HOMA-IR ) , homeostasis model assessment beta-cell function ( HOMA-B ) or BMI ( Table 3 , Table 4 , Table S4 ) . However rs1648707 , at ADIPOQ , was associated with a non-statistically significant trend for its relationship with CHD ( P = 0 . 04 ) and T2D ( P = 0 . 046 ) . In contrast , the risk allele rs4311394-G at ARL15 , which was associated with lower adiponectin levels , was also associated with: an increased risk of CHD in a consortium of 7 CHD cohorts ( Odds ratio [OR] = 1 . 12 , [95% Confidence Interval [CI]: 1 . 06 , 1 . 17] , P = 8 . 5×10−6 , n = 22 , 421 ) ; an increased risk of T2D in the DIAGRAM consortium [15] ( OR = 1 . 11 [95% CI: 1 . 03 , 1 . 18] , P = 3 . 2×10−3 , n = 10 , 128 ) ; and higher glycated hemoglobin in the European Prospective Investigation of Cancer-Norfolk ( EPIC-Norfolk ) cohort ( 0 . 025% per G allele [95% CI: 0 . 01 , 0 . 04] , P = 5 . 0×10−4 , n = 14 , 168 ) ( Table 3 ) . In the MAGIC consortium [16] , the rs4311394-G allele was associated with increased levels of fasting insulin ( P = 2 . 3×10−3 , n = 24 , 614 ) , and demonstrated non-significant trends towards higher HOMA-IR ( P = 0 . 01 , n = 24 , 188 ) and HOMA-B ( P = 0 . 02 , n = 24 , 130 ) ( Table 4 ) . In the GIANT consortium [17] , the same allele demonstrated a modest and non-significant association with decreased BMI ( P = 0 . 016 , n = 32 , 527 ) ( Table S4 ) , indicating that the disease and metabolic trait associations of rs4311394-G are unlikely to be mediated through an increase in BMI . Thus , in sum , the G allele at rs4311394 was consistently associated with an increased risk of T2D and CHD , as well as deleterious changes in the 5 metabolic traits tested . Since the function and distribution of ARL15 expression is unknown , we assessed the level of ARL15 mRNA expression in human tissues using quantitative real-time PCR across a wide set of human tissues . We identified that ARL15 was expressed most abundantly in skeletal muscle at a level 4-fold that of the mean of all other tissues , with adipose expression detectable but low ( Figure 3 ) . Using biopsied tissue from insulin-sensitive tissues ( liver , skeletal muscle and adipose tissue ) in healthy volunteers , immunoblots confirmed ARL15 expression in skeletal muscle , although it was detectable in all 3 tissues ( Figure 4 ) .
By conducting a GWAS for the adipocyte-derived protein adiponectin , we have identified a novel susceptibility variant in ARL15 , which is associated with lower adiponectin levels and increased risk of T2D and CHD . Our results also help clarify which variants at ADIPOQ influence adiponectin levels , thus expanding our understanding of the adiponectin pathway . ARL15 is widely expressed [20] . However its function is unknown , and there have been no phenotypes previously associated with this gene . Based on its predicted protein sequence , ARL15 is structurally similar to ADP-ribosylation factors and Ras-related GTP-binding proteins which play key roles in the regulation of intracellular vesicle trafficking [19] , and which have been specifically implicated in insulin signaling and insulin-stimulated glucose transport [21] , [22] , [23] , [24] . Our preliminary data demonstrate that ARL15 is expressed in insulin-responsive tissues , including adipose tissue . Interestingly , expression was highest in skeletal muscle , which is the main site of insulin-mediated glucose disposal , but which does not synthesize adiponectin . Thus , ARL15 is a good candidate to be involved in cellular insulin resistance and/or adiponectin trafficking and secretion . Its implication in metabolic diseases by a non-hypothesis-based genetic approach provides strong impetus for further functional studies . Our study sheds further light on the role of ADIPOQ SNPs on adiponectin levels — which has been the source of several inconsistent reports [12] , [13] , [14] , [25] — since we have systematically tested all common HapMap CEPH ( Centre d'Étude du Polymorphisme Humain ) SNPs through genotyping and imputation across the ADIPOQ locus in 14 , 733 individuals ( Figure S2 ) . Among the SNPs previously associated with adiponectin levels at ADIPOQ , the rs1648707 SNP achieved genome-wide significance in our analysis for adiponectin . rs1648707 is in moderate linkage disequilibrium with rs266729 ( r2 = 0 . 74 ) , which has previously been associated with adiponectin levels , but not consistently with T2D [12] . We did not assess rare variants , and were thus unable to test the association of rs17366743 ( minor allele frequency = 0 . 075 ) with adiponectin levels , which has been previously associated with T2D and with fasting glucose , but not with adiponectin levels [13] . Interestingly , ADIPOQ SNPs that showed genome-wide significant associations with adiponectin levels did not show associations with T2D or CHD . This raises the question of how ARL15 interacts with adiponectin to influence disease risk . The demonstrated relationship of ARL15 with the metabolic traits and diseases may represent adiponectin-independent effects of ARL15 — a hypothesis that could be tested by adjusting the relationship between ARL15 and CHD or T2D for adiponectin levels ( which was not possible in this study , as the disease cohorts had no measured adiponectin levels ) . Alternatively , recent evidence suggests that adiponectin may be influenced directly by insulin exposure [26]–[35] , allowing adiponectin to act as a surrogate marker for integrated total insulin exposure as a result of its stable half-life and relatively low diurnal variability . Consequently , ARL15 may be an upstream mediator of the relationship between insulin and adiponectin , and may thus impact upon T2D and CHD through an insulin-dependent pathway which involves , but is not entirely dependent upon , adiponectin . In addition , since we demonstrated that the ARL15 variant was associated with adiponectin levels across all age ranges , including children in the Avon Longitudinal Study of Parents and Children ( ALSPAC ) cohort , this variant likely affects lifelong adiponectin levels , which may influence its relationship with T2D and CHD . In conclusion , this study expands our understanding of the genetic influences on adiponectin levels . We have implicated a novel locus , ARL15 , in the regulation of adiponectin levels and clarified the role of variants near ADIPOQ on adiponectin levels . Finally , we provide further evidence that the variant at ARL15 may influence risk of T2D and CHD , thus providing impetus for further study of ARL15 .
All studies including biopsy of liver , skeletal muscle or adipose tissue from healthy volunteers for immunoblotting studies were approved by institutional ethics review committees at the relevant organizations . All participants provided informed written consent . The first stage of the GWAS for adiponectin levels was performed in 3 population-based cohorts utilizing subjects of self-described European ancestry , which were not selected for diabetes , heart disease or any metabolic trait ( Table 1 ) . The discovery cohorts included CoLaus [36] , TwinsUK [37] , [38] , and Genetic Etiology of Metabolic Syndrome ( GEMS ) [39] . Participants of the CoLaus study were individuals of European ancestry , randomly selected from 56 , 694 permanent residents of Lausanne , Switzerland , between the ages of 35 and 75 years . Recruitment took place between April 2003 and March 2006 . TwinsUK is a population-based sample of British twins , which is representative of the general United Kingdom population , and is extensively phenotyped for aging-related traits [40] . GEMS is a case-control study of dyslipidemic individuals between the ages of 20 and 65 years . Cases and controls were matched based on gender and recruitment site . The GEMS and CoLaus studies were sponsored in part by GlaxoSmithKline . All participants were informed of this sponsorship , and consented for the use of their data and biologic samples by GlaxoSmithKline and its subsidiaries . The validation cohorts included the Framingham Offspring Study ( FOS ) [13] , Baltimore Longitudinal Study of Aging ( BLSA ) [41] , InCHIANTI [42] , [43] , ALSPAC [44] and EPIC-Norfolk [45] . The FOS is a population-based sample of residents of Framingham , Massachusetts . Adiponectin was measured at exam 7 ( 1998–2002 ) . BLSA is an observational study that began in 1958 to study normative aging in a cohort of healthy persons 17 years of age and older at study entry . InCHIANTI is a population-based cohort designed to study aging-related traits and disease from the Chianti geographic region ( Tuscany , Italy ) . ALSPAC is a population-based birth cohort study consisting initially of over 13 , 000 women and their children recruited in the county of Avon , UK , in the early 1990s . The EPIC-Norfolk cohort is a British population-based study of white persons recruited from Norfolk , UK , between 1993 and 1997 . All individuals in all replication cohorts were of self-described European descent . Only the SNPs which achieved genome-wide significance for adiponectin levels in the combined analysis of data from all 8 cohorts were assessed for their relationship with adiposity-driven diseases and traits , which included: T2D , CHD , fasting glucose , glycated hemoglobin , BMI and insulin , as well as measures of insulin resistance ( HOMA-IR ) and beta-cell function ( HOMA-B ) estimated by the homeostasis model [46] . T2D risk was estimated from the DIAGRAM consortium ( a meta-analysis of 3 T2D genome-wide association scans [http://www . well . ox . ac . uk/DIAGRAM/] , which included 4 , 107 T2D cases and 5 , 187 controls ) . The 3 populations were the Wellcome Trust Case Control Consortium ( WTCCC ) , the Finland-United States Investigation of NIDDM [Non-Insulin-Dependent Diabetes Mellitus] Genetics ( FUSION ) , and the Diabetes Genetics Initiative ( DGI ) . A full description of this meta-analysis is available elsewhere [15] , [47] . The association between susceptibility alleles and fasting glucose , insulin and measures of insulin resistance and beta-cell function were tested in MAGIC [16] . This consortium includes data from 36 , 610 individuals of European descent who were included in 4 distinct consortia: [a] The European Network for Genetic and Genomic Epidemiology ( ENGAGE ) project , combining data from deCODE , Northern Finland Birth Cohort 1966 , Netherlands Twins Register/Netherlands Study of Depression and Anxiety and the Rotterdam study; [b] the GEMS study , which includes data from the CoLaus and TwinsUK scans; [c] DFS , which includes the DGI , FUSION and SardiNIA scans; and [d] the Framingham Heart Study . Details of all of these studies , phenotyping and genotyping protocols have been published previously [16] . The association between susceptibility alleles and CHD was tested in 8 cohorts ( n = 22 , 421 ) . These cohorts included PennCath [48] , MedStar , the Ottawa Heart Study [49] , the WTCCC coronary heart disease ( CAD ) study [50] , [51] , a case-control study of CHD nested in the EPIC-Norfolk cohort comprising participants with available genome-wide data [52] , German Myocardial Infarction Family Study ( GerMIFS ) I and GerMIFS II [50] , [53] , and the Rotterdam Study [54] ( Table S2 ) . The rs4311394 SNP was assessed by imputation in the GerMIFS I cohort , and did not meet quality control criteria . Thus , results for this SNP are reported for all cohorts except GerMIFS I ( Figure S3 ) . All other SNPs were assessed in all cohorts . Associations with BMI were tested in the GIANT consortium [17] , [18] , which encompasses 15 cohorts of 32 , 527 individuals of European descent . It has been described in detail previously , including information on genotyping and phenotyping [17] . Table S1 outlines the genotyping methods used for each cohort , individual and SNP exclusion thresholds , and imputation algorithms . For the CoLaus and GEMS studies , genotypes were obtained using the Affymetrix Genechip Human Mapping 500k array with the Bayesian Robust Linear Modeling using Mahalanobis distance ( BRLMM ) algorithm [52] . The TwinsUK samples were genotyped using the Illumina calling algorithm on the Illumina HumanHap300 , HumanCNV370 Duo and HumanHap 550 [40] . The FOS employed the Affymetrix 500k and MIPS 50k genotyping arrays . Both the BLSA and InCHIANTI cohorts used the Illumina Human Hap 550 genotyping arrays , while the Illumina Human Hap300 array was used in the ALSPAC cohort . Targeted genotyping was performed in the EPIC-Norfolk cohort using TaqMan SNP genotyping assay ( Applied Biosystems , Warrington , UK ) according to the manufacturer's protocol . Genotype frequencies were in Hardy Weinberg Equilibrium ( HWE ) ( P>0 . 50 ) , call rates were >94% and concordances were >98% for the TaqMan assay . The TwinsUK and EPIC-Norfolk cohorts measured adiponectin levels with an in-house 2-site enzyme-linked immunosorbent assay ( ELISA ) using antibodies and standards from R&D Systems Europe ( Abingdon , Oxford , UK ) in plasma . The day-to-day coefficients of variation ( CV ) for adiponectin were 5 . 4% , 5 . 2% , and 5 . 8% at a concentration of 3 . 6 µg/ml , 9 . 2 µg/ml , and 15 . 5 µg/ml , respectively [38] . The FOS , CoLaus and GEMS measured adiponectin using the ELISA assay ( R&D Systems , Minneapolis , Minnesota , United States of America; Intra-assay CV: 5 . 8% ) [13] . Importantly , while CoLaus and GEMs measured adiponectin in plasma , the FOS measured adiponectin in serum . The ALSPAC cohort measured adiponectin using a commercially available ELISA kit ( R&D systems , Oxon , UK ) previously validated against the corresponding radio-immunoassay ( RIA ) . The inter-assay CV for this adiponectin assay was <7 . 5% . The InCHIANTI and BLSA studies measured adiponectin levels using the adiponectin RIA assay of Linco Research ( St . Charles , Missouri , USA ) . The detectable ranges for the RIA assay used in InCHIANTI and BLSA are 0 . 78 µg/ml–200 µg/ml . Relative levels of ARL15 mRNA in human tissues were assessed by quantitative real-time PCR of a commercially available human tissue panel of RNA ( AMS Biotechnology , Abingdon , UK ) . 500 ng of RNA were reverse-transcribed using 125 ng of random hexamers and 500 µM deoxynucleotide triphosphates ( dNTPs ) ( both from Promega , Madison , Wisconsin , USA ) and 500 ng of Superscript III reverse transcriptase ( Invitrogen ) . Gene expression was quantified on an ABI7900 Real-Time PCR system ( Applied Biosystems , Foster City , California , USA ) in TaqMan Mastermix ( Applied Biosystems ) . Primers and probe for ARL15 were supplied by Applied Biosystems ( ABI Hs00219491_m1 ) , and ARL15 expression was normalized to expression of PPIA ( Cyclophilin A ) . PPIA primers ( 5′-ACGGCGAGCCCTTGG-3′ ( sense ) , 5′- TTTCTGCTGTCTTTGGGACCT-3′ ( antisense ) ) and probe ( 5′-[FAM] CGCGTCTCCTTTGAGCTGTTTGCA[TAMRA]-3′ ) were synthesized by Sigma-Aldrich . Skeletal muscle biopsies were a gift from Dr Anna Krook , from the Karolinska Institute . Frozen skeletal muscle , liver and white adipose tissue samples were homogenized in lysis buffer ( 50 mM Tris-HCl , pH8 . 0 , 150 mM NaCl , 1 mM EDTA , 1% ( v/v ) Triton X-100 , and Complete Protease Inhibitor Cocktail [Roche] ) , and cell debris removed by centrifugation . Cleared supernatants were boiled in sodium dodecyl sulphate ( SDS ) sample buffer and run on an SDS polyacrylamide gel before transfer to a polyvinylidene difluoride ( PVDF ) membrane ( Amersham ) and subsequent immunoblotting with either purified rabbit anti-human ARL15 antibody ( Proteintech Group ) or anti-α-tubulin antibody ( sc-8035; Santa Cruz Biotechnology ) . Full-length human wild type ARL15 cDNA was purchased from Open Biosystems and subcloned into pCDNA 3 . 1 ( Invitrogen ) using the XhoI and HindIII restriction sites . HEK293 cells ( American Type Culture Collection [ATCC] ) were transiently transfected using the CalPhos Mammalian Tranfection Kit ( Clontech ) according to the manufacturer's instructions . In all cohorts , the adiponectin concentrations were natural logarithm transformed to create a normally distributed phenotype . Adiponectin levels were subsequently adjusted for age , sex and BMI — important correlates of adiponectin levels [4] , [5] . All results reported for association of genetic variants with adiponectin levels are adjusted for age , sex and BMI . All statistical tests assumed an additive effect of the effect allele . In the TwinsUK cohort , we found that there was little difference when comparing results both adjusted , and unadjusted , for BMI ( the Spearman coefficients for the beta coefficients was 0 . 94 and 1 . 0 for P-values [P-values for both Spearman coefficients<1×10−5] ) . The SNPTEST software program [51] was used to perform genome-wide association testing in the GEMS and CoLaus cohorts , while the Merlin software package [55] was used to perform association testing in the TwinsUK cohort . The meta-analysis of the discovery phase cohorts ( CoLaus , TwinsUK and GEMS ) was performed using Liptak-Stouffer's method for combination of independent tests , where P-values are converted to Z-scores by a standard normal curve and weighted by each study's sample size [56] . All SNPs that achieved a combined P-value of ≤10−4 in the meta-analysis ( n = 250 ) were tested for their association in the additional cohorts ( InCHIANTI , BLSA , ALSPAC and the Framingham Offspring Cohort ) . Two SNPs that were not near the ADIPOQ locus , and which demonstrated associations of ≤5×10−7 with adiponectin levels in the combined analysis , were further verified in an additional replication cohort ( EPIC-Norfolk ) , where association with adiponectin was tested using a generalized linear model . For the quantitative trait analyses , individuals with known T2D were excluded . For the T2D case-control analyses , each SNP was tested for association using a logistic regression analysis , adjusted for age , sex and BMI . All analyses for the EPIC-Norfolk cohort were performed with SAS 9 . 1 ( SAS Institute Inc . , Cary , North Carolina , USA ) . To perform a meta-analysis of all replication and discovery cohorts , we employed inverse-variance techniques in the STATA software package ( College Station , Texas , USA ) . We declared statistical significance in the GWAS as P≤5×10−8 , where this threshold is based on a Bonferroni correction of α = 0 . 05 divided by one million , the estimated number of independent common tests among common SNPs in the CEU population of the HapMap II project [57] . Using this threshold , 5 SNPs achieved genome-wide significance for their relationship with circulating adiponectin levels in the combined analysis of all adiponectin cohorts . These were subsequently tested for their association with glycated hemoglobin , indices of insulin resistance , beta-cell function and risk of T2D and CHD . The number of independent statistical tests represented by these 5 SNPs , accounting for linkage disequilibrium at ADIPOQ , was assessed by spectral decomposition of matrices of pairwise linkage disequilibrium between the 4 SNPs at the ADIPOQ locus [58] . In total , 3 . 59 independent statistical tests were performed at this locus , and one at the ARL15 locus . Thus , statistical significance in the follow-up studies was declared at P≤0 . 011 ( based on a Bonferroni correction of α = 0 . 05 divided by 4 . 59 , the number of statistically independent SNPs tested in the follow-up analyses ) . Since 2 cohorts measured adiponectin concentrations using an RIA method ( BLSA and InCHIANTI ) whilst all others used an ELISA method , and since one study , ALSPAC , was based on children , rather than adults , we tested for evidence of heterogeneity in the combined analysis using the Q-test P-value [59] . | Through a meta-analysis of genome-wide association studies of 14 , 733 individuals , we identified common base-pair variants in the genome which influence circulating adiponectin levels . Since adiponectin is an adipocyte-derived circulating protein which has been inversely associated with risk of obesity-related diseases such as type 2 diabetes ( T2D ) and coronary heart disease ( CHD ) , we next sought to understand if the identified variants influencing adiponectin levels also influence risk of T2D , CHD , and several metabolic traits . In addition to confirming that variation at the ADIPOQ locus influences adiponectin levels , our analyses point to a variant in the ARL15 ( ADP-ribosylation factor-like 15 ) locus which decreases adiponectin levels and increases risk of CHD and T2D . Further , this same variant was associated with increased fasting insulin levels and glycated hemoglobin . While the function of ARL15 is not known , we provide insight into the tissue specificity of ARL15 expression . These results thus provide novel insights into the physiology of the adiponectin pathway and obesity-related diseases . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"cardiovascular",
"disorders/congenital",
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"diabetes"
] | 2009 | A Genome-Wide Association Study Reveals Variants in ARL15 that Influence Adiponectin Levels |
In many animal species , the sperm DNA is packaged with male germ line–specific chromosomal proteins , including protamines . At fertilization , these non-histone proteins are removed from the decondensing sperm nucleus and replaced with maternally provided histones to form the DNA replication competent male pronucleus . By studying a point mutant allele of the Drosophila Hira gene , we previously showed that HIRA , a conserved replication-independent chromatin assembly factor , was essential for the assembly of paternal chromatin at fertilization . HIRA permits the specific assembly of nucleosomes containing the histone H3 . 3 variant on the decondensing male pronucleus . We report here the analysis of a new mutant allele of Drosophila Hira that was generated by homologous recombination . Surprisingly , phenotypic analysis of this loss of function allele revealed that the only essential function of HIRA is the assembly of paternal chromatin during male pronucleus formation . This HIRA-dependent assembly of H3 . 3 nucleosomes on paternal DNA does not require the histone chaperone ASF1 . Moreover , analysis of this mutant established that protamines are correctly removed at fertilization in the absence of HIRA , thus demonstrating that protamine removal and histone deposition are two functionally distinct processes . Finally , we showed that H3 . 3 deposition is apparently not affected in Hira mutant embryos and adults , suggesting that different chromatin assembly machineries could deposit this histone variant .
The assembly of nucleosome particles on nuclear DNA is the initial step for the formation of chromatin . Nucleosome assembly initiates with the formation of a H3-H4 histone tetramer on DNA followed by the addition of two H2A-H2B dimers to form the octameric particle [1 , 2] . Although this organisation of genomic DNA is remarkably conserved in eukaryotes , sperm cells of many species are characterized by a very different type of chromatin architecture involving non-histone proteins such as protamines [3] . The replacement of histones with protamines or other sperm nuclear basic proteins ( SNBPs ) during the differentiation of post-meiotic spermatids is generally associated with a high level of nuclear condensation , a general shutdown of transcriptional activity , and a state of chromatin that is incompatible with DNA replication [3–5] . Although the precise advantages of acquiring a specialized type of chromatin for the sperm cell are poorly known , the protamine type of chromatin could protect the paternal DNA from damaging agents or allow the resetting of epigenetic marks carried by histones [6–8] . In any case , once entered in the egg cytoplasm , the fertilizing sperm nucleus must replace its SNBPs with maternally provided histones that are stored in the egg cytoplasm . This process , called sperm chromatin remodelling ( SCR ) , allows the paternal DNA to recover a nucleosomal chromatin and thus guarantees the ability of the male pronucleus to replicate its DNA in coordination with its female counterpart [3–5] . SCR can be separated into two key processes . The first process is the removal of SNBPs from the paternal DNA once the sperm nucleus is released in the egg cytoplasm . The second is the assembly of nucleosomes from maternal components before the first round of DNA replication . SCR has been almost exclusively studied in animal models that produce large quantities of eggs , such as amphibians or sea urchins , thereby facilitating the biochemical characterization of factors capable of remodelling sperm nuclei in vitro [3] . Drosophila embryonic extracts have also been used as a source of sperm chromatin decondensation factors [9–12] , but none of the identified molecules has been demonstrated so far to have a function in SCR in vivo . In Drosophila , the sperm DNA is packaged with two protamines , whereas core histones are not detectable in male gamete nuclei [13 , 14] . In this sense , Drosophila represents a good model for the functional study of SCR in vivo . In previous publications , we characterized sésame ( ssm ) , a Drosophila maternal effect mutation that specifically prevented male pronucleus formation [15] and SCR [16] . This mutation was subsequently shown to cause a single amino acid substitution ( R225K ) in the Hira gene [17] . HIRA is a conserved chromatin assembly factor that allows the replication-independent ( RI ) deposition of core histones on DNA , in contrast to the CAF-1 complex whose replication-coupled ( RC ) nucleosome assembly activity is strictly linked to DNA synthesis [18] . Accordingly , it has been established in vitro that HIRA specifically deposits H3-H4 dimers that contain the histone H3 variant H3 . 3 , which is expressed throughout the cell cycle , whereas CAF-1 deposits H3-H4 dimers that contain the replicative histone H3 . 1 [19] . Our functional analysis of the Drosophila Hira gene allowed us to demonstrate in vivo that HIRA was indeed involved in the RI deposition of H3 . 3 [17] . In addition , we observed that maternal HIRA localized in the decondensing sperm nucleus where it deposited H3 . 3-H4 histones before the first zygotic S phase , thus establishing the essential role of HIRA in SCR . Recently , the Hirassm allele was found to enhance the variegation of a white reporter transgene , indicating that HIRA could help counteract the spread of heterochromatin by mediating histone replacement at specific sites [20] . However , because of the subtle nature of the Hirassm mutation and the absence of obvious phenotype in mutant adults , it was not clear whether HIRA could have important functions during development or in adult flies . In this paper , we report the characterization of a loss of function Hira allele that we have generated by homologous recombination . Surprisingly , we show that paternal chromatin assembly at fertilization is the only developmental process that absolutely requires HIRA . We also demonstrate that protamine removal does not depend on HIRA and is thus functionally distinct from the paternal nucleosome assembly process . Finally , we show that H3 . 3 is deposited in the chromatin of mutant embryos and adults , suggesting that other factors are implicated in the assembly of H3 . 3 nucleosomes .
The original ssm185b allele ( referred to as Hirassm ) is a point mutation that replaces an evolutionary conserved arginine with a lysine ( R225K ) in the N-terminus region of HIRA [17] . This region is characterized in all HIRA proteins by the presence of a well-conserved domain containing seven WD-repeats . WD-repeats assemble into a structure called beta-propeller [21] . The Hirassm mutation does not affect the normal recruitment of HIRA in the male nucleus at fertilization [17] . Nevertheless , it completely prevents the deposition of histones on paternal DNA [16 , 17] , suggesting that the beta-propeller domain is important for the nucleosome assembly activity of HIRA . To gain insight into other possible functions of Hira not evident from the subtle Hirassm mutation , we generated a new mutant allele using ends-out homologous recombination [22] . The targeting construct was designed to delete a 319 bp DNA fragment encompassing the complete predicted 5′ UTR , the first exon , the first intron , and the 5′ part of the second exon of Hira . In addition , the recombination arms used in this construct did not overlap any other predicted coding sequence , thus minimizing the risk of damaging adjacent genes . Finally , in the recombined allele , the 319 bp deletion was replaced with a 4778 bp sequence from the pW25 vector [23] , containing the white marker gene flanked with stop codons in the six reading frames ( Figure 1A ) . We recovered 59 independent recombination events on the X chromosome that did not complement the 100% female sterility associated with the Hirassm mutation ( Table 1 ) . Surprisingly , all these lines produced viable and fertile mutant males . In all the lines that were further examined ( n = 7 ) , homozygous mutant females were also viable but produced embryos that never hatched ( unpublished data ) . One line , named HiraHR1 ( homologous recombination 1 ) , was arbitrarily chosen to conduct the rest of the analysis . The nature of the molecular lesion at the HiraHR1 locus was verified by PCR analysis and sequencing of genomic DNA , and the expected recombination event was found , with no other detectable alteration ( Figure 1B and unpublished data ) . We verified that the maternal effect phenotype associated with HiraHR1 remained unchanged in hemizygous HiraHR1/Df ( 1 ) ct4b1 females , Df ( 1 ) ct4b1 being a large X chromosome deficiency that covers the Hira region [15] . In addition , the HiraHR1 phenotype was fully rescued by a single copy of a wild-type Hira transgene [17] , demonstrating that no other important gene was affected by the HiraHR1 recombination event ( unpublished data ) . The HiraHR1 mutation was expected to destroy the normal transcriptional regulation of Hira . However , transcriptional activity was detected by RT-PCR analysis at the junction between the pW25 vector and the beginning of the Hira sequence ( unpublished data ) , suggesting that the HiraHR1 allele could be transcribed from the hsp70 promoter associated with the whs marker gene or from another promoter in or upstream from the pW25 vector . To check for the translation of any truncated HIRA protein from the HiraHR1 allele , we first established transgenic lines containing a pW25-HiraHR1-Flag transgene ( Figure 1A ) . This construct is identical to the donor transgene used for the homologous recombination with the exception of a 3X-Flag tag fused in frame to the C-terminus of HIRA . RT-PCR analysis of two independent pW25-HiraHR1-Flag lines confirmed that the Hira sequence in these transgenes is also transcribed ( unpublished data ) . However , western-blot analysis of embryo extracts from both lines did not detect any HIRA-FLAG protein ( Figure 1C ) . We then directly tested the presence of HIRA in eggs from HiraHR1 females using two independent HIRA polyclonal antibodies . The first antibody was raised against a mix of two synthetic HIRA oligopeptides [17] whose cognate DNA coding sequences are intact in the HiraHR1 allele . The second antibody was raised against a recombinant protein containing residues 381–935 of HIRA ( see Methods ) . Both sera readily detect maternal HIRA in wild-type and Hirassm fixed eggs , as the protein specifically accumulates in the male pronucleus ( Figure 2A , 2C , and 2D ) . As reported before [17] , at the pronuclear apposition stage in Hirassm eggs , the male pronucleus appeared much more condensed and smaller than the female pronucleus and brightly stained with anti-HIRA antibodies ( Figure 2D ) . In HiraHR1 eggs at the same stage , the male pronucleus looked identical to that in Hirassm eggs , but did not contain any detectable HIRA protein ( Figure 2B and 2E ) . Considering the fact that maternal HIRA protein is immediately available at fertilization to assemble paternal chromatin , we speculated that the protein must accumulate in growing oocytes during oogenesis . Indeed , wild-type ovaries stained with anti-HIRA antibodies revealed a specific signal in the oocyte nucleus ( also called germinal vesicle ) that was well visible from stage 10 of egg chamber formation ( Figure 3A ) . The same staining of the oocyte nucleus was obtained with transgenic Hira-Flag ovaries stained with anti-FLAG antibodies ( Figure 3C ) . Strikingly , the germinal vesicle staining was absent in HiraHR1 ovaries and HiraHR1-Flag ovaries stained with anti-HIRA or anti-FLAG antibodies , respectively ( Figure 3B and 3D ) . Altogether , these results strongly support the hypothesis that no HIRA protein is produced from the HiraHR1 mutant allele . Previous studies of the Hirassm allele had revealed that the male nucleus in mutant eggs was unable to undergo SCR [16] . Despite the fact that the mutant HIRA protein normally accumulates in the male nucleus in Hirassm eggs ( [17] and Figure 2D ) , it is unable to assemble chromatin . Consequently , the male nucleus does not achieve its decondensation and does not replicate its DNA . At the cytological level , fertilized eggs from HiraHR1 females appeared phenotypically identical to Hirassm eggs . In all cases observed ( n > 100 ) , the male pronucleus remained abnormally small and condensed after pronuclear apposition ( Figure 2E ) and was unable to participate in the formation of the zygote ( see Figure 4 ) . As a consequence of this early defect , embryos from HiraHR1 females were haploid , with only the maternal chromosome set . To check for any RI nucleosome assembly in HiraHR1 eggs , we used an anti-acetylated histone H4 antibody that brightly and specifically stains the decondensing male nucleus in wild-type eggs [17] . As expected , the massive RI nucleosome assembly that normally occurs during male pronucleus formation was not detected in HiraHR1 eggs ( Figure 4A and 4B ) . In contrast , RC deposition of acetylated H4 was normally detected in maternal nuclei ( Figure 4C ) . Thus both Hirassm and HiraHR1 mutant alleles specifically prevent assembly of paternal chromatin and do not affect maternal nuclei . In Drosophila , during spermiogenesis , post-meiotic spermatid nuclei progressively elongate and condense to eventually reach the typical needle-shape of mature sperm nuclei [24] . This complex process is also characterized by the replacement of histones with SNBPs , including two closely related protamines , ProtA and ProtB [13 , 14] . At fertilization , protamines are removed from the paternal chromatin , and nucleosomes are assembled in an RI process before the onset of the first zygotic S phase . The incapacity of the male nucleus to form in Hirassm eggs led us to hypothesize that this phenotype could result from a defect in protamine removal [16] . Indeed , we would expect the persistence of protamines on paternal DNA to prevent nucleosome assembly and male nucleus decondensation . However , the presence of the HIRA protein in the male nucleus in Hirassm eggs precluded drawing any conclusion about its role in protamine removal [13] . In contrast , the HiraHR1 allele allowed us to address this point because in this case the protein is absent from the male nucleus . To document the dynamics of protamine removal at fertilization , we used transgenic males expressing ProtA-GFP or ProtB-GFP in their germ line [13] . These males are fertile and their testes contain groups of spermatid nuclei that achieve maximum fluorescence toward the end of the condensation process ( Figure 5A , left panel ) . To verify that protamine-GFP can be detected in eggs , we crossed wild-type females with ProtA-GFP males homozygous for sneaky ( snky ) , a paternal effect mutation that prevents sperm plasma membrane breakdown at fertilization and sperm activation [25] . We found that fertilizing sperm nuclei from ProtA-GFP ; snky males were brightly fluorescent in all cases observed ( Figure 5B ) . We then looked at wild-type and HiraHR1 eggs fertilized with ProtA-GFP or ProtB-GFP sperm . Even in the earliest eggs we observed , we never detected any trace of Prot-GFP in the decondensing male nucleus ( Figure 5C and 5D ) . We thus concluded that the removal of protamines from the fertilizing sperm nucleus is a fugacious , HIRA-independent process that must occur immediately after sperm plasma membrane breakdown and before the onset of the second meiotic division . In Drosophila , mature oocytes are arrested in metaphase of the first meiotic division until egg ovulation and activation . In contrast to many animals , egg activation in flies is not dependent on fertilization . Instead , eggs are reactivated during ovulation and immediately resume meiosis [26] . Drosophila females with a mutated sarah ( sra ) gene lay eggs that are defective in several aspects of egg activation , including a meiotic block in anaphase of the first division [27] . Interestingly , these authors observed that the male pronucleus in fertilized sra eggs remained abnormally condensed and did not replicate its DNA . This aspect of the sra phenotype presents striking similarities with the Hira mutant phenotype , raising the possibility that HIRA activity could depend on egg activation . In their paper , Horner et al . observed that the male nucleus and maternal chromosomes stained , although rather diffusely , with an anti-histone H1 antibody . They concluded that paternal chromatin remodelling was not impaired in sra eggs . However , it has been previously reported that early Drosophila embryos lack histone H1 [28] , opening the possibility that anti-H1 antibodies could cross-react with a non-H1 epitope . To directly analyse paternal chromatin assembly in sra eggs , we used anti-acetylated-H4 antibodies . In all cases , the condensed male nucleus , but not the maternal chromosomes , brightly stained with the anti-acetylated-H4 antibody , confirming that paternal chromatin assembly is not dependent on egg activation ( Figure 6A ) . In addition , we verified that ProtA-GFP was not detected from the male nucleus in sra eggs fertilized with ProtA-GFP males ( unpublished data ) . In sra eggs blocked in anaphase of the first meiotic division , the male nucleus frequently presented a rather irregular shape ( Figure 6A ) and an apparent level of DNA condensation that was comparable with the highly condensed maternal chromosomes blocked in anaphase I of the first meiotic division . Hence , the high level of cyclin B in sra eggs that causes the meiotic block [27] could also affect the male nucleus and force it to recondense its unreplicated chromatin . In comparison to sra , the male nucleus in Hirassm mutant eggs is a uniformly round nucleus that systematically adopts its definitive shape by the end of female meiosis II [17] . To see if the Hirassm male nucleus could recondense in sra eggs , we constructed double mutant Hirassm/Hirassm ; sraA108/Df ( 3R ) sbd45 females . In fertilized eggs from these double mutant females , we observed that the male nucleus did not stain with anti-acetylated-H4 antibodies and looked identical in shape and size to the male nucleus in Hirassm eggs ( Figure 6B ) . Thus , in the absence of an assembled chromatin , the male nucleus is unable to recondense in response to the meiotic block of sra eggs . SCR provides a unique opportunity to study de novo nucleosome assembly in vivo at the scale of a whole nucleus and in the absence of DNA synthesis or transcription . A striking feature of this process is the very specific use of the H3 . 3 histone variant to assemble paternal nucleosomes , despite the presence of large quantities of canonical H3 stored in the egg cytoplasm . ASF1 is a conserved histone chaperone involved in the assembly of chromatin during DNA replication ( reviewed in [29] ) . Recent studies have shown that ASF1 specifically interacts with H3-H4 dimers [30 , 31] and with HIR proteins [32 , 33] , and could play a key role in presenting dimers containing specific H3 variants to their corresponding chaperones , such as H3 to CAF-1 and H3 . 3 to HIRA [29 , 31 , 33] . Accordingly , ASF1 proteins are found in both H3 . 1 and H3 . 3 complexes in human cells [19] . To investigate this possibility in our model , we stained fertilized eggs with an antibody against the unique Drosophila ASF1 protein [34] . We observed that ASF1 was systematically detected in replicating nuclei , including the pronuclei ( Figure 7C ) . However , ASF1 was not found on the decondensing male nucleus in wild-type eggs or in the male nucleus in Hira mutant eggs ( Figure 7A , 7B , 7D , and 7E ) . Thus , ASF1 does not directly cooperate with HIRA during the RI assembly of paternal chromatin . This is consistent with a recent report showing that ASF1 is dispensable for direct de novo histone deposition in Xenopus egg extracts [35] . So far , HIRA is the only H3-H4 chaperone involved in SCR in vivo . The analysis of the HiraHR1 allele confirmed the essential role of maternal HIRA for the RI chromatin assembly in the male pronucleus . In Drosophila , early development is under maternal control and zygotic transcription essentially begins at the blastoderm stage [26] . In embryos , HIRA antibodies did not produce any detectable staining , suggesting that the protein , if it plays any role , does not accumulate at high levels in embryo nuclei like in the male pronucleus ( unpublished data ) . Haploid embryos laid by HiraHR1 females ( named HiraHR1 embryos for simplicity ) arrest their development just before hatching . We used this situation to study H3 . 3 deposition in wild-type and HiraHR1 early embryos . We used a previously described transgenic line expressing H3 . 3-FLAG under the regulatory sequences of the Drosophila His3 . 3A gene [17] . Maternally expressed H3 . 3-FLAG was then revealed using anti-FLAG antibodies . Zygotically expressed H3 . 3-FLAG becomes detectable in chromatin only at the gastrula stage ( Figure 8I and 8J ) and was thus not detected in our experiments on early embryos . As reported before [17] , in wild-type eggs , H3 . 3-FLAG is first detected in the decondensing male nucleus shortly after fertilization ( Figure 8A ) . As expected , the male nucleus does not contain any H3 . 3-FLAG in HiraHR1 eggs , confirming the absence of chromatin assembly in the male nucleus ( Figure 8B ) . At the pronuclear apposition stage in wild-type eggs , after the first round of DNA replication , H3 . 3-FLAG is still abundant in the male nucleus , but a faint staining is also visible in the female pronucleus ( Figure 8C ) and polar bodies ( unpublished data ) . Interestingly , this H3 . 3-FLAG staining in the female pronucleus is also detected in HiraHR1 eggs at the same stage ( Figure 8D ) . H3 . 3 can be deposited on DNA through a transcription-coupled ( TC ) assembly mechanism , suggesting that the passage of the RNA polymerase complex displaces nucleosomes and creates a need for RI assembly [36] . In the absence of transcription in early Drosophila embryos , the observed H3 . 3-FLAG must occur through a transcription-independent process , presumably during DNA replication . In wild-type embryos , we observed that the initial enrichment of H3 . 3-FLAG on paternal chromosomes was still detectable during the first 3 or 4 nuclear cycles ( Figure 8E ) . In HiraHR1 early embryos , only a faint H3 . 3-FLAG staining was detected on the sole maternally derived set of chromosomes ( Figure 8F ) . The paternal H3 . 3 mark in wild-type embryos was no longer detectable in later embryos ( unpublished data ) suggesting a rapid dilution by the massive RC deposition of H3 that occurs at each S phase . To verify this point , we used a transgenic line that expresses H3-FLAG with the regulatory sequences of His3 . 3A [17] . Both H3-Flag and H3 . 3-Flag transgenes produce equivalent levels of tagged histones in embryos [17] and allow a direct comparison of their respective deposition during early development . During the earliest mitoses , the H3-FLAG staining on chromosomes was much stronger than the H3 . 3-FLAG staining ( Figure 8K , compare with Figure 8E ) , confirming that H3 is much more efficiently incorporated in chromatin than H3 . 3 at this stage . The difference between H3 . 3-FLAG and H3-FLAG chromosome staining was also visible in blastoderm embryos ( Figure 8G and 8L ) . At the blastoderm stage , H3 . 3-FLAG clearly marked the chromatin of all nuclei in both WT and HiraHR1 ( Figure 8G and 8H ) . In conclusion , although H3 is preferentially deposited during the early nuclear cycles , our results demonstrate that H3 . 3 is also deposited at this stage , through a HIRA-independent assembly pathway . Further work will be required to determine whether this HIRA-independent H3 . 3 deposition occurs during or independently of DNA replication . The migration of nuclei at the embryo periphery correlates with the onset of zygotic transcription , with the notable exception of germ line pole cells that are kept silent until stage 9/10 of embryo development [37] . Interestingly , we observed that H3 . 3-FLAG is deposited at equivalent levels in somatic and in pole cell nuclei in both wild-type and HiraHR1 embryos ( Figure 9 ) . Thus , TC assembly does not seem to contribute substantially to the observed level of H3 . 3-FLAG in chromatin at this stage . The activation of the zygotic genome in blastoderm embryos correlates well with the apparition of histone post-translational modifications associated with transcriptionally active chromatin , such as the methylation of histone H3 at lysine 4 [38] . Figure 9 shows that this active mark is normally detected in HiraHR1 embryos , suggesting that HIRA is not required for the remodelling of chromatin associated with the onset of zygotic transcription . Accordingly , HiraHR1 embryos develop without obvious problems until late embryogenesis and eventually arrest development with a phenotype typical of haploid embryos produced by other mutants ( [39 , 40] and unpublished data ) . That HiraHR1 flies are viable offered us the possibility to evaluate the impact of the mutation on H3 . 3-FLAG distribution in adult tissues . We chose to focus on the testis , an organ where H3 . 3 distribution had been characterized already [41] . In wild-type transgenic adult testis , we observed a strong nuclear staining of H3 . 3-FLAG in all somatic and germline nuclei with the exception of late spermatid and sperm nuclei , similar to previous reports [41] . In HiraHR1 testis we found no detectable alteration of the distribution of H3 . 3-FLAG in both somatic and germ line nuclei ( Figure 10 ) . We then looked at other adult tissues including ovaries , malpighian tubules , and gut; again , we found no difference between control and mutant ( unpublished data ) . We conclude that , with the sole exception of the male pronucleus , HIRA does not seem to play any crucial role for the assembly of H3 . 3 nucleosomes during Drosophila development .
The analysis of maternal effect mutations in the Drosophila Hira gene has revealed that SCR at fertilization involves at least two functionally distinct steps . The first step is a HIRA-independent process that allows the rapid removal of protamines from the activated sperm nucleus . The second step is the RI nucleosome assembly on paternal DNA and requires maternal HIRA . That the male pronucleus seems to be the only nucleus where H3 . 3 deposition is critically dependent on HIRA ( see below ) indicates a peculiar case of RI assembly . This could reflect specific features of the sperm nucleus itself or constraints inherent to the tightly time-controlled , whole paternal genome assembly at fertilization . At least we know that this specific requirement of HIRA for SCR is not directly linked to the removal of protamines . Our finding that SNBP removal activity is functionally uncoupled to nucleosome assembly in Drosophila does not apply to all known cases of SCR in animals . In fact , in the classical example of SCR in Xenopus laevis , it was demonstrated through in vitro experiments that a unique histone chaperone , nucleoplasmin , was necessary and sufficient to perform both SNBP removal and histone deposition [42 , 43] . Nucleoplasmin is a small , acidic protein that is highly abundant in amphibian oocytes and forms pentameric complexes that associate with core histones [2 , 44 , 45] . It is important to consider , however , that the protein composition of Xenopus sperm chromatin is rather peculiar since it essentially retains H3-H4 tetramers on paternal DNA , whereas H2A and H2B are replaced with protamine-like proteins named SPs [43 , 46] . In vitro , nucleoplasmin allows the replacement of SPs with H2A and H2B and reconstitute nucleosomes [43 , 44] . There is apparently no need for a H3-H4 assembly factor such as HIRA for Xenopus SCR . A nucleoplasmin-like protein exists in Drosophila , but studies of its ability to decondense demembranated Xenopus sperm nuclei in vitro have led to contradictory results [11 , 12] . The actual function of Drosophila nucleoplasmin remains to be determined . In addition , other Drosophila embryonic nuclear factors are known to decondense Xenopus sperm in vitro , such as DF31 [10] and NAP-1 [11] , but their protamine removal activity has not been confirmed in vivo . In mouse , as in Drosophila , sperm chromatin is essentially packaged with protamines [47] . Interestingly , the knock-out of NPM2 , the mouse ortholog of Xenopus nucleoplasmin , does not affect SCR [48] . In contrast , HIRA is very likely involved in the assembly of paternal chromatin in the mouse zygote . Indeed , in this species , HIRA is detected in the decondensing male nucleus [49] and H3 . 3 is specifically deposited on paternal DNA in an RI manner [49 , 50] . We thus expect HIRA to be generally involved in the assembly of paternal chromatin in animal species in which histones H3 and H4 are totally or partially replaced with SNBPs in the mature sperm . As an H3 . 3-H4 deposition factor , HIRA itself is not expected to mediate the deposition of H2A-H2B required for the completion of nucleosome assembly on paternal DNA . It will be interesting to identify this H2A-H2B chaperone and see if it is dedicated to RI assembly or involved in both RI and RC assembly pathways . In Hira mutant eggs , the male nucleus is a small , round nucleus that appears homogeneously condensed when stained with a DNA dye . How the paternal DNA is organised in this nucleus is not known . That it is surrounded by a de novo assembled nuclear lamina [16] probably participates in the maintenance of its round shape . Also , it is established that the four centromeric regions are the only regions that are organized with histones , most likely because centromeric chromatin is not replaced with protamines in the sperm nucleus [16] . In this paper , we have demonstrated that the male nucleus in Hira mutant eggs is also devoid of protamines , strongly suggesting that most paternal DNA is free of chromosomal proteins . A similar situation was reported in decondensation assays using sperm from Bufo japonicus , a toad species whose sperm chromatin only contains protamines [51] . In the presence of nucleoplasmin , protamines are efficiently removed but nucleosomes are not assembled . Consequently , B . japonicus sperm nuclei decondensed with egg extracts containing the protamine removal activity possess neither protamines nor core histones , and are very fragile [51] . Similarly , in Hira mutant eggs , the removal of protamines from the male nucleus permits its partial decondensation as the sperm nuclear volume increases when the nucleus loses its specific needle shape and becomes round . However , in the absence of a nucleosomal organisation , the male nucleus cannot achieve its decondensation and does not replicate its DNA . This unique , inert state of the male nucleus in Hira mutant eggs is also well illustrated by its incapacity to recondense in blocked sra mutant eggs . A surprising aspect of this study is the viability of HiraHR1 homozygous flies . This was unexpected , because in mouse the Hira knock-out is embryonically lethal [52] . From a genetic point of view , both Hirassm and HiraHR1 alleles behave as null alleles with respect to the Df ( 1 ) ct4b1 deficiency . In addition , several lines of evidence indicate that no HIRA protein is translated in HiraHR1 flies , including the absence of detection of HIRA in the germinal vesicle and the male pronucleus , and the absence of HIRA-FLAG protein expressed from the pW25-HiraHR1-Flag reporter transgene . In the alternative possibility that some truncated HIRA protein would be translated from this allele and escaped our detection , the first possible translation initiation codon downstream from the deleted region in HiraHR1 is at position 61 , after the second WD repeat . Such a truncated HIRA would thus be expected to have , at best , a destabilized beta-propeller domain , which represents the most evolutionarily conserved part of HIRA proteins [53 , 54] . The fact that both Hirassm and HiraHR1 alleles display identical mutant phenotypes also highlights the very important role of the arginine 225 mutated in Hirassm , and by extension , the important role of the beta-propeller domain for the assembly of paternal chromatin . A recent study implicated Drosophila HIRA and the GAGA factor–FACT complex in a histone replacement mechanism that prevents the spreading of heterochromatin into a white reporter transgene inserted near centromeric heterochromatin [20] . Nakayama et al . observed that silencing of this variegating transgene was enhanced in Hirassm males , and concluded that the mutation affected H3 . 3 replacement at a site near the white gene . Their work suggests that Drosophila HIRA could indeed function in RI assembly in other situations and is consistent with the fact that Hira is expressed throughout development , in addition to its strong maternal expression [17 , 53 , 54] . Nevertheless , the fact that HiraHR1 mutant adults are viable indicates that this function is dispensable . Another important aspect of this study lies in the fact that the HiraHR1 mutation does not have detectable effect on the deposition of H3 . 3-FLAG in embryos or adult cells . First , it clearly establishes that H3 . 3 nucleosomes can be efficiently assembled in the absence of functional HIRA in vivo . So far HIRA is the only chaperone known to deposit the H3 . 3 variant . This study demonstrates the existence of at least one alternative assembly pathway for H3 . 3 nucleosomes , although the nature of the histone chaperone ( s ) involved is unknown . A simple hypothesis is the deposition of H3 . 3 by the CAF-1 complex . In fact , we have shown that in early embryos , the bulk of H3 . 3 is deposited independently of transcription , presumably at each S phase of the early nuclear cycles . Indeed , these cycles consist on a very rapid succession of S and M phases and lack gap phases [26] . The S phase deposition of H3 . 3 is consistent with a previous report showing that overexpressed H3 . 3-GFP was deposited during DNA replication in Drosophila Kc cells [55] . In human cells , only the small subunit of CAF-1 was found in the H3 . 3 complex , whereas all three subunits of the complex were copurified with the replicative histone H3 . 1 [19] . In early cycles , H3 is preferentially deposited compared with H3 . 3 . However , a peculiarity of Drosophila embryos is the storage of large maternal pools of both H3 and H3 . 3 , a situation that could favour a competition of these histones for their interaction with CAF-1 . In contrast , in differentiated cells , the massive expression of S phase histones at the onset of DNA replication could strongly reduce the use of H3 . 3-H4 dimers by the CAF-1 complex . The early Drosophila embryo should represent a good model to address this point . A study of Hira −/− mouse ES showed that these cells undergo early differentiation , suggesting that core histone deposition during this process could use HIRA-independent pathways [56] . Although it is well established that H3 . 3 deposition correlates with active chromatin in many instances , there is yet no link between HIRA and transcription in higher eukaryotes [57] . In budding yeast , nucleosome reassembly at the PHO5 promoter absolutely requires the histone H3-H4 chaperone Spt6 [58] , whereas Hir1 is not absolutely required [59 , 60] . In Drosophila , Spt6 is clearly involved in transcription elongation [61 , 62] and thus represents an interesting candidate for TC deposition of H3 . 3 [57] . The biochemical analysis of H3 . 3 complex in HiraHR1 mutant could help identify alternative H3 . 3 chaperone ( s ) . Our results support the hypothesis that multiple and possibly redundant pathways are involved in the assembly of H3 . 3 nucleosomes in multicellular organisms . Besides , it is now established that H3 . 3 nucleosomes can be assembled independently of RC and TC assembly pathways . For example , nucleosome replacement mechanisms at cis-regulatory elements implicating the deposition of H3 . 3 have been recently reported in Drosophila [20 , 63] . The ability of cells to assemble chromatin independently of DNA replication is apparently common to all eukaryotes . In fact , some organisms such as yeasts have only one type of histone H3 , which is related to H3 . 3 and is deposited throughout the cell cycle [64] . The coexistence of RC and RI histone H3s in most other eukaryotes indicates that these distinct modes of chromatin assembly fulfil important complementary functions . Interestingly and surprisingly , the deletion of all RI H3 histone genes in the protist Tetrahymena thermophila does not compromise survival and , in particular , does not affect nucleosome density at highly transcribed regions [65] . However , RI H3 genes in T . thermophila appear to be critical for the production of viable sexual progeny and for the function of germline micronuclei [65] , suggesting that sexual reproduction and/or developmental processes could have played an important role in the evolution of the RI mode of nucleosome assembly . RI H3 . 3 replacement at fertilization is apparently a conserved mechanism in nematodes , insects , vertebrates , and plants [17 , 49 , 50 , 66 , 67] . That the paternal chromatin assembly is the only essential function of Drosophila HIRA suggests that this factor acquired new important roles during the evolution of vertebrates . So far , in mammals , the implication of the HIRA/H3 . 3 complex has been shown or at least suspected in various remodelling processes , including heterochromatin repair [68] , mammalian meiotic sex chromosome inactivation [69] , fertilization [49 , 50] , and possibly , formation of senescence-associated heterochromatin foci [70] and histone exchange during spermiogenesis [71] . More functional studies should reveal if all these processes strictly rely on HIRA , in the context of the developing organism . Note: After the preparation of our manuscript , a paper by A . Konev et al . [72] was published that reported the implication of the motor protein CHD1 in the deposition of histone H3 . 3 in Drosophila . This finding supports our own conclusions about the existence of Hira-independent H3 . 3 deposition pathways .
The w1118 ssm185b/FM7c stock was described before [15] . The ProtamineA/B-GFP stocks [13] are a gift from S . Jayaramaiah Raja and R . Renkawitz-Pohl . The sraA108 allele [27] is a gift from V . Horner and M . Wolfner . Df ( 3R ) sbd45 is a deficiency that covers the sra locus . The H3 . 3-Flag , H3-Flag , and Hira-Flag stocks have been described before [17] . The y w67c and w1118 stocks were used as wild-type controls . All the other stocks or chromosomes used in this paper were obtained from the Bloomington Drosophila stock center . The Hira gene was targeted by ends-out homologous recombination as described in [22 , 23] . Two DNA fragments from the Hira locus were PCR-amplified from the cosmid genomic DNA clone 107B5 ( European Drosophila Genome Project ) using the following primers: 5′-ATGAAATGAGTGCCAGCAGC-3′ and 5′-GGTACCTATCGGTAACGATGCCCATC-3′ for the Hira upstream arm ( 4209 bp ) and 5′-GGCGCGCCGTGGTCATCTGGAATCTGCT-3′ and 5′-CGTACGATATTGGTTCCCGGTACCAG-3′ for the Hira downstream arm ( 3530 bp ) . These fragments were ligated in the pW25 vector [23] using the following restriction sites: Sac II and Acc65I for the upstream arm and AscI and BsiWI for the downstream arm . The final construct , named pW25-Hira , was verified by PCR and restriction analysis ( unpublished data ) . Six independent autosomal pW25-Hira transgenic lines were established in a y w67c background . Batches of 15–20 virgin y w; P{70FLP}11 P{70I-SceI}2B , Sco/CyO females were crossed with approximately 10 males from a given donor line in plastic vials . Vials containing 24-h egg collections from these crosses were heat shocked for 90 min at 37 °C in a water bath on days 3 , 4 , and 5 after egg laying . pW25-Hira /P{70FLP}11 P{70I-SceI}2B , Sco virgin F1 females with white or mosaic eyes were collected and crossed with w ; P{70FLP}10 males . Non-mosaic , coloured-eyed progenies were then crossed again with w ; P{70FLP}10 to establish individual lines . Each line with a white+ chromosome resistant to constitutive Flipase activity was tested for its complementation with the w Hirassm chromosome . Chromosomes that did not complement the maternal effect embryonic lethality associated with Hirassm were selected , outcrossed with w1118 for five generations , and balanced with the FM7c Chromosome . The pW25-HiraHR1-Flag transgene was constructed by replacing an AgeI-BsiWI restriction fragment in the 3' Hira arm from the pW25-Hira vector with a 729 bp fragment excised from the pW8-Hira-Flag transgene [17] to introduce the 3X-Flag tag at the 3' end of Hira . The final construct was verified by sequencing , and transgenic lines were established . Total RNA was extracted by the Trizol method ( Invitrogen ) and first-strand cDNAs were synthesized with the Superscript II reverse transcriptase ( Invitrogen ) and oligo-dT primers . The primer sequences used for PCR amplification of the cDNAs or genomic DNA are available on request . Anti-Flag M2 mouse monoclonal antibody ( F-3165 , Sigma Aldrich ) was used at 1:2000 , rabbit anti-acetylated histone H4 polyclonal antibody ( 06–598 , Upstate ) at 1:500 , rabbit anti-H3K4me3 polyclonal antibody ( ab8580 , Abcam ) at 1:250 , and mouse monoclonal anti-GFP antibody ( Roche 1814460 , clones 7 . 1 and 13 . 1 ) at 1:500 ( IF ) . The anti-Drosophila ASF1 antibody [34] is a gift from F . Karch and was used at a 1:1000 dilution . The HIRA 830 anti-peptide antibody was described before and used at 1:500 [17] . For the production of the PG1 anti-HIRA polyclonal antibody , a plasmid PW8-Hira-Flag [17] was used as a template to amplify a 1943-pb fragment from 1241 to 3183 ( amino acids 381–935 ) by PCR using primers 5′-ACATATGGTGAACGGTCTGGGAAAGTC-3′ and 5'TGGATCCGTACCCGTTGTCACAGCCAT-3′ . The fragment was cloned into the NdeI and BamHI of pET15b vector ( Novagen ) in frame with the His•Tag® at the N-terminus end of the recombinant protein . The recombinant plasmid was transformed into Escherichia coli BL21-CodonPlus® ( DE3 ) -RIL competent cells ( Stratagene ) and expression of the recombinant protein was induced by IPTG ( isopropyl β-D-thiogalactoside ) as described by the manufacturer and analysed by SDS-PAGE . Two rabbits were immunized with the purified HIRA-HIS-TAG protein purified on a Nickel column . Crude sera were purified on a Proteine-G column ( Proteogenix ) and were used at 1:1000 . Eggs and embryos were collected , fixed in methanol , and immunostained as described [16] . For each experiment , we observed a minimum of 25 eggs/embryos at the desired stage . Testes and ovaries were dissected in PBS-Triton 0 . 1% , fixed in 4% paraformaldehyde for 20 min ( testis ) or 30 min ( ovaries ) at room temperature , rinsed in TBST ( 0 . 1% Triton ) , and stained as for embryos . DNA was stained either with propidium iodide as described [16] or with TO-PRO-3 ( Molecular Probes ) used at a 1:10 , 000 dilution . Preparations were observed under a Zeiss LSM Meta confocal microscope . Images were processed with the LSM and Photoshop ( Adobe ) softwares . WT and transgenic O/N embryos were collected , washed , dechorionated , and homogenized in Laemmli 2X sample buffer ( 125 mM Tris-HCl [pH 6 . 8] , 2% SDS , 10% glycerol , 100mM DTE , 1% bromophenol blue ) with an Eppendorf fitting pestle-homogenizer using the bio-vortexerTM mixer ( Roth ) . Protein samples were centrifuged 5 min at 5000 rpm , boiled for 10 min at 95 °C , and subjected to electrophoresis on an 10% SDS-PAGE gel . Immunoblotting was performed using a tank transfer system ( Mini Trans-Blot Cell , Bio-Rad ) and Hybond-C Extra nitrocellulose membranes ( Amersham Biosciences ) in transfer buffer ( 25 mM Tris , 20 mM glycine , 20% ethanol , 0 . 05% SDS ) . Antibodies incubation was in TBST ( 20 mM Tris-HCl [pH 7 . 5] , 130 mM NaCl , 0 . 1% Tween 20 ) supplemented with 1% ( w/v ) nonfat dry milk as blocking agent . Detection was performed using the ECL western blotting detection system ( Amersham Pharmacia ) . Anti-FLAG M2 ( F-3165 , Sigma Aldrich ) and anti-α-tubulin Dm1A ( T-9026 , Sigma Aldrich ) mouse monoclonal antibodies were used at a 1:20 , 000 dilution . Goat anti-mouse horseradish peroxidase–conjugated antibody ( 170-5047 , Bio-Rad ) was used at 1:15 , 000 dilution . Note that our HIRA antisera did not work on western blots using the extraction and detection procedures that worked very well with the HIRA-FLAG recombinant protein detected with the anti-FLAG antibody . | Chromatin is composed of basic units called nucleosomes , in which DNA wraps around a core of histone proteins . HIRA is a histone chaperone that is specifically involved in the assembly of nucleosomes containing H3 . 3 , a universally conserved type of histone 3 . To understand the function of HIRA in vivo , the authors generated mutant fruit flies with a non-functional Hira gene . Surprisingly , mutant flies were viable , but females were completely sterile . By analysing the female fruit flies' eggs , the authors found that in the absence of HIRA protein , the sperm nucleus was unable to participate in the formation of the zygote . In Drosophila , as in many animals , the condensed sperm chromatin contains protamines instead of histones . The authors found that the only crucial role of HIRA in flies was to assemble nucleosomes containing H3 . 3 in the male pronucleus , after the removal of protamines . This fundamental process , which is presumably also controlled by HIRA in vertebrates , allows the paternal DNA to reconstitute its chromatin and participate in the development of the embryo . | [
"Abstract",
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] | [
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] | 2007 | The Essential Role of Drosophila HIRA for De Novo Assembly of Paternal Chromatin at Fertilization |
Cytotoxic T lymphocytes ( CTL ) are a major factor in the control of HIV replication . CTL arise in acute infection , causing escape mutations to spread rapidly through the population of infected cells . As a result , the virus develops partial resistance to the immune response . The factors controlling the order of mutating epitope sites are currently unknown and would provide a valuable tool for predicting conserved epitopes . In this work , we adapt a well-established mathematical model of HIV evolution under dynamical selection pressure from multiple CTL clones to include partial impairment of CTL recognition , , as well as cost to viral replication , . The process of escape is described in terms of the cost-benefit tradeoff of escape mutations and predicts a trajectory in the cost-benefit plane connecting sequentially escaped sites , which moves from high recognition loss/low fitness cost to low recognition loss/high fitness cost and has a larger slope for early escapes than for late escapes . The slope of the trajectory offers an interpretation of positive correlation between fitness costs and HLA binding impairment to HLA-A molecules and a protective subset of HLA-B molecules that was observed for clinically relevant escape mutations in the Pol gene . We estimate the value of from published experimental studies to be in the range ( 0 . 01–0 . 86 ) and show that the assumption of complete recognition loss ( ) leads to an overestimate of mutation cost . Our analysis offers a consistent interpretation of the commonly observed pattern of escape , in which several escape mutations are observed transiently in an epitope . This non-nested pattern is a combined effect of temporal changes in selection pressure and partial recognition loss . We conclude that partial recognition loss is as important as fitness loss for predicting the order of escapes and , ultimately , for predicting conserved epitopes that can be targeted by vaccines .
HIV replication continues for years despite a highly active immune response . Depletion of cytotoxic CD8+ T cells ( CTL ) in SIV infected animals causes rapid increase in viremia [1] , [2] showing that CTL control HIV/SIV replication; that this response is antigen-specific is evident from rapid genetic evolution of HIV in antigenically important regions . Antigenic escape is one of the major mechanisms of HIV resilience in face of an active immune response , impeding effective vaccine design [3] and implicated in the progression to AIDS [4] . Shortly after infection is initiated , many CTL clones arise to target the transmitted virus strain [5]–[7] , each clone recognizing a distinct 8–10 amino acid viral peptide ( epitope ) presented on the surface of an infected cell by MHC molecules . Escape mutations in CTL epitopes begin to be selected within a month of infection and continue to be selected throughout chronic infection , sometimes causing a decrease in the intrinsic replication rate of the virus ( fitness cost ) [8]–[10] . However , despite a sustained CTL response , not all targeted epitopes escape . Moreover , among epitopes that do escape , the rate of escape slows dramatically over the first 100 days post infection . It remains unclear which parameters decide the timing and rate of escape in a given epitope as well as which epitopes escape and which are preserved throughout chronic infection [11]–[13] . Mathematical models of HIV evolution in the presence of multiple CTL clones have been applied to study the emergence of late escape mutations [14] , [15] and the effect of distributed CTL pressure on the rate of escape [12] , [16] . Previous work has emphasized two parameters , the mutation cost ( ) and the number of active epitopes ( n ) . It can be inferred that escape mutations come at a cost from the observation of occasional reversion of escape mutations upon transmission between MHC mismatched individuals [8] , [9] , [17] , [18] , as well as the frequent acquisition of compensatory mutations outside escaping epitopes . [19] , [20] . Common escape mutations have been shown experimentally to have wide ranging fitness costs [10] , [21] , [22] . However , fitness costs and the number of CTL clones acting on the virus are not the sole determinants of the dynamics of escape . The degree of escape conferred by a mutation is equally important . It has been observed in HIV infected individuals [23] and SIV infected animals [24] that CTL are capable of recognizing different variants of an epitope with different efficiencies . Thus , in general , an escape mutant does not fully abrogate recognition of the corresponding CTL clone . In the present work , we address the process of antigenic escape in terms of a cost-benefit diagram . The benefit of an escape mutation is a partial CTL recognition loss and the mutation cost is a partial reduction in viral replication rate . We extend the basic model introduced by Althaus and De Boer [14] to include partially effective escape mutations and investigate how the two opposing evolutionary forces together determine the observed rate of escape from the CTL response [12] , [25] . The model predicts that a positive correlation between recognition and fitness losses emerges during sequential escape mutations , the strength of which changes over time as pressure from the immune system wanes . We compare our model predictions with existing data showing a correlation between fitness and recognition losses in clinically relevant escape mutations from the Pol gene [26] and estimate the range of recognition losses that occurs in commonly observed escape mutations from three published studies [27]–[29] . Furthermore , the inclusion of partially effective escape mutations in the model can reproduce the diverse patterns of intra-epitope escape that are routinely observed in HIV infected patients . During the majority of escape mutations that have been studied with time-resolved viral sequencing , 2–10 distinct epitope sequences grow in number to replace the transmitted sequence and eventually one mutant epitope spreads to the entire population . Furthermore , the dominant mutated epitope sequence changes over time [11] , [30] , [31] . Although , in some epitopes , mutations are added at new sites in a nested fashion , in a larger number of epitopes , mutations at new sites replace mutations at previous sites leading to a non-nested pattern . This pattern is atypical for models assuming constant selection pressure . The analysis below illustrates how time-dependent selection due to the changing CTL pressure and partial CTL recognition loss can produce the non-nested pattern of escape ( see [32] for review ) .
In order to study escape from the immune response we consider a model that includes target cells , infected cells and multiple CTL clones which recognize regions in the viral genome ( epitopes ) with equal avidities ( Figure 1A and Materials and Methods ) . The model predicts three distinct phases of HIV infection ( Figure 1B ) , as follows . Phase 1: The transmitted HIV strain expands in the population of target cells . Phase 2: All CTL clones that recognize cells infected by the transmitted strain are activated , expand , and reduce the number of infected cells . A steady state is obtained with constant levels of infected cells and CTL , which represents chronic HIV infection ( see Equations S1–S3 ) . Phase 3: Escape mutations in viral epitopes emerge , changing the genetic composition of the population of infected cells and the clonal composition of the CTL population but only weakly affecting their overall sizes . The dynamics of infected cells and CTL during the escape phase ( Phase 3 ) depends on the number of epitopes that are targeted , n , and on the degree of recognition loss per mutation , ( Table 1 ) . In the simplest case , if a single CTL clone is present in steady state and an escape mutation arises which completely abrogates CTL recognition ( ) , the CTL clone contracts and the population of infected cells containing the escape mutation grows uncontrolled until target cells are depleted ( whether and for how long the escape mutation is maintained after disappearance of the CTL clone depends on the fitness cost of the mutation ) . In contrast , if the recognition loss conferred by the escape mutation is partial ( ) , the population of infected cells grows only transiently , and the CTL clone expands until a new steady state is reached . When a group of CTL clones with similar avidity target the infected cell population ( as shown in Figure 1B ) , the spread of either partly or fully effective escape mutation causes the corresponding CTL clone to contract , since it recognizes infected cells less efficiently than other CTL clones ( see Equation S5 ) . Because infected cells continue to be recognized by other CTL clones , the population of infected cells does not grow out of control following the escape mutation . Thus , the model reproduces the three-phase dynamics that can be inferred from kinetic data in HIV infected individuals including the waning of CTL responses to escaped epitopes [11] , [13] , [30] , [31] . Escape mutations begin to be selected once CTL reach sufficiently high levels ( third phase in Figure 1B ) . Mutations in all targeted epitopes begin to grow simultaneously , albeit with different rates due to variation in the amounts of fitness and recognition losses caused by a mutation . The exponential growth rates ( escape rates ) of mutant strains determine which mutant strain will grow to dominate the population . Here we describe the growth of a mutated strain ( ) with a single mutation in epitope 1 . An escape mutation results in both a fractional fitness cost , , and a fractional loss of CTL recognition , . The mutant strain begins to grow as , with initial growth rate : ( 1 ) where is the fraction of CTL population recognizing epitope 1 ( see Equation 7 in Materials and Methods and Text S1 ) . Thus , the escape rate reflects the balance between the partial recognition loss and the partial fitness loss , which determines whether a mutant strain has a selective advantage ( ) . In order to infer , for example , the fitness cost of an escape mutation , it is necessary to measure not only the escape rate , but also the CTL recognition loss . When multiple epitopes are targeted and recognition and fitness losses vary across epitopes , the growth rates of escape mutants vary as well ( Figure 1B ) . In data from HIV infected individuals , it is observed that the rate of escape slows dramatically over the first 100 days post infection [11]–[13] . The rise of escape mutants predicted by the model is consistent with these findings ( Figure S1 ) . Furthermore , assuming only a minor fitness cost , the entire observed variation of escape rates over time and across sites can be simulated from the variation of the recognition loss , . The process of gradual viral escape from the immune response of an infected host continues for years . During this time , the virus shows a limited number of detectable CTL responses against different sites ( [33] ) where a total of 5–30 escape mutations are selected [11] , [30] . We investigate the trajectory connecting these escape mutations in the cost-benefit plane and predict how the average fitness costs and recognition losses incurred by an escape mutation will change as escape progresses . For this aim , we use a simplified version of the main model ( see Materials and Methods ) , focused only on the order of escape mutations ( i . e . , model dynamics are not considered explicitly ) . Parameters and are randomly generated for a genome with multiple epitopes and multiple sites per epitope ( 10 epitopes , with 10 sites per epitope in Figure 2 ) and epitope sites are ranked in the descending order of escape rate ( Equation 1 ) . When many random runs are compared , a correlation between and for escape mutations of a given rank is observed ( Figure 2A ) . The average trajectory of escape in the cost-benefit plane moves from high recognition loss , low fitness cost to low recognition loss , high fitness cost . The maximum escape rate per epitope decreases over many rounds of escape ( Figure 2B ) and each epitope escapes at more than one site . This prediction is consistent with experimental observation , where the majority of escaping epitopes undergo more than one mutation [13] , [30] . The dynamic interplay between CTL and partially effective escape mutations shape the overall course of HIV escape . As mentioned above , an escape mutation results in the decay of the cognate CTL population . In turn , the CTL decay causes the potential benefit of other escape mutations in the escaping epitope to decrease , because the overall CTL pressure on the epitope is lessened . The presence or absence of CTL decay has observable consequences for the trajectory of escape mutations in the cost-benefit plane . Without the CTL decay , the average slope of the trajectory stays constant over time until no more escapes are possible ( Figure 2A ) . In contrast , when CTL clones decay in response to the recognition loss , the trajectory bends towards X-axis ( Figure 2C ) as early escape in an epitope causes escape mutations on the other sites in that epitope to become progressively less advantageous . Therefore , the total number of escape mutations decreases when CTL decay is included ( from 55 in Figure 2A to 20 in Figure 2 ) , which is the result of fewer escape mutations per epitope ( fewer steps in Figure 2D compared to Figure 2B ) . The simulation example in Figure 2 offers an interpretation of the work by Mostowy et al [26] , where a weak but statistically significant correlation was observed between fitness costs and HLA binding losses in clinically derived Pol sequences . The existence of the correlation follows from Equation 1 , which states that more costly mutations will not appear unless they confer a large benefit to the virus . The model relates the weak strength of the correlation ( slope = −0 . 12 between the fitness decrease and the impairment of HLA binding ) to the large number of active epitopes . The existence of the CTL decay caused by escape can further reduce the slope: mutations sampled during the acute phase of HIV infection are predicted to have a much larger slope than those sampled during the chronic phase ( Figure 3B ) . Since the majority of database sequences used in the cited work fall into the latter category , the small slope of the correlation observed can partially be caused by CTL decay . There is an important caveat which must be considered when comparing the model with the cited work [26] . Loss of CTL recognition in the model is given by , which is a composite parameter comprising changes in antigen processing , presentation and recognition , whereas Mostowy et al use the computationally predicted loss of HLA-epitope binding , . To convert the units , we analyzed data from several publications in which both overall loss of CTL recognition as well as HLA binding impairment was measured [27]–[29] . We found a strong correlation between and expressed as a linear relationship ( Figure S2 , Equations S10–S14 ) , which justifies our comparison between the model predictions and this data . Epitopes with many sites can produce different combinations of escape mutations ( haplotypes ) in response to CTL pressure . In [30] it was observed that the mutated sequence changed over time in the majority of epitopes that were studied longitudinally and , interestingly , the order in which escape haplotypes appeared varied from epitope to epitope . We divide the epitopes into three characteristic patterns , based on the order of dominant haplotypes: “simple” , “nested” and “leapfrog” . We can illustrate these patterns in an epitope with two sites , in which four haplotypes are possible . The presence or absence of an escape mutation at each site is denoted by a 1 or a 0 , respectively . The infrequently observed “simple” pattern is characterized by a single escape haplotype . For example , in an epitope with two sites , the sequence of haplotypes observed is . The “nested” escape adds a new mutation sequentially to a previously mutated sequence: . This pattern is predicted if mutations at both sites are under a constant , positive selection pressure ( throughout the course of infection ) . The “leapfrog” pattern is characterized by a switch in the dominant single-site mutation: . In the present model , the leapfrog pattern of escape arises due to time-dependent CTL selection pressure . Below we use the model to determine the distribution of fitness and recognition losses within an epitope that produces the leapfrog pattern of escape . In order to study intra-epitope dynamics , we use the main model ( Equations 6 to 8 ) and consider a genome composed of a large number of two-site epitopes . This simple case can be studied in detail , and the results can illustrate the general case , where epitopes are comprised of many sites . We use Equation 1 to determine the escape rates for each of the three mutant haplotypes has for a given level of CTL clone recognizing epitope : ( 2 ) ( 3 ) ( 4 ) where is the time dependent fraction of the total CTL population comprised by CTL clone and we have neglected the small parameter ( Table 1 ) . We consider the case where the initial escape rate of haplotype 10 in epitope ( ) is higher than the initial escape rate of haplotype 01 ( ) . Once an escape haplotype has reached a sufficiently high frequency , CTL clone will decay monotonically in time , but the virus load will stay stable , since the population of infected cells will be controlled by the other CTL clones . As decreases and the CTL-induced pressure on the epitope declines , the fitness losses at the two sites , , begin to dominate the escape rates ( Equations 2–4 ) , and the favored haplotype will change . Eventually , the decay of the CTL clone will cause the transmitted haplotype ( 00 ) to regain fitness advantage and become a dominant strain again ( the existence of compensatory mutations may cause stabilization of the last escaped clone , see Discussion ) . The sequence of dominant haplotypes can follow one of the patterns , as follows:
We assume that the population of infected cells in acute infection and steady state is large , so that all single escape mutants exist in the population before the rise of CTL ( as in [14] , [15] ) . The assumption is supported by estimates of the large effective HIV population size [4] , [34] , [35] . We can also infer the fact of preexistence of CTL escape mutations from the observed preexistence of drug-resistant mutations evident in the early emergence of resistance to mono-therapy ( e . g . [36] ) . Furthermore , since the number of escape mutations per epitope is typically larger than the number of drug resistance sites per drug , the mutation cost of the least costly escape mutation can be assumed to be lower – and the frequency of preexisting mutations higher – for escape mutations . The preexistence of single mutants casts doubts on the interpretation of very late escape mutations as the result of the late appearance of an escape mutation not initially present in the population [37] . Mutations compensating for fitness losses were documented for drug-resistance mutations ( e . g . [38]–[40] ) and for immune-escape mutations [10] , [19] , [20] ( For an overview of compensatory mutations in HIV see [41] , [42] ) . However , the dynamics of compensation remains poorly understood . Therefore , our model does not explicitly include compensatory mutations and , as a result , predicts the eventual reversion of any escape mutation with a fitness cost . Instead of including compensation , we calculate the period of time that a given mutation would be maintained in the population before reversion occurs , which is the same time interval where compensation would be necessary in order to prevent reversion ( inset of Figures 5 and S3 ) . When the non-nested pattern of escape is observed in infected patients , the first escape variant is typically short-lived compared to the second escape variant , and the transmitted variant is not observed after the initial escape [11] , [30] , [31] . Our interpretation is that the fitness cost of the first variant is large relative to the second variant and does not have time to be compensated before the second variant gains the advantage . The second variant either has a very small fitness cost or is compensated gradually during its lifetime ( compensation is not explicitly simulated ) . Our calculation of the lifetimes of different escape variants in an epitope is a first step towards understanding the timescales associated with compensation . The present analysis is focused on the first year post-infection , the time interval in which most escape mutations occur . Therefore , we consider a group of CTL clones with similar avidities that are present initially in similar numbers . This is a reasonable assumption after escape mutations have occured in the first few immunodominant epitopes during the resolution of acute viremia . At this time , a large number of CTL clones are activated and are maintained for many months at simular levels [6] , [33] . Once all CTL clones are activated , our model predicts that the steady state viral load is proportional to the inverse avidity of the most avid CTL clone ( Equation S2 ) . Kadolsky and Asquith [43] estimated that the average viral load increases by only 0 . 051 log copies/ml per CTL escape . This small increase , which we interpret to be the average difference avidity spacing between CTL clones , justifies our assumption that CTL avidities are , indeed , very closely spaced . Our analysis shows that , given closely spaced avidities , it is recognition and fitness losses that govern the order and timing of escape mutations , rather than variation in CTL avidity . The model includes additional simplifying assumptions , as follows . i ) The proliferation rate of CTL clones saturates with the infected cell number , but not with the total CTL level . In the original model [14] , the authors postulate that the total number of CTL limits the growth of individual CTL clones , which causes clones to interact and enables the co-existence of multiple CTL clones with different avidities . We believe that further study is needed to verify the existence and the possible origin of the interclonal interaction . ( Note that the target availability may not be the cause of interclonal interaction , because most CTL are not bound to their targets even at the peak of infection , and the overall effector to target ratio is less than two [44] . ) ii ) CTL are short-lived in the absence of antigen , with an average lifetime of 10 days , as is consistent with early studies of CTL dynamics in SIV system [45] , [46] and mathematical modeling of these data [47] . iii ) We ignore recombination , which may increase the rate of emergence of escape mutations among different epitopes [15] as well as the long term rate of evolution [48] , [49] . For the intra-epitope dynamics of escape , recombination between neighboring sites will be a small correction . ( iv ) CTL clones against escape mutants are not included in the model . By including partial rather than full recognition loss , we allow CTL to escaped epitopes to continue to exert selection pressure without incorporating additional CTL clones . Though clones to escape mutants may exist , introducing extra CTL clones ( 5 clones for each two-site epitope , instead of 1 ) would complicate analysis of the model without changing the essential results . ( v ) Small fitness costs are assumed . Equation 1 states that costly escapes cannot arise when recognition losses are partial and many CTL clones target the viral genome . We choose to focus on the time interval corresponding to the first year after the resolution of acute infection when these assumptions are fulfilled . Escape mutations entailing a high fitness cost can only escape when only one or two CTL clones dominate strongly , which we estimate from previous data to be during the 3–4 weeks post-infection . ( vi ) Fitness costs are positive . A fraction of mutations that are transmitted to an individual that fall outside the individuals HLA-restricted epitopes revert ( i . e . may have a negative fitness cost ) . However , the rate of reversion is very small [17] and hardly interferes with the faster dynamics of within-epitope mutations . ( vii ) We consider escape processes in different epitopes separately , because typically they do not overlap much in time . In general , however , linkage effects ( clonal interference , background selection ) between epitope mutations and compensatory mutations of same or different epitopes may complicate the picture ( see [32] for review of recent research in this area ) . Thus , our model has demonstrated that partial recognition losses , in addition to fitness costs and the breadth of the CTL response , dramatically affect the rate and the order of escape mutations during an HIV infection . These findings help to interpret the positive correlation between fitness costs and recognition losses observed in the Pol gene [26] and make the testable prediction that the strength of the correlation should decrease with the time post-infection . Our results call for direct measurements of recognition losses for different escape mutations . Combined with the proposed trajectory approach ( Figure 2 ) , these data will serve as a basis for improved prediction of conserved epitopes for use in vaccines .
We model SIV/HIV evolution in the presence of multiple CTL clones ( Figure 1A ) . Each CTL clone recognizes a distinct viral epitope that is presented by an infected cell . Mutations occur in the proviral genomes of infected cell that allow an infected cell to partially evade CTL recognition , but come at a cost in terms of viral replication . The model is given by the following equations: ( 6 ) ( 7 ) ( 8 ) where and are the relative viral replication rate of viral genome i and the relative CTL recognition of sequence i by clone j , as compared to the transmitted sequence , respectively: ( 9 ) ( 10 ) The processes described are , as follows . Highly infectable target cells ( T ) are replenished at a rate cells per day , leave the highly infectable phase with a rate and are infected by virus at a rate that depends on the fitness on the infecting strain , with maximum rate per cell β , and the number of productively infected cells in the system . We consider n epitopes , each consists of m amino acid positions ( sites ) , giving a total of possible strains . An cell infected is labeled by proviral genome i with n epitopes denoted , where each epitope , , has m sites , and indicates the presence or absence of a mutation at an epitope site . The fitness of genome i is reduced by mutations . A mutation in epitope j at site k contributes cost to the logarithm of the reduction in replication rate . The strain with all 0 is the transmitted strain , which has fitness 1 . Effector CTL ( ) are replenished with a constant rate cells per day , divide with a rate dependent on the number of infected cells that they recognize and their avidity , , with maximum rate c , and die with rate . CTL clones each respond to a distinct viral epitope that is presented by an infected cell and kill infected cells at a maximum rate . The recognition ability of the CTL clone to epitope j in strain i is reduced by mutations , as given by Equation 10 . A mutation in epitope j at site k contributes to the logarithm reduction in CTL recognition . In the present work , we assume CTL of equal avidities , . Throughout the text , we describe the relative loss in fitness and recognition due to escape mutations in terms of notation and . Mutations are generated randomly with rate per site per generation of infected cells ( i . e . , the average lifespan of an infected cell , ) between strains that differ by one site . Strains with average copy number above one infected cell are simulated deterministically according to the above equations; below this threshold a strain is considered extinct . The basic model with one escape-conferring site per epitope ( ) has been introduced by Althaus and De Boer [14] . In this work , we adapt the model to focus on the effect of partial recognition and fitness losses during a narrow time interval after acute infection ( see Discussion for a detailed comparison of the two models ) . Further information on model parameters and the estimated range of parameter values are listed in Table 1 . All simulations were performed in Matlab ( Mathworks ) and the source code can be obtained freely upon request from the authors . Following the original work [14] , our model assumes that the majority of infected cells are killed by CTL rather than by viral cytopathicity . It has also been proposed that CTL decrease the viral production in infected cells in a non-lytic fashion , by suppressing viral replication , and that cells die within a fixed period of time ( ∼1 d ) due to viral cytopathicity [50] . A specific choice of the mechanism of viral control does not affect the competition between CTL escape variants ( although the long-range nature of the cytokine interaction may slow down the process of escape [51] ) . However , because there has been considerable controversy on the matter , it deserves a brief discussion . Two variations of the lytic model have been studied: a simple model in which infected cells are described by a single compartment and a slightly more complex model with two linked compartments of infected cells ( cells in the eclipse phase of virion production and cells within a shorter , virus-producing phase ) . Several authors have argued against single-compartment models for the following reasons: ( i ) Although viremia and CTL levels vary strongly among patients , the decay rate of viremia under anti-retroviral therapy ( ART ) ( interpreted as the lifespan of infected cells [52] , [53] ) is approximately 1 d−1 with less than 50% variation between patients [54] . ( ii ) The rate of viremia growth under CD8 T cell depletion is 2–3 fold faster than the rate of viremia decay under ART , whereas in the simplest lytic model , the two rates must be the same [2] . ( iii ) Finally , CD8 depletion does not affect the rate viremia decay rate under ART [55] , [56] . These observations are , however , compatible with two-compartment lytic models [14] , [57]–[60] . In these models the decay rate of viremia under ART is determined by the length of the eclipse phase ( lasting ∼1 d ) , which does not depend on CTL . Elemans et al [61] compared the ability of two-compartment lytic models with non-lytic models to explain data in Refs . [55] , [56] and reported that Akaike information criterion generally favors the non-lytic models . However , they conceded that the accuracy of the cited experiments may be insufficient to detect the effect of CD8 T cells . On the other hand , the short duration of the virus-producing phase measured by Wick et al [62] , [63] and a faster decay under ART including an integrase inhibitor compared to therapies including only protease and reverse transcriptase inhibitors [59] support the lytic models with short-lived virus-expressing cells . For these reasons , we choose a lytic model of HIV control ( though since we do not aim to predict accurately the viremia decay rate under ART we do not include the eclipse phase ) . The matter is open to further investigation . The total CTL killing rate in our model is given by d−1 , which is higher than assumed in the original work . Elemans et al [50] reviewed some estimates of the CTL killing rate in a steady state HIV infection . Using antigenic escape data ( assuming no mutation cost and full recognition loss ) , the killing rate was estimated to be 0 . 1–0 . 2 d−1 per CTL response [64] . Somewhat larger estimates , d−1 for the total CTL response , were obtained by another indirect method , which compared the death rate of SIV infected cells in animals treated with antiretroviral therapy ( ART ) between CD8+ depleted and control groups [61] . The most direct ( and highest ) estimate , 4–10 d−1 for the total CTL response , was obtained by Wick et al [62] , [63] who re-infused into patients autologous , invitro expanded , CD8+ T cells to 2 . 5% of total CD8 count . Wick et al then directly quantified the decay rate of virus-expressing cells . The estimate they obtained is 4–10 fold higher than the rate of viremia decay under ART , indicating a short virus producing phase of 2–8 hours . The estimate agrees with those obtained by modeling data from acute SIV infection and HIV dynamics during ART [47] , [65] and with the killing rate per CTL in acute LCMV infection [66] once the difference in CTL level between the two systems is accounted for . We also introduce the virus-induced infected cell death rate , d−1 , which is several-fold smaller than the CTL killing rate in steady state [55] . Parameter is defined as the inverse length of the highly infectable period of the activated cell cycle , End G1-S-G2-M , which lasts less than 1 day . During that period , a virion entry leads to a successful infection , because the concentrations of nucleotides are maximal , and reverse transcription is completed quickly before HIV RNA is degraded by vigorous innate responses [67] . The choice over , e . g . , d−1 ( often used as an estimate of the total activated cell lifespan ) does not change the dynamics of escape . We introduce a simplified model , which does not explicitly consider dynamics , in order to study the sequence of escape mutations for a realistic size genome . Fractional fitness costs ( ) and recognition losses ( ) are randomly generated from a uniform distribution for 100 sites ( 10 epitopes with 10 sites per epitope ) in order to study the sequence of escaped sites . All sites are ranked in order of and escape sites in similar ranking over many runs are considered together . CTL decay is introduced for all additional sites in an epitope once a site in the epitope escapes: after each round of escape , is reduced for all sites in the epitope by: for all i in the epitope that have escaped . Here parameter is defined as the decay rate per escape , in contrast to in the main model which is defined per day . | Like many viruses , HIV has evolved mechanisms to evade the host immune response . As early as a few weeks after infection is initiated , mutations appear in the viral genome that reduce the ability of cytotoxic T lymphocytes ( CTL ) to control virus replication . However , of the many mutations in the viral genome that could potentially mediate viral escape from the CTL response , a specific subset are typically observed . This suggests that some mutations either entail too high a fitness cost for the virus , or are relatively inefficient escape mutations . A successful vaccine would target the CTL response to these regions in such a way that escape would not be possible . We use a computational model of HIV infection in order to study the factors that determine whether a given escape mutation will occur , how long it will be maintained in the population , and how these changes in the viral genome will affect the CTL response . Our analysis highlights the important role of partial recognition loss conferred by a mutation in producing the complex dynamics of escape that are observed during the course of infection . | [
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] | 2014 | The Route of HIV Escape from Immune Response Targeting Multiple Sites Is Determined by the Cost-Benefit Tradeoff of Escape Mutations |
Human cystic echinococcosis ( CE ) , caused by the larval stage of Echinococcus granulosus , with the liver as the most frequently affected organ , is known to be highly endemic in Tibetan communities of northwest Sichuan Province . Antiparasitic treatment with albendazole remains the primary choice for the great majority of patients in this resource-poor remote area , though surgery is the most common approach for CE therapy that has the potential to remove cysts and lead to complete cure . The current prospective study aimed to assess the effectiveness of community based use of cyclic albendazole treatment in Tibetan CE cases , and concurrently monitor the changes of serum specific antibody levels during treatment . Ultrasonography was applied for diagnosis and follow-up of CE cases after cyclic albendazole treatment in Tibetan communities of Sichuan Province during 2006 to 2008 , and serum specific IgG antibody levels against Echinococcus granulosus recombinant antigen B in ELISA was concurrently monitored in these cases . A total of 196 CE cases were identified by ultrasound , of which 37 ( 18 . 9% ) showed evidence of spontaneous healing/involution of hepatic cyst ( s ) with CE4 or CE5 presentations . Of 49 enrolled CE cases for treatment follow-up , 32 . 7% ( 16 ) were considered to be cured based on B-ultrasound after 6 months to 30 months regular albendazole treatment , 49 . 0% ( 24 ) were improved , 14 . 3% ( 7 ) remained unchanged , and 4 . 1% ( 2 ) became aggravated . In general , patients with CE2 type cysts ( daughter cysts present ) needed a longer treatment course for cure ( 26 . 4 months ) , compared to cases with CE1 ( univesicular cysts ) ( 20 . 4 months ) or CE3 type ( detached cyst membrane or partial degeneration of daughter cysts ) ( 9 months ) . In addition , the curative duration was longer in patients with large ( >10 cm ) cysts ( 22 . 3 months ) , compared to cases with medium ( 5–10 cm ) cysts ( 17 . 3 months ) or patients with small ( <5 cm ) cysts ( 6 months ) . At diagnosis , seven ( 53 . 8% ) of 13 cases with CE1 type cysts without any previous intervention showed negative specific IgG antibody response to E . granulosus recombinant antigen B ( rAgB ) . However , following 3 months to 18 months albendazole therapy , six of these 7 initially seronegative CE1 cases sero-converted to be specific IgG antibody positive , and concurrently ultrasound scan showed that cysts changed to CE3a from CE1 type in all the six CE cases . Two major profiles of serum specific IgG antibody dynamics during albendazole treatment were apparent in CE cases: ( i ) presenting as initial elevation followed by subsequent decline , or ( ii ) a persistent decline . Despite a decline , however , specific antibody levels remained positive in most improved or cured CE cases . This was the first attempt to follow up community-screened cystic echinococcosis patients after albendazole therapy using ultrasonography and serology in an endemic Tibetan region . Cyclic albendazole treatment proved to be effective in the great majority of CE cases in this resource-poor area , but periodic abdominal ultrasound examination was necessary to guide appropriate treatment . Oral albendazole for over 18 months was more likely to result in CE cure . Poor drug compliance resulted in less good outcomes . Serology with recombinant antigen B could provide additional limited information about the effectiveness of albendazole in CE cases . Post-treatment positive specific IgG antibody seroconversion , in initially seronegative , CE1 patients was considered a good indication for positive therapeutic efficacy of albendazole .
Human cystic echinococcosis ( CE ) , caused by the metacestode stage of Echinococcus granulosus , is a complex , chronic disease with a cosmopolitan distribution , and the liver is the most frequently affected organ [1] . Clinical manifestation of this disease ranges from asymptomatic infection to severe , or rarely even fatal disease . Diagnosis of CE remains highly dependent on imaging techniques , due to the fact that immunodiagnosis frequently lacks sensitivity [2] , with about 20% of clinically or surgically confirmed CE cases , and up to 50% of community-detected patients presenting negative serology [3]–[6] . The most common applied imaging techiniques include magnetic resonance imaging ( MRI ) , ultrasonography ( US ) or radiography , for detection of characteristic space-occupying cysts [7] , [8] . MRI is able to show highly specific features of CE , but it is prohibitively expensive and not available in rural areas of many endemic countries . In contrast , US is accessible , much less expensive , and can identify hydatid cyst pathological type ( CE1-CE5 ) [9] . Approaches in clinical management for CE include surgery , percutaneous techniques and antiparasitic treatment for active cysts , and the so-called `watch and wai approach for inactive cysts [9] . Currently , surgery remains the most common approach for CE treatment that has the potential to remove cysts and lead to complete cure , but it involves risks including those associated with any surgical intervention , anaphylactic reactions , and secondary CE owing to spillage of viable parasite ( protoscoleces ) material [10]–[12] . Drug therapy with benzimidazoles ( albendazole or mebendazole ) has increasingly been used to treat CE , and proved to have efficacy against the parasite in humans , with about 30% of patients cured and 30%–50% of cases improved after 12 months follow-up [10] . However , the response to drug therapy is unpredictable , and the optimum duration has not been definitively determined [11] , [13] . Moreover , risk of recurrence remains the major problem in surgical or medical treatment [10] , [12] , [13] . Therefore , post-treatment or post-surgical follow-up of CE patients for several years is usually indicated . Imaging techniques such as MRI , X-ray or ultrasonography , are useful tools for follow-up of CE patients . However , these techniques are sometimes difficult to detect the newly growing small cyst and also to discriminate between dead and viable cysts [14] . Therefore , efforts have been directed at applying immunological tests of significantly diagnostic and prognostic values . ELISA and immunoblotting for serum antibody detection using various antigen preparations , including crude hydatid cyst fluid , purified fractions of antigen 5 or B , and E . granulosus protoscolex soluble extract , have been applied to follow up CE patients [15]–[20] . However , all of these tests exhibited problems mainly related to temporally delayed reactions to clinical changes [18] , [20] . Recombinant antigen B ( rAgB ) proved to have similar diagnostic value to native antigen B in CE patients [21] , [22] . However , there has been little or no application of rAgB for post-treatment follow-up of CE patients . In Tibetan regions of China , human cystic echinococcosis is highly endemic [23] . Albendazole therapy is the primary choice of treatment in the majority of patients owing to remote communities , poor socioeconomics and basic hospital facilities in Tibetan Autonomous Prefectures/communities . The current prospective study was designed to assess the effectiveness of cyclic albendazole treatment in community detected CE patients using ultrasonography as well as ELISA with rAgB as diagnostic/follow-up tools , and also to monitor the changes of serum specific IgG antibody levels against rAgB in these patients during treatment .
The study protocol was approved by the Ethical Committee of Sichuan Centers for Disease Control and Prevention ( Sichuan CDC ) . Clearance to carry out the study was obtained from Shiqu County CDC . Information about the purpose of the post-treatment follow-up study was spread to the villagers . Persons with confirmative ultrasound images of CE were voluntarily self-selected to be involved in this study by written informed consent and were assured free medical treatment with cyclic albendazole therapy if necessary . Recommendations were also provided for possible surgical intervention ( cyst removal ) . At confirmed ultrasound diagnosis , each patient was requested to complete a questionnaire which was designed to get information on demographics . Questions were mainly designed to identify clinical manifestations , history of any previous treatment with albendazole ( regular or irregular , duration ) , as well as history of surgery . At each follow-up , another questionnaire was completed to obtained information on administration of albendazole , surgery associated with echinococcosis , improvement of symptoms if any , adverse effects such as gastrointestinal disturbances , alopecia , jaundice , skin itch , hepatic pain/sting , dizziness etc . Chinese-Tibetan translators were employed when necessary . Diagnosis and classification of cystic echinococcosis ( CE ) was made using portable ultrasound according to the criteria proposed by the World Health Organization Informal Working Group on Echinococcosis for CE [12] , [24] . On the basis of patho-morphological features of cysts , CE lesions were differentiated into six types: CL , CE1 , CE2 , CE3 , CE4 and CE5 ( Figure 1 ) . Briefly , the CL type cyst refers to a cystic lesion of parasite origin without a clear rim indicating a very early stage of parasite development , while the presence of CE1 ( unilocular cyst with thick endo membrane ) or CE2 ( daughter cysts present ) is suggestive of active stages of the disease . While CE3 is broken into CE3a and CE3b characterized by detached cyst membrane and partial degeneration of daughter cysts , respectively , indicating the parasite is at a transitional stage , and CE4 and CE5 implies cyst involution , necrosis , partially calcified or inactive parasite [1] , [12] , [24] . Application of chest X-ray for diagnosis of lung CE was not carried out in this study . During May 2006 to November 2008 , mass ultrasound examination was carried out in eight Tibetan townships of Shiqu County ( Ganzi Prefecture , Sichuan Province ) for detection of individuals with abdominal cystic echinococcosis infection . Patients with CE1/CE2/CE3a/CE3b type cysts were invited to enroll in the current prospective follow-up study . All CE patients , whether enrolled or not , were offered free albendazole treatment . Cyclic treatment with albendazole was provided freely to each patient as 100-mg tablet at a daily dose of 10–15 mg/kg body weight ( in two divided doses , together with fat-rich meal ) . Cyclic treatment of 30 days was followed by a ‘wash out’ period of 7–10 days without albendazole [1] . Albendazole tablets sufficient for six months application were delivered to patients at each follow-up , to whom possible adverse effects were explained . In addition , albendazole was also available freely in the local county CDC clinic . Follow-up was carried out at six months intervals . Once a cystic lesion changed to CE4 type , the patient was requested to cease albendazole , but further regular ultrasound examination was necessary to understand if the cyst remained inactive . According to the questionnaire investigation , patients who took albendazole as requested during follow-up period were included in the regular-treated group , whereas others who did not take albendazole as requested due to poor compliance belonged to the irregular-treated group . The effectiveness of albendazole in CE patients assessed by ultrasound was described as follows: cured , improved , unchanged/static or aggravated . “Cured” was defined as disappearance of cysts , or degeneration of cyst contents . In other words , ‘cured’ referred to a cyst changing to a CE4 or CE5 type from a CE1 , CE2 , CE3a or CE3b type cyst . ‘Improved’ was determined as detachment of cyst membrane , partial degeneration of cyst contents ( or daughter cysts ) and/or reduction of cyst size , indicative of the cyst converting to CE3a/CE3b type from a CE1 or CE2 cyst . A ‘static’ or unchanged cyst showed no morphological and/or size changes . ‘Aggravated’ CE disease was defined as enlargement of the cyst and/or recurrence of daughter cysts . Approximately 3 ml of venous blood was taken voluntarily from patients at diagnosis ( during mass ultrasound screening ) as well as at each follow-up , and then centrifuged on the same day . Sera were aliquoted and stored at −20°C for later serological analysis . Blood transaminase levels were not monitored in the current study , due to the difficulty of doing liver function tests in the field . Information about the characteristics of hydatid cysts for new CE cases and follow-up CE patients was documented in detail , including the cyst type ( CE1-5 ) , the number of cysts ( single or multiple ) , location ( the lobe of the liver , abdominal cavity , pelvic cavity , spleen or kidney ) , and the size ( cm ) . ELISA with recombinant antigen B ( rAgB ) based on previous description [22] was performed on each serum sample for determination of Echinococcus specific IgG . Samples from the same patient were analyzed concurrently . The cut-off point was determined as the mean optical density plus 3 times standard deviation for a panel of serum samples obtained from healthy donors ( n = 30 ) . In these assays , 100-µl volume was applied throughout unless otherwise stated . 96-well microtiter plates ( MaxiSorp; Nalge Nunc International , Roskilde , Denmark ) were coated with diluted rAgB at 0 . 5 µg/ml in PBS overnight at 4°C . Plates were rinsed 3 times with PBST and blocked with 300 µl of 1% casein buffer at 37°C for 1 hr . Sera were diluted 1∶100 in 1% casein buffer . Plates with diluted sera were incubated in duplicate wells at 37°C for 1 hr and then washed five times with PBST . Rabbit anti-human horseradish peroxidase-conjugated protein G ( Zymed Laboratories , Inc . , South San Francisco , Calif . ) was diluted at 1∶4000 in 1% casein buffer and incubated at 37°C for 1 hour . Plates were washed five times with PBST . For colour development , substrate solution ( 0 . 4 mM 2 , 2′-azino-bis[3-ethybenzthiazoline-6-sulfonic acid] in 0 . 1 M citric acid buffer and 0 . 2 M Na2HPO4 ) was added into each well and incubated at room temperature for 30 min . Colour reaction was then stopped by application of 1% SDS in each well . The optical density at 405 nm was evaluated with an ELISA reader . Chi-square test was used to compare the occurrence rate of spontaneous involution between males and females , and the cure rate between the patient groups with albendazole course ≤6 months and those >6 months , ≤12 months and >12 months , ≤18 months and >18 months , and ≤24 months and >24 months . Significance was set at P≤0 . 05 .
A total of 196 persons with CE infection were registered in this study ( male = 83 , female = 113 ) , with a mean age of 37 . 5 years at diagnosis ( range 4–80 years ) . Of these 196 cases , 55 ( 28 . 1% ) had received prior regular albendazole therapy , while an additional 15 ( 8 . 2% ) had prior surgery . However , only 4 of 15 operated cases received regular albendazole treatment following surgery . Totally 98 of 108 CE patients without any previous albendazole treatment at diagnosis were investigated about clinical symptoms and signs , 55 . 1% ( 54 ) reported various degrees of discomfort , while 44 . 9% ( 44 ) were asymptomatic . The most common discomfort was hepatic or epigastric pain in 49 . 0% of patients , other complaints included abdominal distention , palpable abdominal mass , etc . All the 32 patients with cysts of CL , CE4 or CE5 type were asymptomatic , while 74 . 2% ( 49/66 ) of patients with cysts of CE1 , CE2 or CE3 presented various degrees of symptoms . Of these 196 cases , 37 ( 18 . 9% ) were observed at first examination , to have evidence of spontaneous involution of cystic lesions without any interventional procedures , presenting inactive cysts ( CE4 or CE5 ) in the liver . Persons with CE4 cysts had a mean age of 39 . 2 years ( n = 27 ) , while individuals with CE5 cysts had an average age of 61 . 3 years ( n = 10 ) . Of the 37 patients with evidence of spontaneous involution , 23 were male and 14 were female . In other words , spontaneous cure of cystic echinococcosis occurred more frequently in male ( 27 . 7% ) than in female ( 12 . 4% ) , and the difference was significant ( χ2 = 7 . 3 , P<0 . 01 ) . A total of 49 CE patients received regular albendazole treatment for 6 to 30 months , including 19 males and 30 females . The youngest CE case was 4 years old and the oldest was 80 years , with a mean age of 37 . 7 years . Cystic lesions were confined in the liver in 43 cases , and the remaining 6 cases had lesions not only to the liver , but also in the abdominal cavity . Of these 49 patients , 16 had CE1 type cysts , 17 had CE2 cysts , 10 had CE3a type cysts , and the remaining 6 had CE3b cysts ( Table 1 ) . The cyst measured ≥10 cm in 25 patients , the cyst varied in size 5 cm–10 cm in 20 cases , whereas the remaining 4 CE cases had cysts less than 5 cm ( Table 1 ) . Following 6 to 30 months regular therapy , 16 ( 32 . 7% ) of 49 patients were observed to have cysts that changed to CE4 type ( ie . considered cured ) , 24 ( 49 . 0% ) were observed to have cysts converted to CE3a or CE3b from CE1 or CE2 type ( ie . improved ) ( mean duration = 14 months ) , cysts remained unchanged in the other 7 ( 14 . 3% ) patients ( mean = 10 . 3 months ) , and enlargement of hydatid cysts was observed in the remaining 2 ( 4 . 1% ) patients ( Figure 2 . 1; Table 2 ) . The cure rate was 15 . 4% ( 2/13 ) and 38 . 9% ( 14/36 ) in the patient groups with albendazole course ≤6 months and those >6 months , 21 . 4% ( 6/28 ) and 47 . 6% ( 10/21 ) ) for the group with treatment duration ≤12 months and those >12 months , 22 . 9% ( 8/35 ) and 51 . 7% ( 8/14 ) for cases with treatment course ≤18 months and those >18 months , and 26 . 8% ( 11/41 ) and 62 . 5% ( 5/8 ) for patients with albendazole course ≤24 months and those >24 months . Further statistical analysis revealed that the cure rate was significantly different only between the patient group with treatment course ≤18 months and those >18 months ( χ2 = 5 . 24 , P<0 . 05 ) . The 16 ‘cured’ patients were composed of 5 CE1 cases , 5 CE2 , 4 CE3a and 2 cases with CE3b cyst . The treatment course for cure varied in patients with cysts at different stages , that is , the mean curative course was 26 . 4 months in CE2 patients , 20 . 4 months in CE1 cases and 9 months in CE3a/CE3b patients . Moreover , the curative duration also differed in cases with cysts at different size . Patients with large hydatid cysts ( ≥10 cm ) needed 22 . 3 months treatment ( n = 7 ) , whereas cure was achieved following 17 . 3 months therapy in cases with medium cysts ( 5 cm to 10 cm ) ( n = 8 ) and 6 months in patients with small cysts ( ≤5 cm ) ( n = 1 ) . In addition , 5 of these 16 cured CE patients were further followed up with ultrasound for 6 to 24 months , in whom the cysts remained inactive ( CE4 type ) , indicative of no recurrence . In contrast , 12 CE patients who poorly complied with albendazole treatment were observed to have much poorer prognosis during 6 to 30 months follow-up observation ( mean = 17 . 0 months ) . Of these 12 patients , cysts remained unchanged in 8 cases , while enlargement of the cyst or recurrence of daughter cysts was observed in the remaining 4 patients ( Figure 2 . 2 ) . Of 13 CE1 patients without any previous albendazole treatment , 7 ( 53 . 8% ) were sero- negative for a specific IgG response to rAgB ( Figure 2 . 3 ) . However , following 3 to 18 months albendzole therapy , positive IgG seroconversion was observed in 6 ( no . p1-p6 ) of these 7 initially seronegative cases ( Figure 2 . 3 and figure 2 . 4 ) . Concurrently , ultrasound scan detected detachment of cyst membrane and/or partial degeneration of cyst content ( i . e . CE1 type changed to CE3a type ) in all these six patients ( no . p1-p6 ) ( Figure 2 . 4 ) . In another patient ( no . p7 ) in whom serum specific IgG antibody remained negative during 6 months follow-up period ( Figure 2 . 4 ) , ultrasound scan did not detect any changes of the cyst ( the image was not shown ) . A questionnaire investigation revealed that this patient had reported a poor compliance with albendazole therapy . Sequential serum samples ( n = 36 ) were obtained from 8 CE patients ( CE1 = 4 , CE2 = 3 and CE3b = 1 ) , who did not receive any previous chemotherapy at diagnosis and were considered to be cured according to ultrasound images following albendazole therapy in the current study . At least 3 serum samples were monitored in each patient . The follow-up period ranged from 24 months to 78 months ( mean 39 . 8 months ) , and an average 4 . 5 serum samples were taken from each patient . Longitudinal assessment of specific IgG antibody against rAgB in ELISA revealed that serum antibody levels of IgG were initially elevated , and subsequently decreased in five ( 4 CE1 and 1 CE2 ) of these 8 patients ( Figure 2 . 5 ) . In another two patients ( 1 CE2 and 1 CE3b ) , specific IgG antibody levels decreased but with a minor fluctuation during albendazole administration . In the remaining patient with a CE2 type cyst , a cured CE end-point was achieved following treatment , but there was no significant change of specific IgG antibody levels during the period of treatment ( Figure 2 . 6A ) . Serum specific IgG antibody in fact remained positive in all the 8 patients where CE was considered ‘cured’ even 24 months after cure in one CE patient ( ie . P2 ) ( Figure 2 . 5A ) . Consecutively collected 25 sera from 6 patients ( CE1 = 2 and CE2 = 4 ) with improved CE following albendazole treatment were also assessed for specific IgG antibody against rAgB . Of these 6 cases , four ( 1 CE1 and 3 CE2 ) did not receive any previous albendaozle therapy at diagnosis , and initial elevation and subsequent decline of specific IgG antibody levels occurred in all these 4 patients ( Figure 2 . 6B ) . For the other 2 cases with previous albendazole treatment at diagnosis , all antibody levels were observed to progressively decrease following therapy . In contrast , specific IgG antibody levels were observed to sharply increase in one patient with evidently aggravated CE following irregular albendazole administration , presenting enlargement of the cyst and recurrence of daughter cysts ( Figure 2 . 2 H ) . Of 83 CE patients who were investigated about adverse effects related to albendazole administration , 37 ( 44 . 6% ) reported no subjective adverse reactions , while 45 ( 54 . 2% ) reported gastrointestinal disturbance , exhibiting stomach ache , acid regurgitation , nausea or diarrhea . Additionally , headache , dizziness and hepatic pain ( or 'sting' ) each occurred in at least one case besides gastrointestinal disturbance . Evident alopecia was not reported and observed in the current study . Therapy with albendazole was ceased in 2 cases during follow-up , due to occurrence of serious dizziness and stomach ache . Transaminase levels were not monitored in the current study , due to the difficulty of doing liver function tests in the field .
The accession number in GenBank for recombinant antigen B applied in the current study is Z26336 . | Cystic echinococcosis is a serious public health problem in Tibetan communities of northwest Sichuan Province , China . Antiparasitic treatment with albendazole remains the only choice in most cases , due to the poor socio-economy and inadequate hospital facilities in this area . A post-treatment follow-up study was carried out in community-detected 49 CE cases by application of abdominal ultrasound and serology with recombinant antigen B ( rAgB ) in a Tibetan region of Sichuan from 2006 to 2008 . Following 6 to 30 months regular albendazole therapy , 32 . 7% of CE cases were considered cured at ultrasound , 49 . 0% were classed as improved , 14 . 3% remained unchanged or static , and 4 . 1% of cases became aggravated . The treatment course for cure was longer in patients with CE2 type cyst pathology compared to cases with CE1 , CE3a or CE3b type cysts . In addition , patients with large cysts ( ≥10 cm ) had a longer curative duration compared to those with medium cysts ( 5–10 cm ) or small cysts ( <5c m ) . The changes of serum specific IgG antibody levels against rAgB were not strongly associated with the viability of cystic echinococcal lesions , however , post-treatment specific IgG antibody positive sero-conversion in initially seronegative CE1 patients , was an indicator for the albendazole efficacy in specific CE patients . | [
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"imaging"
] | 2011 | Post-Treatment Follow-Up Study of Abdominal Cystic Echinococcosis in Tibetan Communities of Northwest Sichuan Province, China |
Strongyloidiasis is a much-neglected but sometimes fatal soil born helminthiasis . The causing agent , the small intestinal parasitic nematode Strongyloides stercoralis can reproduce sexually through the indirect/heterogonic life cycle , or asexually through the auto-infective or the direct/homogonic life cycles . Usually , among the progeny of the parasitic females both , parthenogenetic parasitic ( females only ) and sexual free-living ( females and males ) individuals , are present simultaneously . We isolated S . stercoralis from people living in a village with a high incidence of parasitic helminths , in particular liver flukes ( Clonorchis sinensis ) and hookworms , in the southern Chinese province Guangxi . We determined nuclear and mitochondrial DNA sequences of individual S . stercoralis isolated from this village and from close by hospitals and we compared these S . stercoralis among themselves and with selected published S . stercoralis from other geographic locations . For comparison , we also analyzed the hookworms present in the same location . We found that , compared to earlier studies of S . stercoralis populations in South East Asia , all S . stercoralis sampled in our study area were very closely related , suggesting a recent common source of infection for all patients . In contrast , the hookworms from the same location , while all belonging to the species Necator americanus , showed rather extensive genetic diversity even within host individuals . Different from earlier studies conducted in other geographic locations , almost all S . stercoralis in this study appeared heterozygous for different sequence variants of the 18S rDNA hypervariable regions ( HVR ) I and IV . In contrast to earlier investigations , except for three males , all S . stercoralis we isolated in this study were infective larvae , suggesting that the sampled population reproduces predominantly , if not exclusively through the clonal life cycles . Consistently , whole genome sequencing of individual worms revealed higher heterozygosity than reported earlier for likely sexual populations of S . stercoralis . Elevated heterozygosity is frequently associated with asexual clonal reproduction .
Soil-transmitted helminths ( STHs ) are parasitic worms that infect hosts by transmitting their eggs or larvae through contaminated soil . Among them are parasitic nematodes like giant roundworms ( Ascaris lumbricoides ) , hookworms ( Necator americanus and Ancylostoma spp . ) , whipworms ( Trichuris ) and threadworms ( Strongyloides ) . STHs infect up to one quarter of the world's population and cause helminthiases , which are considered to be neglected tropical diseases ( NTDs ) . Impoverished populations with limited access to clean water , sanitation , and opportunities for socioeconomic development are disproportionately affected [1] . Strongyloides stercoralis is the prime causative agent of human strongyloidiasis [2] . Estimates of worldwide human infection with S . stercoralis vary but go up to 370 million [3–5] . S . stercoralis is generally more prevalent in tropical and subtropical countries , and local prevalences of over 40% in particular regions have been reported [6 , 7] . However , the presence of S . stercoralis and strongyloidiasis , including fatal cases , have also been reported from well-developed regions with temperate climates such as the European Union and North America [8–15] . S . stercoralis has a rather unique life cycle ( Fig 1A ) with the possibility of forming free-living adults in between parasitic generations [16–18] . The parasitic adults are all females and reproduce by mitotic parthenogenesis . Nevertheless , they can produce progeny of both sexes . Their female progeny have three developmental options . They can develop into infective third stage larvae ( iL3 ) within the host ( 1 in Fig 1A ) and infect the same host without ever leaving the host ( auto infective cycle , notice that auto infective iL3s are not normally present in naturally deposited feces and are therefore not detectable with the methods employed in this study ) . Alternatively , the female larvae can leave the host with the feces as early ( first stage ) larvae and continue their development in the environment . There , in approximately one to two days , they either become iL3s ( 2 in Fig 1A ) , which search for another host ( direct/homogonic cycle ) or develop into free-living adults ( 3 in Fig 1A ) , as do all the males ( 4 in Fig 1A ) ( indirect/heterogonic cycle ) . The free-living adults reproduce sexually . Their progeny are all females and invariably develop into iL3s . The auto infective cycle appears to be specific for S . stercoralis and it is a prerequisite for the severe pathogenicity caused by this species [16] . This explains why strongyloidiasis in humans is a severe disease but Strongyloides spp . are of only very minor veterinary concern [4 , 19] , with the exception of great apes in captivity [20] and dogs [21] , which are also hosts for S . stercoralis . The auto infective cycle allows the infection to persist in one host for many years , which is much longer than the life expectancy of an individual worm . Most of the time , infection with S . stercoralis is asymptomatic in healthy hosts , rendering it rather unlikely to be detected . If the host becomes immuno-deficient , the control of the infection may fail , resulting in a self-enhancing progression of strongyloidiasis , known as hyperinfection syndrome and disseminated strongyloidiasis , which is lethal if not treated [3–5 , 22] . All species of Strongyloides investigated so far may undergo homogonic or heterogonic development . The switch between the two life cycles is influenced by various environmental factors , such as the immune status of the host , temperature or food availability , but also by genetic pre-disposition [23] , such that different isolates may show very different homogonic to heterogonic ratios even under standard laboratory conditions [24] . Hookworms are among the most prevalent parasitic nematodes in humans . The estimation of hookworm human infection is between 576–740 million ( estimate of the CDC , https://www . cdc . gov/parasites/hookworm/index . html , assessed January 21st 2019 ) [25] . Necator americanus and Ancylostoma duodenale are the most common human hookworm species but an increasing number of presumably zoonotic infections with Ancylostoma ceylanicum has been reported from Asia [26] . In China , all these three species are present . Infections with a small number of hookworms are normally asymptomatic , while more severe infections cause medical problems associated with the blood sucking life style of these worms . The life cycle of hookworm is rather simple ( Fig 1B ) . The female and male parasitic adults mate inside the host , producing eggs which are passed by defecation . The larvae hatch and develop into iL3s in the environment and are then ready to infect the next host [27] . Hookworms and Strongyloides spp . are phylogenetically rather distant from each other , belonging to different major clades and their parasitic life styles have presumably arisen independently in evolution [28–30] . Nevertheless , their modes of transmission are very similar . The iL3s of hookworms and Strongyloides mature in the environment , penetrate the skin of the host and undergo a similar body migration . Therefore , hookworm and Strongyloides co-infection were frequently observed all around the world ( Argentina [31] , Brazil [32] , Cambodia [33] , Tanzania [34] , Côte d’Ivoire [35] , Ghana [36] , Thailand [37] ) . Parts of the ribosomal cox1 gene and the nuclear small ribosomal subunit rDNA ( 18S rDNA , SSU ) sequence , in particular the hyper variable regions I and IV ( HVR-I and HVR-IV ) , are frequently used as markers for molecular taxonomy in nematodes ( e . g . [29 , 38–45] ) including Strongyloides spp . [46–53] . In S . stercoralis isolated from humans the HVR-IV appears virtually invariable . Only one instance of a single nucleotide difference to the reference sequence AF279916 has been reported [49] ( accession number M84229 ) . It should , however be noted that [50] found a different HVR-IV sequence in a large portion of the Strongyloides sp . isolated from dogs , which are generally also considered to be S . stercoralis . In HVR-I , several sequence variants were found by multiple authors [47 , 50 , 51 , 53] . In three recent studies , by genotyping individual S . stercoralis isolated from humans in Cambodia [50 , 53] and Myanmar and Japan [51] , two polymorphic positions were identified in the region around the HVR-I of human derived S . stercoralis . One is a sequence of four or five consecutive Ts located in the HVR-I proper corresponding to position number 176–179 of the reference sequence AF279916 , the other one is an A/T polymorphism at position 458 . Interestingly , although all three studies found worms of different haplotypes to occur sympatrically , sometimes even within the same host individual , [53] and [50] found no and [51] only very few hybrids between different haplotypes , sparking the question if multiple , genetically isolated populations of S . stercoralis exist in humans . Whole genome analysis of individual worms suggested that substantial genetic diversity exists among S . stercoralis isolated from human hosts [51 , 54] but did not support the hypothesis of separate populations defined by the different SSU HVR-I haplotypes [50] . To differentiate the different hookworm species Necator spp . and Ancylostoma spp . , the commonly used nuclear molecular markers are ribosomal ITS rather than 18S sequences [40 , 41 , 55–58] . Based on ITS and mitochondrial cox1 sequences , [40 , 41] defined multiple genetically separated groups of what would generally be considered N . americanus in humans in Africa and proposed that they should possibly be considered different species , i . e . N . americanus and N . gorillae . Here we describe the isolation and genomic characterization of a population of S . stercoralis from a village and local clinics in the Guangxi province in Southern China . In contrast to earlier studies , the vast majority of individuals in this population appear heterozygous for different SSU haplotypes and they reproduce predominantly , if not exclusively , through the non-sexual auto-infective and homogonic life cycles . Consistent with asexual reproduction , we found elevated heterozygosity in the genomes of individual worms , compared with individuals from other populations of S . stercoralis . All S . stercoralis isolated in Guangxi were closely related arguing for a rather recent common source of infection . As a comparison , we also analyzed the hookworms isolated from the same village . While all these hookworms belonged to the species Necator americanus , they showed a rather high genetic diversity , even within host individuals , in comparison with other N . americanus populations .
The sampling of human derived material including the procedures to obtain informed consent , was approved by the "Medical Ethics Committee" of the Guangxi Medical University ( no project specific number issued ) . All participants were volunteers and gave informed consent . Because not all people in the study area are literate , a protocol of oral consent was used as follows . The people were informed about the study by representatives of the local Center for Disease Control ( CDC ) . The participants , or in the case of children their parents , showed their consent in two steps . First , they registered for the study and claimed collection containers . At this step , the participants were added to a list and assigned a number such that the health care providers could later identify , inform and treat the infected people but the sample was anonymized for the scientific analysis . Second , the participants submitted the sample the next day . Also this step was voluntary and a number of people did not return samples in spite of having claimed collection containers . Participants received a small financial compensation according to local habits . All participants found to be infected with pathogens were treated with anthelminthic drugs by the local disease control and prevention center ( CDC ) according to the related treatment guidelines . Experiments involving S . stercoralis culture in host animals were in accordance with the "Guiding Opinions on the Treatment of Laboratory Animals" ( issued by the Ministry of Science and Technology of the people's republic of China ) and the Laboratory Animal Guideline for Ethical Review of Animal Welfare ( issued by the National Standard GB/T35892-2018 ) and were reviewed and approved by the "Animal Care and Welfare Committee" of the Guangxi Medical University ( S5 File ) . Human fecal samples were collected in Long An ( LA ) ( 23°13’N 107°25’E ) and Qing Xiu ( QX ) ( 22°46’N 108°39’E ) in May and June 2018 . Both districts are located in the region around Nanning , Guangxi province , Southern China . These two districts were chosen either because a generally high incidence of helminth parasites had been noticed in an earlier survey ( LA ) or because an inhabitant had recently been diagnosed with strongyloidiasis in a local hospital ( QX ) . Sample boxes were distributed to the people who agreed to participate in this study and in the following day fecal samples were collected . In both districts ( LA and QX ) fecal samples were collected for two consecutive days . To identify the helminth eggs , approximately 1g fresh feces from each sample were examined by the Acid Ether Sedimentation method as described [59–61] . In brief , feces were mixed with 7 ml 19% hydrochloric acid and large debris were removed . Then 3 ml ether was added , mixed thoroughly and centrifuged at 1500 rpm for 5 min . The centrifugation resulted in four layers , which were the ether , lipid debris , hydrochloric acid , and the sediment . After removing the upper three layers , one drop of 2% iodine was added to the sediment to stain the fixed eggs , which were observed microscopically . To isolate worms , the rest of the fecal samples were processed as described [50] . In brief , feces were mixed with sawdust and moisturized with water , then cultured at ambient temperature for 24–48 hours to allow the larvae to develop to a stage where individuals destined to become iL3s , free-living males and free-living females can unambiguously be distinguished morphologically . Then the worms were isolated with Baermann funnels . Notice that auto infective iL3s are not normally isolated with this methodology . From the positive Baermann funnels , worms were transferred individually into 10 μl water and stored at -80°C . The samples from local clinics we obtained in the form of isolated worms conserved in 70% ethanol and stored at ambient temperature . These worms were washed twice in water , and transferred individually into 10 μl water and stored at -80°C . Fecal samples were also collected from dogs from S . stercoralis positive households with the consent and the help of the owners . The samples were taken directly from the rectums of the animals . Feces were placed on NGM agar plates and incubated for 24–48 hours at ambient temperature . Any emerging worms were directly examined and collected . Worms stored in 10 μl water were frozen and thawed 3 times with liquid nitrogen . Then 10 μl 2X lysis buffer ( 20 mM Tris-HCl pH 8 . 3 , 100 mM KCl , 5 mM MgCl2 , 0 . 9% NP-40 , 0 . 9% Tween 20 , 240 μg/ml Proteinase K ) were added and incubated at 65°C for 2 hours . The worm lysates were stored at -80°C until they were transported to Tuebingen on wet ice and stored again at -80°C until analyzed . 2 . 5 μl of worm lysate were used as template for PCR amplification of SSU HVR-I , SSU HVR-IV , ITS and cox1 by using Taq DNA polymerase ( M0267 , New England BioLabs ) according to the manufacture’s protocol . Cycling protocol: An initial denaturation step at 95°C for 30 sec was followed by 35 cycles of denaturation at 95°C for 20 sec , annealing for 15 sec , extension at 68°C for 90 sec and a final extension step of 5 minutes at 68°C . The primers and the respective annealing temperatures are listed in Table 1 . Sequencing reactions were done with 1 μl of the PCR products and the sequencing primers listed in Table 1 using the BigDye Terminator v3 . 1 Cycle sequencing Kit ( Applied Biosystems ) according to the manufacturer’s protocol . The reactions were submitted to the in-house sequencing facility at the Max Planck Institute for Developmental Biology at Tuebingen for electrophoresis and base calling . The sequences were analyzed with SeqMan Pro version 12 ( Lasergene package; DNAStar , Inc . , Madison , WI USA ) and inspected manually . For position numbering the following sequences were used as reference: AF279916 for S . stercoralis SSU sequences; LC050212 for S . stercoralis cox1 sequences; AJ920348 and AF217891 for hookworm SSU and ITS , respectively; AJ417719 for hookworm cox1 . For S . stercoralis cox1 the same 552 bp as in [50] were considered . For hookworm cox1 the same 670 bp as in [40] were considered . The cox1 sequences were aligned and phylogenetic analysis was performed using MEGA7 [62] with the maximum-likelihood ( ML ) model as described previously [50] . For the S . stercoralis tree , Necator amercanus ( AJ417719 ) was used as outgroup species . For the Necator amercanus tree , Ancylostoma duodenale ( AJ417718 ) , Ancylostoma caninum ( FJ483518 ) and S . stercoralis ( LC050212 ) were used as outgroup species . For comparison , selected published cox1 sequences were also included in the analysis . The corresponding accession numbers and references are listed in Figs 2 and 3 . In order to determine which HVR -I and HVR-IV haplotypes occurred in the same allele of the SSU heterozygous worms , we amplified 1625 bp out of 1703 bp of the SSU ( positions 39 to 1663 in the reference AJ417719 ) , including both , HVR-I and HVR-IV of individual S . stercoralis . 2 . 5 μl of worm lysate were used as template for PCR amplification by using Q5 Hot Start high-fidelity DNA polymerase ( M0493 , New England BioLabs ) according to the manufacture’s protocol . Cycling protocol: An initial denaturation step at 98°C for 30 sec was followed by 35 cycles of denaturation at 98°C for 10 sec , annealing for 15 sec , extension at 72°C for 90 sec and a final extension step of 2 minutes at 72°C . The primers and the annealing temperature are listed in Table 1 . The PCR products were purified with Wizard SV Gel and PCR Clean-Up System ( A9282 , Promega ) . 3’ A-overhangs were added and cloned into pCR 2 . 1-TOPO vector and transfected into TOP10 competent E . coli cells by using the TOPO TA Cloning Kit ( 45–0641 , Invitrogen ) following the manufacturers protocol . For each S . stercoralis , multiple colonies were selected and cultured overnight at 37°C and 200 rpm in 2 ml LB medium containing 50 μg/ml ampicillin . Plasmids were isolated using the QIAprep Spin Miniprep Kit ( 27106 , QIAGEN ) . The presence of an insert was confirmed by EcoR I ( FD0274 , Thermo Fisher Scientific ) restriction analysis . Sequencing was done using the BigDye Terminator v3 . 1 Cycle sequencing Kit ( Applied Biosystems ) as described above with 1 μl of plasmid DNA as template and the sequencing primers described in Table 1 . The sequences were analyzed as described above . 4-week-old female gerbils were brought from Zhejiang Medical College and injected with prednisolone acetate ( 3 mg ) 2 days before infection and once per week after infection . Around 300 infective larvae isolated from one human host ( QX24 ) were washed three times in tap water and incubated in PBS with antibiotic ( 50 mg/L streptomycin and 50 mg/L penicillin ) for 1h at ambient temperature . Infective larvae were then washed again in water , and injected subcutaneously at the neck of one gerbil . Feces of the gerbil were collected daily for 1 month starting from 7-day post infection . Feces were moisturized with water and incubate at ambient temperature for 1 day followed by Baermann analysis . Genomic libraries of 29 S . stercoralis ( 26 infective larvae , 3 FL males ) isolated from China and 7 S . stercoralis ( 5 FL males , 2 FL females ) isolated from Cambodia were prepared as follows: 12 μl worm lysate were mixed with 4 μl paramagnetic bead-immobilized Tn5 transposomes ( Tn5 expressed and purified according to [63] ) and 4 μl 5X TAPS-DMF MgCl2 buffer ( 50 mM TAPS , 25 mM MgCl2 , 50% DMF ) . The mixture was incubated for 14 min at 55°C . Following tagmentation , Tn5 was stripped from DNA using SDS-containing buffer ( 20 μl , 30 mM Tris pH 8 . 0 , 50 mM NaCl , 0 . 1% Tween 20 , 0 . 6% SDS ) with incubation at 55°C for 4 min . The beads were then washed twice with 125 μl wash buffer ( 30 mM Tris pH 8 . 0 , 50 mM NaCl , 0 . 1% Tween 20 ) on a magnetic stand . PCR amplification , adapter extension and barcoding were done by adding 10 μl 5x Q5 reaction buffer , 1 μl 10 mM dNTP , 1 . 5 μl of 10 μM Nextera i5 primer and i7 primer , 0 . 5 μl Q5 high-fidelity DNA polymerase ( M0491 , New England BioLabs ) and 35 . 5 μl H2O followed by the thermocycling program: 72°C for 5 min , 98°C for 30 sec , followed by 16 cycles of denaturation ( 98°C for 15 sec ) , annealing ( 66°C for 20 sec ) , extension ( 72°C for 90 sec ) and cooling to 4°C . The 250–550 bp fragments of PCR products were selected with HighPrep beads ( MagBio Genomics ) . Libraries were sequenced on an Illumina HiSeq 3000 instrument ( 150 bp paired-end ) at the Genome Core facility at the MPI for Developmental Biology .
In the village LA , fecal samples were collected from 108 persons . We detected liver fluke ( Clonorchis sinensis ) ( 23 = 21 . 3% ) and hookworms ( 12 = 11 . 1% ) but no S . stercoralis . In the village QX , fecal samples were collected from 98 persons . We detected liver fluke ( C . sinensis ) ( 59 = 60 . 2% ) , hookworms ( 17 = 17 . 3% ) and S . stercoralis ( 7 = 7 . 1% ) . For full information see S1 File . Further , we sampled seven of the eight dogs present in the three dog owning households with S . stercoralis positive people . No S . stercoralis were found in these dogs . Since several species of hookworms are present in China [71] and they are not easily distinguishable by morphology , we determined the SSU sequences of 231 hookworms from 19 human hosts ( 11 from LA , 8 from QX ) . All of them were identical with the published sequence of Necator americanus ( AJ920348 ) . In order to connect our work to [40 , 41] , which did not report the SSU sequences of its isolates , we determined the ITS-1 and ITS-2 sequences of 108 hookworms and 670 bp of the mitochondrial gene cox1 of 100 hookworms from the 19 host individuals . All of them had the same ITS sequences ( MK036418 [ITS-1] and MK036419 [ITS-2] ) , which correspond to ITS type I as defined by [40] . For cox1 we identified a total of 18 different haplotypes ( GenBank accession numbers MK040540-MK040557 ) . 6 cox1 haplotypes were present in more than one host individual and multiple cox1 haplotypes were found in 8 of the 19 host individuals . For more details see S2 File . A phylogenetic comparison with the cox1 haplotypes identified in earlier studies [40 , 41] ( Fig 2 ) showed that all our 18 cox1 haplotypes fall into clade A as defined by [40] . Taken together , our molecular data indicate that all hookworms isolated in this study are N . americanus of ITS type I and cox1 clade A sensu [40] . Partial sequence ( 552bp ) of the mitochondrial gene cox1 was obtained from 69 S . stercoralis representing all seven positive human hosts in the village and one of the patients from a local hospital . All the 53 worms from six hosts shared the same haplotype ( H175 , GenBank accession number MK040537 ) while all the 15 worms isolated from the seventh host were of another haplotype ( H205 , GenBank accession number MK040538 ) , which differed at one position from H175 . In the hospital derived sample we identified a third haplotype ( H447 , GenBank accession number MK040539 ) that differed from H205 and H175 by 2 and 1 nt , respectively . For more details see S3 File . To examine the phylogenetic relationships , we reconstructed a maximum-likelihood tree with our and selected published cox1 sequences [6 , 48–51] ( Fig 3 ) . Our cox1 haplotypes group clearly with the ones found in Cambodia in humans and dogs but not with the dog specific ones [50] . With moderate bootstrap support , these haplotypes could be assigned to a group described as clade B by [6] or clade Ib by [51] . Our attempt to culture this isolate of S . stercoralis in gerbils failed . A total of 177 S . stercoralis from 9 humans were sequenced at the SSU HVR-I and/or HVR-IV loci . Only 2 infective larvae and 3 free-living males appeared homozygous or hemizygous ( the SSU is on the X chromosome ) for either one of the HVR-I haplotypes I and III described by [50] ( Tables 2 and 3 ) . In HVR-IV the same 5 worms plus another 28 infective larvae appeared homozygous or hemizygous for either haplotype A or C . Haplotype A is the typical haplotype for S . stercoralis isolated from humans [50] . Haplotype C is a novel haplotype identified in this study , which has a T deleted at position 1265 ( compared with AF279916 ) ( Tables 2 and 3 ) . Interestingly , unlike in earlier studies [50 , 51 , 53] , in this study the vast majority of S . stercoralis individuals appeared to be heterozygous at the SSU locus ( Table 3 ) . All of them can be explained with the hypothesis that the worms were hybrids of two haplotypes described in Table 2 . To confirm this and to determine which combinations of HVR-I and HVR-IV haplotypes existed we amplified a 1625 bp fragment containing both HVRs from the individual worms described in Table 4 , cloned the PCR product and sequenced individual clones . From host QX32 , where all S . stercoralis isolated from this host were hybrids in HVR-I but not in HVR-IV , the two dominant SSU alleles were haplotypes I and III ( HVR-I ) in combination with haplotype A ( HVR-IV ) . From the other three hosts ( QX24 , QX97 and QX105 ) , all S . stercoralis were hybrids in both HVR-I and HVR-IV . Here the two dominant SSU haplotypes were I ( HVR-I ) with C ( HVR-IV ) and III ( HVR-I ) with A ( HVR-IV ) ( Table 4 ) . In several S . stercoralis females analyzed , we identified more ( up to 4 ) than the two SSU haplotypes expected for heterozygous animals ( Table 4: rows 3–11 ) . For comparison , we repeated the experiment with S . stercoralis males from Cambodia [50] ( Table 4: rows 12–19 ) . In 2 cases , we found more than one SSU haplotype ( notice that the SSU is on the X chromosome ) . This suggests that in the population under study and , maybe to a lesser extent , in the population in Cambodia there is appreciable SSU sequence variation among the different rDNA copies within a haploid genome . Intra-individual variability of rDNA sequences , although apparently very rare in animals , has been observed before , for example in American sturgeons [74] or in the plant parasitic nematode Rotylenchulus reniformis [75] . So far , the variant 4T+A , which occurred as a minor variant and in combination with haplotype A ( HVR-IV ) , had not been reported in any of the studies from East Asia but has recently been observed in S . stercoralis from dogs in Switzerland by [21] and named haplotype VI by these authors . In order to extend our analysis beyond the cox1 and SSU sequences , we sequenced the whole genome of individual S . stercoralis . For comparison , the published genome sequences of selected S . stercoralis samples from Cambodia [50] , Myanmar and Japan [54] were also included in this analysis .
Southern China has a subtropical climate which favors a variety of parasites . The main purpose of this study was the isolation and the genomic/genetic characterization of individual S . stercoralis and not to conduct a prevalence study or a general parasitological survey . Accordingly , the number of people surveyed was rather limited . Nevertheless , there are a few observations worth mentioning . As expected based on our experience as routine diagnostics provider , the predominant helminths detectable by egg floatation were liver fluke and hookworm . Usually , in the routine parasitological diagnosis , the species of the hookworms is not determined . However , in this study we used molecular diagnostic tools and could show that all hookworms found belonged to the species N . americanus . Usually , S . stercoralis is not detectable by egg floatation . Therefore , we used culturing and Baermann funnels to test the presence of this parasite and to isolate live individual worms , which is not normally done in our diagnostic routine . Very few studies describing S . stercoralis prevalences in China were recently published in international journals [78–81] . All such studies we are aware of , were conducted by the same research group and in the Yunnan province , which neighbors the Guangxi province . Our study illustrates that S . stercoralis is also prevalent in Guangxi , a fact that does not come as a surprise if also clinical case reports ( usually only available in Chinese language ) are taken into account . As a matter of fact , in a review of such cases between 1973 and 2011[82] more human S . stercoralis cases in Guangxi than in Yunnan were reported . Hookworm co-infection with S . stercoralis are common [31]-[37] and they have similar transmission routes . One could therefore expect , that the dynamics and spreading of these two helminths are similar . This study does not support this conclusion . We found only 2 out of the 7 people infected with S . stercoralis were also positive for N . americanus , which corresponds about to the expectation based on the prevalences ( 17 . 3% for hookworms and 7 . 1% for S . stercoralis ) . Also , the hookworms were genetically diverse , while the S . stercoralis were all very closely related . While the auto infection cycle allows S . stercoralis to maintain an infection for decades , N . americanus , in absence of new infection , can only persist for the life time of the individual parasites which is in the order of a few years [25] . Taken together , this suggests that in our study population the transmission rate of hookworms is much higher than the one of S . stercoralis . The S . stercoralis positive patients have possibly been infected rather long time ago and maintained the infection through the auto infective cycle . Contrary to earlier studies [50 , 51 , 53 , 54] we found most S . stercoralis individuals to be heterozygous for different SSU haplotypes . While the SSU HVR-I haplotypes had all been described in S . stercoralis before ( although almost exclusively in homozygous state ) , we identified a novel SSU HVR-IV haplotype . These findings are of importance for SSU-sequence-based diagnostics and taxonomy of S . stercoralis and closely related species of Strongyloides . The occurrence of S . stercoralis heterozygous for multiple SSU haplotypes may , but not necessarily needs to be , related to our second striking observation , namely the virtual absence of free-living adults . The switch between the clonal direct and the sexual indirect cycle in Strongyloides spp . is influenced by the environment , in particular the temperature and the immune statues of hosts , and the genetic background [23] . We cannot completely exclude that at different times of the year , when temperatures are different , in our study area , more sexual animals could be found . However , the climatic conditions in Guangxi are comparable with northern Cambodia where we found numerous free-living adults of both sexes at the same time of the year . We can also not fully exclude that the immune status of the hosts in this study very strongly favored females developing into iL3s . However , our genomic analyses suggest that our study population is largely , if not exclusively asexual . We detected a genome wide heterozygosity , which was even higher than the one described as elevated in the Japanese samples by [54] . These authors attributed the observed heterozygosity to the fact that the worms had accumulated mutations during the asexual reproduction through the auto infective cycle since the infection of the particular host individual . We do not think clonal reproduction only within individual patients could explain our observations . First , in our study the heterozygosity was higher . Second , the worms in the different patients were very closely related . Third , the observed fraction of around 50% nonsynonymous changes is similar to the ones observed in wild populations of the free-living nematode P . pacificus and C . elegans and considerably lower than in experimental populations of the same two species , where mutations were allowed to accumulate under minimal purifying selection resulting in around 70% - 75% nonsynonymous mutations in coding regions [83 , 84] . This indicates that most variants present in our sample did not arise under conditions of relaxed purifying selection . Nevertheless , we did observe a slightly but significantly higher proportion of nonsynonymous changes among the variants not found in the Cambodian population . Given that presumably only a fraction of these variants were recently introduced to our study population . This observation may be a hint for a limited time period of mutation accumulation with reduced purifying selection . Loss of sexual reproduction has been reported for specific isolates of other species of Strongyloides . For S . ratti , largely asexual populations have been described [24] and in a laboratory strain of S . venezuelensis ( HH1 , originally isolated from Okinawa Japan ) no males and only very few free-living females were observed under various conditions [85] . We favor the hypothesis that our study population has rather recently , but prior to the infection of the current host individuals , become predominantly if not exclusively asexual as a consequence of one or several hybridization events between sexual populations of S . stercoralis . This is consistent with the observations of high heterozygosity and the origin of most non-reference variants during a period with purifying selection at a level normal for sexual reproduction . It is important to notice that , if this hypothesis is true , the first generation of hybrids must have been capable of sexual reproduction because the results shown in S1 Fig can only be explained if meiotic recombination occurred at least once after the hybridization event . Asexual genotypes then arose in the next generations due to the particular combination of genetic material derived from the parental lines . | The vast majority of multicellular organisms reproduce sexually . Sexual reproduction is believed to be advantageous because meiotic recombination separates beneficial and deleterious mutations and generates new , possibly better allele combinations . However , sexual reproduction comes at a cost . Beneficial allele combinations are broken up and , in gonochoristic species , there is the "two-fold cost of sex" due to the investment in "unproductive" males . Strongyloides stercoralis , the causing agent of the grossly neglected but sometimes fatal human strongyloidiasis , appears to get the best of both worlds . Depending on the environmental conditions , S . stercoralis switches between asexual parasitic and sexual free-living generations . In the Guangxi province ( China ) we identified a population of S . stercoralis that appears to have recently transitioned to predominantly if not exclusively reproducing asexually . We failed to detect sexual stages and , in the genomes , we found indication of asexuality such as a high heterozygosity , compared with other populations of S . stercoralis . Additionally , global within-species phylogenetic analysis showed that in our study area , all S . stercoralis form one group of fairly close relatives , suggesting a rather recent common origin . In contrast , the hookworms ( Necator americanus ) , which are phylogenetically distant from but share the infection route with S . stercoralis , in a within-species phylogenetic comparison fall into various groups , suggesting much greater diversity . | [
"Abstract",
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] | 2019 | Characterization of a non-sexual population of Strongyloides stercoralis with hybrid 18S rDNA haplotypes in Guangxi, Southern China |
A central issue in developmental biology is to uncover the mechanisms by which stem cells maintain their capacity to regenerate , yet at the same time produce daughter cells that differentiate and attain their ultimate fate as a functional part of a tissue or an organ . In this paper we propose that , during development , cells within growing organs obtain positional information from a macroscopic physical field that is produced in space while cells are proliferating . This dynamical interaction triggers and responds to chemical and genetic processes that are specific to each biological system . We chose the root apical meristem of Arabidopsis thaliana to develop our dynamical model because this system is well studied at the molecular , genetic and cellular levels and has the key traits of multicellular stem-cell niches . We built a dynamical model that couples fundamental molecular mechanisms of the cell cycle to a tension physical field and to auxin dynamics , both of which are known to play a role in root development . We perform extensive numerical calculations that allow for quantitative comparison with experimental measurements that consider the cellular patterns at the root tip . Our model recovers , as an emergent pattern , the transition from proliferative to transition and elongation domains , characteristic of stem-cell niches in multicellular organisms . In addition , we successfully predict altered cellular patterns that are expected under various applied auxin treatments or modified physical growth conditions . Our modeling platform may be extended to explicitly consider gene regulatory networks or to treat other developmental systems .
The study of stem-cell niche patterns , and specifically how stem cells can maintain their totipotent state while simultaneously giving rise to daughter cells that obtain distinct fates to form differentiated tissues and organs , is fundamental to understanding the development of multicellular organisms [1] . Although plants and animals have key differences in their development ( e . g . lack of cell migration in plant development ) , the cellular organization of stem-cell niches in both lineages reveals striking similarities [1] , [2] . In both plants and animals , stem-cell niches are formed by an organizer group of cells with low rates of division , surrounded by stem cells with slightly higher division rates . Moving distally from the organizer and stem cells , cells proliferate at high rates . This proliferation domain ( also called amplification domain ) is bordered by the elongation and then the differentiation domains where proliferation stops and expansion and differentiation , respectively , take place [1] , [3] . Gene interactions within intracellular complex regulatory networks ( GRN ) [4] , [5] or from morphogen dynamics at supracellular scales ( see [6] , [7] ) are fundamental for proper growth and development . Indeed organ and tissue development , as well as stem cell maintenance relies to a great extent on complex transcriptional regulatory networks and chemical fields . However , these are not the only components of pattern formation . It is now recognized that physical fields are also critical to explain developmental patterns , as they may provide positional information that modifies cell behavior and differentiation ( see [6] , [7] ) . At the cellular level , the simplest physical constraint is space . Cell expansion is driven by turgidity , which is an important force acting on the cell wall [8] . The cell wall is a network of rigid cellulose microfibrils cross-linked by polysaccharides and proteins , that confer stiffness to the wall and allows it to resist turgidity [9] . Expansion of the cell is opposed by the rigidity of the cell wall , producing a real stress field . Recent evidence shows that these kind of mechanical cues are transmitted to the nucleus and , directly or indirectly , regulate transcription factors ( see for instance [10] and references therein ) . Given the complexity of the processes involved in the coupling of developmental restrictions , mathematical and computational tools have become indispensable in our efforts to understand the network of interactions involved in cellular differentiation and organ development . Previously [11] we demonstrated that a simplified version of the originally proposed GRN [12] , [13] involved in floral development , could be coupled with a mesoscopic physical field . This provides positional information to cells in the floral meristem which is required to produce the overall spatial pattern of cells observed during early flower development . This and other similar studies [14] suggest that robust morphogenetic patterns in multicellular organisms emerge from complex interconnected dynamical processes , acting at different levels of organization and spatio-temporal scales . However , models that include such dynamical processes into the dynamics of pattern formation in multicellular organs are in their infancy [15] , [16] . Here we use the Arabidopsis thaliana ( A . thaliana ) root apical meristem as a study system to propose a model that couples cell proliferation and growth with chemical-physical dynamical processes to predict the emergence of patterns in a multicellular and multi-scale system . The A . thaliana root has become an important experimental model for understanding the molecular , cellular and biophysical basis of morphogenesis in complex organs . This is due to its relatively simple cellular structure and its indeterminate growth , which gives rise to a multicellular structure with distinct cell proliferation and elongation domains . Importantly , the root apical meristem exhibits the typical cellular organization of stem cells described above ( see Fig . 1 ) . At the tip of roots stem cells are located surrounding the quiescent centre cells or the organizer cells ( green cells in Fig . 1 ) ; together , they constitute the stem-cell niche ( SCN ) of the Arabidopsis root . Towards the base of the plant , the stem cells transit to a cell proliferation domain ( CPD ) where cells have high rates of cell division ( also called proximal meristem by some authors , for example: [17] ) , then they enter a transition domain ( TD ) , where cells have low or no probability of dividing , but they have not started to elongate [18] . The SCN , the CPD and the TD comprise the root apical meristem ( RAM ) . More distally from the organizer center , cells cease to proliferate and start to grow in the elongation domain ( EZ ) . Upon expanding to their maximum length , cells attain their final fate in the differentiation domain and produce the different tissues of the root . Key experimental data on cell cycle regulation and auxin behavior in the root are used to develop our model . Patterns of cell proliferation along the root longitudinal ( apical-basal ) axis are greatly affected by the dynamics of the cell cycle itself and by the concentration of several plant hormones , including auxin [19]–[23] . Cells in the root proliferation domain of the RAM undergo several rounds of division before starting to elongate in the elongation domain . A complex network of regulatory interactions controls the cell cycle , in which cyclin proteins are key regulators . As their name suggests , the expression of cyclins oscillates during each cell cycle . At the beginning of each cell cycle , D-type cyclins ( CYCD ) induce the expression of the RETINOBLASTOMA-RELATED ( RBR ) gene through E2F-RBR pathway . RBR is a negative regulator of E2F transcription factors , which activate the transcription of mitotic cyclin CYCB . Later , CYCB cyclins are degraded by the Anaphase-promoting complex/cyclosome , thus completing the cycle and returning to the beginning of the cell cycle ( see reviews in: [24] , [25] ) . For the present study , the oscillatory and time differential expressions of CYCD and CYCB are sufficient to represent the cell cycle dynamics . The cell cycle phases and main regulators are illustrated in Fig . 2 . Auxin is a phytohormone involved in almost every aspect of plant development ( see [26]–[32] ) . Auxin is a key regulator of cell proliferation and cell elongation , and also modulates cell cycle progression and cyclins [33]–[35] . Auxin has been shown to upregulate mitotic cyclin ( CYCA and CYCB ) expression , and the over-expression of CYCA can partially recover the phenotype caused by low auxin levels , thus suggesting that auxin promotes cell cycle progression [35] . It is also well-documented that auxin gradients correlate with apical-basal patterns of cell proliferation and elongation along the root ( see [35]–[41] ) . There is an auxin concentration gradient along the longitudinal axis of the root , with the maximum concentration detected at the stem-cell niche , specifically in the quiescent center [41] , [42] . While other hormones are important in root growth and development , we exclusively consider auxin due to its clear role in regulating cell cycle dynamics and its measurable concentration gradient that correlates with root developmental patterning [26] . Theoretical and experimental studies suggest that such auxin gradients depend critically on the polar localization of the auxin efflux transporter proteins , belonging to the PINFORMED gene family ( ) ( see [43]–[47] ) . Five members are expressed throughout the root , namely PIN1 , 2 , 3 , 4 and 7 . The proteins PIN1 , 3 , 4 , and 7 maintain a continuous auxin flow from the base to the apex along the central tissues of the root . At the most apical zone , below the QC , auxin is laterally redistributed to the peripheral tissues by PIN3 , 4 , and 7 . Finally , PIN2 directs flow from the root apex to the base in addition to lateral auxin flow in peripheral tissues . In conjunction , all PIN proteins create a reverse fountain mechanism that maintains an auxin gradient along the root [43] , [46] , [48] . Physical signals have been shown to affect auxin distribution , for instance auxin gradients can be modified by mechanically-induced root bending [49] , [50] , or by changes in gravitational fields [51] , [52] . Polar auxin transport and microtubule orientation also respond to mechanical forces in the shoot apical meristem [53] , [54] . Such evidence suggests that auxin transport is affected by and tightly coupled to physical forces . Furthermore , there is increasing evidence that mechanical stress is extremely important for plant morphogenesis; for instance , experiments show that differentiation of mesenchymal cells is influenced by the rigidity of the intracellular matrix [55] . In this paper we propose a simple model to study the interaction between cell proliferation dynamics , local auxin concentration ( that in turn depends on the polar localization of PIN transporters in the cell membranes ) , and an elastic physical field arising from the inherent growth dynamics of the root . Our model provides a formal tool that can be used to understand and predict the emergence of the cellular patterns in the root tip . This type of model can be extended to explore similarities in stem-cell niche organization and subsequent cellular behaviors ( proliferation , elongation and differentiation ) of plants and animals , and to predict if such cellular organization might be explained by the coupling of generic non-linear physical and chemical fields relevant to cell proliferation dynamics . Our model is validated with experimental measurements on cell size and proliferation patterns along A . thaliana root , and sets the stage for developing similar approaches in other systems .
We start by modeling the space occupied by a cell . Expansion of the cell volume , whether by turgidity or growth , is opposed by the rigidity of the cell wall producing a real stress field [9] , [56] . This field is also present at the larger scale of a group of cells , such as within the root apical meristem , since the increase in volume required by cell growth and division is opposed by the surface forces exerted by the root cap and epidermal cells surrounding it [57] . From this perspective , it is logical to assume that this stress field is self-regulated , that is , the accumulation of local stress ( or pressure ) triggers mechanisms that prevent ( or enhance ) cell division and growth . This assumption of self-regulation has been incorporated into previous models of cellular interactions: Dupuy and collaborators [58] used a rigidity matrix to model the relationship between cell displacement and implied forces . A form of potential energy has likewise been proposed as a way of describing the equilibrium between turgor and cell wall resistance [59] . Finally , in a recent paper investigating the floral meristem of A . thaliana , potential energy was proposed as the means of regulating auxin transport [15] . In our model for the root apex , we define a spatial domain in which a potential function acts . The spatial derivatives of this function render the mechanical force as a function of time and space . Taking advantage of the radial symmetry of the region of the root tip , we consider a two-dimensional space and divide it into cells . We simulate cells by a Voronoi diagram obtained from a collection of generating points that represent the position assigned to each cell . A Voronoi diagram , or tessellation , associated with a collection of points assigns to each point a limited region of space in the form of a convex polygon ( polyhedron in three dimensions ) . Voronoi cells are used nowadays in many fields of science , however it was Honda [60] who first proposed the use of 2D Voronoi to model cells in a biological context . Our domain is defined as follows: 1 ) We construct a regular shape with points on a rectangle and a parabolic tip . The exterior points are fixed and represent the epidermal cells surrounding the ground tissue of the root ( See Videos S1 and S2 ) . 2 ) These points in the border cannot define a convex polygon , so the corresponding cells have a point at infinity . 3 ) We create points with random coordinates in the interior of this domain and perform a Voronoi tessellation using a Delaunay triangulation algorithm . A typical configuration is shown in Fig . 4 . Observe that the areas of the cells ( ) vary in size and shape , and that the generating points shown in the figure ( ) do not correspond , in general , to the centre of mass of the cells ( ) . The average is the space that each cell would occupy in a regular hexagonal lattice . Analogously , the distance is in the regular array . In two dimensions the array of cells with minimal surface energy is the hexagonal lattice , and we use this fact to define a potential function around this equilibrium configuration . Previous studies have used springs to simulate the interactions among cells [61] , and the elements of the cellular walls [62] , [63] . In our case the equilibrium area could be used to fix the size of mature cells , so deviations from this value would represent immature cells . If the cells in the tissue tend to be isotropic in shape , then a value of different from zero would represent cells with the wrong shape and , consequently , largely stressed . Regardless of the actual functional form of the energy potential , it is possible to make a Taylor expansion around the equilibrium state retaining only the first non-zero terms , provided one considers small deviations from equilibrium . The first non-trivial contributions correspond to a quadratic form , whose coefficients can be interpreted as force constants . Therefore , we propose a harmonic potential acting on each cell ( 1 ) where the first term tends to uniformize the size , and the second term is related with the shape of the cells . and are elastic constants . The expressions for the components of the force are: ( 2 ) where is the length of the edge shared between neighboring cells , and we have omitted the time dependence of the variables on the right hand side . All quantities in Eqs . 1 and 2 can be readily calculated with the algorithm used to define the Voronoi diagram . Since this is a conservative system , and there is no reason to assume conservation in the root system , we include dissipation in the form of friction that simulates losses due to the inability of cells to make drastic elastic changes of shape or size . Therefore , the total force should be: ( 3 ) where is the velocity and is a friction coefficient . The coupled dynamical equations of this newtonian system ( 4 ) can be integrated numerically by using a simple Euler method , imposing fixed boundary conditions on the fixed surface points . As an example of the relaxation process with this scheme , in Fig . 5 we show the configuration of points in Fig . 4 after time iterations . The numerical calculation was stopped when the relative changes of the positions and velocities was less than . The magnitude of the constants , and sets the units of the time variations of the dynamical behavior of the system , and should be adjusted to physical units when modeling the growth of the RAM . One should consider the number of cell divisions per unit time ( 2 . 6 events/hr ) , the cell production rate ( between 0 and 6 ) and the cell proliferation rate distribution ( between 0 and 50 ) in the RAM [64] . The final form of the relaxed field suggests that it could be used to transfer positional information to the cells in the meristem . In order to achieve the latter , the auxin concentration must be coupled to the local value of the potential . We introduced the process of cell division and proliferation into the simulation by defining two points inside a cell when it undergoes mitosis . The resulting Voronoi cells then locally alter the field , and the extra space needed for the two daughter cells is obtained by moving the upper border of the domain a proper distance to provide the exact extra space required . We show details of this process below . It is assumed that the field is involved in the processes of auxin transport . In any transport equation there are basically two aspects to be considered: the hydrodynamic forces compelling a fluid to move , and the diffusion phenomena . Both are important for the case of auxins . Furthermore , the process of auxin transport is recognized to be active , meaning that the transfer of matter through the cell membranes could go against the concentration gradient of auxin molecules due to the action of PIN proteins . We propose that the amount of matter transported per unit time from cell to a neighbor cell is proportional to the gradient of the field : ( 5 ) where represents the permeability of the membrane and is the contact surface between the cells and ( the line in 2D ) . Observe that if the values of the 's were constant , this equation would reduce to the well known Darcy's Law in hydraulics , which is analogous to Fourier's law in heat conduction , or Ohm's law in electrical networks . However this is not the case , because of the action of the PIN proteins which are critical . Therefore , the permeability is: ( 6 ) where is a constant related with the time scale of the dynamics , and the direction of the flux with respect to the concentration gradient ( diffusion term ) is given by the logical function . This latter function mimics the action of the PIN molecules , which attach to the membrane according to orientation and position in the domain . We can simplify this action by considering “gates” , which could be opened ( 1 ) or closed ( 0 ) according to specified simple rules . Let be the set of cells at the surface , i . e . in contact with the immobile epidermal cells . We have set the following rules: All gates are closed , except The dynamical equation for the concentration of auxins in cell is then: ( 7 ) where the sum is over all neighboring cells . This expression can be readily integrated numerically in parallel with Eq . 4 , once the parameter has been properly adjusted . In Fig . 6 we show the effect of the logical rules on the formation of auxin gradients . On the left we show a calculation without these rules , that is , maintaining all the membranes permeable . In ( B ) we incorporate the PIN action into the model . Observe that the distribution of the concentration of auxins ( normalized with its maximum value ) is similar to the one observed in real roots [42] . We shall assume that the period of the cell division cycle is regulated by the local concentration of auxins . We are aware that this is an oversimplification of the complex hormonal regulation of the cell cycle in plants , but auxin has been shown to be an important component of such regulation [65] . We therefore need a model for the oscillations of cyclin concentrations . The robustness of these oscillations suggests that a non-linear oscillator would be a good model . We consider a two-component system for simplicity , considering CYCD and CYCB as the two key players . Since both undergo regular out-of phase oscillations with maxima related to the transitions between the G1-S and G2-M phases , respectively ( See Fig . 2 ) , we choose a simple Lotka-Volterra non-linear system with two components , generally used in ecology to model the predator-prey dynamics . This system presents all characteristics required for the observed time behavior of the concentration of cyclins [66] . The adimensional activator-inhibitor dynamical equations are: ( 8 ) where and represent CYCD and CYCB , respectively . This system presents an oscillatory behavior , provided is within a certain range , whose period ( ) and wave shape depend only on and on the boundary conditions . It is easily shown that the period is: ( 9 ) which is inversely proportional to the square root of the ratio of the linear growth rate of the “prey” ( ) to the death rate of the “predator” ( ) . In Fig . 7 we illustrate the oscillations of both variables . Experimental data has shown that the cell cycle is arrested if the auxin concentration is below or above certain threshold values , and that the cycle period increases with auxin concentration [65] . Therefore , we simply assume that the auxin concentration is linearly related to the only parameter of this dynamical system: . Hence , each cell has its individual clock , which runs faster or slower depending on the auxin concentration in the model under consideration . We couple this dynamical feature into the numerical calculation of the model by performing a division of cell when ( where the function is one when the number of iterations , used in the Euler integration , surpasses the period ) . Therefore , is another constant that relates the time scale of reproduction ( ) to the time step used for relaxation dynamics . Parameters and should be fitted according to the observed time scales for each of the three dynamics . Time step ( in seconds ) should be obtained as well . In practice , the act of cellular division is performed in the following manner: The changes in the domain size and the size of the new cells produce a rearrangement of all cells , and this changes the local value of the elastic field , which , at the same time , drives the auxin concentration that , in turn , regulates the division rate of all cells . We hypothesize that coupling among such three dynamics at different time scales is sufficient to produce the growth of the root with cellular patterns that mimic those of real systems in a wide region of the parameter space . We verified that the process is extremely robust against changes of initial conditions .
In Fig . 8 we show the dynamical loop that integrates the dynamical equations with an Euler method . The program is initiated by choosing the values of the number of cells ( ) , the position of each cell ( ) , their proliferation rate ( ) , the gates given by the PIN action between two cells ( ) and the concentration of auxins ( ) at time . It is important to note that we normalize the auxin concentration function with its maximum value at every time step . This allows our model to take into account the role of possible sources and sinks of auxin , since is not a conserved quantity . The final distribution of auxin is insensitive to the initial conditions , but we start with a random distribution of auxin with a maximum at the quiescent centre in accordance with experimental observations . We recovered the same results if auxin concentrations were random at initial conditions ( data not shown ) . The cycle clocks of each cell are set to zero at and reset after a successful cell division . The shape and color of the boxes ( Fig . 8 ) represent the action of the different dynamics as described in experimental systems ( see Fig . 3 ) . The red square indicates a subroutine that includes the logical rules of the PIN action and the red circles represent points of logical decisions at appropriate times . Black arrows represent the direction of flux of the simulation and the black-dash arrow indicates a decision related to the time condition for the dynamics of the cyclins . Eq . 4 is implemented in the blue diamond block that represents the elastic field with time scale . The loop is performed while the time is less than the final time . Eq . 7 is implemented in the green block . The cyclin period is calculated for each cell at the violet block using Eqs . 8 and 9 and the threshold . Cellular divisions are performed as a subroutine represented by the orange block , and cell proliferation alters the conditions of all three dynamics . The first step is to estimate the values of the parameters of the system . The adjustable parameters are the quantities indicated in red in Fig . 8 . We start with the kinematical parameters . The constant is related to the elastic modulus of the cells . This quantity is measured when studying the mechanics of walls , cells , and tissues and is of the order of , as reported in [67] . For simplicity let us consider hexagonal cells in equilibrium . The magnitude of the elastic force is where is the contact area between two cells , is the change in length just after division , where is the area of the hexagon , if is the distance between centroids of two contiguous cells . This should be equal to the corresponding force magnitude in our model . Just after a cell division , , thus , and . Equating the two forces we obtainTaking the average diameter of a mature cell as [64] and the experimental value we obtain . At this stage , should be related to the properties of the cell membrane , the metabolism of cell growth and the turgor pressure . It is difficult to associate the action of the first term of Eq . ( 1 ) to a single biological property . However , the dynamics of this term should produce a restoring force of the same order of magnitude as the second term , if the form and geometry of the domain are to be maintained during the growth dynamics . Therefore , if we use a value of the system should relax to a set of cells with roughly the same size and shape , as shown in the calculation of Fig . 5B . We found numerically that this produces results for the dynamics of growth that are comparable to the experimental quantities measured . Parameter is related to the viscous damping of the cell motion . The dynamical friction constant can be estimated by observing that the amplitude of the oscillatory motion , caused by the harmonic forces should be reduced , to avoid oscillations , by a factor of in a lapse of at most one period , that is . Note that in Eq . ( 4 ) the mass of the cell ( ) is considered to be one . This gives , and . The values used in the calculations are , and . With these values we obtain the real time scale of an iteration step in the numerical calculations , by finding the number of iterations needed to obtain the experimental number of cell divisions in that lapse . In seven days , our observations showed ( see Fig . 1 ) that the number of cells in the meristem is about 350 . In averaged calculations we reproduce this number in 3400 iterations by using and . This means that the lapse representing one iteration is the number of minutes in 7 days over the number of iterations , that is . Considering that the average auxin concentration is , the value of is in units of , which is about 100 times . These values produce a single cell cycle period on the order of 12 hr , as shown in Fig . 7 [66] . In Fig . 9 ( and Video S5 ) we provide an example of the growth of the system . We start with eight points at random in the parabolic tip of the domain , and fix the position of two additional points that represent the quiescent cells in the centre of the domain , marked with a white symbol . These cells reproduce at a rate ten times lower than the others; they divide after ten divisions per cell on average ( in the right panel of the figure these quiescent cells have just divided ) . The auxin concentration in these cells is set to the maximum initially , and this is represented by a dark red color in the figure . The cell's position , shape , and proliferation rate are calculated every time step and the auxin is transported between cells . After 400 time steps the cells are attaining a uniform shape and size ( Fig . 9 ) , and the auxin gradient is already formed . This gradient will dictate the time in which a complete cell proliferation cycle is accomplished locally , followed by a cell division event that produces a sudden increase of the local potential that , in turn , governs auxin transport . Despite these complicated dynamical interactions , the auxin gradient is preserved throughout and the process of growth and cell patterning is by no means random . This can be seen in Fig . 10 . The overall pattern that emerges after some cycles of coupled dynamics is very similar to the apical-basal pattern of cell proliferation and elongation observed in RAM and along the length of the root tip . Such dynamics and emergent pattern are robust to initial conditions . It is interesting to note that the region around the quiescent center in the stem-cell niche shows the greatest concentration of auxin , and a maximum in the potential . Also , the cell division cycle is minimum at this location . An intermediate region in which the auxin concentration diminishes and the potential is very small , but the cell proliferation rate is roughly constant , surrounds the quiescent cells . Finally , the most distal part from the tip ( towards the base of the plant ) is characterized by a very small concentration of auxin , causing the cell proliferation rate to be very small , and the potential to increase enormously . The combination of these effects results in the arrest of cell proliferation and in the formation of the elongation zone at a defined distance from the root tip . The emergent patterns recovered in the model are similar to those observed for the distribution of auxin as reported in Ref . [68] , and the pattern of cell proliferation along the root longitudinal axis reported in Ref . [69] . Our results are also in agreement with the qualitative patterns of cell proliferation and elongation that are observed along the apical-basal , longitudinal axis of the growing A . thaliana root . We can use this model to predict what patterns are expected under different growth conditions . In Fig . 11 we show a histogram of the number of cell divisions occurring at a given distance from tip , as obtained from an example calculation in which we fixed the parameter . Interestingly , we observe that the length of the RAM does not surpass a certain value , which depends on , because the modeled coupled dynamics prevents cells far from the tip to divide . Such types of coupled dynamics could explain the emergence of the transition from proliferation to the elongation cellular states in real roots , as well as the limited ranges or domain sizes of actively proliferating cells in stem-cell niches of plants and animals [1] , [3] . Hence , our model can be used to generate novel predictions concerning the role of the parameters considered in the model , and in determining RAM size and cell proliferation and elongation patterns along the root apical-basal axis for A . thaliana under different environmental or growth conditions . Our general model could eventually be adjusted to model stem-cell niches in other plants and animal systems , as well as modeling growth and differentiation in communities of unicellular organisms if similar physical fields can be postulated in such latter cases . In order to examine the quantitative behavior of the model and validate it with published experimental data , we compared our model's predictions to measurements of the proliferation rates along the axis of the A . thaliana root as a function of the distance from the quiescent centre [64] . We ran numerous iterations of the model in order to obtain a reasonable statistical sample . We show a typical result from the simulations run to the experimental data in Fig . 12 . Panel ( A ) shows the available experimental results for cell proliferation rates along the apical-basal axis of the root reported in Ref . [64] as a continuous red curve . The numerical results from our model are shown in blue . These results were obtained using the estimated parameter values that give the time in hours and the sizes in . We shifted the origin to account for the fact that all quantities in our calculations were measured from the tip of the domain and not from the quiescent centre . Notice that the simulated and experimentally generated curves are very similar . In Fig . 12 ( B ) we show an histogram of the frequency distribution of cell size . This histogram varies with different iterations because of the stochastic nature of cell proliferation and growth dynamics [70] . However , all calculations share the same qualitative characteristics; namely an unimodal distribution between and , with a maximum around . This result was already recovered by Verbelen and collaborators Ref . [71] . The red curve was obtained by measuring the cell size in an Arabidopsis root Fig . 1 . Similar curves have been obtained for many different plant species , including wheat [70] . In Fig . 12 ( C ) we show the variation of cell length along the longitudinal axis of the root . The red curve is the experimental result from Ref . [64] . It should be pointed out that the experiment was obtained by measuring the cell flux in a fixed point and by counting along the axis of the root in two dimensions , which is very convenient when comparing with our two-dimensional model . In order to mimic the experimental procedures , our numerical results were obtained by spotting the centroids of the Voronoi cells in the final time , which corresponds to six days . We calculated the length ( ) by assigning an area of to each cell . Again , a shift of in the horizontal axis was needed to account for the difference in the origin , and the results for each cell are displayed as blue dots in the figure . Once again , the agreement between our simulated results and the experimental data are clear . Finally , in Fig . 12 ( D ) we show the average cell proliferation velocity , as defined in Ref . [64] , as a function of the distance from the quiescent centre ( red line ) , and compare it with our results ( blue dots ) . In the experiment , Beemster and collaborators measured the difference in position of each cell for two subsequent times , averaged over time . In our calculation we measured the difference in position of each cell with respect to the apex of the root , which is itself being displaced every time a cell division takes place . By changing the frame of reference , we can compare the reported experiment with our results . The agreement is also remarkable when one compares the simulation results recovered with our model and the experimental data . This is more significant than the previous validations , since this result reflects the totality of the dynamical behavior in time and not only in a frozen snapshot , as in the previous cases .
We present a dynamical model that couples auxin concentration gradients , cell proliferation and a physical tension field in a two-dimensional spatial domain that mimics the A . thaliana root tip . We have validated our model with both static and dynamic cellular empirical data , and have shown that our model recovers the pattern of rates of cell proliferation observed in the apical-basal axis of roots . The model also recovers the discrete transition from the proliferative to the elongation domains . Thus , our model puts forward a novel theoretical framework to test hypothesis concerning the coupled roles of auxin , cell proliferation , and physical fields dynamics in the emergence of the cellular pattern observed along the A . thaliana root tip . Ultimately , we have postulated a complex system in which the main emergent property of the coupled dynamics is at the appropriate spatial and cellular structure for the intracellular genetic networks to express differentially along the root . However , the explicit consideration of complex gene regulatory networks is out of the scope of this paper . Our model and analysis suggest that the size of the RAM depends on the value of the parameter ( Fig . 13 ) in a rather defined manner . This parameter represents the ratio between the time scales of the potential relaxation and the auxin transport mechanisms . The length of the RAM decreases as the auxin transport parameter increases as a power law . Therefore , this quantitative prediction can be verified experimentally , as auxin concentrations and transport along the root can be modified by manipulating the conditions of root growth ( e . g . adding NPA to the growth medium to block auxin transport ) . Previous experimental work has suggested that the size of the RAM varies depending on growth conditions and is altered with external supplementation of auxin [39] . Given that plant growth is influenced by the mechanical behavior of the cell wall , measurements of the mechanical properties of living cell walls are important to fully understand how cellular organization is achieved . Like most biological materials , material properties of cell walls change as a function of age , the magnitude of forces they are subjected to , and immediate physiological conditions [67] , [72] . This confers spatial and temporal heterogeneity on cell wall constituents , complicating measurements of the mechanical properties of plant living walls even with present-day instrumentation . Furthermore , a single modulus of elasticity is not sufficient because of the structural anisotropy of the cell wall [67] . Therefore , comparisons between the predicted values of Kv and Kc of our model and the values reported for the modulus of elasticity of real cell walls are far from being straightforward . However , the fact that we reproduced the root tip pattern with the selected values suggests that they are likely to be biologically meaningful . More generally , our work reinforces conclusions from recent studies that experimentally demonstrate the importance of physical forces in the regulation of root apical-basal patterning [49] , [53] , such as the mechanical induction of lateral roots or the coordination between auxin concentration and microtubule orientation [49] , [53] , [54] . It is remarkable that simple arguments concerning uniform size , shape and geometry of cell disposition is sufficient to produce a non-uniform field that provides sufficient spatial information to recover the overall dynamical growth pattern observed along the root . It is thus predicted that modification of physical forces would change the size and the pattern of these zones , an issue that could in principle be further explored theoretically and experimentally . Auxin response is modulated not only by auxin concentration , but also by the auxin signaling pathway , which includes many components of different gene families , and which interact through several feedback loops , creating non-linear behaviors . Consequently , auxin concentration at any location does not necessarily coincide with auxin response . Even if this is not the case in the root [68] , it could be important to include an explicit model of the auxin signaling pathway in future extensions of our model . In addition , in our model we considered the polar PIN configuration as fixed , as in Ref . [46] . However , in reality a more robust dynamic auxin transport is observed when the PIN expression is regulated by auxin [73] . In our model we fixed the position and number of the quiescent cells . We are aware that the root stem-cell niches are regulated by a complex regulatory network [74] . WUSCHEL RELATED HOMEOBOX5 ( WOX5 ) is a Quiescent Center identity gene indispensable for the maintenance of the undifferentiated state of stem cells and niche size regulation , and it is part of the proposed root stem-cell niche regulatory network [74] , [75] . Recent theoretical and experimental work has suggested that WOX5 regulates and is regulated by auxin [36] , [74] . In our calculations we input several initial conditions for auxin concentration , and demonstrated that the model is fairly robust to these changes . However , as shown in Fig . 6 , neglecting the action of PIN polarization destroys the auxin gradient along the root . Including these and other regulatory interactions in a future model would enable us to explicitly consider intracellular complex gene regulatory networks , which are likely coupled among cells by physical and hormone fields . The complex network underlying the cell cycle was also reduced to consider two basic components , because for our purposes , only the phases of the oscillations of the concentrations matter . Since CYCA and CYCB oscillate in phase , we consider them as a single variable; and because CYCD oscillates in anti phase , we take this to mean that there is an activator-inhibitor interaction between these two groups of proteins . In our model we stressed the importance of the relationship between auxin concentration and the regulation of cell proliferation , and we neglected the details of the known regulatory processes of the cell cycle , which although important , do not directly affect the overall results of our simulation . Nonetheless , such details of the gene regulatory network underlying the cell cycle , cell differentiation and auxin dynamics should be incorporated in future developments of the model . In conclusion , we have put forward a minimal mathematical model that considers the essential dynamical coupling of cell proliferation with a physical field and chemical ( hormone ) gradients , in order to explore if such processes are sufficient to obtain the emergence of cellular organization during stem-cell niche patterning and organ growth . We have used the A . thaliana root as our study system . Despite the simplification of many biological details , our model is able to recover patterns that greatly resemble those observed in stem-cell niches of plants and animals , and particularly those in the A . thaliana root tip . The remarkable coincidence between the simulated cellular characteristics along the model root apical-basal axis ( shown in Fig . 12 ) , with those that have been observed and quantified in actual roots , validates the qualitative features and utility of our model for understanding the emergence of cellular patterns in such a multicellular organ . Furthermore , the cellular patterns of stem-cells among multicellular plants and animals have generic traits . Our model provides a formal tool to explore if such traits may be explained by the generic non-linear coupling of relevant physical and chemical fields to discover emergent properties of cell proliferation dynamics across biological systems . | The emergence of tumors results from altered cell differentiation and proliferation during organ and tissue development . Understanding how such altered or normal patterns are established is still a challenge . Molecular genetic approaches to understanding pattern formation have searched for key central genetic controllers . However , biological patterns emerge as a consequence of coupled complex genetic and non-genetic sub-systems operating at various spatial and temporal scales and levels of organization . We present a two-dimensional model and simulation benchmark that considers the integrated dynamics of physical and chemical fields that result from cell proliferation . We aim at understanding how the cellular patterns of stem-cell niches emerge . In these , organizer cells with very low rates of proliferation are surrounded by stem cells with slightly higher proliferation rates that transit to a domain of active proliferation and then of elongation and differentiation . We quantified such cellular patterns in the Arabidopsis thaliana root to test our theoretical propositions . The results of our simulations closely mimic observed root cellular patterns , thus providing a proof of principle that coupled physical fields and chemical processes under active cell proliferation give rise to stem-cell patterns . Our framework may be extended to other developmental systems and to consider gene regulatory networks . | [
"Abstract",
"Introduction",
"Model",
"Results",
"Discussion"
] | [
"biology"
] | 2013 | Cell Patterns Emerge from Coupled Chemical and Physical Fields with Cell Proliferation Dynamics: The Arabidopsis thaliana Root as a Study System |
Vaccination with plasmid DNA encoding Ag85A from M . bovis BCG can partially protect C57BL/6 mice against a subsequent footpad challenge with M . ulcerans . Unfortunately , this cross-reactive protection is insufficient to completely control the infection . Although genes encoding Ag85A from M . bovis BCG ( identical to genes from M . tuberculosis ) and from M . ulcerans are highly conserved , minor sequence differences exist , and use of the specific gene of M . ulcerans could possibly result in a more potent vaccine . Here we report on a comparison of immunogenicity and protective efficacy in C57BL/6 mice of Ag85A from M . tuberculosis and M . ulcerans , administered as a plasmid DNA vaccine , as a recombinant protein vaccine in adjuvant or as a combined DNA prime-protein boost vaccine . All three vaccination formulations induced cross-reactive humoral and cell-mediated immune responses , although species-specific Th1 type T cell epitopes could be identified in both the NH2-terminal region and the COOH-terminal region of the antigens . This partial species-specificity was reflected in a higher—albeit not sustained—protective efficacy of the M . ulcerans than of the M . tuberculosis vaccine , particularly when administered using the DNA prime-protein boost protocol .
Buruli ulcer ( BU ) , also known as Bairnsdale ulcer , is an infectious , necrotizing skin disease caused by Mycobacterium ulcerans ( M . ulcerans ) occurring mostly in tropical and subtropical areas . Cases have been reported in several countries in West and Central Africa , in Central and South America , in Southeast Asia and in Australia . BU is emerging as a serious health problem , especially in West Africa , where it is the third leading cause of mycobacterial disease in immunocompetent people , after tuberculosis and leprosy . In some countries in Africa , thousands of cases occur annually and in these areas BU has supplanted leprosy to become the second most important human mycobacterial disease . The natural history of M . ulcerans infection and subsequent development of BU is not completely elucidated . M . ulcerans bacteria have been found in endemic areas in stagnant water or slowly moving water sources and in aquatic snails and carnivorous insects [1] , [2] . So far , person to person transmission has not been reported . The infection causes initially a painless nodular swelling which can eventually develop into an extensive necrotizing lesion . M . ulcerans has the particularity to produce a family of toxin molecules , the so-called mycolactone ( ML ) , polyketides that can suppress the immune system and destroy skin , underlying tissue and bone , causing severe deformities [3]–[5] . ML suppresses the in vitro TNF-α production by murine macrophages infected with M . ulcerans ( 4 ) and it strongly affects the maturation and the migratory properties of DC [5] . On the other hand , ML does not seem to affect the production of the inflammatory cytokine MIP-2 , involved in the recruitment of neutrophils ( 4 ) . M . ulcerans has an initial intracellular infection stage but virulent ML producing strains induce apoptosis of the infected cells and can subsequently be found extracellularly [3] , [6] . Only few Mycobacterium species produce mycolactone toxins [7] . M . ulcerans isolates from different geographical areas produce different types of mycolactone , i . e . mycolactone A/B , C , D , E and F [8] , [9] . The nature of immune protection against M . ulcerans infection remains unclear . In general , resistance to intracellular bacteria is primarily mediated by T cells with pivotal roles of Th1 type cytokines IFN-γ and TNF-α and this apparently is the case for M . ulcerans infection as well [10] . Progression of active Buruli ulcer is characterized by gradual down regulation of systemic and local Th1 type immune responses . Peripheral blood mononuclear cells from Buruli ulcer patients show reduced lymphoproliferation and IFN-γ production in response to specific stimulation with M . ulcerans [11]–[13] . Reduced IFN-γ response does not seem to be caused by decreased interleukin-12 production [14] . Also , semi-quantitative RT-PCR analysis demonstrated high IFN-γ and low IL-10 levels in early , nodular lesions whereas low IFN-γ and high IL-10 mRNA levels are observed in late ulcerative lesions [13] . Using a similar RT-PCR comparison of granulomatous versus non-granulomatous lesions , Phillips et al demonstrated higher expression of IL-12p35 , IL-12p40 , Il-15 , IL-1β and TNF-α in patients from the former group and higher expression levels of IL-8 ( human homologue of MIP-2 ) in the latter group [15] . Finally , Kiszewski et al have also confirmed that in ulcerative lesions without granuloma , there is increased expression of IL-10 and higher bacillary counts . [16] . It is not yet clear whether antibodies play a protective role against BU but the humoral immune response during M . ulcerans infection may be useful for serodiagnosis of BU . In contrast to tuberculosis and leprosy , immunoglobulin IgG antibody production against M ulcerans can be found even in early stages of infection [17] . IgG antibodies cannot be used to readily discern between patients and family controls , but primary IgM antibody responses against M . ulcerans culture filtrate proteins can be detected in sera from 85% of confirmed BD patients and only in a small proportion in sera from healthy family controls [18] . Antibody responses against the M . ulcerans homologue of the M . leprae 18-kDa small heat shock protein -that has no homologues in M . bovis and M . tuberculosis- can be used as serological marker for exposure to M . ulcerans [19] . BU results in considerable morbidity . Because of the late detection of the disease , treatment is principally by excision of the lesion , sometimes necessitating skin grafting [20] . WHO is currently recommending combined rifampicin and streptomycin treatment of nodules for eight weeks in the hope of reducing the need for surgery [21] , [22] . Unfortunately , there is no specific vaccine against BU for the moment [23] . M . bovis BCG ( Bacille Calmette et Guérin ) vaccine , used for the prevention of tuberculosis , has been reported to offer a short-lived protection against the development of skin ulcers [24]–[26] and to confer significant protection against disseminated cases of BU , e . g . osteomyelitis , both in children and in adults [27] , [28] . The precise M . ulcerans antigens that induce a protective immune response are poorly defined . The complete genome sequence of M . ulcerans has recently been published and will hopefully help to advance research and identification of relevant genes [29] . The 65 kD heat shock protein is expressed in considerable amounts by M . ulcerans bacilli in vitro and in vivo , and is immunogenic for both B and T cells in mice . Nevertheless , vaccination of mice with plasmid DNA encoding Hsp65 from M . leprae , having 96% sequence identity with Hsp65 from M . ulcerans , limited only weakly the progression of experimental M . ulcerans infection in tail [30] . We have previously reported that vaccination with BCG or with plasmid DNA encoding Ag85A from M . bovis BCG can partially protect B6 mice against footpad challenge with M . ulcerans [31] . Antigen 85 is a major secreted component in the culture filtrate of many mycobacteria such as M . bovis BCG , M . tuberculosis and M . avium subsp . paratuberculosis [32] . The antigen 85 complex ( Ag85 ) of M . tuberculosis is a family of three proteins , Ag85A , Ag85B and Ag85C , which are encoded by three distinct but highly paralogous genes and that display an enzymatic mycolyl-transferase activity , involved in cell wall synthesis [33] , [34] . Members of the Ag85 family rank among the most promising tuberculosis vaccine candidates , and are actually being tested in clinical trials , formulated as Hybrid-1 fusion protein of Ag85B with ESAT-6 or as recombinant Modified Vaccina Ankara virus encoding Ag85A booster vaccine of BCG [35] , [36] . We have previously sequenced the gene encoding Ag85A from M . ulcerans and reported that it shares 84 . 1% amino acid sequence identity and 91% conserved residues with the gene encoding Ag85A from M . tuberculosis ( which is identical to the Ag85A gene of M . bovis BCG ) [31] . The genes encoding Ag85B and Ag85C of M . ulcerans have recently been sequenced as well and – as for M . tuberculosis- were localized on different loci in the genome [29] . Here , we report on a comparison of the immunogenicity and protective efficacy of vaccines encoding Ag85A from M . tuberculosis and from M . ulcerans . Vaccines were administered as plasmid DNA , purified protein in adjuvant or in a DNA prime-protein boost protocol . We and others have previously reported that DNA priming followed by protein boosting is an effective means to increase the potential of DNA vaccines [37]–[40] .
C57BL/6 mice were bred in the Animal Facilities of the IPH-Pasteur Institute Brussels , from breeding couples originally obtained from Bantin & Kingman ( UK ) . Mice were 8–10 weeks old at the start of the experiments . Female mice were used for immune analysis and male mice for the protection studies . This study has been reviewed and approved by the local Animal Ethics Committee ( file number 030212/05 ) . Virulent M . ulcerans type 1 strain 04-855 from a Benin patient was isolated at the Institute for Tropical Medicine in Antwerp , Belgium . Bacteria grown on Löwenstein-Jensen medium were maintained and amplified in vivo in footpad of the mice . M . bovis BCG strain GL2 was grown for 2 weeks as a surface pellicle at 37°C on synthetic Sauton medium and homogenized by ball mill as described before [41] . Plasmid DNA encoding the mature 32 kD Ag85A from M . tuberculosis in V1J . ns-tPA vector was prepared as described before [31] , [42] . The gene encoding Ag85A from M . ulcerans was amplified by PCR without its mycobacterial signal sequence using BglII restriction site containing primers and ligated into the same V1J . ns-tPA vector . The primers used were 5′-GGAAGATCTTGAGCGCTTGGTACTAGGC-3′ ( forward ) and 5′-GGAAGATCTTTTCGCGGCCGGGCCTGCCGGTGGA-3′ ( reverse ) . In these plasmids the Ag 85A gene is expressed under the control of the promoter of IE1 antigen from cytomegalovirus , including intron A and it is preceded by the signal sequence of human tissue plasminogen activator . Hexa-histidine tagged Ag85A protein from M . tuberculosis was purified from recombinant E . coli as described before [43] . The gene encoding the mature Ag85A protein from M . ulcerans was amplified by PCR from V1J . ns . tPA-85A vector . The primers used were 5′-CGCGGATCCGCGTTTTCGCGGCCGGGCCTGCCGTGGAA-3′ ( forward ) and 5′-CCCAAGCTTGGGCTAGGCGCCCTGGGTGTCACCG-3′ ( reverse ) with respectively BamHI and Hind III restriction sites . Ag85A gene was amplified without its mycobacterial signal sequence . Cloning in expression vector pQE-80L ( QIAGEN ) , containing an NH2-terminal histidine-tag coding sequence , and purification were performed as described before [32] . Briefly , positives clones were screened on LB-ampicillin medium after ligation of the gene in the vector and transformation of E . coli DH5α cells . For expression , Top-10F' E . coli ( Invitrogen ) cells were transformed with plasmid encoding the 85A sequence . Recombinant protein was purified by immobilized metal affinity chromatography ( IMAC ) using gravity flow . The endotoxin level measured with the LAL kinetic chromogenic assay , was inferior to 10 EU/ml ( endotoxin units per millilitre ) or 0 . 03 EU/µg of purified protein ( Cambrex Bioscience , New Jersey , America ) . Peptides spanning the entire mature 295 amino-acid Ag85A sequence of M . tuberculosis were synthesized as 20-mers , with the exception of the 18-mer spanning aa 35–53 and the 21 mer-peptide spanning amino acids 275–295 [44] . Peptides spanning the entire 294-amino acid Ag85A sequence of M . ulcerans were synthesized as 20-mers . All peptides were purchased from Ansynth Service B . V . , The Netherlands . In experiment 1 , B6 mice were anesthesized by intraperitoneal injection of ketamine-xylazine and injected three times intramuscularly ( i . m ) in both quadriceps muscles with 2×50 µg of control V1J . ns-tPA ( empty vector ) , V1J . ns-tPA-Ag85A DNA from M . ulcerans or from M . tuberculosis ( abbreviated as Ag85A-DNA Mu and Ag85A-DNA Mtb in the figures ) . For protein immunization , mice were injected three times subcutaneously ( s . c ) in the back with 10 µg of purified recombinant Ag85A ( abbreviated as rec85A-Mu and rec85A-Mtb in the figures ) , emulsified in Gerbu adjuvant , i . e . water miscible , lipid cationic biodegradable nanoparticles , completed with immunomodulators and GMDP glycopeptide ( GERBU Biochemicals ) . For the DNA prime-protein boost , mice were immunized twice i . m . with Ag85A DNA from M . ulcerans or from M . tuberculosis and boosted s . c . with 20 µg of recombinant Ag85A protein respectively from M . ulcerans or M . tuberculosis in Gerbu adjuvant ( abbreviated as Ag85A-DNA/recMu and Ag85A-DNA/recMtb in the figures ) . All mice received the two first injections at 3 week intervals and the third injection was given two months later . For BCG vaccination , mice were injected intravenously , in a lateral tail vein , at the time of the first DNA injection with 0 . 2 mg ( corresponding to 106 CFU ) of freshly prepared live M . bovis BCG [41] . In experiment 2 , B6 mice were injected intramuscularly ( i . m ) three times , at 3 weeks intervals , in both quadriceps with 2×50 µg of control V1Jns . tPA DNA or plasmid DNA encoding 85A from M . ulcerans or from M . tuberculosis . For protein immunization , mice were injected three times subcutaneously ( s . c ) in the back with 10 µg of purified recombinant Ag85A from M . ulcerans or from M . tuberculosis , emulsified in monophosphoryl lipid A ( MPL-A ) from Salmonella enterica serovar Minnesota ( Ribi ImmunoChem Research , Hamilton , Mont ) ) solubilized in triethanolamine . For the DNA prime-protein boost , mice were immunized twice i . m . with Ag85A DNA from M . ulcerans or from M . tuberculosis and boosted s . c . with 20 µg of purified recombinant Ag85A protein respectively from M . ulcerans or from M . tuberculosis in MPL-A . Naïve and vaccinated B6 mice were infected with M . ulcerans 3 months ( Exp1 ) or 6 weeks ( Exp2 ) after the last vaccination . 105 acid fast bacilli ( AFB ) , obtained by in vivo passage in footpad , were injected in the right footpad of the vaccinated mice . The number of bacilli injected , suspended in Dubos Broth Base medium ( Difco ) , was determined by counting under a microscope after Ziehl Neelsen staining . Viability of the M . ulcerans inoculum was checked by plating on 7H11 Middlebrook agar , supplemented with oleic-acid-albumin-dextrose-catalase enrichment medium . Yellow colonies were counted after 8 weeks of incubation at 32°C . The number of Colony Forming Units corresponded to the number of AFB . Vaccinated mice were sacrificed 3 weeks after the third immunization ( Experiment 1 ) . Spleens were removed aseptically and homogenized in a loosely fitting Dounce homogenizer . Leucocytes ( 4×106 WBC/ml ) from four mice per group were cultivated at 37°C in a humidified CO2 incubator in round-bottom micro well plates individually or pooled ( as indicated ) and analyzed for Th1 type cytokine response to purified recombinant his-tagged Ag85A ( 5 µg/ml ) , and synthetic peptides from M . ulcerans or M . tuberculosis ( 10 µg/ml ) . Supernatants from at least three wells were pooled and stored frozen at −20°C . Cytokines were harvested after 24 h ( IL-2 ) and 72 h ( IFN-γ ) , when peak values of the respective cytokines can be measured . Interleukin-2 ( IL-2 ) activity was determined in duplicate on 24 h culture supernatants using a bio-assay with IL-2 dependent CTLL-2 cells as described before [45] . IL-2 levels are expressed as mean counts per minute ( cpm ) . Assay sensitivity is 10 pg/ml . A typical international standard curve of this assay has been published before [46] . Interferon-γ ( IFN-γ ) activity was quantified by sandwich ELISA using coating antibody R4-6A2 and biotinylated detection antibody XMG1 . 2 obtained from Pharmingen . The standard murine recombinant IFN-γ used was obtained from R&D . The sensitivity of the assay is 10 pg/ml . Sera from immunized mice were collected by tail bleeding 3 weeks after the third vaccination . Levels of M . ulcerans specific total anti-Ag85A Igκ antibodies ( Abs ) were determined by direct enzyme-linked immunosorbant assay ( ELISA ) in sera from individual mice ( four/group ) . The concentration of Ab was expressed by the optical density at a dilution of 1/100 of the sera . For isotype analysis , peroxidase-labeled rat anti-mouse immunoglobulin G1 ( IgG1 ) and IgG2b ( Experimental Immunology Unit , Université Catholique de Louvain , Brussels , Belgium ) were used . Isotype titers were expressed as dilution endpoints ( last serum dilution with an optical density ( OD ) value higher than a cut-off OD value calculated from the OD value plus three standard deviations ( SD ) of the secondary antibody only [42] . In experiment 1 ( Gerbu adjuvant ) , protection was evaluated by enumeration of Acid Fast Bacilli ( AFB ) nine weeks after footpad infection . Briefly , the skin and bones were removed from infected foot pad . Tissues were homogenized in a Dounce homogenizer and suspended in 2 ml of Dubos broth based medium containing glass bead . The number of AFB in 20 fields ( surface of 1 field: 0 . 037994 mm2×20 with the 22 mm ocular diameter used ) was counted on microscope slides after Ziehl-Neelsen staining . In experiment 2 ( MPL-A adjuvant ) , protection was evaluated by monitoring foot pad swelling after M . ulcerans infection . The swelling was measured with a calibrated Oditest apparatus with a resolution of 0 . 01 mm as described previously [47] . Animals were euthanized when footpad swelling exceeded 4mm according to the rules of the local ethical commission . For cytokine production analysis , antibody production and AFB counting , statistical analysis was made according to one-way ANOVA test . Subsequent multiple comparison between the 7 different groups of animals and the antigens used was made by a Tukey's correction test . Statistical results are represented in the figure by *** ( P<0 . 001 ) , ** ( P<0 . 01 ) and * ( P<0 . 05 ) . For the comparison of survival curves , logrank test was used .
Spleen cells from mice vaccinated with the three different vaccine formulations produced significant levels of IL-2 ( Figure 1A ) and IFN-γ ( Figure 1B ) after in vitro stimulation with purified recombinant Ag85A from M . ulcerans or from M . tuberculosis . As expected from the 91% sequence similarity between both antigens , highly cross-reactive immune responses were observed , mice vaccinated with M . ulcerans vaccines recognizing the M . tuberculosis antigen and vice versa . Nevertheless , a certain level of species specificity was observed , particularly in the IL-2 responses . Confirming previous results obtained with a M . tuberculosis DNA vaccine [37] , boosting plasmid DNA vaccinated mice with purified M . ulcerans protein increased significantly Ag 85A specific IL-2 and IFN-γ responses . Significant cross-reactive antibody responses were induced against Ag85A from M . ulcerans ( and from M . tuberculosis , data not shown ) in mice vaccinated with the M . ulcerans and M . tuberculosis vaccines ( Figure 2 ) . Antibody responses in DNA vaccinated mice demonstrated considerable individual variation , and were markedly increased by the protein boost . Vaccination with purified protein in Gerbu adjuvant was also very effective in inducing high level antibody production . DNA vaccination induced very little IgG1 isotype antibodies but biased predominantly an IgG2b isotype response , confirming the well known Th1 inducing properties of intramuscular plasmid DNA . In contrast , vaccination with protein emulsified in Gerbu adjuvant induced antibodies of both IgG1 and of IgG2b isotype . Confirming previous findings , DNA prime- protein boost vaccination resulted in increased and less variable antibody titers of both isotypes [37] . Vaccination with recombinant 85A protein or with the DNA prime /protein boost protocol induced significantly higher levels of total IgG and IgG1 antibodies as compared to plasmid DNA vaccination alone ( P<0 . 001 ) . Despite the high level of sequence similarity ( 91% ) between Ag85A from M . tuberculosis and M . ulcerans but in view of the partial species-specific Th1 type immune responses observed in the previous experiment , we decided to characterize the H-2b restricted immunodominant T cell epitopes , using synthetic 20-mer peptides spanning the entire mature sequence of Ag85A from M . ulcerans and from M . tuberculosis . Figure 3 shows the IL-2 and IFN-γ production induced in response to M . ulcerans peptides in mice vaccinated with M . ulcerans DNA ( white bars ) or M . tuberculosis DNA ( black bars ) . Spleen cells from B6 mice vaccinated with M . ulcerans-Ag85A DNA produced significant levels of IL-2 ( Figure 3A ) and IFN-γ ( Figure 3B ) when stimulated with M . ulcerans peptides both from the NH2-terminal and COOH-terminal part of the protein , whereas IL-2 and IFN-γ responses of B6 mice vaccinated with the M . tuberculosis plasmid were almost exclusively directed against M . ulcerans peptide spanning aa 241–260 . M . ulcerans DNA vaccinated mice also recognized this peptide very effectively . Responses against the NH2-terminal peptides spanning aa 61–80 and 81–100 of M . ulcerans-Ag85A were only observed in M . ulcerans DNA vaccinated mice , indicating that this NH2-terminal region was responsible for the partial species-specificity . This confirmed a previous finding ( Inserts in Figures 3A and 3B ) on species-specific T cell responses induced following in vitro stimulation with a purified , partial M . ulcerans Ag85A protein , spanning aa 17–150 in mice vaccinated with DNA encoding Ag85A from M . ulcerans or M . tuberculosis . IL-2 and IFN-γ responses against M . ulcerans peptide spanning aa 261–280 were also species-specific and only detected in mice immunized with the M . ulcerans vaccine . Responses against M . tuberculosis peptides showed a reciprocal pattern ( Figure 4 ) . Confirming previous findings [37] M . tuberculosis peptide spanning aa 261–280 was very well recognized in M . tuberculosis DNA vaccinated mice ( Figure 4A and 4B ) . It was also recognized by M . ulcerans vaccinated mice . Both DNA vaccinated groups also reacted against M . tuberculosis peptide spanning aa 241–260 , previously found to contain the immunodominant H-2b restricted epitope recognized in BCG vaccinated and M . tuberculosis infected B6 mice ( 10 ) . Responses against this M . tuberculosis peptide were even higher in M . ulcerans than in M . tuberculosis DNA vaccinated mice . IFN-γ responses against M . tuberculosis peptides spanning aa 121–140 and 141–160 were only observed in mice vaccinated with the M . tuberculosis DNA , whereas a cross-reactive immune responses was found against M . tuberculosis peptide spanning aa 81–100 . A sequence comparison of identified immunodominant Th1 peptides of Ag85A from M . ulcerans and from M . tuberculosis , showing conserved and non-conserved amino acid changes is presented in Table 1 . Mice were challenged three months after the third vaccination with 105 AFB of M . ulcerans in the footpad . Nine weeks later , when a significant swelling of the footpad appeared in the control mice vaccinated with empty vector , all animals were sacrificed and the number of AFB in the infected footpad was determined by Ziehl-Neelsen staining . As shown in Figure 5 , a significant and strong reduction in the number of M . ulcerans AFB was observed in mice previously immunized with all three types of vaccine . Vaccination with specific M . ulcerans antigen using the DNA prime-protein boost protocol with Gerbu adjuvant conferred the highest protection with an almost one-hundred fold reduction in number of AFB as compared to the control group . This protection was comparable in magnitude to the protection conferred by the BCG vaccine . Difference between the vaccinated groups was not significant ( ANOVA test; p>0 . 05 ) . In a second experiment , protective efficacy of the vaccines was determined by weekly monitoring appearance and size of footpad swelling and survival as previously reported [47] . Mice were euthanized when footpad swelling was >4 mm . In this experiment mice were challenged with 105 AFB of M . ulcerans 04-855 at 6 weeks after the last immunization . The evolution of footpad swelling is shown in Figure 6A and 6C whereas the survival curves are represented in Figure 6B and 6D . In mice vaccinated with empty control vector , footpad size started to increase 5 weeks after M . ulcerans infection whereas in BCG vaccinated mice , footpad swelling was delayed for 7–8 weeks ( Figures 6A and 6C ) . Vaccination with DNA encoding Ag85A from M . tuberculosis or from M . ulcerans delayed onset of foot pad swelling by only 2 to 3 weeks ( Figure 6A ) . DNA prime/protein boost protocol using the M . tuberculosis Ag85A did not increase vaccine efficacy ( Figure 6C ) whereas vaccination with DNA encoding Ag85A from M . ulcerans boosted with the recombinant Ag85A-MPL-A protein from M . ulcerans delayed onset of foot pad swelling to the same extent as the BCG vaccine by 7 to 8 weeks ( Figure 6C ) . Survival curves reflected the footpad swelling pattern . Median survival time of mice vaccinated with empty vector was 10 . 5 weeks , whereas BCG vaccination delayed significantly the moment when mice had to be euthanized , resulting in a median survival time of 17 . 5 weeks ( Figures 6B and 6D ) ( p<0 . 001 compared to empty vector vaccinated mice; p<0 . 01 compared to 85A-DNA Mu vaccinated mice ) . M . ulcerans DNA vaccinated mice demonstrated a median survival time of 13 weeks ( p<0 . 01 compared to empty vector vaccinated mice according to the log rank test ) . Similar results were observed in mice vaccinated with DNA encoding Ag85A from M . tuberculosis ( median survival time 12 . 5 weeks ) ( Figure 6B ) . Boosting DNA vaccinated mice with protein from M . tuberculosis did not increase protective efficacy of the DNA vaccine but priming with DNA encoding Ag85A from M . ulcerans and boosting with recombinant Ag85A from M . ulcerans was very effective in increasing the protection ( Figure 6D ) ( p<0 . 001 compared to M . ulcerans DNA alone , p<0 . 01 compared to DNA encoding Ag85A from M . tuberculosis boosted with the protein of M . tuberculosis ) . Median survival time in the M . ulcerans DNA primed- M . ulcerans protein boosted mice was 17 weeks . This protection was comparable to that conferred by BCG ( p>0 . 05 ) .
Buruli ulcer belongs to the family of neglected tropical diseases [48] . In 1998 the first International Conference on Buruli Ulcer was organized in Côte d'Ivoire , expressing the poor knowledge about this disease and calling on the international scientific community to support control and research efforts . Currently , no specific vaccine exists against this disease . In 1957 , Fenner demonstrated that a high degree of protection was conferred , in an experimental mouse model , against challenge infection with small doses of M . ulcerans by prior inoculation with M . ulcerans , M . balnei and M . bovis BCG ( BCG ) . Footpad and intravenous BCG administration gave considerable protection against a small dose and a slight protection against a large dose of M . ulcerans given in the other footpad [49] . More recently we have shown in a similar experimental mouse model that BCG vaccine protects to some extent against infection with M . ulcerans but that a booster vaccination with the same BCG vaccine does not increase the protective effect [31] , [47] . In 1969 , a clinical study performed in Uganda reported on a protection rate of 47% of the BCG vaccine . However , protection turned out to be short-lived and was only detected in the first 6 months following BCG vaccination [24] . In 1976 , Smith et al reported another BCG vaccination trial against Buruli ulcer in Uganda giving similar short lived ( one year ) protection rates of about 50% [25] . Although not very effective at preventing the classical skin lesions of Buruli ulcer , the BCG vaccine seems to exert a significant protective effect against its severe , disseminated osteomyelitis form both in children and in adults [26] , [27] . A more effective M . ulcerans vaccine would certainly help to control this debilitating disease that affects particularly children . Unfortunately , the nature of the protective immune response and the precise antigens involved are not fully defined at the moment . Based on biopsy specimens , M . ulcerans was originally thought to reside exclusively as free extracellular bacilli , implying that humoral responses might be protective . However , Coutanceau et al recently demonstrated that the initial phase of M . ulcerans infection proceeds by internalization of bacilli by phagocytic cells and that the extracellular stage results from mycolactone inducing host cell death [6] , [50] . Therefore , recognition of the early intracellular stage by an effective Th1 type immune response may contribute to the control of the infection , that is in so far as it can help to reduce the mycolactone production . Hence , magnitude of mycobacteria-specific Th1 type immune response is a plausible correlate of protection that can be used to analyze the potential of new , experimental vaccines . In this study , we focused on a plasmid DNA vaccine encoding Ag85A from M . ulcerans . Protective efficacy was evaluated using two approaches , in one experiment by enumerating the number of AFB in the footpad at nine weeks after M . ulcerans challenge and in the other experiment by monitoring footpad swelling and long term survival of the mice . We have previously reported that footpad swelling is correlated with bacterial replication and can be used as an alternative read-out for protection against infection [47] . DNA prime–protein boost strategy using specific M . ulcerans antigen 85A was clearly the most effective , reducing about one hundred fold the bacterial number and offering a protection of comparable magnitude as the one induced by the BCG vaccine . Nevertheless , and as for the BCG vaccine , immune protection was not sterilizing and eventually all mice developed footpad swelling . We hypothesize that the vaccines reduced or delayed temporarily mycolactone production by the virulent type 1 strain 04-855 but that immunity was not strong enough to completely block the ML synthesis . Targeting ML production by specific antibodies or by interfering with its synthesis might help to overcome this problem . A study made by Fenner , in 1956 showed that the apparition of footpad swelling depends of the number of viable AFB in the inoculum , small doses of bacilli showing delayed appearance of footpad lesion [51] . As we used a high inoculum size of 105 AFB in our studies , it is possible that more sustained protections could have been observed if we had administered a lower number of bacteria . The Gerbu adjuvant is less well known as immunomodulator than other adjuvants such as alumn or monosphoshoryl-lipd-A ( MPL-A ) [52] . Here we have shown that this adjuvant has a strong Th1 inducing capacity , as indicated by the elevated levels of antigen-specific IL-2 and IFN-γ that could be detected in spleen cell cultures from mice vaccinated with protein in this adjuvant . Antibodies of both IgG1 but also of IgG2b isotype were induced , which was another indication of its Th1 favouring properties . Vaccination with recombinant M . ulcerans Ag85A protein in Gerbu adjuvant induced comparable Th1 cytokine and antibody levels as the prime-boost DNA vaccination . This protein vaccine also induced considerable protection ( albeit somewhat lower that the DNA based vaccine ) as indicated by significantly reduced number of AFB in the footpad at nine weeks after M . ulcerans challenge . We have previously shown that DNA vaccination induces a broader T cell repertoire ( more protein epitopes recognized ) than infection with tuberculosis [53] , [54] , vaccination with BCG [44] or with protein [46] and this may explain the better protection conferred by the DNA prime-protein boost vaccination . It is also possible that immune memory induced with this combined immunization protocol was stronger and longer lasting than immune memory induced with protein only vaccination . Analysis of the H-2b restricted Th1 T cell epitopes of antigen 85A from M . ulcerans and from M . tuberculosis revealed some extent of species specificity , both in the NH2-terminal and in the COOH-terminal half of the protein . In contrast to the response induced with DNA encoding M . tuberculosis Ag85A , which was preferentially directed against Ag85A peptide spanning aa 261–280 , T cell response induced with DNA encoding the M . ulcerans protein was directed preferentially against peptide spanning aa 240–259 . Remarkably , mice vaccinated with the M . tuberculosis DNA reacted more strongly to this peptide region of M . ulcerans ( 25 , 000 cpm of IL-2/5 , 000 pg of IFN-γ ) than to the same region in M . tuberculosis ( 10 , 000 cpm of IL-2/1 , 000 pg of IFN-γ ) . We have previously reported that B6 mice vaccinated with DNA encoding Ag85B from M . tuberculosis also react more strongly to 85B peptide spanning aa 244–260 than to peptide spanning aa 262–279 [46] . Sequence analysis of the 241–260 region of Ag85 revealed that the Ag85A sequence from M . ulcerans is more similar to the Ag85B sequence of M . tuberculosis ( only 1 aa ( A–D ) change in position 242 ) than to the Ag85A sequence of M . tuberculosis ( 4 aa changes ) . Interestingly , it was demonstrated by Yanagisawa et al that vaccination of B6 mice with killed M . tuberculosis triggered preferentially a vβ11+ CD4+ T cell response against the peptide spanning amino acids 240 to 254 of Ag85B [55] . All these data taken together seem to indicate that the M . ulcerans Ag85A241–260 region is more immunogenic than the corresponding M . tuberculosis Ag85A region and this may explain the better protective efficacy that we have observed with the species specific M . ulcerans vaccine . In conclusion , our results show that specific Ag85A-DNA priming followed by protein boosting is an effective way to induce robust Th1 type immune responses and strong protection against experimental footpad infection with M . ulcerans in mice . This is a promising vaccination approach that warrants further analysis . Combination with vaccines targeting mycolactone or with vaccines targeting enzymes involved in mycolactone synthesis may be a way to strengthen its protective efficacy . | Buruli ulcer ( BU ) is an infectious disease characterized by deep , ulcerating skin lesions , particularly on arms and legs , that are provoked by a toxin . BU is caused by a microbe belonging to the same family that also causes tuberculosis and leprosy . The disease is emerging as a serious health problem , especially in West Africa . Vaccines are considered to be the most cost-effective strategy to control and eventually eradicate an infectious disease . For the moment , however , there is no good vaccine against BU , and it is still not fully understood which immune defence mechanisms are needed to control the infection . The identification of microbial components that are involved in the immune control is an essential step in the development of an effective vaccine . In this paper , we describe the identification of one of these microbial components , i . e . , antigen 85A , a protein involved in the integrity of the cell wall of the microbe . Our findings obtained in a mouse model now need to be extended to other experimental animals and later to humans . Combination with a vaccine targeting the toxin may be a way to strengthen the effectiveness of the vaccine . | [
"Abstract",
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] | [
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] | 2008 | Improved Protective Efficacy of a Species-Specific DNA Vaccine Encoding Mycolyl-Transferase Ag85A from Mycobacterium ulcerans by Homologous Protein Boosting |
Thermoanaerobic bacteria are of interest in cellulosic-biofuel production , due to their simultaneous pentose and hexose utilization ( co-utilization ) and thermophilic nature . In this study , we experimentally reconstructed the structure and dynamics of the first genome-wide carbon utilization network of thermoanaerobes . The network uncovers numerous novel pathways and identifies previously unrecognized but crucial pathway interactions and the associated key junctions . First , glucose , xylose , fructose , and cellobiose catabolism are each featured in distinct functional modules; the transport systems of hexose and pentose are apparently both regulated by transcriptional antiterminators of the BglG family , which is consistent with pentose and hexose co-utilization . Second , glucose and xylose modules cooperate in that the activity of the former promotes the activity of the latter via activating xylose transport and catabolism , while xylose delays cell lysis by sustaining coenzyme and ion metabolism . Third , the vitamin B12 pathway appears to promote ethanologenesis through ethanolamine and 1 , 2-propanediol , while the arginine deiminase pathway probably contributes to cell survival in stationary phase . Moreover , by experimentally validating the distinct yet collaborative nature of glucose and xylose catabolism , we demonstrated that these novel network-derived features can be rationally exploited for product-yield enhancement via optimized timing and balanced loading of the carbon supply in a substrate-specific manner . Thus , this thermoanaerobic glycobiome reveals novel genetic features in carbon catabolism that may have immediate industrial implications and provides novel strategies and targets for fermentation and genome engineering .
Renewable liquid fuels derived from lignocellulose , the most abundant biological polymer on earth , could alleviate global energy shortages and climate change . Consolidated bioprocessing ( CBP ) , which is one proposed scheme of lignocellulosic ethanol production , combines cellulase production , cellulose degradation , hexose fermentation and pentose fermentation in a single bioreactor , thus maximizing energy- and cost-saving [1] . Thermophilic , gram-positive , anaerobic bacteria ( TGPAs ) are of exceptional interest in a CBP scheme , due to several advantages , including the capability of rapid cellulose degradation ( e . g . , Clostridium thermocellum ) [2] , the ability to ferment a wide range of monosaccharides and oligosaccharides ( e . g . , Thermoanaerobacter ) , and optimal growth at high temperature ( 60–70°C ) , which avoids iterative heating/cooling steps , saves energy for downstream product recovery and minimizes microbial contamination [2] . Furthermore , TGPAs , such as Thermoanaerobacter , not only metabolize both hexose and pentose but also simultaneously ferment them into ethanol ( “co-utilization” ) . This feature is of particular industrial interest because the pentose D-xylose is the primary ingredient of the hemicellulose fraction of lignocellulosic biomass . However , optimization of TGPA cellular machineries for cellulosic ethanol production through fermentation or genetic engineering has been a challenge . First , continuous ethanol production is highly dependent on the efficient and simultaneous use of all of the di- and monosaccharides ( both pentoses and hexoses ) released from lignocellulose . Second , along with ethanologenic pathways , all described TGPAs have branched organic acid pathways [3] . Currently , the ethanol yield ( typically <2% ) [3] , carbon substrate loading ( e . g . , <27 g/L cellulose for C . cellulolyticum and <20 g/L xylose for T . ethanolicus [4] ) , ethanol tolerance ( usually <1∼1 . 5% ( w/v ) [5] ) and sugar to ethanol conversion rate ( usually <30% ) [3] have hindered the direct industrial application of TGPAs . Genetic engineering of TGPA ethanologens has focused thus far on knocking out individual enzyme genes in the acetate and lactate pathways [3] . However , the highest yield reported ( 3 . 7% ( w/v ) ) is still far below the industrial demand ( 7% or higher ) [1] . Alternatively , few studies have engineered carbon-influx machineries in which shared or specific catabolic pathways of various carbohydrate substrates interact to mediate the carbon flux in the cell . In organisms such as Escherichia coli and Bacillus subtilis , the catabolism of monosaccharides and oligosaccharides is tightly controlled by key regulatory processes , such as carbon catabolite repression ( CCR ) , resulting in a cellular preference for hexoses over pentoses [6] . However , little is known about how the unusual phenotype of hexose-pentose co-utilization found in some TGPAs occurs , and few genome-wide models of thermoanaerobic carbohydrate catabolism ( “thermoanaerobic glycobiomes” ) have been reported [7] . Employing the thermophilic ethanologen Thermoanaerobacter sp . X514 as a model [8] , [9] , we devised a rational strategy to unravel the structure and dynamics of the thermoanaerobic glycobiome . Whole-genome expression profiles and physiological responses at various growth phases were measured while culturing X514 in glucose , xylose , fructose and cellobiose as sole carbon sources or in pairwise combinations . Diversity , interactions and dynamics of functional modules were identified and interrogated via co-expression analysis and comparative genomics . To the best of our knowledge , these efforts enabled the reconstruction of the first genome-wide functional network operating and regulating a thermoanaerobic glycobiome .
Among the glucose , xylose , fructose and cellobiose carbon sources , X514 more rapidly and efficiently catabolized monosaccharides than disaccharides ( Figure 1A and Figure S1E ) . Moreover , different carbon substrates resulted in distinct product sets ( Part I of Text S1 ) . However , X514 simultaneously and efficiently metabolized both hexose and pentose when both were present , suggesting an absence of CCR ( Figure 1B ) . To investigate the cause of this phenomenon , a high-density , oligonucleotide-based , whole-genome , gene-expression microarray for X514 was constructed and analyzed via two-dimensional transcriptome sampling ( Figure 2 ) : one by carbon substrate ( glucose , xylose , fructose and cellobiose ) and the other by growth phase ( early , mid and late exponential ) . The sampling and analyses were further organized into three “Views” ( Figure 2 ) . View I investigated carbon substrate-specific cellular machineries where X514 transcriptomes of mono-carbohydrate cultures ( glucose , xylose , fructose or cellobiose alone ) were collected and compared at mid exponential phase . View II was designed to explore the interactions between the hexose and pentose pathways where mid exponential cultures under glucose alone , xylose alone and the equimolar presence of glucose and xylose ( glucose-xylose ) were collected and investigated . In View III , network dynamics were captured by sampling early , mid and late exponential phase cultures under different substrates ( glucose alone , xylose alone or glucose-xylose ) . All of these experiments were performed with three biological replicates to improve the discriminating power of the subsequent co-expression analyses . A genome-wide functional network consequently emerged . ( I ) Overview of the Network . The network includes a total of 614 genes ( 24 . 8% of the genome ) that are partitioned into thirteen modules ( Figure 3A and Table S1 ) . Each module represents a group of more than four genes that are highly connected among themselves but have fewer connections with those in other modules [10] . Thus , each module consists of a functionally coherent set of genes ( ‘nodes’ ) and a set of connections ( ‘links’ ) that suggest positive co-expression ( i . e . , functional correlation ) between the two nodes . Module 1 ( mostly genes involved in amino acid metabolism , translation , transcription or encoding hypothetical proteins ) , Module 3 ( mostly genes related to DNA replication or encoding hypothetical proteins ) , Module 8 ( mostly xylose catabolism and energy production genes ) and Module 10 ( mostly 30S or 50S ribosomal protein genes ) are the most predominant ( Figure 3A ) , suggesting their contribution as the core sugar-responsive pathways in X514 . Modules 5 ( mostly genes involved in de novo vitamin B12 synthesis and related cobalt transport ) , Module 6 ( genes responsible for heavy metal transport ) , Module 9 ( mostly V-type ATP synthesis genes ) and Module 11 ( mostly genes involved in chaperones and stress responses ) also are identified , although they are generally much smaller in size ( Figure 3A ) . Furthermore , distinct modules that correspond to xylose , fructose and cellobiose utilization are revealed ( Module 8 , 7 and 4 respectively; Figure 3A ) . ( II ) Structures of Individual Modules . The network serves as an experimental basis for unraveling , validating and annotating the structure and function of “hypothetical genes” , operons and regulatory circuits ( Part II of Text S1; Figure S2 , Figure S3 and Table S2 ) . Because there are 13 prominent modules in the network , only Module 8 , which is responsible for xylose catabolism , is presented here as an example . Module 8 includes the majority of genes in xylose catabolism and includes 138 nodes ( genes ) and 2001 links . Three major Clusters of Orthologous Groups ( COG ) categories exist in this module: carbohydrate transport and metabolism ( COG G , 19 genes ) , energy production and conversion ( COG C , 19 genes ) and amino acid transport and metabolism ( COG E , 12 genes ) ( Figure 3B ) . These 50 genes account for 36% of all genes in this module . Genes in COG G occupy two groups , one of which is the xylose-specific monosaccharide ATP-binding cassette ( ABC ) transporter systems ( teth5140155 ( xylF ) , 0157-0158 ( xylGH ) , 0166 , 0225 and 0989 ) . Similar to Thermoanaerobacter ethanolicus 39E , which is a strain closely related to X514 , and E . coli , the xylose ABC transporter of X514 consists of three molecular components: an ATP-binding protein ( XylG , Teth5140157 ) , a membrane transporter ( XylH , Teth5140158 ) and a substrate-binding protein ( XylF , Teth5140155 ) ( teth5140156 encodes an RNA-directed DNA polymerase ) [11] , [12] . However , the organization of these genes in the X514 genome is distinct from in E . coli and 39E ( Figure S4 ) . In X514 , xylF and xylAB ( encoding xylose metabolic enzymes ) are in one putative operon , whereas xylGHR resides in another locus located downstream of xylABF . Moreover , in X514 , the xylABF and xylGH are both upregulated under xylose ( Table S3 ) , while in 39E the expression of xylH is relatively constant [11] , regardless of xylose . Thus , X514 is unique in that its xylose ABC transporters are xylose-dependent . The other group includes genes encoding xylose metabolic enzymes ( teth5140154 ( xylB ) , 0969 and 1351 ) , which are strongly induced under xylose ( Table S3 ) . Interestingly , xylA ( teth5140153; Module 1 ) , a member of one Module 8 predicted operon ( teth5140153-0155 ) , serves as the connection between an ATPase gene ( teth5140280; Module 1 ) and the rest of the putative operon , thus identifying Teth5140280 as the ATP provider for xylose transport and initial metabolism ( Figure 3B ) . The COG C genes in Module 8 include two predicted operons related to butanoate metabolism ( teth5140936-0938 and teth5140939-0947 ) , two loci involved in the ethanolamine utilization pathway ( teth5141937-1939 and teth5141943-1944 ) , acetate kinase ( teth5141936 ) and alcohol dehydrogenase ( teth5141935 ) . These genes , all of which are key participants of energy ( ATP ) and ethanol generation , are upregulated under xylose ( Table S4 ) . Therefore , xylose supports a vigorous ethanologenic program in X514 . The COG E genes in Module 8 mainly occupy two putative operons: one encoding oligopeptide/dipeptide ABC transporters ( teth5141792-1796 ) and the other for ethanolamine utilization proteins ( teth5141945-1946 ) . In fact , genes involved in vitamin B12-dependent ethanolamine utilization , including those in COG C and COG E , are all grouped into this module . Moreover , they are all upregulated under xylose ( Table S5 ) . Ethanolamine produces acetaldehyde , which is subsequently converted to acetate and ethanol by aldehyde dehydrogenase ( Aldh; Teth5141942 ) and alcohol dehydrogenase ( Adh; Teth5141935 ) ( Figure 3C and Figure S5D ) . Consistent with this , microcompartment proteins ( Teth5141938-1939 ) , which protect cytosolic proteins from aldehyde toxicity [13] , are also found in Module 8 and are highly upregulated , representing a potential cellular detoxification mechanism against aldehydes in X514 . Aside from the three major COG categories , an additional 54 genes ( 39 . 1% of total ) are found in the Module 8 , including several newly recognized sub-modules , such as ABC transporters and the B12-dependent propanediol utilization pathway ( Part III of Text S1; Figure S3 ) . The most notable is a regulatory sub-module centering on the transcriptional antiterminator bglG ( teth5140269 ) in the putative mannitol-specific phosphotransferase system ( PTS ) operon ( Figure S3A ) . In the network , the bglG is directly linked to the other members of this locus ( teth5140268-0270 ) , suggesting that it regulates their expression . Surprisingly , the bglG is also positively linked to the genes encoding oligopeptide/dipeptide ( teth5141792-1796 ) and xylose ( teth5140157 ) ABC transporters . However , no links are found between the xylose transporters ( teth5140155-0157 ) and the Module 1 xylR ( teth5140159 ) , which encodes the transcriptional factor regulating xylose transporters in other bacteria , including E . coli [12] , [14] ( Figure S3B ) . Moreover , the expression of bglG but not xylR is induced under xylose ( Table S3 ) . These data suggest that , in response to xylose availability , BglG regulates xylose transport ( instead of XylR ) via activation of the putative operons in X514 . In addition to Modules 8 , 7 and 4 , which are involved in xylose , fructose and cellobiose utilization respectively ( Part IV and Part V of Text S1; Figure S2B , Figure S5A and Tables S6 , S7 , S8 , S9 , S10 ) , the network unravels previously unrecognized functional modules in the X514 glycobiome ( Figure 3A; Part VI of Text S1 ) . For example , the presence of Module 5 demonstrates de novo B12 cofactor biosynthesis in X514 , which simplifies nutrient requirements for cells , and reveals the molecular link between B12 and carbon metabolism . Additionally , the crucial role of arginine metabolism in the glycobiome is revealed by the discovery of Module 13 , which includes arginine metabolism genes ( Figure 3A ) . Arginine serves as a precursor for additional ATP and NH3 via the arginine deiminase ( ADI ) pathway to protect the cell against damage caused by energy depletion and acidification [15] . Under xylose , arginine ( argABCDFGH , teth5140657-0663 ) and glutamate biosynthesis ( the precursor of arginine biosynthesis; teth5140505 and teth5140468-0470 ) genes are highly expressed ( Table S5 ) during mid exponential phase . Furthermore , the ADI pathway locus ( teth5140483-0485 ) is activated during late exponential phase under xylose . Therefore , we propose that , in response to xylose , arginine is actively synthesized during mid exponential phase , which could serve as an alternative energy source for ATP generation during late exponential or stationary phases , consistent with the observation that X514 had a longer stationary phase under xylose than glucose ( Figure 1A ) . How xylose activates the ADI pathway remains unclear . ( III ) Interactions among the Modules . One crucial feature and contribution of the network is the inter-module interactions , revealed via the links among modules , that manifest the intricate relationship among biological pathways . Among the 13 modules in the network ( Figure 3A ) , Modules 1 , 3 , 5 and 8 each interacts with another five , four , three and three modules , respectively , while Module 10 is involved in binary interactions with another two modules . For example , the modules of DNA replication , transcription , amino acid metabolism and protein biosynthesis ( Modules 1 , 3 and 10 ) interact as expected . However , from the interaction relationship between Modules 5 and 8 ( Figure 3C ) , a novel pathway is discovered that explains the positive correlation between B12 and ethanol yield ( Figure S5D ) : genes encoding ethanolamine and propanediol ( the alternative energy sources for ethanol production ) utilization proteins , acetate kinase ( producing acetate and ATP ) and alcohol dehydrogenase ( the final step in ethanol fermentation ) are all linked to the porphobilinogen deaminase ( teth5140318; a key enzyme in B12 synthesis ) and ATP-cobalamin adenosyltransferase ( teth5141943; the enzyme converts vitamin B12 to coenzyme B12 ) ( Figure 3C ) . Thus , via this pathway , B12 synthesis interacts with and promotes ethanol production . Therefore , the network reveals unrecognized pathway interactions and indentifies genes serving crucial junctions . Additionally , the absence of inter-module interactions also contains crucial information . There are seven “standalone” modules in the network . For example , Module 8 , Module 7 ( mostly related to fructose utilization ) and Module 4 ( related to cellobiose utilization ) are free of any connections or links , suggesting relative structural separation and functional independence among glucose , xylose , fructose and cellobiose catabolism in X514 ( View II , Figure 2 and Figure S6; Part VII of Text S1 ) . Similarly , based on the number of links for each node , the network also reveals the relative importance of genes in biological processes [16] . Genes encoding the oligopeptide ABC transporter , xylose ABC transporter , PTS , binding protein-dependent transport systems , pyruvate oxidoreductase , acetate kinase and ethanolamine utilization protein are examples of genes with the highest number of connections ( Table S11 ) , suggesting active and important contributions by these genes to the glycobiome . Furthermore , when integrated in an evolutionary perspective , the network reveals crucial metabolic and regulatory junctions . One prevalent feature of microbial genomes is the apparent redundancy of paralogs with individual contributions that can be elusive . One such example is the nine alcohol dehydrogenase ( adh ) genes in the X514 genome , which could play crucial roles in ethanologenesis by mediating the last and shared step of both pentose and hexose fermentations that converts aldehyde to ethanol . Two of them are NADPH-dependent adhs: teth5140653 ( adhB ) and teth5140654 ( adhA ) ; one of them is a bifunctional alcohol dehydrogenase/aldehyde dehydrogenase ( teth5140627 , adhE ) [17] , [18] . These three genes and four iron-containing adhs ( teth5140145/0241/0564/1935 ) cluster in COG C , whereas an iron-containing adh ( teth5141808 ) and a short-chain adh ( teth5141882 ) cluster into COG Q ( secondary metabolite catabolism ) and COG E , respectively ( Figure S7 and Table S12 ) . The network reveals the various roles that these adhs play ( Part VIII of Text S1 ) . In particular , teth1541935 is one of the most prominent ( i . e . , well-connected ) nodes in the network . It is located in Module 8 ( mostly xylose catabolism and energy production genes ) and directly linked to 21 genes involved in ethanol conversion , particularly in B12-dependent ethanolamine utilization and propanediol utilization ( Figure S5B ) . Moreover , teth1541935 is induced under both fructose and xylose compared to glucose and is positively correlated with higher ethanol yields via the ethanolamine and propanediol pathways ( Figure 3C ) . The protein sequence-based phylogeny of all adhs in X514 and 39E ( 39E harbors seven adhs; Figure S7B ) reveals that , except for teth5141935 and teth5140145 in X514 and teth391597 in 39E , most X514 adhs have orthologs in 39E ( a total of six such orthologous pairs ) and are under stringent negative selection , suggesting a strong evolutionary pressure to preserve their functions ( Figure S7B ) . Teth5141935 and 0145 are the only two strain-specific adhs in X514 , and the former coincides with the teth5141935 , which prominently stands out in the network with 21 links ( Figure S5B and Table S12; teth5140145 is absent in the network ) . All of these data strongly identify teth5141935 as a crucial junction in ethanol production and a key target in the rational perturbation of the network . Thus , of the nine adhs in the genome , seven are constitutively expressed , six are under negative selection ( Ka/Ks <1 ) , and two are X514 lineage-specific innovations with one of the two ( teth5141935 ) serving as a crucial junction in energy production . By aligning the three choreographies ( glucose , xylose , and dual carbohydrate ) progressing across the three growth phases , the network not only provides a “static” model for glycobiome structure and regulation , but reveals the dynamics of this process ( View III in Figure 2 ) . Among the three choreographies , the differentially expressed genes ( Figure S8 ) are mostly those in energy production ( C ) , carbohydrate transport and metabolism ( G ) , amino acid metabolism ( E ) , coenzyme metabolism ( H ) , and inorganic ion transport ( P ) ( Figure S8C and S8D ) . However , there are a number of prominent discordances . First , glucose quickly switched on genes involved in carbon transport and metabolism ( glycolysis and pentose phosphate pathway ( PPP ) ) during early exponential phase , in both glucose alone and glucose-xylose growth conditions . In glucose alone , the two loci ( teth5141115-1118 and teth5142194-2201 ) encoding carbohydrate-binding proteins ( and associated transporters in each locus ) peaked at early exponential phase , whereas the glucose-specific IIA/IIBC of PTS ( teth5140412-0413 ) peaked during mid exponential phase ( Figure 4A ) . However , in the xylose-alone choreography , a relative delay ( “xylose lag” ) was observed: the xylose-specific binding protein genes ( teth5141099-1100 ) peaked during mid exponential phase with the ABC transport systems ( teth5140986-0992 and teth5140157-0158 ) peaking during late exponential phase ( Figure 4A ) . However , under the dual carbohydrate condition , the “xylose lag” disappeared . Indeed , genes encoding xylose- and glucose-binding proteins peaked during early exponential phase ( except teth5141100 ) , while carbohydrate transport systems of glucose-specific PTSs and xylose ABC transporters both peaked during mid exponential phase . For PPP and glycolysis pathway genes , this finding was consistent with that observed for transporter genes ( Figure 4A ) . Thus , when both glucose and xylose are present , glucose apparently activates xylose transport and catabolism via monosaccharide binding proteins ( during early exponential phase ) and via monosaccharide transport , PPP and glycolysis genes ( during mid exponential phase ) . Second , xylose extended cellular coenzyme activities and sustained cellular growth during late exponential phase , whether alone or as one of the dual substrates ( Figure 1; also see Figure 5A ) . In each of the three choreographies , most genes involved in inorganic ion transport and coenzyme metabolism were poorly transcribed during early exponential phase but abundantly expressed during mid exponential phase ( Figure 4A ) . However , their expression quickly decreased during late exponential phase under glucose but not under xylose or xylose-glucose . Examples of such genes include the putative cobalt transport ( teth5140323-0326 ) and vitamin B12 coenzyme biosynthesis ( teth5140296-0321 ) operons . The sustained high expression of these genes probably explains the prolonged stationary phase whenever xylose is present . Therefore , the dual carbohydrate glycobiomes include the respective choreographic features of xylose- and glucose-alone glycobiomes ( Figure 4B ) . In particular , glucose accelerated xylose utilization via activating xylose transport and catabolism genes , whereas xylose maintained and extended coenzyme activities and ion metabolism to delay cellular lysis ( Figure 1A and Figure 5A ) . Such a structurally independent yet functionally collaborating interaction between pentose and hexose catabolism explains the robust glucose-xylose co-utilization and appears to be a key feature of the Thermoanaerobacter glycobiome . To validate the network-derived findings on the distinct roles and cooperative nature of pentose and hexose , a series of batch fermentation experiments were devised in which , in a substrate-specific manner , the relative loading and timing ( and their combinations ) of the carbon supply were varied ( Figure 5 ) . Under dual carbohydrate conditions , the growth curve was similar to that of glucose alone , while the duration of the stationary phase was similar to that of xylose alone ( Figure 5A ) . Moreover , when introducing xylose but not glucose into a glucose-alone culture , either at inoculation or at mid exponential phase , the stationary phase was extended ( Figure 5A and Figure S9 ) . Both findings are consistent with the hypothesized roles and cooperative nature of glucose and xylose . Furthermore , the ethanol yields were also examined ( Figure 5 ) . First , the dual substrate condition yields significantly more ethanol than did either substrate alone . For example , glucose plus xylose ( at 50 mM each ) produced 89% more ethanol than did 100 mM glucose ( p = 0 . 0002; Figure 5B ) and 113% more ethanol than did 100 mM xylose ( p = 0 . 0017; Figure 5C ) . Second , under dual substrate , there is an additive , substrate concentration-dependent effect on the ethanol yield when supplementing one substrate for the other at inoculation . However , the effect disappeared beyond ∼10 mM of the supplementary substrate . Beyond this threshold , adding more of the supplementary substrate did not further improve ethanol yield . For example , on top of 50 mM glucose , 10 mM supplementary xylose yielded a level of ethanol similar to 50 mM supplementary xylose ( p value not significant; Figure 5B ) . On top of 50 mM xylose , 10 mM supplementary glucose yielded a similar amount of ethanol to 50 mM supplementary glucose ( p value not significant; Figure 5C ) . Third , the timing of introducing the supplementary substrate is an important factor in determining ethanol yield . When supplementing 10 mM xylose with 50 mM glucose , no significant difference in ethanol yield was found whether introducing the supplementary substrate at inoculation or during mid exponential phase ( Figure 5B ) In fact , replacing the supplementary xylose with glucose lowered the ethanol yield ( data not shown ) . However , adding 10 mM supplementary glucose at inoculation to 50 mM xylose yielded 31% more ( p = 0 . 01 ) ethanol than adding the glucose during mid exponential phase ( Figure 5C ) . Replacing supplementary glucose with xylose lowered the ethanol yield ( data not shown ) . This finding is also consistent with the distinct yet collaborative action modes of glucose and xylose . Finally , the substrate-specific and timing-dependent features were exploited to achieve optimal yield . For example , supplementing 10 mM xylose during mid exponential phase to 50 mM glucose ( added at inoculation ) yielded 39% more ethanol ( p = 0 . 002 ) than did supplementing 10 mM glucose during mid exponential phase to 50-mM xylose ( added at inoculation ) ( Figure 5D ) . Therefore , the distinct yet collaborative glucose and xylose catabolism can be exploited to enhance ethanol yield via optimized timing and balanced loading of the carbon supply in a substrate-specific manner . The structure and dynamics of the glycobiome enables the construction of a hypothetical cellular model of carbohydrate catabolism for thermophilic anaerobes ( Figure 6 ) , which highlights the features shared with and distinct from model microbes , such as E . coli . First , transcription factors , such as the BglG family anti-terminators , respond to carbon-substrate availability and activate both hexose-specific PTS transport systems and ABC pentose transport systems ( in contrast to E . coli in which XylR regulates ABC xylose transport ) [12] . Second , when the monosaccharides are transported into cells , another set of regulators is expressed: the DeoR-family regulator , responding to hexoses ( e . g . , fructose ) , activates glycolysis genes , while lacI and other members of the putative PPP operon that includes lacI are all induced under xylose ( Figure 6 ) . The two core pathways of carbon metabolism , glycolysis and PPP , consequently metabolize hexoses , pentoses and disaccharides . Third , the vitamin B12 pathway promotes ethanol production through B12-dependent ethanolamine and 1 , 2-propanediol , while the ADI pathway revives the cell for sustained growth ( Figure 6 ) .
Our study demonstrates a strategy for the efficient and productive reconstruction of functional and regulatory networks based on a rational experimental design , a limited number of transcriptomes sampled and appropriate computational theories . First , to maximize the discriminating power of co-expression-based analysis , time courses ( View III; Figure 2 ) were introduced in the experimental design in contrast to most previous studies in which only substrates ( View I and II; Figure 2 ) were considered and only the mid exponential phase was sampled [19] . In this study , when only the 15 datasets from different substrates ( 15 microarrays ) were used for network construction , no prominent biological insights were revealed ( data not shown ) . Thus , the absence of the time course datasets would have prevented crucial insights , such as the nature of interactions between the glucose and xylose modules . Second , we demonstrated that valuable insights can emerge from a transcriptome dataset of a reasonable size ( 33 total microarrays; View I/II/III; Figure 2 ) . Previous networks were usually constructed with thousands of microarrays [20] , and the notion that the construction of biologically meaningful co-expression networks demands “scale” has largely limited such studies to only a few model organisms [21] . Third , one crucial factor determining network quality is the threshold of the Pearson correlation between genes . Methods for rationally choosing this threshold include those based on known biological information [22] and on the statistical comparison of randomized expression data [23] , which usually are manual , subjective and require organisms with well-established biological backgrounds . Random matrix theory in an automatic and objective fashion distinguishes system-specific , non-random properties embedded in complex systems [10] , [16] , and thus is especially valuable in non-model organisms where little or no prior knowledge exits . Hexose and pentose co-utilization is an extraordinary and industrially valued property among ethanologens . Saccharomyces cerevisiae and Zymomonas mobilis are not able to ferment pentose to ethanol [4] , while E . coli and B . subtilis prefer hexoses over pentoses , due to CCR [6] . In Thermoanaerobacter , the intriguing trait of hexose and pentose co-utilization may be tentatively interpreted as an adaptation to barren environments where carbohydrates ( and , in particular , monosaccharides , are scarce [8] ) . Simultaneous activation of catabolic pathways for all monosaccharides available may thus be viewed as an evolutionary acquisition with adaptive value . In addition , as an anaerobe , these cells lack an oxidative PPP for converting hexoses to pentoses [24] . Consequently , any preference for hexose would have compromised the many crucial metabolic processes where pentose played essential roles . Such environmental and evolutionary pressure left striking footprints on their regulatory mode as well . For example , in both E . coli and B . subtilis , the activation of xylose utilization genes uses XylR [12] , [25] , which activates xylose utilization loci . In B . subtilis , when glucose and xylose are both present , the transcriptional regulator catabolite control protein A ( CcpA ) , which is activated by the hexose-induced phosphorylated histidine-containing protein ( HPr ) , blocks XylR-binding sites upstream of xylose utilization loci , thus preventing xylose from being consumed until glucose is depleted . Alternatively , in Thermoanaerobacter , both the hexose- and pentose-transport systems appear to be regulated by BglGs , rather than by XylR for pentose . Our model suggests that one BglG ( Teth5140269 ) positively regulates xylose ABC transporters ( Teth5140157 ) under both xylose alone and dual carbon source ( glucose and xylose ) , a second ( Teth5140823 ) activates fructose-specific PTS transporters ( Teth5140824-0826 ) , and a third ( Teth5140414 ) activates glucose-specific PTS transporters ( Teth5140412-0413 ) ( Figure 6 and Figure S3 ) . This difference may contribute to the pentose-hexose co-utilization . Compared to aerobes , de novo B12 synthesis is another advantageous trait in Thermoanaerobacter , a strict anaerobe , because B12 promotes ethanol production in the thermophilic anaerobe C . thermocellum [26] . Only strict anaerobes contain this trait because B12 is only anaerobically synthesized [27] . This trait simplifies nutrient requirements and promotes ethanol yields via ethanolamine and 1 , 2-propanediol utilization pathways in X514 , a clear advantage in ethanol production ( Figure 3C ) . Furthermore , the ADI pathway , which is a survival strategy that delays host cell lysis and ethanol damage [15] , functions only anaerobically . Under aerobic conditions , another arginine catabolism pathway ( arginine to glutamate ) is induced [28] . These features and their genetic basis distinguish the Thermoanaerobacter glycobiome and suggest novel exploitation strategies to enhance Thermoanaerobacter fermentation . Mono- or co-cultures of cellulolytic and/or ethanologenic TGPAs ( e . g . , C . thermocellum and T . ethanolicus [2] , [29] ) are promising CBP organisms , but several challenges in their utilization remain . Our study reveals new strategies and targets for overcoming these hurdles . First , glucose and xylose utilization can be enhanced by engineering specific transport apparatuses . At least five predicted operons , including both ABC transporters and PTSs , may be involved in transporting xylose or glucose , and their individual contributions can be elusive . Our results identify putative operons that are specifically affected by glucose ( teth5140412-0414 ) and xylose ( teth5140153-0155 and teth5140157-0158 ) , thereby presenting a prioritized list of engineering targets in X514 and related organisms . Second , cellobiose , which is abundant in lignocellosic hydrolysates , is a dimer of glucose . However , in X514 , lower biomass and end products were produced under cellobiose than under glucose ( Figure S1 ) . The presence of Module 4 ( related to cellobiose catabolism ) in the network ( Figure 3A ) and the finding that these genes require a longer induction identify cellobiose utilization as one bottleneck in Thermoanaerobacter ethanol yield , reveal a relatively independent mechanism of cellobiose hydrolysis and transport and pinpoint teth5140262-0267 , which encodes a disaccharide-specific transporter and glycoside hydrolyase cassette , as a key engineering target to improve cellobiose utilization . Third , one of the nine adhs in the genome , teth5141935 , is identified as a top target for engineering ethanol yield . Adhs regulate the balance leading to ethanol; therefore , the perturbation of teth5141935 should improve ethanol yield [30] . Because large arrays of paralogous adhs are the norm rather than the exception in microbes ( 9 in X514 , 7 in E . coli and 7 in S . cerevisiae; [31] ) , our network-based approach for functionally distinguishing such paralogs should have broader applications . Finally , we uncovered the distinct properties of glucose and xylose as cellular fuels . Glucose and xylose modules overlap when both monosaccharides are present: the former activates xylose transport and catabolism during early exponential phase ( via monosaccharide binding proteins ) and mid exponential phase ( via monosaccharide transport and PPP and glycolysis genes ) , while the latter delays cell lysis by sustaining coenzyme and ion metabolisms . This finding could be valuable to the design of more efficient carbon utilization devices and modules in synthetic biology . Furthermore , we experimentally demonstrated that these properties can be rationally exploited to enhance ethanol yield . In summary , the newly discovered modular and precisely regulated network unveiled unique features of the Thermoanaerobacter glycobiome and revealed novel perturbation strategies and targets . Together with the genetic systems under development [30] , [32] , this network forms the foundation for rational engineering of both fermentation and cellular machineries in TGPA and other ethanologens for efficient hexose-pentose co-utilization . Furthermore , we demonstrated a strategy for cost-effective network reconstruction . The expanded applications of such a strategy and approaches in monocultures ( e . g . , exploring network fluidity or evolution ) and mixed communities could provide new insights into interacting pathways , cells and cellular populations .
Microarrays for X514 were constructed with 70-mer nucleotide probes . This microarray includes 2315 probes covering 2322 annotated gene sequences . Among these probes , 20 probes were designed from 20 ORFs randomly selected from the human genome and were used as negative controls . Of the 2365 gene sequences used for probe design , no probes could be designed for 43 of them , which were thus not covered by the microarray . Each probe was replicated twice on the microarray , resulting in two sub-arrays on the slide . Gene-specific and group-specific oligonucleotide probes were designed with CommOligo 2 . 0 software [33] based on the following criteria: identity ≤85% , stretch ≤20 bases , and free energy ≥−40 kCal/Mol to non-target sequences for gene-specific probes; and identity ≥96% , stretch ≥55 bases , and free energy ≤−90 kCal/Mol to target sequences for group-specific probes . All designed oligonucleotides were commercially synthesized by MWG Biotech Inc . ( High Point , NC ) and prepared , as described by Li et al . [33] . Detailed information of the probes and their targets were reported in Table S13 . Thermoanaerobacter sp . X514 was grown anaerobically in defined medium [8] supplemented with either 50 mM glucose , xylose , fructose or cellobiose as the sole carbon source or 50 mM glucose plus 50 mM xylose as dual carbohydrates at 60°C without shaking . X514 was passaged five times on the substrate of interest in defined medium before inoculation . Each sample was harvested during the early exponential ( OD600∼0 . 1 ) , mid exponential ( OD600∼0 . 17 ) and late exponential ( OD600∼0 . 27 ) phases . Next , cell pellets were frozen immediately in liquid N2 and stored at −80°C prior to RNA extraction . All of the samples were prepared and analyzed in triplicate . Concentrations of acetate , ethanol and lactate were analyzed using a high-performance liquid chromatograph ( Agilent Technologies , CA ) equipped with a variable wavelength ( 190–600 nm ) detector ( VWD , with UV absorption at 245 nm ) and an ion exclusion column ( Aminex HPX-87H , 300 mm×7 . 8 mm , Bio-Rad Laboratories , CA ) at a column temperature of 55°C . The mobile phase consisted of 0 . 025% sulfuric acid at a flow rate of 0 . 6 ml/min . An Aminex HPX-87P column ( Bio-Rad ) was used with deionized water as the mobile phase to measure the sugar concentrations . Total cellular RNA and genomic DNA were isolated and labeled , as previously described [34] , [35] . The Cy3-labeled genomic DNA was used as a common reference to co-hybridize the Cy5-labeled RNA samples on each slide . Hybridization was performed using a TECAN HS4800 Pro Hybridization Station ( Tecan , NC ) by following the manufacturer's instructions . After hybridization , slides were scanned using a ProScanArray microarray analysis system ( Perkin Elmer , MA ) . To determine the signal intensity of fluorescence for each spot , the scanned 16-bit TIFF images were analyzed by ImaGene 6 . 1 software ( Biodiscovery Inc . , EI Segundo , CA ) as described by [34] . Details of the microarray data analysis were previously described [34] . Typically , a cutoff |log2R| ≥1 . 0 and |Z score| ≥2 . 0 were used to determine changes of significance . The complete microarray dataset was deposited as NCBI GEO GSE24458 . The robustness of the high-density oligonucleotide microarray-based expression profiles was tested by qRT-PCR of a selected set of 20 genes distributed in different putative operons ( Table S14 ) . The specific primer pair for each gene and their respective sequences were listed in Table S14 . The qRT-PCR analysis was performed according to a previously described protocol [34] . Copy numbers of the target gene transcripts were determined via comparison with standard curves , and log2R ratios were subsequently determined . A high correlation coefficient of 0 . 932 was observed between the qRT-PCR and the microarray results ( Figure S10 ) , validating the reproducibility of the microarray data . All 33 microarray datasets from the sole and dual carbon sources were used for the construction of a gene co-expression network based on random matrix theory [16] . For each spot on the microarray , a normalized Cy5/Cy3 ratio was calculated , and then logarithmic transformation of the ratio was performed . The network was generated with a Pearson correlation coefficient cut-off at 0 . 94 between each pair of genes [16] . The modules were separated by fast greedy modularity optimization [36] . This algorithm divides an entirely unclustered network where each node in the graph forms a singleton community into organized modular structures by computing modularity between two communities based on the connections between nodes until a maximum modularity value is reached [37] . The amino acid sequences of the nine and seven adhs of X514 and 39E , respectively , were extracted from the NCBI reference genome sequences NC_010320 . 1 and NC_010321 . 1 . These adh sequences were analyzed by ClustalW based on MEGA 4 software . Ka/Ks was calculated by the PAML software . Ka/Ks <1 suggests negative selection while Ka/Ks>1 indicates positive selection [38] . | Renewable liquid fuels derived from lignocellulosic biomass could alleviate global energy shortage and climate change . Cellulose and hemicellulose are the main components of lignocellulosic biomass . Therefore , the ability to simultaneously utilize pentose and hexose ( i . e . , co-utilization ) has been a crucial challenge for industrial microbes producing lignocellulosic biofuels . Certain thermoanaerobic bacteria demonstrate this unusual talent , but the genetic foundation and molecular mechanism of this process remain unknown . In this study , we reconstructed the structure and dynamics of the first genome-wide carbon utilization network of thermoanaerobes . This transcriptome-based co-expression network reveals that glucose , xylose , fructose , and cellobiose catabolism are each featured on distinct functional modules . Furthermore , the dynamics of the network suggests a distinct yet collaborative nature between glucose and xylose catabolism . In addition , we experimentally demonstrated that these novel network-derived features can be rationally exploited for product-yield enhancement via optimized timing and balanced loading of the carbon supply in a substrate-specific manner . Thus , the newly discovered modular and precisely regulated network elucidates unique features of thermoanaerobic glycobiomes and reveals novel perturbation strategies and targets for the enhanced thermophilic production of lignocellulosic biofuels . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"systems",
"biology",
"biology",
"microbiology",
"genetics",
"and",
"genomics"
] | 2011 | The Thermoanaerobacter Glycobiome Reveals Mechanisms of Pentose and Hexose Co-Utilization in Bacteria |
Advances in reporters for gene expression have made it possible to document and quantify expression patterns in 2D–4D . In contrast to microarrays , which provide data for many genes but averaged and/or at low resolution , images reveal the high spatial dynamics of gene expression . Developing computational methods to compare , annotate , and model gene expression based on images is imperative , considering that available data are rapidly increasing . We have developed a sparse Bayesian factor analysis model in which the observed expression diversity of among a large set of high-dimensional images is modeled by a small number of hidden common factors . We apply this approach on embryonic expression patterns from a Drosophila RNA in situ image database , and show that the automatically inferred factors provide for a meaningful decomposition and represent common co-regulation or biological functions . The low-dimensional set of factor mixing weights is further used as features by a classifier to annotate expression patterns with functional categories . On human-curated annotations , our sparse approach reaches similar or better classification of expression patterns at different developmental stages , when compared to other automatic image annotation methods using thousands of hard-to-interpret features . Our study therefore outlines a general framework for large microscopy data sets , in which both the generative model itself , as well as its application for analysis tasks such as automated annotation , can provide insight into biological questions .
Detailed knowledge of the precise location and time span of gene expression is mandatory to deciphering dynamic cellular mechanisms . The application of microarray technology has led to genome-wide quantitative overviews of the relative changes of transcript levels in many organisms ( such as Drosophila embryonic development [1]–[4] ) , but these rarely provide spatial information . In contrast , microscopy of colored or fluorescent probes , followed by imaging , is able to deliver spatial quantitative phenotype information such as gene expression at high resolution [5] , [6] . For instance , RNA in situ hybridization localizes specific mRNA sequences by hybridizing complementary mRNA-binding oligonucleotides and a suitable dye [7] . This approach has been used as part of large scale compendia of gene expression in Drosophila embryos [4] , and the adult mouse brain [9] , [10] . Available image data therefore constitute a repertoire of distinctive spatial expression patterns , allowing us to obtain significant insights on gene regulation during development or in complex organs . One of the fastest growing expression pattern data collections is the Berkeley Drosophila Genome Project RNA in situ hybridization database [8] ) , which contains annotations of spatial expression patterns using a controlled vocabulary , following the example of the Gene Ontology ( GO ) [11] . The annotation terms integrate the spatial gene expression dimensions of a developing “path” from the cellular blastoderm stage until organs are formed . Over images for genes have thus been manually acquired , curated and annotated [4] . Due to the complex nature of the task , these Drosophila images were manually annotated by human experts . Automatic image annotation systems are fairly routinely used in cell-based assays , e . g . for the classification of protein subcellular localization in budding yeast [12] . The increasing number of expression images for complex organisms has motivated the design of computational methods to automate these analyses [13] . In general , this requires solving two sub-problems: identifying objects in a potentially noisy image and normalizing the morphology of the objects , followed by analysis on the actual expression patterns . Typically , studies have focused on the task to recapitulate the expert-provided annotation , based on bottom-up approaches utilizing large sets of low-level features extracted from the images . For instance , Ji et al . [14] proposed a bag-of-words scheme in which invariant visual features were first extracted from local patches on the images , followed by a feature quantization based on precomputed “visual codebooks” and finally classification . Peng et al . [15] developed an automatic image annotation framework using three different feature representations ( based on Gaussian Mixture Models , Principal Component Analysis ( PCA ) and wavelet functions ) and several classifiers , including Linear Discriminant Analysis ( LDA ) , Support Vector Machines ( SVM ) and Quadratic Discriminant Analysis ( QDA ) . Heffel et al . [16] proposed an embryo outline extraction and transformation and conversion to Fourier coefficients-based feature representation . One potential drawback of the above mentioned approaches is the high dimensional and complex feature space ( thousands of features per image ) which implies a potential for high redundancy and computational difficulties . In contrast to such large feature sets , a spatial expression pattern typically consists of a limited number of discrete domains , defined by a small set of upstream regulatory factors . As an alternative , Frise et al . [17] therefore set out to identify a concise set of basic expression patterns in Drosophila . Starting with an unsupervised clustering approach on a manually selected small set of distinct images , the clusters were extended to a broader data set comprising lateral views of early development through a binary SVM classification . This pipeline revealed a set of well defined clusters describing specific regions of expression with good correspondence to developmental structures and shared biological functions of the genes within clusters . While the authors gave many individual examples for the possible meaning of clusters , they did not use them in further applications to annotate patterns or infer regulatory relationships . As with most of the described approaches , the study involved significant human intervention , which generally includes manual selection of “good” images for training , clustering , and/or evaluation: selection of a subset of viewpoints ( images show different embryo orientations , e . g . lateral or dorsal/ventral ) , or selection of successfully registered images only . While this may lead to highly encouraging results , the significant work for manual image selection represents a potential shortcoming , considering that available data are rapidly increasing and an automatic computational method is essential . We here propose a new approach to close the gap between the feature-oriented approaches for pattern annotation , and the identification of expression domains to gain functional insights . The central part is the application of sparse Bayesian factor analysis ( sBFA ) , which describes a large number of observed variables ( image features ) by linear combinations of a much smaller number of unobserved variables ( factors ) . This framework aims at explaining the variability of the original high dimensional feature space by a much smaller set of latent factors , through a completely unsupervised process . [Note that the mathematical usage of the word “factor” is distinct from its biological meaning] . It can also be seen as a clustering method , where samples belong to different clusters , based on their corresponding linear combination mixing weights . Another advantage of the sBFA model is that any information about the underlying structure can be easily incorporated through priors [18]; for instance , we here use a sparseness prior placed on the number of factors used to “reconstruct” each image . Using such sparse Bayesian approaches we identify a basic expression vocabulary directly from the image data , and show that this small subset of features is highly interpretable in terms of biological function or co-regulation . This vocabulary is then used for gene annotation with performance comparable or exceeding current systems , and stability when applied on the complete and noisy data set , without any human intervention or selection of “representative” images . The top-down generative nature of this approach ( rather than traditional bottom-up approaches ) also promises high utility in other application areas , by integrating the model with various information on gene expression and regulation .
One of the most popular data sets to explore the use of image expression data is the Berkeley Drosophila Genome Project ( BDGP ) data set . It consists of over images which document embryonic expression patterns for over of the protein-coding genes identified in the Drosophila melanogaster genome . A gene's expression pattern can be reflected in the accumulation of its product in subsets of cells as embryonic development progresses . In this case , the patterns of mRNA expression were studied by RNA in situ hybridization , which has the potential to reveal the spatial aspects of gene expression during development at genome-wide scale . The RNA in situ hybridization used digoxygenin-labeled RNA probes derived primarily from sequenced cDNAs to visualize gene expression patterns and documented them by digital microscopy . For each expressed gene , representative low and high magnification images were captured at key developmental stages . These developmental stages clearly define emerging embryonic structures such as gastrulation , midblastula transition and organogenesis onset . For practical reasons , the first hours of Drosophila development , spanning embryonic stages , , , , and , were chosen for analysis , as this interval is manageable in terms of data annotation . As examples , stages are associated with the time interval 1h20min–3h , while the later developmental stages occur between 5h20min–9h20min . Genes are annotated with ontology terms from a controlled vocabulary describing developmental expression patterns ( cf . [11] ) . Any gene is thus associated to one or multiple terms , and often represented by more than one image . Images can display non-informative patterns due to poor quality staining/washing , and a gene can show distinct and different expression patterns due to different embryo orientations or the relatively long developmental time spanned by a stage range . Images with lateral orientation have now been annotated as such , information not available until recently . As proof of concept , the model is demonstrated on a variety of images , covering two distinct developmental stage ranges ( , ) and multiple orientations ( lateral , dorsal/ventral ) . The first data set ( ) includes genes ( images ) with arbitrary orientation ( mostly lateral and dorsal/ventral ) , acquired during the time window of developmental stages . The second data set ( ) covers a subset of genes ( images ) restricted to lateral views; we used this smaller data set to evaluate the effect of integrating images from multiple views , and to be able to compare against earlier approaches which were frequently applied on lateral views only . Genes in these two sets were annotated with non-trivial terms ( i . e . excluding no or ubiquitous expression ) . The third data set ( ) covers genes ( images ) with arbitrary orientation from the later developmental stage range . At this point , the problem is complicated by the more developed embryo morphology , which gives rise to intricate spatial expression patterns . Consequently , genes in this set were annotated with unique non-trivial terms . The last data set ( ) contains manually selected genes from data set as used in a previous study [15] , comprising images with lateral view only . The image registration process used throughout this paper was previously introduced by Mace et al . [19] in which individual embryos were extracted and rotated in an automatic fashion . We then scaled the registered images to × pixel resolution and extracted grid-based features by calculating the mean pixel value within each patch . Details can be found in the “Materials and Methods” section . To illustrate the potential of a sparse set of factors to represent complex expression patterns , we started with data set . We evaluated different values for the number of factors in the model ( ) and different resolution – , and factors for grid sizes of × , × and × , respectively . Representative images ( original , grid-based , and reconstructed factor-based ) for the annotation terms with the highest number of associated genes are shown in Figure 1 . While the resulting images are somewhat noisier , they clearly recapitulate the overall expression domains . sBFA was successful in automatically extracting interpretable patterns based on our choice of pixel intensities as input features . Figure 2 illustrates this for an example grid size of × and factors , and the estimated sparse factor loading matrix is shown in Figure S1 . In particular , many factors correspond to prototypical lateral view patterns along the anterior/posterior axis , reflecting the activity of the segmentation network . Others represent expression differences along the dorsal/ventral axis , and patterns from different views , showcasing the ability of the method to automatically extract distinct patterns for different embryo orientations . In addition , some factors do not represent distinct expression patterns but rather the embryo shape or lighting artifacts . While these factors certainly reflect commonalities among the input data , they show the potential of sBFA to automatically separate meaningful patterns from noise . Besides image reconstruction , the factor loading matrix provides for an elegant way for clustering and co-expression analysis: the factors ( rows in the factor matrix ) represent cluster centroids and the mixing weights ( entries in the factor loading matrix ) describe co-expression between genes . Each cluster can then be referred to through its corresponding factor . To illustrate this , we selected the entry/factor in the factor loading matrix with the highest absolute value for each gene in data set . The resulting clusters divided the expression landscape into distinct categories , defining clusters of genes with various expression patterns . Compared to Frise et al . [17] , who illustrated the correspondence of clusters to a developmental fate map , the sBFA framework was thus able to discover highly similar expression domains and the underlying relationships among them , but with no prior manual initialization . Within the largest clusters ( Figure 2 ) , we noticed broadly expressed genes , anteriorly expressed genes , posteriorly expressed genes , as well as dorsal/ventral expression . We further investigated co-expression by identifying instances where two clusters shared genes ( two columns in the factor loading matrix contain informative mixing weights for common genes; for informative weights , we selected all loading matrix entries within of the absolute highest value , in accordance with the sparsity assumption of the model ) . In most of the cases , linked clusters correspond to a general trend of temporally progressing gene expression , from larger expression domains to more narrowly defined spatial expression ( Figure 2 ) . Categorizing the factors revealed that among lateral views , a larger number of genes in the data set were expressed anteriorly and ventrally , and fewer genes posteriorly and dorsally ( Figure 3A ) . Among dorsal/ventral views , most of the expressed genes have ventral view and predominantly anterior orientation . As mentioned earlier , data set covered images with arbitrary orientation ( lateral , dorsal/ventral ) . The inferred set of factors and factor loading matrix unveiled another important strength of the proposed framework: for any given image , factors which represent the same embryo orientation are more likely to contribute to the image decomposition , through more informative weights . As a result , estimated factors that show a clear lateral gene expression would be highly used by lateral gene expressed images in their corresponding factor linear combination; furthermore , estimated factors with dorsal/ventral expressions would be most likely used by dorsal/ventral input gene patterns . The four examples in Figure 3B illustrate lateral , dorsal/ventral , and non-informative expression . As expected , for non-informative maternal expression , all factors share relatively low weights in their image decomposition . As co-regulated genes are frequently co-regulated by transcription factors , we next inspected the similarities between estimated factors ( matrix in our model ) and the FlyTF database of Drosophila site-specific transcription factors [20] . This database contains manually annotated site-specific transcription factors , identified from a list of candidate proteins with transcription-related GO annotation as well as structural DNA-binding domains assignments . Careful visual inspection revealed that a number of inferred factors were close to the expression patterns of the experimentally verified transcription factors ( Figure 4 ) , thus suggesting that the model factors are reflecting underlying biological functions . Moreover , the majority of the discovered similarities ( out of cases ) correspond to the top ranked factors shown in Figure 2 . Clusters of co-regulated genes inferred from microarray analyses have frequently been shown to reflect groups of genes with distinct functions . A popular approach is to determine enrichments of functional annotations , such as provided by the Gene Ontology , to genes within each cluster . For this aim , we selected the absolute highest value entries from the factor loading matrix to find enriched GO biological process terms ( corrected p-value for hypergeometric test ) . The early development during stages is largely centered on specifying the body axes and layout , and we thus examined the later stage data set which included a broader range of ontology terms ( Figure 5 ) . Compared to the stage analysis , we used a larger matrix with factors to allow for the identification of a larger number of distinct patterns . Among the entire selection of biological process terms ( GO:0009987 ) , we found biological processes with significant enrichments mapping to one or more of out of clusters . In particular , cluster had a clear enrichment of genes with heart development function ( GO:0007507 ) which agrees with the gene expression showed by the factor itself ( at stage , heart precursors have been specified within the dorsal mesoderm ) . Cluster , with a pattern localized around the germ band , is highly enriched in germ cell migration genes ( GO:0008354 ) . Finally , cluster shows central/posterior development , related to the enrichment of genes with gonad development function ( GO:0008406 ) . The availability of recent genome-wide regulatory information made it possible to additionally investigate regulatory relationships between transcription factors and their target genes . Using the same clusters as for the GO enrichment analysis , we examined the agreement of factors with the “physical” regulatory network published by the modENCODE consortium [21] ) . This static network was inferred from TFs with experimentally derived binding profiles , combining chromatin immunoprecipitation data from multiple cultured cell lines with chromatin information and conserved sequence elements . It covers more than target genes; on average , genes were targeted by TFs , with up to regulatory inputs . We carefully selected the subset of TFs with demonstrated expression during Drosophila embryogenesis as profiled in the BDGP database as well as FlyBase , and identified the significant ones for every set of genes with high value entries in the factor loading matrix ( following the GO analysis described before ) . For developmental stages , we found significant TFs ( corrected p-value for Pearson's Chi-square test ) mapping to one or more of out of clusters ( Figure 6 ) . Out of these significant clusters , are shared with the clusters found in the GO analysis ( Figure 5 ) . There are clusters that only show significant enrichments among biological functions and clusters with solely significant TFs ( shaded areas ) . Nevertheless , most of the clusters of interest share biological functions as well as physical regulatory relationships and illustrate a strong consistency between the two analyses . Moreover , clusters with significance for both biological function and transcription regulation revealed term associations between transcription factors and biological processes currently not found in the Gene Ontology database . For instance , Trl targets ( FBgn0013263 ) are enriched in germ cell migration ( cluster ) and heart development ( cluster ) ; Trl mutants have been reported to exhibit defects in oogenesis [22] . Twi targets ( FBgn0003900 ) are associated with cell adhesion ( cluster ) , consistent with findings from genome wide ChIP analyses [23]; a complete list with term associations between transcription factors and biological processes can be found in Figure S2 . To put these results into context , we identified the set of modENCODE TFs enriched within the gene sets of the most frequent developmental terms of the controlled vocabulary as annotated by human experts ( Figure S3 ) . Among the enriched TFs , a subset of TFs are shared with the sBFA cluster-based transcription regulation analysis . The TFs that were only identified in the CV analysis are mostly general regulators; e . g . involved in chromatin remodeling and silencing ( trx , BEAF-32 , CTCF , TfllB , or CBP ) . These enrichments are not function-specific and therefore spurious hits . On the other hand , there are only four TFs specific to the sBFA cluster-based analysis: among them , bab1 targets ( FBgn0004870 ) are enriched during ectoderm development , consistent with recent reports based on sequence motif analyses [24] . The automatically inferred factors are therefore more enriched in specific TF targets , and lead to a cleaner and more extensive set of links between TFs , expression patterns , and biological functions . Lastly , we visually inspected similarities between spatial expression of estimated sparse model factors ( cluster centroids ) and corresponding TFs with significant p-values . Three example cases are shown in Figure S4 , and they suggest that the estimated factors not only reflect biological functions but also explain correlations within the physical regulatory network . In conclusion , our method can be used to find physical/functional networks that are relevant to Drosophila embryonic developmental stages of interest . In this case , the network associated to stages appears to be a highly modular cohesive component of the full physical regulatory network introduced in [21]; the multitude of highly significant TFs advance the hypothesis of a self-contained , highly evolvable structure . While gene expression data is often analyzed in an unsupervised fashion , the expert annotation of images with anatomical terms also allows for a direct evaluation whether extracted features reflect distinct biological patterns . To demonstrate the effectiveness of the sparse factor analysis in exploiting the hidden structure shared among different genes , entries in the factor loading matrix ( ) were subsequently used as features by two state-of-the-art classifiers: the SVM ( polynomial kernel ) [25] and a sparse multinomial logistic regression model , SMLR [26] . In evaluating the relative performance of the classifiers for individual annotation terms , we trained binary classifiers , one for each anatomical annotation term . We only considered terms associated to more than genes; terms with too few annotated genes were statistically too weak to be learned and evaluated effectively ( for the developmental stages , this selection translated into removing of the initial non-trivial terms mentioned before ) . For each of these remaining terms , the question was whether the factor loadings would be effective features to discriminate genes with a particular annotation term from those without one ( to automatically identify the anatomical regions that express a gene , given a training set of annotations ) . We chose sparse classifiers , as some factors appeared to reflect common sources of noise ( e . g . illumination differences ) and should thus be uninformative for annotation . The accuracy of sBFA-based classifiers is represented by the area under the ROC curve ( AUC values , [27] ) . We started with data set , which contained 1 , 231 genes annotated with a total of terms , and the SMLR classifier , which allows one to assess the importance of features for a classification task by the weights assigned to each feature . We first analyzed the SMLR weights on the entire set of features ( three different resolutions with corresponding number of factors of , and leading to a combined factors ) , and examined the number of times factors were selected as relevant by the SMLR algorithm during leave-one-out cross-validation ( LOO-CV ) . During cross-validation , all images corresponding to a single gene were left out and the model was trained on the remaining set of images . A few common factors were not selected as relevant by any annotation term model , which confirmed our initial belief that some factors were uninformative for at least some annotations . In addition , there is strong consistency in factor selection , and most factors are either always or never included . Figure 7 shows the mixing weights on the factors for two randomly selected annotation terms , as well as a histogram of the number of times each factor is selected as relevant over the entire set of trials , with a cut-off value for feature selection at . Specifically , for the ‘amnioserosa anlage in statu nascendi’ annotation term , factors were never selected while were always selected . To evaluate the success of annotation prediction , we computed AUC values achieved by the SMLR framework on data set using LOO-CV ( Figure 8A ) . To assess the influence of a particular classifier , we compared the SMLR results to those achieved by polynomial SVMs . The AUC value for each annotation term was computed using majority voting across all genes ( see ‘Materials and Methods’ ) . We see that on average , the annotation process reached similar performances with both classifiers , above across all terms ( exception are the ‘pole cell’ and ‘ventral ectoderm anlage’ annotation terms; the ‘pole cell’ lower performance can be explained by the fact that these germline precursor cells migrate and may have little overlapping spatial expression during stage ) . In the next phase , we evaluated the effect of integrating images with multiple views at early stages in Drosophila development , by running the sBFA on data set ; as previously mentioned , it covers genes ( images ) with arbitrary orientation ( most lateral and dorsal/ventral ) . Similar to the previous case , we carefully examined different numbers of factors for different image resolutions and observed the following good matches: , and factors for a grid size of × , × and × , respectively . On the images in this set , the SVM- slightly outperforms the SVM- and both SMLR- and SMLR- results and leads to overall consistent results despite the large variety of patterns , inconsistency among patterns associated with the same term , and variable orientation ( Figure 8B ) . AUC values fall largely between and with a few exceptions , where we believe that either the annotation terms were assigned to the wrong images , or the corresponding images had some tilted viewing angle , making the understanding of the 2D pattern difficult to accomplish . Figure S5 shows two scenarios where several images corresponding to the same genes are either uninformative , out of focus or under tilted viewing angles , or show expression at different time points , making it impossible for an automated annotation process to reach perfect accuracy . To assess how the good performance of the sBFA model would translate to later development , we applied it to the full set of images from stage 11–12 ( ) , representing a more complicated image annotation problem , given the variety of orientations ( lateral , dorsal/ventral ) and very intricate spatial expression patterns . The sBFA framework was run on both the complete data and the lateral subset; classifiers were trained/tested on the top most frequent annotation terms . As the above results did not show a clear advantage of using features from multiple resolutions , we used the highest resolution ( grid size ) of × on the complete set , and a total number of factors . Due to the larger number of images , training and test data sets were generated times by randomly selecting each with and without a specific annotation from the total set of images . On the set of lateral view images only ( images ) , the sBFA model was run on the same grid-size and a smaller number of factors ; in this case , the training and test data sets were generated times by randomly selecting from lateral views ( to achieve a comparable number of images between the two scenarios ) . The AUC values for each annotation term obtained by the sBFA framework ( SVM classifier ) were computed using both minority and majority voting , i . e . counting a gene as a true positive hit if it had at least one of its images , or the majority of images , correctly classified . According to our expectations , minority voting reaches AUC values of – , with a high performance corresponding to ‘posterior midgut primordium’ . When using majority voting , the performance is in the same range ( – ) as on the images from early development , this time with a slight advantage of SMLR over SVM , indicating that sBFA was successfully able to represent more complex expression patterns ( Table 1 ) . The overall improved performance of minority over majority voting ( in the range of – AUC percent points ) is a direct reflection of the nature of the actual images used by our model . For a given gene , this can happen when most , but not all , of the images are of poor quality ( out of focus , poor quality of staining/washing ) ; the existence of at least one clear and representative image can lead to a successful minority classification . Additional complications arise from errors in the automatic normalization ( such as incorrect orientation ) , and outlier images from different views . Several such examples are shown in Figure S6: gene FBgn0033227 is annotated with ‘posterior midgut primordium’ on a total of three images , two of which were impossible to classify due to poor quality staining and washing; FBgn0002174 is incorrectly annotated on a total of three images , two of which contain non-informative patterns; FBgn0015774 was incorrectly majority voted for two different controlled vocabulary terms , in both cases , images are either out of focus , with non-informative patterns or improperly rotated by the automated registration process . The analysis of integrating images with multiple views revealed that , for stages , the annotation performance consistently increased when incorporating images from views other than lateral . In comparison , the average AUC performance on the lateral view only data set from stages slightly outperformed the annotation using multiple views . In , the additional views increased the number of genes as much as the number of images , meaning that most genes were represented by either lateral or other views . Additional dorsal/ventral view images are less informative for annotating purposes during early stages in Drosophila embryogenesis , which generally follows simple expression dynamics oriented along the A/P or D/V axis . In contrast , at later developmental stages with more complex patterns , the dorsal/ventral view images become more informative for embryo annotation , as certain expression patterns cannot be fully represented by one 2D view only . In summary , our results confirm that a fully automatic image analysis pipeline without any human intervention can lead to highly successful expression pattern classification , despite variations in orientation and the presence of uninformative images and/or registration errors . Since both classifiers ( SVM and SMLR ) achieved similar annotation results , it further demonstrates the general effectiveness of the sparse Bayesian factor representation . To put our approach in context , we compared our results to two state-of-the-art systems representing bottom-up approaches using many low-level features . The automatic image annotation platform IANO was introduced by Peng et al . [15]; in the original study , it used three different feature representations and several classifiers to predict annotations , which were reported on lateral-view images only . To provide for a fair comparison on the same set of genes , we ran the first comparison on data set , using the IANO code as provided by the authors . In its current version , SVMs were the only available classifier; furthermore , binary prediction labels were provided , which prevented the use of AUC as evaluation metric . Instead , we followed the authors' example and used the absolute recognition rate , despite its flaws on unbalanced data sets which leads to inflated results , as opposed to the balanced view obtained by AUC ( for more details , see ‘Materials and Methods’ ) . With this in mind , the results from both sBFA ( majority voting ) and IANO systems on the most frequent annotation terms showed that the sBFA model clearly outperformed IANO ( Table 2 ) , at lower dimensionality . The proposed sBFA model consists of a fixed grid-based feature extraction technique followed by a sparse Bayesian factor analysis framework , whereas IANO considers three local and global feature extraction analyses which might result in higher-dimensional feature spaces . The original IANO results focused on a manually selected data set of representative gene images with lateral views from stages [15] . While we were able to obtain identifiers for the genes , the exact images used in their work were no longer available from the authors; as a result , for the second comparison , we considered all BDGP images from stage for the genes ( images , data set ) . Using sBFA with a polynomial kernel SVM classifier , we obtained results using both minority and majority voting . The average recognition rate for the annotation terms evaluated by Peng et al . are shown in Table 3; minority voting is the measure most likely to recapitulate the IANO results reported for the smaller , manually curated data set [15] . Altogether , sBFA lead to clearly improved results when applied on the same data sets , or on a prediction scheme aimed at recapitulating the original scenario , demonstrating the robustness of our generative feature extraction method when using SVM classifiers . A more recent study used dense Scale-Invariant Feature Transform ( SIFT ) descriptors [28] that were converted into sparse codes to form a codebook to represent registered images , and proposed a local regularization procedure for the learning process [14] . An unbiased comparison between our model and this system was hard to establish since the image IDs were not published in detail , results were based at least partially on selected orientations and not full sets , and annotation terms did not exactly correspond to the BDGP ontology . However , our results based on a much smaller feature space ( effectively around features for the SMLR classifiers , as opposed to several thousand ) , are in a similar range to the ones reported by their system .
Digital images are a quickly increasing new source of data for problems in computational biology . Given the very diverse nature of imaging technology , samples , and biological questions , approaches are oftentimes very tailored and ad hoc to a specific data set . At the same time , high content screening of phenotypes is moving from cell-based assays to whole organisms , and phenotypes can no longer be manually annotated due to large volumes of data . In this paper we presented a general method for the automatic decomposition of spatial quantitative information , applied on the dissection and annotation of gene expression images . The algorithm is based on a fully Bayesian factor analysis formulation , and annotates images based on a trained SVM or SMLR model . We also employed the biologically justified prior assumption that the models for both factor inference and classification are sparse , implying that only a small subset of factors are used to define expression domains . Indeed , the classifiers make use of only a dozen or two of features , orders of magnitude less than state-of-the-art approaches addressing the same problem . We also demonstrated that genes with strong weights to the same factor share specific biological functions or are targets of the same transcription factor , providing important starting point for future in-depth analysis . Our approach is probably closest to Pan et al . [29] , which introduced an image mining system to discover latent spatial “themes” of gene expressions , by using PCA and independent component analysis ( ICA ) based features . ICA assumes independence at the regulatory level , and the resulting decomposition may lack the physical or biological association to sBFA factors , by not imposing sparsity within the model ( as the biological prior assumption ) . Unlike PCA , sBFA includes sparseness constraints and allows for independent additive measurement errors on the observed variables . Whereas the earlier study was mostly exploratory and did not include a specific application , we provided extensive results on fruit fly embryonic expression pattern annotation from early and late stages . Our results showed that sBFA automatically identifies and separates patterns corresponding to different views , and subsequently makes successful predictions even when presented with images of the same gene taken from different angles . In addition to the automatic pattern separation , factor loadings can also automatically identify and filter non-informative ( such as ubiquitous ) gene expression patterns . To illustrate this , we manually selected a set of informative images ( lateral , dorsal/ventral expression ) and non-informative images ( mostly maternal expression ) from data set and computed the Euclidean distances between their corresponding estimated sparse mixing weights ( rows in matrix ) and the null vector as reference . Choosing a threshold to separate the informative images from non-informative images ( please see Figure S7 ) , we succesfully filtered the original data set by removing a total of non-informative images ( about of the total number of images ) . The subsequently obtained AUC values on the filtered data set of ( images ) displayed the further improvement achieved by this simple Euclidean-based analysis ( Figure S8 ) . For future work , we plan to extend the sparse Bayesian analysis to the entire BDGP database ( multiple developmental stage analysis ) ; after extracting stage-window specific factors , a classifier taking factor weights from different stage windows as input may be able to increase the baseline performance obtained on one stage window only . In constructing the sparse FA representation , we only used simple grid-based features . This provided for an easy human interpretation of the factors and was possible because we used previously registered data as input . However , results from other groups have shown that multiple feature integration ( over features including wavelet and rotation/translation invariant coefficients ) can improve performance . Given that we are addressing spatial patterns , features can also take the correlation to neighboring features into account ( cf . Frise et al . , [17] ) . As preliminary example , we ran the sparse Bayesian factor analysis on the features used by the platform which was concurrently developed to our system [30] . registers every raw Drosophila ISH image via foreground object extraction , alignment , orientation detection and concise gene expression pattern extraction ( using a Markov random field model ) . Resulting images are then converted into a low-dimensional feature representation using the ‘triangulated image’ idea , first introduced in [17] , in which embryo expression is represented on a deformable mesh of equilateral triangles in the shape of an ellipse . A comparison between the sparse Bayesian factor analysis applied on fixed grid-based filtered registered images used here [19] , and the ‘triangulated’ features [30] , is shown in Figure S9 and Table S1 . Overall , the features showed a slight advantage over the fixed grid-based technique . However , the mean absolute recognition rates achieved by both feature sets when used in a sparse Bayesian factor analysis model were in a highly similar range , and filtering of non-informative images as discussed above showed a comparatively stronger positive effect . This demonstrated the robustness of the sBFA framework , and its ability to identify and separate gene expression patterns , regardless of the complexity of the feature space . In the future , application of the sBFA model on the actual set of registered images may potentially generate accuracies even better than the mesh-based features . The correspondence of inferred factors to expression patterns of regulatory proteins , as well as the enrichment of targets for specific TFs , suggests the potential for a more sophisticated model that incorporates known transcription factor expression patterns into the factor analysis , possibly in the shape of prior information , or as higher level information in hierarchical models . Finally , the approach can be extended by the integration of image data with other genomic sources . A previous study automatically inferred positive ( spatial gene co-expression ) and negative pairwise constraints ( distinctive spatial expression patterns ) from image data , and used them in a semi-supervised analysis of microarray time-course data [31] . In summary , factor analysis provides a flexible unsupervised framework to identify the basic vocabulary in complex image ( expression ) data , which leads to competitive prediction results while using only a small set of features . This sparse approach is general and applicable in other microscopy application domains , such as protein localization in subcellular domains and expression data from other organisms , and as such holds promise as a general framework for high-throughput screeening , to identify candidate gene sets with consistent altered expression under changed environmental or genetic conditions .
Factor analysis is a statistical method first introduced by Gorsuch [32] when modeling many dependent random variables with linear combinations of a few hidden variables . Hinton et al . [33] pioneered an Expectation-Maximization ( EM ) algorithm for factor analysis in order to model the manifolds of digitized images of handwritten digits . West [34] was the first to introduce a framework for using factor analysis on gene expression data . In matrix notation , the Bayesian factor analysis on image data can be represented as ( 1 ) where is a dimensional data matrix , with the number of features , quantifying the associated gene-expression values for images ( genes ) under investigation . Each row of is called a gene pattern with dimension . Here , we assume that each gene pattern is already normalized to zero mean . is the factor loading matrix with dimension , which contains the linear weights . is the factor matrix with dimension , with each element modeled by a standard normal distribution . Each column of is the factor score for feature and each row is called a factor . is the additive Gaussian noise with dimension . Both and are inferred by the model simultaneously . From the model we can see that each row of is modeled by a linear combination of the factors ( rows of ) , indicating that the variability of the original feature patterns can be explained by only latent factors . The model can also be written in a vector form as follows ( 2 ) where and denote the row of and , respectively and the basis matrix is shared across all samples . Indeed , factor analysis is an unsupervised dimensionality reduction method used widely in data analysis and signal processing [35] . To promote sparseness required by the underlying biological assumption of gene expression data , West [34] suggested the use of a mixture prior on the factor loading matrix . Thus each row of the matrix should only have a small number of non-zero elements in order to comply with the biological assumption and to make the model more interpretable . In order to follow the biological assumption where spatial gene expression patterns are modeled only by a few domains ( factors ) , coupled with the benefit of a relatively simple inference , we employed the Student-t distribution as sparseness prior , which takes the following hierarchical form ( 3 ) with , , indicating the precision parameters and , the shape and scale parameters of the gamma prior distribution on . By integrating out the precision parameter , the marginal prior on is a sparseness inducing Student-t distribution . The sparseness is controlled by the precision parameter . The objective of imposing this sparse prior is to automatically shrink most elements in to near zero , in order to yield a more interpretable model . A comprehensive review of sparse factor analysis for gene expression data analysis is given by Pournara and Wernisch [18] , with various sparse priors taken into consideration . The full likelihood for the Student-t sparse factor analysis model can be expressed as ( 4 ) where denotes the column of , represents the precision parameters on the additive noise , while and indicate the shape and scale hyperparameters on precision , respectively . The posterior distribution for the sparse factor analysis framework is approximated using a Markov Chain Monte Carlo ( MCMC ) inference . –Sample the factor matrix from ( 5 ) where ( 6 ) and denotes conditional density of on all other random variables . –Sample the factor loading matrix from ( 7 ) where ( 8 ) Here denotes the row of ; and are similarly defined . –Sample the precision parameters from ( 9 ) with and , where ( 10 ) –Sample the precision parameters from ( 11 ) where ( 12 ) The sBFA algorithm was generally run for a total of Gibbs iterations , discarding the first and estimating the model parameters on the remaining iterations . The sparse prior on the factor loading matrix ( ) was controlled by the hyperparameter on the precision parameters , , while the scale parameter of the Gamma prior distribution on was set to . Running on a typical modern PC ( Quad-core Intel Xeon 1 . 86 GHz processors and 4 . 0 GB memory ) , the computation times for data sets and are summarized in Table 4 . The sBFA model is demonstrated on a large set of image expression data collected within the Berkeley Drosophila Genome Project . The use of brightfield microscopy and the color of the staining made it hard to separate object and expression pattern from the same image , although heuristic normalization steps have been proposed [17] . We here used previously segmented and registered images [19] , in which a state-of-the-art framework provided simultaneous , fully automated image segmentation and registration without human intervention . Due to the complex nature of this task , the final registration process was not perfectly accurate in terms of precise embryo extraction as well as orientation , which increased the challenge of automatic annotation . We scaled the registered images to × pixel resolution , containing a single embryo and no background . We defined a grid of fixed size ( e . g . , × patches ) and calculated the mean pixel value within each patch; all mean values were stacked into a single feature vector . Each scaled and registered image was classified individually , with each gene being represented by one or more images . As a results of a one gene to a multiple image mapping , many earlier approaches chose representative images , one per gene , with a clearly defined informative pattern . However , for a fully automated approach , the data offer the possibility to combine results from several images to annotate a gene . The challenge is illustrated by the examples in Figure S5 , which demonstrate inconsistencies and presence of noise in large image data sets; as a result , some images are more informative than others and may even lead to contradicting images within the same gene . Regardless of evaluation metric , we here use two strategies: ( i ) majority voting , in which a label is assigned based on the predominant label over all images associated to a particular gene; ( ii ) minority voting , in which a gene is considered correctly classified if at least one image has been predicted with the correct annotation term . While the latter is not a realistic metric for unseen data , it provides for a reasonably fair evaluation when comparing against previous approaches which evaluated manually selected single representative images for each gene . The agreement between the predicted annotations and the ground truth provided by human curators was measured using AUC values for a leave-one-out cross-validation procedure . To allow for a fair comparison to previously published work , we also used the absolute recognition rate ( ARR ) to measure the classification accuracy . When a data set is unbalanced , this metric is not representative of the true performance of the classifier: if the larger class comprises of the data , an ARR of is trivially achieved by classifying all samples into the larger class . Image data sets are heavily unbalanced , as only comparatively few out of a total set of images are annotated with any given term . | High throughput image acquisition is a quickly increasing new source of data for problems in computational biology , such as phenotypic screens . Given the very diverse nature of imaging technology , samples , and biological questions , approaches are oftentimes very tailored and ad hoc to a specific data set . In particular , the image-based genome scale profiling of gene expression patterns via approaches like in situ hybridization requires the development of accurate and automatic image analysis systems for understanding regulatory networks and development of multicellular organisms . Here , we present a computational method for automated annotation of Drosophila gene expression images . This framework allows us to extract , identify and compare spatial expression patterns , of essence for higher organisms . Based on a sparse feature extraction technique , we successfully cluster and annotate expression patterns with high reliability , and show that the model represents a “vocabulary” of basic patterns reflecting common function or regulation . | [
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] | 2011 | Automatic Annotation of Spatial Expression Patterns via Sparse Bayesian Factor Models |
Reevaluation of treatment guidelines for Old and New World leishmaniasis is urgently needed on a global basis because treatment failure is an increasing problem . Drug resistance is a fundamental determinant of treatment failure , although other factors also contribute to this phenomenon , including the global HIV/AIDS epidemic with its accompanying impact on the immune system . Pentavalent antimonials have been used successfully worldwide for the treatment of leishmaniasis since the first half of the 20th century , but the last 10 to 20 years have witnessed an increase in clinical resistance , e . g . , in North Bihar in India . In this review , we discuss the meaning of “resistance” related to leishmaniasis and discuss its molecular epidemiology , particularly for Leishmania donovani that causes visceral leishmaniasis . We also discuss how resistance can affect drug combination therapies . Molecular mechanisms known to contribute to resistance to antimonials , amphotericin B , and miltefosine are also outlined .
The leishmaniases are complex diseases of ( sub ) tropical regions of the world caused by Leishmania spp . ( Protozoa , Kinetoplastida , Trypanosomatidae ) and spread by sand flies . The World Health Organization ( WHO ) considers the leishmaniases to be prominent among the global causes of death by infectious diseases [1] . Clinical manifestations produced by Leishmania comprise the visceral ( VL ) and tegumentary forms . The tegumentary forms of the disease include the cutaneous ( CL ) , diffuse ( DCL ) , and mucocutaneous ( MCL ) leishmaniases [1] , but infections remain asymptomatic in many cases [2 , 3] . Leishmania may also appear as an opportunistic parasite in immunosuppressed individuals . Chemotherapy constitutes the main approach to manage the disease although it is generally not applied to asymptomatic subjects . For a number of other neglected tropical diseases , mass drug administration—even without diagnosis—is possible , given the safety of particular drugs used in these circumstances ( e . g . , praziquantel in schistosomiasis and ivermectin for lymphatic filariasis ) . The combined problems of parenteral administration and toxicity of anti-leishmanials precludes such programs for leishmaniasis . Furthermore , the selection of resistant parasites carrying genetic mutations that lessen the parasite's response to drugs may emerge upon mass drug administration . Leishmania has an intricate life cycle , and one of the developmental forms , the amastigote , dwells within immunological cells of the mammalian host , which adds to the challenge of accessing the parasites with specific drugs . Nevertheless , the aim of chemotherapy is to kill intracellular parasites; therefore , chemotherapy remains the best means available to cure the disease [4] . Antimonials ( sodium stibogluconate [SSG] ) are the primary drugs employed against leishmaniasis . They have been in use since the 1920s . These toxic compounds have a narrow therapeutic window , and their use has been largely superseded in the ISC , where resistance has become widespread . However , they are still in use in other regions of the world , including Latin America and East Africa [5 , 6] . MIL has replaced SSG in the ISC in the context of the kala-azar elimination program , but efficacy of this drug as well had already dropped within a decade of its introduction [7 , 8] . Initially , this decrease in MIL efficacy could not be related to increasing parasite resistance to the drug [8] , although recently a few resistant clinical isolates have been described , first in France in an HIV-coinfected patient and later in two Indian patients [9 , 10 , 11] . AmB is highly efficacious but relatively toxic when injected in its free deoxycholate form . Administration in a liposomal formulation ameliorates the toxicity risk , although the high cost of this formulation has left its range of use restricted; this is the case even though it is provided free of charge to WHO for use in strategically important areas by its manufacturers ( Gilead Sciences; with up to 350 , 000 vials over the next five years ) . Unfortunately , a risk of resistance is becoming apparent for AmB as well ( discussed below ) . Paromomycin has a relatively restricted range of targeting Leishmania species , and the situation regarding resistance in the field is unclear , although laboratory-derived resistant isolates have been created [12–15] . Even combination therapies are not immune from selection of resistance , which poses potential challenges to the WHO’s proposed next generation of combination therapies in the treatment of leishmaniasis . It is critical to consider that TF and drug resistance ( DR ) are not necessarily synonymous . TF goes far beyond DR , with numerous factors in the host ( such as immunity or nutritional status ) , the parasite ( e . g . , whether or not the parasite resides in tissues not accessible to drugs ) , and the environment ( e . g . , global warming contributing to the expansion of the disease to new geographical areas ) influencing treatment outcomes . Notwithstanding , DR is a fundamental determinant of treatment outcome , and understanding the mechanisms whereby parasites become resistant to drugs is essential . In this Review , we discuss the phenomenon of DR associated with SSG , AmB , and MIL and outline the molecular mechanisms associated with the selection of resistance in combination therapies , as well as the possible consequences of emergent resistance on the use of these drugs in the field .
A growing number of questions apply to treatment options for leishmaniasis . For example , why do patients with the same clinical form of the disease , living in the same areas , infected by the same species of Leishmania , and treated with the same drug regimen differ in their response to treatment ? What are the causes of TF ? To help address these questions from the parasitological point of view , two key requirements are ( 1 ) to find a sensitive and practical technique to determine the persistence , or the elimination , of viable parasites in clinical samples after treatment and ( 2 ) to define and standardize tests of sensitivity and resistance in vitro . Numerous features are known to impact the final therapeutic outcome ( see Fig 1 ) . Host factors play a role . For example , because an effective immune response is required to support anti-leishmanial drugs , patients with immunodeficiency can be particularly hard to cure [16] . Less extreme natural variation in host immunological response can also influence the ability of a drug to work . To date , little has been done to assess the effect of individual variation in pharmacokinetics or other drug-related responses in the host . Differences between individuals are normal for most drugs , and stratified responses are clearly observed for the leishmaniases . Examples include when comparing children versus adults or males versus females [17 , 18 , 19] and in instances in which a patient who may have failed initial treatment responds to a second cycle of a drug given at a higher dose [20] . It is also clear that parasite factors other than explicitly acquired resistance can also play a role . These factors comprise the inherent virulence of the infecting isolate of Leishmania [21] and—in some instances—parasite infection by RNA viruses [22 , 23] , whose occurrence can stimulate a different kind of immunological response in the host [24] . TF may occur with susceptible strains in immunocompromised patients [25 , 26] , and ill-defined regional differences in response are known [27 , 28] . Finally , TF may occur in similar clinical cases that respond to a given therapy in some patients but fail in others when the causative Leishmania species or strain is different [29] . Factors related to the drug itself can be crucial as well . Inappropriate dosing by inexperienced health workers ( or self-administration in the case of MIL ) may lead to subtherapeutic dosage and introduce the risk of selecting parasites resistant to those drugs [18] . Administering agents in difficult-to-work-in environments can also be a problem , e . g . , with the application of a product that is faulty or the expiration of its activity due to long-term storage in inappropriately hot conditions [30 , 31] . "To cure or not to cure" is the main issue in clinical practice . The patient must be evaluated at the end of treatment to determine the outcome . Should this evaluation be solely clinical , or should it also include parasitological confirmation ? While clinical evaluation is mandatory , and some clinical scores have been proposed for CL [32] , parasitic evaluation ( test of cure [TOC] ) includes aggressive , sophisticated , and costly tests ( bone marrow biopsy , splenic aspirate , parasite culture , and PCR techniques , among others ) . It is , nevertheless , generally required , depending on the specific circumstances surrounding the patient . New , confirmatory assays are emerging that appear promising for the prognosis of treatment outcome in VL . These include rapid diagnostic tests capable of detecting anti-Leishmania IgG1 [33] and loop-mediated isothermal amplification ( LAMP ) [34] . Unfortunately , molecular diagnosis remains a challenge in American tegumentary leishmaniasis because the variety of causative Leishmania species may confuse the outcome . This is especially true when parasites are scarce in tissues , as is the case for L ( V . ) braziliensis in CL and MCL patients . If inflammatory signs are absent , and complete scarring of the lesion ( determined by the size of the inner and outer borders of the wound ) or re-epithelialization of ulcerative lesions at three-month follow-up occurs , then definitive cure for CL can be claimed . For VL , a good clinical response correlates to the normalization of temperature; disappearance of symptoms; decreased size of the liver and spleen; increased counts in peripheral blood of leukocytes , hemoglobin , and platelets; and increased appetite and weight gain [20] . Parasitological cure is defined by the absence of Leishmania parasites detected by microscopy or culture from tissue samples ( skin , spleen , bone marrow , or lymph nodes ) . The detection of parasite DNA by PCR in tissues is substantially more sensitive than conventional parasitological techniques , but it may give false positive results when performed too early because of the persistence of nonviable Leishmania parasites . Nevertheless , RNA targets have been exploited for the assessment of Leishmania viability , e . g . , by reverse transcription PCR ( RT-PCR ) amplification of the 7SL RNA transcript , which proved a rapid and efficient method to detect and quantify viable parasites in tissue samples [35] . Quantitative PCR in peripheral blood can also be useful for measuring the initial parasitic load and for monitoring VL responses to treatment ( but these tests are not standardized and are not widely available for clinical use ) . Biomarkers—including cytokines , their receptors , and acute-phase proteins , among others—are under evaluation . Serological tests are generally not useful for follow-up , given the persistence of seroconversion post cure [36] . In drug trials , TOC is usually performed one month after the last dose of treatment , which may be too early because residual parasites can be found in patients who , when diagnosed , had a very high parasite load ( this may be the case of immunosuppressed patients ) . In clinical practice , TOC is generally not recommended in patients showing a timely clinical response . Two main forms of TF can be defined as follows: ( 1 ) nonresponse or the persistence of symptoms at the end of treatment and ( 2 ) relapse , that is , a second episode after initial apparent patient cure . In both cases , clinical symptoms might be verified by parasitological means . Because relapses usually occur within the first 6 to 12 months after the end of treatment , following the patients for at least one year before considering them as cured would seem warranted [8] . In conclusion , the relative importance of parasite resistance , as measured using currently available techniques , is still unclear in clinical practice because host immunological or clinical features are at least as likely to underpin TF . Assays to determine whether parasites are sensitive or resistant to drugs have not been standardized and are not available in the vast majority of medical clinics and/or centers where the disease is treated . Theoretically , these assays should be essential in monitoring drug sensitivity and/or resistance in specific geographical areas , but they are of little use in clinical practice and thus only rarely requested in particular relapsing cases . In fact , experimental assays that might answer this key question can currently only be used in an epidemiological context , for surveillance , not for diagnosis . Substantial improvements in techniques for molecular diagnostics are needed before the implementation of such tests into clinical practice is feasible . Last but not least , information on drug sensitivity is essential when designing therapeutic guidelines in a given geographical area . Thus , although TF certainly goes far beyond DR , treatment outcome should be considered in the context of DR as an important contributing factor .
As mentioned previously , SSG were for a long time the mainstay treatment in the fight against leishmaniasis but were abandoned in 2005 in some areas , including the ISC , due to TF related to DR . SSG remain , nevertheless , the drugs of choice in many countries around the world [4] . MIL , however , is an alternative drug that was developed in the mid-1980s and first became available in 2002 for VL in India , then in 2005 for CL in Latin America [37] . Recent reports indicate that the effectiveness of MIL in India and Nepal is decreasing [7 , 8] . In these areas , the drug has now been replaced by liposomal AmB for the kala-azar elimination program [6] . Classically , resistance emerges as genetic mutations that lessen the parasite’s response to a drug when the parasite is under drug pressure . This situation is easy to translate to selective conditions in the field . However , an intriguing point to consider is the description of Leishmania parasites resistant to SSG , even in cases in which parasites have not been exposed to the drug [38] . Antimony is a heavy metal whose action against Leishmania shares characteristics with the related heavy metal arsenic . Under experimental conditions , resistance selected to arsenic renders parasites cross-resistant to antimony . In North Eastern India , high levels of arsenic in drinking water may have led to selection of parasites with reduced sensitivity to both heavy metals , a plausible explanation for the spread of antimony resistance in this region [39] . This has been evaluated in a retrospective epidemiological survey performed in Bihar , India , the conclusions of which suggest that arsenic-contaminated groundwater may well be associated with SSG TF . The failure rate with SSG was found to be 59% . Of note , patients living in areas with high mean local arsenic levels showed a higher risk ( albeit statistically nonsignificant ) to SSG TF than patients living in areas in which the wells had low arsenic concentrations <10 μg/L [40] .
A significant amount of work has been devoted to understanding how SSG exert their selective action against Leishmania and how resistance emerges ( see Fig 2 ) . To obtain the anti-leishmanial products SSG and potassium antimony tartrate , a chemical reaction must occur between various components; e . g . , stibonic and gluconic acids are combined to produce SSG , i . e . , it is a complex chemical mixture and not a single compound [57] . Pentavalent antimony ( SbV ) must be reduced to its trivalent form ( SbIII ) for activity . Some of this reduction occurs within the host macrophage , and the resultant SbIII enters via the AQP1 membrane carrier . SbV also enters the parasite via another , as yet uncharacterized , carrier mechanism and is further reduced to SbIII within the cell . Antimony accumulation is lower in resistant parasites when compared to sensitive parasites , although it is unclear whether or not actual levels of accumulation relate to sensitivity in wild-type cells [41] . Overexpression of AQP1 renders the parasites hypersensitive to SbIII , whereas gene deletion renders them resistant [42] , and reduced levels of AQP1 expression also relate to resistance [43] . As described later , a mutation to AQP1 that renders the gene inactive is associated with a high level of antimony resistance in India . Diminished biological reduction of SbV to SbIII , decreased internalization of the drug , and increased levels of trypanothione , which provides increased thiol redox potential , have also been implied in resistance [44–46] . Overexpression of ATP-binding cassette ( ABC ) transporters involved in ATP-dependent transport of a variety of molecules across biological membranes has also been shown to influence the efflux of drugs and can play a role in antimony resistance in Leishmania . MRPA is an ABC transporter localized in membrane vesicles close to the flagellar pocket [47 , 48] . Its overexpression confers antimonial resistance both in amastigotes and promastigotes by sequestering thiol–metal conjugates in intracellular vesicles . The molecular epidemiological analysis of field isolates in the ISC also showed that an intrachromosomal amplification of MRPA that probably emerged in the mid-19th century predisposed L . donovani to develop resistance to SSG ( see below ) . Trypanothione is an unusual bis-glutathionyl spermidine adduct found uniquely in the phylogenetic group that includes Leishmania . Trypanothione binds to SbIII , and the resultant metal–trypanothione conjugates are either sequestered into an intracellular organelle by MRPA or extruded from the cell by other efflux pumps [49] . Overexpression of key enzymes in trypanothione synthesis ( ornithine decarboxylase and gamma-glutamylcysteine synthase ) has also been associated with resistance in conjunction with overexpression of MRPA [49] . PRP1 is another ABC transporter protein originally identified due to an ability to confer resistance to pentamidine [50] . It has been postulated that it can also confer resistance to antimony , although its localization and mechanism of resistance have not yet been determined [48] . Other ABC transporters in Leishmania , including ABCI4 [51] and ABCG2 [52] , can also contribute to antimony resistance by the efflux of the drug as conjugated metal–thiol conjugates . Finally , overexpression of tryparedoxin peroxidase has been associated with SbIII resistance [53–55] through elevated levels of reduced intracellular thiols . Many resistant mutants exhibit significantly increased levels of intracellular thiols , including cysteine , glutathione , and trypanothione , and lowering intracellular thiol levels in SSG-resistant ( SSG-R ) mutants can cause partial reversion of the resistance phenotype . Antimony induces oxidative stresses within the cells; therefore , an increased ability to deal with such stresses contributes to resistance . It is , then , clear that a variety of different mechanisms can all contribute—to varying degrees—to the ability of Leishmania parasites to resist antimony in a complex fashion . Also of note is the fact that some changes to parasite physiology can also influence resistance by orchestrating changes to macrophage biochemistry , including increases in the macrophage’s ability to extrude SbV- SSG [56] . Complex glycans present on the surface of SSG-R parasites but not on SSG-sensitive parasites contribute through a cascade of cellular events to the up-regulation of the anti-inflammatory cytokine interleukin 10 ( IL-10 ) levels in the host . This , in turn , provokes overexpression of multidrug resistance protein 1 ( MDR1 ) efflux systems in the macrophage [56] , which diminishes the total levels of antimony reaching the parasites . This also shows how coinfection status could influence treatment response; e . g . , other agents that stimulate IL-10 levels could diminish the response of Leishmania to SSG . The complexity of host and parasite factors that affect outcomes of Leishmania infections treated with SSG is becoming clear and , as outlined above , understanding of mechanisms that have been observed in laboratory analyses have been shown to be pertinent in explaining the emergence and spread of SSG resistance in the field .
The main factors behind the epidemiology of the leishmaniases as ( re ) emerging diseases spreading to new geographical areas include human-made and environmental changes , changes in host immune status , and TF ( due to either DR or other factors ) . These factors may act synergistically to enhance the endemicity of the disease . For example , immunocompetence of patients impacts the emergence of DR [9] . Demographic changes , with movement of infected people from endemic rural regions to sprawling conurbations , lead to increasing incidence of HIV–Leishmania coinfection , as is the case in Brazil [58] . The epidemiological and clinical context of any given endemic region can thus influence DR and should be considered in depth . Resistance to SSG , in L . donovani of the ISC , is the best-documented example of molecular epidemiology of DR in leishmaniasis and is discussed below . Poor socioeconomic conditions [59] seem to be a fundamental contributory factor for antimonial resistance in the North Bihar region of India . Toxicity of SSG and suboptimal dosing ( <10 mg/kg/day ) , including a steady-step but smooth increase and even recommendations of drug-free intervals , may also have contributed to this situation by presenting conditions amenable to the selection of resistance . Parasites with varying susceptibility to SSG coexist in the field and also influence the variable responses that occur in different geographical areas [60] . Studying the molecular epidemiology of VL in the ISC has been hindered by the lack of high-resolution tools enabling analysis of genetic markers associated with resistance . DNA microsatellite sequences , which are generally useful for Leishmania genotyping , could not discriminate most of the strains in the ISC [61] because of the high genetic homogeneity of the ISC population . However , the advent of whole genome sequencing ( WGS ) provided the level of resolution needed to answer questions regarding the molecular epidemiology of L . donovani in the ISC , both generally and in the context of DR . This was demonstrated in 2016 by Imamura et al . [62] in a pioneering WGS study of 204 Indian and Nepalese isolates that had been well documented both clinically and epidemiologically . The fundamental results of this study were as follows: The occurrence of this amplicon in the whole core population indicates that it is an ancestral character and was already present in 1850; its occurrence also suggests that it constitutes a preadaptation that facilitated the emergence of true resistance once SSG was introduced many years later . How it was selected at a time when SSG was not yet in use remains to be elucidated , although the relationship between arsenic in drinking waters and selection of SbIII resistance discussed above demonstrates how environmental stimuli can influence DR in leishmaniasis . A second level of adaptation occurred specifically in ISC5 , where all of the sequenced isolates contain a homozygous two–base-pair insertion in the gene encoding AQP1 ( another known player in antimony resistance , see above ) [42] . AQP1 activity is abolished in the resistant lines , and this is probably responsible for the high level of resistance observed . Interestingly , hybrid strains showing intermediate SSG-R were heterozygous for the AQP1 insertion [42] . WGS offers unprecedented insight into the molecular epidemiology of Leishmania , in particular in the context of DR . As such , this method should be implemented in surveillance schemes , when and where it is available . Of course , molecular tracking could be simplified and new assays translated from this genomic knowledge , as seen with the single locus genotyping ( SLG ) for the detection of ISC groups [63] or a PCR assay for the detection of the 2-nt insertion in AQP1 . However , such targeted assays do have limitations . For example , when SLG was evaluated in Nepal , about 50% of the isolates could not be typed , possibly because other genotypes circulating in this region are not detected by SLG [63] . Furthermore , different molecular modifications can alter the functionality of a gene such as AQP1 . For example , a reduction in number of the chromosome bearing the gene , deletion of the gene , or different single nucleotide polymorphisms ( SNPs ) affecting key residues can all affect gene function , a concept recently introduced under the name of “Many Roads to Drug Resistance” [64] . A PCR test specifically targeting the AQP1 2-nt insertion will not detect the other genomic alterations and thus would fail to identify other types of mutation affecting this gene . Therefore , untargeted methods such as WGS remain the best choice until a full catalogue of all mutations that can cause resistance is created . Recent development and validation of WGS protocols allowing direct sequencing in clinical samples represent a promising and currently needed alternative because they avoid the problems and biases related to isolation and cultivation of parasites—and new markers in parasites found in situ may emerge . Finally , transmission patterns of different Leishmania species can impact the spread of resistance . This is clearly exemplified by the role of reservoirs that are generally untreated , such as asymptomatics or post–kala-azar dermal leishmaniasis ( PKDL ) patients for L . donovani and animal reservoirs both for L . braziliensis and L . tropica . Transmission into conditions where selective pressure is removed can slow down the rate at which resistance is fixed within the population [65–67] . Lessons from WGS to understand the epidemiology of DR to SSG in the ISC should influence how information is gathered on the increase and spread of resistance to other drugs as well and underpin the development of surveillance strategies for implementation into leishmaniasis control programs globally .
Leishmania has a highly plastic genome ( see Fig 1 ) , with an enormous potential for aneuploidy , local copy number variations ( CNVs ) of specific loci , and extrachromosomal circular or linear amplification of sets of genes [68 , 69] . This plasticity relates to the Leishmania genome containing numerous pairs of short repeats flanking groups of genes , which promote genome-wide stochastic adaptive DNA amplification [70] . Such variability may play key roles in the adaptive and evolutionary biology of the parasite . For example , it permits an increase in the quantity of transcripts of given genes , a good solution for an organism that cannot regulate transcriptional initiation [71] . Additionally , this plasticity allows—at low risk—the creation of genetic diversity among the additional copies of amplified genes , a useful adaptive strategy for immunogens like GP63 [72] . In this context , it is not surprising that genome plasticity has also been exploited by the parasite to generate DR . In experimental conditions , episomal amplification of different sets of genes is a molecular adaptation often found in parasites selected for DR [73] . This is the case , for example , for MRPA in the context of SSG resistance [74 , 75] and in methotrexate-resistant parasites of dihydrofolate reductase-thymidylate synthase [76 , 77]; in both cases , increased activity of the proteins encoded by these genes leads to resistance . By contrast , episomal amplification has not been observed for AQP1 , which is because the AQP1 transporter imports drug; therefore , loss of activity is required for resistance associated with this gene . The same is true for the MIL-resistant Leishmania donovani transporter ( LdMT1 ) involved in MIL uptake [78] . Experimental episomal amplification can emerge rapidly , but it also generally disappears rapidly , when the drug pressure is stopped . In natural conditions , gene amplification is also observed , e . g . , the H-locus containing the MRPA gene [79] and a mitogen-activated protein kinase 1 ( MAPK1 ) gene in the core population of L . donovani [9] on the ICS . In this case , however , it is an intrachromosomal amplification rather than episomal amplification , which is likely explained by the higher stability offered by amplification within a chromosome . A second strategy exploited by Leishmania to alter copy number of particular genes is to increase copy number of the entire chromosome upon which they reside , generating aneuploidy . Under laboratory conditions , the emergence of aneuploidy usually occurs early under drug selection , while other mutations—including SNPs , indels , or gene deletions—arise later . Aneuploidy allows increasing or decreasing gene dosage , according to the driver gene ( s ) need of gain- or loss-of-function . For example , for LdMT ( one of the main transporters accountable for MIL uptake in L . donovani—for which DR is linked to loss-of-function through diminished drug uptake ) —a decrease of somy is selected in some resistant parasites [78] . In the case of MRPA ( DR linked to gain-of-function through drug sequestration ) , an increase in chromosome copy number is observed [75] . Of noteworthy importance is that the extent of aneuploidy is not the same among individual cells constituting a strain . This mosaicism allows the population to have cells in different genomic states , the fittest being selected depending on the selective conditions [80] . Extensive aneuploidy is also observed in natural populations . For example , in ISC L . donovani clinical isolates , the contrast was striking between the high genetic homogeneity ( only 2 , 418 SNPs across the whole genome of 191 isolates ) and the strain-specific aneuploidy , with several chromosomes showing a somy >2 [9] . Surprisingly , no correlation was found between somy and DR patterns in this study . This may be because these genomic studies were performed on promastigote forms , cultivated in vitro subsequent to their collection from patients , rendering it possible that the observed aneuploidy was selected during this postcollection cultivation . This was supported experimentally in a recent study that followed chromosome number of L . donovani throughout the life cycle by sequencing different life stages , including amastigotes obtained from infected hamsters [81] . Starting from highly aneuploid ( 8 chromosomes with somy >2 ) in vitro promastigotes , all chromosomes but one showed a decrease in somy upon adaptation to growth in the hamster , while two new trisomies appeared , affecting chromosomes bearing genes essential for virulence , e . g . , amastins and GP63 [81] . In brief , these data suggest that aneuploidy is adaptive [82] and sensitive to environmental conditions . Accordingly , chromosome amplification related to DR in patients could be diluted and disappear upon in vitro isolation and cultivation . This finding means that future WGS studies aiming to establish a link between genome features and phenotypes like DR should be performed on amastigotes directly isolated from clinical isolates rather than cultivating strains as promastigotes prior to analysis . This presents significant technical challenges given that parasite DNA derived directly from host is relatively scarce and at much lower levels than host DNA . However , advances in sequencing technology , including capturing Leishmania DNA before sequencing—e . g . , with SureSelect technology—may offer a solution . Further discoveries on DR should , therefore , be expected from the application of this technology .
MIL is the first and only oral drug available against leishmaniasis . Since its registration in India in 2002 , it has replaced the use of SSG as first-line treatment in the ISC [83 , 84] , with cure rates higher than 94% . MIL , a phosphorylcholine ester of hexadecanol , was originally developed as an anticancer drug . The exact mode of action of MIL is not well understood , although it has been described to have a direct effect on the parasites by interfering with biosynthesis of phospholipids and metabolism of alkyl-lipids [85 , 86] , affecting mitochondrial cytochrome c oxidases and inducing mitochondrial depolarization and decrease of intracellular levels of ATP [87] , and an apoptosis-like cell death [88–90] . The uptake of MIL and other alkyl-glycerophospholipids in Leishmania requires a translocation machinery that includes a P-type ATPase named the Leishmania miltefosine transporter ( LMT ) , which is responsible for the translocation of phospholipids from the exoplasmic to the cytoplasmic leaflet of the plasma membrane of Leishmania [91] . The function of LMT depends on its binding to a specific B subunit of LMT called LRos3 [92] , which belongs to the CDC50/LEM3 protein family . Both proteins are mutually dependent for their function and their localization at the plasma membrane of Leishmania [92 , 93] , being required for MIL uptake and susceptibility . MIL has a long elimination half-life ( approximately 120 h ) that leads to subtherapeutic levels remaining for some weeks after a standard treatment course [94] . Following this observation , it was predicted that resistance to MIL would rapidly emerge in the regions where it was extensively used . Ten years after the implementation of MIL in the ISC , its efficacy was shown to be decreasing with a relapse rate of 10% in India [7] and up to 20% in Nepal upon 12-month follow-up [8] . However , this increasing TF was not initially associated to drug resistance , with other factors invoked to explain the situation , including parasite virulence [95] and host factors [96] . Recently , however , two clinical isolates resistant to MIL were isolated in the ISC [10] . Although slower to emerge in the field than some had feared , laboratory-based experimentation has demonstrated that in vitro selection of promastigotes resistant to MIL was easily achieved [97–102] . The main mechanism of experimental resistance observed is associated with a significant reduction in drug internalization due , mainly , to a reduced uptake or an increased efflux of MIL . The acquisition of inactivating mutations or deletions in MIL translocation machinery LMT and/or LRos3 in L . donovani was shown to drastically increase MIL resistance in both in vitro and in vivo assays [91 , 92 , 103] . LMT and/or LRos3 have also been shown to represent MIL-resistant markers in clinical samples obtained from leishmaniasis patients showing therapeutic failure to MIL [10 , 11 , 102] . In addition , the overexpression of ABC transporters ABCB4 ( MDR1 ) , ABCG4 , and ABCG6 has also been described to be associated with an increased resistance to several alkyl-lysophospholipids analogues , including MIL in Leishmania [104–106] , due to a reduced intracellular accumulation because of increased efflux of the drug across the plasma membrane . Other cellular modifications have also been proposed to contribute to MIL resistance in Leishmania . These include changes in the length and levels of unsaturation of fatty acids , as well as a reduction in ergosterol levels [107]; altered expression of genes involved in thiol metabolism , protein translation and folding , as well as DNA repair and replication machinery [93]; and higher ability to resist reactive oxygen species ( ROS ) [108] as well as a better tolerance towards , or reduced production of , oxidative stress [109 , 110] . An increase in metacyclogenesis and infectivity has also been described in MIL-resistant promastigotes [109] . Recently , the use of omics techniques ( whole-genome and RNA sequencing ) in MIL experimental resistant L . donovani lines has revealed mutations in genes encoding proteins other than LMT . These include pyridoxal kinase and α-adaptin line protein as well as up- and down-regulation of specific genes associated with stress , membrane composition , and amino acid and folate metabolism [98 , 100] . Specific roles in the drug’s mode of action or resistance mechanisms are not known , but a picture of a multifactorial process contributing to resistance to MIL is emerging .
AmB has been used as an antifungal agent for the last 70 years [111] and as an anti-leishmanial since the 1960s [112] . Its ability to bind to ergosterol-related sterols in cell membranes explains its specificity [113] . Because Leishmania parasites , in common with fungi , use ergosterol as a primary membrane sterol , they too are sensitive to this drug . Mammalian cells use cholesterol instead and are accordingly less sensitive to the drug . AmB is a natural product produced by Streptomyces nodosus and has an amphipathic nature with hydrophilic and hydrophobic moieties [113] . Once within the vicinity of the membrane , it spontaneously assembles with its hydrophobic surface in contact with membrane lipids while hydrophilic surfaces of adjacent molecules produce a pore . The exchange of ions across the surface via the pores contributes to cell death [114] . However , the drug also induces oxidative stress , and binding to sterol per se irrespective of pore formation also contributes to the death of yeast cells [115 , 116] . WHO has been promoting the use of single-dose Ambisome , a relatively harmless liposomal formulation of the drug for VL patients , particularly on the ISC . This campaign , which enables the widespread use of the drug without needing patient hospitalization and repeated injections , has clear advantages from a public health perspective [117–119] . However , the dose available in this single shot is not far from the minimum required to treat the disease [118] . This poses the risk that this single shot , not being always curative , could select for parasites with reduced vulnerability to the drug . These may then transmit as a less sensitive population , itself then potentially capable of developing further resistance . As discussed below in the section on combination therapies , AmB is currently recommended as a potential partner drug in a number of regimens . If resistance genes to AmB are selected during a monotherapy phase , there is the risk that they will render ineffective the AmB part of any combination . In that case , the partner drug will be effectively used as monotherapy and—worse still—possibly be used against parasites for which that drug is used at doses that are suboptimal for monotherapy , while assuming that the AmB part is effective ( i . e . , in combination therapies , drugs are often given at lower doses than in monotherapy ) . Thus selection of resistance to the second drug as well becomes increasingly likely . Resistance , however , has been considered of low risk for AmB . This is partly because resistance has been relatively rare in fungal infections , in spite of 70 years of use [111] . Moreover , reports of AmB resistance in leishmaniasis have also been rare . Notwithstanding , there are multiple reports of AmB resistance in fungal infections [e . g . , 119–123] . Moreover , the first cases of TF with AmB have already appeared in India [124 , 125] , where resistant parasites were clearly associated with one case [124] . In France , TF in HIV–Leishmania coinfections has been reported [126] , and AmB unresponsiveness in an immunosuppressed patient in Switzerland was reported as well [127] . In laboratory studies , it has been possible to select for resistance to AmB in several species of Leishmania , and both promastigote and amastigote forms of the parasite resistant to AmB have been selected [124 , 128 , 129] . It seems , therefore , that serious attention should be given to the risk of selecting resistance to AmB in Leishmania . Several studies have started to elucidate the mode of action and resistance mechanisms to the drug . For example , it was shown that treatment with AmB permeabilized leishmanial lipid bilayers to ion and dye exchanges [114 , 130] , indicating that the drug binds to the membrane as in yeast . A number of selected resistant lines revealed changes in the sterol content . Notably , ergosterol and related sterols were replaced by cholesta-related sterols . In one case , this was attributed to possible changes in sterol methyltransferase , causing disruption in the sterol pathway and accumulation of intermediates in the ergosterol synthetic pathway [124 , 131] . Several other studies have shown that loss of ergosterol is associated with resistance [128 , 129] , which links to the drug’s dependency on binding this sterol to exert its mode of action . It has recently been demonstrated that mutations to the gene encoding sterol 14α-demethylase underlie resistance [132] with an accumulation of that enzyme’s product , which indicates that the demethylase is still active itself , but its product no longer enters the remainder of the pathway . Other studies have also indicated separate changes associated with resistance . For example , a number of selected lines have increased parts of their oxidative defense mechanism [128 , 133 , 134] , which indicates that part of the drug’s mode of action is via the induction of oxidative stress and that prevention of this oxidative damage can yield a reduced sensitivity to the drug . In another resistant line , alterations to the MIL transporter were identified , and in this case , cross-resistance between AmB and MIL was detected , although the functional impacts of these mutations were unclear because the impact on MIL uptake was minimal [135] . This may be attributed to changes in the lipid composition of the membrane because the MIL transporter plays a key role in arranging lipids within the membrane; indeed , extensive changes to lipid profile were observed in this resistant line and were partially restored by complementation with wild-type MIL transporter [135] . It appears , therefore , that resistance to AmB can be selected in Leishmania . Although it appears that this is more readily achieved in promastigote forms than in amastigote forms ( possibly because a combination of changes in membrane sterol composition and response to oxidative stress are required ) , the latter form can indeed develop resistance to the drug , and this has been found already in cases of TF . More work is required to collect a comprehensive inventory of the mechanisms that can underpin resistance and to help develop a tool kit that can assist in diagnosing potentially resistant lines in the field .
In most antimicrobial drug scenarios , there is a growing awareness that combination therapies offer substantial benefits , including overcoming resistance . Using at least two drugs with different mechanisms of action should also improve the control of leishmaniasis . In the case of malaria , AIDS , and tuberculosis , substantial effort has gone into seeking optimal combination therapy based on both mode of action of partner drugs and their pharmacokinetic parameters . Characteristics such as parasite clearance time and drug half-life are guiding the rational selection of combinations . So far , relatively little has been done with respect to combination therapy in Leishmania , although preferred pairings are emerging [136–139] . WHO recommendations [140] for combination therapy in Indian VL patients are for a single dose of liposomal AmB together with MIL [136 , 141] or a single dose of liposomal AmB plus paromomycin [136] . SSG and paromomycin together comprise the WHO recommendation for Sudan [137 , 142] . Finally , for other East African countries , WHO recommends several combination therapies , including a single dose of liposomal AmB followed by MIL [143] , or AmB administered simultaneously with SSG [138] , or SSG plus paromomycin [142] . The efficacy of combination therapy must be established for each Leishmania species , and it is increasingly appreciated that it should be implemented for all clinical manifestations of leishmaniasis , including CL , in order to obtain lower relapse rates in comparison with the use of monotherapy . Resistance to individual drugs in Leishmania appears to arise easily , partly due to plasticity in their genome [69 , 73] . Whether drug combinations can also select resistant Leishmania has also been explored . At least under our experimental conditions , L . donovani promastigotes were shown to develop resistance even to combinations [12] , particularly to MIL/paromomycin and SSG/paromomycin pairings [12] . These results were confirmed in intracellular amastigotes [12] although intrinsic technical difficulties—including low replication rates and the limited time of survival of infected cell cultures—limit the time available to select resistant lines . Metabolomics analysis was used to investigate Leishmania lines resistant to different drug combinations [144] alongside the investigation of the phenotypic adaptations and fitness of parasites resistant to drug combinations [145] . These studies suggest that combination-resistant lines develop metabolic changes in multiple pathways , including proline and lipid metabolism [144] , thereby activating stress responses—including enhanced ability to neutralize drug-induced ROS production and decreases in membrane fluidity [144] . Drug-combination–resistant parasites are more tolerant to ATP loss , have increased thiol levels , resist depolarization of the mitochondrial membrane , show no DNA fragmentation under drug pressure , and sustain membrane integrity . Using a transgenic L . donovani line expressing luciferase ( LUC ) as a reporter to assess viability and dynamics of mixed populations of wild-type and combination-therapy–resistant parasites , it was observed that combination-resistant parasites acquire an overall increase in fitness over wild type under different stress situations , including nutrient starvation and heat-shock pH stress and thus have the ability to survive as intracellular amastigotes [145] . In conclusion , these results clearly suggest that , although it is more difficult for Leishmania to acquire resistance to combination therapy over monotherapy , resistance to combinations is possible ( especially when paromomycin is one of the partner drugs ) . This has important clinical relevance for the use of combination therapies and their impact on leishmaniasis control programs .
SSG TF offers a challenge to our efforts to control the leishmaniases . Several factors have contributed to diminished efficacy of drugs registered for use against these diseases , including changes in host immunity associated with the global HIV/AIDS epidemic and a changing demography in the range of the disease . Molecular mechanisms for resistance have all been determined in laboratory-selected lines , and the advent of new technologies , particularly next-generation sequencing , is increasing our ability to understand resistance in the field . In the case of SSG resistance in India , a clear pattern of genetic change associated with resistance is known . The discovery of genes underpinning resistance to other drugs , including MIL and AmB , will similarly offer an ability to monitor the emergence and spread of resistance to these drugs in the field as well . This will inform surveillance strategies to monitor drug efficacy and should feed into coordinated measures to enable further research and the setting of policies to optimize our capability to continue efforts to control the leishmaniases as a global health problem [146] . | Chemotherapy is central to the control and management of leishmaniasis . Antimonials remain the primary drugs against different forms of leishmaniasis in several regions . However , resistance to antimony has necessitated the use of alternative medications , especially in the Indian subcontinent ( ISC ) . Compounds , notably the orally available miltefosine ( MIL ) , parenteral paromomycin , and amphotericin B ( AmB ) , are increasingly used to treat leishmaniasis . Although treatment failure ( TF ) has been observed in patients treated with most anti-leishmanials , its frequency of appearance may be important in patients treated with MIL , which has replaced antimonials within the kala-azar elimination program in the ISC . AmB is highly efficacious , and the associated toxic effects—when administered in its free deoxycholate form—are somewhat ameliorated in its liposomal formulation . Regrettably , laboratory experimentation has demonstrated a risk of resistance towards AmB as well . The rise of drug resistance impacts treatment outcome , and understanding its causes , spread , and impact will help us manage the risks it imposes . Here , we review the problem of TF in leishmaniasis and the contribution of drug resistance to the problem . Molecular mechanisms causing resistance to anti-leishmanials are discussed along with the appropriate use of additional available drugs , as well as the urgent need to consolidate strategies to monitor drug efficacy , epidemiological surveillance , and local policies . Coordination of these activities in national and international programs against leishmaniasis might represent a successful guide to further research and prevention activities . | [
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"organisms"
] | 2017 | Drug resistance and treatment failure in leishmaniasis: A 21st century challenge |
MicroRNAs ( miRNAs ) regulate gene expression posttranscriptionally by interfering with a target mRNA's translation , stability , or both . We sought to dissect the respective contributions of translational inhibition and mRNA decay to microRNA regulation . We identified direct targets of a specific miRNA , miR-124 , by virtue of their association with Argonaute proteins , core components of miRNA effector complexes , in response to miR-124 transfection in human tissue culture cells . In parallel , we assessed mRNA levels and obtained translation profiles using a novel global approach to analyze polysomes separated on sucrose gradients . Analysis of translation profiles for ∼8 , 000 genes in these proliferative human cells revealed that basic features of translation are similar to those previously observed in rapidly growing Saccharomyces cerevisiae . For ∼600 mRNAs specifically recruited to Argonaute proteins by miR-124 , we found reductions in both the mRNA abundance and inferred translation rate spanning a large dynamic range . The changes in mRNA levels of these miR-124 targets were larger than the changes in translation , with average decreases of 35% and 12% , respectively . Further , there was no identifiable subgroup of mRNA targets for which the translational response was dominant . Both ribosome occupancy ( the fraction of a given gene's transcripts associated with ribosomes ) and ribosome density ( the average number of ribosomes bound per unit length of coding sequence ) were selectively reduced for hundreds of miR-124 targets by the presence of miR-124 . Changes in protein abundance inferred from the observed changes in mRNA abundance and translation profiles closely matched changes directly determined by Western analysis for 11 of 12 proteins , suggesting that our assays captured most of miR-124–mediated regulation . These results suggest that miRNAs inhibit translation initiation or stimulate ribosome drop-off preferentially near the start site and are not consistent with inhibition of polypeptide elongation , or nascent polypeptide degradation contributing significantly to miRNA-mediated regulation in proliferating HEK293T cells . The observation of concordant changes in mRNA abundance and translational rate for hundreds of miR-124 targets is consistent with a functional link between these two regulatory outcomes of miRNA targeting , and the well-documented interrelationship between translation and mRNA decay .
MicroRNAs ( miRNAs ) are small noncoding RNAs whose complementary pairing to target mRNAs potentially regulates expression of more than 60% of genes in many and perhaps all metazoans [1]–[6] . Destabilization of mRNA and translational repression have been suggested as the mechanisms of action for miRNAs [1] , [3] , [7]–[15] , and recent work directly measuring endogenous protein levels in response to altered miRNA expression levels found that specific miRNAs modestly inhibit the production of hundreds of proteins [16] , [17] . The importance and functional range of miRNAs are evidenced by the diverse and often dramatic phenotypic consequences when miRNAs are mutated or misexpressed , leading to aberrant development or disease [7] , [18]–[24] . Although regulation by miRNAs is an integral component of the global gene expression program , there is currently no consensus on either the mechanism by which they decrease the levels of the targeted proteins or even the steps in gene expression regulated by miRNAs [3] , [25]–[29] . The proposal that miRNAs decrease protein levels without affecting mRNA stability arose from the observation that the miRNA lin-4 down-regulates lin-14 expression in the absence of noticeable changes in lin-14 mRNA abundance in Caenorhabditis elegans [7] , [30]–[33] . Subsequent studies in mammalian cell culture provided further support for this model [34]–[37] . Several studies have found that repressed mRNAs as well as protein components of the miRNA regulatory system accumulate in P-bodies , suggesting that repressed mRNAs may be sequestered away from the translation pool [38]–[44] . Other evidence points to deadenylation of miRNA-targeted mRNAs , an effect that can inhibit translation [14] , [45]–[53] . Some studies have argued that initiation of translation is blocked at either an early , cap-dependent stage or later during AUG recognition or 60S joining [10] , [44] , [52] , [54]–[58] . Others have argued that a postinitiation step is targeted , resulting in either slowed elongation , ribosome drop-off , or nascent polypeptide degradation [7] , [59]–[62] . One factor contributing to the lack of a consensus model for miRNA function is the evidence that miRNA targeting of an mRNA significantly reduces message levels ( despite previous reports to the contrary ) [9] , [11] , [12] , [14] , [52] , [63] , [64] . Indeed , very recent studies from Baek et al . and Selbach et al . found that the changes in mRNA abundance are not only correlated with the repression of many targets , but also can account for most of the observed reduction in protein expression [16] , [17] . mRNA targets of the same miRNA can either be translationally repressed with little change in mRNA abundance , translationally repressed and have concordant changes in mRNA abundance , or have little translation repression with large changes in mRNA abundance [52] , [65] , [66] . That miRNAs can affect both protein production and abundance of their mRNA targets raises the question of to what extent these outcomes of miRNA regulation are mediated by a common mechanism or by competing or complementary processes . The regulatory consequence of a particular miRNA–mRNA interaction might be influenced by miRNA-independent factors such as cellular context or by additional information encoded by the target mRNA , e . g . , presence of binding sites for other RNA-binding proteins and miRNAs , secondary structure around miRNA binding sites , or the intrinsic decay rate of the mRNA [25] , [51] , [67] , [68] . Experiment-specific effects of in vitro translation assays , reporter constructs , or procedural differences that alter properties of gene expression could account for some of the wide variation in the apparent mechanisms by which miRNAs alter expression [25] , [27] . To date , most studies on translational regulation by miRNAs have used reporter assays . Although assays that rely on engineered reporter transcripts are powerful , assay-specific anomalies are a concern; artificial mRNAs may lack key pieces of regulatory information , overexpression of reporter mRNAs could mask subtle regulatory functions , and DNA transfection can lead to indirect effects on cell physiology [26] . Indeed , recent reports have found that differences in experimental setup , such as the method of transfection , type of 5′-cap , or the promoter sequence of the DNA reporter construct can drastically alter the degree or even the apparent mode of regulation by miRNAs [59] , [69] . In addition , some models have been based on studies in which only one or a few targets were studied , which introduces the possibility of generalizing the behavior of a single miRNA–mRNA interaction that may not represent the dominant biological mechanism . Two recent studies avoided many of these caveats by overexpressing , inhibiting , or deleting specific miRNAs and systematically measuring changes in endogenous mRNA and protein levels using DNA microarrays and stable isotope labeling with amino acids in cell culture ( SILAC ) , respectively [16] , [17] . Both studies found mostly concordant changes in mRNA levels and protein levels , with changes in mRNA levels accounting for much , but not all , of the changes in protein abundance . With data for hundreds of endogenous targets , these studies were the first to provide genome-wide evidence that mRNA degradation accounts for much of the reduction in protein levels . And whereas these results suggest that translation inhibition accounts for some of the observed changes in protein abundance of miRNA targets , they do not provide direct evidence of this , nor do they provide insight into which steps in translation are regulated , the extent this regulation contributes to reduced gene expression of specific mRNAs , or its possible links to mRNA decay . To investigate how miRNAs regulate gene expression , we systematically identified direct targets of the miRNA miR-124 by measuring the recruitment of target mRNAs to Argonaute ( Ago ) proteins , the core components of the miRNA effector complex , as previously described [70]–[72] . We then measured , in parallel , mRNA abundance and two indicators of translation rate , ribosome occupancy and ribosome density , for more than 8 , 000 genes , using DNA microarrays and a novel polysome encoding scheme . This strategy allowed us to directly investigate the behavior of miRNA–mRNA target pairs with respect to both mRNA fate and translation , on a genomic scale .
To study the effects of miR-124 on expression of mRNA targets , we first had to identify those targets . Recruitment to Ago complexes in response to the expression of a particular miRNA appears to be the most reliable criterion for target identification [70] . To this end , we lysed human embryonic kidney ( HEK ) 293T cells transfected with miR-124 and isolated Ago-associated RNA by immunopurification ( IP ) using a monoclonal antibody that recognizes all four human Ago paralogs [73] . We measured mRNA enrichment in Ago IPs by comparative DNA microarray hybridization of samples prepared from immunupurified RNA and total RNA from cell extracts . Three replicates of Ago and control IPs were performed from both miR-124 and mock-transfected cells ( Datasets S1 and S5 ) . To examine the enrichment profiles of the IPs , we first clustered the microarray results by their similarity and visualized the results as a heatmap , with the degree of enrichment of each RNA shown on a green ( least enriched ) to red ( most enriched ) scale ( Figure S1 ) . The Ago IP enrichment profiles were reproducible as evidenced by an average Pearson correlation coefficient between mRNA enrichment profiles of Ago IPs in mock-transfected cells and miR-124–transfected cells of 0 . 90 and 0 . 94 , respectively . Thousands of mRNAs were reproducibly enriched in the Ago IPs from mock-transfected cells ( Figures S1 and S2 , and Text S1 ) . We found that the presence of sequence matches to two highly expressed microRNA families , miR-17-5p/20/92/106/591 . d and miR-19a/b , in the 3′-untranslated regions ( UTRs ) of mRNAs significantly correlated with Ago IP enrichment ( Text S2 ) , suggesting that association with Ago is in large part a reflection of the relative occupancy of each mRNA with the suite of miRNAs endogenously expressed in HEK293T cells . High-confidence Ago-associated mRNAs ( at least 4-fold enriched over the mean , 1 , 363 mRNAs ) disproportionately encode regulatory proteins ( 409 , p = 0 . 001 ) , with roles including “transcription factor activity” ( 95 , p = 0 . 01 ) , “signal transduction” ( 230 , p = 0 . 02 ) , and “gene silencing by RNA” ( 7 , p = 0 . 02 ) . To identify RNAs specifically recruited to Agos by miR-124 , we compared the mRNA enrichment profiles of Ago IPs from miR-124–transfected cells to Ago IPs from mock-transfected cells using the significance analysis of microarrays ( SAM ) modified two-sample unpaired t-test ( Datasets S1 and S5 ) . At a stringent 1% local false-discovery rate ( FDR ) threshold , we identified 623 distinct mRNAs significantly enriched in Ago IPs from lysates of miR-124–transfected cells compared to Ago IPs from mock-transfected cells ( Figure 1A ) . Previous work established that the 5′-end of the miRNA , the “seed region , ” is particularly important for interactions with mRNA targets [4] , [11] , [37] , [74]–[77] . In most cases , there is a 6–8 bp stretch of perfect complementarity between the seed region of the miRNA and a “seed match” sequence in the 3′-UTR of the mRNA [4] , [11] , [37] , [74]–[77] . We reasoned that if the mRNAs specifically recruited to Agos by miR-124 transfection were physically associated with miR-124 , seed match sequences would be significantly enriched in miR-124–specific IP targets compared to nontargets . Indeed , we found strong enrichment of 6–8 base seed matches to miR-124 in the 3′-UTRs of miR-124 Ago IP targets ( Figure 1B ) . We also found enrichment within the coding sequences of miR-124 Ago IP targets , as previously reported ( Figure 1B ) [11] , [16] , [17] , [70] , [71] , [78] , [79] . For instance , 60% of miR-124 Ago IP targets contain a perfect match to positions 2–8 of miR-124 ( called 7mer-m8 ) in their 3′-UTRs , compared to 10% of nontargets ( p<10−185 , hypergeometric distribution ) , and 23% of miR-124 Ago IP targets contain a perfect match to positions 2–8 of miR-124 in their coding sequence , compared to 10% of nontargets ( p<10−21 ) . After removing mRNAs with 7mer seed matches in their 3′-UTRs , the remaining miR-124 IP targets were still significantly , albeit weakly , enriched for 3′-UTR 6mer matches to miR-124 ( 6mer 2–7 , p = 0 . 008 , 6mer 3–8 , p<10−5 ) . These data argue that most miR-124 Ago IP targets were recruited to Agos by direct association with miR-124 , via seed matches in their 3′-UTRs or coding sequences . The standard approach to assess translation in vivo has been the analysis of “polysome profiles . ” After treatment with cycloheximide to trap elongating ribosomes , mRNAs with no associated ribosomes and those with varying numbers of ribosomes bound can be separated by velocity sedimentation through a sucrose gradient . The polysome profile of a gene's mRNA provides information on two key parameters in translation: ( 1 ) the fraction of the mRNA species bound by at least one ribosome , and presumably undergoing translation , referred to as “ribosome occupancy , ” and ( 2 ) the average number of ribosomes bound per 100 bases of coding sequence to mRNAs that have at least one bound ribosome , referred to as the “ribosome density . ” We previously developed a method to systematically measure ribosome occupancy and ribosome density by measuring the relative amount of each gene's mRNA in each fraction of a polysome profile using DNA microarray hybridization [80] . We have since developed and implemented a more streamlined approach that uses one DNA microarray hybridization to measure ribosome occupancy and only a single additional microarray hybridization to measure ribosome density ( Figure 2 and Figure S3 ) . We measured ribosome occupancy by first pooling ribosome bound fractions and unbound fractions and adding exogenous doping control RNAs to each ( Figure 2A ) . Poly ( A ) RNA from bound and unbound pools was isolated , amplified , coupled to Cy5 and Cy3 dyes , respectively , and comparatively hybridized to DNA microarrays . The ribosome occupancy for each gene's mRNA was obtained after scaling the microarray data using the doping controls ( see Materials and Methods for details ) . We determined the ribosome density for each gene's mRNA by a “gradient encoding” strategy in which a graded ratio of each fraction from the ribosome bound fractions was split into a “heavy” and a “light” pool , respectively . For instance , 99% of the first fraction ( ∼one ribosome bound ) was added to the light pool and 1% was added to the heavy pool . Then , 98% of the second fraction ( 1 . 5–2 ribosomes bound ) was added to the light pool and 2% was added to the heavy pool , and so on , such that the light pool was enriched for mRNAs associated with fewer ribosomes , and the heavy pool was enriched for mRNAs associated with a greater number of ribosomes ( Figure 2B ) . The RNA in each pool was amplified , labeled with Cy5 or Cy3 , mixed , and comparatively hybridized to DNA microarrays . Thus , the Cy5/Cy3 ratio measured at each element on the array is a monotonic function of the mass-weighted average sedimentation coefficient of the corresponding mRNA , which is primarily determined by the number of ribosomes bound to it . The validity of this approach is supported by the very strong concordance between ribosome density measurements in yeast obtained with the gradient-encoding method and our previously published ribosome density measurements obtained using the traditional approach of analyzing each fraction on separate DNA microarrays ( Pearson r = 0 . 95 ) ( unpublished data ) [80] . Further details of the methodology , as well as control experiments and additional analyses , will be described elsewhere . To measure the effects of miR-124 on translation , we performed translation profiling on cell extracts generated from the same miR-124–transfected , or mock-transfected cell cultures that were used for Ago IPs and mRNA expression profiling ( see below ) . We obtained high-quality ribosome occupancy and ribosome density measurements on 16 , 140 sequences ( representing 10 , 455 genes ) from three independent mock-transfected cultures and two miR-124–transfected cultures ( Datasets S2 , S3 , and S5 ) . There was a strong concordance between replicate experiments for both the ribosome occupancy and ribosome number/density measurements , both in terms of the correlation of the gene-specific measurements ( Pearson correlation for ribosome occupancy = 0 . 85–0 . 89 , ribosome number = 0 . 91–0 . 97 ) and the means ( mean ribosome occupancy = 0 . 83–0 . 87 , mean ribosome number = 5 . 6–6 . 1 per mRNA ) , which were derived independently for each experiment based on the exogenous doping controls . The measurements from mock-transfected cells provide some general insights into the translation regulatory program in proliferating human cells . Here , we focus on 8 , 385 genes that correspond to a RefSeq mRNA for which we obtained high-quality measurements in both Ago IP and mRNA expression DNA microarray experiments . The average ribosome occupancy for the mRNAs from these 8 , 385 genes was 85% ( 25th and 75th quartiles = 0 . 81 and 0 . 94 , respectively ) ( Figure 3A ) suggesting , that for most genes , most polyadenylated mRNAs are associated with ribosomes under these growth conditions and that there are not abundant pools of polyadenylated mRNAs in an untranslated “compartment . ” For more than 97% of the genes analyzed , a majority of the transcripts were associated with ribosomes; mRNA transcripts of 3% of these genes ( 224 ) were predominantly unassociated with ribosomes ( ribosome occupancy <50% ) . The reason for the relative exclusion of this small set of mRNAs from the highly translated pool remains to be determined: possibilities include sequestration from the translation machinery or a relatively short half-life that results in these mRNAs spending a correspondingly small fraction of their lives in the translated pool . We searched for common biological themes among these non–ribosome-associated mRNAs using gene ontology ( GO ) term analysis , and found that an unexpectedly large fraction of these mRNAs encode proteins involved in “regulation of transcription” ( 64 , p<10−7 ) . On the flip side , there were 342 genes whose mRNAs were almost completely ( 98% or greater ) associated with ribosomes . Many of these mRNAs encoded proteins involved in metabolism and gene expression , including “oxidative phosphorylation” ( 21 , p<10−10 ) , “nuclear mRNA splicing” ( 23 , p<10−5 ) , “proteasome complex” ( 11 , p = 0 . 0003 ) , and “glycolysis” ( 10 , p = 0 . 0002 ) . mRNAs with low ribosome occupancy ( less than 50% ) were significantly less abundant than mRNAs with high ribosome occupancy ( greater than 98% ) ( Kolmogorov-Smirnov test , p<10−15 ) , consistent with the hypothesis that a lower rate of decay , and hence a greater fraction of the lifespan spent in the translated pool , contributes to ribosome occupancy . The average ribosome density for the 8 , 385 genes with technically high-quality data across this set of experiments was 0 . 53 ribosomes per 100 nucleotides ( nts ) ( 25th and 75th quartiles = 0 . 27 and 0 . 67 , respectively ) , which corresponds to one ribosome per 189 nts ( Figure 3B ) . Given that ribosomes are believed to span ∼30 nts of the mRNA , the average ribosome density would be approximately one sixth of the maximal packing density [81] . This spacing suggests that translation initiation is rate limiting for most mRNAs . We previously observed a strong negative correlation between an mRNA's ribosome density and its coding sequence length in yeast cells rapidly growing in rich medium [80] . Subsequent experiments suggested that this relationship is due to either a strong inverse correlation between initiation rate and coding sequence length [82] , or a decrease in ribosome density as a function of position along the mRNA [83] . We found the same inverse relationship between the size of a coding sequence and ribosome density in proliferating mammalian cells ( Spearman r = −0 . 90 ) ( Figure 3C ) . Sucrose gradient sedimentation did not clearly resolve polysomes containing more than seven ribosomes , so it is possible that our method underestimates the number of ribosome bound to mRNAs with long coding sequences , which could , in principle , lead to a spurious negative correlation between coding sequence length and ribosome density . However , the inverse relationship between coding sequence length and ribosome density is still readily evident when only mRNAs with coding sequences less than 1 , 000 nts are considered ( r = −0 . 73 ) , strongly supporting the validity of this relationship . These broad similarities between translational programs in proliferating HEK293T cells and proliferating S . cerevisiae grown in rich medium , suggest that the overall organization of the program , and perhaps some of the fundamental mechanisms underlying the regulation of translation , may be similar in rapidly growing yeast and human cells [80] . To measure the effects of miR-124 on mRNA expression levels , we profiled mRNA expression in the same cell cultures that we used for the Ago IPs and translation profiling . We obtained high-quality measurements for 15 , 301 genes from three independent mock-transfected cultures and three independent miR-124–transfected cultures ( Datasets S4 and S5 ) . There was strong concordance between replicate experiments ( Pearson r = 0 . 95–0 . 97 ) . To study the effects of miR-124 on the expression of its mRNA targets , we first compared the changes in mRNA abundance of Ago IP targets of miR-124 ( 560 mRNAs; 1% local FDR ) and nontargets ( 7 , 825 mRNAs ) between cells transfected with miR-124 and cells that were mock transfected . Samples were taken 12 h after the respective treatments . We plotted the cumulative distributions of miR-124–dependent Ago IP targets ( Figure 4A , green curve ) and nontarget mRNAs ( Figure 4A , black curve ) as a function of the differences in their mRNA abundance between miR-124 and mock-transfected cells . miR-124 target mRNAs were much more likely to decrease in abundance after miR-124 transfection than nontargets ( p<10−173 , one-sided Kolmogorov-Smirnov test ) . For example , 74% of miR-124 IP targets decreased at least 15% at the mRNA level , compared to 13% of nontargets . The average abundance of miR-124 Ago IP targets decreased by 35% compared to nontargets ( Figure 4C , green bar on the left ) . The results are consistent with miRNAs having significant , but modest effects on the mRNA levels of most of their endogenous mRNA targets . Previous work has established that perfect seed matches to the miRNA in 3′-UTRs are important to elicit effects on mRNA abundance [4] , [11] , [37] , [74]–[77] . To test the importance of 3′-UTR seed matches on the expression of miR-124 targets , we plotted the cumulative distributions of miR-124 IP targets with at least one 7mer 3′-UTR seed match ( 379 , Figure 4A , red curve ) and miR-124 IP targets that lacked a 7mer 3′-UTR seed match ( 181 , Figure 4A , blue curve ) . We found that mRNA targets with 7mer 3′-UTR seed matches were more likely than targets that lacked a 7mer 3′-UTR seed match to decrease in abundance in the presence of miR-124 ( 90% of miR-124 IP targets with a 3′-UTR seed match decreased at least 15% , compared to 49% of targets that lacked a 7mer 3′-UTR seed match ) . On average , IP target mRNAs with 7mer 3′-UTR seed matches decreased 40% , whereas IP targets that did not have a 7mer seed match in their 3′-UTR decreased 17% , compared to nontargets ( Figure 4C , left ) . These results underscore the importance of 3′-UTR seed matches for regulation at the mRNA level , but also demonstrate that a large fraction of miR-124 IP targets that lack 7mer seed matches to miR-124 in their 3′-UTR are nevertheless regulated at the mRNA level by miR-124 . To study the effects of miR-124 on translation of targeted mRNAs , we estimated the change in the translation rates between miR-124-transfected and mock-transfected cells ( Tr ) for each mRNA as: ( 1 ) where multiplying O , the fraction of the mRNA that is ribosome-bound ( ribosome occupancy ) , by D , the average number of ribosomes per 100 nts for bound mRNAs ( ribosome density ) provides the weighted ribosome density for each mRNA; Er is an unmeasured value for the elongation rate of any given mRNA and was assumed not to change ( discussed further below ) . Values Tr obtained from miR-124 transfected cells were divided by those from mock-transfected cells to estimate the change . We plotted the cumulative distribution of Tr for miR-124 Ago IP targets and nontargets ( Figure 4B ) . miR-124 targets ( Figure 4B , green curve ) were much more likely to decrease in translation rate than nontarget mRNAs ( Figure 4B , black curve ) ( p<10−62 , one-sided Kolmogorov-Smirnov test ) . The apparent translation rate of 47% of miR-124 Ago IP targets , but only 10% of nontargets , decreased by at least 10% . In line with what we observed for changes in mRNA abundance , miR-124 IP targets with at least one 7mer seed match in their 3′-UTR were more likely to decrease in translation rate than miR-124 IP targets that lacked a 7mer 3′-UTR seed match ( 56% percent of miR-124 IP targets with a 7mer 3′-UTR seed match decreased at least 10% in translation versus 27% of IP targets that lacked a 7mer 3′-UTR seed match ) . The overall effects on translation , while highly significant , were very modest; on average , the estimated translation rates of miR-124 Ago IP targets decreased by 12% relative to nontargets ( 15% for miR-124 IP targets with a 7mer 3′-UTR seed match and 5% for miR-124 IP targets without a 7mer 3′-UTR seed match ) ( Figure 4C , right ) . These results show that miR-124 has modest effects on the abundance , translation rate , or both for most its targets . In some cases , mRNAs that are translationally repressed are deadenylated and stored , rather than degraded [84]–[86] . All of our measurements were of mRNAs amplified based on their poly ( A ) tails . Therefore , it was possible that the effects on translation were underestimated and the effects on abundance were overestimated because a large percentage of targets mRNAs were translationally repressed , and stored without a poly ( A ) tail . To test this possibility , we measured the differences in total RNA levels irrespective of poly ( A ) tail for each gene between miR-124–transfected and mock-transfected cells . We found that the differences in RNA abundance between miR-124–transfected and mock-transfected cells as measured with unamplified total RNA were similar to those measured for amplified poly ( A ) -selected mRNA for miR-124 targets ( Pearson r = 0 . 82 , slope of least-squares regression fit in linear space = 0 . 82 ) ( Figure S4 ) . These data suggest that the apparent decrease in abundance of miR-124 target mRNAs results primarily from degradation rather than deadenylation alone . Many steps in protein synthesis have been proposed to be regulated by miRNAs . The proposed mechanisms include: ( i ) blocking initiation , e . g . , by preventing eiF4F binding to mRNA caps or joining of the 40S and 60S ribosomal subunits; ( ii ) promoting poly ( A ) tail deadenylation , which can slow initiation by preventing interactions between the poly ( A ) tail and 5′-cap , and by increasing the rate of mRNA decay , which reduces the fraction of the mRNA's lifespan spent in the translated pool; ( iii ) promoting premature ribosome release during elongation; ( iv ) slowing translation elongation; ( v ) promoting cotranslational proteolysis; and ( vi ) concerted slowing of initiation and elongation [7] , [10] , [30] , [34]–[37] , [45] , [51] , [52] , [54]–[62] . The first four proposed mechanisms make specific predictions about the effects of miRNAs on the ribosome occupancy and ribosome density of targets . Proposed mechanisms ( i ) , ( ii ) , and ( iii ) predict that both occupancy and density will decrease; mechanism ( iv ) predicts that ribosome density will increase as a function of the extent to which elongation is slowed . In contrast , proposed mechanism ( v ) does not predict that ribosome occupancy or ribosome density will change , and the effects on ribosome occupancy and ribosome density in mechanism ( vi ) depend on the relative effects of the miRNA on the two steps . We tested these predictions by comparing ribosome occupancy and density profiles of mRNAs from miR-124 and from mock-transfected cells . We found that miR-124 Ago IP targets were much more likely than nontarget mRNAs to exhibit both reduced ribosome occupancy ( Figure 5A ) ( p<10−31 , one-sided Kolmogorov-Smirnov test ) and reduced ribosome density ( Figure 5B ) ( p<10−51 , one-sided Kolmogorov-Smirnov test ) following miR-124 transfection . Thirty-nine percent of miR-124 Ago IP targets decreased at least 5% in ribosome occupancy , compared to 13% of nontargets; 55% of miR-124 Ago IP targets decreased at least 5% in ribosome density , compared to 18% of nontargets . On average , the ribosome occupancy of miR-124 Ago IP targets decreased by 4% , and their ribosome density decreased by 8% ( Figure 5C , green bars ) . We hypothesized that mRNAs with fewer associated ribosomes might exhibit larger changes in ribosome occupancy as a result of the increased likelihood of losing all ribosomes . In support of this hypothesis , on average , all ten miR-124 target mRNAs with ribosome occupancy changes greater than 20% had significantly shorter coding sequences and fewer bound ribosomes than mRNAs that changed less than 20% ( p = 0 . 0003 , one-sided Mann-Whitney test ) ( Figure S5A ) . The effects on ribosome occupancy and ribosome density were significantly larger for miR-124 Ago IP targets that contain at least one 3′-UTR 7mer seed match ( 45% and 65% decreased at least 5% in ribosome occupancy and ribosome density , respectively ) , compared to miR-124 Ago IP targets that lack a 3′-UTR 7mer seed match ( 26% and 34% decreased at least 5% in ribosome occupancy and ribosome density , respectively ) , providing direct evidence for the general importance of 3′-UTR seed matches for miRNA-mediated translational repression of endogenous mRNAs [16] , [17] . The observed effects on ribosome occupancy and density could , in principle , be the result of multiple independent regulatory mechanisms . For instance , the decrease in ribosome occupancy and density could be a result of mechanisms ( i ) , ( ii ) , and ( iii ) . If however , the effects on ribosome occupancy and ribosome density were due to the same regulatory mechanism , we would expect a large overlap between mRNAs that show appreciable decreases in ribosome occupancy and ribosome density in the presence of miR-124 . Indeed , 77% of miR-124 IP targets that decreased at least 5% in ribosome occupancy also decreased at least 5% in ribosome density ( 30% of all miR-124 IP targets decreased at least 5% in both ribosome occupancy and ribosome density compared to 2% of nontargets ) , which is significantly more than expected by chance ( p<10−18 , hypergeometric distribution ) . There was also a modest , but highly significant , correlation between changes in ribosome occupancy and ribosome density of miR-124 Ago IP targets ( Spearman r = 0 . 45 , p<10−25 ) ( Figure S6A ) , although many mRNAs appeared to differentially change in either ribosome occupancy or ribosome density ( some miR-124 mRNA targets even appeared to increase appreciably in ribosome occupancy; Figure S6 and Text S3 ) . These results are consistent with the effects on ribosome occupancy and ribosome density arising from the same regulatory mechanism . If miR-124 induced ribosome drop-off ( mechanism ( iii ) ) stochastically along the coding sequence , the change in ribosome density would be exponentially related to the length of the coding sequence . To test this , we plotted the change in ribosome density as a function of mRNA length for miR-124 IP targets and found that although they are correlated ( Spearman r = 0 . 30 ) , it is highly unlikely there is a first-order exponential relationship between the change in density and the length of the mRNA's coding sequence ( p<10−211 , F-test with the null hypothesis that the observed change in density fits the predicted change in density from an exponential least-squares fit ) ( Figure S5B ) . Thus , if ribosome drop-off is the predominant mode of miR-124 regulation , it occurs preferentially near the translation start site . The observation that many miR-124 targets decreased in both ribosome occupancy and ribosome density after transfection with miR-124 is consistent with regulation of translation initiation ( mechanisms ( i ) or ( ii ) ) or ribosome drop-off preferentially near the translation start site ( mechanism ( iii ) ) by miR-124 and suggests that slowed elongation ( model ( iv ) ) is not the predominant mode of regulation of translation by miR-124 under these conditions . Without measurements of the actual effects on protein synthesis , these results , however , do not rule out the possibility that miR-124 also induces cotranslational proteolysis ( v ) or coordinately represses translation initiation and translation elongation ( vi ) , resulting in modest decreases in ribosome occupancy and ribosome density , but large effects on protein synthesis . To analyze the overall effect of the observed decreases in mRNA abundance and translation on protein production , we calculated the estimated change in protein synthesis as: ( 2 ) where the estimated change in protein synthesis ( Pc ) can then be derived by multiplying the change in mRNA abundance by the estimated change in translation rate ( Tr ) . The change in relative mRNA abundance is calculated by dividing relative mRNA abundance values from miR-124 transfection experiments by values from the mock condition . Although the overall effect on predicted protein production was on average quite modest ( ∼2-fold decrease compared to nontargets ) , for a small fraction of miR-124 targets , the predicted changes in protein production were fairly large; 45 of the 560 identified miR-124 targets were predicted to have a decrease of at least 4-fold in protein production 12 h after miR-124 transfection . A disproportionate fraction of the most significantly affected mRNAs encoded proteins associated with membrane compartments ( 28 , p = 0 . 001 ) , including endoplasmic reticulum ( seven ) and plasma membrane ( nine ) ; these mRNAs are likely to be translated on the rough endoplasmic reticulum . A similar observation was reported with different miRNAs in a recent study [17] . These results suggest that mRNAs that are translated on the rough endoplasmic reticulum might be particularly susceptible to miRNA-mediated regulation , possibly while stalled prior to engagement with the endoplasmic reticulum [87] . To test whether our estimated changes in protein synthesis predict actual changes in protein abundance , we measured changes in protein abundance of a diverse set of proteins encoded by mRNAs that are highly enriched in miR-124 Ago IPs by Western blot analysis based on the availability of reliable antibodies . We chose 14 proteins encoded by mRNAs that are highly enriched in miR-124 Ago IPs , with predicted decreases in protein synthesis ranging from no change to 3-fold ( Table S1 ) . We collected cell lysates 60 h ( four to five cell divisions ) after miR-124 or mock-transfection to reduce the likelihood of underestimating the change in protein synthesis for long-lived proteins . Twelve of the 14 antibodies detected bands at the predicted molecular weight ( Figure 6A ) . We observed a significant correlation between the estimated changes in protein synthesis ( Figure 6B , x-axis ) and the measured changes in protein levels ( Figure 6B , y-axis ) in response to miR-124 transfection ( Spearman r = 0 . 54 , p = 0 . 07 , slope of least-squares regression fit = 0 . 54 , grey line in Figure 6B ) , with one exception . Only RNF128 , with a predicted 3 . 7-fold reduction in protein synthesis , drastically disagreed with our measured decrease of 1 . 2-fold reduction . It is possible that the discordance in RNF128 protein levels is due to posttranslational autoregulation , which is common among ring finger proteins [88]–[90] . After excluding RNF128 from analyses , there is a strong concordance between the two measurements ( Spearman r = 0 . 90 , p = 0 . 0001 , slope = 0 . 95; red line , Figure 6B ) for the remaining 11 proteins . The high correlation and the fact that the slope of the best-fit line excluding RNF128 is close to one , suggests that miR-124–induced changes in transcript abundance and translation rate can almost completely account for the changes in abundance of the targeted proteins . Thus , cotranslation proteolysis ( proposal ( v ) ) and coordinate repression of initiation and elongation ( proposal ( vi ) ) are unlikely to play more than a minor role in miR-124 regulation under these conditions . Multiple distinct miRNA regulatory pathways have been proposed , such that translational repression and mRNA degradation can be regulated independently , and these two regulatory consequences are differentially affected by specific features of miRNA–mRNA interaction [34] , [37] , [67] , [71] , [91] . The relative magnitude of effects on translation and decay of targeted mRNAs might be influenced by the sequence context of the miRNA–mRNA interaction and the particular suite of RNA-binding proteins associated with the mRNA [38] , [43] , [65] , [92] . If the balance between effects on translation and effects on decay were influenced in a gene-specific way by features of the mRNA , we would expect that some targets of miR-124 would have relatively large changes in translation with little change in mRNA abundance or vice versa . If , however , miRNA–mRNA interactions act through a single dominant regulatory pathway that affects both translation and decay , we would expect a strong correlation between the changes in abundance and translation of mRNA targets of miR-124 . We compared the changes in mRNA abundance ( Figure 7 , x-axis ) to apparent changes in translation rate ( Figure 7 , y-axis ) for miR-124 Ago IP targets following miR-124 transfection . There was strong correlation between these two regulatory effects ( Pearson r = 0 . 60 , see Text S4 and Figure S7 for estimates of significance of the correlation ) , and we found no subpopulation of mRNAs whose translation was appreciably diminished without corresponding changes in mRNA abundance , and few mRNAs whose abundance changed significantly without a corresponding change in translation . To test whether the apparent correlation might be driven solely by mRNAs with the largest measured changes in abundance and translation , we calculated the average changes in mRNA abundance and translation in moving windows of ten mRNAs ranked by their change in mRNA abundance . As shown in Figure 7 ( red curve ) , we found a persistent , nearly monotonic , relationship between changes in mRNA abundance and translation that closely matches the least-squares fit of all the data ( Pearson r = 0 . 91 ) . We obtained similar results when we analyzed miR-124 Ago IP targets with 7mer 3′-UTR seed matches and those that lacked a 7mer 3′-UTR seed match ( Figure S8 ) , although the correlation was stronger for targets with 7mer 3′-UTR seed matches ( r = 0 . 60 versus 0 . 42 ) . The correlation between changes in mRNA abundance and estimated translation rate , and the absence of a subgroup of mRNAs regulated at the translational level without corresponding effects on abundance , is consistent with a model in which these two regulatory programs are functionally linked . Although there was a measurable decrease in mRNA abundance for almost all miR-124 targets that significantly decreased in translation , only about half of the targets that decreased in mRNA abundance registered a measurable reduction in translation . It is possible that some mRNA targets are degraded without any appreciable effect on translation ( e . g . , the mRNAs are degraded while still associated with ribosomes ) or that translation of these mRNAs is indirectly stimulated in response to miR-124 , resulting in no apparent effect on translation at the time we performed translation assays . Alternatively , as the changes in translation tended to be smaller than the changes in mRNA abundance , we may have been unable to accurately measure the small effects on translation of many targets . Although most functional microRNA seed matches are located in 3′-UTRs as judged by mRNA expression data , phylogenetic conservation analysis , Ago IPs , and reporter studies , some sites in coding sequences and 5′-UTRs can also confer regulation by miRNAs [4] , [16] , [17] , [59] , [70] , [71] , [78] , [79] , [93]–[95] . The 560 high-confidence miR-124 Ago IP targets for which we obtained high-quality measurements in expression and translation analyses were strongly enriched for mRNAs that contained miR-124 seed matches in 3′-UTRs and coding sequences ( Figure 1B ) , but they were also significantly , albeit weakly , enriched , for seed matches in 5′-UTRs ( 16 , p = 0 . 009 ) . We compared the effectiveness of 7mer seed matches in the 3′-UTR , coding sequence , and 5′-UTR , and 6mer seed matches in the 3′-UTR in effecting changes in mRNA abundance and estimated translation rate . We found that both the abundance and translation rate of IP targets , regardless of the location of seed matches , decreased relative to nontarget mRNAs in miR-124 transfected cells compared to mock-transfected cells ( Figure S9 ) . The estimated effects on protein production were greatest for mRNAs with 7mer seed matches in the 3′-UTR , consistent with previous studies reporting that 3′-UTR seed matches confer the highest degree of regulation [11] , [70] , [71] , [93] . Changes in mRNA abundance were significantly greater than changes in translation for miR-124 Ago IP targets with 3′-UTR and coding sequence seed matches ( Figure S9 ) . IP targets that did not contain any 6mer seed matches were also significantly more likely to decrease in mRNA abundance than nontargets ( Figure S9 ) , which suggests that many of these mRNAs are specifically recruited to Agos by miR-124 and regulated by miR-124 , even though they do not appear to have canonical recognition elements . The extent to which each of the thousands of genes expressed in a given mammalian cell is regulated by the suite of ( often hundreds of ) miRNAs expressed in that cell is not known . We reasoned that our Ago immunopurification strategy , by quantitatively measuring association of mRNAs with microRNA effector complexes , could serve as a direct readout of the potency of the regulatory effects of miRNAs on each mRNA . We compared the change in Ago IP enrichment following transfection with miR-124 to the estimated changes in protein production ( Equation 2 ) for mRNAs with seed matches to miR-124 in their 3′-UTR or coding sequence ( Figure S10 ) . For mRNAs with 7mer or 8mer seed matches to miR-124 in their 3′-UTR , there was a strong negative correlation between the magnitude of their enrichment by the Ago IP and the estimated changes in production of the protein they encode ( 3′-UTR 7mer: Pearson r = −0 . 72 , p<10−192; 3′-UTR 8mer: r = −0 . 72 , p<10−26 ) ( Figure S10A ) . For mRNAs with 7mer or 8mer seed matches to miR-124 in their coding sequences , but no 7mer seed matches in their 3′-UTRs , there was also a significant , albeit weaker , correlation ( coding sequence 7mer: r = −0 . 39 , p<10−33; coding sequence 8mer: r = −0 . 38 , p<10−4 ) ( Figure S10B ) . There was also a weak , but still significant , correlation between IP enrichment and the estimated change in protein production for mRNAs that lacked 7mer seed matches in their 3′-UTR or coding sequence or that lacked even 6mer seed matches in their 3′-UTR or coding sequence , respectively ( 3′-UTR 6mer: r = −0 . 40 , p<10−75; no 3′-UTR or CDS 6mer: r = −0 . 23 , p<10−24 ) ( Figure S10C ) . Most of the mRNAs with 7mer or 8mer seed matches in their 3′-UTR or coding sequence that decreased significantly in protein production were enriched in the Ago IPs . Thus , Ago IP enrichment following transfection with a specific miRNA appears to be a good predictor of the corresponding effects on protein production . Because changes in mRNA abundance and translation following transfection of a specific miRNA are quantitatively smaller and less specific than their change in association with Agos , the IP method appears to be a more sensitive assay to identify the direct regulatory targets of specific miRNAs .
miRNAs regulate the posttranscriptional fates of most mammalian mRNAs , yet for endogenous mRNAs , the effects of miRNAs on translation , the steps in translation that are regulated by miRNAs , and the relationship between regulation of translation and mRNA decay by miRNAs have not been systematically explored . To address these effects and relationships , we determined the effect of a human miRNA , miR-124 , on translation and abundance of hundreds of endogenous mRNAs that were recruited to Argonaute proteins in response to ectopic expression of miR-124 in HEK293T cells . We developed a simple and economical method to quantitatively measure two key parameters of translation , ribosome occupancy and average ribosome density , on a genome-wide scale with single DNA microarray hybridizations for each ( Figure 2 ) . This method allowed us to address the effects of miR-124 on translation of endogenous mRNAs; it is also more broadly applicable to the study of translational regulation . In this initial application , we found many parallels between the translation programs in proliferating human embryonic kidney cells and S . cerevisiae ( Figure 3 ) , suggesting common features of translational programs in eukaryotes [80] . Direct identification of the mRNAs specifically recruited by miR-124 to Ago proteins , core components of miRNA-effector complexes , defined functional targets of this miRNA in this model system , providing a starting point for dissecting miRNA regulation [70]–[72] , [96] , [97] . mRNA expression profiling then allowed us to recognize the specific effects of miR-124 on the abundance of these targets . Three major conclusions emerged from our studies: ( i ) miR-124 reduces translation and abundance of its mRNA targets over a broad range; changes in mRNA abundance accounted for ∼75% of the estimated effect on protein production; ( ii ) miR-124 predominantly targets translation at the initiation stage or stimulates ribosome drop-off preferentially near the translation start site; and ( iii ) miR-124–mediated regulation of translation and mRNA decay are correlated , indicating that most mRNAs are not differentially targeted for translational repression versus mRNA decay . Transfection of miR-124 consistently reduced the translation and abundance of most of its several hundred high-confidence targets; the resulting decrease in translation averaged 12% and the decrease in target mRNA abundance averaged 35% ( Figure 4 ) . The observation that there were several mRNAs ( CD164 , VAMP3 , and DNAJC1 ) that had about 10-fold reductions in mRNA levels ( Figure S7 ) , and the fact that 90% of control-transfected cells expressed the transfected GFP marker , suggests that more than 90% cells were transfected with functionally significant quantities of miR-124; thus the small magnitude of the effects on translation and abundance of most of the mRNA targets of miR-124 identified by Ago IP was not likely a result of poor transfection efficiency . The correlation between predicted changes in protein synthesis and observed changes in protein levels for 11 of 12 proteins following miR-124 transfection ( Figure 6 ) , suggests that our assays capture most ( or all ) of the effects of miR-124 on protein synthesis . Although we need to be cautious in generalizing from these model systems , in these cells under the condition examined , miRNAs appears to modulate production for hundreds of proteins through joint regulation of target mRNA translation and stability over a strikingly large dynamic range . While the repressive effects on most targets were modest ( 1–3-fold ) , there were eight targets ( DNAJC1 , VAMP3 , CD164 , SYPL1 , MAGT1 , HADHB , ATP6V0E1 , and SGMS2 ) that were substantially down-regulated with decreases in protein synthesis of 10-fold or greater . In addition , 45 targets were estimated to have greater than 4-fold changes in protein synthesis . Regardless of the magnitude of regulation , mRNA destabilization accounted for ∼75% of the change in estimated protein synthesis . This range of regulation is in good accord with previous studies with genetically characterized endogenous miRNAs as well as with studies introducing exogenous miRNAs introduced into human tissue culture [7] , [9] , [16] , [17] , [33] . However , our observation that miR-124 had only modest effects on the translation of hundreds of targets contrasts dramatically with several previous studies in which miRNAs reduced protein expression by 5–25-fold while only modestly decreasing mRNA levels ( 1 . 1–2-fold ) , suggesting substantial inhibitory effects on translation [37] , [44] , [61] , [69] , [91] . The previous studies , however , measured the effect of a specific miRNA on reporter constructs in which the 3′-UTRs of the encoded mRNAs were not derived from mammalian mRNAs , but were either short ( ∼250 nts ) modified viral sequences or artificial . In contrast , mammalian mRNA 3′-UTRs tend to be much longer ( on average ∼1 , 000 nts ) and include regulatory sites for RNA-binding proteins and regulatory RNAs that influence mRNA localization , translation , and decay . The basis for the discrepancy in the results from these two experimental designs remains to be determined , and the answer is likely to provide useful mechanistic insights . One possibility is that mRNAs containing exogenous 3′-UTRs might have anomalously long mRNA half-lives that obscure the normal contribution of mRNA degradation to the miRNA-directed inhibition of protein expression . The large magnitude of effects observed in reporter-based assays , compared to what we and others have observed with endogenous mRNAs , is likely to be partially due to the multiple ( four to eight ) engineered miRNA binding sites in the reporter constructs used in those studies [37] , [44] , [61] , [69] , [91] . Further , these sites were in close proximity , and adjacent miRNA binding sites have been reported to act cooperatively [36] , [93] , [98] . Indeed , two studies that measured the effects of specific miRNAs on protein and mRNA levels of reporters with endogenous mammalian 3′-UTRs found more modest effects on translation , less than 2-fold on average [11] , [72] . Moreover , the magnitude of the effects we observed on translation of the mRNAs targeted by miR-124 were in agreement with two recent studies that inferred the repressive effect of miRNAs on translation by measuring miRNA-mediated effects on mRNA and protein abundance [16] , [17] . Those reports , based on directly measured changes in protein levels by quantitative mass spectrometry , concluded that the effects of miRNAs on translation were small ( less than 2-fold for hundreds of target mRNAs ) . Although we believe that our experimental design provided a good model of miRNA regulation as it normally operates in vivo , our results do represent the full range of possible regulatory consequences of miRNA–mRNA interactions . Our results suggest that miRNAs have a large dynamic range of effects on endogenous protein expression , achieved via regulation of both translation and mRNA abundance; this pattern is generally quite consistent with previous results from cells grown in culture and limited in vivo observations . However , in specific developmental or physiological programs , or for specific mRNAs , the effects on abundance and translation , as well as the apparent mode of translation regulation may differ from what we observed in this study [7] , [33] , [60] , [62] , [99] . Thus , the effects we observed for miR-124 targets after ectopically expressing the microRNA in Hek293T cells may not capture the full scope of regulation by miRNAs in their endogenous context; miR-124 is endogenously expressed in neuronal cells , and the regulatory effects of miR-124 interactions may be modulated by the physiological demands of the cell and the specific suite of specific RNA-binding proteins and regulatory RNAs that also associate with miR-124 target mRNAs . miR-124 negatively affected both the ribosome occupancy and ribosome density of hundreds of its targets ( Figure 5 ) . These parallel effects , combined with the close match between changes in protein synthesis predicted from miRNA-induced effects on mRNA abundance and translation and changes in protein levels for 11 of 12 proteins , suggest that the step in translation principally targeted by miR-124 and presumably other miRNAs is initiation or elongation processivity near the translation start site . We favor the initiation model because it is in accord with several previous studies that focused on one or a few mRNAs [10] , [44] , [52] , [54]–[58] , and there is a paucity of empirical evidence supporting ribosome drop-off , which predicts that ribosome density of miRNA-regulated mRNAs declines between the 5′- and 3′-ends of the coding sequence and that there should be an overrepresentation of incompletely synthesized N-terminal nascent polypeptides [61] . The small apparent magnitude of the effects on translation initiation , combined with the strong correlation between changes in translation and mRNA abundance , can be explained by a model in which repression of translational by miR-124 rapidly leads to mRNA decay . Such a model would explain why observable effects on translation appear to be smaller than the changes in mRNA abundance: if mRNAs whose translation is inhibited are quickly destroyed , their diminished translation would not be detected in our translation assay . There is already compelling evidence that translational repression and mRNA decay are linked [39] , [100]–[112] . Our observation that an overwhelming majority of polyadenylated mRNAs are associated with ribosomes in HEK293T cells may be a manifestation of this relationship ( Figure 3A ) . Thus , miRNA-mediated inhibition of translation may be linked to a general system for removal of the mRNA from the translational pool , involving recruitment to P-bodies and subsequent destruction [39]–[44] . Regulated decoupling of miRNA-mediated translation repression and mRNA decay would then allow organisms to tilt the balance of effects in favor of translational repression during physiological and developmental conditions where mRNA destruction is a disadvantage [99] . Our results are also consistent with a model in which miRNA-mediated regulation of translation and mRNA decay are functionally independent , but are similarly controlled by the same cis-elements . Determining whether the concordant regulation of translation and mRNA abundance represents a mechanistic coupling of miRNA-mediated regulation of translation and mRNA decay , and understanding the molecular links between these two regulatory consequences of miRNA–mRNA interactions are important goals for future investigation .
miR-124 siRNA: sense: 5′-UAA GGC ACG CGG UGA AUG CCA-3′ antisense: 5′-GCA UUC ACC GCG UGC CUU AAU-3′ HEK293T cells were obtained from ATCC ( Cat# CRL-11268 ) and grown in Dulbecco's modified Eagle's medium ( DMEM ) ( Invitrogen ) with 10% fetal bovine serum ( Invitrogen ) and supplemented with 100 U/ml penicillin , 100 mg/ml streptomycin , and 4 mM glutamine at 37°C and 5% CO2 . Transfections of HEK293T cells were carried out with calcium phosphate . Cells were plated in 15-cm dishes 12 h prior to transfection at 2×105 cells per ml ( 25 ml total ) . We made mock-transfection mixture ( 1/10 volume of growth medium ) by diluting 152 µl of 2 M CaCl2 into 1 . 25 ml of nuclease-free H2O and then slowly adding this solution to 1 . 25 ml of 2× HBS ( 50 mM Hepes [pH 7 . 1] , 280 mM NaCl , 1 . 5 mM Na2HPO4 ) . After 1 min , the mixture was added to a 15-cm plate at a medium pace . Transfections with miR-124 oligonucleotides were performed analogously with 30 nM of oligonucleotides in 2 . 5 ml of transfection mixture . Ago-specific 4f9 hybridoma was grown in suspension and adapted to 10% FBS-enriched DMEM [73] . We purified the antibody by passing supernatant from 1 l of culture over a 5-ml protein L-agarose column ( Pierce Cat# 89929 ) as per the vendor's instructions . Eluent fractions were pooled and dialyzed into PBS with Slide-A-Lyzer Dialysis Cassettes ( Pierce Cat# 66382 ) . We then biotinylated the purified 4f9 antibody with No-Weigh NHS-PEG4-Biotin Microtubes ( Pierce Cat# 21329 ) . We quantified biotinylation with EZ Biotin Quantitation Kit ( Pierce Cat# 28005 ) . Biotinylated 4f9 antibody was aliquoted and stored at −80°C until use . For Ago immunopurifications , we coupled biotinylated 4f9 antibody to DYNAL Dynabeads M-280 Streptavidin magnetic beads ( Invitrogen Cat# 112-06D ) ( 50 µg of antibody per ml of beads ) as per vendor's instructions and stored the coupled beads at 4°C for up to 1 wk before use . Twelve hours after transfection , we washed each 15-cm plate once with phosphate-buffered saline ( usually two plates were used per IP ) , then added 1 ml of 4°C lysis buffer ( 150 mM KCl , 25 mM Tris-HCl [pH 7 . 4] , 5 mM Na-EDTA [pH 8 . 0] , 0 . 5% Nonidet P-40 , 0 . 5 mM DTT , 10 µl protease inhibitor cocktail [Pierce Cat# 78437] , 100 U/ml SUPERase•In [Ambion Cat# AM2694] ) . Following a 30-min incubation at 4°C , we scraped the plates , combined the lysates , and then spun them at 4°C for 30 min at 14 , 000 RPM in a microcentrifuge . We collected the supernatant and filtered it through a 0 . 45-µm syringe filter . We froze an aliquot of lysate in liquid nitrogen for reference RNA isolation . We then added 0 . 22 mg/ml heparin to the lysate . We mixed the lysate with 2 . 5 mg of Dynal M-280 Streptavidin beads ( 250 µl from original storage solution ) coupled to biotinylated 4F9 ago antibody ( ∼12 . 5 µg ) , which we equilibrated immediately prior to use by washing twice with 1 ml of lysis buffer . We incubated the beads with the lysate for 2 h at 4°C and then washed the beads twice with 1 . 25 ml of ice-cold lysis buffer for 5 min each . Five percent of the beads were frozen for SDS PAGE analysis after the second wash . RNA was extracted directly from the remaining beads using lysis buffer from Invitrogen's Micro-to-Midi kit ( Invitrogen Cat# 12183-018 ) . We purified RNA from the lysate and RNA extracted from the beads with the Micro-to-Midi kit as per vender's instructions , except that the percentage isopropanol used for binding to the column was 70% , instead of 33% , to promote the binding of small RNAs . Sixty hours after transfection , HEK293T cell lysate was prepared using the same protocol for immunoaffinity purifications . The concentration of protein in each sample was quantified using the BCA assay ( Pierce Cat#23255 ) . For SDS-PAGE separation , 25 µg of protein from each sample was used . Protein was then transferred on to a polyvinylidene fluoride ( PVDF ) membrane for detection with the following specific antibodies: DUSP9 ( Abcam Cat# ab54941-100 ) ; PTPN11 ( Bethyl Laboraties Cat# a301–544a ) ; ITGB1 ( BD Transduction Laboratories Cat# 610467 ) ; AURKA , DHCR24 , MAPK14 , and PLK1 ( Cell Signaling Cat# 4718 , 2033s , 9212 , and 4513 , respectively ) ; AHR , ACTN4 , CDK4 , RNF128 , NRAS , and PTBP2 ( Santa Cruz Biotechnology Cat# sc-5579 , sc-17829 , sc-260 , sc-101967 , sc-519 , and sc-101183 , respectively ) ; and TUBA1A ( Sigma Cat# 096K4777 ) . GAPDH and TUBB1 ( Abcam Cat# ab9484 , ab6046 ) were used as loading controls to check for lane-specific differences from loading , transfer , and detection errors . Protein bands were quantified using the BioRad Quantity One software package . For translation experiments , two 15-cm dishes of cells ( per condition ) were seeded , grown , and transfected as described above . Twelve hours after transfection , high-purity cyclohexamide ( Calbiochem Cat# 239764 ) was added at a final concentration of 0 . 1 mg/ml directly into growth media , and the plate was agitated for 1 min at room temperature . Plates were then placed on ice and washed twice with 10 ml of ice-cold buffer A ( 20 mM Tris [pH 8 . 0] , 140 mM KCl , 5 mM MgCl2 , 0 . 1 mg/ml cycloheximide ) . After the second wash was aspirated , the plates were tilted and left for 1 min on ice to facilitate removal of excess liquid . Each plate was then washed 1× with 2 ml of ice-cold buffer A that contained 0 . 22 mg/ml of heparin . After removal of excess liquid , cells were scraped from each dish and collected in a 1 . 5-ml microcentrifuge tube on ice . Each plate typically yielded about 300 µl ( for 600 µl total ) of cells and residual buffer . This mixture was then brought to 1× protease inhibitor cocktail ( Pierce Cat# 78437 ) , 100 U/ml SUPERASin , and 0 . 5 mM DTT . To lyse the cells , the cell-buffer mixture was brought to 0 . 1% Brij 58 ( Sigma Aldrich Cat# P5884-100G ) and 0 . 1% sodium deoxycholate ( Sigma Aldrich Cat# D6750-100G ) and vortexed for 1 min . The lysate was subsequently spun at 3 , 500 rpm in a microcentrifuge for 5 min at 4°C . Supernatant was collected in a fresh tube and spun at 9 , 500 rpm in a microcentrifuge for 5 min at 4°C . Supernatant was collected , flash frozen in liquid nitrogen , and then stored at −80°C until use . Sucrose gradients were prepared using the Gradient Master ( Biocomp ) according to the manufacturer's suggestions . Five percent and 60% ( w/v ) sucrose solutions were prepared by dissolving sucrose in Gradient Buffer ( 20 mM Tris-HCl [pH 8 . 0] , 140 mM KCl , 5 mM MgCl2 , 0 . 5 mM DTT , 0 . 1 mg/ml cycloheximide ) at room temperature . The 60% solution was dispensed into an SW41 ultracentrifuge tube through a cannula underneath the 5% solution . Using an 11-step program ( Biocomp , SW41 SHORT SUCR 5–50 11 ) , the two solutions were mixed on the Gradient Master to form a linear gradient . After preparation , gradients were placed in chilled SW41 ultracentrifuge buckets and equilibrated for several hours at 4°C . Immediately before centrifugation , 300 µl of lysate ( ∼300 µg of total RNA ) was transferred to the surface of the gradient . Gradients were centrifuged at 41 , 000 rpm ( RCFave = 207 , 000 ) for 70 min at 4°C using a SW41 rotor and then stored at 4°C until fractionation . The Gradient Station ( Biocomp ) trumpet tip was pushed into the ultracentrifuge tube at a rate of 0 . 17 mm per second . Fractions ( 550 µl ) were collected into a 96-well plate containing 600 µl of lysis solution ( Invitrogen ) using a fraction collector ( Teledyne-Isco ) . The absorbance of the gradient at 260 nm was measured during fractionation using a UV6 system ( Teledyne-Isco ) . Immediately after fractionation , a unique set of four to five polyadenylate-tailed control RNAs , corresponding to Methanococcus jannaschii mRNAs that do not share significant identify to sequences in the human genome , were added at 100 pg each to fractions that contained the 80S ribosome and polysomes ( Table S2 ) . The solution was mixed well by inverting the plate several times , and liquid was collected in the well bottom by a brief centrifugation . A Precision XS liquid handler ( BioTek Intruments ) was used to transfer a defined volume of each of the fractions to one of four tubes ( Fisher Cat# 14-959-11B ) ( Table S2 ) ; the solutions in each tube are referred to as pool “A , ” “B , ” “C , ” and “D , ” respectively . Upon completion of liquid handling , eight additional control RNAs ( Ambion Cat# 1780 ) ( Table S1 ) were added to each pool , and the pools were stored at −20°C . Pools A–D were thawed at room temperature for 30 min . Two volumes of isopropanol was added to each pool , and the RNA in each pool was isolated from the mixture using the Micro-to-Midi RNA isolation kit ( Invitrogen Cat# 12183-018 ) . HEEBO oligonucleotide microarrays were printed on epoxysilane-coated glass ( Schott Nexterion E ) by the Stanford Functional Genomic Facility . The HEEBO microarrays contain ∼45 , 000 70-mer oligonucleotide probes , representing ∼30 , 000 unique genes . A detailed description of this probe set can be found at ( http://microarray . org/sfgf/heebo . do ) [113] . Prior to hybridization , slides were first incubated in a humidity chamber ( Sigma Cat# H6644 ) containing 0 . 5× SSC ( 1× SSC = 150 mM NaCl , 15 mM sodium citrate [pH 7 . 0] ) for 30 min at room temperature . Slides were snap-dried at 70–80°C on an inverted heat block . The free epoxysilane groups were blocked by incubation with 1M Tris-HCl ( pH 9 . 0 ) , 100 mM ethanolamine , and 0 . 1% SDS for 20 min at 50°C . Slides were washed twice for 1 min each with 400 ml of water , and then dried by centrifugation . Slides were used the same day . Amplified RNA was used for most DNA microarray experiments . Poly-adenylated RNAs were amplified in the presence of aminoallyl-UTP with Amino Allyl MessageAmp II aRNA kit ( Ambion Cat# 1753 ) . For mRNA expression experiments , universal reference RNA was used as an internal standard to enable reliable comparison of relative transcript levels in multiple samples ( Stratagene Cat# 740000 ) . Amplified RNA ( 3–10 µg ) was fluorescently labeled with NHS-monoester Cy5 or Cy3 ( GE HealthSciences Cat# RPN5661 ) . Dye-labeled RNA was fragmented ( Ambion Cat# 8740 ) , then diluted into in a 50-µl solution containing 3× SSC , 25 mM Hepes-NaOH ( pH 7 . 0 ) , 20 µg of human Cot-1 DNA ( Invitrogen Cat# 15279011 ) , 20 µg of poly ( A ) RNA ( Sigma Cat# P9403 ) , 25 µg of yeast tRNA ( Invitrogen Cat# 15401029 ) , and 0 . 3% SDS . The sample was incubated at 70°C for 5 min , spun at 14 , 000 rpm for 10 min in a microcentrifuge , then hybridized at 65°C using the MAUI hybridization system ( BioMicro ) for 12–16 h . For translation experiments , amplified RNA from pools A and C was fluorescently labeled with NHS-monoester Cy5 , and RNA from pools B and D was fluorescently labeled with NHS-monoester Cy3 . Amplified RNA from pools A and B were comparatively hybridized to a DNA microarray to obtain the average ribosome density , and amplified RNA from pools C and D were comparatively hybridized to a DNA microarray to measure ribosome occupancy . To compare total RNA levels in miR-124 and mock-transfected cells ( Figure S3 ) , 5–10 µg of total RNA from miR-124–transfected cells or mock-transfected cells or universal reference RNA ( Stratagene Cat# 740000 ) was reverse transcribed with Superscript III ( Invitrogen Cat# 18080085 ) in the presence of aminoallul-dUTP 5- ( 3-aminoallyl ) -dUTP ( Ambion Cat# AM8439 ) and natural dNTPs ( GE Healthsciences Cat# US77212 ) with 10 µg of N9 primer ( Invitrogen ) . Subsequently , amino-allyl–containing cDNAs from miR-124 and mock-transfected cells were covalently linked to Cy5 NHS-monoesters , and universal reference cDNA was covalently linked to Cy3 NHS-monoesters ( GE HealthSciences Cat# RPN5661 ) . Cy5- and Cy3-labeled cDNAs were mixed and diluted into 50 µl of solution containing 3× SSC , 25 mM Hepes-NaOH ( pH 7 . 0 ) , 20 µg of human Cot-1 DNA ( Invitrogen Cat# 15279011 ) , 20 µg of poly ( A ) RNA ( Sigma Cat# P9403 ) , 25 µg of yeast tRNA ( Invitrogen Cat# 15401029 ) , and 0 . 3% SDS . The sample was incubated at 95°C for 2 min , spun at 14 , 000 rpm for 10 min in a microcentrifuge , then hybridized at 65°C for 12–16 h . Following hybridization , microarrays were washed in a series of four solutions containing 400 ml of 2× SSC with 0 . 05% SDS , 20058 SSC , 1× SSC , and 0 . 2× SSC , respectively . The first wash was performed for 5 min at 65°C . The subsequent washes were performed at room temperature for 2 min each . Following the last wash , the microarrays were dried by centrifugation in a low-ozone environment ( <5 ppb ) to prevent destruction of Cy dyes [114] . Once dry , the microarrays were kept in a low-ozone environment during storage and scanning ( see http://cmgm . stanford . edu/pbrown/protocols/index . html ) . Microarrays were scanned using AxonScanner 4000B ( Molecular Devices ) . PMT levels were autoadjusted to achieve 0 . 1–0 . 25% pixel saturation . Each element was located and analyzed using SpotReader ( Niles Scientific ) and GenePix Pro 6 . 0 ( Molecular Devices ) . For IP and mRNA expression experiments , the microarrays were submitted to the Stanford Microarray Database for further analysis [115] . Data were filtered to exclude elements that did not have one of the following: a regression correlation of ≥0 . 7 between Cy5 and Cy3 signal over the pixels compromising the array element , or an intensity/background ratio of ≥3 in at least one channel . Ribosome density ( pool A versus B ) and ribosome occupancy ( pool C versus D ) measurements were normalized using exogenous doping control RNAs to correct for experimental variation between the two pools from RNA isolation , labeling efficiency , and scanning levels . In most cases , oligonucleotides that were designed to measure the exogenous doping control RNAs were represented multiple times on the DNA microarray ( up to eight ) and printed from different plates with different print tips . For ribosome occupancy experiments , the measured Cy5/Cy3 ratios of features on the microarray that correspond to the eight RNA controls added to pools C and D were fit to their expected Cy5/Cy3 ratios using least-squares linear regression in the statistical computing program R . The slope and y-intercept were used to rescale the measured Cy5 value of all other features on the DNA microarray . The ribosome occupancy for each RNA was then calculated as the corrected Cy5 intensity/ ( corrected Cy5 intensity + Cy3 intensity ) ( Figure S3C ) . To calculate the average number of ribosomes bound to each mRNA , the measured Cy5/Cy3 ratios of features on the microarray that correspond to the 85 M . jannaschii doping control RNAs that were added to fractions that contained ribosomes pools was fit to their expected Cy5/Cy3 ratios using least-squares linear regression . The slope and y-intercept were used to rescale the measured Cy5 value of all other features on the DNA microarray ( Figure S3B ) . The average ribosome density was calculated by dividing the average ribosome number by coding sequence length and then multiplying the result by 100 to give density per 100 nts . The average ribosome number was calculated using two relationships . For each ribosome peak in the profile , the distance traveled from the start point was determined . In all gradients , we could clearly resolve peaks for up to seven bound ribosomes , and we used least-squares regression to relate the ribosome peaks 1–7 to their distance traveled in the gradient according to the following equation: ( 3 ) where R represents the number of ribosomes bound , DT represents the distance traveled , and a and b are the slope and y-intercept , respectively . We then recorded the distance between the midpoint of each fraction to the start of the profile for each of the 15 ribosome-bound fractions and used the slope and y-intercept from Equation 3 to calculate the number of ribosomes at each fraction midpoint . The gradient encoding ratio at each fraction midpoint is the result of differential partitioning of each fraction in a predetermined manner into the heavy and light pools , and the ratio can be related to the ribosome number at each fraction midpoint using least-squares linear regression as described by Equation 4: ( 4 ) where R represents the average ribosome number , and ER represents the encoding ratio . Finally , the average number of ribosomes bound for each gene's mRNAs was calculated using the slope and y-intercept from Equation 4 . Prior to normalization , spots with intensity/background of less than 1 . 5 for either Cy3 or Cy5 channel were filtered . The microarray data are available from Gene Expression Omnibus ( GEO ) ( http://www . ncbi . nlm . nih . gov/geo/ ) and Stanford Microarray Database . Hierarchical clustering was performed with Cluster 3 . 0 [116] and visualized with Java TreeView 1 . 0 . 12 [117] . For SAM , unpaired two-class t-tests were performed with default settings ( R-package samr; http://cran . r-project . org/web/packages/samr/index . html ) . Microarray features that passed quality filtering in all experiments were used as input . Ago IP experiments ( Dataset S1 ) and mRNA expression experiments ( Dataset S4 ) were mean centered at log2 0 prior to running SAM . The ribosome occupancy ( Dataset S2 ) and ribosome number/density measurements ( Dataset S3 ) from miR-124 and mock-transfected cells were highly correlated , but had slightly different means ( see main text ) . Because of the small changes in ribosome occupancy and ribosome density between miR-124–transfected and mock-transfected samples , we conservatively adjusted the means of each experiment to be the same by subtracting the difference between the mean of that experiment and the mean of all the experiments to ensure that differences observed between miR-124–transfected and mock-transfected cells were not due to the doping control normalization . Enrichment of GO terms was performed with Genetrail [118] . p-Values were corrected for multiple-hypothesis testing by the Bonferroni method [119] . The significance of correlations was estimated in R by recalculating the correlations with 10 , 000 permuted sets of data , then estimating the p-value with the normal distribution function using the mean and standard deviation from the permuted data . We used a bootstrap method to estimate 95% confidence intervals for the average changes in mRNA abundance , estimated translation rate , ribosome occupancy , and ribosome density ( Figures 4 and 5 , and Figure S8 ) of IP targets compared to nontargets . To do this , we sampled with replacement measurements for each gene from the mock and miR-124 replicates , respectively , 10 , 000 times , then calculated the respective changes between miR-124 IP targets and nontargets for the 10 , 000 bootstrapped samples . For molecular features that mapped to genomic loci with an Entrez ID , the RefSeq sequence with the longest 3′-UTR was used . In cases with multiple RefSeqs with the same 3′-UTR length , the one that was alphanumerically first was used . RefSeq 3′-UTR , coding , and 5′-UTR sequences were retrieved from UCSC genome browser ( hg18 ) http://genome . ucsc . edu/ . Seed match sites in these sequences were identified with Perl scripts . miR-124 seed matches: 6mer_n2-7 “UGCCUU , ” 6mer_n3-8 “GUGCCU , ” 7mer-m8 “GUGCCUU , ” 7mer-A1 “UGCCUUA , ” 8mer “GUGCCUUA . ” In many instances , there were multiple probes on the DNA microarrays that mapped to the same RefSeq . In these cases , we used the probe that was most enriched in Ago IPs from miR-124–transfected cells compared to mock-transfected cells . | The human genome contains directions to regulate the timing and magnitude of expression of its thousands of genes . MicroRNAs are important regulatory RNAs that tune the expression levels of tens to hundreds of specific genes by pairing to complimentary stretches in the messenger RNAs from these genes , thereby reducing their stability and their translation into protein . Although the importance of microRNAs is appreciated , little is known about the relative contributions of degradation or repression of translation of the cognate mRNAs to the overall effects on protein synthesis , or the links between these two regulatory mechanisms . We devised a simple , economical method to systematically measure mRNA translation profiles , then applied this method , in combination with gene expression analysis , to measure the effects of the human microRNA miR-124 on the abundance and apparent translation rate of its mRNA targets . We found that for the ∼600 mRNA targets of miR-124 that were identified by their association with microRNA effector complexes , around three quarters of the reduction in estimated protein synthesis was explained by changes in mRNA abundance . Although the apparent changes in translation efficiencies of the targeted mRNAs were smaller in magnitude , they were highly correlated with changes in the abundance of those RNAs , suggesting a functional link between microRNA-mediated repression of translation and mRNA decay . | [
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] | 2009 | Concordant Regulation of Translation and mRNA Abundance for Hundreds of Targets of a Human microRNA |
During muscle development , myosin and actin containing filaments assemble into the highly organized sarcomeric structure critical for muscle function . Although sarcomerogenesis clearly involves the de novo formation of actin filaments , this process remained poorly understood . Here we show that mouse and Drosophila members of the DAAM formin family are sarcomere-associated actin assembly factors enriched at the Z-disc and M-band . Analysis of dDAAM mutants revealed a pivotal role in myofibrillogenesis of larval somatic muscles , indirect flight muscles and the heart . We found that loss of dDAAM function results in multiple defects in sarcomere development including thin and thick filament disorganization , Z-disc and M-band formation , and a near complete absence of the myofibrillar lattice . Collectively , our data suggest that dDAAM is required for the initial assembly of thin filaments , and subsequently it promotes filament elongation by assembling short actin polymers that anneal to the pointed end of the growing filaments , and by antagonizing the capping protein Tropomodulin .
Striated muscles contain cylindrical structures , myofibrils , composed of repeating elements called sarcomeres , the basic contractile units of muscle . A sarcomere , defined as the region between neighboring Z-discs , contains two filament systems , the actin-containing thin filaments and the myosin II-containing thick filaments , and their associated proteins . The thin filaments are anchored into the Z-disc where they are cross-linked by dimeric α-actinin and a number of other proteins [1] . These filaments extend in both directions from the Z-disc into neighboring sarcomeres . They consist of a filamentous actin ( F-actin ) core decorated with the regulatory proteins Tropomyosin ( TM ) and Troponin . Interdigitated with thin filaments are the bipolar thick filaments , composed largely of myosin molecules , that are at the middle of the sarcomere and crosslinked by the M-band proteins . Whereas the structural properties of these macromolecular complexes have been determined in detail in recent decades , much less is known about the in vivo assembly of the filaments and Z-discs to form the very regular sarcomeric structures [2] . In particular , the initial assembly of thin filaments and the regulation of actin dynamics during myofibril formation and maintenance remains poorly understood . Owing to the regular assembly of actin monomers ( G-actin ) into F-actin , these filaments display a polarized morphology and dynamics with barbed ( + ) and pointed ( − ) ends . In vivo filament growth likely occurs only at the barbed end , whereas the pointed end is favored for depolymerization [3] . New actin filament formation critically requires a nucleation step , during which a few actin monomers combine to form a nucleation seed , prior to elongation . As nucleation is not favored kinetically , and spontaneous in vivo nucleation would lead to anarchic filament assembly , this step is promoted by nucleation factors . Nucleation factors described so far include the Arp 2/3 complex , formins , Spire , Cordon-bleu and Leimodin ( Lmod ) [4] , [5] . Although actin nucleation factors have been extensively studied in many different model systems , the essential nucleation factors in developing muscles have not been clearly identified . Lmod and the mammalian formin Fhod3 have both been implicated in actin assembly in vertebrate striated muscles [6] , [7] but subsequent work concluded that they are unlikely to contribute to actin nucleation during the initial stages of myofibril assembly [8] , [9] , [10] , [11] . In fruit flies , the genome harbors no clear Lmod ortholog , and genetic analysis of the Drosophila Fhod ortholog , Fhos , and other members of the formin family , such as Diaphanous , Cappuccino or Form3 , revealed no clear role in muscle development [12] , [13] , [14] , [15] . Regulation of thin filament elongation and length , thought to be controlled by elongation factors and capping proteins , are also important aspects of actin dynamics in muscles . Elongation factors , such as Ena/VASP proteins or the barbed end binding formins that also function as nucleation factors , promote filament growth , whereas capping protein binding blocks polymerization . In contrast to non-muscle cells where thin filament growth is restricted to the barbed ends , sarcomeric actin filaments elongate from their pointed ends [16] . In each half sarcomere the thin filaments are aligned with the same polarity and their barbed ends are within the Z-discs , where they are capped by CapZ , whereas their pointed ends are capped by Tropomodulin ( Tmod ) . So far Tmod , TM , Lmod and the Sarcomere Length Short ( SALS ) proteins have all been implicated in thin filament length regulation . Of these , Tmod binding causes thin filament shortening; conversely , loss of Tmod function causes lengthening of actin filaments [16] , [17] . TM enhances Tmod binding affinity , whereas Lmod and SALS seem to antagonize the capping activity of Tmod and promote filament elongation from their pointed ends [9] , [18] . Surprisingly , instead of promoting elongation , SALS appears to inhibit filament elongation in vitro . These results together with the observation that no protein was yet isolated which would catalyze F-actin assembly at the pointed end , mean that the mechanism which enables muscle thin filaments to elongate from their pointed ends remains mysterious . Here we show that the Drosophila formin DAAM ( Dishevelled associated activator of morphogenesis ) plays an important role in sarcomerogenesis . The absence of dDAAM reduces larval motility , causes a flightless phenotype and complex defects in sarcomere organization . The latter include shorter and thinner sarcomeres with reduced thin filament levels and an absence of both Z-disc and M-band organization . Our protein localization studies revealed that , despite being a barbed end binding protein in non-muscle cells , dDAAM is highly enriched near the thin filament pointed ends both in Drosophila and mouse muscle cells . We propose that members of the DAAM family of formins are very good candidates for the long sought-after muscle actin/thin filament nucleators .
In studies of the Drosophila formin DAAM , we noticed that ∼16% of adults homozygous for the viable , hypomorphic dDAAMEx1 allele were flightless ( 16 . 2±5 . 3% , mean±SEM , n = 740 , p = 0 . 02 ) . As dDAAM null alleles are homozygous lethal , we used two dDAAM specific RNAi lines ( KK102786 from VDRC and T129M constructed in our laboratory , targeting two non-overlapping parts of the mRNA ) to verify the flight effect . In the presence of UAS-Dicer2 and an IFM ( indirect flight muscle ) specific driver ( UH3-Gal4 ) [19] , both RNAi lines produced strong flightless phenotypes ( RNAiVDRC: 94 . 7±5 . 3% , mean±SEM , n = 103 , p<0 . 001; RNAiT129M: 87 . 1±3 . 9% , mean±SEM , n = 334 , p = 0 . 002 ) ( Figure 1A ) . RNAi silencing in a dDAAM mutant background ( dDAAMEx1 , UH3-Gal4; UAS-Dicer2; UAS-dDAAMRNAi-T129M , subsequently referred to as dDAAMEx1 , UDT ) caused nearly all males to be flightless ( 98 . 9±1 . 1% , mean±SEM , n = 327 , p<0 . 001 ) ( Figure 1A ) . The strength of the flightless phenotypes correlates with the partial reduction of dDAAM protein levels in dDAAMEx1 IFM and its near absence in IFM from the RNAi genotypes ( Figure 1B ) . The flightless phenotype exhibited by dDAAMEx1 mutants could be rescued by muscle-specific expression of the dDAAM protein ( 4 . 1±2 . 9% , mean±SEM , n = 134 , p = 0 . 043 ) ( Figure 1A ) . In wild type or UH3-Gal4; UAS-Dicer2 flies ( used as parental control ) , the IFM displayed , as visualized by phalloidin ( labels F-actin ) and anti-Kettin ( a Z-disc marker ) staining , its typical regular sarcomeric organization ( Figure 1C–C″ ) , with the sarcomere length of 3 . 19±0 . 04 µm ( mean±SD , n = 63 ) found in young adults . In contrast , the IFM of dDAAM mutant flies showed significant structural alterations ( Figure 1D–E″ ) . The IFM of flightless dDAAMEx1 mutants looked largely normal , but about 25% of the myofibrils were thinner ( 1 . 42±0 . 32 µm , mean±SD , n = 50 , p<0 . 001 ) than wild type ( 1 . 72±0 . 11 µm , mean±SD , n = 150 ) and part of the sarcomeres exhibited a reduced length ( down to 2 . 59±0 . 13 µm , mean±SD , n = 73 , p<0 . 001 ) ( Figure S1A ) . In contrast , IFM from the dDAAMEx1 , UDT mutant combination showed gross alterations in IFM fiber morphology ( Figure S3A , B ) . The myofibrils were thinner than in wild type ( 1 . 18±0 . 3 µm , mean±SD , n = 64 , p<0 . 001 ) and their organization was irregular ( Figure 1D–E″ ) . Mutant IFMs exhibited reduced F-actin staining ( Figure 1D–E″ ) without significant alterations in the amount of G-actin ( Figure S1F ) . Additionally , phalloidin staining suggested that many of the thin filaments were of unequal length , and similar to dDAAMEx1 mutants , shorter sarcomeres ( 1 . 97±0 . 28 µm , mean±SD , n = 62 , p<0 . 001 ) could often be detected . M-lines could hardly be identified by Myosin immunostaining ( Figure S1B–C″ ) , while the Z-discs displayed a highly irregular and delocalized pattern compared to wild type ( Figure 1D–E″ ) . Thus , loss of dDAAM function impairs IFM structure from overall muscle shape to myofibrillar and sarcomeric organization . Electron microscopy ( EM ) of the IFM of dDAAMEx1 , UDT mutants ( Figure 2 ) confirmed and extended all the major myofibrillar defects seen in the confocal images . Notably , in longitudinal sections ( Figure 2A–D ) we revealed irregularly shaped , thin myofibrils with frayed edges , strong Z-disc defects , absence of M-lines and shorter sarcomeres . The thick and thin filament organization was also severely altered . Thick filaments rarely ran parallel to each other , the average thick filament number per sarcomere was strongly reduced compared to controls , and filament packing was much looser than wild type ( Figure 2A–D ) . Most strikingly , instead of thin filaments running in parallel between the myosin filaments ( Figure 2C ) , this space was filled with many thin filaments that formed a meshwork in some places ( Figure 2D ) . Although the organization of these filaments was very different from wild type , their dimensions argue that they are disordered thin filaments . Possibly , but we consider it unlikely , they are connecting filaments . Such filaments containing the Sallimus/Kettin proteins link the Z-disc to the thick filaments in insect flight muscle . The transverse sections of dDAAM mutant IFMs confirmed the presence of irregularly shaped myofibrils consisting of thick filament clusters with grayish material in between of them , whereas individual thin filaments could hardly be seen ( Figure 2E–H ) . Clearly the regular myofibrillar lattice was missing . Unlike wild type thick filaments which appear ring-shaped or hollow in transverse sections ( except at the level of the M-line ) ( Figure 2G ) [20] , dDAAM mutant thick filaments were very dark , irregular in shape and almost never hollow ( Figure 2H ) . At 48 hours after puparium formation ( APF ) ( at 29°C ) the pupal IFM of dDAAMEx1 , UDT flies already showed all the muscle phenotypes observed in adults . These include irregular myofibrillar organization , reduced F-actin levels , lack of visible M-lines and disorganized , unequally spaced Z-discs ( Figure S2A–B″ ) . Accordingly , EM analysis revealed sarcomere shortening ( 2 . 3±0 . 05 µm , n = 22 in wild type; 1 . 53±0 . 08 µm , mean±SD , n = 22 in mutants , p<0 . 001 ) , absence of M-lines , erratic filament packing and strong Z-disc defects ( Figure 2I–J ) . Together , these data suggest that the IFM phenotypes observed in newly hatched dDAAM mutant adults were likely to be a consequence of loss of dDAAM function during early muscle development . To test whether the structural alterations observed in dDAAM mutant myofibrils affect their mechanical properties an Atomic Force Microscope ( AFM ) was used to measure the transverse elasticity of individual myofibrils in rigor conditions ( Figure S2C ) . The elasticity ( Young's modulus ) of dDAAMEx1 and dDAAMEx1 , UDT mutant myofibrils was significantly lower , 6±1 . 63 kPa ( n = 35 ) and 4±1 . 24 kPa ( n = 15 ) , than that of wild type , 22±4 . 91 kPa ( n = 25 ) . In summary , the genetic impairment of dDAAM function severely affects the structural and mechanical properties of the flight muscles . These results argue that this formin is an important regulator of muscle development affecting multiple aspects of myofibril formation in flies . To ask whether dDAAM plays a role generally in muscle development , larval body wall muscles and the heart tube were examined . The body size and somatic musculature of dDAAMEx68 null mutant early third instar ( L3 ) appeared normal , but late in L3 , 100 hours after eggs laying ( AEL ) , the larvae were shorter ( 2 . 08±0 . 31 mm; n = 30 ) than wild type ( 3 . 24±0 . 25 mm; n = 30; p<0 . 001; Figure 3E , I ) . Although gross alterations were not evident in the overall structure of the musculature , mutant muscles were also smaller , some myofibers were split and their general organization was looser than in wild type ( Figure 3A–D ) . Measurements of the ventral longitudinal 3 ( VL3 ) muscle showed a 53% length reduction and 38% reduction in width ( Figure 3K , L ) compared to wild type . Shortening of VL3 in dDAAM mutants arises both by sarcomere shortening and a reduction in sarcomere numbers ( Figure 3M , N ) . The mean sarcomere length of wild type VL3 muscles at 100 hours AEL was 6 . 2±1 . 6 µm ( n = 477 sarcomeres; 12 muscles ) , but was decreased in dDAAM mutants to 3 . 8±0 . 7 µm ( n = 241 sarcomeres; 8 muscles; p<0 . 001 ) . The serial sarcomere number of VL3 was also decreased in dDAAM mutants ( 30 . 1±2 . 1; n = 8 ) compared to wild type ( 39 . 7±4 . 3; n = 12; p<0 . 001 ) . To investigate the physiological relevance of the muscular defects observed , we examined the larval motility of dDAAM mutant larvae . Until the early L3 stages there were no differences between the wild type and the dDAAM mutant larvae , possibly due to maternally derived dDAAM ( in ∼10% of dDAAMEx68 larvae the dDAAM protein could still be clearly detected at 100 hours AEL , Figure S3E , F ) . Consistent with the findings of the structural analysis , kinematic studies of linear larval crawling at 72 hours AEL showed that velocities of wild type and mutant larvae did not significantly differ ( Figure 3F ) . Subsequently at 100 hours AEL their velocity was decreased by ∼60% compared to wild type ( Figure 3F , J ) . Although , we observed a strong correlation between larval body length and crawling velocity ( Figure 3G , H ) , the dDAAM mutant larvae are much slower than their reduced size would indicate . Rescue experiments with DMef2-Gal4 driven expression of UAS-DAAM constructs confirmed that the observed phenotypes are specific to loss of dDAAM function . Western blot analysis revealed that the IFM expresses two dDAAM protein isoforms , a short ( 130 kD ) minor isoform and a long ( 163 kD ) major isoform ( Figure 1B ) . These correspond respectively to the predicted DAAM-PB and DAAM-PD proteins ( Flybase annotation ) . The rescue experiments ( above ) were performed with UAS-DAAM-PB as well as with UAS-DAAM-PD . UAS-DAAM-PB expression partly rescued the velocity decrease and almost fully rescued the body and muscle size of dDAAMEx68 mutant larvae , whereas UAS-DAAM-PD expression almost completely rescued all the phenotypic traits ( Figure 3I–N ) . Moreover , muscle-specific expression of these constructs not only rescued the larval muscle defects , but partly rescued the lethality of dDAAMEx68 to adulthood ( 3% for PB and 6 . 1% for PD ) . Importantly , unlike the wild type constructs , the actin polymerization incompetent mutant forms , UAS-DAAM-PBI732A and UAS-DAAM-PDI1042A mimicking the Bni1 I1431A mutation [21] , failed to rescue ( Figure 3I–N ) . These data demonstrate that the effect of dDAAM on muscle structure and larval motility is muscle autonomous , and that the actin-assembling activity of dDAAM is essential for normal muscle development . Additionally , it appears that the two muscle-specific dDAAM isoforms play largely , but not completely , redundant roles in larval muscle . Muscle-specific expression of UAS-DAAM-PB and UAS-DAAM-PD , in a wild type background , produced significantly longer larvae ( PB: 4 . 26±0 . 15 mm , n = 10 , p<0 . 001; PD: 4 . 24±0 . 19 mm , n = 10 , p<0 . 001 ) than wild type . Their VL3 muscles were longer , although in both cases sarcomere size was slightly shorter than wild type ( Figure 3I , K , M ) . Muscle lengthening occurred by significantly increasing sarcomere number compared to wild type ( PB: 56±2 . 8 , n = 14 , p<0 . 001; PD: 54±2 . 5 , n = 12 , p<0 . 001 ) ( Figure 3N ) . Interestingly , the aforementioned structural aspects were almost identical in larvae overexpressing either isoform . Nevertheless , larvae expressing the PB isoform were much faster ( ∼55% faster , n = 10 ) than wild type larvae ( Figure 3J ) , while the velocity of larvae expressing PD ( ∼5% faster , n = 10 ) and the controls ( Figure 3J ) were not significantly different . Lengths of PB and PD overexpressing larvae were indistinguishable but PB larvae had significantly wider VL3 muscles compared to PD larvae . Thus increasing dDAAM isoform levels is sufficient to enhance the number of sarcomeres initiated , but efficient sarcomere elongation may require cooperation of both isoforms and regulation of their ratio . Larval heart tube size was also reduced in dDAAM mutants compared to wild type ( ∼40% reduction in diameter ) . In 100 hour old wild type larvae the maximum heart diameter was 100 . 33±7 . 39 µm; n = 9 whereas in dDAAM mutants 60 . 44±6 . 18 µm; n = 9 , p<0 . 001 and they displayed reduced F-actin levels ( Figure S3C , D ) . Many mutant myofibrils appeared thinner than in wild type and often deviated from the normal orientation ( Figure S3D ) . These observations strongly suggest that the formin dDAAM may be a crucial regulator of muscle development in Drosophila with an effect in every muscle type and developmental stage examined . To further characterize the role of dDAAM in myofibril formation , we examined its localization pattern in the IFM . In newly eclosed adults the anti-dDAAM serum [22] produced a strong staining in the middle of IFM sarcomeres in the M-line region and a weaker staining was evident at the Z-disc and within the sarcoplasm ( Figure 4C ) . This pattern persisted from 48 hours APF ( the earliest analyzable pupal developmental timepoint ) ( Figure 4A–B′ ) until young adulthood . However , in slightly older adults the signal gradually decreased at the M-line and by 4 days after hatching , equally strong signals were detected at the M-line and Z-disc ( Figure 4D ) . In a dDAAMEx1 , UDT mutant , which is nearly protein null for dDAAM ( Figure 1B ) , only background staining was detectable demonstrating the specificity of the antibody ( Figure S4B ) . To complement the immunostaining we created a C-terminally GFP tagged dDAAM knock-in allele ( dDAAMEGFP ) . The dDAAMEGFP allele is viable and fertile in either homo- or hemizygous states , and expression of this protein is entirely under the control of endogenous regulatory sequences . The dDAAM::EGFP fusion protein displayed a roughly equally strong enrichment at the M-line and Z-disc in young and 4 day-old IFMs ( Figure S1D–E′ ) . Thus , although the anti-dDAAM serum detects a partial difference between the early and late dDAAM pattern , which is not seen with dDAAMEGFP ( presumably due to a difference in the accessibility of the native and the EGFP tagged C-termini ) , both tools confirm that sarcomeric dDAAM protein is present at both the Z-disc and the M-line . As thin and thick filaments overlap almost entirely in Drosophila IFM , it was not possible to determine unambiguously whether dDAAM enrichment in the middle of wild type sarcomeres reflects binding to the M-line or to the thin filament ends that extend close to the M-line . In UH3-Gal4/+; UAS-Tmod/+ mutant flies excess Tmod resulted in shorter thin filaments that were not in perfect register and varied in length ( Figure 4G ) while M-line organization remained largely normal ( Figure 4H ) , as judged by F-actin and Obscurin staining , respectively . In such IFMs the dDAAM protein no longer formed a distinct band at the M-line . Instead a punctate intra-sarcomeric staining occurred that mostly co-localized with the pointed end region of the actin filaments ( Figure 4G–G″ ) . This suggests that the mid-sarcomeric dDAAM enrichment , seen in wild type , is likely to be thin filament binding and not an M-line association . Consistent with this conclusion , in developing larval body wall muscles ( 72 hours AEL ) the dDAAM staining clearly resolves into two bands along the M-line ( Figure 4E ) . Interestingly , in full-grown larval myofibrils the dDAAM staining relocated to a region flanking the Z-disc ( Figure 4F ) , which is similar to the pattern observed for SALS and Tmod [18] . All together these localization data indicate that dDAAM is present in the growing sarcomeres at a location consistent with a role in thin filament regulation . As many muscle proteins are evolutionary highly conserved , and the mouse Daam1 ( mDaam1 ) gene was shown to be involved in heart development [23] , we examined the localization of mDaam1 by immunostaining of skeletal muscle sections from 15 day-old animals . Interestingly , in the m . tibialis anterior two bands of sarcomeric enrichment occurred at either side of the M-line , whereas in m . vastus lateralis most protein was detected along the Z-discs ( Figure 5A–B″ ) . To verify this mDaam1 localization pattern further and its development during the early phases of myofibrillogenesis , we used the mouse myogenic cell line C2C12 [24] and α-actinin , known to be one of the earliest marker of myofibril formation [25] . In C2C12 cells that were induced to differentiate for 24 hours , mDaam1 was detected in two broad bands in the sarcomeres between the Z-bodies and the M-line ( Figure 5E ) . In C2C12 cells differentiated for 48 or 96 hours , the same mDaam1 distribution was detected as after 24 hours of differentiation ( Figure 5F ) . To resolve the sarcomeric position of the two bands labeled by anti-mDaam1 , double staining was carried out with the anti-titin 9D10 and the anti-myomesin B4 antibodies in C2C12 cells differentiated for 96 hours . The 9D10 antibody labels the PEVK region of the giant titin protein located in the I-band close to the I-A border [26] , [27] , whereas B4 labels the M-line [28] . The mDaam1 staining did not significantly overlap with that of either 9D10 or B4 ( Figure 5C–D″ ) confirming that most of the mDaam1 protein is accumulated between the M-line and the I-A border , corresponding to the thin and thick filament overlap region . The sarcomeric localization pattern of mDaam1 suggests two important conclusions . Firstly , despite some muscle-specific differences , the subsarcomeric localization of mDaam1 appears similar to that of Drosophila DAAM with regard to accumulation at the Z-disc and alongside the M-line . Secondly , because mDaam1 is recruited to sarcomeric complexes as early as the actin cross-linker α-actinin protein , this formin is likely to be an early determinant of myofibrillogenesis . To collect further evidence for our proposal that dDAAM has an important role in thin filament formation and regulation , genetic interactions with the IFM-specific Act88FKM88 and Tm23 mutations [29] , [30] were tested . IFM structure was analyzed in heterozygous mutants in wild type and dDAAMEx1 mutant backgrounds . The results revealed that the mild dDAAMEx1 IFM phenotype ( Figure 6A ) is strongly enhanced by Act88FKM88 and enhanced by Tm23 . Myofibrils of Act88FKM88 heterozygotes were thinner than wild type and some Z-discs were not entirely straight ( Figure 6B ) , but the precisely repeating organization of the sarcomeres remained . In contrast , the IFM of dDAAMEx1; Act88FKM88/+ double mutants exhibited a network of very thin myofibrils often with a branched appearance , in which Z-disc and sarcomeric organization appeared to be completely abolished ( Figure 6C ) . Similarly , in dDAAMEx1; Tm23/+ mutants the myofibrils appeared disorganized , displaying strongly varying width , unequal sarcomere and thin filament length and the frequent appearance of mini-sarcomeres ( Figure 6E ) . As controls we examined Act5C null mutants , affecting the major non-muscle cell specific actin isoform [31] and a strong loss-of-function allele of the cytoplasmic Tm1 isoform , Tm102299 [32] . As expected , these mutations did not alter the IFM phenotype of dDAAMEx1 ( Figure S5 ) . The strong dominant genetic interaction between dDAAM and the IFM-specific Act88F and Tm2 alleles , and the complete lack of interaction with the non-muscle cell specific isoforms , suggests that the major function of dDAAM during muscle development is indeed linked to the regulation of sarcomeric actin filament formation . Under in vitro conditions the FH2 or FH1–FH2 domains of dDAAM behave as bona fide formins possessing both actin nucleation and elongation activities [33] . The observation that the thin filaments are shorter in dDAAM mutants than in wild type , suggested that dDAAM is a positive regulator of thin filament elongation . Consistent with the view that muscle thin filaments elongate from their pointed ends , dDAAM is present at the pointed end of actin filaments in the IFM , although , as expected for a formin , it also accumulates at barbed ends . To determine whether dDAAM is functionally important for pointed end elongation we investigated genetic interactions of dDAAM with mutations affecting the pointed end regulator proteins SALS and Tmod . SALS promotes filament elongation in vivo [18] , whereas Tmod binding is thought to prevent elongation [16] . The presence of salsf07849/+ in a dDAAMEx1 mutant background had no obvious phenotypic effect . In contrast , the tmod00848 mutation entirely suppressed the weak flightless phenotype of dDAAMEx1 ( 4 . 9±0 . 5% , mean±SEM , n = 160 , p = 0 . 027 ) ( Figure 1A ) suggesting that dDAAM and Tmod may act antagonistically during thin filament growth . To investigate the dDAAM/Tmod interaction in more detail we first examined the IFM-specific RNAi silencing of tmod , and we found that in most myofibrils it severely disrupted myofibrillogenesis ( Figure 7A ) . However , approximately 10% of the myofibrils had almost normal looking Z-discs allowing us to determine that these sarcomeres were shorter ( 2 . 62±0 . 11 µm; n = 26; mean±SD; p<0 . 001 ) than wild type . Phalloidin staining revealed the presence of thin filaments in the mid-sarcomeric region ( Figure 7B ) and impaired M-lines are evident by EM analysis ( Figure 7H ) . The strong effect on myofibrillogenesis is in accordance with previous reports that Tmod1 in mouse and Unc-94 ( tmd-1 ) in C . elegans are required for myofibril assembly [34] , [35] , [36] , [37] . The decreased sarcomere length was unexpected as the inhibition of Tmod function increases sarcomere length in cultured cardiomyocytes [38] or in Drosophila primary muscles [18] . We noted however , that although sarcomere length of UH3-Gal4; UAS-tmodRNAi flight muscles was reduced , some of the thin filaments clearly failed to terminate in the H-zone of these mutant sarcomeres ( Figure 7H ) . Therefore , individual filament length can be longer than in wild type , which would be consistent with the known function of Tmod in filament length regulation . To study whether the tmodRNAi phenotype is sensitive to dDAAM protein level , tmod silencing was carried out in a dDAAMEx1 mutant background . Most ( ∼80% ) myofibrils displayed a striated pattern with distinct M-lines and somewhat aberrant Z-discs , and nearly normal sarcomere length ( 2 . 8±0 . 13 µm; n = 30; mean±SD; p<0 . 001 ) ( Figure 7C ) . This phenotype suggests that the reduced dDAAM levels suppress the “over elongation” of the thin filaments seen in the IFM of tmodRNAi flies , and hence , these results further confirm that these two proteins have antagonistic activities in thin filament elongation . Although dDAAM protein is detected in the vicinity of the pointed ends of sarcomeric thin filaments , former structural studies indicated that formins are strictly barbed end binding proteins [21] , [39] , [40] . This paradox would be resolved if pointed end elongation relies on the formation of short actin filaments that anneal sequentially to growing thin filaments anchored to the Z-disc . In this model , dDAAM would mediate the assembly of short actin filaments by acting as a classical barbed end binding formin , but would additionally either actively promote actin filament annealing , or at least not block it . To test this expectation , an in vitro F-actin annealing assay was carried out with the barbed end binding FH1–FH2 domains of dDAAM . We found that the presence of the FH1–FH2 fragment ( 100 nM ) allowed the end-to-end annealing of actin filaments ( Figure 7G ) , although in previous in vitro assays the FH1–FH2 domains of dDAAM significantly reduced barbed end assembly under similar conditions [33] . Capping protein and TM were used as controls . In accordance with former studies [41] , [42] , the barbed end blocking capping protein had an inhibitory effect , whereas TM enhanced the end-to-end annealing of actin filaments , and the combined effect of TM and dDAAM was even slightly higher than the one of TM alone ( Figure 7G ) . The annealing model suggests that , even if at the pointed end sarcomeric region , dDAAM acts as a barbed end binding protein . Hence it follows that dDAAM is unlikely to directly interfere with the binding of pointed end cappers , such as Tmod . To address this issue , we investigated the effect of dDAAM and Tmod in overexpression assays . The IFM specific overexpression of Tmod resulted in thin filament shortening [43] ( Figure 7D–D″ ) , whereas the excess of dDAAM had no obvious phenotypic effect in the IFM ( Figure 7E–E″ ) . When the two proteins were expressed together , we observed the same phenotypic effect as the overexpression of Tmod alone ( Figure 7F–F″ ) . Therefore these results support the annealing model of dDAAM mediated thin filament elongation and the interaction studies are also consistent with the proposal that dDAAM affects thin filament assembly at pointed ends .
The sarcomeric actin filaments are critical structural and functional elements of muscles , yet the mechanism of actin filament formation and its regulation during myofibrillogenesis remained unclear . The initial steps of actin filament formation require nucleation factors , of which Lmod and Fhod3 have been previously identified as muscle-specific nucleators [6] , [7] . However , functional analysis led to the conclusion that Lmod and Fhod3 are crucial to myofibril maintenance but are unlikely to contribute to filament nucleation during the initial stages of myofibril assembly . Recent work in C . elegans revealed that two members of the formin family , Cyk-1 ( a Diaphanous ortholog ) and Fhod-1 , are both enriched at the Z-disc and promote filament lattice growth and its maintenance in striated muscles [44] . Surprisingly however , the muscle phenotypes displayed by cyk-1 or fhod-1 single mutants were relatively mild and it is unresolved whether other nucleation factors are required in worm muscles . Here we provide in vivo evidence that DAAM , another formin family member , is important for sarcomeric thin filament formation . We have found that dDAAM is required for thin filament elongation and that the actin-assembling activity of dDAAM is indispensable for formation of functional muscles . In addition , we have shown that in differentiating C2C12 cells the mouse Daam1 ortholog is incorporated into sarcomeric complexes at least as early as α-actinin . Thus DAAM family formins are strong candidates for being involved in the initial assembly of myofibrillar actin filaments . Interestingly , although the F-actin content of dDAAM mutant muscles is reduced , some filaments still form . Notably however , the dDAAM mutants available for muscle studies are not protein null . This prevents us from determining whether an additional nucleation factor , such as Dia or Fhos , is involved or that residual dDAAM activity is sufficient to promote some level of F-actin formation . Nevertheless , our results demonstrate that dDAAM is a developmentally important sarcomere-associated actin assembly factor in Drosophila . Remarkably , expression of the vertebrate DAAM orthologs are known to be abundant in developing somites and heart [23] , [45] , and genetic analysis of mDaam1 indicated a role in sarcomere organization in cardiomyocytes [23] . Overall this suggests that the regulation of sarcomeric actin filament formation is an evolutionary conserved DAAM function . Our studies revealed that in the IFM the dDAAM protein is mostly enriched at either end of the thin filaments , the expected positions for proteins affecting thin filament assembly . We formerly showed that in vitro dDAAM behaves as a bona fide formin , possessing all the major properties reported for other formin family members [33] . Here we propose that at Z-discs dDAAM may regulate G-actin incorporation with the well described barbed end processive capping mechanism of formins . Given that the sarcomeric dDAAM expression in the IFM , including the Z-disc accumulation , is maintained during adulthood , it appears likely that dDAAM also contributes to the maintenance of normal muscle structure and function . Besides the Z-disc enrichment , dDAAM also accumulates at the pointed end region of the thin filaments . Since dDAAM promotes thin filament formation and acts antagonistically to the F-actin pointed end capping protein , Tmod , the simplest interpretation of these data is to assume that dDAAM is involved in filament elongation from the pointed end . This is in good accordance with the evidence that in cardiac myocytes and in Drosophila primary cultures actin dynamics predominate at the pointed ends [17] , [18] , yet the presence at the pointed ends is unexpected for a formin , a barbed end binding protein . Because available structural studies exclude the possibility that a formin directly binds to the pointed end , dDAAM might be recruited to the pointed end by binding to a different protein than actin , or our findings indicate the presence of F-actin barbed ends in the vicinity of the pointed end of the thin filaments . Although we cannot strictly exclude the first possibility , at present the functional importance of such an association is unclear . Therefore we favor the second alternative that has interesting mechanistic implications . If barbed ends indeed exist in the region of the pointed ends , then pointed end elongation could be achieved through the end-to-end annealing of short actin filaments to the Z-disc anchored growing “mother filament” ( Figure 8 ) . Such a mechanism , demonstrated in vitro , would allow rapid filament elongation at the pointed ends . Our data are compatible with the model in which dDAAM promotes the formation of these short filaments by acting as an F-actin barbed end binding processive capper that also allows filament annealing . An important question is how long these short filaments are ? In this regard , it is interesting to note that during contractile ring formation in fission yeast the formin Cdc12p was shown to nucleate short actin filaments that anneal to each other in the presence of TM [42] , and consistently , TM increased the annealing process by ∼2 fold in our in vitro assay . As TM is a major myofibrillar protein , and the IFM-specific Tm2 mutation dominantly enhanced the thin filament defects of dDAAMEx1 , we propose that the length of the filaments involved in the annealing process is unlikely to be shorter , but could be equal to an F-actin fragment covered by one TM dimer which is about 37–38 nm or 14 actin monomers . Whereas the ability to anneal end-to-end is an intrinsic property of actin filaments , a better understanding of this mechanism during myofibril formation awaits future studies , most importantly the visualization of the short protofilaments . Nonetheless , it is remarkable that the formin Fhod3 , implicated in myofibril maintenance and maturation [10] , [46] , also displays an accumulation in the pointed end region [7] , [47] and might regulate actin assembly with a similar mechanism as dDAAM . Previously presented models of thin filament growth in Drosophila proposed a two-step mechanism [18] , [43] . According to this view , during the first step short filaments are assembled , and during the second step these filaments extend to their final length . Moreover , it is presumed that , at least in larval muscles , the initial phase is SALS-independent , whereas subsequent elongation from the pointed end requires SALS activity that is thought to antagonize the effect of Tmod [18] . The shorter sarcomeres observed in dDAAM mutant muscles argue that dDAAM is required during the second step of thin filament formation . On the other hand , the severe Z-disc organization defects and the reduced sarcomere number in larval muscles , that are also typical for dDAAM mutants , indicate an earlier function that may be linked directly to the initial steps of thin filament formation . Our mDaam1 protein localization data during C2C12 cell differentiation is also consistent with an early function during sarcomerogenesis , therefore dDAAM is a good candidate for being involved already in the first steps of sarcomeric thin filament formation . Whether the annealing mechanism is at work during the first , second or both steps of actin filament formation/elongation , and whether SALS and dDAAM cooperate or act through independent mechanisms during the second step , remain open questions . Interestingly , beyond the strong effect on thin filaments , dDAAM also affects thick filament and myofibrillar lattice organization . While these phenotypes can be the indirect consequences of the severe impairment of the sarcomeric thin filament system , another alternative could be that dDAAM plays a more complex role in sarcomerogenesis . In favor of this idea we note that the dDAAM mutants display a more poorly organized filament system than observed in Act88F null mutants which completely lack the sarcomeric thin filaments [48] . Additionally , dDAAM affects the shape of the thick filaments which is not reported for Act88F [48] . Moreover , we found that despite the lack of thin filaments , in Act88F mutants the dDAAM protein remains associated with the muscle fibers displaying a non-uniform distribution with foci that largely overlap with those of Myosin staining ( Figure S4C ) . Taking all these observations together with the unusually strong effect on lattice organization , we speculate that , besides actin binding , dDAAM might play an important role in the integration of the thin and thick filament systems during sarcomerogenesis . Remarkably , unlike the actin isoforms [49] , overexpression of the wild type dDAAM protein in larval muscles significantly increased sarcomere number and muscle size while sarcomere length remained nearly normal . Therefore dDAAM appears to play an instructive role in sarcomere formation , and to our knowledge , this is the first example reported where overexpression of a single muscle protein results in such an effect on muscle development .
Unless indicated otherwise , flies were raised and crossed at 25°C according to standard procedures . w1118 was used as wild-type control . In addition , the following fly stocks were used: dDAAMEx1 , dDAAMEx68/FM7c , Kr-GFP and w; UAS-FLDAAM or UAS-DAAM-PB [50] , y w; DMef2-Gal4 ( Bloomington Stock Center ) , w; UH3-Gal4 [19] , w; UAS-Dcr2 ( Bloomington ) , ry506 tmod00848/TM3 ( Bloomington ) , w; UAS Tmod ( gift from J . Bai , Harvard Medical School , Boston ) , ry506 Act88FKM88 e [51] , y w; Tm23 ( Bloomington ) , ry506 Tm102299/TM3 ( Bloomington ) , w; salsf07849/TM6B ( Bloomington ) , w Act5CG0025/FM7c ( Bloomington ) , w; sls-GFP [52] , w; UAS-TmodRNAi ( NIG-FLY , Kyoto ) and w; UAS-dDAAMKK102786 ( VDRC , Vienna ) . The UAS-dDAAMRNAi-T129M dDAAM specific RNAi line , targeting nucleotides 2562–3068 of the RE67944 dDAAM cDNA clone , was created by standard cloning and transformation techniques . To create a UAS-DAAM-PD clone , the PD isoform specific region was amplified from a cDNA pool generated by reverse transcription of mRNAs isolated from the L3 stage . We first created a pENTR3C-DAAM-PD clone that subsequently was used to create pTW-DAAM-PD ( UAS-DAAM-PD ) destination clones suitable for transgenesis . The UAS-DAAM-PBI732A and UAS-DAAM-PDI1042A mutants were created by standard cloning techniques using pENTR3C-DAAM-PB and pENTR3C-DAAM-PD as templates for in vitro mutagenesis . The dDAAMEGFP knock-in mutant was created by a two-step P-element mediated gene conversion experiment . First a targeting construct was assembled in a modified pBS vector where we inserted a 1 . 3 kb 3′ dDAAM genomic region until the last codon , this was followed by a 2 . 3 kb Gal4::VP16 fragment flanked with I-SceI cut sites on both sides , next we inserted a 1150 bp fusion fragment containing the 3′ dDAAM region encoding the last 83 C-terminal aminoacids fused to an EGFP coding sequence ending with a stop codon . This was followed with the entire 3′ UTR of dDAAM and a 1 . 1 kb genomic region further downstream of it . This way , besides the genomic flanking sequences , the construct carries Gal4::VP16 that can be used as a marker gene which is flanked both by I-SceI sites and a ∼250 bp long genomic duplication encoding the most C-terminal dDAAM coding sequences . This targeting construct was converted into the dDAAM genomic region after remobilizing the EP ( 1 ) 1542 P-element insertion located 200 bp downstream of dDAAM ( see Flybase for details ) . To this end , EP ( 1 ) 1542 virgins were crossed to ry502 Fab-71 Δ2-3 ( gift from L . Sipos , BRC HAS , Szeged ) males and the embryonic progeny of this cross was injected with the targeting construct . Offspring of the previous cross was crossed to w; UAS-EGFP flies en masse and put on egg laying medium . Embryos were collected on apple-juice plates , and the hatching larvae were screened for GFP fluorescence with an MZ FLIII stereo microscope ( Leica , Switzerland ) . Larvae with GFP expression in the tracheal and nervous system were collected individually and used to set up stocks . Conversion events were confirmed by PCR and sequencing . Once the Gal4::VP16 containing construct has been successfully converted into the dDAAM gene , in a subsequent round of crosses I-SceI was used to induce DNA double strand breaks that could eventually be repaired through homologous recombination between the ∼250 bp duplicated dDAAM regions . This event , confirmed by PCR and sequencing , led to the removal of all non Drosophila sequences with the exception of EGFP , and resulted in a C-terminally GFP-tagged dDAAM allele . dDAAMEGFP is fully viable and fertile in hemi- or homozygous state indicating that the presence of EGFP does not significantly alter dDAAM function . Newly eclosed adult IFMs were dissected from bisected half thoraces in 4% paraformaldehyde ( PF ) , incubated for 15 minutes , then washed with relaxing solution ( 6 mM MgCl2 , 5 mM EGTA , 5 mM ATP , 90 mM potassium propionate , 20 mM NaPi , pH 7 . 0 ) . The muscles were permeabilized overnight in Triton-X/glycerol solution ( 50% v/v glycerol , 0 . 5% Triton X-100 , 20 mM NaPi , 2 mM MgCl2 , 1 mM EGTA , 5 mM DTT , pH 7 . 0 ) at 4°C , and washed in PBS supplemented with 0 . 5% Triton X-100 ( PBT ) , then labeled with primary and secondary antibodies . For pupal IFM preparations , timed pupae were removed from their puparia and pinned by the head on dry Sylgard ( Dow Corning ) , dorsal side down , and then submerged into 4% PF in PBS . After dissection along the ventral midline , unattached material was flushed gently away using a syringe to expose the IFMs . These were detached and incubated in fixative ( 4% PF in PBS ) for a further 15 minutes , then transferred back to relaxing solution . For larval heart tube dissections larvae were cut along the ventral midline in relaxing solution . Then fixed with 4% PF in PBS [53] . Fat bodies and other organs were removed , then labeled with primary and secondary antibodies . For developmental staging , white pre-pupae with everted spiracles were removed into fresh vials at 25°C and harvested at required time-points . Adult flies were selected as ‘newly eclosed’ between 0 and 8 hr post-eclosion . Primary antibodies , listed below , were applied overnight at 4°C . Muscles and heart tubes were washed three times in PBST , secondary antibodies were applied for 3 hr , then samples were rinsed three times again in PBST . The following primary antibodies were used: rat monoclonal anti-Kettin ( MAC 155 , 1∶200; Abcam ) ; rat monoclonal anti-Myosin ( MAC 147 , 1∶200; Abcam ) , rabbit polyclonal anti-GFP ( 1∶1000; Sigma ) and rabbit polyclonal anti-dDAAM ( 1∶1000 ) [22] . For secondary antibodies we used the appropriate Alexa-488 , Alexa-546 and Alexa-647 ( 1∶600 ) , actin was stained with Rhodamine-Phalloidin ( 1∶100 ) ( all from Life Technologies ) . Samples were mounted in PBS∶glycerol ( 1∶4 ) . Confocal images were captured on an Olympus FV1000 LSM microscope , images were edited with ImageJ ( NIH ) and Olympus FW10-ASW ( version1 . 7a . ) . Aged larvae were collected and rinsed with tap water , and then gently placed onto agar plates . The plates were placed under an Olympus SZX12 dissecting microscope equipped with an Olympus C7070 digital camera . Total illumination was applied , images were acquired at 25 Hz . The recording environment ( temperature , humidity , illumination ) was stationary . Twenty seconds of movie was recorded with DScaler ( The DScaler Project Team ) for each larva . During this period of time most wild-type larvae moved out from the field of view . ImageJ ( NIH ) software was used to analyze the image sequences . Persistent forward movements were selected to characterize larval crawling velocity and larval length . Larval length was calculated as the average of the minimum and the maximum head to tail distance for each larva . Maximum intensity projections were used to generate larval tracks . To calculate larval crawling velocity , the lengths of these tracks were divided by the time . Tracked larvae were dissected , stained and subjected to muscle measurements made on VL3 ( ventral longitudinal 3 ) muscles . Muscle length was measured manually as the major axis of the VL3 muscles; muscle width was measured as the minor axis of the VL3 muscles . Sarcomere number and sarcomere length were measured from gray scale intensity plots across phalloidin stained sarcomeres , sarcomere size being the distance between adjacent peaks . Flight tests were carried out with three day old flies [54] . Flies were released inside a perspex box illuminated from above , and scored for the ability to fly up , horizontally or down . Flies falling into the third category ( down ) were counted as flightless . The mouse myogenic cell line , C2C12 ( ATCC ) , was maintained in growth medium ( DMEM supplemented with 10% FBS; GIBCO/Life Technologies ) . Cells were initially plated into 100-mm-diameter dishes ( Greiner ) at a density of 104/cm2 . When cultures reached ∼80% confluence they were subcultured onto sterile glass coverslips in 35-mm-diameter dishes . Cultures were kept in growth medium until they reached 60% confluence and subsequently were switched to differentiation medium ( DMEM containing 2% horse serum; GIBCO/Life Technologies ) . This medium was replaced every day , and samples were processed for immunostaining at selected time points . Cells were fixed in 4% formaldehyde in PBS for 10 minutes , and permeabilized in PBS+0 . 1% Triton-X100 for 3 minutes before staining . Primary antibodies were applied for 1 hr RT , and after 3×5 minutes washing in PBS , cells were incubated with secondary antibodies for another 1 hr . After washing three times for 5 minutes in PBS , samples were mounted in PBS∶glycerin ( 1∶4 ) . For sections of m . tibialis anterior and m . vastus lateralis , C57Bl/6 adult male mice were sacrificed by cervical dislocation . Leg muscle was dissected , embedded in Tissue-Tek O . C . T . compound ( Sakura Finetek ) and snap-frozen in isopentane cooled by liquid nitrogen . 10 µm cryosections were fixed in prechilled acetone and kept at −80°C . For mammalian muscle and C2C12 staining the following antibodies were used: rabbit polyclonal anti-mDaam1 ( 1∶2000; Sigma ) , rabbit polyclonal anti-mDaam1 ( 1∶200; Abnova ) , mouse monoclonal anti-α-actinin ( 1∶80; Sigma ) , mouse monoclonal anti-titin 9D10 ( 1∶20; DSHB ) and mouse monoclonal anti-myomesin ( B4 , 1∶1; DSHB ) . For secondary antibodies we used the appropriate Alexa-488 , Alexa-546 and Alexa-647 ( 1∶600; Life Technologies ) . Images were taken and analyzed in a similar ways as flight muscles described above . Muscles were dissected and fixed in 3 . 2% paraformaldehyde , 0 . 5% glutaraldehyde , 1% sucrose , 0 . 028% CaCl2 in 0 . 1 N sodium cacodylate ( pH 7 . 4 ) overnight at 4°C , and washed 2× overnight in 0 . 1 N sodium cacodylate ( pH 7 . 4 ) at 4°C . Samples were postfixed in 0 . 5% osmium-tetroxide for 1 hr at room temperature , and embedded into Durcupan ( Fluka ) by following the manufacturer's recommendations . 70–80 nm ultrathin sections were prepared from 2–3 animals per genotype , stained in Reynold's lead citrate , and evaluated using a JEM-1011 electron microscope ( JEOL ) equipped with Morada camera and iTEM software ( Olympus ) . IFM muscle fibers , falling apart for individual myofibrils upon preparation , were mounted on a poly-L-lysine coated glass surface and measured in phosphate buffered saline . To identify the target points at which to perform individual force measurements , a rough and low resolution scan was taken ( not shown ) . Experiments were carried out with Asylum MFP-3D head and controller ( Asylum Research , Santa Barbara , CA ) . The driver program was written in IGOR Pro software ( version 5 . 04 , Wavemetrics , Lake Oswego , OR ) . Rectangular , gold coated , silicon nitride cantilevers were used , with a nominal spring constant of 30 pN/nm and a V shaped tip with radius of curvature of roughly 30 nm ( Bio-Lever , BL-RC150 VB-C1 , Olympus Optical Co . Ltd ) . The measurements were performed in contact mode in liquid , with the vertical piezo working in a closed loop . Constant speed of 0 . 6 µm/s ( scan rate 0 . 1 Hz ) and total load was kept below 1 nN during experiments . Simple force curves were measured by lowering the probe until a desired deflection is reached and pulling it back . To calculate the sample's elasticity the contact region of the lowering part from force curves has been used . By subtracting a reference curve , recorded on a hard surface from those measured on the object of interest , the sample's force vs . indentation curve can be obtained , which provides the Young's modulus of the measured sample [55] . Several points were examined recording multiple force curves at each selected place; the average and standard deviation of which was calculated . Adult IFM samples were dissected as described above . Tissues were immediately placed in ice-cold RIPA lysis buffer and kept overnight . SDS-PAGE and Western blot analyses were carried out according to standard protocols . Membranes were stained with rabbit anti-dDaam ( 1∶1000 ) , and rabbit anti-glycogen phosphorylase ( 1∶20000 ) ( gift from A . Udvardy , BRC HAS , Szeged ) used as a loading control . Secondary antibody was α-rabbit-HRPO ( 1∶10000; Sigma ) . For chemiluminescent detection we used a Millipore Immobilon kit . To measure the annealing of actin filaments fluorescence microscopy assays were performed . Actin filaments ( 10 µM , F-actin ) were polymerized for 2 hr at room temperature in 4 mM Tris-HCl ( pH 7 . 0 ) , 0 . 1 mM CaCl2 , 0 . 2 mM ATP , 0 . 5 mM DTT , 1 mM EGTA , 1 mM MgCl2 and 50 mM KCl ( F-buffer ) . The F-actin solution was then diluted to 1 µM using F-buffer in the absence or presence of actin-binding proteins ( 100 nM capping protein or 100 nM dDAAM FH1-FH2 or 1 µM skeletal muscle TM or 100 nM dDAAM FH1-FH2 and 1 µM skeletal muscle TM ) . The samples were incubated overnight . For investigation of the annealing , Alexa-488-phalloidin labeled samples were sheared five times through a 26 gauge needle . Samples were diluted 100 fold into microscopy buffer ( F-buffer supplemented with 50 mMDTT , 5 mM DABCO and 0 . 5% ( w/v ) methylcellulose ) 0 and 60 minutes after shearing and processed for microscopy observations . Single actin filaments were observed with an Olympus IX81 inverted fluorescence microscope using a 100× objective ( NA1 . 4 ) and a CCD camera ( Orca ERG Hamamatsu ) . The length of the actin filaments was measured and analyzed with ImageJ . Under each condition 3–4 independent measurements were performed and 300–600 filaments were analyzed . Statistical analysis was carried out using Microsoft Excel or Microcal Origin 6 . 0 . Excel ( Microsoft ) was used to collect and organise data . Statistical analysis was carried out using Prism 5 ( GraphPad Software Inc . ) and/or SigmaPlot 12 ( Systat Software Inc . ) . Normality of the data was verified by Shapiro-Wilk test . Pairwise comparisons were made using the Student's t test or the Mann-Whitney U test according to the normality , p<0 . 05 was considered as statistically significant . | Sarcomeres , the smallest contractile units of muscle , are formed by two major filament systems , the myosin containing thick and the actin containing thin filaments . Although it is well established that sarcomerogenesis involves the formation of novel actin filaments , so far it remained largely unclear how these filaments form . In this study , we show that the Drosophila and mouse members of the DAAM formin subfamily are sarcomere associated actin assembly factors . Genetic analysis revealed that dDAAM plays an essential role in thin filament formation and sarcomere organization . In addition , we demonstrate that mDaam1 is an early determinant of myofibrillogenesis . Our data suggest that besides a role at the barbed end of the thin filaments , dDAAM also functions at the pointed end where it antagonizes the capping protein Tropomodulin . Based on these observations , we propose that DAAM family formins are very good candidates for being the long sought-after muscle actin nucleators , that also promote filament elongation by assembling short actin polymers that anneal to the Z-disc anchored growing filament . Given that cardiomyopathies , muscular dystrophies and the cardiovascular disease related heart muscle degenerations belong to the major health problems worldwide , understanding the mechanism of how muscles normally form is of immense biomedical relevance . | [
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] | 2014 | DAAM Is Required for Thin Filament Formation and Sarcomerogenesis during Muscle Development in Drosophila |
Dengue , the predominant arthropod-borne viral disease affecting humans , is caused by one of four distinct serotypes ( DENV-1 , -2 , -3 or -4 ) . A literature analysis and review was undertaken to describe the molecular epidemiological trends in dengue disease and the knowledge generated in specific molecular topics in Latin America , including the Caribbean islands , from 2000 to 2013 in the context of regional trends in order to identify gaps in molecular epidemiological knowledge and future research needs . Searches of literature published between 1 January 2000 and 30 November 2013 were conducted using specific search strategies for each electronic database that was reviewed . A total of 396 relevant citations were identified , 57 of which fulfilled the inclusion criteria . All four dengue virus serotypes were present and co-circulated in many countries over the review period ( with the predominance of individual serotypes varying by country and year ) . The number of countries in which more than one serotype circulated steadily increased during the period under review . Molecular epidemiology data were found for Argentina , Bolivia , Brazil , the Caribbean region , Colombia , Ecuador , Mexico and Central America , Paraguay , Peru and Venezuela . Distinct lineages with different dynamics were found in each country , with co-existence , extinction and replacement of lineages occurring over the review period . Despite some gaps in the literature limiting the possibility for comparison , our review has described the molecular epidemiological trends of dengue infection . However , several gaps in molecular epidemiological information across Latin America and the Caribbean were identified that provide avenues for future research; in particular , sequence determination of the dengue virus genome is important for more precise phylogenetic classification and correlation with clinical outcome and disease severity .
Dengue disease , caused by a dengue virus ( DENV ) , is the predominant arthropod-borne viral disease affecting humans . The primary vector is the Aedes aegypti ( A . aegypti ) ( Linnaeus ) mosquito . Dengue is caused by one of four distinct serotypes ( DENV-1 , -2 , -3 or -4 ) that are members of the Flaviviridae family ( genus: Flavivirus ) . The economic burden and the size of the at-risk population confirm the global importance of dengue infections [1] . The direct and indirect costs of dengue are large , with the worldwide costs of medical care , surveillance , vector control and lost productivity estimated to be approximately US$39 billion per year ( 2010 base ) [2] . In the Americas , the economic and societal costs of dengue have been an estimated at between US$1 billion and US$4 billion each year ( 2010 base ) [3] . Dengue is a major public health concern [4–7]; a recent epidemiology systematic review indicated that the incidence of dengue within the Latin America–Caribbean region increased over the period 1995–2010 ( pooled incidence was 72 . 1/100 , 000 person-years ) [8] . The reasons for the spread of dengue in the tropics and subtropics are complex . Population growth , rapid and unplanned urbanisation of tropical regions with poor sanitary conditions , deterioration of the public health infrastructure , decreased access to health care and inadequate vector-control efforts have also contributed to the increase of disease burden [6] . Globalisation of the economy , international travel ( recreational , business , and military ) and climate change might also explain the disease expansion [6] . The introduction of Aedes albopictus , a secondary vector reported for the first time in the continent in 1985 , could also play a role in the maintenance of the virus cycle [6] . The relationship between DENV and other co-circulating arboviruses , such as Zika virus ( ZIKV ) or chikungunya virus ( CHIKV ) , may also influence human infectivity , diagnosis and reported incidence . Brathwaite et al . have segmented the history of dengue in the region into four phases [9] . DENV RNA is a single-strand positive-sense genome ( approximately 10 , 700 bases ) , surrounded by a nucleocapsid covered by a lipid envelope . The genome comprises a single open reading frame ( ORF ) , which is co- and post-translationally cleaved into three structural ( capsid [C] , pre-membrane [prM] and envelope [E] ) and seven non-structural ( NS1 , NS2A , NS2B , NS3 , NS4A , NS4B , NS5 ) proteins . The precursor polyprotein is flanked by two non-translated regions ( 59 and 39 ) [22] . DENV genetic diversity can be attributed to the RNA polymerase , which does not have proofreading activity; it is thought to produce approximately one mutation per round of genome replication [23] . DENV ‘genotypes’ can be defined as virus clusters for which associations could be inferred on epidemiological grounds with sequence divergence ≤6% within the chosen genome region [24] . This definition relies on two factors , the genomic region selected and the length of RNA analysed . Differences across the region in the genotyping methods used by the different researches groups appear to be related to nomenclature rather than real differences in methodology . Our systematic review objectives were to describe the molecular epidemiological trends and to identify knowledge gaps relating to dengue disease in Latin America from January 2000 to November 2013 . Our definition of ‘Latin America’ included all countries within Central and South America and the Caribbean ( Fig 1 ) . Within some of the phylogenetic studies described in this review , the term ‘lineage’ has been used to describe viruses clustered in clades at a taxonomic level beneath the genotypes [25] . However , as the terms lineage and clade are sometimes used synonymously , we have kept the terminology used in the original sources .
A literature review group ( LRG ) guided the systematic review process , defining and preparing the search strategies , protocol and review of documents . Predetermined search strategies were developed , designed to find a high proportion of relevant studies . A number of pilot searches were carried out using terms developed with reference to the expanded Medical Subject Headings ( MeSH ) thesaurus of the US National Library of Medicine ( used for indexing articles in PubMed ) , after which specific search strategies were devised for each target electronic database . The following basic search string was employed: dengue AND ( Latin America OR Caribbean ) AND ( molecular epidemiology OR cluster analysis OR dengue/virology OR dengue virus/classification OR dengue virus/genetics OR genotype OR phylogeny OR sequence analysis OR outcomes ) . To improve accuracy , this was augmented by the addition of specific countries to the generic Latin America and Caribbean terms . Initial searches were conducted in PubMed , the World Health Organization Library database ( WHOLIS ) ; the WHO Regional Database; the Virtual Health Library ( VHL; Latin American and Caribbean Center on Health Sciences Information , LILACS ) ; and the Scientific Electronic Library Online ( SciELO ) . Studies ( as well as official reports and bulletins , and conference materials ) published in English , Spanish , Portuguese or French between 1 January 2000 and 30 November 2013 were included . References found in databases that did not allow language and/or date limitations and not meeting these criteria were deleted manually at the first review stage . No limits by sex , age and ethnicity of study participants or by study type were imposed , although single-case reports and incomplete surveillance reports ( i . e . those with <52 weeks of data ) were excluded . As duplicate publications of data ( e . g . in meta-analyses and other reviews ) could lead to over-sampling , literature reviews and editorials involving previously published peer-reviewed data were also excluded . Publications not identified by the approved search strategy and unpublished data sources meeting the inclusion criteria were included if recommended by LRG members . Following LRG review of the titles and abstracts , duplicates and articles that did not satisfy the inclusion criteria were removed . Full papers of the first selection of references were retrieved and reviewed as a basis for the final selection . We chose not to exclude articles and other data sources nor formally rank them based on the quality of evidence , as we were of the view that given the nature of molecular epidemiology studies , such quality assessment would not add value to our review . The selected sources were collated and summarised using a series of Excel ( Microsoft Corp . , Redmond , WA ) spreadsheets . Data from literature reviews of previously published peer-reviewed studies and pre-2000 data published within the search period were not extracted . The original data sources and the extraction tables were made available to all members of the LRG for review and analysis . A meta-analysis was not conducted; a narrative synthesis of our findings is presented .
Our review provides a comprehensive overview of the evolving molecular epidemiology of dengue disease in Latin America over the period 2000−13 . Our literature search was thorough; we screened almost 400 sources to identify relevant data and we developed a comprehensive data extraction instrument to facilitate the capture of those data . However , our review does have some limitations . We could only review published data found by our search techniques and we recognise that some isolated reports from some countries may have escaped our attention and that unusual findings may have a more prominent place in the literature than mainstream findings . Furthermore , our searches may have obscured any bias ( by country and time ) although we included sources ( as well as official reports and bulletins , and conference materials ) available in English , Spanish , Portuguese or French over a long time period to reduce the level of selection and publication bias . In addition , we did not formally rank articles and other data sources based on the quality of evidence and any limitations of the original studies are carried forward into our review . Co-circulation of different serotypes in a region may be a factor in the association between dengue infection and the severity of disease . All four dengue serotypes were found circulating in Latin America , with predominance of individual serotypes varying by country and year . Under these circumstances , populations may exhibit a wide range of humoral and cellular immune responses to DENV , which may increase the likelihood of severe dengue . One such example is the emergence in north-eastern Peru in 2010 of the virulent DENV-2 lineage II clade F , which was associated with the largest DHF epidemic experienced in the region [55] . Although consideration should also be given to the possible effects of co-infections in patients with dengue , these are relatively uncommon . During epidemics , it does happen that some patients have concurrent serotype infections when more than one DENV serotype is being transmitted , but there have been few studies assessing the clinical impact of DENV co-infections . Reports of ZIKV/DENV and CHIKV/DENV co-infections are similarly scarce . Recent studies described a co-infection DENV-2 and ZIKV , isolated from a patient with travel to Haiti who developed fever , rash , arthralgia , and conjunctivitis [83] and a pregnant woman from Colombia with a triple co-infection caused by DENV , CHIKV AND ZIKV [84] . The picture is complicated by multiple introductions of viral lineages of each serotype throughout the countries of Latin America . Intra-serotype antigenic variation and the resulting differential generation of protective antibodies and immune responses is postulated as one of the reasons for the high epidemiological impact of certain DENV serotypes in the Americas [60] . The lack of consistent differences in the E gene of Brazilian DENV-2 isolates from cases with different clinical manifestations suggests that if disease severity has a genetic basis , it is not solely due to differences observed in the E gene [33] . Distinct lineages with different dynamics were identified in each of the countries and co-existence , extinction and replacement of lineages occurred over the review period . Phylogenetic studies suggest that geographical micro-evolution may be operating with regional foci of virus extinction and selection . For example , the Caribbean is the main source of DENV-3 viruses introduced into Brazil , and the northern and south-eastern Brazilian regions seem to be important hubs for the dissemination of those DENV-3 lineages . Phylogenetic analysis also highlights the role played by intensive exchange between bordering countries , particularly that due to tourism and trade [47] . Viral lineages that have circulated in Mexico appear to have the same evolutionary origin as those detected more widely in the Americas ( particularly in countries geographically close to Mexico ) , indicating considerable local diffusion across borders [67] . The resulting pattern of dengue evolution can present an increased risk for subsequent epidemics and severe disease . Further examples are the cultural and economic influence on viral spread and gene flow between the French Territories , the Caribbean and South America [82] , and the effect of high viral genetic diversity and a large naive population on the expansion and collapse of DENV-3 in Puerto Rico [81] . Although regions adjacent to the cluster radius might have been protected by herd immunity or cross-protection from other serotypes , re-introduction of a particular DENV serotype or lineage replacement after a period of prolonged absence would expose to infection a population with no immunity to dengue [82] . Micro-evolutionary changes within dengue serotypes have resulted in substantial genetic diversity , with emergence of endemic and epidemic strains . One explanation for the interval between the introduction of DENV in a region and its subsequent detection could be that new DENV genotypes or lineages remain undetected until the number of infections and/or disease incidence reaches a threshold of detection that is high enough to be detected by the local surveillance system . In Brazil , the clustering of DENV-2 isolates in the São José do Rio Preto BR3 lineage ( 2008 ) with two strains from the northern region and a 2007 Jamaican strain support suggestions that this lineage was circulating in the country before its first detection in 2007 , possibly remaining undetected due to poor surveillance [36] . Similarly , DENV-4 may have re-emerged in Brazil before 2010 , but was undetected due to a higher prevalence of DENV-1 and DENV-2 , and the failure of the surveillance system to identify the milder disease commonly associated with DENV-4 [52] . These situations highlight the importance of systematic surveillance of dengue viruses because cryptic introduction or circulation of a new DENV serotype into endemic areas is generally considered to increase the risk for severe dengue disease . Regional extinction and emergence of new lineages is associated with periodic dengue outbreaks and possibly also with the severity and fatality of the disease . The exact cause and effect of viral virulence and lineage changes is yet to be proven , although the extinction of earlier strains and the appearance of new epidemic strains suggest a genetic bottleneck as a cause of regional replacement . Nevertheless , the relative inferiority of the American genotype of DENV-2 in Mexico , coupled with the non-overlapping nature of lineage distributions , suggests that more attention should be paid to the possible role of natural selection in determining patterns of lineage turnover . One hypothesis to explain the possible virulence of emerging clades in the region is an improved ability to avoid neutralisation by serotypes’ cross-reactive antibodies [85] . Although vector competence may be expected to influence the emergence of new lineages , overall many events other than vector competence influence DENV evolution . While vector competence and DENV evolution have been directly tested , no interaction has been demonstrated to date [86] , although in some cases enhanced clade transmission was demonstrated in the mosquito [87] . Furthermore , although , genotypes with higher rates of replication are preferentially transmitted by mosquitoes when they are co-infected [88] , DENV replication in mosquitoes results in less diversity than in humans [89] . Nevertheless , additional studies are needed to assess whether mosquito vector-driven selection plays a significant role in DENV micro-evolution . Despite some gaps in the molecular epidemiological information limiting the possibility for comparison , our review has described the molecular epidemiological trends of dengue infection across Latin America and the Caribbean . However , fundamental gaps in our understanding of epidemiological and evolutionary dynamics and its relation with disease remain . Although the molecular characterisation of DENV strains in a number of the studies included in this review has identified mutations or polymorphisms that appear to be characteristic of specific serotypes , or are associated with increased clinical severity [30 , 77] , it is not possible to correlate accurately spatial or temporal trends in disease epidemiology , disease severity or the genetic diversity of DENV . Sequence determination of the DENV genome is important for more precise phylogenetic classification and correlation with clinical outcome and disease severity . Although it is unlikely that DENV genetic variation will completely explain the incidence of severe disease or the scale of outbreaks , differences in virulence are apparent among DENV lineages [38] . However , how those distinct viral populations are maintained or transmitted is not fully understood . Additionally , a clearer understanding of the spatial and temporal dynamics of dengue transmission , particularly whether specific lineages are spreading more rapidly than others , will add to our knowledge of local transmission patterns . Furthermore , appreciating the processes that increase the diversity of DENV lineages and the pathogenicity associated with genetic variation will improve our understanding of the mechanisms that govern epidemic development . It may also be of use in vaccine development [30] , particularly with regard to the effect of partially effective vaccines on viral population , structure and virulence [90] . Finally , a consistent approach to genotyping would aid cross-study comparisons . One limitation relates to the definition of the DENV ‘genotype’ and its reliance on the genomic region selected and the length of RNA analysed . The genomic region affects sensitivity because it seems that when a partial , usually short , region of the genome ( e . g . the E-NS1 or domain III of the E gene ) is analysed , the number of genotypes is equal or lower than the result of analysing the entire E gene or the full genome sequence [23] . The length proportionally affects the resolution of tree branches , which could have a high impact if phylodynamic analysis is pursued [91]; for taxonomic purposes , the length also affects sensitivity . In general , as passage through a laboratory culture medium may aid the introduction of mutations [92 , 93] , it is also desirable to use samples that have not undergone laboratory culture to generate amplicon-sequencing data , particularly in viral evolution studies [92] , that are not representative of circulating viruses [94] . In addition , the software used should be selected according to the target sequence . For example , the BEAST suite ( a cross-platform program for Bayesian analysis of molecular sequences ) [95] is currently the best for full genome sequencing or E gene , while E/NS1 junction or partial NS5 can be analysed with the phylogeny model of R software ( see https://www . r-project . org ) . Overall , the impact of the genomic region selected or the length of the sequence analysed is not critical for a descriptive study; however , when a more detailed study is done ( such as phylodynamics or phylogeographic analyses ) , bigger is better . In this review , we found the heterogeneity introduced from the classification used by a particular group for their findings to be more critical some groups assign numbers to the clusters determined according to their phylogeny , which could prevent comparison with the findings of other groups that may have assigned different numbers . This is an important consideration when trying to make data available for healthcare professionals who do not have a molecular analysis background . It should also be recognised that the use of sequences in the included studies that were downloaded from GenBank and other public domains may constitute a further limitation in that information about geographical origin or how they were sequenced may be incomplete and thus may compromise any conclusions drawn , which might be based on incomplete or faulty data . It is important that future sequencing methodologies are revised and optimised , with the objective of providing high quality data for future reference . Finally , if we are to identify spatial or temporal trends in disease epidemiology , disease severity or the genetic diversity of DENV , one target must be the development of a network of national/regional laboratories using the same protocols , reagents and hardware and , importantly , the elaboration of controls , standards and calibrators required for a proper quality control . The Network of PAHO/WHO Collaborating Centers and National Reference Laboratories for Dengue in the Americas ( http://www1 . paho . org/english/ad/dpc/cd/den-cc . htm ) is currently undertaking an initiative to strengthen diagnostic laboratory networks in the region and promote exchange and technological transfer among them . However , one issue for future surveillance programmes will be to establish the most informative resolution level ( e . g . type , genotype , clade ) and whether there are any particular mutations that it is important to determine . As there are no odds ratios associated with specific clades or mutations , this would suggest that phylogeography should meet the resolution requirements at the present time . Another significant obstacle to standardising surveillance methods remains sampling . Although some countries have specific regulations ( e . g . Mexican regulations require testing 10% of all dengue cases for virology surveillance and 100% for severe dengue cases ) , the level of under-sampling is very high . Moreover , there is no information available to make power calculations about the number of samples needed to determinate associations among virus clades and dengue incidence . Consequently , although the PAHO/WHO network will facilitate the monitoring of the introduction and spread of the clades and determine the hot spots of dengue control , it will be extremely difficult , and possibly not economically viable , to build a robust , meaningful molecular virology surveillance system that will answer all the outstanding questions relating to dengue molecular epidemiology .
In conclusion , the re-emergence of dengue in the Americas may be ascribed jointly to i ) the spread of different DENV serotypes in bordering countries , ii ) the permanent migration flow of viremic travellers , and iii ) an increase in vector infestation due to inconstant vector-control strategies and economic support . In addition , urbanisation has probably had the most impact on the increase of dengue within a country , and the high frequency of low-cost international travel has had the greatest effect on the global spread of dengue [96] . Moreover , DENV seems to take advantage of diverse mechanisms to generate genetic diversity via genetic variability and the ( mainly human ) movement of the host , as well as exploiting the increasing density of human hosts and urbanisation . Continuous epidemiological surveillance ( employing consistent reporting , definitions and terminology ) and sequencing of viral strains circulating in all countries of the region are important for the detection of new DENV lineages and to improve understanding about the regional patterns of DENV dissemination . | The wide distribution of the mosquito vector and the co-circulation of multiple dengue virus serotypes has led to increases in the incidence of dengue in the Americas , where it is a major public health concern . Identifying molecular epidemiological trends may help to identify the reasons for the re-emergence of dengue across Latin America and the Caribbean , and , in turn , enable disease control and management . We conducted this review using well defined methods to search for and identify relevant research according to predetermined inclusion criteria . The objective was to obtain a clearer understanding of changes occurring within dengue serotypes that have resulted in substantial genetic diversity and the emergence of endemic and epidemic strains in different parts of the region . There remain fundamental gaps in our understanding of the epidemiological and evolutionary dynamics of dengue and its relation with disease , and it is not possible to correlate accurately spatial or temporal trends in disease epidemiology , disease severity , or the genetic diversity of DENV . It is important to maintain comprehensive epidemiological surveillance throughout the region ( including sequencing of viral strains ) to detect new DENV lineages and to understand the regional patterns of DENV dissemination . | [
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] | 2017 | Dengue in Latin America: Systematic Review of Molecular Epidemiological Trends |
Estimates of current global rabies mortality range from 26 , 000 to 59 , 000 deaths per annum . Although pre-exposure prophylaxis using inactivated rabies virus vaccines ( IRVs ) is effective , it requires two to three doses and is regarded as being too expensive and impractical for inclusion in routine childhood immunization programmes . Here we report the development of a simian-adenovirus-vectored rabies vaccine intended to enable cost-effective population-wide pre-exposure prophylaxis against rabies . ChAdOx2 RabG uses the chimpanzee adenovirus serotype 68 ( AdC68 ) backbone previously shown to achieve pre-exposure protection against rabies in non-human primates . ChAdOx2 differs from AdC68 in that it contains the human adenovirus serotype 5 ( AdHu5 ) E4 orf6/7 region in place of the AdC68 equivalents , enhancing ease of manufacturing in cell lines which provide AdHu5 E1 proteins in trans . We show that immunogenicity of ChAdOx2 RabG in mice is comparable to that of AdC68 RabG and other adenovirus serotypes expressing rabies virus glycoprotein . High titers of rabies virus neutralizing antibody ( VNA ) are elicited after a single dose . The relationship between levels of VNA activity and rabies virus glycoprotein monomer-binding antibody differs after immunization with adenovirus-vectored vaccines and IRV vaccines , suggesting routes to further enhancement of the efficacy of the adenovirus-vectored candidates . We also demonstrate that ChAdOx2 RabG can be thermostabilised using a low-cost method suitable for clinical bio-manufacture and ambient-temperature distribution in tropical climates . Finally , we show that a dose-sparing effect can be achieved by formulating ChAdOx2 RabG with a simple chemical adjuvant . This approach could lower the cost of ChAdOx2 RabG and other adenovirus-vectored vaccines . ChAdOx2 RabG may prove to be a useful tool to reduce the human rabies death toll . We have secured funding for Good Manufacturing Practice- compliant bio-manufacture and Phase I clinical trial of this candidate .
Despite the development of an efficacious rabies vaccine by Pasteur in 1885 , estimates of current global annual human rabies mortality range from 24 , 000 to 59 , 000 [1–3] . Among the neglected tropical diseases , the burden of mortality due to rabies is exceeded only by that due to leishmaniasis [1] . Across large and populous areas of Africa and Asia , rabies-attributable mortality rates exceed 1 per 100 , 000 people per year [3] . More than 200 million individuals live in countries with rabies-attributable mortality rates exceeding 5 per 100 , 000 per year , corresponding to a lifetime risk of death due to rabies exceeding 0 . 1% [3] . Such death rates exceed those attributable to some diseases included in the Expanded Programme on Immunization ( EPI ) and/or supported by GAVI . Calculations suggest that , in such settings , a highly-effective , simple to deliver pre-exposure prophylactic intervention costing less than USD 4 per recipient would have a cost per death averted of less than USD 4000 and a cost per DALY of less than USD 200 , competitive with GAVI-funded interventions [4] . Most human cases of rabies are the result of dog bites [5] . There is a strong argument for investment in dog vaccination: feasibility and cost-effectiveness of rabies control and human rabies elimination by dog vaccination has been demonstrated in some low and middle income country ( LMIC ) settings [6 , 7] . However , the countries with the highest rabies incidence are those which are least developed and most politically unstable , including Somalia , the Democratic Republic of the Congo , and Afghanistan . It remains doubtful whether adequate coverage ( >60% in a canine population which turns over approximately every two years [7] ) is achievable in such settings . Similarly , implementation of a robust programme of human post-exposure prophylaxis ( PEP ) is likely to be challenging in such settings . Following a dog bite , PEP is needed urgently and requires repeated vaccination and the administration of expensive rabies immune globulin ( RIG ) . Achieving continuous local availability of PEP to meet such urgent yet unpredictable and intermittent demand is substantially more challenging than implementing intermittent planned mass vaccination campaigns . Concerns about the complexity of implementation of such an ‘as-needed’ intervention were the basis of GAVI’s 2013 decision not to fund PEP , despite analysis suggesting that the cost per death averted could compare favourably to that of other GAVI-funded interventions; review of this decision is expected in the near future [4] . Licensed human rabies vaccines are all based upon inactivated rabies virus ( IRV ) , and may be used either for pre-exposure prophylaxis ( PrEP ) as well as PEP . PrEP regimes have until recently involved three doses of IRV vaccine spread over 28 days with typical costs of around USD 25 per vaccinee [8] . The WHO recently endorsed the use of a PrEP regime involving intradermal administration of two smaller doses on each of two visits , separated by seven days: such intradermal regimes can reduce cost substantially , but still require a total of four injections over two visits and are not licensed in some countries [9 , 10] . Although there are data suggesting that IRVs can be stable for a few weeks at 37 °C , their regulator-approved labels mandate refrigerated storage at 2–8 °C [11–14] . As a result of these cost and delivery characteristics , rabies PrEP is not included in routine childhood immunization programmes in most rabies-endemic areas [15] . It is recommended that previous PrEP recipients who then receive a dog bite should still receive PEP , but this ‘post-PrEP PEP’ is an abbreviated and much less expensive course over one or two visits , without RIG [9] . Importantly , data suggest that PrEP alone—without any PEP—can achieve potentially protective antibody titers lasting many years [16 , 17] . This suggests that PrEP may provide substantial benefit even in contexts in which the reliable availability of PEP cannot be assured . Given the substantial proportion of children in rabies-endemic areas who receive dog bites and for whom PEP is then indicated ( estimated to exceed 30% across a typical childhood in some areas [8] ) , it is estimated that routine PrEP could be not only more effective but cost-saving relative to PEP-based strategies in many contexts , particularly if the cost of PrEP is beneath USD 4 per child [8 , 18] . Child-population-wide PrEP is thus an attractive intervention in areas in which Expanded Programme on Immunization ( EPI ) vaccines are delivered but which have otherwise limited capacity for reliable urgent PEP or for control of rabies-transmitting animals . This role for mass PrEP has been recognised , for example , in the Peruvian Amazon: in this setting , vampire bat rabies is problematic and difficult to control , access to PEP is limited , and a PrEP programme appears to have been successful [15] . Here , we have set out to develop a tool intended to enable cost-effective PrEP against rabies within routine population-wide immunization programmes . The immunological mechanism of vaccine-induced immunity against rabies is well characterised . A virus neutralizing antibody ( VNA ) titer exceeding 0 . 5 international units per milliliter ( IU/mL ) is accepted as a marker of adequate immunization [19] . This threshold is thought by many to signify clinical efficacy: indeed , in animal challenge studies , 100% protection is achieved at 0 . 1–0 . 2 IU/mL , with incomplete but substantial protection at even lower titers [20] . Simian adenovirus-vectored vaccines are an attractive platform technology for induction of antibody responses , circumventing the problem of pre-existing anti-vector antibody to human adenovirus serotypes and readily manufacturable at large scale and low cost [21 , 22] . A chimpanzee adenovirus serotype 68 ( AdC68 ) -vectored rabies vaccine and the ability of a single low dose of this vaccine to achieve long-lasting protection against rabies challenge in non-human macaques has been reported previously [23 , 24] . We now describe the development of a closely-related simian-adenovirus-vectored rabies vaccine , ChAdOx2 RabG , which is suitable for good manufacturing practice ( GMP ) -compliant production . We also describe additional approaches which may make this particularly suitable for use in low-income settings , namely thermostabilisation of the vaccine and dose-sparing adjuvantation . ChAdOx2 RabG may prove to be suitable for PrEP in highly rabies-endemic settings which have adequate infrastructure to achieve appreciable levels of childhood immunization coverage [25] but which currently lack capacity for reliable dog vaccination or PEP .
Plasmid pC68 010-Rabgp comprising the E1- and E3-deleted AdC68 genome with the full-length ERA strain rabies virus glycoprotein coding sequence under a human cytomegalovirus immediate-early ( HCMV-IE ) promoter was constructed based on a virus obtained from ATCC ( ATCC VR-594 , Genbank accession: FJ025918 . 1 ) . The wildtype AdC68 was propagated in HEK 293 cells and purified by CsCl gradient centrifugation , followed by viral genomic DNA purification as described [26] . To generate the E1-deleted AdC68 molecular clone , the 5’ right inverted terminal repeat ( ITR ) was amplified by PCR and cloned into the pNEB193 vector . Using restriction enzyme sites that are unique in assembly but not necessarily unique to the full AdC68 genome , approximately 2 . 6 Kb of the E1 region between SnaBI and NdeI sites ( from 455bp to 3028bp ) were removed and replaced with a linker which contains the rare enzyme sites of I-CeuI and PI-SceI . The resultant was the pC68 000 plasmid . To delete the E3 domain , a 3 . 6 kb fragment was excised using AvrII and NruI ( from 27793bp to 31409bp ) : briefly , the pC68 000 was digested by AvrII , the 5 . 8kb fragment was subcloned into a pUC19-like backbone ( generating pXY-AvrII ) , and NruI was used to excise a 1 . 4 kb fragment ( generating pXY-E3 deleted ) . Later , pXY-E3 deleted ( insert donor ) was digested with AvrII and SpeI and the insert was ligated into pAdC68 000 , to produce plamid pC68 010 . The HCMV-IE promoter—rabies virus glycoprotein cassette was inserted as previously reported [23] , generating pC68 010-Rabgp ( differing from previously published constructs , notably in the deletion of the E3 region ) . To construct a vector for transient mammalian expression of rabies virus glycoprotein and as a precursor to adenovirus vector production , the full-length coding sequence was PCR amplified from pC68 010-Rabgp using oligonucleotides providing flanking Acc65I ( 5’ ) and NotI ( 3’ ) restriction enzyme sites . This permitted restriction-enzyme mediated cloning of the PCR product into pENTR4 LPTOS , a plasmid providing HCMV-IE promoter with intron A and tetracycline operator elements [27 , 28] . This transgene is referred to henceforth as SPrab-Gnative , or simply ‘G’ . A codon-optimized version of the ERA strain glycoprotein gene in which the viral signal peptide was replaced by that of the human tissue plasminogen activator ( tPA ) was synthesized by ThermoFisher and cloned into pENTR4 LPTOS similarly; this transgene is referred to henceforth as SPtPA-Gopt . A third pENTR4 LPTOS plasmid in which the codon-optimized gene was preceded by the viral signal peptide was generated by InFusion cloning ( Takara ) ; this transgene is referred to henceforth as SPrab-Gopt . To produce the ChAdOx2 RabG adenoviral vector , Gateway LR recombination ( ThermoFisher ) was then used to transfer the SPrab-Gnative transgene cassette into the ChAdOx2 parent bacterial artificial chromosome ( BAC ) [21] . Adenoviral destination vectors for the expression of RabG by AdHu5 , chimpanzee adenovirus serotype 63 ( ChAd63 ) and ChAdOx1 were produced similarly , using previously described viral backbones [29 , 30] and a full-length non-codon-optimized glycoprotein gene , with the exception that the transgene used was derived from the SAD B19 strain ( Addgene plasmid 15785 , a kind gift of Miguel Sena-Esteves [31] ) . SAD B19 and ERA were both derived from the Street Alabama Dufferin ( SAD ) isolate; their glycoprotein genes differ at only 4 of 524 amino acid loci [32] . AdHu5 , AdC68-010 , ChAd63 , ChAdOx1 , and ChAdOx2 adenoviruses were produced from the plasmids/ BACs described above ( and subsequently titered ) by the Jenner Institute Viral Vector Core Facility , as previously described [27] . Prior to genetic stability studies , ChAdOx2 RabG was subjected to two rounds of plaque picking . Virus was then propagated on adherent HEK293 cells ( Oxford Clinical Biomanufacturing Facility proprietary cell bank ) for five passages , prior to caesium chloride ( CsCl ) purification , phenol-chloroform DNA extraction and enzymatic restriction analysis , as previously described [33] . G expression from the three ERA G transgene constructs in mammalian cells was assessed by flow cytometry . Two or three independent DNA preparations of each pENTR4 LPTOS plasmid were produced and transfected into HEK293E cells ( National Research Council , Canada ) using 25kDa linear polyethylene-imine ( Polysciences ) , as previously described [34] . Identical cells were incubated either with serum from AdHu5 RabG-immunized mice , or with naïve mouse serum ( negative control ) , and then with Alexa488-conjugated goat-anti-mouse secondary antibody ( ThermoFisher ) . The ratio of median fluorescence intensity ( MFI ) in fully-stained versus negative controls was used to quantify glycoprotein expression . Expression of codon-optimized G constructs ( Gopt , i . e . SPtPA-Gopt and SPrab-Gopt ) was compared to that of the base-case construct ( SPrab-Gnative ) by dividing the Gopt MFI ratio by that obtained in the same experiment with SPrab-Gnative . All animal work was performed in accordance with the U . K . Animals ( Scientific Procedures ) Act 1986 ( ASPA ) , and was approved by the University of Oxford Animal Welfare and Ethical Review Body ( in its review of the application for the U . K . Home Office Project Licenses PPL 30/2889 and P9804B4F1 ) . Female CD-1 outbred mice ( Harlan , Charles River and Envigo ) were used throughout . Mice were housed in a specific-pathogen-free facility and were 6–7 weeks old at the initiation of each experiment . All adenovirus vaccine doses were calculated on the basis of infective unit ( IU ) titers , but viral particle ( VP ) titers and hence particle: infectivity ( P:I ) ratios were also measured . In viral preparations used for mouse immunization , P:I ratios were 23 for AdHu5 , 132 for ChAd63 , 65 for ChAdOx1 , 63 for AdC68 , and 173 for ChAdOx2 . The range of P:I ratios seen here is typical of our experience with these vectors: P:I ratio is known to vary both between preparations of the same adenovirus-vectored vaccine and between serotypes , in some cases by orders of magnitude [30 , 35] . Standardization of IU dose and VP dose ( and hence P:I ratio ) is not possible , but immunogenicity has previously been shown to correlate with the IU dose ( 30 ) . Addavax was purchased ( Invivogen ) . SWE , a non-branded squalene-in-water emulsion was prepared at the Vaccine Formulation Laboratory in Lausanne , using a GMP-compatible manufacturing process as previously described [36] . Both adjuvants were used at a dose of 25 μL per mouse , mixed by vortexing for two seconds with the appropriate adenovirus dose ( diluted to 25 μL in phosphate buffered saline [PBS] ) 1–2 hours prior to administration . As comparators , we used two IRV vaccines . Rabipur ( Novartis ) consists of a liquid formulation of unadjuvanted inactivated Flury LEP strain rabies virus , produced on purified chick embryo cells ( PCEC ) , licensed for human use , and with a potency of >2 . 5 IU/mL in the NIH mouse potency assay , as described [37] . Nobivac Rabies ( MSD Animal Health ) consists of a liquid formulation of aluminium phosphate adjuvanted inactivated Pasteur strain rabies virus , produced on BHK-21 cells , licensed for animal use , and with a potency of >2 IU/mL . Vaccine doses used for each experiment are set out in the corresponding figure legends . All vaccinations were diluted in PBS to a total volume of 50 μL ( with the exception of the highest doses of Rabipur and Nobivac Rabies [used in Fig 2] ) . The highest doses of Rabipur and Nobivac Rabies were limited by the maximum volume permitted for intramuscular injections in mice within our institution to 100 μL of undiluted vaccine ( i . e . 1/10 of the 1 mL human or companion-animal dose ) . Vaccine was administered intramuscularly , split equally between the gastrocnemius muscles of each hind limb . Serum samples were obtained by tail bleeding at timepoints as indicated in figure legends . Upon completion of experiments , mice were euthanized by cervical dislocation . Coded serum samples were assayed for VNA . For data shown in Fig 2 , the fluorescent antibody virus neutralization ( FAVN ) assay was performed at the Animal and Plant Health Agency ( APHA ) as previously described , with quadruplicate serum dilutions and pre-determined acceptance criteria for virus dose as measured by back titration [38] . For data shown in Fig 3 , the rapid fluorescent focus inhibition test ( RFFIT ) was performed at the Wistar Institute , again as previously described [39] , using MNA cells . Both assays used the CVS-11 reference rabies virus strain and the WHO international reference standard to derive titers expressed in IU/mL . The two methods are known to correlate closely [38] . To produce soluble rabies virus glycoprotein ( RabGsol ) for ELISA plate coating , the sequence encoding amino acids 1–453 of the SAD B19 strain G protein ( MVPQ…DLGL ) was PCR-amplified from Addgene plasmid 15785 ( as above ) . The primers used added a 5’ Acc65I site and 3’ sequence encoding a C-tag and NotI site [40] , enabling cloning into pENTR4 LPTOS ( as described above for adenovirus generation ) . This plasmid was then transfected into Expi293 cells using Expifectamine ( both from ThermoFisher ) , in accordance with the manufacturer’s instructions . The RabGsol protein was purified using a CaptureSelect C-tag column ( ThermoFisher ) , appearing as a band of c . 55 kDa on Coomassie blue-stained reducing SDS-PAGE and detected by Western blot using serum from Rabipur-immunized mice . The ELISA was performed as described previously [41] . In brief , plates were coated with RabGsol protein ( 100 ng/well in 50 μL PBS ) . Dilutions of test sera ( in triplicate ) and a standard curve produced using serial dilutions of an in-house reference serum pool from mice immunized with AdHu5 RabG , were added to the plate . Washing , secondary Ab binding , final washing , and detection were all as previously described [41] , with the exception that the secondary Ab used was alkaline-phosphatase–conjugated goat anti-mouse IgG ( Sigma-Aldrich ) . The OD405 was quantified using ELx800 or Clariostar plate readers ( Bio-Tek and BMG respectively ) . Results were expressed in arbitrary antibody units ( AU ) , defined using the in-house reference standard , by interpolation of OD405 readings on the standard curve . Negative control sera ( from mice immunised with AdHu5 expressing ovalbumin [42] ) gave no detectable response . Sugar-matrix thermostabilisation ( SMT ) was performed essentially as previously described [43] . In brief , adenovirus was formulated in thermostabilisation buffer ( 0 . 4M trehalose , 0 . 1M sucrose ) . Virus was applied to Whatman Standard-14 paper ( GE Healthcare ) at a ratio of 50 μL/cm2 . The virus-loaded paper was then dried for 48 hours in a glovebox ( Coy Laboratory Products ) at room temperature ( 24 +/- 2 °C ) and controlled humidity ( % relative humidity <5% ) before being transferred to airtight vials . Moisture content of the dried product was measured by Karl-Fischer analysis using an 851 Titrando coulometer equipped with 860 Thermoprep oven ( Metrohm ) , in accordance with the manufacturer’s instructions . The mass of water measured per cm2 of product was used to estimate percentage moisture content of the sugar glass , based upon the calculated 9 . 3mg mass of solute present in 50 μL of the thermostabilisation buffer . As a comparator for the stability of SMT product , virus was formulated in the liquid buffer ‘A438’ ( 10 mM Histidine , 7 . 5% sucrose , 35 mM NaCl , 1 mM MgCl2 , 0 . 1% PS-80 , 0 . 1 mM EDTA , 0 . 5% ( v/v ) Ethanol pH 6 . 6 ) [44] . Thermostabilised vaccine was reconstituted by the addition of 500 μL/cm2 of phosphate buffered saline and vortexing for 2 seconds . The IU titer of recovered virus was measured as described for adenovirus production . Prism 7 software ( Graphpad ) was used for data analysis and production of graphs . Statistical analyses are described in full where reported in the Results section and Figure legends . All analyses used log10-transformed ELISA and VNA data; where negative ELISA results were obtained , they were assigned an arbitrary value of 3 AU ( just below the detection limit ) to permit log10 transformation and analysis .
A promising AdC68-vectored rabies vaccine has previously been described [23 , 45] . ChAdOx2 RabG differs from the previously reported vaccine in the following respects: We also explored the possibility of modifying the RabG transgene by codon optimisation for mammalian cells and the incorporation of the human tissue plasminogen activator signal peptide . We have previously observed enhanced levels of transgene expression and immunogenicity upon making similar changes to transgenes in other adenovirus vectored vaccines . In the case of RabG , we observed no beneficial impact of such changes upon transgene expression ( Fig 1A and 1B ) . ChAdOx2 RabG thus uses the unmodified transgene ( SPrab-Gnative , henceforth simply ‘G’ ) . To ensure suitability for manufacture in non-transgene-repressing cells ( which are often used for GMP manufacture but in which production of some adenoviruses is frequently problematic [33] ) , we assessed virus yield in HEK293A cells ( ATCC ) . In two independent preparations results were as follows: Yield was thus approximately 1x105 VP per cell , favourably comparable to our experience with other viruses and with yields reported in the literature for other adenoviruses [49] . Following 5 passages in HEK293A cells , CsCl purification and DNA extraction , enzymatic restriction analysis demonstrated a banding pattern consistent with the starting virus sequence ( Fig 1C ) . Although this clearly does not rule out point mutations , it does confirm genetic stability of the vector to the level required for GMP-compliant manufacture for early phase clinical trials . To assess the immunogenicity of our adenovirus-vectored vaccine candidates , we immunized mice with one of a range of adenovirus serotypes expressing rabies virus glycoprotein , or one of the licenced IRVs Rabipur ( unadjuvanted ) and Nobivac Rabies ( alum-adjuvanted ) . For each vaccine , dose-response was assessed . In the case of the adenoviruses , the highest dose tested was 1x108 IU ( c . 5x109 VP , dependent upon P:I ratio ) . This is around 1/10 of a typical human dose of 5x1010 VP . In the case of the IRVs , the highest dose tested was similarly 1/10 of the manufacturer’s recommended human or dog/cat dose , i . e . >0 . 25 IU for Rabipur and >0 . 2 IU for Nobivac: this was the highest dose which could be given within the constraints of a 100 μL intramuscular injection volume . Vaccines were assessed in two groups in separate experiments . In an initial exploratory experiment , ( prior to the availability of the ChAdOx2 vector ) , we assessed AdHu5 , ChAd63 , ChAdOx1 and the IRVs . Induction of RabGsol-binding antibody , as measured by ELISA , was apparent for all adenovirus vectors , regardless of the dose used ( though it should be noted that all vaccines were given as a single dose rather than the repeated dosing used for IRVs in humans ) ( Fig 2A ) . While responses to the IRVs did not rise beyond the initial timepoint ( 4 weeks ) , the adenovirus-vectored vaccines induced antibody responses which gradually rose over 12 weeks ( Fig 2B ) . We previously observed a similar kinetic with AdC68 . rabgp in macaques [23] . In this initial experiment , VNA was assessed at week 12 for the highest-dose groups . All vaccines induced high VNA titers ( Fig 2C ) . Interestingly , the relationship between RabGsol-binding antibody ELISA response and VNA titer was markedly different for adenovirus-vectored vaccines and IRVs ( Fig 2D ) . In a subsequent experiment , we compared the immunogenicity of the ChAdOx2 RabG vector with AdC68 . 010 . rabgp . Differences between the ChAdOx2 RabG vector , the previously reported AdC68 . rabgp vector [24] and AdC68 . 010 . rabgp are described above; the removal of E3 from AdC68 . 010 . rabgp is not expected to affect immunogenicity . Mice receiving 1x107 IU ChAdOx1 RabG were included as a group for bridging/ comparison with the previous experiment . ELISA and VNA responses induced by ChAdOx2 RabG were significantly higher than those induced by AdC68 . 010 . rabgp ( Fig 3 ) . Interestingly the dose-response relationships differed markedly , with similar responses to the two vectors at high dose ( 1x108 IU ) , but substantially stronger responses to ChAdOx2 than AdC68 . 010 at lower doses . Chemical adjuvants are widely used with protein and inactivated-virus vaccines , both to induce stronger immune responses and to achieve an antigen-dose-sparing effect . The cost-of-goods of adenovirus-vectored vaccines is likely to be relatively low ( as a result of the development of high-yielding scalable manufacturing processes ) . Nonetheless , any reduction in the adenovirus dose required to achieve a protective response would be valuable as it could enhance the cost-efficacy of population-wide vaccination campaigns in low-income settings . We therefore sought to explore whether responses to our adenovirus-vectored rabies vaccines could be enhanced using a chemical adjuvant . We have previously reported that co-administration of adenovirus-vectored vaccines with certain adjuvants could enhance CD8+ T cell responses but , in contrast with others’ observations , we had not seen such effects upon antibody responses to viral vectors in the absence of co-administered protein antigen [42 , 50 , 51] . Here , we focussed upon squalene oil-in-water adjuvants which , to our knowledge , have not previously been explored in combination with adenovirus-vectored vaccines . In particular , we evaluated Addavax ( Invivogen ) and SWE ( produced at VFL , Lausanne , Switzerland ) : both of these have a composition similar to MF59 ( previously developed by Novartis and now marketed by GSK ) , which has been given to millions of people in licensed vaccines and has an excellent safety record [52] . We observed a beneficial impact of such squalene emulsion adjuvants upon immunogenicity of our vaccines in each of three independent experiments , together encompassing ChAdOx1 , ChAdOx2 , Addavax and SWE ( Fig 4 ) . The enhancement in ELISA response at doses ≥1x106 IU was small ( statistically significant increases of c . 2-fold in antibody titer in two experiments- Fig 4A and 4B; not detectable in one of the three experiments- Fig 4C , right-hand side ) . A more marked benefit of adjuvant was apparent when very low doses of vaccine ( ≤5x104 IU ) were used ( Fig 4C , left-hand side and Fig 4D ) ; remarkably , in the presence of adjuvant , 11/12 mice detectably sero-converted with a dose of 5x103 IU ( approximately 100 , 000-fold below a typical human adenovirus-vectored vaccine dose ) . These data suggest that the use of squalene oil-in-water emulsions with this vaccine- or indeed other adenovirus-vectored vaccines- may achieve either a modest increase in immunogenicity at high dose , or perhaps more likely a substantial dose-sparing and hence cost-reducing effect . We have previously described a simple method for thermostabilization of adenovirus-vectored vaccines by formulating the virus in a disaccharide-based solution and drying it onto a fibrous pad [43 , 53] . The materials required for this SMT technique are inexpensive ( <USD 0 . 10 per dose ) and the method is suitable for adaptation to GMP production . In-process losses of viral infectivity are close to zero . Other reported approaches to adenoviral thermostabilization include optimisation of liquid buffers [54 , 55] , lyophilization [56] and spray-drying [57 , 58] . To our knowledge , SMT is the only method reported to achieve adenoviral stability at temperatures in excess of 40 °C , such as may be encountered during ambient-temperature distribution in some low-income countries . The SMT technique has not previously been applied to AdC68 or ChAdOx2 vectors . Here , we tested the ability of the technique to stabilize our ChAdOx2 RabG vector . Water content of the vitrified sugar-glass in the SMT product was 3 . 7% ( median , n = 3 , range 3 . 4–3 . 8% ) . We observed excellent stability over 1 month at 45 °C , considerably out-performing virus formulated in the liquid buffer A438 [44]: median log10 IU titer loss was 0 . 4 in the SMT product ( Fig 5 ) . No viable virus remained detectable in the A438 . We are not aware of any other liquid buffer formulation which out-performs A438 for the stabilization of species E adenoviruses such as AdC68/ChAdOx2 .
Global rabies mortality remains unacceptable for a disease which is technically straightforward to prevent- there is no immunological mystery regarding how to achieve protection against rabies . The obstacles to its control are primarily practical and economic . It is clear that increased effort and investment in existing methods of canine rabies control is both necessary and likely to prove productive . Nonetheless , the fact that rabies is concentrated in the least developed parts of the least developed countries poses significant challenges . In some such settings , there may be a role for pre-exposure vaccination of children against rabies , enabled by established infrastructure for the delivery of other childhood immunizations . We have therefore aimed to develop a technology which may render such an approach practical and cost-effective . Here , we have built upon recent encouraging data with a closely related vaccine [23] to develop an iteratively improved candidate , ChAdOx2 RabG . We have shown that this has suitable genetic stability for GMP manufacture ( Fig 1 and Results ) . Adenovirus vector yield can vary widely for multiple reasons: transgene-specific inhibition of virus replication is a major factor [33] . Here , even without transgene repression , we obtained preliminary indications of yield favourably comparable to other adenoviruses reported in the literature [49] . Scalable adenovirus manufacturing platforms capable of producing virus titers exceeding 1x1012 VP per mL of culture have been developed [22 , 59]; this corresponds to a yield of 20 doses per mL at the typically-used human dose of 5x1010 VP , or 1000 doses per mL at the dose of 1x109 VP at which AdC68 . rabgp was protective in macaques [23] . Such productivity is likely to be compatible with low-cost manufacturing . In mice , a single dose of ChAdOx2 RabG reliably induced rabies virus neutralizing antibody ( Fig 3 ) . Responses to ChAdOx2 RabG compared favourably to those to AdC68 . 010 rabgp , the latter being closely related to the vector which achieved robust protection in a macaque-rabies virus challenge study [23] . It is unclear why ChAdOx2 RabG appeared to outperform AdC68 . 010 rabgp at low vector doses; of the differences between the vectors , the one most likely to explain differing immunogenicity ( as opposed to differing manufacturing characteristics ) is the altered ( intron-A containing ) promoter used in ChAdOx2 RabG , which has previously been shown to enhance immunogenicity of other adenoviruses [28] . This possibility could be investigated further by in vitro assays of transgene expression from cells transfected with plasmids carrying the two transgene cassettes . Our hope is that the ability of adenoviruses to achieve reliable , single-dose seroconversion in humans [60 , 61] will enable ChAdOx2 RabG to achieve similar results in clinical trials , and hence to offer a single-dose alternative to existing PrEP regimes . The relatively slow-rising kinetic of acquisition of antibody responses after ChAdOx2 RabG immunization ( Fig 2B ) may be related to ongoing antigen expression from adenovirus-transduced cells . Antigen expression has been shown to remain detectable for a number of weeks after immunization with other adenovirus vectors , and we have previously observed stronger transgene antigen-specific germinal centre reactions five weeks after adenovirus-vector immunization than after protein/adjuvant vaccination [41 , 62] . While these phenomena probably contribute to the eventual responses to adenovirus-vectored immunization , the slowness to achieve peak titers may make the candidate less suitable for use in the PEP context , when urgent seroconversion is needed; indeed results were disappointing when AdC68 . rabgp was used for PEP in non-human primates [23] . We were interested to observe that the relationship between ELISA-measured antibody and VNA titers differed for adenovirus-vectored and IRV vaccines ( Fig 2D ) . We thus relied upon this ELISA assay only for relative immunogenicity comparisons between different adenovirus-vectored immunizations , not between adenovirus-vectored and IRV vaccines: the ELISA is clearly not a substitute for the VNA assay of functional antibody induction . Importantly , satisfactory VNA activity was achieved by the adenovirus-vectored vaccines ( Fig 2C ) . Nonetheless , this altered relationship suggests that the adenovirus-vectored vaccines may be inducing a considerable amount of antibody which is detected by ELISA ( i . e . binds soluble glycoprotein ) but which does not neutralize the virus . Secreted soluble G ( lacking the transmembrane domain ) is known to adopt a conformation differing from that of the pre-fusion native G trimer , notably in that soluble G is predominantly monomeric [63] . The antigen expressed by the adenovirus vectors is the full length ( transmembrane-domain-containing ) glycoprotein and would therefore be expected to form trimers on the surface of vector-infected cells . The epitopes displayed to B cells by such cells are still likely to differ from those displayed by rabies virions , not least in their spatial arrangement and , perhaps , steric accessibility of membrane-proximal regions . This observation suggests that , despite the good VNA results achieved with the current adenovirus-vectored vaccines , there may yet be scope for improvement in their efficacy , for example by engineering of the expressed antigen to focus the B cell response towards neutralizing epitopes . We are encouraged by the observation that a dose-sparing effect may be achievable by formulating ChAdOx2 RabG with a squalene oil-in-water emulsion . Such adjuvants have an excellent clinical safety record and there are no longer intellectual property barriers to their use in most countries [52 , 64] . Their raw materials cost cents per dose; manufacturing processes are published and sufficiently simple for transfer to academic or small commercial manufacturing organisations [36] . The effect we have observed in mice is modest yet statistically significant and consistent across experiments with different vectors . Assessing whether such an effect is seen in humans would be relatively simple and low in cost , given access to existing GMP-grade adjuvant and adenovirus-vectored vaccine . Attainment of a 5 or 10-fold virus-dose-sparing effect using an inexpensive adjuvant could make a significant difference to the economic case for adoption of this or other adenovirus-based vaccines in low-resource settings . As well as manufacturing cost , a further obstacle to vaccine delivery in such settings is the maintenance of a cold chain . This is particularly pertinent when considering the deployment of a live ( albeit replication-deficient ) viral-vectored vaccine . We have therefore sought to ‘build in’ thermostability from the early stages of development of the ChAdOx2 RabG candidate . Here , we demonstrated stabilization of ChAdOx2 resulting in ability to withstand 45 °C for 1 month with modest infectivity loss ( 0 . 4 log10-fold ) . This modest loss would be expected to have minimal effect upon immunogenicity ( eg see the shallow dose-response curves in Fig 3A ) , but does suggest caution regarding longer-term storage at such temperatures . In its current form , SMT is probably adequate to allow adenovirus-vectored vaccines to meet the requirements for distribution via the WHO’s ‘controlled temperature chain’ ( CTC ) programme ( i . e . maintenance of compliance with product specification after a single exposure to at least 40 °C for a minimum of 3 days just prior to administration ) and/or use with chromogenic vaccine vial monitors [65 , 66] . There remains scope for further improvement of the SMT approach to enable true long-term ambient-temperature storage of such vaccines: we are currently pursuing a number of optimization strategies . We have recently secured funding for GMP bio-manufacture and Phase I clinical trial of ChAdOx2 RabG in both liquid and SMT formulations . We hope ChAdOx2 RabG may eventually enable low-cost rabies PrEP in low-income settings . It may also have a role in PrEP for travellers to such settings ( potentially facilitating clinical development by providing a ‘dual market’ in high-income as well as low/middle-income countries ) . The SMT technology is applicable to multiple adenovirus-vectored vaccines and other vaccine platforms [43 , 53]; we thus hope that , as well as developing a tool with potential for rabies control , this trial will advance a technology with broad utility for the delivery of vaccination to the millions of individuals who live in resource-poor settings . This work coincides with resurgent interest in the possibility of shortened vaccination regimes using licensed IRVs . The same WHO position paper which recently recommended two-visit PrEP also discussed completed and ongoing trials of single-visit PrEP: this is currently only recommended under circumstances inapplicable to programmatic use in low-income countries ( last-minute travel not permitting two visits , prompt access to PEP , and administration of a second dose on return ) [9 , 67] . Currently available evidence suggests that single-visit IRV regimes achieve VNA >0 . 5 IU/mL in most ( but by no means all ) recipients , and robust recall responses upon simulated PEP in all recipients , but that without boosting , VNA in many recipients falls below 0 . 5 IU/mL within a year [68 , 69] . These results suggest scope for improvement , particularly for use in contexts where PEP is not reliably available . We hope that our work , alongside ongoing studies to further characterise the efficacy of single-dose IRV regimes , may assist further movement towards low-cost and practical PrEP regimes suitable for resource-poor contexts . | Rabies was , after smallpox , the second human disease for which an efficacious vaccine was developed , by Pasteur in 1885 . Although it is eminently preventable , with highly efficacious vaccines available for both humans and animals , it still causes considerable mortality in low and middle-income countries . It is a particular problem in areas with the weakest healthcare and veterinary infrastructure , where achieving prompt post-exposure prophylaxis or high-coverage dog vaccination are challenging . Here , we report the development of a new candidate rabies vaccine , designed to enable low-cost single-dose pre-exposure human rabies prophylaxis in such settings . ChAdOx2 RabG is based upon a simian adenovirus-vectored candidate previously shown to achieve protection after a single dose in non-human primates , now modified to allow clinical-grade bio-manufacture . We show that it induces a potent immune response in mice , that this response can be further enhanced by a clinically-relevant adjuvant , and that we can stabilise it such that it can withstand temperatures of up to 45 °C for a month . We will be performing a clinical trial of this candidate in the near future . | [
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] | 2018 | A simian-adenovirus-vectored rabies vaccine suitable for thermostabilisation and clinical development for low-cost single-dose pre-exposure prophylaxis |
The bacterial flagellar motor can rotate either clockwise ( CW ) or counterclockwise ( CCW ) . Three flagellar proteins , FliG , FliM , and FliN , are required for rapid switching between the CW and CCW directions . Switching is achieved by a conformational change in FliG induced by the binding of a chemotaxis signaling protein , phospho-CheY , to FliM and FliN . FliG consists of three domains , FliGN , FliGM , and FliGC , and forms a ring on the cytoplasmic face of the MS ring of the flagellar basal body . Crystal structures have been reported for the FliGMC domains of Thermotoga maritima , which consist of the FliGM and FliGC domains and a helix E that connects these two domains , and full-length FliG of Aquifex aeolicus . However , the basis for the switching mechanism is based only on previously obtained genetic data and is hence rather indirect . We characterized a CW-biased mutant ( fliG ( ΔPAA ) ) of Salmonella enterica by direct observation of rotation of a single motor at high temporal and spatial resolution . We also determined the crystal structure of the FliGMC domains of an equivalent deletion mutant variant of T . maritima ( fliG ( ΔPEV ) ) . The FliG ( ΔPAA ) motor produced torque at wild-type levels under a wide range of external load conditions . The wild-type motors rotated exclusively in the CCW direction under our experimental conditions , whereas the mutant motors rotated only in the CW direction . This result suggests that wild-type FliG is more stable in the CCW state than in the CW state , whereas FliG ( ΔPAA ) is more stable in the CW state than in the CCW state . The structure of the TM-FliGMC ( ΔPEV ) revealed that extremely CW-biased rotation was caused by a conformational change in helix E . Although the arrangement of FliGC relative to FliGM in a single molecule was different among the three crystals , a conserved FliGM-FliGC unit was observed in all three of them . We suggest that the conserved FliGM-FliGC unit is the basic functional element in the rotor ring and that the PAA deletion induces a conformational change in a hinge-loop between FliGM and helix E to achieve the CW state of the FliG ring . We also propose a novel model for the arrangement of FliG subunits within the motor . The model is in agreement with the previous mutational and cross-linking experiments and explains the cooperative switching mechanism of the flagellar motor .
Bacteria such as Escherichia coli and Salmonella enterica swim by rotating multiple flagella , which arise randomly over the cell surface . Each flagellum is a huge protein complex made up of about 30 different proteins and can be divided into three distinct parts: the basal body , the hook , and the filament . The basal body is embedded in the cell envelope and acts as a reversible motor powered by a proton motive force across the cytoplasmic membrane . The hook and the filament extend outwards in the cell exterior . The filament is a helical propeller that propels the cell body . The hook connects the basal body with the filament and functions as a universal joint to transmit torque produced by the motor to the filament . The flagellar motor can exist in either a counterclockwise ( CCW ) or clockwise ( CW ) rotational state . CCW rotation causes the cell to swim smoothly in what is termed a run , whereas brief CW rotation of one or more flagella causes a tumble . The direction of motor rotation is controlled by environmental signals that are processed by a sensory signal transduction pathway to generate chemotaxis behavior [1]–[3] . Five flagellar proteins , MotA , MotB , FliG , FliM , and FliN , are involved in torque generation . Two integral membrane proteins , MotA and MotB , form the stator , which converts an inwardly directed flux of H+ ions through a proton-conducting channel into the mechanical work required for motor rotation . The FliG , FliM , and FliN proteins form the C ring on the cytoplasmic side of the MS ring , which is assembled from 26 subunits of a single protein , FliF , and this complex acts as the rotor of the flagellar motor [1]–[3] . An electrostatic interaction between the cytoplasmic loop of MotA and FliG is thought to be involved in torque generation [4] , [5] and in stator assembly around the rotor [6] . The protonation-deprotonation cycle of a highly conserved aspartic acid residue in MotB is coupled to the movement of the MotA cytoplasmic loop to generate torque [7]–[9] . Because FliG , FliM , and FliN are also responsible for switching the direction of motor rotation , their assembly is called the switch complex [10] . Binding of a chemotactic signaling protein CheY-phosphate ( CheY-P ) to FliM and FliN is presumed to induce conformational changes in FliG that result in a conformational rearrangement of the rotor-stator interface , allowing the motor to spin in the CW direction [11] , [12] . The switching probability is also affected by motor torque , suggesting that the switch complex senses the stator-rotor interaction as well as the concentration of CheY-P [13] , [14] . Recently , turnover of FliM and heterogeneity in the number of FliM subunits within functioning motors have been reported [15] , [16] . The turnover rate is increased by the presence of CheY-P , implying that turnover of FliM may be directly involved in the switching process [15] . FliG forms a ring on the cytoplasmic face of the MS ring with 26-fold rotational symmetry [17] , [18] . FliG consists of three domains , FliGN , FliGM , and FliGC . FliGN is responsible for association with the cytoplasmic face of the MS ring [17] , [19] , and FliGM and FliGC are required for an interaction with FliM [20] . The FliGM domains of adjacent subunits are fairly close to each other in the FliG ring [21] . The crystal structure of FliGMC of Thermotaoga martima ( Tm-FliGMC ) shows that FliGM and FliGC are connected by an extended α-helical linker ( helix E ) [22] . The linker contains two well-conserved Gly residues and hence might be flexible [22] . This finding is supported by genetic analyses of FliG and a computer-generated prediction of its secondary structure [23] , [24] . Critical charged residues , which are responsible for an interaction with MotA [4]–[6] , are clustered together along a prominent ridge on FliGC [25] . It has been shown that the elementary process of torque generation by the stator-rotor interaction is symmetric in CCW and CW rotation [26] , although the torque-speed curves are distinct between them [27] . A recent report on the full-length FliG structure of Aquifex aeolicus has shown two distinct conformational differences between the full-length FliG and FliGMC structures [28] . The helix E linker is held in a closed conformation by packing tightly against an α-helix ( helix n ) , which connects FliGN to FliGM in a way similar as helix E connects FliGM and FliGC in the full-length FliG structure . Helix E is dissociated from FliGM in the Tm-FliGMC structure , resulting in its being in an open conformation . The conformation of FliGC is also different in these two structures . Combined with the previous genetic data , it has been proposed that the closed conformation represents FliG during CCW rotation and that switching to CW rotation may be accompanied by the dissociation of helix E from FliGM to form an open conformation . The S . enterica FliG ( ΔPAA ) mutant protein has three-amino-acid deletion at positions 169 to 171 . Motors containing this protein are extremely CW biased [29] . The mutant motors remain in CW rotation even in the presence of a cheY deletion , indicating that the motor is locked in the CW state [29] . Therefore , it is likely that binding of CheY-P to FliM may introduce a conformational change in FliG similar to the one introduced by the in-frame PAA deletion . To elucidate the switching mechanism , we crystallized a fragment of a T . maritima FliG mutant variant , FliGMC ( ΔPEV ) , which contains a deletion equivalent to S . enterica FliGMC ( ΔPAA ) , and determined its structure at 2 . 3 Å resolution . Based on the structural difference among full-length A . aeolicus FliG , wild-type Tm-FliGMC , and its deletion variant , we suggest that a reorientation of helix E relative to FliGM is important for switching and propose a new model for the arrangement of FliG subunits in the motor .
The motors of the fliG ( ΔPAA ) mutant rotated only CW ( Figure S1A ) , whereas wild-type motors rotated exclusively CCW under our experimental conditions . The motors of the deletion mutant produced normal torque under a wide range of external-load conditions , indicating that the deletion does not affect the torque generation step ( Figure S1B ) . Introduction of a cheA-Z deletion , which causes wild-type motors to spin exclusively CCW [30] , into the fliG ( ΔPAA ) mutant did not change the CW-locked behavior . These results are in good agreement with a previous report [29] . Switching between the CW and CCW states is highly cooperative [31]–[34] . The switching mechanism can be explained by a conformational spread model , in which a switching event is mediated by conformational changes in a ring of subunits that spread from subunit to subunit via nearest-neighbor interactions [34] , [35] . Therefore we investigated rotation of a single motor composed of wild-type and mutant FliG subunits at different ratios . FliG ( ΔPAA ) inhibited expansion of wild-type colonies in semi-solid agar ( Figure 1A ) , even when its expression level was ca . 5-fold lower than the level of wild-type FliG expressed from the chromosome ( Figure 1B ) . Bead assays revealed that the decrease in colony expansion results from an increase in both switching frequency and prolonged pausing ( Figure 1C ) . In addition , a low level expression of FliG ( ΔPAA ) partially increased the colony expansion of the ΔcheA-Z smooth-swimming mutant , presumably because switching now occurred ( Figure 1D , upper and middle panels ) . These results suggest that even a small fraction of FliG ( ΔPAA ) in a motor can affect the CW-CCW switching . The CW-CCW transition , which is very fast in wild-type motors , became significantly longer in mixed motors ( Figure 1 ) , suggesting that , as proposed previously [24] , the motor can exist in multiple states . A much higher expression of FliG ( ΔPAA ) completely inhibited wild-type motility ( Figure 1D ) and did not increase the colony size of the ΔcheA-Z mutant in semi-solid agar plates because of the extreme CW-biased rotation of its flagella ( Figure 1C and D , lower panel ) , in agreement with data showing that a higher expression level of wild-type FliG is required for complementation of the fliG ( ΔPAA ) mutant ( Figure S2 ) . Therefore , we conclude that wild-type FliG is more stable in the CCW state than in the CW state , whereas FliG ( ΔPAA ) is more stable in the CW state than in the CCW state . To identify structural differences between the CW and CCW states of FliG , we carried out limited trypsin proteolysis of the wild-type and mutant FliG proteins and analyzed the products by matrix-assisted laser desorption ionization time-of-flight ( MALDI-TOF ) mass spectrometry and N-terminal amino-acid sequencing ( Figure 2 ) . Both the wild-type and mutant FliG proteins were cleaved between helix E and FliGC , producing the T1 and T2a fragments . This indicates that there is a flexible region between them . The T1 fragment derived from FliG ( ΔPAA ) was less stable than the T1 fragment from wild-type FliG , suggesting that the deletion causes a conformational change in FliGM and helix E . In contrast , the T2a fragment was more stable in FliG ( ΔPAA ) than in the wild-type . The T2a fragment derived from the wild-type FliG protein was detected by MALDI-TOF but not on SDS-PAGE gels , indicating that the wild-type T2a fragment is rapidly converted into the T2 fragment . These results suggest that the deletion also influences the conformation in the region between helix E and FliGC . We tried crystallizing both wild-type FliG and FliG ( ΔPAA ) from S . enterica but did not succeed in obtaining crystals . It has been reported that the crystal structure of a fragment ( residues 104–335 ) of T . martima FliG ( Tm-FliGMC ) consists of FliGM , FliGC , and helix E connecting the two domains ( [22]; PDB ID , 1lkv ) . FliGC can be further divided into two sub-domains ( FliGCN and FliGCC ) . Therefore , we introduced the deletion ( ΔPEV ) , equivalent to ΔPAA , into Tm-FliGMC ( Tm-FliGMC ( ΔPEV ) ) and determined its structure at 2 . 3 Å resolution by X-ray crystallography ( Figure 3 ) . FliGM , FliGCN , and FliGCC are composed of five ( n , A–D ) , three ( F–H ) , and six ( I–N ) helices , respectively ( Figure 3 ) . Since the residues between G186 and V195 are invisible in the crystal , there are two possible ways to connect FliGM with FliGCN: one is to connect FliGM with its adjacent FliGCN ( G186 to V195 in Figure 3A upper panel and Figure S3A ) , and the other is with a distant FliGCN ( G186 to V195' in Figure 3A upper panel and Figure S3A ) . The Cα distance between G186 and V195 , and G186 and V195' is 16 . 9 Å and 27 . 9 Å , respectively . Therefore , to connect with the distant FliGCN , the invisible chain would have a fully extended conformation . We thus conclude that the connection with the adjacent FliGCN is more plausible . Compared with the structure of wild-type Tm-FliGMC , FliG ( ΔPEV ) showed a significant conformational change in the hinge between helix E and FliGM , leading to a very different orientation of helix E relative to FliGM ( Figure 3A and B , and Figure 4A and C ) . As a result , some of the residues in FliGM are exposed to solvent in the Tm-FliGMC ( ΔPEV ) structure . This result is in good agreement with the data obtained by limited proteolysis ( Figure 2 ) . Thus , the conformational difference in the FliGM-helix E hinge between the wild-type and mutant structures may represent the conformational switch between the CW and CCW states of the motor . The C-terminal half of helix E is disordered and protrudes into the solvent channel in the Tm-FliGMC ( ΔPEV ) crystal ( Figure S3A ) . In contrast , helix E in the wild-type crystal is stabilized by forming an anti-parallel four-helix bundle structure with the E helices of three adjacent subunits related by crystallographic symmetry ( Figure S3B ) [22] . Therefore , the orientation of FliGC relative to FliGM is different between the wild-type and the deletion variants ( Figure 3A and B upper panel ) . Because the disordered region of helix E is far from the PEV deletion , we conclude that helix E has a highly flexible nature , which may be responsible for the switching mechanism , as suggested before [23] , [24] . Tm-FliGMC ( ΔPEV ) also showed a conformational difference in the H–I loop , resulting in a rigid body movement of FliGCC relative to FliGCN ( Figure 3A and B middle and lower panels , and Figure 4A ) . This movement is consistent with the limited proteolysis data because , in the Tm-FliGMC ( ΔPEV ) structure , FliGCC almost covers D199 , which is the residue corresponding to R198 in S . enterica FliG . It is , however , unclear how the deletion affects the conformation of the H–I loop , because neither direct contact between FliGCC and helix E nor significant structural difference in FliGCN is observed . The crystal structure of full-length A . aeolicus FliG ( Aa-FliG ) showed that the conformation of helix E and the orientation of FliGCN relative to FliGCC are quite distinct from those of wild-type Tm-FliGMC [28] . We compared the Aa-FliG structure with the Tm-FliGMC ( ΔPEV ) structure and found that the conformation of helix E and the relative conformation of FliGCC to FliGCN are also different in those two structures ( Figure 3A and C , and Figure 4B and C ) . The conformational differences are greater than those between Tm- FliGMC and Tm-FliGMC ( ΔPEV ) . The conformation of helix E in Aa-FliG seems to be stabilized by interactions of helix E with FliGM and helix n in the crystal ( Figure S3C ) . As mentioned earlier , the conformation of helix E and the orientation of FliGCC to FliGCN are also different between the wild-type and mutant Tm-FliGMC structures . Therefore , these conformational differences among the three structures strongly suggest that both helix E and the linker connecting FliGCN to FliGCC are highly flexible . The interaction between FliGM and FliGCN , which share the armadillo repeat motif [36] that is often responsible for protein-protein interaction , is very tight in the Tm-FliGMC ( ΔPEV ) crystal , in agreement with a previous report [28] . FliGM and FliGCN can be identified as a single domain , although it is unclear whether the two domains belong to the same molecule or not because the residues between Gly-186 and Val-195 are invisible in the crystal ( Figures 3A and S3A ) . The interaction surface between FliGM and FliGCN is formed by the C-terminal portion of αB , αC , and αD of FliGM , and αF , αG , and the N-terminal portion of αH of FliGCN , respectively ( Figure 5A and B ) . The interface is highly hydrophobic . Ala-143 , Ala-144 , Leu-147 , Leu-156 , Leu-159 , Ile-162 , and Ala163 of FliGM , and Ile-204 , Met-205 , Leu-208 , Ile-216 , Leu-220 , Leu-227 , and Ile-231 of FliGCN are mainly involved in the tight domain interaction . Leu-159 is located at the center of the hydrophobic interface ( Figure 5C ) . Around the hydrophobic core , hydrophilic interactions between Arg-167 and Glu-230 , and Gln-155 and Thr-212 , also contribute to the domain interaction ( Figure 5C ) . These interactions are also conserved in the wild-type Tm-FliGMC and Aa-FliG crystals , in which FliGM interacts with FliGCN of an adjacent molecule related by crystallographic symmetry ( Figures 3 and S3B ) . The FliGM-FliGCN unit in the wild-type Tm-FliGMC structure can be superimposed onto that in Tm-FliGMC ( ΔPEV ) with root mean square deviation of 0 . 46 Å for corresponding Cα atoms ( Figure 4A and C ) , and that in Aa-FliG with 0 . 79 Å ( Figure 4B and C ) . These observations support the idea that the FliGM-FliGCN unit is a functionally relevant structure [28] . This is in good agreement with the previous mutational study showing that most of the known point mutations that affect FliM-binding [37] are located either on the bottom surface of the FliGM-FliGCN unit or on the interaction surface between FliGM and FliGCN ( Figure 6A and C ) .
The default direction of the wild-type flagellar motor of Salmonella enterica is CCW , and the binding of CheY-P to FliM and FliN increases the probability of CW rotation . CheY-P binding induces conformational changes in FliM and FliN that are presumably transmitted to FliG , which directly interacts with MotA to produce torque [1] , [2] . Mutations located in and around helix E FliG , which connects the FliGM and FliGC domains , generate a diversity of phenotype , including motors that are strongly CW biased , infrequent switchers , rapid switchers , and transiently or permanently paused , suggesting that helix E is directly involved in the switching of the flagellar motor [24] . However , it remains unclear how helix E affects the switch . To investigate the switching mechanism , we characterized an extreme CW-biased S . enterica mutant in which an in-frame deletion of three residues , Pro-169 , Ala-170 , and Ala-171 , in FliG caused an extreme CW-biased rotation even in the absence of CheY . Motors containing the FliG ( ΔPAA ) protein showed normal torque generation under a wide range of external-load conditions ( Figure 1 and Figure 1S ) . Thus , the conformational change in FliG induced by ΔPAA is presumably similar to one induced by CheY-P binding to FliM and FliN . Limited proteolysis revealed that ΔPAA induces conformational changes in the hinge between FliGM and helix E ( Figure 2 ) . This result is in agreement with the crystal structure of Tm-FliGMC ( ΔPEV ) , which shows that the orientation of helix E relative to FliGM has changed significantly compared to wild-type FliG ( Figure 3 ) . FliG forms a ring on the cytoplasmic face of the MS ring [17] , [18] . In vivo disulfide cross-linking experiments using Cys-substituted FliG proteins have suggested that helix A is close to the D–E loop of the adjacent FliG molecule in the FliG ring [21] . Both a conserved EHPQR motif in FliGM and a conserved surface-exposed hydrophobic patch of FliGCN are important for the interactions with FliM [21] . Because the conserved charged residues on helix M in FliGCC are responsible for its interaction with MotA [4] , [5] , [25] , which is embedded in the cytoplasmic membrane , helix M must lie on top of FliGCC [21] , [28] . Considering those facts in light of the crystal structure of Tm-FliGMC ( ΔPEV ) described here , we propose a new model for arrangement of FliG subunits in the motor ( Figures 6 and 7 ) . In the proposed model , the conserved charged residues on helix M are located on the top of the FliGM-FliGC unit and the EHPQR motif is present at the bottom of the unit ( Figure 6B and C ) . The conserved hydrophobic patch , and most of the point mutation sites involved in the interaction with FliM , is localized at the bottom of the FliGMFliGCN units around the EHPQR motif or on the interface between the FliGM and FliGCN . The D–E loop and helix E interact with the FliGM domain in the neighboring subunit , in agreement with data of in vivo cross-linking experiments , which show that residues 117 and 120 ( 118 and 121 in T . martima ) on helix A of one subunit lie close to residues 166 and170 ( 167 and 171 in T . martima ) on the D–E loop of the neighboring subunit [21] . In fact , these residues are very close to each other in our model in positions in which disulfide-crosslinking should occur . Moreover , the position of Cys residues that do not participate in disulfide cross-linking are far from each other in the model ( Figure 6D ) . Our model can also explain the results of mutational studies of CW and CCW-biased fliG mutants [37] , [38] . The mutation sites are widely distributed from helix A to the H–I loop . Most of them are localized in three regions in our model ( Figure 6A and B ) . In the first region , the CCW-biased mutations , which are located on helix A , affect residues close to residues targeted by CW-biased mutations , which are on a segment between helix D and E of the adjacent subunit ( Figure 6A and B , 1 ) . Because these residues are distributed on the interaction surface between the neighboring subunits , they presumably affect cooperative changes in subunit conformation . A second cluster of residues targeted by CW-biased mutations is located on the C-terminal half of helix B and the E–F loop ( Figure 6A and B , 2 ) . These mutations may change the orientation of the E–F loop and probably alter the orientation of helix E , resulting in unusual switching behavior . The third cluster of residues affected by mutations causing a CW switching bias is located near the loop between helices H and I ( Figure 6A and B , 3 ) . This region determines the relative orientation of FliGCC to the FliGM-FliGCN unit , and therefore the mutations may change the orientation of FliGCC to cause anomalous switching behavior . Helix E is directly involved in the switching mechanism , but how does the structure of helix E affect the orientation of the FliGM-FliGC unit ? Since the D–E loop and helix E interact with FliGM in the neighboring subunit , we propose that a hinge motion of helix E may directly change the orientation of the neighboring FliGM domain ( Figure 7A ) . This mechanism could explain the cooperative switching of the motor . The conformational changes of FliM induced by association or dissociation of CheY-P may trigger conformational changes in the FliGM-FliGC unit that it contacts , leading to a large change in the interaction between FliGCC and MotA . The conformational change in one unit is probably accompanied by a conformational change in the loop between FliGM and helix E . This change could influence the orientation of the neighboring subunit through the interaction between helix E and FliGM of the neighbor , thereby propagating the conformational change to the neighboring subunit ( Figure 7A ) . If helix E actually contacts the more-distant FliGCN in the crystal structure , an alternative interaction could be responsible for the cooperative switching ( Figure 7B ) . However , the same general mechanism involving changes in the conformation of helix E would still be responsible for the cooperative switching . Recently , Lee et al . have proposed a model for FliG arrangement and switching based on the structural differences in Aa-FliG and Tm-FliGMC [28] . In the crystal structure of Aa-FliG , the hydrophobic patch in FliGM is covered by the N-terminal hydrophobic residues of helix E ( closed conformation ) , whereas the patch is exposed in Tm-FliGMC ( open conformation ) . Because mutations that may disturb the hydrophobic interaction result in strong CW-bias in motor rotation [38] , the structures of Aa-FliG and Tm-FliGMC are proposed to be in the CCW and CW states , respectively [28] . The hydrophobic patch is also exposed in the Tm-FliGMC ( ΔPEV ) structure , although the conformation of helix E is different from that of Tm-FliGMC . Since ΔPAA in S . enterica FliG ( ΔPEV in T . maritima ) caused an extreme CW-bias , it is possible that the dissociation of helix E from FliGM leads to CW rotation . In our model , however , the hydrophobic patch of the FliGM is covered by the hydrophobic residues in the C-terminal half of helix E of the adjacent subunit . This arrangement raises the possibility that the closed conformation of helix E found in the Aa-FliG structure is an artifact of crystal packing . Lee et al . assume that the FliGM-FliGC unit is present in the rotor ring , and hence is in agreement with the results of most of mutational studies . However , the arrangement of the subunits and the mechanism of switching are different than in our model . In their model , dynamic motion of helix E and helix n induces a large conformational change of the FliGM-FliGC unit , including the rotation of FliGM-FliGCN unit and relative to the FliGCC to the unit , leading to a change in the arrangement of the charged residues on helix M ( Figure 7C ) [28] . Cooperative switching is explained by the strong interaction between FliGCN of one subunit and FliGCC of the adjacent subunit . However , helix A of one subunit and the D–E loop of the adjacent subunit are always at a considerable distance in both the CW and CCW states . Hence , their model cannot explain the in vivo disulfide cross-linking experiments ( Figure 7C ) [21] . Since our new model can explain the cross-linking data , it appears to be more plausible than the model proposed by Lee et al . [28] . Although our model is consistent with most of the previous experimental data , it still contains ambiguity . The available density map of the basal body obtained by electron cryo-microscopy is not high enough to allow fitting of the atomic model . Thus , a higher-resolution rotor-ring structure will be required to build a more precise model to explain the molecular mechanism of directional switching .
S . enterica strains and plasmids used in this study are listed in Table 1 . L-broth , soft agar plates , and motility media were prepared as described [39] , [40] . Ampicillin was added to a final concentration of 100 µg/ml . Fresh colonies were inoculated on soft tryptone agar plates and incubated at 30°C . Bead assays were carried out using polystyrene beads with diameters of 0 . 8 , 1 . 0 , and 1 . 5 mm ( Invitrogen ) , as described before [8] . Torque calculation was carried out as described [8] . Cultures of S . enterica cells grown at 30°C were centrifuged to obtain cell pellets . The cell pellets were resuspended in SDS-loading buffer , normalized in cell density to give a constant amount of cells . Immunoblotting with polyclonal anti-FliG antibody was carried out as described [41] . His-FliG and His-FliG ( ΔPAA ) were purified by Ni-NTA affinity chromatography as described before [39] . His-FliG and its mutant variant ( 0 . 5 mg/ml ) were incubated with trypsin ( Roche Diagnostics ) at a protein to protease ratio of 300∶1 ( w/w ) in 50 mM K2HPO4-NaH2PO4 pH 7 . 4 at room temperature . Aliquots were collected at 0 , 5 , 15 , 30 , 60 , 90 , and 120 min and trichloroacetic acid was added to a final concentration of 10% . Molecular mass of proteolytic cleavage products was analyzed by a mass spectrometer ( Voyager DE/PRO , Applied Biosystems ) as described [42] . N-terminal amino acid sequence was done as described before [42] . Tm-FliGMC ( ΔPEV ) was purified as described previously [23] . Crystals of Tm-FliGMC ( ΔPEV ) were grown at 4°C using the hanging-drop vapor-diffusion method by mixing 1 µl of protein solution with 1 µl of reservoir solution containing 0 . 1 M sodium phosphate-citrate buffer pH 4 . 2–4 . 4 , 36%–50% PEG200 , and 200 mM NaCl . Initially , we tried to solve the structure by the molecular replacement method using Tm-FliGMC structure ( PDB ID: 1 lkv ) as a search model . However , no significant solution was obtained , even though individual domains were used as search models . Therefore , we prepared heavy-atom derivative crystals and determined the structure using the anomalous diffraction data from the derivatives . Derivative crystals were prepared by soaking in a reservoir solution containing K2OsCl6 at 50% ( v/v ) saturation for one day . Crystals of Tm-FliGMC ( ΔPEV ) and its Os derivatives were soaked in a solution containing 90% ( v/v ) of the reservoir solution and 10% ( v/v ) 2-Methyl-2 , 4-pentanediol for a few seconds , then immediately transferred into liquid nitrogen for freezing . All the X-ray diffraction data were collected at 100 K under nitrogen gas flow at the synchrotron beamline BL41XU of SPring-8 ( Harima , Japan ) , with the approval of the Japan Synchrotron Radiation Research Institute ( JASRI ) ( Proposal No . 2007B2049 ) . The data were processed with MOSFLM [43] and scaled with SCALA [44] . Phase calculation was performed with SOLVE [45] using the anomalous diffraction data from Os-derivative crystals . The best electron-density map was obtained from MAD phases followed by density modification with DM [44] . The model was constructed with Coot [46] and was refined against the native crystal data to 2 . 3 Å using the program CNS [47] . About 5% of the data were excluded from the data for the R-free calculation . During the refinement process , iterative manual modifications were performed using “omit map . ” Data collection and refinement statistics are summarized in Tables S1 and S2 , respectively . | The bacterial flagellum is a rotating organelle that governs cell motility . At the base of each flagellum is a motor powered by the electrochemical potential difference of specific ions across the cytoplasmic membrane . In response to environmental stimuli , rotation of the motor switches between counterclockwise and clockwise , with a corresponding effect on the swimming direction of the cell . Switching is triggered by the binding of the signaling protein phospho-CheY to FliM and FliN , and achieved by conformational changes in the rotor protein FliG . The actual switching mechanism , however , remains unclear . In this study , we characterized a fliG mutant of Salmonella that shows an extreme clockwise-biased rotation , and determined the structure of a fragment of FliG ( FliGMC ) of the equivalent mutant variant of Thermotoga maritima . FliGMC is composed of two domains and covers the regions essential for torque generation and FliM binding . We showed that the mutant structure has a conformational change in the helix connecting the two domains , leading to a domain orientation distinct from that of the wild-type FliG . On the basis of this structure , we propose a new model for the arrangement of FliG subunits in the rotor that is consistent with the previous mutational studies and explains how cooperative switching occurs in the motor . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biophysics/macromolecular",
"assemblies",
"and",
"machines",
"microbiology",
"biophysics"
] | 2011 | Structural Insight into the Rotational Switching Mechanism of the
Bacterial Flagellar Motor |
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