But despite the fundamental importance of FDR estimates in ensuring the utility of large lists of identifications, there is surprisingly little consensus on exactly how TDA should be applied to minimize the chances of biased FDR estimates. In fact, since less rigorous TDA/FDR estimates tend to result in more identifications (at higher 'true' FDR), there is often little incentive to enforce strict TDA/FDR procedures in studies where the major metric of success is the size of the list of identifications and there are no follow up studies imposing hard cost constraints on the number of reported false positives.Automated database search engines are one of the fundamental engines of high-throughput proteomics enabling daily identifications of hundreds of thousands of peptides and proteins from tandem mass (MS/MS) spectrometry data. Nevertheless, this automation also makes it humanly impossible to manually validate the vast lists of resulting identifications from such high-throughput searches. This challenge is usually addressed by using a Target-Decoy Approach (TDA) to impose an empirical False Discovery Rate (FDR) at a pre-determined threshold factual FDR estimator of 'true' FDR we evaluate several popular variants of the TDA procedure in a variety of database search contexts. We show that the fraction of false identifications can sometimes be over 10× higher than reported and may be unavoidably high for certain types of searches. In addition, we further report that the two-pass search strategy seems the most promising database search strategy.Here we address the problem of the accuracy of TDA estimates of empirical FDR. Using MS/MS spectra from samples where we were able to define a While unavoidably constrained by the particulars of any specific evaluation dataset, our observations support a series of recommendations towards maximizing the number of resulting identifications while controlling database searches with robust and reproducible TDA estimation of empirical FDR. The score threshold must be appropriately determined because low thresholds lead to excessive false positives and high thresholds lead to too many false negatives.Mass spectrometry (MS) based proteomics studies often generate millions of tandem mass spectra. These spectra are usually assumed to come from peptides and typically interpreted using a database search engine. There are numerous database search engines available such as SEQUEST , Mascot decoy database. A decoy database is a reversed, shuffled  or otherwise randomized database of the same size as the target database. It is assumed that the positive PSMs from the decoy database (decoy PSMs) are false and that the expected number of decoy PSMs equals the expected number of false positive PSMs from the target database. Thus, by counting the number of decoy PSMs, one can estimate the False Discovery Rate (FDR) - the proportion of false PSMs among positive PSMs. Estimating FDRs via TDA is currently the standard in high-throughput MS studies because it is simple, easily implementable, and widely applicable to various experimental set-ups while successfully distinguishing correct and incorrect PSMs.The target-decoy approach (TDA) ,8 is curHowever, there is no consensus on the exact procedure for TDA - a worrisome situation since the quality of the resulting FDR estimates and the number of resulting PSMs are strongly dependent on such procedural variations. For example, there are multiple methods to generate decoy databases  but it remains an open problem to determine the optimal way of generating and using decoy databases. Also, it is questionable whether to search the target and the decoy database separately or to search the concatenated target and decoy databases. Furthermore, even after the score threshold is determined, it is ambiguous what formula to use to calculate the FDR. Because of all these "variations", the same FDR  may mean a different confidence level depending on the specific procedure, and it is often difficult to determine how much trust can be allowed for FDRs reported in research papers on MS studies.We compare various TDA procedures and assess them in terms of how accurate they estimate the "true" FDRs and how many PSMs they identify at a fixed true FDR. We also show how different database search parameters such as the the choice of the protein database, parent mass tolerance and enabling/disabling two-pass searches affect the accuracy of FDR estimation and the resulting set of PSMs. Based on our results, we recommend a set of TDA guidelines and search parameters towards improving the accuracy of FDR estimates while also producing more resulting PSMs.We used X!Tandem  and MS-GISB Standard Protein Mix Database [ISB-All) from 10 replicates generated from tryptic digests of 18 proteins called ISB Standard Protein Mix. For most experiments, a subset containing 4,966 spectra from replicate 02 (denoted by ISB-02) were used.The main MS/MS spectra dataset used in this study was the LTQ-Orbitrap dataset in Mix 7 from the Database . It consStudy 6 LTQ-XL-Orbitrap@86 data set generated by the clinical proteomic technology assessment for cancer (CPTAC) network [Y-All dataset and further randomly selected 9,758 spectra out of Y-All dataset to form Y-Small dataset.We also analyzed the  network . This daAB-All). Among them, 6,319 Spectra from trypsin and chymotrypsin digests (denoted by AB-TC) were mainly used for most experiments.To compute factual FDRs (to be defined below), we additionally obtained a dataset of monoclonal antibody spectra from a previous protein sequencing study by Bandeira et al.  consistiISB) for the ISB-All and ISB-02 datasets and the yeast database  for the Y-All and Y-Small datasets.We used the protein database of ISB Standard Protein Mix . The Arabidopsis thaliana database (denoted by AT) was also used to compute factual FDRs.We also obtained an Arabidopsis thaliana database from the Arabidopsis Information Resource (TAIR) (We used X!Tandem version 12/01/2011)  and MS-G2/01/2011Standard TDA Protocol) is as follows:The most commonly used TDA procedure  and a database search engine,1. Generate a decoy database by reversing the target database.2. Concatenate the target and decoy database and run a database search engine against the concatenated database. For each spectrum, consider only the best scoring (either target or decoy) PSM.3. Sort all PSMs in decreasing (or increasing) order of match scores .t, estimate the FDR as decoy/Ntarget Nwhere target N(decoyN) is the number of positive target (decoy) PSMs .4. For a threshold t and a corresponding FDR.5. Report the set of target PSMs with scores better than decoy/N(target N+ decoyN) instead of decoy/Ntarget Nas the formula to compute FDRs. Depending on such choices, the set of resulting PSMs is very likely to vary.Although the above TDA procedure is frequently used, many researchers do not follow exactly these steps. For example, instead of using the reversed database, some generate decoy databases by shuffling protein sequences in the target database  or enumeIn addition to the specific TDA procedure, one may get significantly different resulting PSMs depending on the choice of the protein database and search parameters. Below, we evaluate how these factors affect FDR estimation and change the resulting set of PSMs. In particular, we address the following cases/issues:1. How to construct a decoy database: reversed vs shuffled2. Concatenated vs separate decoy3. Choice of formula to calculate FDR4. Impact of the size of the database5. How the number of spectra affects the results6. Expected gains from accurate peptide parent masses7. How the score normalization affects the results8. PSM-level vs Peptide-level FDR9. Two-pass searches and TDAfactual FDR. The factual FDR is defined as follows: If we are given a dataset where all spectrum identifications are perfectly known (a fully-labeled approach) in advance then one can easily validate the FDR estimated via TDA  because it would be possible to compute the "true" FDR. But since such a dataset is not readily available, similar to Granholm et al. [semi-labeled approach where false PSMs (termed dummy PSMs) are intentionally introduced using the following three ways.To address these issues, we designed a set of experiments by varying the set of spectra, protein database, TDA procedure, and search parameters Table . For eacm et al. , we use dummy proteins be the proteins from which the searched spectra are not supposed to be generated. The dummy database is a database containing only dummy proteins. For example, consider the search of ISB-All spectra against ISB+Yeast  or ISB+AT database. We do not expect any significant match between the spectra in ISB-All and proteins in Yeast or AT databases. Thus, in this case, Yeast or AT databases are dummy databases for ISB-All dataset. All PSMs matched to dummy databases are dummy PSMs.1. Dummy databases: let dummy spectra are the spectra that are not supposed to be matched to the database searched against. For instance, we sometimes appended the spectra from either AB-TC or AB-All to ISB-02, ISB-All, Y-Small, or Y-All datasets and searched the merged datasets. Since we do not expect any significant match between the spectra in AB-All or AB-TC dataset and any protein sequence database used, AB-TC or AB-All spectra are dummy spectra for all experiments. All PSMs from dummy spectra are dummy PSMs.2. Dummy spectra: the dummy tolerance) instead. Dummy parent mass tolerance was applied only to the experiments for the ISB-All and ISB-02 datasets. All PSMs with parent mass error larger than 50 ppm are dummy PSMs.3. Dummy parent mass tolerance: all spectra used in our experiments were obtained with a LTQ-Orbitrap, using an MS acquisition mode where the parent mass error is usually less than 30-50 ppm . Although running database searches with parent mass tolerance 50 ppm would be enough to find most correct matches, we used 2.5 Da parent mass tolerance (putative PSMs) are correct. To compute FDR  we have to estimate the number of false positive target PSMs. In case of empirical FDR, we estimate this number by the number of decoy PSMs (decoyN) without distinguishing between dummy and putative PSMs. In case of factual FDR, however, we use the information that positive dummy PSMs always represent false positive PSMs; the total number of false positive PSMs is thus the number of positive dummy PSMs (denoted by dummyN) plus the number of false positive PSMs among the putative PSMs . Since dummy Nis given, we only need to estimate falseN.Note that all dummy PSMs are regarded as false but not all remaining PSMs . Since false Nis estimated via TDA, the factual FDR also may suffer from the bias introduced by TDA as the empirical FDR does. However, since the factual FDR is using "extra information" of dummy PSMs (not available to the database search engine nor to TDA), it is expected to be closer to true FDR than empirical FDR in particular when the number of dummy PSMs (dummyN) is large. The definition of the factual FDR for two-pass searches is more complicated and is discussed below.where targetN). Also for each experiment, we evaluated how significant the difference between empirical FDR and factual FDR is using the Fisher's exact test [×2 tables given in Table For each experiment, we fixed the factual/empirical FDR thresholds to 5% for searches I-1 to I-23 and 1% for searches Y-1 to Y-13 and reported the corresponding empirical/factual FDR values and the number of positive target PSMs . We only evaluate how FDR estimation via TDA is reliable and how the number of positive PSMs (or peptides) changes for different search strategies with different parameters or protocols.The decoy database can be generated by reversing the target proteins (reversed), shuffling amino acids of proteins (shuffled), or enumerating amino acids randomly (randomized) . To avoiThe reversed database meets all these conditions when fully-tryptic peptide digestion is not enforced. Moreover, there is only one possible reversed database for every target database. This is beneficial because it removes the dependence on the randomization procedure and makes the FDR calculation deterministic and reproducible. Moreover, shuffled or randomized databases usually do not contain as many shared peptides (peptides that are shared between multiple proteins) as target protein database. This makes the actual search space in the decoy database larger than the search space in the target database, thus resulting in conservative FDR estimates . Elias aTo assess the impact of this choice of reversed vs shuffled decoy databases, we performed various pairs of searches Table . For eacNo notable difference was observed between both approaches. Regardless of the database, both approaches reported similar numbers of PSMs at a fixed factual FDR (5% or 1%). The Fisher's p-value exceeded 5% for all cases; in contrast with popular belief, we did not observe a conservative estimation of FDR with shuffled decoy when compared to the reverse decoy database.Based on these results, we would recommend the utilization of reversed decoy databases rather than shuffled decoy databases. While there was no noticeable disadvantage, there are several advantages of using reversed decoy databases: it is easy to generate, deterministic, reproducible and maintains the amino acid composition and distribution of shared peptides/parent masses between target and decoy databases.Given target and decoy databases, it is common to concatenate them and search the concatenated database  but some0) [0 were suggested  ,15. Seved , but weWe compared both approaches using pairs of searches that differ only by the database search method - concatenated or separated search. For all searches, the standard TDA procedure was followed except step 2 for the separate search. For separate searches, the target and decoy databases are searched separately and the best scoring PSM is selected from each database and used for the empirical FDR calculation. Table 0 factor is expected to be smaller for small databases than for large databases.The results show that the separate-decoy searches tend to estimate FDR conservatively. In particular for small databases, the separate-decoy searches resulted in more conservative FDR estimation than concatenated-decoy searches. For instance, the Fisher's p-value for the search I-9 was far less than 5% for both MS-GFDB and X!Tandem. This is because the π0 factor, in particular for searches using small databases.Thus, we recommend to use concatenated-decoy search. Separate-decoy searches should be used with reliable estimation of the πtarget Nand decoy Nrespectively), one can estimate FDR as decoy/Ntarget Nas in the standard TDA procedure. However, the first review on TDA by Elias and Gygi [decoy/N(target N+ decoyN) and both formulas are used in MS experiments . The latter formula assumes the database search engine reports both target and decoy PSMs as positive discoveries. However, decoy PSMs do not need to be included in the final set of positive discoveries since these are obviously known to be false.Given the numbers of target and decoy positive PSMs . For searches using the alternative formula, we used the second table in Table decoy/N(target N+ decoyN)) resulted in conservative FDR estimation, yielding less positive target PSMs than the original formula decoy/NtargetN. For example, the Fisher's p-value was less than 5% in the search I-12 for both MS-GFDB and X!Tandem, indicating inaccurate FDR estimation from the alternative formula.For most cases, the alternative formula  can measure masses very accurately and are commonly configured to generate high-accuracy MS spectra  and low-accuracy MS/MS spectra  . The avaAs expected, when using strict parent mass tolerance more PSMs were identified  in most cases. For the searches I-17 and I-18, the empirical FDRs reported by MS-GFDB were rather inaccurate. However, while the empirical FDR in I-17 was too conservative, that in I-18 was too liberal. This indicates that the empirical FDR in searches using strict tolerance is not strongly biased toward one direction. Thus, we recommend using strict tolerance in database searches.S1, P1) and  where S1 ≠ S2, if Score ≥ Score , the chances of  being correct should be higher than the chances of  being correct  is better than ). However, this is not true for all scoring functions. For example, SEQUEST Xcorr tends to assign large scores to long peptides. Thus, even if Score ≥ Score , if Length(P1) >> Length(P2), it is possible that  is a worse match than . This score normalization problem is an important issue for TDA to work effectively.TDA implicitly assumes that given two PSMs  is a good solution to obtain a good normalization. Most database search engines nowadays report a pair of scores: a "raw" score and a probabilistic score. For example, Mascot reports ion scores and E-values and MS-GFDB reports MS-GF score and spectral probability. Alternatively, one can get probabilistic scores by running post-processing tools like Peptide-Prophet .To estimate the effect of score normalization, we ran pairs of MS-GFDB searches. For each pair of searches, one used the spectral probability (probabilistic score) and the other used the MS-GF score (raw score) to compute FDR. The spectral probability can be considered simply as "better normalized" score of the MS-GF score for this experiment . Table 9Using the well-normalized score  always produces substantially more resulting PSMs, with higher gains for larger databases. Furthermore, as in the search Y-7, the TDA-determined empirical FDR tended to be more accurate when well-normalized score was used. Thus, we recommend to use well-normalized scoring function  to maximize the number of positive target PSMs at a fixed FDR. To compute FDRs separately depending on the precursor charge is also recommended if the scoring function is not well normalized across the spectra of different precursor charges. For example, most engines using peptide sequence tags  identifP then P is said to be identified). These identified peptides are in turn used to identify proteins . However, while multiple correct PSMs often correspond to a single correct peptide, false PSMs typically correspond to distinct false peptides. Consequently, even a set of PSMs with a very low (PSM-level) FDR may result in excessive false peptide identifications.In MS experiments, it is common to compute FDRs at the PSM-level , and use the resulting PSMs to identify peptides  were used, the resulting empirical PSM-level FDRs seriously underestimated the factual peptide-level FDRs (up to 10 folds), indicating that the peptide-level FDR is more important when large datasets/experiments are considered. On the other hand, Table Thus, in MS experiments where peptide identifications are used in downstream applications  peptide-level FDR should be used instead of PSM-level FDR. Other applications choosing to use empirical PSM-level FDR should be required to present supporting evidence that such FDR estimates are accurate and appropriate for the proposed goals.Craig and Beavis  pioneereRecently, it was recognized that TDA should be carefully applied when estimating FDRs for two-pass searches -27. TradBK decoy database. On the other hand, the decoy database constructed by retaining only candidate decoy protein sequences is specified by traditional decoy database.To remedy this problem, Everett et al.  suggesteWe tested two methods - the traditional and the BK decoy database - with or without the MSR step . For thdummyN) can be counted as previously described (in Methods section), but the number of false positives out of putative PSMs  should be estimated differently because the search space of a two-pass search is typically different from a single-pass search. We call the estimation method of false Nfor single-pass searches described in Methods section the single-pass estimation method.For two-pass searches, the number of dummy PSMs  and the proteins in the generated BK decoy database. false Nis given by the number of decoy positive PSMs in this search. This estimation method for two-pass searches is specified by the two-pass estimation method.To estimate S1 matched spectra in the first step and S2 remaining spectra. To estimate the false positives in the first pass, we use the single-pass estimation method with the spectra S1 instead of all the spectra. To estimate the false positives in the second pass, we use the two-pass estimation method with the spectra S2 instead of all the spectra. The final estimation of false Nis given by summing up the two estimated numbers of the false positives.Second, in case in which the MSR step is applied, we first divide the set of spectra into two groups: The results of the four two-pass search methods are shown in Table - 2588 = 674 and 3103 - 2588 = 515 additional PSMs in the searches Y-12 (without the MSR step) and Y-13 (with the MSR step), respectively, as compared to the search Y-1. The factual FDRs of these additional 674 and 515 PSMs were 1.8% and 1.4%, respectively. This indicates that the additional PSMs without the MSR step result in rather high FDR.For most cases, the two-pass searches produced significantly more PSMs than the single-pass search at the same factual FDR. The empirical FDR from traditional decoy database significantly underestimated the factual FDR, in particular when the MSR step was not used (shown in the search Y-10). On the other hand, the empirical FDR from the BK decoy database was close to the factual FDR, whether the MSR step was used or not (shown in the searches Y-12 and Y-13). The numbers of target PSMs in these searches were still larger than in the single-pass search. For example, MS-GFDB reported 3262 Based on the results, we recommend to use two-pass searches using the BK decoy database because it outputs more target PSMs than single-pass searches with reliable FDR estimation.factual FDR estimator of 'true' FDR using strong indicators of false identifications that were not available to TDA or the database search engine.Reliable estimation of false discovery rates is a necessary precondition for the downstream utility of high throughput proteomics studies. Without accurate FDR estimates it is not possible to meaningfully compare results across different labs or search procedures and substantial amounts of time and resources may be wasted following 'surprising leads' later shown to be no more than just false positives. While the final decision of which FDR  is reasonable and appropriate for a particular experiment should ultimately rest with the researcher responsible for the analysis, it is important to be aware of the expected statistical consequences of the possible procedural choices to allow for both amelioration and critical evaluation of their effects in the resulting lists of identifications. Here we evaluated these possible effects using MS/MS data from samples where we were able to define a × higher than the only reported FDR. In fact, we argue that peptide-level FDR should be the norm when reporting identification results and PSM-level FDR should be avoided whenever possible and require additional evidence from the authors showing that there are substantial reasons to avoid imposing peptide-level FDR. The main reason behind this strong assertion is that most MS based experiments are conducted with the purpose of identifying peptides and proteins for biological interpretation where one is not concerned about the identity of any particular spectrum but rather with the expected number of false positives in the list of identifications used for follow up analysis. Another reasonable way to control FDR is to impose protein-level FDR; however, these procedures usually faces difficulties of their own  and should be addressed separately in a different study. Other aspects beyond the scope of this study that could also have a significant impact on the accuracy of TDA estimation of FDR are post-translational modifications, MS/MS acquisition modes , local FDR , spectral library searches [While the particulars of specific experiments may warrant additional exploration, the results presented here indicate that the adoption of a simple set of guidelines could substantially improve the odds that TDA estimates of 'true' FDR will be within an acceptable interval around measured empirical FDRs. Conversely, we show that there are cases where PSM-level FDR is highly inappropriate since it results in a peptide-level FDR over 10searches , etc.Out of the recommendations derived and supported by the results above, we observed that two-pass searches seem to be the most promising search strategy. Out of all tested strategies, two-pass searches came closest to identifying as many peptides as would be possible with perfect advance knowledge of the exact list of proteins in the sample of interest. Of course, it should be noted that such gains are likely to deteriorate for higher complexity samples where the second pass database is not substantially smaller than the initial database. Also we remark that the increased number of identified peptides does not necessarily mean the increased number of identified proteins in two-pass searches because the candidate proteins are fixed in the first pass of the searches.The authors declare that they have no competing interests.


Reproducibility of results can have a significant impact on the acceptance of new technologies in gene expression analysis. With the recent introduction of the so-called next-generation sequencing (NGS) technology and established microarrays, one is able to choose between two completely different platforms for gene expression measurements. This study introduces a novel methodology for gene-ranking stability analysis that is applied to the evaluation of gene-ranking reproducibility on NGS and microarray data.The same data used in a well-known MicroArray Quality Control (MAQC) study was also used in this study to compare ranked lists of genes from MAQC samples A and B, obtained from Affymetrix HG-U133 Plus 2.0 and Roche 454 Genome Sequencer FLX platforms. An initial evaluation, where the percentage of overlapping genes was observed, demonstrates higher reproducibility on microarray data in 10 out of 11 gene-ranking methods. A gene set enrichment analysis shows similar enrichment of top gene sets when NGS is compared with microarrays on a pathway level. Our novel approach demonstrates high accuracy of decision trees when used for knowledge extraction from multiple bootstrapped gene set enrichment analysis runs. A comparison of the two approaches in sample preparation for high-throughput sequencing shows that alternating decision trees represent the optimal knowledge representation method in comparison with classical decision trees.Usual reproducibility measurements are mostly based on statistical techniques that offer very limited biological insights into the studied gene expression data sets. This paper introduces the meta-learning-based gene set enrichment analysis that can be used to complement the analysis of gene-ranking stability estimation techniques such as percentage of overlapping genes or classic gene set enrichment analysis. It is useful and practical when reproducibility of gene ranking results or different gene selection techniques is observed. The proposed method reveals very accurate descriptive models that capture the co-enrichment of gene sets which are differently enriched in the compared data sets. DNA microarray technology has extended to all fields of genomic research and has become practically the primary tool for gene expression analysis . SignifiMa  wrote onThe goal of the recent large reproducibility study named Microarray Quality Control (MAQC) Project  was to mOur study focuses on the comparison of gene ranking result reproducibility, using simple stability metrics and a more advanced pathway level of comparison of results obtained from microarray and NGS platforms. In addition to empirical evaluation, we propose a novel gene set enrichment-based analysis methodology that can significantly facilitate the process of gene set enrichment analysis when one intends to compare results of different studies, platforms, or even gene-ranking methods. It has to be noted that, in contrast to Mane et al. and the original MAQC study, where technical reproducibility of gene expression measurement is observed, this study focuses on reproducibility of gene-ranking results, sometimes also referred to as gene- ranking stability.Our study used microarray data from Affymetrix data sets that were used in the MAQC study. This platform was chosen due to the high number of test sites (six data sets), allowing more accurate results of reproducibility and pathway-level analysis. MAQC CEL file data were analyzed with BioConductor to generate probeset-level data using the justPlier function. Probe-level data were quantile-normalized before PLIER summarization per test site. An offset value of 16 was added to each probeset-level data point. All six normalized data sets, each containing five replicates of sample A (pooled human cell lines) compared with sample B (pooled human brain), were obtained from the official MACQ website.Affymetrix HG-U133 Plus 2.0 GeneChip probe ids were collapsed into gene symbols using maximal expression for multiple probes mapped to a single gene symbol. This step reduced the dimensionality of data sets from the original 54,675 probes to 20,647 gene symbols.Two data sets from a recent paper by Mane et al. were used to compare the reproducibility of microarray versus NGS data sets. Deep sequencing of the MAQC reference RNA samples was done using Roche's 454 Genome Sequencer FLX (GS FLX). More than 3.6 million sequence reads with an average length of 250 bp were generated for cDNA from the MAQC A and B samples. Using RefSeq database, 64% of all reads could be matched to annotated genes using BLAST. Following mapping to RefSeq IDs, so-called digital gene expression can be measured by counting the numbers of reads that map to individual genes. A supplementary table presenting counts for all mapped reads from the paper by Mane et al. was used to compare the NGS and microarray data in this study. It contains hit counts for the sequencing runs for the A and B samples processed using either the Transcriptome Sequencing (TSEQ) or Oligo DT (ODT) protocols. The data were divided into two data sets- ODT and TSEQ. There were 10 samples (five from MAQC A sample and five from MAQC B sample) in the ODT data set and 12 samples (MAQC B sample was sequenced seven times) in the TSEQ dataset. An initial pool of 24,655 RefSeq symbols was mapped to 16,578 gene symbols for compatibility with microarray data sets, especially for pathway-based analysis.The ODT and TSEQ cDNA sample preparation methods were introduced by Mane et al in their paper comparing more technical aspects of NGS to microarrays. The TSEQ method used random primers and had to be applied to heat-fragmented mRNA strands to generate a single-stranded cDNA library for sequencing using the standard Roche GC Amplicon sequencing procedure. The random primers were used to remove a potential 3' bias in the ODT preparation. On the other hand, the ODT preparation method was used to prepare double-stranded cDNA for the standard Roche GS DNA Library preparation and sequencing procedure. To eliminate reads with long poly A strings, modified oligo dT primers ending with two different additional nucleotides were used in some sequencing runs for this method. Furthermore, both methods required a thorough depletion of rRNA, which can constitute as much as 98% of the total RNA, to minimize the number of sequencing reads from rRNA contamination. After multiple-reduction steps, the rRNA reads were finally reduced to less than 10%. General information on data sets used in this study is summarized in Table Reproducibility of experiments is one of the most important measures to consider when different gene expression analysis platforms are compared. This study used the so-called percentage of overlapping genes (POG) metric that was already proposed in the original MAQC study. POG can be calculated from two lists of ranked genes that are of equal length. It is calculated as the number of genes in common divided by the number of genes in each of the two equal-length lists. Usually, all available genes are ranked and POG is calculated for sublists of different lengths. Results of such comparison are the most suitable for visual representation.lo and k ranked lists l1, l2, ..., lk of genes on each subset. Average POG score POGavg was measured by averaging all pairwise comparisons of k ranked gene lists with the original ranking on the initial data set.In our study, only gene symbols present in both compared platforms  were used to allow an objective comparison of POG score. Initially, k subsets of original data sets were created using sampling with replacement, also called bootstrapping. Eleven gene-ranking methods from the Bioconductor package GeneSelector by Boulesteix and Slawski  were useAltogether, POG scores were calculated using the 11 gene selection methods from GeneSelector summarized in Table Current high-throughput-based studies usually generate large lists of differently expressed genes as their outputs. However, the biological interpretation of such lists (ranging in size from hundreds to thousands of genes) is still a challenging task. Over the last few decades, bioinformatics specialists have collected a wide spectrum of biological knowledge that is deposited in public databases and scientific papers. It is therefore possible to assemble a summary of genes that are present in similar clinical conditions in a collection of gene sets that can be used for so-called functional analysis. A number of high-throughput enrichment tools, like Onto-Express , MAPPFinThis study employed a widely used tool for gene set enrichment, simply called gene set enrichment analysis. An earlier version of this approach, also called gene set enrichment analysis, has been previously described by Lamb et al.  and MootAn analysis of gene set enrichment using GSEA application was performed on each of the eight data sets as described in . As recoOur proposed meta-learning-based GSEA originates from the idea to automate comparisons of multiple GSEA results that have to be done manually. The novel approach is called gene set enrichment meta-learning analysis (GSE-MLA), inspired by the meta-learning theory . By defiFrom a technical point of view, one needs enough meta-data samples to build a reliably supervised classification model. In our study, GSEA was run 100 times on bootstrapped samples for each of two compared original data sets. Each sample, containing NES measurements for all observed gene sets where FDR <25%, was labeled according to the data set of origin and deposited in a meta-learning data set. This data set was then used to build a final knowledge representation model that was built using 200 meta-data samples. Figure The ability to track and evaluate every step in the decision-making process is the most important factor for trusting the decisions gained from data-mining methods. Examples of such techniques are decision trees that possess an important advantage in comparison with competitive classification methods-i.e., the symbolic representation of the extracted knowledge. Decision trees, along with rule-based classifiers, represent a group of classifiers that perform classification by a sequence of simple, easy-to-understand tests whose semantics are intuitively clear to domain experts . Data anWhile J48 and SimpleCART represent two classical decision tree-building techniques that were widely used in the past, ADTree deserves a short introduction. It is an advanced decision tree-building technique based on boosting  algorithThe performance of each decision tree built during this study was evaluated by measuring the classification accuracy (ACC) and area under ROC curve (AUC) metrics. Cross-validation with ten folds was used to calculate both performance metrics. J48 and SimpleCART trees were used with default Weka parameters, while the number of ADTree boosting iterations was lowered from 10 to 5 to allow better comprehensibility of the built models. Our empirical results showed no significant loss of ACC or AUC when using only five boosting iterations.The pseudo-code of the algorithm used is summarized below:n times.1) Repeat the bootstrapping of the samples from each of the two compared gene expression data sets (GED) 2n bootstrapped data sets, calculate normalized enrichment scores (NES) for all gene sets.2) For each of the 3) Label NES vectors by their origin (GED1 or GED2).2n labeled NES vectors (decision tree is recommended).4) Build comprehensible classification model using all In addition to knowledge extraction and classification performance evaluation, GSE-MLA results can be used as input data for GeneSelector to assess overlap of gene sets between different data sets. In our case, we can measure the POGS between gene set enrichment analysis on microarray versus NGS data sets.In the initial experiment, we tried to find the gene ranking method with the highest reproducibility score. It is important to check whether NGS data follow similar characteristics to microarray data sets (the MAQC study determined that simple fold change ranking guarantees the highest reproducibility scores). To our knowledge, there is no similar study that would evaluate the stability of NGS of MAQC data using the POG metric. Therefore, POG was calculated for 11 gene selection methods implemented in GeneSelector. Altogether, there are 15 gene selection methods, but due to technical reasons , we were not able to run all of them on our data. Figure As already noted in the MAQC study, fold change gives the most stable results overall. One can notice that AFX1 provides a higher level of stability than ODT or TSEQ; however, POG for all data sets still lies above 80%. This is not the case for the second (Wilcoxon) and third  most stable metrics where significantly lower POG scores were achieved, especially for top ranked genes. Again, microarrays outperformed NGS in terms of POG. There was actually only a single gene ranking method where results on microarrays were not the most stable, the Ebam . HoweveTo compare the reproducibility of the two abovementioned platforms for gene expression measurements, classical GSEA was performed on six microarray data sets measured on Affymetrix platform and two different NGS data sets included in this study -defined overexpressed genes in CD138-enriched plasma cells for a subgroup of multiple myeloma patients ; and• POMEROY_MD_PTREATMENT_GOOD_VS_POOR_DN gene set containing 24 genes highly associated with medulloblastoma treatment failure .A collection of the remaining decision trees for results from Table GSE-MLA represents a novel approach to gene set enrichment analysis where reproducibility of results is observed at the pathway level. This paper demonstrates an effective way of uncovering the differences in enriched gene sets, comparing microarray and NGS experiments using decision tree-based knowledge extraction. Classic GSEA allows a comparison of different platforms by comparing NES or ranks of single gene set enrichments between platforms, whereas GSE-MLA also uncovers the hidden co-enrichments of gene sets from two compared data sets.Additionally, this study demonstrates that one should be very careful when choosing a gene-ranking method for NGS data analysis. Even the simplest techniques like fold change give lower POG scores in comparison with microarray-based POG. Therefore, it is advised that one must consider specialized NGS gene-ranking methods tailored to count data instead of continuous gene expression values.Classical gene set enrichment analysis shows only minor differences when TSEQ data are compared with AFX1-6 data. Relatively different enrichment levels in the top enriched gene sets between ODT and other data sets might also be contributed by a potential 3' bias in the ODT sample preparation procedure.With the GSE-MLA results, one can notice that even the most enriched gene sets tend to suffer from instability of polarity-i.e., in some bootstrap samples, their value is extremely enriched in one direction and in some, in the opposite direction. Therefore it would also be possible to work with absolute values of NES and avoid the instability of NES polarity. There are still many ways to improve and extend the current GSE-MLA methodology. One of the promising possibilities is discretization of NES values to three  or two  classes.http://ri.fzv.uni-mb.si/nextGene/sup. Java source code for GSE-MLA is also available in Additional File All data sets and source code (in R and Java) used for the experiments described in the paper are available at this supplementary website: The authors declare that they have no competing interests.GS and PK conceived and designed the method. GS and MB wrote the program and analyzed the data. GS and PK drafted the manuscript. All authors read and approved the final manuscript.Percentage of overlapping genes R code. Source file of Figure Click here for fileHeat map R code. Source file of a heat map presented in Figure Click here for fileGSE-MLA ODT results archive. Comma separated values (CSV) files containing results of GSE-MLA for ODT data sets.Click here for fileGSE-MLA TSEQ results archive. Comma separated values (CSV) files containing results of GSE-MLA for TSEQ data sets.Click here for fileGSE-MLA source code archive. Java source code for GSE-MLA and visualization using decision trees.Click here for file


HeT-A retrotransposon. The sequence of the HeT-A retrotransposon changes rapidly resulting in differentiated subfamilies. This pattern of sequence change contrasts with the essential function with which the HeT-A is entrusted and brings about questions concerning the extent of sequence variability, the telomere contribution of different subfamilies, and whether wild type and mutant Drosophila stocks show different HeT-A scenarios.Telomere replication in Drosophila depends on the transposition of a domesticated retroelement, the HeT-A reveals that both the level of variability and the number of subfamilies are higher than previously reported. Comparisons between GIII, a strain with longer telomeres, and its parental strain Oregon-R indicate that both strains have the same set of HeT-A subfamilies. Finally, the presence of a highly conserved splicing pattern only in its antisense transcripts indicates a putative regulatory, functional or structural role for the HeT-A RNA. Interestingly, our results also suggest that most HeT-A copies are actively expressed regardless of which telomere and where in the telomere they are located.A detailed study on the variability of HeT-A sequence changes much faster than previously reported resulting in at least nine different subfamilies most of which could actively contribute to telomere extension in Drosophila. Interestingly, the only significant difference observed between Oregon-R and GIII resides in the nature and proportion of the antisense transcripts, suggesting a possible mechanism that would in part explain the longer telomeres of the GIII stock.Our study demonstrates how the  HeT-A, TART and TAHRE [HeT-A and TART have been cloned and studied from species more than 60 MY distant (D.melanogaster - D.virilis), demonstrating that the telomeric retrotransposons predate the separation of the extant species as well as the robustness and reliability of this mechanism of telomere maintenance [HeT-A and TART orthologues, although committed to the essential function of telomere replication, are far from being static, and while maintaining their basic structures allow their sequence to change rapidly, evolving faster than euchromatic genes and other retrotransposons [D. melanogaster HeT-A element two previous studies have suggested the presence of a small number of subfamilies coexisting in the same stock [Drosophila has a unique mechanism of telomere maintenance. Instead of using the telomerase holoenzyme as most eukaryotes, Drosophila replenishes the telomeres by specific transpositions onto the end of the chromosomes of three retrotransposons, nd TAHRE ,2. The tntenance ,4. Surprnsposons . This trme stock .HeT-A element in several subfamilies according to their variability in the 3'UTR [HeT-A element.Previous studies have attempted to classify the genomic copies of the he 3'UTR  and alsohe 3'UTR . These sHeT-A subfamilies has a putative role related to its own survival as a retrotransposon or to its telomeric function is still unknown. Studies comparing the number and dynamics of the different subfamilies between wild type and telomeric mutant stocks are needed to answer this question.Other retroelements also form subfamilies in a given genome, as for example Tnt1 in tobacco and L1 in mammalian genomes ,9. In th1y); cinnabar (1cn) brown (1bw) speck (1sp) [Drosophila melanogaster. Because the telomeric retrotransposons suffer from terminal erosion while being at the end of the chromosome, 5' truncated copies were expected. These two studies actually revealed that more HeT-A copies in the telomeric arrays have maintained ORFs and other regions needed for function than had originally been expected. The existence of functional copies in proximal regions of these long telomere arrays suggests that these interior sequences may be renewed more frequently than previously thought. In this case, the turnover in these arrays does not simply replace terminal sequence lost in DNA replication but is also necessary for rebuilding a large fraction of the telomere when needed [HeT-A copies capable of active transposition to replenish or make up new telomeres whenever needed. Alternatively, full length elements in the middle of the array could also be explained by the simultaneous transposition of more than one HeT-A element, or access of HeT-A transposition intermediates to the end of the chromosome when terminal erosion has not yet taken place, for example when the capping complex is disassembled. Finding which HeT-A copies are actively being transcribed and whether transcriptional differences exist in response to subfamily affiliation or specific position in the telomeric array would help to better understand telomere biology in Drosophila.With the completion of the heterochromatic genome project  and the ck (sp1) ,13) it wn needed . If thisHeT-A and TART are also transcribed from the antisense strand [HeT-A and TART at Drosophila telomeres become crucial.Besides specialized structures composed of repeated sequences, telomeres are also composed of specific proteins and RNA . Becausee strand ,18 and ae strand . TherefoHeT-A variability and determining if different HeT-A subfamilies could become more successful in a given stock. For this, we have chosen two different stocks, a wild type stock, Oregon-R, and a mutant stock with longer telomeres, the GIII stock bearing an Oregon-R background where the third chromosome from the Tel-1 mutant was introduced almost 10 years ago [Tel-1 mutation has been determined (3L (69)), it is still unknown the molecular cause responsible for the extremely long telomeres of the GIII stock (approximately ten times longer than in any wild type stock). We have amplified and sequenced from DNA and RNA sources in these two stocks some of the most variable regions inside the HeT-A sequence, and have been able to identify all previously defined HeT-A subfamilies. Moreover we describe five previously unreported HeT-A families, demonstrating that HeT-A variability is even greater than expected. Our results show that most HeT-A subfamilies are actively transcribed and some of the found variations allow us to draw the recent history for a number of HeT-A copies. Finally, we have done a wide study on HeT-A antisense transcription and found that most antisense transcripts suffer different alternative splicing with remarkable conservation. Interestingly, we find that the GIII and Oregon-R stocks mainly differ in which subfamilies contribute the most to antisense transcription. This leads us to suggest that due to the importance of non-coding RNAs in gene and heterochromatin regulation, the reported differences could explain in part the greater expression of HeT-A and ultimately the longer telomeres of the GIII stock.The work presented here aims at better characterizing the extent of ears ago . AlthougHeT-A, we decided to carry out a genome wide study analyzing two different regions along the retrotransposon, inside the HeT-A ORF (gag) and the 3' Untranslated Region (UTR). For this, we designed primers that would anneal to highly conserved sequences among copies, maximizing the number of different subfamilies that would be amplified, and in turn flanking highly variable regions in order to maximize the obtained information. Highly variable regions were selected by performing alignments of the amplified fragment from all HeT-A sequences already present in the databases in which the primers were contained  and nine (A to J minus E) subfamilies attending to gag and 3'UTR respectively. In spite of being full length, some of the previously classified HeT-A sequences, had only been analyzed for the gag or the 3'UTR region. Interestingly, our study has now found that the phylogenetic relationship among subfamilies is maintained for both fragments and therefore families A to D for the gag fragment correspond to families A to D for the 3'UTR fragment . The absence of subfamily J in GIII might be due to the insufficient coverage of our approach, or a lower fitness of this subfamily as suggested by its low presence also in Oregon-R. Alternatively, the absence of the J family in GIII suggests that the major increase in HeT-A copy number in this stock may be the result of an increase of only the telomeric copies of the parental stock Oregon-R. Previous studies have reported few HeT-A defective copies outside the telomeres in the heterochromatic region of the III and Y centromeres [HeT-A fragments mostly correspond to the 3'UTR and have not been previously classified in subfamilies. All the sequences, from subfamilies I and J collected in this study show 99% or higher sequence identity with the centromeric sequences previously reported, suggesting that sequences belonging to these subfamilies come from non-telomeric regions.Figures tromeres -23. ThesHeT-A copies obtained in the Oregon-R and GIII stocks . RNA amplification was performed to distinguish sense and antisense strands, since antisense expression of HeT-A has previously been reported in germ line tissues [HeT-A content and variability on these two strains. If our coverage had been too low to carry out these studies, we would expect to find a fair number of expressed or genomic sequences with no counterpart.In order to investigate the specific contribution of each  tissues . Here, wI, all existing subfamilies of both strains are expressed. Figures A and H stand out as the ones with higher expression rate in both strains. Interestingly, subfamily A has a low copy number in both GIII and Oregon-R, suggesting the recent birth of this subfamily or its low transposition efficiency. Subfamilies C (with the exception of the 3'UTR fragment in GIII) and G would be an example of the opposite situation, the expression detected for these subfamilies is lower than expected for the genomic copies integrated in these genomes, nevertheless the differences in this case are subtle and could be explained by level of coverage of this approach.The first result from integrating the genomic and the transcriptomic data for both fragments Figures  and 5 inHeT-A defective copies have been found outside the telomeres in the heterochromatic region of the III and Y centromeres [I and J demonstrating that the 3'UTR primers used in this study are able to amplify sequences belonging to subfamilies I and J. Nevertheless, these copies are expressed at a very low level since we have only detected one sequence out of 44 for the J subfamily in Oregon-R and no sequence for the I subfamily. This result indicates that HeT-A expression coming from other genomic locations outside telomeres is very limited demonstrating that the expression of the HeT-A retrotransposon comes almost exclusively from the telomeres. Again, most subfamilies are expressed similarly in both stocks, supporting the evidence that the major HeT-A content in GIII comes from a proportional increase of the HeT-A subfamilies already present in the parental genetic background, Oregon-R.A few tromeres -23. AlthHeT-A sequences from the two assembled telomeres ; cinnabar (1cn) brown (1bw) speck (1 sp)).Because we have also included in our analysis the gag fragment are free of frame-shifts and other mutations resulting in stop codons, suggesting that all expressed sequences containing a gag fragment could produce a putative functional Gag protein. The phylogenetic relationships of the protein sequences corresponding to the DNA sequences for the gag fragment were analyzed and resulted very similar to the ones presented in Figure Finally, it is also important to mention that all the analyzed RNA sequences from the gag fragment is nearly undetectable in Oregon-R and very poor in GIII  and subfamily H in the case of Oregon-R (40%) are the main producers of 3'UTR antisense transcripts. The comparison between the data from Figure B and D, and finally some subfamilies have almost no antisense expression, subfamilies F and G, that behave otherwise in the sense strand, or subfamilies I and J which have very low expression from both strands. As mentioned before, the sequences from subfamilies I and J belong to defective copies located outside the telomeres and this study demonstrates that their contribution to the total antisense pool of HeT-A transcripts, as it was also the case for sense transcription, is very low .Figure  of GIII 0% and suH is the most abundant, while in GIII subfamily H does not even represent the 10% of total antisense expression ; cinnabar (1cn) brown (1bw) speck (1sp)), we were forced to compare our results with the only mapped telomere sequences  and mRNAs from truncated copies could be explained by a complex regulatory mechanism in which full length and small RNAs (sense and antisense) would interact and control HeT-A transposition and the telomeric chromatin (see below). Work by Shpiz et al. [HeT-A in ovaries and the rasiRNA pathway. Because the antisense HeT-A transcripts reported here do not come from germ line tissues, we suggest that the antisense transcription of HeT-A in somatic tissues could also be involved in a mechanism of RNA interference working in somatic cells. It is worth mentioning that we have detected expression of sequences with high identity with truncated HeT-A copies located in pericentromeric heterochromatin [HeT-A expression. Thereby, we propose that the truncated copies of HeT-A retrotransposon at the telomeres could act in a similar way than truncated copies from master loci such as flamenco, acting to control transposition of other Drosophila retrotransposons  [HeT-A and the majority of Drosophila retrotransposons, the number of full length copies of HeT-A per genome is much higher. The maintenance of a good reservoir of full length copies is probably related to its involvement in building the telomeres, and might require a slightly different regulation than other retrotransposons with no apparent function in the genome, which allows the integration of HeT-A copies whenever telomeres are extended.Although the strain used to assemble the telomeres (2057 see below)  is diffez et al.  demonstrhromatin -23, but Ideafix) ,25. NeveHeT-A copies have additional functions besides the regulation of its own transcription. Recently the world of the non-coding RNAs is being expanded with new functions often involved in chromatin structure and regulation. Examples like the Xist RNA involved in mammalian dosage compensation or, of course, the TERRA RNA in telomere regulation, and the capacity of other non-coding RNAs to recruit histone modifying enzymes like the HOTAIR and the Ezh2 enzyme for regulation of the human HOXC [HeT-A RNAs could have important additional roles at Drosophila telomeres. In this direction, a recent paper has demonstrated how some HeT-A small RNAs coming from the telomeres are essential to recruit the capping complex that protects Drosophila telomeres [HeT-A copy has to be entirely transcribed from the sense strand. Transcription begins at the 3'UTR end of the immediately upstream HeT-A element and extents along the complete downstream copy [HeT-A is also transcribed in the antisense strand in ovaries [HeT-A, most of which are spliced through a small number of very conserved splicing alternatives. The antisense transcripts mainly come from the 3'UTR since only a few transcripts seem to extent into the gag gene  represents more than 60% of the total pool of antisense transcripts in this stock  in an Oregon-R background [All Drosophila stocks were maintained at 25°C on a standard yeasted corn meal-molasses medium. Oregon-R is a standard laboratory wild type strain. The Gaiano III (GIII) stock carries the third chromosome from the Gaiano strain . DNAse I treatment as follows: once with RNase-Free DNase set (Qiagen ref.79254) on-column, as manufacturer instructions, and twice for 3 hours with the same DNAse I in solution, as manufacturer instructions. The RNA concentration and quality were checked using NanoDrop® ND-1000.5 female and 5 male 3HeT-A. In order to normalize the data, we also quantified the genomic copy number of actin in all samples using the primers: Actin F (5' GCGCCCTTACTCTTTCACCA 3') and Actin R (5' ATGTCACGGACGATTTCACG 3'). The qPCR was carried out using the iQ SYBR Green super mix (Biorad) in iQ Real-Time PCR thermal cycler (Biorad), following standard qPCR protocols. The data was analyzed with the iQ software (Biorad).The primers used to quantify the genomic copy number in both strains were: HeT-A Real Time F (5' ACAGATGCCAAGGCTTCAGG 3') and HeT-A Real Time R (5' GCCAGCGCATTTCATGC 3') that bind to the 3'UTR of gag fragment were: GAG F (5' GGCGCCAAAAGCAGCATC 3') and GAG R (5' AACGTCCGGCTTGGGGTT 3'). The primers used to amplify the 3'UTR fragment were: 3UTR F (5' GCTCCAAGCTGCCAATCC 3') and 3UTR R (5' GCCAGAAGGACGGAAGCAC 3'). Genomic amplification was performed by standard PCR using Biotools DNA Polymerase (Cat Nº 10047). RNA amplification was performed by RT-PCR (Transcriptor One-Step RT-PCR Kit (Roche ref. 04655877001)) as directed by the manufacturer, specific for sense or antisense transcript amplification. Amplified products were directly ligated into pSTBlue-1 plasmid, using the AccepTor™ Vector Kit (Novagene ref. 70595-3). Plasmid DNA was purified by standard alkaline miniprep protocol. Insert presence was checked by EcoRI  restriction. The plasmid DNA was sequenced by the Value Service of Macrogen Inc. (Korea) using the T7 promoter primer.The primers used to amplify the The error test of the Biotools DNA Polymerase (Cat Nº 10047) was performed amplifying, cloning and sequencing an already known clone. Eleven sequences comprising 459 nucleotides were analyzed and no error was found.The sequence alignments were carried out using MAFFT 6.833b program . The phyMY: Millions of Years; ORF: Open Reading Frame; TERRA: Telomeric-Repeat containing RNA; qPCR: quantitative Polymerase Chain Reaction; UTR: Un-Translated Region; EDTA: Ethylenediaminetetraacetic acid; SDS: Sodium Dodecyl Sulfate; bp: base pairs.The authors declare that they have no competing interests.DP performed research and wrote the paper, EL-P and ML-P performed research and EC conceived, directed the study and wrote the paper. All authors read and approved the final manuscript.Schematic representation of the assembled data from Drosophila telomeres (source: http://flybase.org). XL, left telomere chromosome X (aprox. 20 Kb). 4R, right telomere chromosome 4 (aprox. 70 Kb). Names of HeT-A copies are indicated. Domains of complete HeT-A element, 5'UTR, gag gene and 3'UTR  are indicated.Click here for fileHeT-A sequences previously available on databases. Names and accession numbers are given. X indicates if HeT-A elements are complete and if the sequence has been used in the gag and/or 3'UTR fragment analysis.Click here for file


Although total left ventricular (LV) stroke volume (SV) is equal in healthy subjects and heart failure patients with mild LV remodeling, the SV’s transventricular flow paths to ejection and diastolic energetics are different. These flow-specific markers of inefficiency of the failing heart are detectable prior to clinical decompensation, and might be predictors of progressive adverse cardiac remodeling.Heart failure is a common disorder with a dismal prognosis. Cardiac remodeling is a key component of heart failure that progresses from adaptive to maladaptive as the disease worsens, and is associated with increased risks of symptoms and mortality. In earlier stages of heart failure with adaptive remodeling, patients may remain clinically compensated and the failing heart’s left ventricular (LV) stroke volume (SV) remains the same as in the normal heart. Recently, time-varying and complex three-dimensional flow patterns and energetics within the normal LV have been demonstrated. Based on such 4D flow-specific measures, we hypothesized that while the SV of normal and failing LVs may be equal, the SV’s diastolic kinetic energy (KE) and transventricular routes to ejection are different in the two states.In ten patients with dilated cardiomyopathy (DCM)  and ten healthy subjects , 4D phase-contrast CMR velocity data and morphological data were acquired at 1.5T (Philips Achieva). A previously validated method was used for the analysis: The LV endocardium was segmented from the morphological images at end-diastole (ED) and end-systole. Pathlines were emitted from the ED blood volume and traced forward and backward in time until end-systole, allowing separation of the Total SV into two flow components . While the Total SV was equal in the two groups , the Direct SV represented a significantly smaller part of the Total SV in DCM compared to healthy  (figure Although total LV stroke volume is equal in healthy subjects and in clinically compensated heart failure patients with mild LV remodeling, the diastolic routes through the LV and pre-systolic energetics of the SV are significantly different in the two states. Inefficient flow may augment the chronic stress on the failing heart’s reserve, promoting progressive maladaptive cardiac remodeling.The Swedish Research Council


Understanding the spatio-temporal pattern of malaria transmission where prevention and control measures are in place would help fine-tuning of strategies. The objective of this study was to assess the effect of mass bednets distribution and indoor residual spraying with insecticides on spatio-temporal clustering of malaria in one malaria endemic village in south Ethiopia.A longitudinal study was conducted from April 2009 to April 2011. The study population was 6631 in 1388 households. We used active and passive searches for malaria cases for 101 weeks. A software for spatial and space-time statistics, SatScan v9.1.1 was used to identify statistically significant retrospective space-time clusters. A discrete Poisson based model was applied with an aim to get areas with high rates. PASW Statistics 18 was used to analyze individual level factors using generalized Poisson loglinear model.Plasmodium Falciparum and Vivax was 316 (22.9 per 1000 per year) and 306 (22.2 per 1000 per year), respectively. Indoor residual spraying with DDT (and later with Deltamethrin) and free mass insecticide treated nets distribution were carried out during the study period. There was space-time clustering of malaria episodes at a household level. The spatio-temporal clustering of malaria was not influenced by free mass insecticide treated nets distribution, however, the time-span of the spatio-temporal clustering of malaria ended after indoor residual spraying with Deltamethrin. The reason for having the clusters on the east-south edge of the village was consistent with the finding showing increased risk of acquiring malaria infection for individuals living nearer to the identified vector breeding place.The total number of all types of malaria episodes was 622 resulting 45.1 episodes per 1000 per year; of which, episodes of The risk of getting malaria infection varied significantly within one village. Free mass insecticide treated nets distribution did not reduce spatio-temporal clustering of malaria, but indoor residual spaying affected malaria clustering in the village.


Here, we use mathematical modeling to investigate the potential impact of early killing of HIV-infected cells on viral replication. We suggest that the majority of CTL-mediated killing could occur during the viral eclipse phase, and that the killing of virus-producing cells could be substantially lower at later stages due to MHC-I-down-regulation. Such a mechanism is in agreement with several experimental observations that include CD8+ T cell depletion and antiretroviral drug treatment. This indicates a potentially important role of CTL-mediated killing during the non-productive stage of HIV-infected cells.Patients infected with HIV exhibit orders of magnitude differences in their set-point levels of the plasma viral load. As to what extent this variation is due to differences in the efficacy of the cytotoxic T lymphocyte (CTL) response in these patients is unclear. Several studies have shown that HIV-infected CD4 It has been shown that the viral load levels can vary over orders of magnitude between patients + T cells recognize and inhibit viral replication very early after infection of a cell Klenerman et al. + CTL and NK cells are depleted Interestingly, HIV evolved a mechanism to partially evade killing by CTLs through down-regulation of MHC-I molecules in infected cells + depletion experiments during antiretroviral therapy + T cells cannot be ruled out, our study indicates a potentially important role of cytotoxic killing of HIV-infected cells during the intracellular eclipse phase.We devise a mathematical model of HIV dynamics to investigate the impact of CTL-mediated killing of HIV-infected cells during the intracellular eclipse phase. Our model can account for large variations in viral set-point levels without affecting the typical decay rate of virus load after drug treatment. We further test the consistency of the hypothesis that HIV-infected cells are cleared during the non-productive stage of infection with several experimental observations such as the unexpected effects of CD8Our model considers two types of cells infected with HIV. Infected cells during the viral eclipse phase, articles , top. BoFirst, we analyze which parameter regimes realistically describe the characteristic early down slope of + cells, To explicitly analyze the effect of early CTL-mediated killing of HIV-infected cells on the set-point viral load, we describe the death rate of infected cells as First, consider the simple case with a constant CTL response, i.e. ral load , and incSecond, consider CTL effector cells t levels . The cored to  d .This analysis demonstrates that ‘early killing’ is an efficient control mechanism, i.e., can account for large variations in the viral load between patients by substantial differences in the efficacy of the immune response, and for a critical killing rate at which the infection is cleared. However, it does not imply that early killing is more efficient than late killing, or that the critical killing rate of approximately 5 dtion see  results Summarizing, we have shown that if CTL-mediated killing happens before the infected cell starts to produce new viral particles, the viral load can be suppressed very effectively by the CTL response, independent of the predicted downslope of the viral load during drug treatment. The infection can be cleared when the killing rate during the eclipse phase exceeds a rate of 5 per day. Such a high killing rate has never been consistent with the viral load decay data, and has therefore has not been considered in studies of the effect of CTL on controlling HIV-1 infection + T cell depletion also suggested that CTL-mediated killing is not responsible for the death of virus-producing cells. In SIV-infected rhesus macaques, the viral load decay following the administration of antiretroviral drugs was not different in CD8+ T depleted animals compared to controls in silico experiment where CD8+ depletion takes place at the same time when drug treatment is started. Technically speaking, we set the infection rate + depletion is performed. This is due to the fact that a larger fraction of the cells in the eclipse phase at the start of treatment will move into the state of virus-producing cells when CTLs are removed.We demonstrated above that ‘early killing’ can account for large differences in viral set-point levels while the observed death rate of virus-producing cells during antiretroviral therapy would remain largely invariant. This characteristic behavior is depicted by the solid lines in + T cell depletion. Monkeys infected with simian immunodeficiency virus (SIV) show a rapid and transient increase in their viral load over orders of magnitude upon depleting their CD8+ T cells + T cell depletion affects the death rate of virus-producing cells. Jin et al. + T cell depletion experiments + T cell depletion, the number of cells becoming virus-producing cells increases rapidly leading to a transient increase in the viral load . We have shown that cytotoxicity during the eclipse phase is indeed perfectly consistent with the absence of an affect of CD8+ T cell depletion during antiretroviral treatment. An experiment to test whether CTL-mediated killing acts during the intracellular eclipse phase would be to follow the dynamics of different cell populations after drug treatment. The virus-producing cells are expected to decline with the same rate as the viral load. In contrast, we hypothesize that infected cells during the early stage before MHC-I-down-regulation experience strong CTL-mediated killing, and therefore should decay with a faster rate than the viral load.Another observation that caused controvery on the role of CTL in controlling HIV-1 infection was the absence of an effect of depleting CD8+ T cells also seem to play an important role during the later productive stage of an infected cell (at least during the acute phase of infection). Whether or not this occurs mainly through late killing in combination with a slow eclipse phase The intriguing implication of the studies from Sacha et al. de novo protein expression kinetics. One possible explanation for this apparent discrepancy comes from the observation that not all epitopes from a specific protein are presented with the same kinetics, i.e., epitopes derived from the same viral protein experience differential antigen presentation kinetics Nevertheless, the studies by Sacha et al. + T cell depletion indeed challenges the view that CTL-mediated killing is effective during the late, productive stage of the cell + T cell depletion experiments and antiretroviral treatment.Lastly, it needs to be investigated how efficient an HIV-infected cell can escape recognition by CTLs through down-regulation of MHC-I For the mathematical analysis on the influence of clearing HIV-infected cells early or late during their viral life cycle, we devise a model of HIV dynamics that is based on standard models of within-host virus dynamics + target cells + T cells are discussed below.Here, non-infected CD4To investigate the influence of cell death on virus production we calculate the expected duration of viral production by a cell that becomes infected with a virus. Increasing the death rate of infected cells, which suggests that changing After the administration of antiretroviral drugs, the viral load declines exponentially during the first week and is typically related to the loss of virus-producing cells. Since the turnover of the virus is a fast process For any value of From the general scheme where an unbound CTL effector cell, where CTL effector cells are stimulated by cells presenting viral antigen on their surface. Similar as for the killing of an infected cell, we can derive a general scheme where an unbound CTL effector cell, where The basic reproductive number, To calculate + T cells can also mediate non-cytotoxic effects on HIV-infected cells CD8+ T cells + T cells reduce the number of new infections, the process function where CD8+ T cells on the virus dynamics are the same as shown in The effects of a model with non-cytotoxic effects of CD8


Proteins involved in mitochondrial metabolic pathways engage in functionally relevant multi-enzyme complexes. We previously described an interaction between short-chain 3-hydroxyacyl-coenzyme A dehydrogenase (SCHAD) and glutamate dehydrogenase (GDH) explaining the clinical phenotype of hyperinsulinism in SCHAD-deficient patients and adding SCHAD to the list of mitochondrial proteins capable of forming functional, multi-pathway complexes. In this work, we provide evidence of SCHAD's involvement in additional interactions forming tissue-specific metabolic super complexes involving both membrane-associated and matrix-dwelling enzymes and spanning multiple metabolic pathways. As an example, in murine liver, we find SCHAD interaction with aspartate transaminase (AST) and GDH from amino acid metabolic pathways, carbamoyl phosphate synthase I (CPS-1) from ureagenesis, other fatty acid oxidation and ketogenesis enzymes and fructose-bisphosphate aldolase, an extra-mitochondrial enzyme of the glycolytic pathway. Most of the interactions appear to be independent of SCHAD's role in the penultimate step of fatty acid oxidation suggesting an organizational, structural or non-enzymatic role for the SCHAD protein. Previous studies have identified functionally relevant protein-protein interactions across these mitochondrial metabolic pathways. For example, mitochondrial aspartate aminotransferase (AST) has been shown to form complexes with both glutamate dehydrogenase (GDH) in the glutamine degradation pathway and carbamoyl phosphate synthase-1 (CPS-1), the first enzyme committed to ammonium removal. Several groups have independently identified this interaction of GDH with CPS-1 More recently, a large protein complex association between inner mitochondrial membrane-associated enzymes of fatty acid oxidation and complexes of the respiratory chain has been identified In our own studies of the mitochondrial matrix-associated fatty acid oxidation enzyme short-chain 3-hydroxy acyl-CoA dehydrogenase (SCHAD), we have demonstrated a novel regulatory role and distinct physical association between SCHAD and GDH. This interaction defined the mechanism of hyperinsulinism in children with SCHAD deficiency In earlier pull down studies from murine liver using histidine-tagged SCHAD (His-Tag SCHAD), we recognized that, in addition to GDH, both CPS-1 and 3-hydroxy-3-methylglutaryl-CoA lyase (HMG-CoA lyase) from the hepatic mitochondrial ketogenesis pathway were also identified Mouse tissues from SCHAD knockout and wild type mice were excess tissues that had previously been snap frozen during earlier published experiments 6 colonies screened, 25 were positive. Plasmids recovered from positive clones were transformed into KC8 bacteria to select colonies containing only target plasmids. The β-galactosidase filter assay was used to assess interaction of SCHAD with other proteins and positives were placed on a grid to small plates, and LacZ filter assay was performed. To confirm the association and identify the site(s) of interaction of SCHAD and positive interactants from initial Lac Z assay, a mating assay was performed. SCHAD and each of the fragments in pEG202 were transformed with pSH18–34 reporter plasmid into RFY206 cells. Proteins coded by cDNA in pJG4.5 (target plasmid) were transformed into EGY48 cells.The DupLEX-A yeast two-hybrid system  was used to screen a human placental LexAcDNA library  with human SCHAD cDNA encoding amino acids 75–119 fused to LexA in plasmid pEG202 as bait. Fragments of this clone, similarly fused in pEG202, are shown in 5′TCCTGGCAAAATCCAAAAAG3′ and reverse 5′ACAGACTTGGTGGTGGAAG3′.SCHAD bait was amplified using a human cDNA library prepared from human liver tissue . PCR was performed using Amplitaq Gold kit (Applied Biosystems) using the following primers, forward Recombinant SCHAD  linked to agarose beads were used for pulldown experiments from various mouse tissue homogenates using identical conditions described for SCHAD. Recombinant SCHAD was expressed and purified in our laboratory using Ni-his columns . SCHAD-protein pull down was performed using AminoLinkPlus Immobilization Kit  following the manufacturer's instructions. Briefly, SCHAD was linked to agarose beads using cyanoborohydride and excess SCHAD protein was washed out. Bovine serum albumin  was used as the control protein and immobilized to agarose beads similar to the SCHAD protein.Tissues were homogenized in 50 mM Tris-HCl buffer containing 0.1% Triton-X 100 and centrifuged at 3000 rpm to separate nuclear and non disrupted tissues. Protein concentration was determined using Lowry's method and protein concentration was adjusted to 1 mg/mL. Homogenates from tissues were incubated for interactions with immobilized SCHAD, polyclonal fructose bisphosphate aldolase antibody or BSA at 4°C overnight. Proteins were eluted from agarose beads following the manufacturer's instructions and identified using mass spectrometric analysis for the SCHAD pulldown experiments and by Western blot using methods we have previously validated for the identification of SCHAD Samples were lysed in SDT  and digested with trypsin using the FASP method essentially as described Amino acids were measured in plasma from wild type and knockout mice in both fasted and fed conditions by ultra-performance liquid chromatography using an assay that has been validated for clinical activities To identify additional protein-protein interactions with SCHAD, we first undertook a yeast 2-hybrid screen using a human SCHAD (AA 77–119)-LexA fusion protein as bait. This portion of SCHAD is shown by X-ray crystallography to form a loop structure that may interact with other proteins. After screening a 2-hybrid library derived from human placental cDNA library, this screen yielded 25 positives clones which encoded 19 different proteins. Sequences were searched against the Genbank database, and SCHAD-interacting proteins identified in these experiments  are listed in In order to provide a second, independent approach to identify protein-protein interactions of SCHAD, we undertook a pulldown strategy in conjunction with protein identification by tandem mass spectrometry. Histidine-tagged SCHAD was immobilized on agarose beads, and interacting proteins from murine tissue extracts were isolated using affinity chromatography in preparation for LC-MS/MS identification. This approach allowed us to identify conserved SCHAD interactions as well as to extend our initial work across a variety of tissue types. In this study we analyzed tissues from both wild-type and SCHAD knockout mice. This was based on our earlier observation that, at least in pancreas, in the wild type mouse, GDH was fully occupied by SCHAD due to molar excess of SCHAD in this tissue. We determined that there was a risk that proteins such as GDH may not be available to pulldown in the wild type situation. Our results demonstrated that the two murine lines demonstrated comparable pull down profiles across most of the associations. This complementary strategy recognized many of the same protein interactions that we had identified using the yeast-2-hybrid assay thus providing additional evidence that the initial proteins were true interactants with SCHAD. In order to confirm this unexpected association of SCHAD with non-mitochondrial cytosolic enzymes, we undertook additional immunoprecipitation experiments directed at confirming the association with one of the glycolytic enzymes, fructose-bisphosphate aldolase. Pulldown from tissue extracts derived from mice with wild-type SCHAD were undertaken using an anti-aldolase antibody, and Western blot analysis confirmed the copurification of aldolase and SCHAD . MoreoveOur results provide evidence that SCHAD interacts with enzymes from a diverse variety of metabolic pathways and that these enzymes form an interacting metabolic super-complex. The results of our SCHAD pulldown experiments are organized by individual metabolic pathways as well as by tissue specificity.The SCHAD pulldown experiments showed that SCHAD protein associated in all tissues with components of the glycolytic pathway and, to a lesser extent, enzymes of the citric acid cycle. The major glycolytic enzymes indentified were fructose bisphosphate aldolase and phosphoglycerate kinase1, and citric acid cycle enzymes included fumarate hydratase and isocitrate dehydrogenase. There was also a consistent association with the proteins of the pyruvate dehydrogenase complex responsible for the generation of acetyl-CoA to fuel the citric acid cycle. We also identified a physical interaction of SCHAD with glycogen phosphorylase, a non-mitochondrial enzyme that is important for the regulation of blood glucose levels from glycogen.SCHAD-interacting proteins that are involved in amino acid metabolism include GDH, AST, responsible for the transamination of glutamate to α-ketoglutarate and glutamine synthetase which converts glutamate to glutamine and also salvages ammonium ions. These proteins were found to be associated with SCHAD in liver, kidney, and brain. We did not identify GDH as an associated protein in skeletal and cardiac muscle, and it is well established that GDH enzyme activity is not expressed in these tissues.SCHAD interacted with a variety of membrane-associated and matrix proteins involved in fatty acid β-oxidation in all tissues types tested. Proteins identified include both subunits of the inner mitochondrial membrane-associated trifunctional protein, which contains the 3-hydroxyacyl-CoA dehydrogenase responsible for oxidation of long chain acyl-CoA species; three chain-length specific members of the acyl CoA dehydrogenase family ; and 3-keto-acyl CoA thiolase, which is responsible for the subsequent and final reaction of fatty acid oxidation after SCHAD.ACAD10, a recently described member of the acyl-CoA dehydrogenase family is known to be primarily expressed in brain and liver and was only seen in these tissues in our studies. Of particular interest, although present in the wild-type tissue pulldown in liver, heart and brain, we did not see association of SCHAD with the membrane associated enzymes responsible for long-chain fatty acid oxidation in the liver and brain from SCHAD knockout tissues. Cardiac muscle, however, did demonstrate the association in the knock-out mouse.In liver, we identified interactions with CPS-1, argininosuccinate (ASA) synthase and ASA lyase, important components of ureagenesis. As seen with carbohydrate metabolism, in addition to mitochondrial proteins, we identified some enzymes that are localized to the cytosolic fraction (ASA synthase and ASA lyase). We did identify individual enzymes of the urea cycle in other tissues with isolated expression of CPS-1, ASA synthase ASA lyase in association with SCHAD in kidney, and arginase-2, the mitochondrial form of arginase in brain.HMG-CoA synthase and HMG-CoA lyase play a role in mitochondrial ketogenesis and were identified in liver using both yeast-2 hybrid and the affinity pulldown-proteomic techniques. Succinyl-CoA: 3-ketoacid acyl transferase, an important enzyme for ketone body utilization was identified in all tissues except the heart muscle. One electron transfer protein (ETF-Ubiquinone oxidoreductase) was identified in liver using the yeast-2 hybrid assay, whilst both ETF and ETF ubiquinone oxidoreductase were identified using pulldown-proteomic techniques in all of the tissues studied. Using the yeast 2 hydbrid technique, we identified three related TNF receptor associated factor family of proteins: TRAF3, 5 and 6. We also identified association of SCHAD with several oxidative phophorylation enzymes in our study, with the majority of them identified in brain. These enzymes include NADH-cytochrome oxidoreductase, cytochrome bc1 complex, succinate dehydrogenase, cytochrome oxidase, and members of the ATP synthase complex.Amino acid profiles and blood ammonia levels were obtained from wild type and SCHAD knockout mice in both fasted and fed conditions. A total of 5 animals in each subgroup were studied. We did not detect significant changes in any circulating amino acid level between the wild type or knockout in either fasting or fed status. In particular we did not see evidence of altered ureagenesis through changes in the levels of ammonia, glutamine, ornithine, citrulline or arginine, or alterations in the levels of glutamate or aspartate.In this study, we have identified protein-protein interactions between SCHAD, the penultimate enzyme of mitochondrial fatty acid β-oxidation and multiple other mitochondrial and some non-mitochondrial metabolic proteins. We initially examined SCHAD interactions using a yeast-2 hybrid system and identified 21 interacting proteins as candidates for a metabolic super-complex. Next, we used immobilized SCHAD on agarose beads coupled with mass spectrometric proteomic analysis to confirm our initial yeast-2-hybrid findings. The process of pulldown with mass-spectrometric identification has greater inherent sensitivity for detecting proteins and has the advantage of allowing us to rapidly test whether different interaction sets are identified within different tissues. Our results from these affinity chromatography protein pull down experiments coupled with mass-spectrometric identification provided valuable verification of the proteins that were identified using the yeast-2 hybrid technique. Using these complimentary approaches, we have confirmed our previously published protein-protein interaction between SCHAD and several other mitochondrial proteins in liver including GDH, CPS1 and HMG-CoA lyase; further, we have revealed new interactions that have not previously been recognized and that together provide evidence for the possible existence of a cellular “metabolic super complex” integrating multiple metabolic pathways.The identification of these novel interactions underscore a role for the SCHAD protein as an essential component not only of metabolic pathways within the mitochondria but also with those that are not currently regarded as being mitochondrial. Given the surprising nature of this result, we performed reciprocal co-immunoprecipitation to confirm an interaction between SCHAD and fructose bisphosphate aldolase. These experiments appear to confirm a direct or indirect physical interaction between SCHAD and a glycolytic enzyme known to be localized in another intracellular compartment.ATP channels Our previous observations of the role of SCHAD in non-enzymatically regulating the function of GDH suggest the possibility that SCHAD might regulate or modulate the function of other critical enzymes through protein-protein interactions In our earlier studies on SCHAD-GDH interaction in pancreatic islet cells we observed that the association between these two proteins in the wild-type mouse was complete and that in this scenario GDH was blocked. We were only able to confirm pancreatic islet association in the SCHAD knockout mouse. Subsequently, we performed the current study using tissues from both wild type and SCHAD knockout mice for comparison. The majority of proteins identified by mass spectrometry had similar profiles and recoveries, based on peptide sequence numbers, from both wild type and knockout mice. The major exception that we observed was that in the knock out mouse a number of enzymes of mitochondrial fatty acid oxidation were no longer seen to be associated in some tissues. This was particularly apparent for the trifunctional enzyme subunit beta and very long-chain specific acyl-CoA dehydrogenase in brain and liver and also short-chain specific acyl-CoA dehydrogenase in liver and medium-chain specific acyl-CoA dehydrogenase in skeletal muscle. However, all of these enzymes were identified as equal components in both wild type and SCHAD knockout cardiac muscle. Taken together, this evidence suggests that the loss of SCHAD does result in loss of both membrane associated and matrix associated members of the fatty acid oxidation pathway in some tissues due to structural changes in the super-complex. This loss of structure is not seen in cardiac muscle suggesting a more stable complex in heart which is a tissue that has an obligate need for fatty acid oxidation as an energy source. Also, cardiac muscle was the only tissue that did not show association with succinyl-CoA: 3-ketoacid acyl transferase, an important enzyme in ketone body utilization as an energy source, indicating that ketones may not be important fuels for heart energetics.We also identified associations between SCHAD and several urea cycle enzymes, including the extra-mitochondrial ASA synthase and ASA lyase in liver. We speculate that the functional significance of the association between the SCHAD and urea cycle enzymes may be related to the requirement of N-acetylglutamate (NAG) as an activator of the first enzymatic step of ureagenesis, which is catalyzed by CPS-1. The end product of fatty acid oxidation through the thiolase step is acetyl-CoA which in addition to glutamate is essential for activation of CPS-1. Hardy et al. Our pull down studies revealed an interaction between SCHAD and several components components of the mitochondrial oxidative phosphorylation system. Interactions with several functional components of the OX/PHOS system, including NADH-ubiquinone oxidoreductase, succinate dehydrogenase, cytochrome bc1 complex, cytochrome c oxidase and others, were identified. In contrast, the relative lack of OX/PHOS structural components identified suggests that interactions of SCHAD may be more specific to catalytic components of this pathway. This is in keeping with the role of SCHAD and also the trifunctional protein as NADH-generating enzymes demonstrating a logical physical association with the regeneration of NAD through the respiratory chain.+ T cells Our yeast-2-hybrid studies have identified physical interactions between SCHAD and some of the TRAF family of proteins, specifically TRAF3, 5 and 6. Studies of the TRAF6 knockout mouse model have shown a defect in mitochondrial fatty acid oxidation as well as in AMP activated kinase activation in CD8A model of how SCHAD and its associated proteins link the metabolic pathways in the mitochondria is shown in Hardy et al, + ATPases suggesting a role in regulating mitochondrial membrane potential In conclusion, we have demonstrated a physical association between SCHAD and important components of other key metabolic pathways. Most of the interactions are with enzymes in mitochondrial pathways and most of the interactions appear to be independent of the primary role of SCHAD as an enzyme responsible for the penultimate step of fatty acid oxidation. These interactions would define a metabolic super-complex where multiple metabolic pathways converge. Unexpectedly, we also identified a number of non-mitochondrial protein reactions on classical pathways that are related to those of the mitochondria including energy metabolism through glycolysis and ureagenesis through extra-mitochondrial urea cycle proteins. Pulldown using fructose bisphosphate aldolase confirmed these associations. It is well known that glycolytic enzymes associate with mitochondria Table S1Full pulldown proteomic details from wild type and SCHAD knockout brain.(DOCX)Click here for additional data file.Table S2Full pulldown proteomic details from wild type and SCHAD knockout kidney.(DOCX)Click here for additional data file.Table S3Full pulldown proteomic details from wild type and SCHAD knockout heart.(DOCX)Click here for additional data file.Table S4Full pulldown proteomic details from wild type and SCHAD knockout skeletal muscle.(DOCX)Click here for additional data file.Table S5Full pulldown proteomic details from wild type and SCHAD knockout liver.(DOCX)Click here for additional data file.