Detecting Differential and Correlated Protein Expression in Label-Free Shotgun Proteomics

Recent studies have revealed a relationship between protein abundance and sampling statistics, such as sequence coverage, peptide count, and spectral count, in label-free liquid chromatography−tandem mass spectrometry (LC−MS/MS) shotgun proteomics. The use of sampling statistics offers a promising method of measuring relative protein abundance and detecting differentially expressed or coexpressed proteins. We performed a systematic analysis of various approaches to quantifying differential protein expression in eukaryotic Saccharomyces cerevisiae and prokaryotic Rhodopseudomonas palustris label-free LC−MS/MS data. First, we showed that, among three sampling statistics, the spectral count has the highest technical reproducibility, followed by the less-reproducible peptide count and relatively nonreproducible sequence coverage. Second, we used spectral count statistics to measure differential protein expression in pairwise experiments using five statistical tests:  Fisher's exact test, G-test, AC test, t-test, and LPE test. Given the S. cerevisiae data set with spiked proteins as a benchmark and the false positive rate as a metric, our evaluation suggested that the Fisher's exact test, G-test, and AC test can be used when the number of replications is limited (one or two), whereas the t-test is useful with three or more replicates available. Third, we generalized the G-test to increase the sensitivity of detecting differential protein expression under multiple experimental conditions. Out of 1622 identified R. palustris proteins in the LC−MS/MS experiment, the generalized G-test detected 1119 differentially expressed proteins under six growth conditions. Finally, we studied correlated expression of these 1119 proteins by analyzing pairwise expression correlations and by delineating protein clusters according to expression patterns. Through pairwise expression correlation analysis, we demonstrated that proteins co-located in the same operon were much more strongly coexpressed than those from different operons. Combining cluster analysis with existing protein functional annotations, we identified six protein clusters with known biological significance. In summary, the proposed generalized G-test using spectral count sampling statistics is a viable methodology for robust quantification of relative protein abundance and for sensitive detection of biologically significant differential protein expression under multiple experimental conditions in label-free shotgun proteomics. Keywords: label-free • LC−MS/MS • shotgun proteomics • differential expression • correlated expression • clustering • Saccharomyces cerevisiae • Rhodopseudomonas palustris</i

Detecting Differential and Correlated Protein Expression in Label-Free Shotgun Proteomics

Recent studies have revealed a relationship between protein abundance and sampling statistics, such as sequence coverage, peptide count, and spectral count, in label-free liquid chromatography−tandem mass spectrometry (LC−MS/MS) shotgun proteomics. The use of sampling statistics offers a promising method of measuring relative protein abundance and detecting differentially expressed or coexpressed proteins. We performed a systematic analysis of various approaches to quantifying differential protein expression in eukaryotic Saccharomyces cerevisiae and prokaryotic Rhodopseudomonas palustris label-free LC−MS/MS data. First, we showed that, among three sampling statistics, the spectral count has the highest technical reproducibility, followed by the less-reproducible peptide count and relatively nonreproducible sequence coverage. Second, we used spectral count statistics to measure differential protein expression in pairwise experiments using five statistical tests:  Fisher's exact test, G-test, AC test, t-test, and LPE test. Given the S. cerevisiae data set with spiked proteins as a benchmark and the false positive rate as a metric, our evaluation suggested that the Fisher's exact test, G-test, and AC test can be used when the number of replications is limited (one or two), whereas the t-test is useful with three or more replicates available. Third, we generalized the G-test to increase the sensitivity of detecting differential protein expression under multiple experimental conditions. Out of 1622 identified R. palustris proteins in the LC−MS/MS experiment, the generalized G-test detected 1119 differentially expressed proteins under six growth conditions. Finally, we studied correlated expression of these 1119 proteins by analyzing pairwise expression correlations and by delineating protein clusters according to expression patterns. Through pairwise expression correlation analysis, we demonstrated that proteins co-located in the same operon were much more strongly coexpressed than those from different operons. Combining cluster analysis with existing protein functional annotations, we identified six protein clusters with known biological significance. In summary, the proposed generalized G-test using spectral count sampling statistics is a viable methodology for robust quantification of relative protein abundance and for sensitive detection of biologically significant differential protein expression under multiple experimental conditions in label-free shotgun proteomics. Keywords: label-free • LC−MS/MS • shotgun proteomics • differential expression • correlated expression • clustering • Saccharomyces cerevisiae • Rhodopseudomonas palustris</i

Detecting Differential and Correlated Protein Expression in Label-Free Shotgun Proteomics

Recent studies have revealed a relationship between protein abundance and sampling statistics, such as sequence coverage, peptide count, and spectral count, in label-free liquid chromatography−tandem mass spectrometry (LC−MS/MS) shotgun proteomics. The use of sampling statistics offers a promising method of measuring relative protein abundance and detecting differentially expressed or coexpressed proteins. We performed a systematic analysis of various approaches to quantifying differential protein expression in eukaryotic Saccharomyces cerevisiae and prokaryotic Rhodopseudomonas palustris label-free LC−MS/MS data. First, we showed that, among three sampling statistics, the spectral count has the highest technical reproducibility, followed by the less-reproducible peptide count and relatively nonreproducible sequence coverage. Second, we used spectral count statistics to measure differential protein expression in pairwise experiments using five statistical tests:  Fisher's exact test, G-test, AC test, t-test, and LPE test. Given the S. cerevisiae data set with spiked proteins as a benchmark and the false positive rate as a metric, our evaluation suggested that the Fisher's exact test, G-test, and AC test can be used when the number of replications is limited (one or two), whereas the t-test is useful with three or more replicates available. Third, we generalized the G-test to increase the sensitivity of detecting differential protein expression under multiple experimental conditions. Out of 1622 identified R. palustris proteins in the LC−MS/MS experiment, the generalized G-test detected 1119 differentially expressed proteins under six growth conditions. Finally, we studied correlated expression of these 1119 proteins by analyzing pairwise expression correlations and by delineating protein clusters according to expression patterns. Through pairwise expression correlation analysis, we demonstrated that proteins co-located in the same operon were much more strongly coexpressed than those from different operons. Combining cluster analysis with existing protein functional annotations, we identified six protein clusters with known biological significance. In summary, the proposed generalized G-test using spectral count sampling statistics is a viable methodology for robust quantification of relative protein abundance and for sensitive detection of biologically significant differential protein expression under multiple experimental conditions in label-free shotgun proteomics. Keywords: label-free • LC−MS/MS • shotgun proteomics • differential expression • correlated expression • clustering • Saccharomyces cerevisiae • Rhodopseudomonas palustris</i

Detecting Differential and Correlated Protein Expression in Label-Free Shotgun Proteomics

Recent studies have revealed a relationship between protein abundance and sampling statistics, such as sequence coverage, peptide count, and spectral count, in label-free liquid chromatography−tandem mass spectrometry (LC−MS/MS) shotgun proteomics. The use of sampling statistics offers a promising method of measuring relative protein abundance and detecting differentially expressed or coexpressed proteins. We performed a systematic analysis of various approaches to quantifying differential protein expression in eukaryotic Saccharomyces cerevisiae and prokaryotic Rhodopseudomonas palustris label-free LC−MS/MS data. First, we showed that, among three sampling statistics, the spectral count has the highest technical reproducibility, followed by the less-reproducible peptide count and relatively nonreproducible sequence coverage. Second, we used spectral count statistics to measure differential protein expression in pairwise experiments using five statistical tests:  Fisher's exact test, G-test, AC test, t-test, and LPE test. Given the S. cerevisiae data set with spiked proteins as a benchmark and the false positive rate as a metric, our evaluation suggested that the Fisher's exact test, G-test, and AC test can be used when the number of replications is limited (one or two), whereas the t-test is useful with three or more replicates available. Third, we generalized the G-test to increase the sensitivity of detecting differential protein expression under multiple experimental conditions. Out of 1622 identified R. palustris proteins in the LC−MS/MS experiment, the generalized G-test detected 1119 differentially expressed proteins under six growth conditions. Finally, we studied correlated expression of these 1119 proteins by analyzing pairwise expression correlations and by delineating protein clusters according to expression patterns. Through pairwise expression correlation analysis, we demonstrated that proteins co-located in the same operon were much more strongly coexpressed than those from different operons. Combining cluster analysis with existing protein functional annotations, we identified six protein clusters with known biological significance. In summary, the proposed generalized G-test using spectral count sampling statistics is a viable methodology for robust quantification of relative protein abundance and for sensitive detection of biologically significant differential protein expression under multiple experimental conditions in label-free shotgun proteomics. Keywords: label-free • LC−MS/MS • shotgun proteomics • differential expression • correlated expression • clustering • Saccharomyces cerevisiae • Rhodopseudomonas palustris</i

Genome-wide DNA methylation analysis reveals loci that distinguish different types of adipose tissue in obese individuals

Background: Epigenetic mechanisms provide an interface between environmental factors and the genome and are known to play a role in complex diseases such as obesity. These mechanisms, including DNA methylation, influence the regulation of development, differentiation and the establishment of cellular identity. Here we employ two approaches to identify differential methylation between two white adipose tissue depots in obese individuals before and after gastric bypass and significant weight loss. We analyse genome-wide DNA methylation data using (a) traditional paired t tests to identify significantly differentially methylated loci (Bonferroni-adjusted P≤1×10−7) and (b) novel combinatorial algorithms to identify loci that differentiate between tissue types. Results: Significant differential methylation was observed for 3239 and 7722 CpG sites, including 784 and 1129 extended regions, between adipose tissue types before and after significant weight loss, respectively. The vast majority of these extended differentially methylated regions (702) were consistent across both time points and enriched for genes with a role in transcriptional regulation and/or development (e.g. homeobox genes). Other differentially methylated loci were only observed at one time point and thus potentially highlight genes important to adipose tissue dysfunction observed in obesity. Strong correlations (r>0.75, P≤0.001) were observed between changes in DNA methylation (subcutaneous adipose vs omentum) and changes in clinical trait, in particular for CpG sites within PITX2 and fasting glucose and four CpG sites within ISL2 and HDL. A single CpG site (cg00838040, ATP2C2) gave strong tissue separation, with validation in independent subcutaneous (n=681) and omental (n=33) adipose samples. Conclusions: This is the first study to report a genome-wide DNA methylome comparison of subcutaneous abdominal and omental adipose before and after weight loss. The combinatorial approach we utilised is a powerful tool for the identification of methylation loci that strongly differentiate between these tissues. This study provides a solid basis for future research focused on the development of adipose tissue and its potential dysfunction in obesity, as well as the role DNA methylation plays in these processes. </div

Race-gender specific geographic distribution of the Common Factor.

<p>USA. 1999–2006.</p

Relationship between ErGeNs and counterpart networks in RMA expression data.

<p>Both S-score networks, ErGeN1 and ErGeN2, had counterpart networks in the basal saline and post-ethanol expression data: ErGeN1 significantly overlapped with saline network 1 and ethanol network 1; ErGeN3 significantly overlapped with saline network 4 and ethanol network 2 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033575#pone.0033575.s003" target="_blank">Figure S3</a>). Each point represents a gene that belongs to a given ErGeN's counterpart saline network (blue), ethanol network (red) or both (green). Filled-in points indicate the gene also belongs to the overlapping ErGeN. The X- and Y-axes measure gene connectivity (|Pearson correlation coefficient|≥0.7) within the saline and ethanol expression datasets, respectively.</p

Acute ethanol transcriptional response profiles.

<p>(<b>A</b>) Strain frequency distributions of gene transcriptional-response classes based on PFC S-score analysis. S-scores >2 indicate a gene was up-regulated by acute ethanol, S-scores <2 indicate down-regulation and S-scores between these thresholds were considered unchanged. (<b>B</b>) S-score strain distributions for three significantly ethanol responsive genes that each represent a different class of ethanol response profile.</p

RNAi knockdown of <i>tsp-7</i> increases worm lifespan.

Survival curves C. elegans fed either empty vector (EV) RNAi (black, n = 317) or tsp-7(RNAi) (blue, n = 312).</p

Conserved pathways between process of cellular senescence and functional decline.

A group of 10 genes was identified as common to functional decline and senescence. Many of these genes interact in the MAP kinase pathway, as shown by this protein-protein interaction plot from STRING.</p