To elucidate cellular events underlying the pluripotency of human embryonic stem cells (hESCs), we performed parallel quantitative proteomic and phosphoproteomic analyses of hESCs during differentiation initiated by a diacylglycerol analog or transfer to media that had not been conditioned by feeder cells. We profiled 6521 proteins and 23,522 phosphorylation sites, of which almost 50% displayed dynamic changes in phosphorylation status during 24 hours of differentiation. These data are a resource for studies of the events associated with the maintenance of hESC pluripotency and those accompanying their differentiation. From these data, we identified a core hESC phosphoproteome of sites with similar robust changes in response to the two distinct treatments. These sites exhibited distinct dynamic phosphorylation patterns, which were linked to known or predicted kinases on the basis of the matching sequence motif. In addition to identifying previously unknown phosphorylation sites on factors associated with differentiation, such as kinases and transcription factors, we observed dynamic phosphorylation of DNA methyltransferases (DNMTs). We found a specific interaction of DNMTs during early differentiation with the PAF1 (polymerase-associated factor 1) transcriptional elongation complex, which binds to promoters of the pluripotency and known DNMT target genes encoding OCT4 and NANOG, thereby providing a possible molecular link for the silencing of these genes during differentiation.

During recent years, increased efforts have focused on elucidating the secretory function of skeletal muscle. Through secreted molecules, skeletal muscle affects local muscle biology in an auto/paracrine manner as well as having systemic effects on other tissues. Here we used a quantitative proteomics platform to investigate the factors secreted during the differentiation of murine C2C12 skeletal muscle cells. Using triple encoding stable isotope labeling by amino acids in cell culture, we compared the secretomes at three different time points of muscle differentiation and followed the dynamics of protein secretion. We identified and quantitatively analyzed 635 secreted proteins, including 35 growth factors, 40 cytokines, and 36 metallopeptidases. The extensive presence of these proteins that can act as potent signaling mediators to other cells and tissues strongly highlights the important role of the skeletal muscle as a prominent secretory organ. In addition to previously reported molecules, we identified many secreted proteins that have not previously been shown to be released from skeletal muscle cells nor shown to be differentially released during the process of myogenesis. We found 188 of these secreted proteins to be significantly regulated during the process of myogenesis. Comparative analyses of selected secreted proteins revealed little correlation between their mRNA and protein levels, indicating pronounced regulation by posttranscriptional mechanisms. Furthermore, analyses of the intracellular levels of members of the semaphorin family and their corresponding secretion dynamics demonstrated that the release of secreted proteins is tightly regulated by the secretory pathway, the stability of the protein, and/or the processing of secreted proteins. Finally, we provide 299 unique hydroxyproline sites mapping to 48 distinct secreted proteins and have discovered a novel hydroxyproline motif. Molecular & Cellular Proteomics 9:2482-2496, 2010.

Mass spectrometry-based proteomics critically depends on algorithms for data interpretation. A current bottleneck in the rapid advance of proteomics technology is the closed nature and slow development cycle of vendor-supplied software solutions. We have created an open source software environment, called MSQuant, which allows visualization and validation of peptide identification results directly on the raw mass spectrometric data. MSQuant iteratively recalibrates MS data thereby significantly increasing mass accuracy leading to fewer false positive peptide identifications. Algorithms to increase data quality include an MS(3) score for peptide identification and a post-translational modification (PTM) score that determines the probability that a modification such as phosphorylation is placed at a specific residue in an identified peptide. MSQuant supports relative protein quantitation based on precursor ion intensities, including element labels (e.g., (15)N), residue labels (e.g., SILAC and ICAT), termini labels (e.g., (18)O), functional group labels (e.g., mTRAQ), and label-free ion intensity approaches. MSQuant is available, including an installer and supporting scripts, at http://msquant.sourceforge.net .

The stimulation of fibroblast growth factor receptors (FGFRs) with distinct FGF ligands generates specific cellular responses. However, the mechanisms underlying this paradigm have remained elusive. Here, we show that FGF-7 stimulation leads to FGFR2b degradation and, ultimately, cell proliferation, whereas FGF-10 promotes receptor recycling and cell migration. By combining mass-spectrometry-based quantitative proteomics with fluorescence microscopy and biochemical methods, we find that FGF-10 specifically induces the rapid phosphorylation of tyrosine (Y) 734 on FGFR2b, which leads to PI3K and SH3BP4 recruitment. This complex is crucial for FGFR2b recycling and responses, given that FGF-10 stimulation of either FGFR2b_Y734F mutant- or SH3BP4-depleted cells switches the receptor endocytic route to degradation, resulting in decreased breast cancer cell migration and the inhibition of epithelial branching in mouse lung explants. Altogether, these results identify an intriguing ligand-dependent mechanism for the control of receptor fate and cellular outputs that may explain the pathogenic role of deregulated FGFR2b, thus offering therapeutic opportunities.

A fascinating conundrum in cell signaling is how stimulation of the same receptor tyrosine kinase with distinct ligands generates specific outcomes. To decipher the functional selectivity of EGF and TGF-α, which induce epidermal growth factor receptor (EGFR) degradation and recycling, respectively, we devised an integrated multilayered proteomics approach (IMPA). We analyzed dynamic changes in the receptor interactome, ubiquitinome, phosphoproteome, and late proteome in response to both ligands in human cells by quantitative MS and identified 67 proteins regulated at multiple levels. We identified RAB7 phosphorylation and RCP recruitment to EGFR as switches for EGF and TGF-α outputs, controlling receptor trafficking, signaling duration, proliferation, and migration. By manipulating RCP levels or phosphorylation of RAB7 in EGFR-positive cancer cells, we were able to switch a TGF-α-mediated response to an EGF-like response or vice versa as EGFR trafficking was rerouted. We propose IMPA as an approach to uncover fine-tuned regulatory mechanisms in cell signaling.

Nonalcoholic fatty liver disease (NAFLD) represents a spectrum of conditions ranging from simple steatosis (NAFL), over nonalcoholic steatohepatitis (NASH) with or without fibrosis, to cirrhosis with end-stage disease. The hepatic molecular events underlying the development of NAFLD and transition to NASH are poorly understood. The present study aimed to determine hepatic transcriptome dynamics in patients with NAFL or NASH compared with healthy normal-weight and obese individuals. RNA sequencing and quantitative histomorphometry of liver fat, inflammation and fibrosis were performed on liver biopsies obtained from healthy normal-weight ( n = 14) and obese ( n = 12) individuals, NAFL ( n = 15) and NASH ( n = 16) patients. Normal-weight and obese subjects showed normal liver histology and comparable gene expression profiles. Liver transcriptome signatures were largely overlapping in NAFL and NASH patients, however, clearly separated from healthy normal-weight and obese controls. Most marked pathway perturbations identified in both NAFL and NASH were associated with markers of lipid metabolism, immunomodulation, extracellular matrix remodeling, and cell cycle control. Interestingly, NASH patients with positive Sonic hedgehog hepatocyte staining showed distinct transcriptome and histomorphometric changes compared with NAFL. In conclusion, application of immunohistochemical markers of hepatocyte injury may serve as a more objective tool for distinguishing NASH from NAFL, facilitating improved resolution of hepatic molecular changes associated with progression of NAFLD. NEW & NOTEWORTHY Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease in Western countries. NAFLD is associated with the metabolic syndrome and can progress to the more serious form, nonalcoholic steatohepatitis (NASH), and ultimately lead to irreversible liver damage. Using gold standard molecular and histological techniques, this study demonstrates that the currently used diagnostic tools are problematic for differentiating mild NAFLD from NASH and emphasizes the marked need for developing improved histological markers of NAFLD progression.

SUMMARY Ubiquitin-mediated inactivation of caspases has long been postulated to contribute to the regulation of apoptosis. However, detailed mechanisms and functional consequences of caspase ubiquitylation have not been demonstrated. Here we show that the Drosophila Inhibitor of Apoptosis 1, DIAP1, blocks effector caspases by targeting them for polyubiquitylation and nonproteasomal inactivation. We demonstrate that the conjugation of ubiquitin to drICE suppresses its catalytic potential in cleaving caspase substrates. Our data suggest that ubiquitin conjugation sterically interferes with substrate entry and reduces the caspase’s proteolytic velocity. Disruption of drICE ubiquitylation, either by mutation of DIAP1’s E3 activity or drICE’s ubiquitin-acceptor lysines, abrogates DIAP1’s ability to neutralize drICE and suppress apoptosis in vivo. We also show that DIAP1 rests in an “inactive” conformation that requires caspase-mediated cleavage to subsequently ubiquitylate caspases. Taken together, our findings demonstrate that effector caspases regulate their own inhibition through a negative feedback mechanism involving DIAP1 “activation” and nondegradative polyubiquitylation.

Recent technological advances have made it possible to identify and quantify thousands of proteins in a single proteomics experiment. As a result of these developments, the analysis of data has become the bottleneck of proteomics experiment. To provide the proteomics community with a user-friendly platform for comprehensive analysis, inspection and visualization of quantitative proteomics data we developed the Graphical Proteomics Data Explorer (GProX)1 . The program requires no special bioinformatics training, as all functions of GProX are accessible within its graphical user-friendly interface which will be intuitive to most users. Basic features facilitate the uncomplicated management and organization of large data sets and complex experimental setups as well as the inspection and graphical plotting of quantitative data. These are complemented by readily available high-level analysis options such as database querying, clustering based on abundance ratios, feature enrichment tests for e.g. GO terms and pathway analysis tools. A number of plotting options for visualization of quantitative proteomics data is available and most analysis functions in GProX create customizable high quality graphical displays in both vector and bitmap formats. The generic import requirements allow data originating from essentially all mass spectrometry platforms, quantitation strategies and software to be analyzed in the program. GProX represents a powerful approach to proteomics data analysis providing proteomics experimenters with a toolbox for bioinformatics analysis of quantitative proteomics data. During the last decade, identification and quantitation of proteomes has been facilitated by the constant developments in mass spectrometry instrumentation, fractionation techniques, quantitation-strategies, and data analysis software. Using state-of-the-art technology it has become possible to quantify several thousands of proteins (1-10), and even complete proteomes within a single proteomics experiment (11, 12). Powerful software solutions for protein identification and quantitation have been developed that allow users to process the information stored in the raw mass spectrometry data. These software solutions have been developed by both the scientific community (13-16) and by instrument vendors, exemplified by PEAKS (Bioinformatics Solutions) and Proteome Discoverer (Thermo Scientific). In face of these advances in the field, we find that data analysis is currently the bottleneck of proteomics experiments. Familiarity with several advanced bioinformatics tools, and preferably programming skills, are nowadays essential to perform a comprehensive analysis of large proteomics data sets (17). So far, experimenters without familiarity with computer programming have typically been required to use spreadsheet applications that are not per se developed for analysis of biological data and are therefore of limited use for working with the large amount of data produced from modern proteomics experiments. Alternatively a number of software s...