Proteomic Analyses Identify a Novel Role for EZH2 in the Initiation of Cancer Cell Drug Tolerance.

Acquisition of drug resistance remains a chief impediment to successful cancer therapy, and we previously described a transient drug-tolerant cancer cell population (DTPs) whose survival is in part dependent on the activities of the histone methyltransferases G9a/EHMT2 and EZH2, the latter being the catalytic component of the polycomb repressive complex 2 (PRC2). Here, we apply multiple proteomic techniques to better understand the role of these histone methyltransferases (HMTs) in the establishment of the DTP state. Proteome-wide comparisons of lysine methylation patterns reveal that DTPs display an increase in methylation on K116 of PRC member Jarid2, an event that helps stabilize and recruit PRC2 to chromatin. We also find that EZH2, in addition to methylating histone H3K27, also can methylate G9a at K185, and that methylated G9a better recruits repressive complexes to chromatin. These complexes are similar to complexes recruited by histone H3 methylated at K9. Finally, a detailed histone post-translational modification (PTM) analysis shows that EZH2, either directly or through its ability to methylate G9a, alters H3K9 methylation in the context of H3 serine 10 phosphorylation, primarily in a cancer cell subpopulation that serves as DTP precursors. We also show that combinations of histone PTMs recruit a different set of complexes to chromatin, shedding light on the temporal mechanisms that contribute to drug tolerance.

MsViz: A Graphical Software Tool for In-Depth Manual Validation and Quantitation of Post-translational Modifications.

Mass spectrometry (MS) has become the tool of choice for the large scale identification and quantitation of proteins and their post-translational modifications (PTMs). This development has been enabled by powerful software packages for the automated analysis of MS data. While data on PTMs of thousands of proteins can nowadays be readily obtained, fully deciphering the complexity and combinatorics of modification patterns even on a single protein often remains challenging. Moreover, functional investigation of PTMs on a protein of interest requires validation of the localization and the accurate quantitation of its changes across several conditions, tasks that often still require human evaluation. Software tools for large scale analyses are highly efficient but are rarely conceived for interactive, in-depth exploration of data on individual proteins. We here describe MsViz, a web-based and interactive software tool that supports manual validation of PTMs and their relative quantitation in small- and medium-size experiments. The tool displays sequence coverage information, peptide-spectrum matches, tandem MS spectra and extracted ion chromatograms through a single, highly intuitive interface. We found that MsViz greatly facilitates manual data inspection to validate PTM location and quantitate modified species across multiple samples.

Mass spectrometric quantification of histone post-translational modifications by a hybrid chemical labeling method.

Mass spectrometry is a powerful alternative to antibody-based methods for the analysis of histone post-translational modifications (marks). A key development in this approach was the deliberate propionylation of histones to improve sequence coverage across the lysine-rich and hydrophilic tails that bear most modifications. Several marks continue to be problematic however, particularly di- and tri-methylated lysine 4 of histone H3 which we found to be subject to substantial and selective losses during sample preparation and liquid chromatography-mass spectrometry. We developed a new method employing a "one-pot" hybrid chemical derivatization of histones, whereby an initial conversion of free lysines to their propionylated forms under mild aqueous conditions is followed by trypsin digestion and labeling of new peptide N termini with phenyl isocyanate. High resolution mass spectrometry was used to collect qualitative and quantitative data, and a novel web-based software application (Fishtones) was developed for viewing and quantifying histone marks in the resulting data sets. Recoveries of 53 methyl, acetyl, and phosphoryl marks on histone H3.1 were improved by an average of threefold overall, and over 50-fold for H3K4 di- and tri-methyl marks. The power of this workflow for epigenetic research and drug discovery was demonstrated by measuring quantitative changes in H3K4 trimethylation induced by small molecule inhibitors of lysine demethylases and siRNA knockdown of epigenetic modifiers ASH2L and WDR5.

Peptide level immunoaffinity enrichment enhances ubiquitination site identification on individual proteins.

Ubiquitination is a process that involves the covalent attachment of the 76-residue ubiquitin protein through its C-terminal di-glycine (GG) to lysine (K) residues on substrate proteins. This post-translational modification elicits a wide range of functional consequences including targeting proteins for proteasomal degradation, altering subcellular trafficking events, and facilitating protein-protein interactions. A number of methods exist for identifying the sites of ubiquitination on proteins of interest, including site-directed mutagenesis and affinity-purification mass spectrometry (AP-MS). Recent publications have also highlighted the use of peptide-level immunoaffinity enrichment of K-GG modified peptides from whole cell lysates for global characterization of ubiquitination sites. Here we investigated the utility of this technique for focused mapping of ubiquitination sites on individual proteins. For a series of membrane-associated and cytoplasmic substrates including erbB-2 (HER2), Dishevelled-2 (DVL2), and T cell receptor α (TCRα), we observed that K-GG peptide immunoaffinity enrichment consistently yielded additional ubiquitination sites beyond those identified in protein level AP-MS experiments. To assess this quantitatively, SILAC-labeled lysates were prepared and used to compare the abundances of individual K-GG peptides from samples prepared in parallel. Consistently, K-GG peptide immunoaffinity enrichment yielded greater than fourfold higher levels of modified peptides than AP-MS approaches. Using this approach, we went on to characterize inducible ubiquitination on multiple members of the T-cell receptor complex that are functionally affected by endoplasmic reticulum (ER) stress. Together, these data demonstrate the utility of immunoaffinity peptide enrichment for single protein ubiquitination site analysis and provide insights into the ubiquitination of HER2, DVL2, and proteins in the T-cell receptor complex.

Ubiquitination profiling identifies sensitivity factors for IAP antagonist treatment.

Evasion of cell death is one crucial capability acquired by tumour cells to ward-off anti-tumour therapies and represents a fundamental challenge to sustaining clinical efficacy for currently available agents. Inhibitor of apoptosis (IAP) proteins use their ubiquitin E3 ligase activity to promote cancer cell survival by mediating proliferative signalling and blocking cell death in response to diverse stimuli. Using immunoaffinity enrichment and MS, ubiquitination sites on thousands of proteins were profiled upon initiation of cell death by IAP antagonists in IAP antagonist-sensitive and -resistant breast cancer cell lines. Our analyses identified hundreds of proteins with elevated levels of ubiquitin-remnant [K-GG (Lys-Gly-Gly)] peptides upon activation of cell death by the IAP antagonist BV6. The majority of these were observed in BV6-sensitive, but not-resistant, cells. Among these were known pro-apoptotic regulators, including CYC (cytochrome c), RIP1 (receptor-interacting protein 1) and a selection of proteins known to reside in the mitochondria or regulate NF-κB (nuclear factor κB) signalling. Analysis of early time-points revealed that IAP antagonist treatment stimulated rapid ubiquitination of NF-κB signalling proteins, including TRAF2 [TNF (tumour necrosis factor) receptor-associated factor 2], HOIL-1 (haem-oxidized iron-regulatory protein 2 ubiquitin ligase-1), NEMO (NF-κB essential modifier), as well as c-IAP1 (cellular IAP1) auto-ubiquitination. Knockdown of several NF-κB pathway members reduced BV6-induced cell death and TNF production in sensitive cell lines. Importantly, RIP1 was found to be constitutively ubiquitinated in sensitive breast-cancer cell lines at higher basal level than in resistant cell lines. Together, these data show the diverse and temporally defined roles of protein ubiquitination following IAP-antagonist treatment and provide critical insights into predictive diagnostics that may enhance clinical efficacy.

Visualization of protein sequence features using JavaScript and SVG with pViz.js.

UNLABELLED: pViz.js is a visualization library for displaying protein sequence features in a Web browser. By simply providing a sequence and the locations of its features, this lightweight, yet versatile, JavaScript library renders an interactive view of the protein features. Interactive exploration of protein sequence features over the Web is a common need in Bioinformatics. Although many Web sites have developed viewers to display these features, their implementations are usually focused on data from a specific source or use case. Some of these viewers can be adapted to fit other use cases but are not designed to be reusable. pViz makes it easy to display features as boxes aligned to a protein sequence with zooming functionality but also includes predefined renderings for secondary structure and post-translational modifications. The library is designed to further customize this view. We demonstrate such applications of pViz using two examples: a proteomic data visualization tool with an embedded viewer for displaying features on protein structure, and a tool to visualize the results of the variant_effect_predictor tool from Ensembl. AVAILABILITY AND IMPLEMENTATION: pViz.js is a JavaScript library, available on github at https://github.com/Genentech/pviz. This site includes examples and functional applications, installation instructions and usage documentation. A Readme file, which explains how to use pViz with examples, is available as Supplementary Material A.

neXtProt: a knowledge platform for human proteins.

neXtProt (http://www.nextprot.org/) is a new human protein-centric knowledge platform. Developed at the Swiss Institute of Bioinformatics (SIB), it aims to help researchers answer questions relevant to human proteins. To achieve this goal, neXtProt is built on a corpus containing both curated knowledge originating from the UniProtKB/Swiss-Prot knowledgebase and carefully selected and filtered high-throughput data pertinent to human proteins. This article presents an overview of the database and the data integration process. We also lay out the key future directions of neXtProt that we consider the necessary steps to make neXtProt the one-stop-shop for all research projects focusing on human proteins.

Early activation of the fatty acid metabolism pathway by chronic high glucose exposure in rat insulin secretory beta-cells.

Pancreatic beta-cells are responsible for insulin secretion that regulates blood glucose homeostasis. In the development of type II diabetes, a progressive impairment of insulin secretion by the pancreatic beta-cells occurs called beta-cell dysfunction or beta-cell failure. Chronic hyperglycemia has been shown being involved in beta-cell dysfunction, a phenomenon known as glucotoxicity. The molecular mechanisms underlying the impairment of insulin secretion by beta-cells induced by glucotoxicity are still not fully understood. In this work, quantitative proteomics was employed to identify early key players involved in beta-cell dysfunction induced by glucotoxicity. For this, the stable isotope labeling by amino acids in cell culture strategy was used on the slowly-growing rat beta-cell line INS-1E. We showed that the stable isotope labeling by amino acids in cell culture approach did not induce any detectable biological effects on these beta-cells, as measured at both the transcriptomic and proteomic levels. Proteins differentially expressed between control cells and cells submitted to chronic high glucose concentrations were identified and verified. The results obtained reinforce the link between glucotoxicity and lipogenesis and suggest that the fatty acid metabolism pathway may rapidly be stimulated in beta-cells submitted to chronic high glucose concentrations.

A simple workflow to increase MS2 identification rate by subsequent spectral library search.

Searching a spectral library for the identification of protein MS/MS data has proven to be a fast and accurate method, while yielding a high identification rate. We investigated the potential to increase peptide discovery rate, with little increase in computational time, by constructing a workflow based on a sequence search with Phenyx followed by a library search with SpectraST. Searching a consensus library compiled from the search results of the prior Phenyx search increased the number of confidently matched spectra by up to 156%. Additionally matched spectra by SpectraST included noisy spectra, spectra representing missed cleaved peptides as well as spectra from post-translationally modified peptides.

SwissPIT: An workflow-based platform for analyzing tandem-MS spectra using the Grid.

The identification and characterization of peptides from MS/MS data represents a critical aspect of proteomics. It has been the subject of extensive research in bioinformatics resulting in the generation of a fair number of identification software tools. Most often, only one program with a specific and unvarying set of parameters is selected for identifying proteins. Hence, a significant proportion of the experimental spectra do not match the peptide sequences in the screened database due to inappropriate parameters or scoring schemes. The Swiss protein identification toolbox (swissPIT) project provides the scientific community with an expandable multitool platform for automated in-depth analysis of MS data also able to handle data from high-throughput experiments. With swissPIT many problems have been solved: The missing standards for input and output formats (A), creation of analysis workflows (B), unified result visualization (C), and simplicity of the user interface (D). Currently, swissPIT supports four different programs implementing two different search strategies to identify MS/MS spectra. Conceived to handle the calculation-intensive needs of each of the programs, swissPIT uses the distributed resources of a Swiss-wide computer Grid (http://www.swing-grid.ch).

X-Rank: a robust algorithm for small molecule identification using tandem mass spectrometry.

The diversity of experimental workflows involving LC-MS/MS and the extended range of mass spectrometers tend to produce extremely variable spectra. Variability reduces the accuracy of compound identification produced by commonly available software for a spectral library search. We introduce here a new algorithm that successfully matches MS/MS spectra generated by a range of instruments, acquired under different conditions. Our algorithm called X-Rank first sorts peak intensities of a spectrum and second establishes a correlation between two sorted spectra. X-Rank then computes the probability that a rank from an experimental spectrum matches a rank from a reference library spectrum. In a training step, characteristic parameter values are generated for a given data set. We compared the efficiency of the X-Rank algorithm with the dot-product algorithm implemented by MS Search from the National Institute of Standards and Technology (NIST) on two test sets produced with different instruments. Overall the X-Rank algorithm accurately discriminates correct from wrong matches and detects more correct substances than the MS Search. Furthermore, X-Rank could correctly identify and top rank eight chemical compounds in a commercially available test mix. This confirms the ability of the algorithm to perform both a straight single-platform identification and a cross-platform library search in comparison to other tools. It also opens the possibility for efficient general unknown screening (GUS) against large compound libraries.

swissPIT: a novel approach for pipelined analysis of mass spectrometry data.

The identification and characterization of peptides from tandem mass spectrometry (MS/MS) data represents a critical aspect of proteomics. Today, tandem MS analysis is often performed by only using a single identification program achieving identification rates between 10-50% (Elias and Gygi, 2007). Beside the development of new analysis tools, recent publications describe also the pipelining of different search programs to increase the identification rate (Hartler et al., 2007; Keller et al., 2005). The Swiss Protein Identification Toolbox (swissPIT) follows this approach, but goes a step further by providing the user an expandable multi-tool platform capable of executing workflows to analyze tandem MS-based data. One of the major problems in proteomics is the absent of standardized workflows to analyze the produced data. This includes the pre-processing part as well as the final identification of peptides and proteins. The main idea of swissPIT is not only the usage of different identification tool in parallel, but also the meaningful concatenation of different identification strategies at the same time. The swissPIT is open source software but we also provide a user-friendly web platform, which demonstrates the capabilities of our software and which is available at http://swisspit.cscs.ch upon request for account.

Exploring glycopeptide-resistance in Staphylococcus aureus: a combined proteomics and transcriptomics approach for the identification of resistance-related markers.

BACKGROUND: To unravel molecular targets involved in glycopeptide resistance, three isogenic strains of Staphylococcus aureus with different susceptibility levels to vancomycin or teicoplanin were subjected to whole-genome microarray-based transcription and quantitative proteomic profiling. Quantitative proteomics performed on membrane extracts showed exquisite inter-experimental reproducibility permitting the identification and relative quantification of >30% of the predicted S. aureus proteome. RESULTS: In the absence of antibiotic selection pressure, comparison of stable resistant and susceptible strains revealed 94 differentially expressed genes and 178 proteins. As expected, only partial correlation was obtained between transcriptomic and proteomic results during stationary-phase. Application of massively parallel methods identified one third of the complete proteome, a majority of which was only predicted based on genome sequencing, but never identified to date. Several over-expressed genes represent previously reported targets, while series of genes and proteins possibly involved in the glycopeptide resistance mechanism were discovered here, including regulators, global regulator attenuator, hyper-mutability factor or hypothetical proteins. Gene expression of these markers was confirmed in a collection of genetically unrelated strains showing altered susceptibility to glycopeptides. CONCLUSION: Our proteome and transcriptome analyses have been performed during stationary-phase of growth on isogenic strains showing susceptibility or intermediate level of resistance against glycopeptides. Altered susceptibility had emerged spontaneously after infection with a sensitive parental strain, thus not selected in vitro. This combined analysis allows the identification of hundreds of proteins considered, so far as hypothetical protein. In addition, this study provides not only a global picture of transcription and expression adaptations during a complex antibiotic resistance mechanism but also unravels potential drug targets or markers that are constitutively expressed by resistant strains regardless of their genetic background, amenable to be used as diagnostic targets.

Structure of the BRAF-MEK complex reveals a kinase activity independent role for BRAF in MAPK signaling.

Numerous oncogenic mutations occur within the BRAF kinase domain (BRAF(KD)). Here we show that stable BRAF-MEK1 complexes are enriched in BRAF(WT) and KRAS mutant (MT) cells but not in BRAF(MT) cells. The crystal structure of the BRAF(KD) in a complex with MEK1 reveals a face-to-face dimer sensitive to MEK1 phosphorylation but insensitive to BRAF dimerization. Structure-guided studies reveal that oncogenic BRAF mutations function by bypassing the requirement for BRAF dimerization for activity or weakening the interaction with MEK1. Finally, we show that conformation-specific BRAF inhibitors can sequester a dormant BRAF-MEK1 complex resulting in pathway inhibition. Taken together, these findings reveal a regulatory role for BRAF in the MAPK pathway independent of its kinase activity but dependent on interaction with MEK.

Assessing peptide de novo sequencing algorithms performance on large and diverse data sets.

De novo peptide sequencing algorithms are often tested on relatively small data sets made of excellent spectra. Since there are always more and more tandem mass spectra available, we have assembled six large, reliable, and diverse (three mass spectrometer types) data sets intended for such tests and we make them accessible via a web server. To exemplify their use we investigate the performance of Lutefisk, PepNovo, and PepNovoTag, three well-established peptide de novo sequencing programs.

InSilicoSpectro: an open-source proteomics library.

We present a new proteomics open-source project, InSilicoSpectro, aimed at implementing recurrent computations that are necessary for proteomics data analysis. Illustrative examples are mass list file format conversions, protein sequence digestion, theoretical peptide and fragment mass computations, graphical display, matching with experimental data, isoelectric point estimation, and peptide retention time prediction. The project library is written in Perl, a widely used scripting language in bioinformatics, and it offers a unique framework of integrated objects to implement complex proteomics data analyses. For instance, only a few lines of code are required to digest a protein with fixed and variable modifications, label peptides with 18O, compute the fragmentation spectra and display their match with experimental spectra. We believe that InSilicoSpectro will be of great help to bioinformaticians, without detailed knowledge of proteomics specifics, and to mass spectrometrists with computer programming interest as well.

OLAV-PMF: a novel scoring scheme for high-throughput peptide mass fingerprinting.

We propose a new type of probabilistic scoring scheme framework for protein identification from peptide masses. We first introduce the framework itself and explain its requirements. In a second part, we describe a particular implementation and test it on a data set of more than 8000 MALDI-TOF spectra with known contents. Doing so, we also compare its performance to two widely used scoring schemes, thereby demonstrating the potential of the proposed approach.

Improved peptide charge state assignment.

We present a new approach capable of assigning charge states to peptides based on both their intact mass spectrum and their fragmentation mass spectrum. More specifically, our approach aims at fully exploiting available information to improve correct charge assignment rate. This is achieved by using information provided by the fragmentation spectrum extensively. For low-resolution spectra, charge assignment based on fragmentation mass spectrum is better than charge assignment based on intact peptide signal only. We introduce two methods that allow to integrate information contributing to successful peptide charge state assignment. We demonstrate the performance of our algorithms on large ion trap data sets. The application of these algorithms to large-scale proteomics projects can save significant computation time and have a positive impact on identification false positive rates.

OLAV: towards high-throughput tandem mass spectrometry data identification.

Mass spectrometry combined with database searching has become the preferred method for identifying proteins in proteomics projects. Proteins are digested by one or several enzymes to obtain peptides, which are analyzed by mass spectrometry. We introduce a new family of scoring schemes, named OLAV, aimed at identifying peptides in a database from their tandem mass spectra. OLAV scoring schemes are based on signal detection theory, and exploit mass spectrometry information more extensively than previously existing schemes. We also introduce a new concept of structural matching that uses pattern detection methods to better separate true from false positives. We show the superiority of OLAV scoring schemes compared to MASCOT, a widely used identification program. We believe that this work introduces a new way of designing scoring schemes that are especially adapted to high-throughput projects such as GeneProt large-scale human plasma project, where it is impractical to check all identifications manually.

High-performance peptide identification by tandem mass spectrometry allows reliable automatic data processing in proteomics.

In a previous paper we introduced a novel model-based approach (OLAV) to the problem of identifying peptides via tandem mass spectrometry, for which early implementations showed promising performance. We recently further improved this performance to a remarkable level (1-2% false positive rate at 95% true positive rate) and characterized key properties of OLAV like robustness and training set size. We present these results in a synthetic and coherent way along with detailed performance comparisons, a new scoring component making use of peptide amino acidic composition, and new developments like automatic parameter learning. Finally, we discuss the impact of OLAV on the automation of proteomics projects.