Céline Hernandez

ExPASy: SIB bioinformatics resource portal

ExPASy (http://www.expasy.org) has worldwide reputation as one of the main bioinformatics resources for proteomics. It has now evolved, becoming an extensible and integrative portal accessing many scientific resources, databases and software tools in different areas of life sciences. Scientists can henceforth access seamlessly a wide range of resources in many different domains, such as proteomics, genomics, phylogeny/evolution, systems biology, population genetics, transcriptomics, etc. The individual resources (databases, web-based and downloadable software tools) are hosted in a ‘decentralized’ way by different groups of the SIB Swiss Institute of Bioinformatics and partner institutions. Specifically, a single web portal provides a common entry point to a wide range of resources developed and operated by different SIB groups and external institutions. The portal features a search function across ‘selected’ resources. Additionally, the availability and usage of resources are monitored. The portal is aimed for both expert users and people who are not familiar with a specific domain in life sciences. The new web interface provides, in particular, visual guidance for newcomers to ExPASy.

Hybrid Modeling and Simulation of Genetic Regulatory Networks: A Qualitative Approach

The study of genetic regulatory networks has received a major impetus from the recent development of experimental techniques allowing the measurement of patterns of gene expression in a massively parallel way. This experimental progress calls for the development of appropriate computer tools for the modeling and simulation of gene regulation processes. We present a method for the hybrid modeling and simulation of genetic regulatory networks, based on a class of piecewiselinear (PL) differential equations that has been well-studied in mathematical biology. Distinguishing characteristics of the method are that it makes qualitative predictions of the behavior of regulatory systems and that it deals with discontinuities in the right-hand side of the differential equations. The simulation method has been implemented in Java in the computer tool Genetic Network Analyzer (GNA). The method and the tool have been used to analyze several networks of biological interest, including the network underlying the initiation of sporulation in Bacillus subtilis.

Dynamic Impacts of the Inhibition of the Molecular Chaperone Hsp90 on the T-Cell Proteome Have Implications for Anti-Cancer Therapy

The molecular chaperone Hsp90-dependent proteome represents a complex protein network of critical biological and medical relevance. Known to associate with proteins with a broad variety of functions termed clients, Hsp90 maintains key essential and oncogenic signalling pathways. Consequently, Hsp90 inhibitors are being tested as anti-cancer drugs. Using an integrated systematic approach to analyse the effects of Hsp90 inhibition in T-cells, we quantified differential changes in the Hsp90-dependent proteome, Hsp90 interactome, and a selection of the transcriptome. Kinetic behaviours in the Hsp90-dependent proteome were assessed using a novel pulse-chase strategy (Fierro-Monti et al., accompanying article), detecting effects on both protein stability and synthesis. Global and specific dynamic impacts, including proteostatic responses, are due to direct inhibition of Hsp90 as well as indirect effects. As a result, a decrease was detected in most proteins that changed their levels, including known Hsp90 clients. Most likely, consequences of the role of Hsp90 in gene expression determined a global reduction in net de novo protein synthesis. This decrease appeared to be greater in magnitude than a concomitantly observed global increase in protein decay rates. Several novel putative Hsp90 clients were validated, and interestingly, protein families with critical functions, particularly the Hsp90 family and cofactors themselves as well as protein kinases, displayed strongly increased decay rates due to Hsp90 inhibitor treatment. Remarkably, an upsurge in survival pathways, involving molecular chaperones and several oncoproteins, and decreased levels of some tumour suppressors, have implications for anti-cancer therapy with Hsp90 inhibitors. The diversity of global effects may represent a paradigm of mechanisms that are operating to shield cells from proteotoxic stress, by promoting pro-survival and anti-proliferative functions. Data are available via ProteomeXchange with identifier PXD000537.

Addressing Trypsin Bias in Large Scale (Phospho)proteome Analysis by Size Exclusion Chromatography and Secondary Digestion of Large Post-Trypsin Peptides

In the vast majority of bottom-up proteomics studies, protein digestion is performed using only mammalian trypsin. Although it is clearly the best enzyme available, the sole use of trypsin rarely leads to complete sequence coverage, even for abundant proteins. It is commonly assumed that this is because many tryptic peptides are either too short or too long to be identified by RPLC-MS/MS. We show through in silico analysis that 20-30% of the total sequence of three proteomes (Schizosaccharomyces pombe, Saccharomyces cerevisiae, and Homo sapiens) is expected to be covered by Large post-Trypsin Peptides (LpTPs) with M(r) above 3000 Da. We then established size exclusion chromatography to fractionate complex yeast tryptic digests into pools of peptides based on size. We found that secondary digestion of LpTPs followed by LC-MS/MS analysis leads to a significant increase in identified proteins and a 32-50% relative increase in average sequence coverage compared to trypsin digestion alone. Application of the developed strategy to analyze the phosphoproteomes of S. pombe and of a human cell line identified a significant fraction of novel phosphosites. Overall our data indicate that specific targeting of LpTPs can complement standard bottom-up workflows to reveal a largely neglected portion of the proteome.

Quantitative proteomics identifies the membrane‐associated peroxidase GPx8 as a cellular substrate of the hepatitis C virus NS3‐4A protease

The hepatitis C virus (HCV) NS3‐4A protease is not only an essential component of the viral replication complex and a prime target for antiviral intervention but also a key player in the persistence and pathogenesis of HCV. It cleaves and thereby inactivates two crucial adaptor proteins in viral RNA sensing and innate immunity, mitochondrial antiviral signaling protein (MAVS) and TRIF, a phosphatase involved in growth factor signaling, T‐cell protein tyrosine phosphatase (TC‐PTP), and the E3 ubiquitin ligase component UV‐damaged DNA‐binding protein 1 (DDB1). Here we explored quantitative proteomics to identify novel cellular substrates of the NS3‐4A protease. Cell lines inducibly expressing the NS3‐4A protease were analyzed by stable isotopic labeling using amino acids in cell culture (SILAC) coupled with protein separation and mass spectrometry. This approach identified the membrane‐associated peroxidase GPx8 as a bona fide cellular substrate of the HCV NS3‐4A protease. Cleavage by NS3‐4A occurs at Cys 11, removing the cytosolic tip of GPx8, and was observed in different experimental systems as well as in liver biopsies from patients with chronic HCV. Overexpression and RNA silencing studies revealed that GPx8 is involved in viral particle production but not in HCV entry or RNA replication. Conclusion: We provide proof‐of‐concept for the use of quantitative proteomics to identify cellular substrates of a viral protease and describe GPx8 as a novel proviral host factor targeted by the HCV NS3‐4A protease. (Hepatology 2014;59:423–433)

A Novel Pulse-Chase SILAC Strategy Measures Changes in Protein Decay and Synthesis Rates Induced by Perturbation of Proteostasis with an Hsp90 Inhibitor

Standard proteomics methods allow the relative quantitation of levels of thousands of proteins in two or more samples. While such methods are invaluable for defining the variations in protein concentrations which follow the perturbation of a biological system, they do not offer information on the mechanisms underlying such changes. Expanding on previous work [1], we developed a pulse-chase (pc) variant of SILAC (stable isotope labeling by amino acids in cell culture). pcSILAC can quantitate in one experiment and for two conditions the relative levels of proteins newly synthesized in a given time as well as the relative levels of remaining preexisting proteins. We validated the method studying the drug-mediated inhibition of the Hsp90 molecular chaperone, which is known to lead to increased synthesis of stress response proteins as well as the increased decay of Hsp90 “clients”. We showed that pcSILAC can give information on changes in global cellular proteostasis induced by treatment with the inhibitor, which are normally not captured by standard relative quantitation techniques. Furthermore, we have developed a mathematical model and computational framework that uses pcSILAC data to determine degradation constants kd and synthesis rates Vs for proteins in both control and drug-treated cells. The results show that Hsp90 inhibition induced a generalized slowdown of protein synthesis and an increase in protein decay. Treatment with the inhibitor also resulted in widespread protein-specific changes in relative synthesis rates, together with variations in protein decay rates. The latter were more restricted to individual proteins or protein families than the variations in synthesis. Our results establish pcSILAC as a viable workflow for the mechanistic dissection of changes in the proteome which follow perturbations. Data are available via ProteomeXchange with identifier PXD000538.

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).

Database Construction and Peptide Identification Strategies for Proteogenomic Studies on Sequenced Genomes

Since the advent of high-throughput DNA sequencing technologies, the ever-increasing rate at which genomes have been published has generated new challenges notably at the level of genome annotation. Even if gene predictors and annotation softwares are more and more efficient, the ultimate validation is still in the observation of predicted gene product( s). Mass-spectrometry based proteomics provides the necessary high throughput technology to show evidences of protein presence and, from the identified sequences, confirmation or invalidation of predicted annotations. We review here different strategies used to perform a MS-based proteogenomics experiment with a bottom-up approach. We start from the strengths and weaknesses of the different database construction strategies, based on different genomic information (whole genome, ORF, cDNA, EST or RNA-Seq data), which are then used for matching mass spectra to peptides and proteins. We also review the important points to be considered for a correct statistical assessment of the peptide identifications. Finally, we provide references for tools used to map and visualize the peptide identifications back to the original genomic information.

Early activation of the fatty acid metabolism pathway by chronic high glucose exposure in rat insulin secretory β‐cells

Pancreatic β‐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 β‐cells occurs called β‐cell dysfunction or β‐cell failure. Chronic hyperglycemia has been shown being involved in β‐cell dysfunction, a phenomenon known as glucotoxicity. The molecular mechanisms underlying the impairment of insulin secretion by β‐cells induced by glucotoxicity are still not fully understood. In this work, quantitative proteomics was employed to identify early key players involved in β‐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 β‐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 β‐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 β‐cells submitted to chronic high glucose concentrations.

Labeling of Bifidobacterium longum Cells with 13C-Substituted Leucine for Quantitative Proteomic Analyses

ABSTRACT Stable isotope labeling of amino acids in cell culture was used for Bifidobacterium longum. A comprehensive proteomic strategy was developed and validated by designing an appropriate semidefined medium that allows stable replacement of natural leucine by [13C6]leucine. Using this strategy, proteins having variations of at least 50% in their expression rates can be quantified with great confidence.