Vers une meilleure caractérisation du protéome par le développement de méthodes de spectrométrie de masse quantitative et par l'analyse protéogénomique

The high intrinsic complexity of biological samples, the technical variability and the dependency of Bottom-up Proteomics to consensus protein sequence databases handicap the comprehensive analysis of an entire Proteome. My doctoral work was focused on method development in quantitative Proteomics and Proteogenomics in order to achieve a better proteome characterization. First, I focused on the development of global and targeted quantitative methods. The introduction and development of standard samples to assess the performances at any level of the analytical workflow is also described. These methods were applied to answer different biological questions. My PhD also focused on Proteogenomic method development. A high throughput N-terminomic analysis approach was developed and applied to the analysis of the human mitochondria. Finally, this manuscript presents a personalized multi-omics profiling strategy to improve the proteome analysis with the use of personalized databases.L'extrême complexité des échantillons biologiques, la variabilité technique et la dépendance de la protéomique envers les banques protéiques empêchent l'analyse complète d'un protéome. Ce travail de thèse s'est focalisé sur le développement de méthodes pour la protéomique quantitative et la protéogénomique afin d'améliorer la caractérisation du protéome. Premièrement mon travail s'est centré sur le développement de méthodes quantitatives globales et ciblées. La mise en place de standards pour évaluer les performances de tous les niveaux de la stratégie analytique est aussi décrite. Ces méthodes ont été optimisées pour répondre à diverses questions biologiques. Mon doctorat s'est focalisé aussi autour de la protéogénomique. Une méthode d'analyse N-terminomique à haut débit a été développée et appliquée à l'étude de la mitochondrie humaine. Enfin, ce manuscrit présente une approche multi-omique visant à améliorer l'analyse du protéome avec la création de banques de données personnalisées

Doublet N-Terminal Oriented Proteomics for N-Terminomics and Proteolytic Processing Identification

The study of the N-terminome and the precise identification of proteolytic processing events are key in biology. Dedicated methodologies have been developed as the comprehensive characterization of the N-terminome can hardly be achieved by standard proteomics methods. In this context, we have set up a trimethoxyphenyl phosphonium (TMPP) labeling approach that allows the characterization of both N-terminal and internal digestion peptides in a single experiment. This latter point is a major advantage of our strategy as most N-terminomics methods rely on the enrichment of N-terminal peptides and thus exclude internal peptides.We have implemented a double heavy/light TMPP labeling and an automated data validation workflow that make our doublet N-terminal oriented proteomics (dN-TOP) strategy efficient for high-throughput N-terminome analysis

A Combined N-terminomics and Shotgun Proteomics Approach to Investigate the Responses of Human Cells to Rapamycin and Zinc at the Mitochondrial Level

International audienceAll but thirteen mammalian mitochondrial proteins are encoded by the nuclear genome, translated in the cytosol and then imported into the mitochondria. For a significant proportion of the mitochondrial proteins, import is coupled with the cleavage of a presequence called the transit peptide, and the formation of a new N-terminus. Determination of the neo N-termini has been investigated by proteomic approaches in several systems, but generally in a static way to compile as many N-termini as possible. In the present study, we have investigated how the mitochondrial proteome and N-terminome react to chemical stimuli that alter mitochondrial metabolism, namely zinc ions and rapamycin. To this end, we have used a strategy that analyzes both internal and N-terminal peptides in a single run, the dN-TOP approach. We used these two very different stressors to sort out what could be a generic response to stress and what is specific to each of these stressors. Rapamycin and zinc induced different changes in the mitochondrial proteome. However, convergent changes to key mitochondrial enzymatic activities such as pyruvate dehydrogenase, succinate dehydrogenase and citrate synthase were observed for both treatments. Other convergent changes were seen in components of the N-terminal processing system and mitochondrial proteases. Investigations into the generation of neo-N-termini in mitochondria showed that the processing system is robust, as indicated by the lack of change in neo N-termini under the conditions tested. Detailed analysis of the data revealed that zinc caused a slight reduction in the efficiency of the N-terminal trimming system and that both treatments increased the degradation of mitochondrial proteins. In conclusion, the use of this combined strategy allowed a detailed analysis of the dynamics of the mitochondrial N-terminome in response to treatments which impact the mitochondria

N-terminome analysis of the human mitochondrial proteome

The high throughput characterization of protein N-termini is becoming an emerging challenge in the proteomics and proteogenomics fields. The present study describes the free N-terminome analysis of human mitochondria-enriched samples using trimethoxyphenyl phosphonium (TMPP) labelling approaches. Owing to the extent of protein import and cleavage for mitochondrial proteins, determining the new N-termini generated after translocation/processing events for mitochondrial proteins is crucial to understand the transformation of precursors to mature proteins. The doublet N-terminal oriented proteomics (dN-TOP) strategy based on a double light/heavy TMPP labelling has been optimized in order to improve and automate the workflow for efficient, fast and reliable high throughput N-terminome analysis. A total of 2714 proteins were identified and 897 N-terminal peptides were characterized (424 N-α-acetylated and 473 TMPP-labelled peptides). These results allowed the precise identification of the N-terminus of 693 unique proteins corresponding to 26% of all identified proteins. Overall, 120 already annotated processing cleavage sites were confirmed while 302 new cleavage sites were characterized. The accumulation of experimental evidence of mature N-termini should allow increasing the knowledge of processing mechanisms and consequently also enhance cleavage sites prediction algorithms. Complete datasets have been deposited to the ProteomeXchange Consortium with identifiers PXD001521, PXD001522 and PXD001523 (http://proteomecentral.proteomexchange.org/dataset/PXD001521, http://proteomecentral.proteomexchange.org/dataset/PXD0001522 and http://proteomecentral.proteomexchange.org/dataset/PXD001523, respectively)

MetAP1 and MetAP2 drive cell selectivity for a potent anti-cancer agent in synergy, by controlling glutathione redox state.

International audienceFumagillin and its derivatives are therapeutically useful because they can decrease cancer progression. The specific molecular target of fumagillin is methionine aminopeptidase 2 (MetAP2), one of the two MetAPs present in the cytosol. MetAPs catalyze N-terminal methionine excision (NME), an essential pathway of cotranslational protein maturation. To date, it remains unclear the respective contribution of MetAP1 and MetAP2 to the NME process in vivo and why MetAP2 inhibition causes cell cycle arrest only in a subset of cells. Here, we performed a global characterization of the N-terminal methionine excision pathway and the inhibition of MetAP2 by fumagillin in a number of lines, including cancer cell lines. Large-scale N-terminus profiling in cells responsive and unresponsive to fumagillin treatment revealed that both MetAPs were required in vivo for M[VT]X-targets and, possibly, for lower-level M[G]X-targets. Interestingly, we found that the responsiveness of the cell lines to fumagillin was correlated with the ability of the cells to modulate their glutathione homeostasis. Indeed, alterations to glutathione status were observed in fumagillin-sensitive cells but not in cells unresponsive to this agent. Proteo-transcriptomic analyses revealed that both MetAP1 and MetAP2 accumulated in a cell-specific manner and that cell sensitivity to fumagillin was related to the levels of these MetAPs, particularly MetAP1. We suggest that MetAP1 levels could be routinely checked in several types of tumor and used as a prognostic marker for predicting the response to treatments inhibiting MetAP2

Causal interactions from proteomic profiles: Molecular data meet pathway knowledge

Summary: We present a computational method to infer causal mechanisms in cell biology by analyzing changes in high-throughput proteomic profiles on the background of prior knowledge captured in biochemical reaction knowledge bases. The method mimics a biologist's traditional approach of explaining changes in data using prior knowledge but does this at the scale of hundreds of thousands of reactions. This is a specific example of how to automate scientific reasoning processes and illustrates the power of mapping from experimental data to prior knowledge via logic programming. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at http://causalpath.org. The bigger picture: Molecular profiling of biological organisms provides us with a great amount of information on cellular differences, but converting it to mechanistic insights is still a very challenging task. A prominent approach is to integrate new measurements with the mechanistic knowledge described in the scientific literature and build a model that is supported by both. Although this can be done in many ways, an adept approach will use the literature knowledge in detail and follow high standards of logical reasoning while integrating the known and the new. This article describes an approach that utilizes the details in human biological pathways to identify pairs of changes with a likely cause-effect relation within. The approach automatically converts comparative proteomic and other molecular profiles into hypotheses of differentially active mechanistic relations that explain how the profiles came to be

Multi-OMICS analyses unveil STAT1 as a potential modifier gene in mevalonate kinase deficiency

International audienceObjectives The objective of the present study was to explain why two siblings carrying both the same homozygous pathogenic mutation for the autoinflammatory disease hyper IgD syndrome, show opposite phenotypes, that is, the first being asymptomatic, the second presenting all classical characteristics of the disease.Methods Where single omics (mainly exome) analysis fails to identify culprit genes/mutations in human complex diseases, multiomics analyses may provide solutions, although this has been seldom used in a clinical setting. Here we combine exome, transcriptome and proteome analyses to decipher at a molecular level, the phenotypic differences between the two siblings.Results This multiomics approach led to the identification of a single gene—STAT1—which harboured a rare missense variant and showed a significant overexpression of both mRNA and protein in the symptomatic versus the asymptomatic sister. This variant was shown to be of gain of function nature, involved in an increased activation of the Janus kinase/signal transducer and activator of transcription signalling (JAK/STAT) pathway, known to play a critical role in inflammatory diseases and for which specific biotherapies presently exist. Pathway analyses based on information from differentially expressed transcripts and proteins confirmed the central role of STAT1 in the proposed regulatory network leading to an increased inflammatory phenotype in the symptomatic sibling.Conclusions This study demonstrates the power of a multiomics approach to uncover potential clinically actionable targets for a personalised therapy. In more general terms, we provide a proteogenomics analysis pipeline that takes advantage of subject-specific genomic and transcriptomic information to improve protein identification and hence advance individualised medicine.This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds

While gene expression profling has traditionally been the method of choice for large-scale perturbational profling studies, proteomics has emerged as an efective tool in this context for directly monitoring cellular responses to perturbations. We previously reported a pilot library containing 3400 profles of multiple perturbations across diverse cellular backgrounds in the reduced-representation phosphoproteome (P100) and chromatin space (Global Chromatin Profling, GCP). Here, we expand our original dataset to include profles from a new set of cardiotoxic compounds and from astrocytes, an additional neural cell model, totaling 5300 proteomic signatures. We describe fltering criteria and quality control metrics used to assess and validate the technical quality and reproducibility of our data. To demonstrate the power of the library, we present two case studies where data is queried using the concept of “connectivity” to obtain biological insight. All data presented in this study have been deposited to the ProteomeXchange Consortium with identifers PXD017458 (P100) and PXD017459 (GCP) and can be queried at https://clue.io/proteomics