Le but de mon projet est de créer et d’appliquer des outils pour l’analyse de la biologie des systèmes musculaires en utilisant différentes données OMICS. Ce projet s’intéresse plus particulièrement à la dysferlinopathie due la déficience d’une protéine appelée dysferline qui est exprimée principalement dans les muscles squelettiques et cardiaque. La perte du dysferline due à la mutation (autosomique-récessive) du gène DYSF entraîne une dystrophie musculaire progressive (LGMD2B, MM, DMAT). Nous avons déjà développé des outils bio-informatiques qui peuvent être utilisés pour l’analyse fonctionnelle de données OMICS, relative à la dyspherlinopathie. Ces derniers incluent le test dit «gene set enrichment analysis», test comparant les profils OMICS d’intérêts aux données OMICS musculaires préalablement publiées ; et l’analyse des réseaux impliquant les diffèrent(e)s protéines et transcrits entre eux/elles. Ainsi, nous avons analysé des centaines de données omiques publiées provenant d’archives publiques. Les outils informatiques que nous avons développés sont CellWhere et MyoMiner. CellWhere est un outil facile à utiliser, permettant de visualiser sur un graphe interactif à la fois les interactions protéine-protéine et la localisation subcellulaire des protéines. Myominer est une base de données spécialisée dans le tissu et les cellules musculaires, et qui fournit une analyse de co-expression, aussi bien dans les tissus sains que pathologiques. Ces outils seront utilisés dans l'analyse et l'interprétation de données transcriptomiques pour les dyspherlinopathies mais également les autres pathologies neuromusculaires. / The aim of this project was to build and apply tools for the analysis of muscle omics data, with a focus on Dysferlin deficiency. This protein is expressed mainly in skeletal and cardiac muscles, and its loss due to mutation (autosomal-recessive) of the DYSF gene, results in a progressive muscular dystrophy (Limb Girdle Muscular Dystrophy type 2B (LGMD2B), Miyoshi myopathy and distal myopathy with tibialis anterior onset (DMAT)). We have developed various tools and pipelines that can be applied towards a bioinformatics functional analysis of omics data in muscular dystrophies and neuromuscular disorders. These include: tests for enrichment of gene sets derived from previously published muscle microarray data and networking analysis of functional associations between altered transcripts/proteins. To accomplish this, we analyzed hundreds of published omics data from public repositories. The tools we developed are called CellWhere and MyoMiner. CellWhere is a user-friendly tool that combines protein-protein interactions and protein subcellular localizations on an interactive graphical display (https://cellwhere-myo.rhcloud.com). MyoMiner is a muscle cell- and tissue-specific database that provides co-expression analyses in both normal and pathological tissues. Many gene co-expression databases already exist and are used broadly by researchers, but MyoMiner is the first muscle-specific tool of its kind (https://myominer-myo.rhcloud.com). These tools will be used in the analysis and interpretation of transcriptomics data from dysferlinopathic muscle and other neuromuscular conditions and will be important to understand the molecular mechanisms underlying these pathologies.

Comunidades microbianas possuem papéis importantes em processos que ocorrem em diversos ambientes, tais como solos, oceanos e o trato gastrointestinal dos seres humanos. Portanto, é de interesse a compreensão da estrutura e do funcionamento dessas comunidades. A estrutura dessas comunidades, em termos de organismos componentes, pode ser determinada com o uso do sequenciamento de nova geração em conjunto com as técnicas meta-ômicas e pela análise taxonômica das sequências obtidas com programas de classificação taxonômica. Se por um lado diversos programas estão disponíveis, por outro lado eles cometem erros, como a identificação parcial dos organismos presentes na amostra e a identificação de organismos que não estão presentes na amostra (os falsos positivos - FPs). Algumas abordagens foram propostas para a melhoria das classificações taxonômicas obtidas por esses programas com a redução desses FPs, porém elas abordam apenas um tipo de meta-ômica, a metagenômica. Neste trabalho, propomos uma nova abordagem através da integração de diferentes meta-ômicas - metagenômicas shotgun e de amplicons de 16S, e metatranscritômica. Exploramos os resultados de classificações de dados simulados e mocks para a extração de variáveis e desenvolvemos modelos de classificação para discriminação de predições de espécies de bactérias classificadas como corretas ou incorretas. Comparamos o desempenho dos resultados obtidos entre as meta-ômicas individuais e os obtidos através da integração observando o balanceamento entre a precisão e a sensibilidade. De acordo com as medidas calculadas com nossos conjuntos de dados, nossa abordagem demonstrou melhorias na classificação com a redução de FPs e aumentos para a medida F1, quando comparada com abordagens não integrativas, inclusive com o uso de métodos de combinação de classificadores. Para facilitar seu uso, desenvolvemos o Gunga, uma ferramenta que incorpora a abordagem desenvolvida em formato de pacote do R, com funcionalidades para a integração de dados de classificação taxonômica com diferentes meta-ômicas e a classificação das predições incorretas. / Microbial communities play important roles in processes that occur in diverse environments, such as soils, oceans, and the gastrointestinal tract of humans. Therefore, it is of interest to understand the structure and functioning of these communities. The structure of these communities, in terms of component organisms, can be determined by the use of the next generation sequencing in conjunction with the meta-omics techniques and by the taxonomic analysis of the sequences obtained with taxonomic classification programs. If on the one hand several programs are available, on the other hand they make mistakes, such as the partial identification of the organisms present in the sample and the identification of organisms that are not present in the sample (the false positives - FPs). Some approaches have been proposed to improve the taxonomic classifications obtained by these programs with the reduction of these FPs, but they address only one type of meta-omics, the metagenomics. In this work, we propose a new approach by integrating different meta-omics - shotgun and 16S amplicon metagenomics, and metatranscriptomics. We explored the classifications results of simulated data and mocks for variable extraction and developed classification models for discriminating predictions of bacterial species classified as correct or incorrect. We compared the performance of the results obtained between the individual meta-omics and the obtained through the integration observing the balance between precision and sensitivity. According to the measures calculated with our data sets, our approach has shown improvements in the classification with the reduction of the FPs and increases for the F1 measure, when compared to non-integrative approaches, including the use of classifiers combination methods. To facilitate its use, we developed the Gunga, a tool that incorporates the developed approach in R package format, with features for the integration of taxonomic classification data with different meta-omics and the classification of the incorrect predictions.

Le tissu adipeux est un organe important pour la régulation de l'homéostasie énergétique de l'organisme. Constitué majoritairement de lipides, ce tissu représente de plus un lieu de stockage pour de nombreux composés environnementaux lipophiles. Les conséquences de cette accumulation, et plus généralement de l'exposition du tissu adipeux à des polluants environnementaux ont été encore peu étudiées. Pourtant, en modulant les fonctions physiologiques ou le développement de ce tissu, ces polluants pourraient jouer un rôle dans le développement de pathologies telles que l'obésité ou le diabète. Le but de cette thèse était d'améliorer notre compréhension des conséquences de l'exposition du tissu adipeux humain à des xénobiotiques environnementaux. Dans un premier temps, nous avons regardé les capacités de métabolisation des xénobiotiques in situ dans le tissu adipeux humain en caractérisant l'expression des 23 isoformes de cytochromes P450 impliquées dans le métabolisme des xénobiotiques. Nous avons montré que seuls les CYP1B1 et CYP2U1 étaient exprimés dans le tissu adipeux humain et que la voie d'induction AhR était fonctionnelle, alors que les voies d'induction CAR et PXR ne semblaient pas l'être. Dans un deuxième temps, nous avons effectué une étude non ciblée par transcriptomique et métabonomique par 1H-RMN des perturbations induites par le traitement de cultures primaires de préadipocytes humains différenciés in vitro avec 2 polluants en particulier : la 2,3,7,8-tetrachlorodibenzo-p-dioxine (TCDD) et le mono-2-ethyl hexyl-phthalate (MEHP). L'intégration de ces données a permis la génération d'un nombre important d'hypothèses quant aux mécanismes de toxicité induits par ces composés. Le MEHP en particulier semble posséder un effet pro-différenciateur sur les préadipocytes humains et induire la glycéronéogenèse dans les adipocyte matures, deux mécanismes par lesquels ce polluant pourrait avoir un effet pro-obésifiant chez l'Homme.

The information flow through the regulatory networks in biological systems has been a rapidly growing field of research. Translation, being a very important regulatory check point, presents itself as a legitimate process for investigation. Only few regulatory factors and pathways are currently delineated that regulate translation through intermediary components in a remote manner, with global implications. In this context, this thesis studies the proteomics and transcriptomics data of Escherichia coli (E. coli) mutants, defective in translation, with the aim to unravel such regulatory factors or pathways and thus probe their regulatory aspects. Two main mics techniques are the backbone of this study; proteomics and transcriptomics. These provide a holistic view of cell states which allow us to investigate the regulation happening at the translation as well as at the transcription level. Two different proteomics techniques are used to resolve the proteomes; two-dimensional gel electrophoresis (2DE) and LC-coupled mass spectrometry. These have been introduced in the first chapter. Transcriptomics and proteomics being an evolving field, most of the techniques need optimization before applied for actual experiments and data acquisition. As part of our experimental strategy, we performed both transcriptomics and proteomics experiments in parallel. During application of 2DE based proteomics, we observed significant deficiencies in the 2DE technique itself, which we addressed as our first priority. We ran numerous optimization protocols to arrive at an optimized protocol to remove acidic region streaking (ARS) in 2DE, which is a well-known artifact. We describe the development of the modified protocol and discuss the detailed comparative analyses with recently published 2DE gels confirming the efficacy of the method in Chapter 2. The optimized 2DE technique developed by us was exploited in combination with MALDI mass spectrometry for the comparative proteomic analysis between the wild type E. coli and a mutated (ΔmetZWV::kan) strain. The proteomics results and its functional validation revealed a direct link between the flux of 10-formyltetrahydrofolate and the regulation of purine metabolism. The experimental observations were computationally modelled using flux balance analysis to understand the mechanistic detail involved in the remote regulation driven by purine metabolism and other peripheral pathways. The experimental details and the computational modelling are covered in chapter three. To gather wider perspective on the regulatory links in the E. coli organism, related to translation, we extended the omics studies using microarray technique on newer mutant strains. Our experiments aimed at obtaining differential transcript levels in the whole cell and the polysomal fraction of the E. coli cells. Three different E. coli mutants were used in this study; infC135, PthTs and folD122, which were defective in translation initiation, recycling and one carbon metabolism, respectively. The analysis revealed important routes of metabolic regulation. Few of them are worth mentioning; for example, purine and 10-fTHF metabolism that controls macromolecular synthesis, energy generation and inter-conversion of metabolites through pyruvate and also flagellar biosynthesis which is remote to translation. Transcriptomics data available from GEO database was analyzed as a background and based on the analysis we propose which of the differentially expressed genes are of generic in nature or unique to our mutants. These interesting observations about regulatory pathways are discussed in chapter four. To validate our transcriptomics results at the proteomics level and with a higher sensitivity than 2DE proteomics, we studied the whole cell proteomics data from two E. coli mutants, infC135 and PthTs, using high resolution FT-ICR mass spectrometry. Although a small number of differentially expressed proteins compared to microarray data, we could correlate the results with our transcriptomics data, especially, the proteins in the catabolic pathways. We elaborate the aforesaid study in chapter five. At the end we summarize the above omics studies to notice the following aspects emerging out. Translation, being a fundamental and essential process for the cell, disturbing it from any angle should affect many other processes which might seem remotely or not at all related to protein synthesis. This is evident from the whole study; we have been able to see some regulations which are very close to translation, but most are not directly related to translation. Apart from this we were able to point out routes of regulation which might control the amount of macromolecules synthesized, utilization of energy and metabolites and flagellar biogenesis. Another aspect is that we were able point out the gap in information between our regulation of pathways close to and remote to protein biosynthesis. Lastly, few master regulators were pointed out which might have potential functions in addition to what is known till date. A concluding discussion about these aspects has been discussed in the sixth chapter.

L'importance des symbioses dans l'évolution du vivant est désormais admise et les associations symbiotiques sont observées dans une grande diversité d'habitats. Notre étude porte sur une symbiose au sein d'un écosystème réduit, les sources hydrothermales de l'océan profond. Bathymodiolus azoricus est un bivalve hydrothermal vivant le long de la ride Médio-Atlantique, qui héberge dans des cellules branchiales spécialisées, deux types de γ-protéobactéries différentes : des méthanotrophes (MOX) et des sulfo-oxydantes (SOX). Ces dernières sont capables d'oxyder les composés réduits présents dans le fluide hydrothermal fournissant ainsi énergie et/ou source de carbone à leur hôte. Cette double endosymbiose est plastique ainsi, l'abondance relative du type de symbionte hébergé (SOX vs. MOX) varie en fonction des concentrations en composés réduits présent dans le milieu (H2S, CH4). L'objectif de ce travail de thèse est d'identifier les acteurs moléculaires impliqués dans l'acquisition, le maintien et la régulation des bactéries symbiotiques. Pour ce faire, une analyse OMICs globale (protéomique -nano LC-MS/MS- et transcriptomique -micro-array-) a été mise en ¿uvre sur des individus symbiotiques issus de population naturelle (site hydrothermal Lucky Strike, -1700m) et sur des individus ayant expérimentalement perdu ou maintenu leurs symbiotes. Suite à cette approche globale et exploratoire, une approche plus spécifique a été menée sur des familles de protéines impliquées dans des processus immunitaire et/ou d'interactions hôte/symbiotes. Cette thèse apporte un éclaircissement sur les mécanismes régissant les relations et la communication hôte/symbiote. / Hydrothermal vents are located on the mid-ocean ridges, and are characterized by challenging physico-chemical conditions. Despite these conditions dense hydrothermal communities develop down around hydrothermal fluid emissions. The presence of marine invertebrates relies on their capacity to cope with these challenging factors, and, for those forming most of the biomass, on their ability to live in symbiosis with chemoautotrophic bacteria. Bathymodiolus azoricus is one of these symbiotic species that harbors two types of γ-proteobacteria, a sulfide-oxidizing bacterium (SOX) (using the oxidation of H2S as the source of energy and CO2 as source of carbon) and a methane-oxidizing bacterium (MOX) (that uses the oxidation of CH4 as both a source of energy and carbon). These bacteria are located in specific epithelial cells in the gill tissue of the mussel. The proportion and number of these symbiont types (SOX vs. MOX) in B.azoricus can change in response to environmental conditions, and especially on the relative concentration of reduced compounds. The aim of our study is to understand the molecular mechanisms of acquisition, regulation and maintenance of the symbiotic charge in B .azoricus gills. We therefore, performed a global OMICs analysis (proteomics –nano LC-MS/MS and transcriptomics- micro-array) on mussels from natural population (Lucky Strike, -1700m) and on mussels that experimentally loose or maintain their symbiotic rate. This exploratory approach was followed by a more specific approach on family of proteins involved in immunity process and/or in host/symbiont interactions. This PhD provides hypotheses on the mechanisms governing the relationship and communication between host and symbionts.

A primary challenge in the analysis of high-throughput biological data is the abundance of correlated variables. A small change to a gene's expression or a protein's binding availability can cause significant downstream effects. The existence of such chain reactions presents challenges in numerous areas of analysis. By leveraging knowledge of the network interactions that underlie this type of data, we can often enable better understanding of biological phenomena. This dissertation will examine network-based statistical approaches to the problems of mechanism-of-action inference, characterization of gene expression changes, and prediction of drug response. First, we develop a method for multi-target perturbation detection in multi-omics biological data. We estimate a joint Gaussian graphical model across multiple data types using penalized regression, and filter for network effects. Next, we apply a set of likelihood ratio tests to identify the most likely site of the original perturbation. We also present a conditional testing procedure to allow for detection of secondary perturbations. Second, we address the problem of characterization of cellular phenotypes via Bayesian regression in the Gene Ontology (GO). In our model, we use the structure of the GO to assign changes in gene expression to functional groups, and to model the covariance between these groups. In addition to describing changes in expression, we use these functional activity estimates to predict the expression of unobserved genes. We further determine when such predictions are likely to be inaccurate by identifying GO terms with poor agreement to gene-level estimates. In a case study, we identify GO terms relevant to changes in the growth rate of S. cerevisiae. Lastly, we consider the prediction of drug sensitivity in cancer cell lines based on pathway-level activity estimates from ASSIGN, a Bayesian factor analysis model. We use penalized regression to predict response to various cancer treatments based on cancer subtype, pathway activity, and 2-way interactions thereof. We also present network representations of these interaction models and examine common patterns in their structure across treatments.

No description available.

Telomeres are repeated sequences at the ends of eukaryotic chromosomes that play prominent role in normal aging and disease development. They are dynamic structures that normally shorten over the lifespan of a cell, but can be elongated in cells with high proliferative capacity. Telomere elongation in stem cells is an advantageous mechanism that allows them to maintain the regenerative capacity of tissues, however, it also allows for survival of cancer cells, thus leading to development of malignancies. Numerous studies have been conducted to explore the role of telomeres in health and disease. However, the majority of these studies have focused on consequences of extreme shortening of telomeres that lead to telomere dysfunction, replicative arrest or chromosomal instability. Very few studies have addressed the regulatory roles of telomeres, and the association of genomic, transcriptomic and epigenomic characteristics of a cell with telomere length dynamics. Scarcity of such studies is partially conditioned by the low-throughput nature of experimental approaches for telomere length measurement and the fact that they do not easily integrate with currently available high-throughput data. In this thesis, we have attempted to build algorithms, in silico pipelines and software packages to utilize high-throughput –omics data for telomere biology research. First, we have developed a software package Computel, to compute telomere length from whole genome next generation sequencing data. We show that it can be used to integrate telomere length dynamics into systems biology research. Using Computel, we have studied the association of telomere length with genomic variations in a healthy human population, as well as with transcriptomic and epigenomic features of lung cancers. Another aim of our study was to develop in silico models to assess the activity of telomere maintenance machanisms (TMM) based on gene expression data. There are two main TMMs: one based on the catalytic activity of ribonucleoprotein complex telomerase, and the other based on recombination events between telomeric sequences. Which type of TMM gets activated in a cancer cell determines the aggressiveness of the tumor and the outcome of the disease. Investigation into TMM mechanisms is valuable not only for basic research, but also for applied medicine, since many anticancer therapies attempt to inhibit the TMM in cancer cells to stop their growth. Therefore, studying the activation mechanisms and regulators of TMMs is of paramount importance for understanding cancer pathomechanisms and for treatment. Many studies have addressed this topic, however many aspects of TMM activation and realization still remain elusive. Additionally, current data-mining pipelines and functional annotation approaches of phenotype-associated genes are not adapted for identification of TMMs. To overcome these limitations, we have constructed pathway networks for the two TMMs based on literature, and have developed a methodology for assessment of TMM pathway activities from gene expression data. We have described the accuracy of our TMM-based approach on a set of cancer samples with experimentally validated TMMs. We have also applied it to explore TMM activity states in lung adenocarcinoma cell lines. In summary, recent developments of high-throughput technologies allow for production of data on multiple levels of cellular organization – from genomic and transcriptiomic to epigenomic. This has allowed for rapid development of various directions in molecular and cellular biology. In contrast, telomere research, although at the heart of stem cell and cancer studies, is still conducted with low-throughput experimental approaches. Here, we have attempted to utilize the huge amount of currently accumulated multi-omics data to foster telomere research and to bring it to systems biology scale.

Schizophrenia and bipolar disorder are two severe mental disorders that affect more than 65 million individuals worldwide. The aim of thisproject was to find co-prediction mechanisms for genes associated with schizophrenia and bipolar disorder using a multi-omics data set and a transparent machine learning approach. The overall purpose of theproject was to further understand the biological mechanisms of these complex disorders. In this work, publicly available multi-omics data collected from post-mortem brain tissue were used. The omics types included were gene expression, DNA methylation, and SNP array data. The data consisted of samples from individuals with schizophrenia, bipolar disorder, and healthy controls. Individuals with schizophrenia or bipolar disorder were considered as a combined CASE class. Using machine learning techniques, a multi-omics pipeline was developedto integrate these data in a manner such that all types were adequately represented. A feature selection was performed on methylation and SNP data, where the most important sites were estimated and mapped to their corresponding genes. Next, those genes were intersected with the gene expression data, and another feature selection was performed on the gene expression data. The most important genes were used to develop an interpretable rule-based model with an accuracy of 88%. The model wasthen visualized as a network. The graph highlighted genes that may be of biological importance, including CACNG8, RTN4, TERT, OSBPL8, and ANTXR1. Moreover, strong co-predictions were found, most notable between CNKSR4 and KDM4C in CASE samples. However, further investigations would need to be performed in order to prove that these are real biological interactions. Through the methods developed and the results found in this project, we hope to shed new light towards analyzing multi-omics data as well as to reveal more about the underlying mechanisms of psychiatric disorders.

Cyanobacteria are photosynthetic organisms with the potential to sustainably produce carbon-based end products by fixing carbon dioxide from the atmosphere. Optimizing the growth or biochemical production in cyanobacteria is an ongoing challenge in metabolic engineering. Rational design of metabolic pathways requires a deep understanding of regulatory mechanisms. Hence, a deeper understanding of photosynthetic regulation of the influence of the environment on metabolic fluxes provides exciting possibilities for enhancing the photosynthetic Calvin-Benson-Bassham cycle. One approach to study metabolic processes is to use omic-level techniques, such as proteomics and fluxomics, to characterize varying phenotypes that result from different environmental conditions or different genetic perturbations.<br><br>This dissertation examines the influence of light intensity on enzymatic abundances and the resulting Calvin-Benson-Bassham cycle fluxes using a combined proteomic and fluxomic approach in the model cyanobacteria Synechocystis sp. PCC 6803. The correlation between light intensity and enzymatic abundances is evaluated to determine which reactions are more regulated by enzymatic abundance. Additionally, carbon enrichment data from isotopic labelling experiments strongly suggest metabolite channeling as a flexible and light-dependent regulatory mechanism present in cyanobacteria. We propose and substantiate biological mechanisms that explains the formation of metabolite channels under specific redox conditions. <br><br>The same multi-omic approach was used to examine genetically modified cyanobacteria. Specifically, genetically engineered and conditionally growth-enhanced Synechocystis strains overexpressing the central Calvin-Benson-Bassham cycle enzymes FBP/SBPase or transketolase were evaluated. We examined the effect of the heterologous expression of each of these enzymes on the Calvin-Benson-Bassham cycle, as well as on adjacent central metabolic pathways. Using both proteomics and fluxomics, we demonstrate distinct increases in Calvin-Benson-Bassham cycle efficiency as a result of lowered oxidative pentose phosphate pathway activity. This work demonstrates the utility of a multi-omic approach in characterizing the differing phenotypes arising from environmental and genetic changes.<br><br>