Développement et évaluation de méthodes bioinformatiques pour la détection de séquences cis-régulatrices impliquées dans le développement de la drosophile

L'objectif de ce travail est de développer et d'évaluer des approches méthodologiques pour laprédiction de séquences cis-régulatrices. Ces approches ont été intégrées dans la suite logicielleRSAT (Regulatory Sequences Analysis Tools). Ces séquences jouent un rôle important dans larégulation de l'expression des gènes. Cette régulation, au niveau transcriptionnel, s'effectue àtravers la reconnaissance spécifique entre les facteurs de transcription et leurs sites de fixation(TFBS) au niveau de l'ADN.Nous avons développé et évalué une série d'outils bioinformatiques qui utilisent les matricesposition-poids pour prédire les TFBS ainsi que les modules cis-régulateurs (CRM). Nos outilsprésentent l'avantage d'intégrer les différentes approches déjà proposées par d'autres auteurs touten proposant des fonctionnalités innovantes.Nous proposons notamment une nouvelle approche pour la prédiction de CRM basé sur ladétection de régions significativement enrichies en TFBS. Nous les avons appelés les CRER (pourCis-Regulatory Elements Enriched Regions). Un autre aspect essentiel de toute notre approcheréside dans le fait que nous proposons des mesures statistiques rigoureuses pour estimerthéoriquement et empiriquement le risque associé aux différentes prédictions. Les méthodes deprédictions de séquences cis-regulatrices prédisent en effet un taux de fausses prédictionsgénéralement élevé. Nous intégrons un calcul des P-valeurs associées à toutes les prédictions.Nous proposons ainsi une mesure fiable de la probabilité de faux positifs.Nous avons appliqué nos outils pour une évaluation systématique de l'effet du modèle debackground sur la précision des prédictions à partir de la base de données de TRANSFAC. Nosrésultats suggèrent une grande variabilité pour les modèles qui optimisent la précision desprédictions. Il faut choisir le modèle de background au cas par cas selon la matrice considérée.Nous avons ensuite évalué la qualité des matrices de tous les facteurs de transcription dedrosophile de la base de données ORegAnno, c'est à dire leur pouvoir de discrimination entre lesTFBS et les séquences génomiques. Nous avons ainsi collecté des matrices des facteurs detranscription de drosophile de bonne qualité.A partir des matrices de drosophile que nous avons collectées, nous avons entamé une analysepréliminaire multi-genome de prédictions de TFBS et de CRM dans la région de lʼenhancer dorsocentral(DCE) du complexe achaete-scute de drosophile. Les gènes de ce complexe jouent unrôle important dans la détermination des cellules système nerveux périphérique de drosophile. Il aété prouvé expérimentalement qu'il existe un lien direct entre le phénotype du système nerveuxpériphérique et les séquences cis-régulateurs des gènes de ce complexe.Les outils que nous avons développés durant ce projet peuvent s'appliquer à la prédiction desséquences de régulation dans les génomes de tous les organismes.Doctorat en Sciencesinfo:eu-repo/semantics/nonPublishe

Evaluating the prediction of cis-acting regulatory elements in genome sequences

Transcriptional regulation plays an essential role in all steps of morphogenesis, by controlling the specific subsets of genes that will be expressed in different cell types, and at different times during embryonic development. The control of gene expression is also crucial to maintain the basic cellular functions (e.g. cell divisions) and the response of the organism to its environment (e.g. metabolic regulation). The spatio-temporal control of gene expression is ensured by interactions between transcription factors and specific loci, called cis-acting regulatory elements.SCOPUS: ch.binfo:eu-repo/semantics/publishe

Using RSAT to scan genome sequences for transcription factor binding sites and cis-regulatory modules.

This protocol shows how to detect putative cis-regulatory elements and regions enriched in such elements with the regulatory sequence analysis tools (RSAT) web server (http://rsat.ulb.ac.be/rsat/). The approach applies to known transcription factors, whose binding specificity is represented by position-specific scoring matrices, using the program matrix-scan. The detection of individual binding sites is known to return many false predictions. However, results can be strongly improved by estimating P value, and by searching for combinations of sites (homotypic and heterotypic models). We illustrate the detection of sites and enriched regions with a study case, the upstream sequence of the Drosophila melanogaster gene even-skipped. This protocol is also tested on random control sequences to evaluate the reliability of the predictions. Each task requires a few minutes of computation time on the server. The complete protocol can be executed in about one hour.Comparative StudyJournal ArticleResearch Support, Non-U.S. Gov'tinfo:eu-repo/semantics/publishe

RNA-sequencing identifies Nova1 as a major splicing regulator in pancreatic beta cells.

info:eu-repo/semantics/nonPublishe

MCPIP1 regulates the sensitivity of pancreatic beta-cells to cytokine toxicity

The autoimmune-mediated beta-cell death in type 1 diabetes (T1DM) is associated with local inflammation (insulitis). We examined the role of MCPIP1 (monocyte chemotactic protein–induced protein 1), a novel cytokine-induced antiinflammatory protein, in this process. Basal MCPIP1 expression was lower in rat vs. human islets and beta-cells. Proinflammatory cytokines stimulated MCPIP1 expression in rat and human islets and in insulin-secreting cells. Moderate overexpression of MCPIP1 protected insulin-secreting INS1E cells against cytokine toxicity by a mechanism dependent on the presence of the PIN/DUB domain in MCPIP1. It also reduced cytokine-induced Chop and C/ebpβ expression and maintained MCL-1 expression. The shRNA-mediated suppression of MCPIP1 led to the potentiation of cytokine-mediated NFκB activation and cytokine toxicity in human EndoC-βH1 beta-cells. MCPIP1 expression was very high in infiltrated beta-cells before and after diabetes manifestation in the LEW.1AR1-iddm rat model of human T1DM. The extremely high expression of MCPIP1 in clonal beta-cells was associated with a failure of the regulatory feedback-loop mechanism, ER stress induction and high cytokine toxicity. In conclusion, our data indicate that the expression level of MCPIP1 affects the susceptibility of insulin-secreting cells to cytokines and regulates the mechanism of beta-cell death in T1DM.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

RSAT: regulatory sequence analysis tools.

The regulatory sequence analysis tools (RSAT, http://rsat.ulb.ac.be/rsat/) is a software suite that integrates a wide collection of modular tools for the detection of cis-regulatory elements in genome sequences. The suite includes programs for sequence retrieval, pattern discovery, phylogenetic footprint detection, pattern matching, genome scanning and feature map drawing. Random controls can be performed with random gene selections or by generating random sequences according to a variety of background models (Bernoulli, Markov). Beyond the original word-based pattern-discovery tools (oligo-analysis and dyad-analysis), we recently added a battery of tools for matrix-based detection of cis-acting elements, with some original features (adaptive background models, Markov-chain estimation of P-values) that do not exist in other matrix-based scanning tools. The web server offers an intuitive interface, where each program can be accessed either separately or connected to the other tools. In addition, the tools are now available as web services, enabling their integration in programmatic workflows. Genomes are regularly updated from various genome repositories (NCBI and EnsEMBL) and 682 organisms are currently supported. Since 1998, the tools have been used by several hundreds of researchers from all over the world. Several predictions made with RSAT were validated experimentally and published.Journal ArticleResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe

SRp55 regulates splicing networks controlling key pathways for human beta cell function and survival.

info:eu-repo/semantics/nonPublishe

Regulation of pancreatic beta cell function and survival by alternative splicing: the role of the splicing factor SRp55.

info:eu-repo/semantics/publishe

SRp55 Regulates a Splicing Network that Controls Human Pancreatic Beta Cell Function and Survival.

Progressive failure of insulin-producing beta cells is the central event leading to diabetes, but the signalling networks controlling beta cell fate remain poorly understood. Here we show that SRp55, a splicing factor regulated by the diabetes susceptibility gene GLIS3, has a major role in maintaining function and survival of human beta cells. RNA-seq analysis revealed that SRp55 regulates the splicing of genes involved in cell survival and death, insulin secretion and JNK signalling. Specifically, SRp55-mediated splicing changes modulate the function of the pro-apoptotic proteins BIM and BAX, JNK signalling and endoplasmic reticulum stress, explaining why SRp55 depletion triggers beta cell apoptosis. Furthermore, SRp55 depletion inhibits beta cell mitochondrial function, explaining the observed decrease in insulin release. These data unveil a novel layer of regulation of human beta cell function and survival, namely alternative splicing modulated by key splicing regulators such as SRp55 that may crosstalk with candidate genes for diabetes.SCOPUS: ar.jinfo:eu-repo/semantics/publishe