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Identification et classification de composés reprotoxiques par des approches de toxicogénomique prédictive

Abstract : The core aim of my thesis project is to develop predictive toxicology approaches based on the integration of massive toxicogenomics datasets using bioinformatics and biostatistics methodologies. Specific objectives include: (1) classification of chemicals based on toxicogenomics signatures, i.e. the set of genes whose expression is known to be positively or negatively altered after exposure to these compounds; (2) the association of the resulting classes with human disorders or deleterious phenotypes based on the well-known toxicants present in those classes; (3) the prediction of novel reprotoxicants and/or endocrine disruptors based on toxicogenomics signature similarities with known chemicals affecting testis development and function. The assembled toxicogenomics dataset contains 23,657 samples covering 7092 experimental conditions (one chemical, one dose, one exposure time, one tissue) for 541 chemicals in seven distinct tissues in the rat from 18 different studies. From this dataset, 3,022 experimental conditions corresponding to 452 distinct compounds are associated to a toxicogenomics signature containing more than ten genes showing an altered expression pattern after exposure. Using unsupervised classification methods, 95 chemical clusters were defined showing close toxicogenomics signatures. The phenotype association analysis using data extracted from de Comparative Toxicogenomics Database (CTD) allowed us to identify three clusters significantly enriched in known endocrine-disrupting chemicals. Currently, 22 compounds are being tested on a human cell line expressing the enzymes of steroidogenesis (NCI-H295R) to evaluate their potential endocrine disrupting effects. These researches allowed us to demonstrate the relevance of integrating massive toxicogenomics datasets to predict adverse effects of compounds tested in different organs. It is currently being pursued through the development of a novel repository, TOXsIgN. This resource provides a flexible environment to facilitate online submission, storage and retrieval of toxicogenomics signatures by the scientific community. Similarly, the current PhD project also yielded to the implementation of several tools dedicated to predictive toxicology and data visualization including the ReproGenomics Viewer (RGV).
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Submitted on : Thursday, March 29, 2018 - 1:26:08 PM
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Thomas Darde. Identification et classification de composés reprotoxiques par des approches de toxicogénomique prédictive. Médecine humaine et pathologie. Université Rennes 1, 2017. Français. ⟨NNT : 2017REN1B022⟩. ⟨tel-01751766⟩

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