Approches transcriptionelles dans des modèles animaux de stress et de dépression majeure

Authors: Fatma, Mena
Advisor: Labonté, Benoit
Abstract: Major depressive disorder (MDD) is the leading cause of disability for three decades with over 300 million affected worldwide. Indeed, it is a major contributor to the overall global economic burden of disease. Despite its significant societal impact, the biological mechanisms of depression remain poorly understood. Unfortunately, only around 30% of patients treated for depression show complete improvement in their symptoms. Given, the high failure rate of antidepressant clinical trials, there has been increased scrutiny recently regarding their use for deciphering the neurobiology of depression and to design potential treatment interventions. Given the fact that most of our knowledge of the field comes from animal models, indeed, these models reproduce some aspects of human MDD but to what degree remains unknown. This work elucidates the extent to which they recapitulate the molecular pathology of the human disorder. In this thesis, we leveraged differential expression and co-expression network analyses to catalogue the overlap between human MDD and 3 mouse model of stress, namely chronic variable stress, social isolation and chronic social defeat stress, and evaluated their capacity of reproducing the transcriptional profiles associated with human MDD in two brain regions, mPFC and NAc, widely implicated in depression. Our results show that each model efficiently reproduces common but also unique transcriptional features of the human syndrome.Overall, by identifying strongly co-expressed groups of genes shared between humans and mice, our results suggest that these transcriptional signatures are similarly involved in the control of functional pathways in both species and confer strong support for the use of these mouse models for the study of the molecular alterations seen in MDD while providing important implications for future research and clinical applications.
Document Type: Thèse de doctorat
Issue Date: 2021
Open Access Date: 22 March 2021
Grantor: Université Laval
Collection:Thèses et mémoires

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