[From Gene to Cell: Functional Validation of RYR1 Variants].
M S-MEDECINE SCIENCES（2024）
Univ Grenoble Alpes
Abstract
Le depistage genetique des maladies rares permet d'identifier le(s) gene(s) responsable(s) chez environ 50 % des patients. Les cas restants se trouvent dans une impasse diagnostique, car les connaissances actuelles ne permettent pas d'identifier le bon gene ou de determiner si le(s) variant(s) detecte(s) sur le gene est(sont) pathogene(s) ou benin(s). On parle alors de << variants de signification inconnue >> (VSI). Dans le cas des maladies neuromusculaires, le gene RYR1 est souvent mis en cause, mais la majorite de ses variants identifies sont classes comme VSI, ce qui met a mal le diagnostic precis des patients. Notre projet vise a creer un pipeline d'analyses en combinant differentes approches (l'intelligence artificielle, les donnees de biologies structurales et les analyses fonctionnelles), afin d'obtenir une classification des variants de RYR1 plus efficace et d'ameliorer le diagnostic genetique des maladies liees a ce gene. Genetic screening of rare diseases allows identification of the responsible gene(s) in about 50% of patients. The remaining cases are in a diagnostic deadlock as current knowledge fails to identify the correct gene or determine if the detected variant on the gene is pathogenic. These are named "variants of unknown significance" (VUS). In the case of neuromuscular diseases, the RYR1 gene is often implicated, with the majority of variants classified as VUS, requiring reliable classification to help patient diagnosis. Our project aims to create an efficient classification pipeline, integrating artificial intelligence, structural biology data, and functional analyses to enhance genetic diagnosis of RYR1-related diseases.
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