Prédiction in silico de l’impact des Variant de Signification Incertaine du gène KSR2 sur la survenue de l’Obésité Monogénique

Abstract

Monogenic obesity represents a rare form of obesity caused by mutations affecting a single gene involved in the regulation of appetite, energy metabolism, and overall energy balance. It is generally characterized by severe, early-onset obesity and constitutes a relevant model for understanding the molecular mechanisms underlying obesity in general. Among the genes involved, KSR2 plays a key role in the regulation of energy metabolism and cellular homeostasis, and its alterations may contribute to the development of monogenic obesity. In this context, the present study focused on analyzing the deleterious effects of missense mutations classified as Variants of Uncertain Significance (VUS) in this gene. From a total of 873 missense variants identified in the KSR2 gene in the GnomAD database, a multi-step filtering process based on different criteria was applied. Subsequently, the effects of the selected mutations were evaluated through an in-silico approach using several bioinformatics tools, including SIFT, PolyPhen-2, REVEL, CADD, DynaMut2, and Project HOPE. The filtering process led to the selection of 21 variants of the KSR2 gene. Predictive analyses showed that the majority of the studied variants exhibit a deleterious profile, with 96.4% predicted as deleterious by SIFT and 82.1% as probably deleterious by PolyPhen-2. Furthermore, structural analysis revealed alterations in protein stability and dynamics, as well as changes in the physicochemical properties of amino acids, which may disrupt intra-protein interactions and impair the function of the KSR2 protein. The consistency of the results obtained from the different tools strengthens the reliability of in silico approaches and highlights the importance of combining multiple methods to improve prediction accuracy. In conclusion, this study contributes to a better understanding of the impact of KSR2 gene mutations and emphasizes the importance of bioinformatics in the interpretation of genetic variants. It also provides perspectives for improving diagnosis and developing personalized therapeutic strategies within the framework of precision medicine.