Diabesity: New Candidate Genes and Structural and Functional Effects of Non-Synonymous Single Nucleotide Polymorphisms Identified by Computational Biology.

Abstract

Diabesity is a comorbidity of type 2 diabetes mellitus and obesity. Diabesity is a major global epidemic and a veritable planetary health burden. With diabesity, several clinical signs are present such as excess accumulation of fat, altered lipid metabolism, chronic inflammation, insulin resistance, disordered pancreatic β-cell metabolism, and hyperglycemia. We report here new potential candidate genes for diabesity, and the structural and functional effects of non-synonymous single nucleotide polymorphisms (nsSNPs) in these genes using a computational biology approach. A protein-protein interaction (PPI) network was constructed using Human Integrated Protein-Protein Interaction rEference (HIPPIE') data for 186 diabesity-associated genes from the Disease Gene Network (DisGeNET). Subsequently, the top 2% of nine centrality-ranked genes were identified as hub genes. Gene ontology enrichment analysis was performed with the same gene list using the Gene Ontology enRIchment anaLysis and visuaLizAtion (GORILLA) tool, and importantly, 63 enriched hub genes with no prior disease association were selected and their differential expressions in adipose, skeletal, and hepatic tissues were analyzed using Gene Expression Omnibus (GEO) profiles. Finally, the nsSNPs in the top five prioritized genes (EGFR, SRC, SQSTM1, CCND1, and RELA) were retrieved from Database of Single Nucleotide Polymorphisms (dbSNP) and subjected to deleterious variant analysis. The significant variants were subjected to structural prediction using AlphaFold, stability analysis, and molecular dynamics simulation using GROningen MAchine for Chemical Simulations (GROMACS). Taken together, the present computational biology research reports new molecular insights on diabesity candidate genes and the role of nsSNPs that may potentially contribute to diabesity. As diabesity and diabetes continue to be major planetary health challenges, these findings warrant further in vitro and clinical translation research with an eye to precision medicine and therapeutics innovation. Understanding the differences between wild type and variant proteins is crucial for developing interventions aimed at stabilizing these proteins in the prevention and treatment of diabesity.