Gene Network Analysis and Computational Modelling of Proteins Causing 'Diabesity'

Background and Aims: Diabetes resulting from obesity is referred to as diabesity. This study aimed to establish a genetic link between the two complex polygenic diseases and to define the role of these genes in the critical metabolic pathways and their contribution to the clinical symptoms and phenotypes. Methods: The dataset for the current research was collated from obesity and co-morbid diseases database (OCDD), PubMed, and Google Scholar. The interactions between the genes were analyzed using the STRING tool after the compilation of obesity genes associated with T1D, T2D, and MODY. Cytoscape software was used for the visualization of these interactions. The top 3 gene clusters of the interactive network were extracted using the MCODE plugin. Enrichment analysis of the genes listed was followed by functional annotation using ClueGo/CluePedia and DAVID software. Databases such as UniProt/SwissProt, HGMD, and Clinvar will be used to retrieve the variants from shortlisted genes responsible for diabesity. Tools such as HapCUT, WhatHap, and HapCompass will be used to perform haplotyping. Results: We identified a total of 546 genes from the literature survey that are obesity genes associated with T1D, T2D, and MODY. The network backbone of the identified genes comprised of 514 nodes and 4126 edges with an estimated clustering coefficient of 0.242. The MCODE has generated 3 clusters with a score of 33.61, 16.788, and 6.783, respectively. The highest scoring nodes of the clusters are AGT, FGB, and LDLR genes. The genes from cluster 1 were enriched in FOXO-mediated transcription of oxidative stress, renin secretion, and regulation of lipolysis in adipocytes. The cluster 2 genes enriched in SHC-related events triggered by IGF1R, regulation of lipolysis in adipocytes, and GRB2:SOS provides linkage to MAPK signaling for integrins. The cluster 3 genes enriched in IGF1R signaling cascade and insulin signaling pathway. The genes involved in the essential molecular signaling pathways will be identified. Several computational tools will be applied to identify the genetic variants from the genes identified. Conclusion: The study will assist in identifying novel genetic biomarkers that could help in early diagnostics, preventive measures. Further, we will obtain the possible chemicals for the molecular modeling of a target protein. These possible outcomes will serve as a platform for drug discovery and provides a bright future to the clinical trials and better treatment strategies for diabesity.