Integrative Bioinformatic Analysis of TCF7L2 (Q9NQB0) Polymorphisms Associated with Type 2 Diabetes

Abstract

Background:
Type 2 diabetes mellitus (T2DM) is a multifactorial metabolic disorder influenced by both genetic and environmental factors. Among the susceptibility genes, TCF7L2 is one of the most significant contributors to insulin secretion and glucose metabolism.

Objective:
This study aimed to investigate the structural and functional impact of TCF7L2 polymorphisms associated with T2DM using an integrated bioinformatics approach.

Methods:
Disease-associated genes were identified using DisGeNET and MalaCards databases, and overlapping genes were analyzed using Venny 2.1. Functional enrichment analysis was performed using ShinyGO to determine the biological roles of TCF7L2. A missense variant, c.4C>G (p.Pro2Ala), was selected from the gnomAD database for pathogenicity assessment. The deleterious nature of the variant was predicted using SIFT, PolyPhen-2, and MutationTaster. Three-dimensional structures of wild-type and mutant proteins were modeled using AlphaFold. Molecular docking with resveratrol was performed using AutoDock Vina to evaluate binding interactions.

Results:
Functional enrichment analysis indicated that TCF7L2 is involved in insulin signaling, pancreatic β-cell development, and glucose metabolism pathways. The selected missense mutation was predicted to be deleterious by multiple in silico tools. Molecular docking revealed stable ligand–protein interactions, with altered binding affinity and interaction profiles between wild-type and mutant proteins.

Conclusion:
The TCF7L2 variant (p.Pro2Ala) may influence protein function and contribute to T2DM pathogenesis. Additionally, resveratrol demonstrated notable binding affinity toward TCF7L2, suggesting its potential therapeutic relevance.