Integrated Genetic and Protein Mechanisms Underlying Glucagon-like Peptide-1 Receptor Agonists in Treating Diabetes Mellitus and Weight Loss

Abstract

Glucagon-like peptide-1 receptor (GLP1R) agonists, such as semaglutide, are used for treating type 2 diabetes mellitus and promoting weight loss. This study investigates genetic and molecular mechanisms underlying GLP1R activation using a novel in silico approach to identify effects on metabolism, glucose and insulin production, gastrointestinal motility, behavior, and satiety. This approach used three separate searchable web-based programs and databases (STRING, Pathway Commons, and BioGRID) to identify and analyze functional gene and protein interactions with mechanisms to query GLP1R and related metabolic and appetite regulatory networks with disease associations. We examined integrated gene-gene and protein-protein interactions, pathways, molecular functions, associated diseases, and biological processes for GLP1R, that reportedly involved in diabetes and obesity. GLP1R signaling cascades were described with the activation of the adenylate cyclase-modulating G protein-coupled receptor and increased intracellular cyclic AMP, collectively impacting glucagon production, insulin, glycogenolysis, vasoactive intestinal peptide, and other peptides and hormones required for satiety. Additional factors found were obesity-related peptides (i.e., POMC), hormone signaling, renin secretion, electrolytes and diuresis, circadian rhythm, and insulin secretion. These associations and interactions shift from hypoglycemia to broader endocrine dysfunction. A relationship was noted for GNAS having a role in growth, electrolytes, and skeletal disturbances with specific hormone sensitivity patterns. Understanding established and new interactions with genetics and gene-protein variants that impact type 2 diabetes and obesity would provide further insight into therapeutic GLP1R agonists response and consequences. Potential long-term systemic effects should be monitored, studied, and recorded with treatment protocols adjusted accordingly.

Key Biomarkers

Symptom Clusters