Computational Analysis of Missense Single Nucleotide Variants (SNVs) in the GHSR Gene Linked to Obesity

Background/Objectives: In recent years, efforts to understand obesity’s pathophysiology have focused on satiety signals in the hypothalamus and hormonal signalling in orexigenic and anorexigenic neurons. These signals, linked to hunger, satiety, and energy expenditure, are influenced by peptides that activate or suppress specific pathways. However, different phenotypes related to body composition result from mutations (allelic variants) in genes that encode these proteins, particularly peptide receptors. Specifically, the hormone receptor ghrelin (GHSR), located on the surface of orexigenic neurons, has been linked to the regulation of hunger. Additionally, the production and secretion of ghrelin, a peptide hormone produced by the stomach, may exhibit varying sensitivity in its receptor based on an individual’s nutritional status. Moreover, allelic variants of the GHSR gene may potentially lead to significant alterations in signalling provided by the GHSR receptor, resulting in modified hormone-binding phenotypes. In this context, the search for allelic variants that can account for diverse phenotypes, whether thinness or overweight/obesity, can aid in comprehending the pathway and defining new strategies for early laboratory diagnosis or target peptides for treatment. Methods: Initial mining produced 373 non-random SNPs located in missense regions. A total of 373 missense variants were initially identified in the GHSR gene. After applying a global minor allele frequency (MAF) filter of <1%, 20 rare missense variants remained. Results: These variants were subsequently analyzed using nine in silico pathogenicity prediction tools, resulting in the prioritization of eight variants predicted as deleterious by at least four algorithms. These variants were further analysed using the HOPE project web server and the SwissModel database. Conclusions: Through these analyses and future investigations into these mutations, we may gain a more comprehensive understanding of the implications of these mutations and their potential correlation with the pathophysiology of obesity.