In silico approach to identify non-synonymous missense variants in human obesity-related genes: Comprehensive analyses in variants reported in Brazilian databases

Bratti, Letícia de Oliveira Souza; Nunes, Bruno Fonseca; Gorges, Daphany Marah; Filippin-Monteiro, Fabíola Branco

doi:10.1016/j.humgen.2023.201174

Cited by 2 publications

(2 citation statements)

References 45 publications

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“…Non-synonymous SNPs including rs765425383, rs752071352, rs759555652, rs200326086, and rs766267373 have been reported to be deleterious although no structural alteration of the ADIPOR1 protein was caused by these polymorphic changes [29]. A missense variant, rs766665118, in MC4R, was detected from the Brazilian database as a potential pathogenic variant for obesity [30]. Multiple other non-synonymous SNPs of MC4R (E308K, P299L, D298H, C271F, C271R, P260L, T246N, G243R, C196Y, W174C, Y157S, D126Y, and D90G) were also observed to be causative agents of childhood obesity [31].…”

Section: Introductionmentioning

confidence: 99%

Predictive Analysis of the Leptin-Melanocortin and Adiponectin Signaling Pathways in Obesity through In Silico Techniques

Ghosh,

Dhar,

Roy

et al. 2024

Preprint

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Genetic and epigenetic alterations have been reported to significantly influence the global burden of obesity. Single nucleotide polymorphisms (SNPs) including both coding and non-coding amino acid changes are the key regulators of the protein structural and functional modifications. The current computational study utilizing in silico techniques focused on the screening and identification of the most pathogenic missense SNPs of the selected candidate genes of the leptin-melanocortin and adiponectin signaling pathways provoking obesity. A total of 2424 SNPs from 9 candidate genes were extracted from the NCBI database followed by pathogenicity prediction using seven servers, SIFT, PANTHER, Meta-SNP, PhD-SNP, PredictSNP, PolyPhen-2, and SNAP2. The shortlisted variants (n = 7) were analyzed for structural stability using DynaMut, iMutant, INPS3D, MuPro, and iStable followed by the functional stability analysis (n = 3) using Mut-Pred2, Project HOPE, and I-TASSER. Gene-network analysis of the finally screened SNPs (n = 3) was created using the STRING database. Two SNPs of ADIPOR1 (rs1419320091 and rs1654109863) and one variant of MC4R (rs1159323398) were predicted in the study to be the most pathogenic resulting in altered protein functionality. Therapeutic approaches designed based on early pathogenicity predictions using in silico analysis techniques would be a new horizon for the effective control of disease prevalence.

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Section: Introductionmentioning

confidence: 99%

Predictive Analysis of the Leptin-Melanocortin and Adiponectin Signaling Pathways in Obesity through In Silico Techniques

Ghosh,

Dhar,

Roy

et al. 2024

Preprint

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“…Given the notable variability in individual biological responses to increased food intake or decreased energy expenditure, and considering the growing popularity of genetic studies, it has become essential to investigate the genetic influences on body weight [35]. Particularly in our geographical region, there is a lack of published data exploring the connections between genetic nucleotide polymorphisms and their role as predisposing factors in cardiometabolic alterations [36]; this region exhibits a significant prevalence of being overweight and obesity, with rates of 21.1% and 21.8%, respectively [37].…”

Section: Introductionmentioning

confidence: 99%

Gene Polymorphisms LEP, LEPR, 5HT2A, GHRL, NPY, and FTO-Obesity Biomarkers in Metabolic Risk Assessment: A Retrospective Pilot Study in Overweight and Obese Population in Romania

Penes,

Weber,

Pop

et al. 2024

Cardiogenetics

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Genome-wide association studies (GWAS) have successfully revealed numerous susceptibility loci for obesity. The PREDATORR study (2014) shows that in Romania, 346% of adults aged 20–79 y/o are overweight, and 31.4% are obese with a high risk of cardiometabolic complications, a number that puts almost 67% of Romania’s population in the abnormal weight group. Our study aims to investigate the current status of the genetic foundation in metabolic disease associated with obesity, applied to a pilot group of patients specifically examining the impact of known polymorphisms and their haplotype of six food intake-regulating genes, namely leptin (LEP), leptin receptor (LEP-R), serotonin receptor (5HTR2A), ghrelin (GHRL), neuropeptide Y (NPY), and fat-mass and obesity-associated protein (FTO) with the following polymorphisms: LEP A-2548G, LEPR A-223G, 5HTR2A G-1439A, GHRL G-72T, NPY T-29063C, FTO A-T, and body mass index (BMI). A notable link between the LEP-2548 rs7799039 gene’s AG genotype and the risk of obesity was observed, particularly pronounced in males aged 40–49, with an approximately seven-fold increased likelihood of obesity. The 5HTR2A rs6311 AA genotype was associated with a higher BMI, which was not statistically significant. The FTO rs9939609 gene’s AA genotype emerged as a significant predictor of obesity risk. Besides these significant findings, no substantial associations were observed with the LEPR, 5HTR2A, GHRL, and NPY genes. Haplotype association analysis showed a suggestive indication of GRGMLA (rs7799039, rs1137101, rs6311, rs696217, rs16139, rs9939609 sequence) haplotype with a susceptibility effect towards obesity predisposition. Linkage disequilibrium (LD) analysis showed statistically significant associations between LEP and LEPR gene (p = 0.04), LEP and GHRL gene (p = 0.0047), and GHRL and FTO gene (p = 0.03). Our study, to the best of our knowledge, is one of the very few on the Romanian population, and aims to be a starting point for further research on the targeted interventional strategies to reduce cardiometabolic risks.

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In silico approach to identify non-synonymous missense variants in human obesity-related genes: Comprehensive analyses in variants reported in Brazilian databases

Cited by 2 publications

References 45 publications

Predictive Analysis of the Leptin-Melanocortin and Adiponectin Signaling Pathways in Obesity through In Silico Techniques

Predictive Analysis of the Leptin-Melanocortin and Adiponectin Signaling Pathways in Obesity through In Silico Techniques

Gene Polymorphisms LEP, LEPR, 5HT2A, GHRL, NPY, and FTO-Obesity Biomarkers in Metabolic Risk Assessment: A Retrospective Pilot Study in Overweight and Obese Population in Romania

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