Functional Characterization of Variants inMC4RGene Promoter Region Found in Obese Children

Tan, Karen; Ooi, Delicia Shu Qin; Ong, Siong Gim; Kwan, Charmaine Shuyi; Chan, Raymond Ming En; Poh, Larry Kok Seng; Mendoza, Jedeane; Heng, Chew-Kiat; Loke, Kah Yin; Lee, Yung Seng

doi:10.1210/jc.2013-3711

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…Several rare variants were described in the coding region of MC4R gene, including deletions (n=6), frameshift (n=21), nonsense (n=24), and missense mutations (n=318) 9. Pathogenic mutations could lead to complete or partial loss of function, which may impair receptor signaling through intracellular retention of the protein, reduced ligand binding, or reduced downstream signaling 9,25,26. In this present study, we aimed to investigate the prevalence of MC4R variants in a cohort of Brazilian patients.…”

Section: Discussionmentioning

confidence: 99%

Identification of the MC4R start lost mutation in a morbidly obese Brazilian patient

Fonseca¹,

Abreu²,

Zembrzuski³

et al. 2019

DMSO

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Background Melanocortin 4 receptor gene ( MC4R ) is an important regulator of food intake, body weight, and blood pressure. Mutations in MC4R are associated with the most common form of nonsyndromic monogenic obesity. MC4R variations have an autosomal co-/dominant model of inheritance. MC4R screening could reveal individuals previously unrecognized with Mendelian form of obesity for further clinical management and genetic counseling. However, there are limited data regarding MC4R variants in patients with obesity from Brazil. The aim of this study was to screen the coding region of the MC4R gene in a Brazilian cohort of severely obese adults and to investigate the phenotype–genotype correlation within MC4R variant carriers. Methods This study comprised 157 adult participants, stratified according to the period of obesity onset. The first group included 97 patients with childhood-onset obesity (0–11 years) and the second group comprised 60 subjects with adolescence/youth-onset obesity (12–21 years). The entire coding region of MC4R gene was screened by Sanger sequencing. Results As a result, five previously described variants (Met1?, Ser36Thr, Val103Ile, Ile98=, and Phe202Leu) were identified. Met1? is a start lost codon variant, which affects the translation of MC4R . It was found in a female patient with childhood-onset obesity. We also compared the anthropometric and metabolic parameters between patients with MC4R missense variants (Ser36Thr, Val103Ile, and Phe202Leu) and noncarriers. Patients carrying MC4R variants had higher median of waist–hip ratio when compared to noncarriers ( P =0.048). These missense variants were also associated with hypertension ( P =0.014). Additionally, Val103Ile carriers had lower diastolic blood pressure and lower systolic blood pressure compared to noncarriers ( P =0.020 and P =0.065, respectively). Val103Ile was also associated with hypertension ( P =0.003). Conclusion This study showed the prevalence of MC4R variants in a cohort of Brazilian adults with severe obesity. We also identified significant phenotype differences between carriers and noncarriers of missense variants in our sample, suggesting an important role of MC4R on body fat distribution and blood pressure.

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Section: Discussionmentioning

confidence: 99%

Identification of the MC4R start lost mutation in a morbidly obese Brazilian patient

Fonseca¹,

Abreu²,

Zembrzuski³

et al. 2019

DMSO

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“…SNP rs12970134 is located upstream of gene MC4R, the defects of which have been identified as a cause for autosomal dominant obesity. 44 SNP rs11807640 is located downstream to gene CDK4PS, which has been associated with obesity traits among some postmenuposal women. 45 Both rs9540221, which is intergenic between NFYAP1 and LGMNP1 and rs4365558, which is intergenic between A4GALT and RPL5P34, are SNPs situated between genes that have been associated with obesity as well.…”

Section: Discussionmentioning

confidence: 99%

Genome-Wide Genetic Interaction Analysis of Glaucoma Using Expert Knowledge Derived From Human Phenotype Networks

Darabos

Cricco³

et al. 2014

Biocomputing 2015

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The large volume of GWAS data poses great computational challenges for analyzing genetic interactions associated with common human diseases. We propose a computational framework for characterizing epistatic interactions among large sets of genetic attributes in GWAS data. We build the human phenotype network (HPN) and focus around a disease of interest. In this study, we use the GLAUGEN glaucoma GWAS dataset and apply the HPN as a biological knowledgebased filter to prioritize genetic variants. Then, we use the statistical epistasis network (SEN) to identify a significant connected network of pairwise epistatic interactions among the prioritized SNPs. These clearly highlight the complex genetic basis of glaucoma. Furthermore, we identify key SNPs by quantifying structural network characteristics. Through functional annotation of these key SNPs using Biofilter, a software accessing multiple publicly available human genetic data sources, we find supporting biomedical evidences linking glaucoma to an array of genetic diseases, proving our concept. We conclude by suggesting hypotheses for a better understanding of the disease.

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Transcriptional Regulation of Hypothalamic Energy Balance Genes

Good

2018

Textbook of Energy Balance, Neuropeptide Hormones, and Neuroendocrine Function

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Functional Characterization of Variants inMC4RGene Promoter Region Found in Obese Children

Abstract: We have described, for the first time, two novel human MC4R gene promoter variants found in obese children that resulted in a decrease in basal transcriptional activity.

Cited by 6 publications

References 16 publications

<p>Identification of the <em>MC4R</em> start lost mutation in a morbidly obese Brazilian patient</p>

<p>Identification of the <em>MC4R</em> start lost mutation in a morbidly obese Brazilian patient</p>

Genome-Wide Genetic Interaction Analysis of Glaucoma Using Expert Knowledge Derived From Human Phenotype Networks

Transcriptional Regulation of Hypothalamic Energy Balance Genes

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