Generic placeholder image

Current Chinese Science

Editor-in-Chief

ISSN (Print): 2210-2981
ISSN (Online): 2210-2914

Research Article Section: Genetics

Identification of Functional Variants Associated with Obesity in Pakistani Kindred

Author(s): Ayesha Aftab, Syed Babar Jamal and Syeda Marriam Bakhtiar*

Volume 1, Issue 1, 2021

Published on: 09 September, 2020

Page: [58 - 68] Pages: 11

DOI: 10.2174/2210298101666200909160022

Abstract

Background: Obesity is an emerging pandemic considered to be an outcome of change in lifestyle owing to more processed food and the use of mechanical locomotives. Obesity has not only appeared as a problem in the esthetic appearance of an individual rather is a serious health issue due to its associations with various chronic diseases such as coronary and cardiovascular problems, hypertension, osteoarthritis, type-II diabetes mellitus, hyperlipidemia, and certain cancers. It is estimated that 30 percent of the world’s population, i.e. approximately 2.1 billion people, are victims of obesity. In addition to environmental causes, various genes and a group of genes are reported to be increasing the suceptibility of obesity.

Objective: Pakistan is a heterogeneous population, an amalgam of various races, therefore, narrowing down the list of obesity-associated genes and their functional variance could help molecular biologists to select potential SNPs in the Pakistani population for molecular diagnosis and treatment.

Method: The extraction of a set of obesity-associated genes has been performed by using Polysearch2. SNPs for each gene are retrieved from dbSNP. RegulomeDB and SNPinfo tools have been used for the functional analysis of SNPs retrieved against the Pakistani population. For the prediction of potential deleterious SNPs, SIFT, Polyphen-2, MUTTASTER, MUTASSESSOR, and LRT (likelihood ratio test) are utilized. Functional analysis of potential deleterious SNPs has been performed by studying protein stability and mapping of identified SNPs to protein structure. For the protein stability analysis, I-Mutant and SNPs3D have been used.

Results: Four genes FTO, POMC, LEPR, and MC4R and further analysis revealed 3 deleterious SNPs in FTO, 4 in POMC, 1 in LEPR, and 1 in MC4R.

Conclusion: This research was designed to identify obesity-associated genes and the most impactful deleterious SNPs in these genes. These findings will be helpful for the molecular biologists and pharmacists to design better and focused diagnosis and treatment strategies.

Keywords: Obesity, SNPs, obesity-associated genes, functional variants, protein stability analysis, FTO, MC4R, LEPR, POMC.

Graphical Abstract
[1]
James PT, Leach R, Kalamara E, Shayeghi M. The worldwide obesity epidemic Obesity research. Obes Res 2001.
[http://dx.doi.org/doi.org/10.1038/oby.2001.123] [PMID: 11707546]
[2]
Hossain P, Kawar B, El Nahas M. Obesity and diabetes in the developing world - a growing challenge. N Engl J Med 2007.
[http://dx.doi.org/doi.org/10.1056/NEJMp068177]
[3]
Swinburn BA, Sacks G, Hall KD, McPherson K, Finegood DT, Moodie ML. The global obesity pandemic: Shaped by global drivers and local environments. The Lancet 2011; 378: 804-14.
[http://dx.doi.org/10.1016/S0140-6736(11)60813-1]
[4]
Campos P, Saguy A, Ernsberger P, Oliver E, Gaesser G. The epidemiology of overweight and obesity: public health crisis or moral panic? Int J Epidemiol 2006.
[http://dx.doi.org/10.1093/ije/dyi254] [PMID: 16339599]
[5]
Hruby A, Hu FB. The epidemiology of obesity: A big picture. Pharmacoeconomics 2015; 33: 673-89.
[http://dx.doi.org/10.1007/s40273-014-0243-x]
[6]
Chan RSM, Woo J. Prevention of overweight and obesity: How effective is the current public health approach. Int J Environ Res Public Health 2010; 7(3): 765-83.
[http://dx.doi.org/10.3390/ijerph7030765]
[7]
Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: A systematic analysis for the global burden of disease study 2013. Lancet 2013; 384(9945): 766-81.
[http://dx.doi.org/10.1016/S0140-6736(14)60460-8]
[8]
Zahid N, Claussen B, Hussain A. High prevalence of obesity, dyslipidemia and metabolic syndrome in a rural area in Pakistan. Diabetes Metab Syndr 2008; 2(1): 13-9.
[http://dx.doi.org/10.1016/j.dsx.2007.11.001]
[9]
Dennis B, Aziz K, She L, Faruqui AMA, Davis CE, Manolio TA. High rates of obesity and cardiovascular disease risk factors in lower middle class community in Pakistan: the Metroville Health Study. J Pak Med Assoc 2006; 56(6): 267-72.
[PMID: 16827250]
[10]
Robbens S, Rouzé P, Cock JM, Spring J, Worden AZ, Van De Peer Y. The FTO gene, implicated in human obesity, is found only in vertebrates and marine algae. J Mol Evol 2008; 66(1): 80-4.
[http://dx.doi.org/10.1007/s00239-007-9059-z] [PMID: 18058156]
[11]
Liu Y, Liang Y, Wishart D. PolySearch2: a significantly improved text-mining system for discovering associations between human diseases, genes, drugs, metabolites, toxins and more. Nucleic Acids Res 2015; 43(W1)W535-42
[http://dx.doi.org/10.1093/nar/gkv383] [PMID: 25925572]
[12]
Sherry ST, Ward MH, Kholodov M, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res 2001; 29(1): 308-11.
[http://dx.doi.org/doi.org/10.1093/nar/29.1.308]
[13]
Bairoch A, Apweiler R, Wu CH, et al. The universal protein resource (UniProt). Nucleic Acids Res 2005; 33(Database issue): D154-9.
[http://dx.doi.org/doi.org/10.1093/nar/gki070] [PMID: 15608167]
[14]
Xu Z, Taylor JA. SNPinfo: Integrating GWAS and candidate gene information into functional SNP selection for genetic association studies. Nucleic Acids Res . 2009; 37(Web Server issue): W600-5.
[http://dx.doi.org/10.1093/nar/gkp290] [PMID: 19417063]
[15]
Boyle AP, Hong EL, Hariharan M, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res 2012; 22(9): 1790-7.
[http://dx.doi.org/10.1101/gr.137323.112] [PMID: 22955989]
[16]
Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res 2003; 31(13): 3812-4.
[http://dx.doi.org/10.1093/nar/gkg509]
[17]
Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet 2013; 20.
[http://dx.doi.org/10.1002/0471142905.hg0720s76] [PMID: 23315928]
[18]
Schwarz JM, Rödelsperger C, Schuelke M, Seelow D. MutationTaster evaluates disease-causing potential of sequence alterations. Nat Methods 2010; 7(8): 575-6.
[http://dx.doi.org/10.1038/nmeth0810-575]
[19]
Karolchik D, Hinrichs AS, Kent WJ. The UCSC genome browser. Curr Protoc Bioinforma 2012.
[http://dx.doi.org/10.1002/0471250953.bi0104s40]
[20]
Capriotti E, Fariselli P, Casadio R. I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 2005.
[http://dx.doi.org/10.1093/nar/gki375] [PMID: 15980478]
[21]
Yue P, Melamud E, Moult J. SNPs3D: candidate gene and SNP selection for association studies. BMC Bioinformatics 2006; 7(1): 166.
[http://dx.doi.org/10.1186/1471-2105-7-166.]
[22]
Niknafs N, Kim D, Kim R, et al. MuPIT interactive: webserver for mapping variant positions to annotated, interactive 3D structures. Hum Genet 2013; 132(11): 1235-43.
[http://dx.doi.org/10.1007/s00439-013-1325-0] [PMID: 23793516]
[23]
Vohra S, Biggin PC. Mutationmapper: a tool to aid the mapping of protein mutation data. PLoS One 2013; 8(8): e71711-1.
[http://dx.doi.org/10.1371/journal.pone.0071711]
[24]
Jamal S, Goyal S, Shanker A, Grover A. Computational screening and exploration of disease-associated genes in alzheimer’s disease. J Cell Biochem 2017; 118(6): 1471-9.
[http://dx.doi.org/10.1002/jcb.25806] [PMID: 27883225]
[25]
Shabana Hasnain S. Effect of the common fat mass and obesity associated gene variants on obesity in pakistani population: A case-control study. BioMed Res Int 2015.
[http://dx.doi.org/10.1155/2015/852920]
[26]
Liu C, Mou S, Pan C. The FTO gene rs9939609 polymorphism predicts risk of cardiovascular disease: a systematic review and meta-analysis. PLoS One 2013; 8(8)e71901
[http://dx.doi.org/10.1371/journal.pone.0071901] [PMID: 23977173]
[27]
Saeed S, Butt TA, Anwer M, Arslan M, Froguel P. High prevalence of leptin and melanocortin-4 receptor gene mutations in children with severe obesity from Pakistani consanguineous families. Mol Genet Metab 2012; 106(1): 121-6.
[http://dx.doi.org/10.1016/j.ymgme.2012.03.001] [PMID: 22463805]
[28]
Koh KK, Park SM, Quon MJ. Leptin and cardiovascular disease: response to therapeutic interventions. Circulation 2008; 117(25): 3238-49.
[http://dx.doi.org/10.1161/circulationaha.107.741645] [PMID: 18574061]
[29]
Duggal P, Guo X, Haque R, et al. A mutation in the leptin receptor is associated with Entamoeba histolytica infection in children. J Clin Invest 2011; 121(3): 1191-8.
[http://dx.doi.org/10.1172/JCI45294] [PMID: 21393862]
[30]
Adan RAH, Tiesjema B, Hillebrand JJG, la Fleur SE, Kas MJH, de Krom M. The MC4 receptor and control of appetite. Br J Pharmacol 2006; 149(7): 815-27.
[http://dx.doi.org/10.1038/sj.bjp.0706929] [PMID: 17043670]
[31]
Fernandes AE, de Melo ME, Fujiwara CTH, et al. Associations between a common variant near the MC4R gene and serum triglyceride levels in an obese pediatric cohort. Endocrine 2015; 49(3): 653-8.
[http://dx.doi.org/10.1007/s12020-015-0616-8] [PMID: 25948074]
[32]
Simpson KA, Martin NMR, Bloom S. Hypothalamic regulation of food intake and clinical therapeutic applications. Arq Bras Endocrinol Metabol 2009; 53(2)
[http://dx.doi.org/10.1590/S0004-27302009000200002]
[33]
Krude H, Biebermann H, Luck W, Horn R, Brabant G, Grüters A. Severe early-onset obesity, adrenal insufficiency and red hair pigmentation caused by POMC mutations in humans. Nat Genet 1998; 19(2): 155-7.
[http://dx.doi.org/10.1038/509] [PMID: 9620771]
[34]
Yazdi FT, Clee SM, Meyre D. Obesity genetics in mouse and human: back and forth, and back again. PeerJ 2015.
[http://dx.doi.org/10.7717/peerj.856] [PMID: 25825681]

© 2024 Bentham Science Publishers | Privacy Policy