Computational determination of human PPARG gene: SNPs and prediction of their effect on protein functions of diabetic patients

Howeida Abdullah Mustafa; Afraa Mohamed Suliman Albkrye; Buthiena Mohamed AbdAlla; Mona AbdelRahman Mohammed Khair; Nidal Abdelwahid; Hind Abdelaziz Elnasri

doi:10.1186/s40169-020-0258-1

Computational determination of human PPARG gene: SNPs and prediction of their effect on protein functions of diabetic patients

Clin Transl Med. 2020 Feb 17;9(1):7. doi: 10.1186/s40169-020-0258-1.

Authors

Howeida Abdullah Mustafa^{1

2}, Afraa Mohamed Suliman Albkrye³, Buthiena Mohamed AbdAlla^{3

4}, Mona AbdelRahman Mohammed Khair³, Nidal Abdelwahid³, Hind Abdelaziz Elnasri³

Affiliations

¹ Department of Molecular Biology and Bioinformatics, College of Veterinary Medicine, University of Bahri, Khartoum, Sudan. howeida.mustafa@gmail.com.
² Department of Biochemistry, Faculty of Veterinary Medicine, University of Khartoum, Khartoum, Sudan. howeida.mustafa@gmail.com.
³ Department of Molecular Biology and Bioinformatics, College of Veterinary Medicine, University of Bahri, Khartoum, Sudan.
⁴ Department of Biochemistry, College of Applied and Industrial Science, University of Bahri, Bahri, Sudan.

Abstract

Background: The Peroxisome proliferator-activated receptor gamma gene (PPARG), encodes a member of the peroxisome-activated receptor subfamily of nuclear receptors. PPARs form heterodimers with retinoid X receptors (RXRs) which regulate transcription of various genes. Three subtypes of PPARs are known: PPAR-alpha, PPAR-delta and PPAR-gamma. The protein encoded by this gene is PPAR-gamma which is a regulator of adipocyte differentiation. PPARG-gamma has been implicated in the pathology of numerous diseases including obesity, diabetes, atherosclerosis and cancer.

Aim: This study aimed to perform insilico analysis to predict the effects that can be imposed by SNPs reported in PPARG gene.

Methodology: This gene was investigated in NCBI database (http://www.ncbi.nlm.nih.gov/) during the year 2016 and the SNPs in coding region (exonal SNPs) that are non-synonymous (ns SNPs) were analyzed by computational softwares. SIFT, Polyphen, I-Mutant and PHD-SNP softwares). SIFT was used to filter the deleterious SNPs, Polyphen was used to determine the degree of pathogenicity, I-Mutant was used to determine the effect of mutation on protein stability while PHD-SNP software was used to investigate the effect of mutation on protein function. Furthermore, Structural and functional analysis of ns SNPs was also studied using Project HOPE software and modeling was conducted by Chimera.

Results: A total of 34,035 SNPs from NCBI, were found, 21,235 of them were found in Homo sapiens, 134 in coding non synonymous (missense) and 89 were synonymous. Only SNPs present in coding regions were selected for analysis. Out of 12 deleterious SNPs sorted by SIFT, 10 were predicted by Polyphen to be probably damaging with PISC score = 1 and only two were benign. All these 10 double positive SNPs were disease related as predicted by PHD-SNPs and revealed decreased stability indicated by I-Mutant.

Conclusion: Based on the findings of this study, it can be concluded that the deleterious ns SNPs (rs72551364 and rs121909244SNPs) of PPARG are important candidates for the cause of different types of human diseases including diabetes mellitus.

Keywords: Diabetes insilico; PPARG; Polyphen; SIFT; SNP.