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Computational determination of human PPARG gene: SNPs and prediction of their effect on protein functions of diabetic patients
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 kno...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7024687/ https://www.ncbi.nlm.nih.gov/pubmed/32064572 http://dx.doi.org/10.1186/s40169-020-0258-1 |
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author | Mustafa, Howeida Abdullah Albkrye, Afraa Mohamed Suliman AbdAlla, Buthiena Mohamed Khair, Mona AbdelRahman Mohammed Abdelwahid, Nidal Elnasri, Hind Abdelaziz |
author_facet | Mustafa, Howeida Abdullah Albkrye, Afraa Mohamed Suliman AbdAlla, Buthiena Mohamed Khair, Mona AbdelRahman Mohammed Abdelwahid, Nidal Elnasri, Hind Abdelaziz |
author_sort | Mustafa, Howeida Abdullah |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7024687 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-70246872020-03-02 Computational determination of human PPARG gene: SNPs and prediction of their effect on protein functions of diabetic patients Mustafa, Howeida Abdullah Albkrye, Afraa Mohamed Suliman AbdAlla, Buthiena Mohamed Khair, Mona AbdelRahman Mohammed Abdelwahid, Nidal Elnasri, Hind Abdelaziz Clin Transl Med Research 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. Springer Berlin Heidelberg 2020-02-17 /pmc/articles/PMC7024687/ /pubmed/32064572 http://dx.doi.org/10.1186/s40169-020-0258-1 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Research Mustafa, Howeida Abdullah Albkrye, Afraa Mohamed Suliman AbdAlla, Buthiena Mohamed Khair, Mona AbdelRahman Mohammed Abdelwahid, Nidal Elnasri, Hind Abdelaziz Computational determination of human PPARG gene: SNPs and prediction of their effect on protein functions of diabetic patients |
title | Computational determination of human PPARG gene: SNPs and prediction of their effect on protein functions of diabetic patients |
title_full | Computational determination of human PPARG gene: SNPs and prediction of their effect on protein functions of diabetic patients |
title_fullStr | Computational determination of human PPARG gene: SNPs and prediction of their effect on protein functions of diabetic patients |
title_full_unstemmed | Computational determination of human PPARG gene: SNPs and prediction of their effect on protein functions of diabetic patients |
title_short | Computational determination of human PPARG gene: SNPs and prediction of their effect on protein functions of diabetic patients |
title_sort | computational determination of human pparg gene: snps and prediction of their effect on protein functions of diabetic patients |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7024687/ https://www.ncbi.nlm.nih.gov/pubmed/32064572 http://dx.doi.org/10.1186/s40169-020-0258-1 |
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