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Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes
Genome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic β cells and insulin signaling in target tissues. However, effect of genetic risk variants on continuous g...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3538588/ https://www.ncbi.nlm.nih.gov/pubmed/23308243 http://dx.doi.org/10.1371/journal.pone.0053522 |
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author | Jain, Priyanka Vig, Saurabh Datta, Malabika Jindel, Dinesh Mathur, Ashok Kumar Mathur, Sandeep Kumar Sharma, Abhay |
author_facet | Jain, Priyanka Vig, Saurabh Datta, Malabika Jindel, Dinesh Mathur, Ashok Kumar Mathur, Sandeep Kumar Sharma, Abhay |
author_sort | Jain, Priyanka |
collection | PubMed |
description | Genome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic β cells and insulin signaling in target tissues. However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidates perturbation of insulin secretion. Also, the disease associated genes do not clearly converge on functional categories consistent with the known aspects of T2D pathophysiology. We used a systems biology approach to unravel genome to phenome correlation in T2D. We first examined enrichment of pathways in genes identified in T2D GWASs at genome-wide or lower levels of significance. Genes at lower significance threshold showed enrichment of insulin secretion related pathway. Notably, physical and genetic interaction network of these genes showed robust enrichment of insulin signaling and other T2D pathophysiology related pathways including insulin secretion. The network also overrepresented genes reported to interact with insulin secretion and insulin action targeting antidiabetic drugs. The drug interacting genes themselves showed overrepresentation of insulin signaling and other T2D relevant pathways. Next, we generated genome-wide expression profiles of multiple insulin responsive tissues from nondiabetic and diabetic patients. Remarkably, the differentially expressed genes showed significant overlap with the network genes, with the intersection showing enrichment of insulin signaling and other pathways consistent with T2D pathophysiology. Literature search led our genomic, interactomic, transcriptomic and toxicogenomic evidence to converge on TGF-beta signaling, a pathway known to play a crucial role in pancreatic islets development and function, and insulin signaling. Cumulatively, we find that GWAS genes relate directly to insulin secretion and indirectly, through collaborating with other genes, to insulin resistance. This seems to support the epidemiological evidence that environmentally triggered insulin resistance interacts with genetically programmed β cell dysfunction to precipitate diabetes. |
format | Online Article Text |
id | pubmed-3538588 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-35385882013-01-10 Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes Jain, Priyanka Vig, Saurabh Datta, Malabika Jindel, Dinesh Mathur, Ashok Kumar Mathur, Sandeep Kumar Sharma, Abhay PLoS One Research Article Genome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic β cells and insulin signaling in target tissues. However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidates perturbation of insulin secretion. Also, the disease associated genes do not clearly converge on functional categories consistent with the known aspects of T2D pathophysiology. We used a systems biology approach to unravel genome to phenome correlation in T2D. We first examined enrichment of pathways in genes identified in T2D GWASs at genome-wide or lower levels of significance. Genes at lower significance threshold showed enrichment of insulin secretion related pathway. Notably, physical and genetic interaction network of these genes showed robust enrichment of insulin signaling and other T2D pathophysiology related pathways including insulin secretion. The network also overrepresented genes reported to interact with insulin secretion and insulin action targeting antidiabetic drugs. The drug interacting genes themselves showed overrepresentation of insulin signaling and other T2D relevant pathways. Next, we generated genome-wide expression profiles of multiple insulin responsive tissues from nondiabetic and diabetic patients. Remarkably, the differentially expressed genes showed significant overlap with the network genes, with the intersection showing enrichment of insulin signaling and other pathways consistent with T2D pathophysiology. Literature search led our genomic, interactomic, transcriptomic and toxicogenomic evidence to converge on TGF-beta signaling, a pathway known to play a crucial role in pancreatic islets development and function, and insulin signaling. Cumulatively, we find that GWAS genes relate directly to insulin secretion and indirectly, through collaborating with other genes, to insulin resistance. This seems to support the epidemiological evidence that environmentally triggered insulin resistance interacts with genetically programmed β cell dysfunction to precipitate diabetes. Public Library of Science 2013-01-07 /pmc/articles/PMC3538588/ /pubmed/23308243 http://dx.doi.org/10.1371/journal.pone.0053522 Text en © 2013 Jain et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Jain, Priyanka Vig, Saurabh Datta, Malabika Jindel, Dinesh Mathur, Ashok Kumar Mathur, Sandeep Kumar Sharma, Abhay Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes |
title | Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes |
title_full | Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes |
title_fullStr | Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes |
title_full_unstemmed | Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes |
title_short | Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes |
title_sort | systems biology approach reveals genome to phenome correlation in type 2 diabetes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3538588/ https://www.ncbi.nlm.nih.gov/pubmed/23308243 http://dx.doi.org/10.1371/journal.pone.0053522 |
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