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dc.contributor.authorLiu, Manwayen_US
dc.contributor.authorLiberzon, Arthuren_US
dc.contributor.authorKong, Sek Wonen_US
dc.contributor.authorLai, Weil R.en_US
dc.contributor.authorPark, Peter J.en_US
dc.contributor.authorKohane, Isaac S.en_US
dc.contributor.authorKasif, Simonen_US
dc.date.accessioned2012-01-11T00:41:23Z
dc.date.available2012-01-11T00:41:23Z
dc.date.issued2007-6-15
dc.identifier.citationLiu, Manway, Arthur Liberzon, Sek Won Kong, Weil R Lai, Peter J Park, Isaac S Kohane, Simon Kasif. "Network-Based Analysis of Affected Biological Processes in Type 2 Diabetes Models" PLoS Genetics 3(6): e96. (2007)
dc.identifier.issn1553-7404
dc.identifier.urihttps://hdl.handle.net/2144/3046
dc.description.abstractType 2 diabetes mellitus is a complex disorder associated with multiple genetic, epigenetic, developmental, and environmental factors. Animal models of type 2 diabetes differ based on diet, drug treatment, and gene knockouts, and yet all display the clinical hallmarks of hyperglycemia and insulin resistance in peripheral tissue. The recent advances in gene-expression microarray technologies present an unprecedented opportunity to study type 2 diabetes mellitus at a genome-wide scale and across different models. To date, a key challenge has been to identify the biological processes or signaling pathways that play significant roles in the disorder. Here, using a network-based analysis methodology, we identified two sets of genes, associated with insulin signaling and a network of nuclear receptors, which are recurrent in a statistically significant number of diabetes and insulin resistance models and transcriptionally altered across diverse tissue types. We additionally identified a network of protein–protein interactions between members from the two gene sets that may facilitate signaling between them. Taken together, the results illustrate the benefits of integrating high-throughput microarray studies, together with protein–protein interaction networks, in elucidating the underlying biological processes associated with a complex disorder. Author Summary Type 2 diabetes mellitus currently affects millions of people. It is clinically characterized by insulin resistance in addition to an impaired glucose response and associated with numerous complications including heart disease, stroke, neuropathy, and kidney failure, among others. Accurate identification of the underlying molecular mechanisms of the disease or its complications is an important research problem that could lead to novel diagnostics and therapy. The main challenge stems from the fact that insulin resistance is a complex disorder and affects a multitude of biological processes, metabolic networks, and signaling pathways. In this report, the authors develop a network-based methodology that appears to be more sensitive than previous approaches in detecting deregulated molecular processes in a disease state. The methodology revealed that both insulin signaling and nuclear receptor networks are consistently and differentially expressed in many models of insulin resistance. The positive results suggest such network-based diagnostic technologies hold promise as potentially useful clinical and research tools in the future.en_US
dc.description.sponsorshipNational Science Foundation (ITR-048715); National Human Genome Research Institute (R01 HG003367-01A1); National Institute of General Medical Sciences (K25-GM67825); National Institute of Diabetes and Digestive and Kidney Diseases (TO1DK60837-01A1); National Institutes of Health National Center for Biomedical Computing (5U54LM008748–02)en_US
dc.language.isoenen_US
dc.publisherPublic Library of SCienceen_US
dc.titleNetwork-Based Analysis of Affected Biological Processes in Type 2 Diabetes Modelsen_US
dc.typeArticleen_US
dc.identifier.doi10.1371/journal.pgen.0030096
dc.identifier.pmid17571924
dc.identifier.pmcid1904360


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