Published within the June 2007 Issue of PLoS Genetics
Open Access
Research Article
Writer Summary
Type 2 diabetes mellitus at the moment has an effect on numerous folks. It really is clinically characterized by insulin resistance additionally to an impaired glucose response and related to numerous problems including heart disorder, stroke, neuropathy,
Cheap Office 2007, and kidney failure, among other folks. Exact identification with the underlying molecular mechanisms with the disorder or its problems is a vital study dilemma that could result in novel diagnostics and treatment. The main problem stems through the simple fact that insulin resistance is a advanced disorder and affects a multitude of biological processes, metabolic networks, and signaling pathways. With this report, the authors produce a network-based methodology that seems to be a lot more delicate than previous techniques in detecting deregulated molecular processes inside a condition state. The methodology uncovered that equally insulin signaling and nuclear receptor networks are consistently and differentially expressed in many models of insulin resistance. The beneficial final results advise this kind of network-based diagnostic technologies maintain promise as possibly beneficial medical and study tools in the future.
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Type 2 diabetes mellitus is actually a complex disorder associated with multiple genetic, epigenetic, developmental,
Microsoft Office 2007 Pro, and environmental factors. Animal models of kind two diabetes differ based on diet, drug treatment, and gene knockouts, and yet all display the medical hallmarks of hyperglycemia and insulin resistance in peripheral tissue. The recent advances in gene-expression microarray technologies present an unprecedented opportunity to study kind 2 diabetes mellitus at a genome-wide scale and across different models. To date,
Office 2010 Home And Student, a key challenge has been to identify the biological processes or signaling pathways that play significant roles within the condition. Here,
Windows 7 X86, using a network-based analysis methodology, we identified two sets of genes, related to insulin signaling and a network of nuclear receptors, which are recurrent inside a statistically significant number of diabetes and insulin resistance designs and transcriptionally altered across diverse tissue types. We additionally identified a network of protein–protein interactions between members in the two gene sets that may facilitate signaling between them. Taken together, the final 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 complicated disorder.
Writer Summary Top
Type two diabetes mellitus currently has an effect on millions of individuals. It can be clinically characterized by insulin resistance in addition to an impaired glucose response and linked to many difficulties which includes heart disease, stroke, neuropathy, and kidney failure, between other people. Exact identification of the underlying molecular mechanisms from the disease or its problems is an important analysis issue that can bring about novel diagnostics and remedy. The main problem stems through the simple fact that insulin resistance is actually a advanced problem and affects a multitude of biological processes, metabolic networks, and signaling pathways. On this report, the authors create a network-based methodology that seems to be a lot more sensitive than prior ways in detecting deregulated molecular processes within a disorder state. The methodology unveiled that each insulin signaling and nuclear receptor networks are constantly and differentially expressed in many models of insulin resistance. The constructive results advise these network-based diagnostic technologies maintain promise as probably helpful clinical and investigation tools in the future.
Citation: Liu M, Liberzon A, Kong SW, Lai WR, Park PJ, et al. (2007) Network-Based Analysis of Affected Biological Processes in Variety 2 Diabetes Versions. PLoS Genet 3(6): e96. doi:10.1371/journal.pgen.0030096
Editor: Kathleen Kerr, University of Washington, United States of America
Received: December 19, 2006; Accepted: May 1, 2007; Printed: June 15, 2007
Copyright: © 2007 Liu et al. This is definitely an open-access article distributed under the terms of your Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original writer and source are credited.
Funding: ML, AL, and SK were supported in part by National Science Foundation grant number ITR-048715 and National Human Genome Investigation Institute grant number R01 HG003367-01A1. PJP was supported in part by National Institute of General Medical Sciences grant number K25-GM67825. ISK was supported in part by National Institute of Diabetes and Digestive and Kidney Diseases DGAP grant number TO1DK60837-01A1. This work was supported in part by the National Institutes of Health National Center for Biomedical Computing grant number 5U54LM008748–02.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: DEA,
Office Pro Plus 2007, hypergeometric enrichment test on differentially expressed genes; DGAP, Diabetes Genome Anatomy Project; DM2, sort two diabetes mellitus; GNEA, gene network enrichment analysis; GO, gene ontology; GSEA, gene-set enrichment analysis; HNF4A, hepatocyte nuclear factor 4 alpha 1; HPRD, Human Protein Reference Database; HSN, high-scoring subnetwork; IS-HD, insulin-signaling gene set used within the analysis of the DGAP dataset and the HPRD protein–protein interactions; NR-HD, nuclear receptor signaling gene set used inside the analysis of your DGAP dataset and the HPRD protein–protein interactions
* To whom correspondence should be addressed. E-mail: manwayl@bu.edu (ML); kasif@bu.edu (SK)
# These authors contributed equally to this work.
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