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Published from the June 2007 Problem of PLoS Genetics

Open Access
Research Article
Author Summary
Type 2 diabetes mellitus presently has an effect on millions of men and women. It's clinically characterized by insulin resistance furthermore to an impaired glucose response and associated with several complications such as heart disorder,Microsoft Office 2010 Home And Student, stroke,Office Professional Plus 2010, neuropathy, and kidney failure, amid others. Exact identification of your underlying molecular mechanisms of the disease or its complications is an important research problem that might lead to novel diagnostics and remedy. The primary problem stems through the fact that insulin resistance is really a advanced problem and affects a multitude of biological processes, metabolic networks, and signaling pathways. With this report, the authors develop a network-based methodology that appears for being a lot more delicate than preceding ways in detecting deregulated molecular processes inside a illness state. The methodology unveiled that the two insulin signaling and nuclear receptor networks are consistently and differentially expressed in many types of insulin resistance. The constructive outcomes recommend this kind of network-based diagnostic technologies hold promise as probably valuable medical and analysis tools in the future.

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Type 2 diabetes mellitus is actually a complicated disorder related to multiple genetic, epigenetic, developmental, and environmental factors. Animal versions of sort 2 diabetes differ based mostly 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 kind two diabetes mellitus at a genome-wide scale and across different versions. To date, a key problem has been to identify the biological processes or signaling pathways that play significant roles from the condition. 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 types 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 outcomes illustrate the benefits of integrating high-throughput microarray studies, together with protein–protein interaction networks, in elucidating the underlying biological processes connected with a complex disorder.
Writer Summary Top
Type 2 diabetes mellitus at present impacts an incredible number of individuals. It can be clinically characterized by insulin resistance in addition to an impaired glucose response and related to quite a few difficulties which includes heart condition, stroke, neuropathy, and kidney failure, amongst other folks. Exact identification with the underlying molecular mechanisms of the illness or its problems is a vital study problem that might bring about novel diagnostics and remedy. The principle challenge stems through the fact that insulin resistance is actually a advanced problem and impacts a multitude of biological processes, metabolic networks, and signaling pathways. In this report,Office 2010 Sale, the authors create a network-based methodology that seems to become much more sensitive than preceding methods in detecting deregulated molecular processes inside a illness state. The methodology uncovered that the two insulin signaling and nuclear receptor networks are constantly and differentially expressed in many versions of insulin resistance. The beneficial results advise these network-based diagnostic technologies hold promise as probably useful medical and research instruments later on.

Citation: Liu M, Liberzon A, Kong SW, Lai WR, Park PJ,Windows 7 License, et al. (2007) Network-Based Analysis of Affected Biological Processes in Kind 2 Diabetes Models. 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 surely an open-access post 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 credited.
Funding: ML, AL, and SK were supported in part by National Science Foundation grant number ITR-048715 and National Human Genome Research 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, hypergeometric enrichment test on differentially expressed genes; DGAP, Diabetes Genome Anatomy Project; DM2, kind 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,Office Professional 2010 Key, 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 in the analysis of the 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.