Office 2010 Home And Business PLoS Genetics Netwo

[letter98]

Published from the June 2007 Situation of PLoS Genetics

Open Access
Research Article
Writer Summary
Type 2 diabetes mellitus at present impacts an incredible number of people. It's clinically characterized by insulin resistance furthermore to an impaired glucose response and associated with numerous problems which includes heart disease, stroke, neuropathy, and kidney failure, amongst other individuals. Correct identification with the underlying molecular mechanisms of the disorder or its complications is a crucial investigation issue that may result in novel diagnostics and remedy. The principle problem stems in the truth that insulin resistance is actually a advanced condition and has an effect on a multitude of biological processes, metabolic networks, and signaling pathways. On this report, the authors build a network-based methodology that seems to become much more delicate than earlier ways in detecting deregulated molecular processes inside a disorder state. The methodology uncovered that each insulin signaling and nuclear receptor networks are persistently and differentially expressed in lots of models of insulin resistance. The positive results suggest this sort of network-based diagnostic technologies maintain promise as potentially useful medical and analysis resources in the future.

Jump to
Abstract Top
Type 2 diabetes mellitus is actually a advanced disorder related to multiple genetic, epigenetic, developmental, and environmental factors. Animal models of kind two diabetes differ primarily 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 sort two diabetes mellitus at a genome-wide scale and across different types. To date, a key problem has been to identify the biological processes or signaling pathways that play significant roles in 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 very 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 associated with a complex condition.
Author Summary Top
Type two diabetes mellitus at the moment affects millions of folks. It really is clinically characterized by insulin resistance furthermore to an impaired glucose response and related to several problems which includes heart illness, stroke, neuropathy, and kidney failure,Windows 7 Ultimate, amid other individuals. Precise identification of your underlying molecular mechanisms with the illness or its issues is an important research issue that could cause novel diagnostics and remedy. The principle challenge stems from the simple fact that insulin resistance can be a complex problem and impacts a multitude of biological processes, metabolic networks, and signaling pathways. In this report,Office 2010 Home And Business, the authors build a network-based methodology that seems to become more delicate than preceding ways in detecting deregulated molecular processes inside a disorder state. The methodology unveiled that the two insulin signaling and nuclear receptor networks are consistently and differentially expressed in lots of versions of insulin resistance. The constructive results advise these network-based diagnostic technologies maintain guarantee as potentially helpful clinical and investigation equipment in the future.

Citation: Liu M, Liberzon A, Kong SW, Lai WR,Office 2007 Pro Key, Park PJ, et al. (2007) Network-Based Analysis of Affected Biological Processes in Type two Diabetes Designs. PLoS Genet 3(6): e96. doi:10.1371/journal.pgen.0030096
Editor: Kathleen Kerr,Office 2010 Activation, University of Washington, United States of America
Received: December 19, 2006; Accepted: May 1, 2007; Revealed: June 15, 2007
Copyright: © 2007 Liu et al. This is surely an open-access post distributed under the terms with the 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 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 2 diabetes mellitus; GNEA, gene network enrichment analysis; GO, gene ontology; GSEA, gene-set enrichment analysis; HNF4A,Office 2007 Professional Key, hepatocyte nuclear factor 4 alpha 1; HPRD, Human Protein Reference Database; HSN, high-scoring subnetwork; IS-HD, insulin-signaling gene set used from the analysis of your DGAP dataset and the HPRD protein–protein interactions; NR-HD, nuclear receptor signaling gene set used inside the analysis from 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.