Office 2007 Ultimate PLoS Genetics Network-Based

Published inside the June 2007 Concern of PLoS Genetics

Open Access
Research Article
Author Summary
Type 2 diabetes mellitus currently has an effect on countless men and women. It is clinically characterized by insulin resistance furthermore to an impaired glucose response and related to quite a few difficulties such as heart condition, stroke, neuropathy, and kidney failure, amongst other folks. Exact identification from the underlying molecular mechanisms of your disorder or its issues is a vital investigation dilemma that may cause novel diagnostics and therapy. The principle problem stems from the simple fact that insulin resistance is really a advanced disorder and affects a multitude of biological processes, metabolic networks, and signaling pathways. Within this report, the authors create a network-based methodology that appears for being far more delicate than earlier techniques in detecting deregulated molecular processes in a very disorder state. The methodology revealed that both insulin signaling and nuclear receptor networks are regularly and differentially expressed in lots of models of insulin resistance. The positive outcomes propose such network-based diagnostic technologies hold promise as probably beneficial clinical and research tools later on.

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Type two diabetes mellitus is actually a advanced condition associated with multiple genetic, epigenetic, developmental, and environmental factors. Animal models of variety two diabetes differ based mostly 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 type 2 diabetes mellitus at a genome-wide scale and across different designs. To date, a key challenge has been to identify the biological processes or signaling pathways that play significant roles inside the condition. Here, 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 within 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 through the two gene sets that may facilitate signaling between them. Taken together,Microsoft Office Pro 2007, 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 condition.
Author Summary Top
Type 2 diabetes mellitus at the moment impacts countless individuals. It's clinically characterized by insulin resistance moreover to an impaired glucose response and linked to numerous complications like heart disorder, stroke, neuropathy, and kidney failure, amid others. Accurate identification with the underlying molecular mechanisms of your disease or its complications is a vital investigation dilemma that could bring about novel diagnostics and remedy. The principle problem stems in the simple fact that insulin resistance is a complicated condition and influences a multitude of biological processes, metabolic networks, and signaling pathways. In this report, the authors build a network-based methodology that appears to get more delicate than prior techniques in detecting deregulated molecular processes inside a condition state. The methodology revealed that the two insulin signaling and nuclear receptor networks are constantly and differentially expressed in lots of models of insulin resistance. The positive results propose these network-based diagnostic technologies maintain promise as potentially valuable medical and investigation resources later on.

Citation: Liu M, Liberzon A, Kong SW, Lai WR, Park PJ, et al. (2007) Network-Based Analysis of Affected Biological Processes in Sort 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,Office 2007 License, 2007; Published: June 15, 2007
Copyright: © 2007 Liu et al. This is surely an open-access report distributed under the terms from 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,Windows 7 Pro Key, and SK were supported in part by National Science Foundation grant number ITR-048715 and National Human Genome Analysis 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 2007 Ultimate, insulin-signaling gene set used from the analysis from the DGAP dataset and the HPRD protein–protein interactions; NR-HD,Office Enterprise 2007 Key, nuclear receptor signaling gene set used inside 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.