Microsoft Office 2007 PLoS Genetics Network-Based

Published from the June 2007 Situation of PLoS Genetics

Open Access
Research Article
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Type two diabetes mellitus presently affects an incredible number of people. It is clinically characterized by insulin resistance in addition to an impaired glucose response and linked to many complications like heart disease, stroke, neuropathy, and kidney failure, among other people. Correct identification from the underlying molecular mechanisms of the illness or its problems is an important investigation difficulty that could lead to novel diagnostics and treatment. The principle challenge stems through the fact that insulin resistance is actually a advanced condition and impacts a multitude of biological processes, metabolic networks, and signaling pathways. In this report, the authors create a network-based methodology that seems to become much more delicate than previous ways in detecting deregulated molecular processes in a illness state. The methodology revealed that the two insulin signaling and nuclear receptor networks are consistently and differentially expressed in lots of versions of insulin resistance. The good outcomes propose this sort of network-based diagnostic technologies hold guarantee as probably useful medical and investigation tools in the future.

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Type 2 diabetes mellitus is actually a complex problem associated with multiple genetic, epigenetic, developmental, and environmental factors. Animal versions of kind two diabetes differ based mostly on diet, drug treatment, and gene knockouts,Cheap Office 2007, 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 types. To date, a key challenge has been to identify the biological processes or signaling pathways that play significant roles within 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,Microsoft Office 2007 Standard, which are recurrent inside 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, the results illustrate the benefits of integrating high-throughput microarray studies, together with protein–protein interaction networks, in elucidating the underlying biological processes linked to a complex condition.
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Type two diabetes mellitus at the moment influences millions of folks. It's clinically characterized by insulin resistance furthermore to an impaired glucose response and connected with several issues which includes heart condition, stroke, neuropathy, and kidney failure, amongst other individuals. Correct identification of the underlying molecular mechanisms of the disease or its issues is an important analysis problem that can bring about novel diagnostics and therapy. The principle problem stems in the reality that insulin resistance is actually a advanced disorder and influences a multitude of biological processes, metabolic networks, and signaling pathways. On this report, the authors develop a network-based methodology that appears to be a lot more sensitive than prior ways in detecting deregulated molecular processes in a very illness state. The methodology uncovered that both insulin signaling and nuclear receptor networks are regularly and differentially expressed in lots of designs of insulin resistance. The positive results recommend these network-based diagnostic technologies hold guarantee as potentially valuable clinical and study instruments in the future.

Citation: Liu M, Liberzon A, Kong SW, Lai WR,Office Standard 2007, Park PJ, et al. (2007) Network-Based Analysis of Affected Biological Processes in Kind 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 surely an open-access article distributed under the terms from the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium,Microsoft Office 2007, provided the original author and source are credited.
Funding: ML,Windows 7 X86, AL, and SK were supported in part by National Science Foundation grant number ITR-048715 and National Human Genome Study 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, type 2 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 in the analysis of your DGAP dataset and the HPRD protein–protein interactions; NR-HD, nuclear receptor signaling gene set used from the analysis with 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.