A gene expression signature for insulin resistance

Konstantopoulos, Nicky and Foletta, Victoria C. and Segal, David H. and Shields, Katherine A. and Sanigorski, Andrew and Windmill, Kelly and Swinton, Courtney and Connor, Tim and Wanyonyi, Stephen and Dyer, Thomas D. and Fahey, Richard P. and Watt, Rose A. and Curran, Joanne E. and Molero, Juan C. and Krippner, Guy and Collier, Greg R. and James, David E. and Blangero, John and Jowett, Jeremy B. and Walder, Ken R. (2011) A gene expression signature for insulin resistance. Physiological Genomics, 43 (3). pp. 110-120. ISSN 1094-8341

Abstract

Insulin resistance is a heterogeneous disorder caused by a range of genetic and environmental factors, and we hypothesize that its etiology varies considerably between individuals. This heterogeneity provides significant challenges to the development of effective therapeutic regimes for long-term management of type 2 diabetes. We describe a novel strategy, using large-scale gene expression profiling, to develop a gene expression signature (GES) that reflects the overall state of insulin resistance in cells and patients. The GES was developed from 3T3-L1 adipocytes that were made “insulin resistant” by treatment with tumor necrosis factor-α (TNF-α) and then reversed with aspirin and troglitazone (“resensitized”). The GES consisted of five genes whose expression levels best discriminated between the insulin-resistant and insulin-resensitized states. We then used this GES to screen a compound library for agents that affected the GES genes in 3T3-L1 adipocytes in a way that most closely resembled the changes seen when insulin resistance was successfully reversed with aspirin and troglitazone. This screen identified both known and new insulin-sensitizing compounds including nonsteroidal anti-inflammatory agents, β-adrenergic antagonists, β-lactams, and sodium channel blockers. We tested the biological relevance of this GES in participants in the San Antonio Family Heart Study (n = 1,240) and showed that patients with the lowest GES scores were more insulin resistant (according to HOMA_IR and fasting plasma insulin levels; P < 0.001). These findings show that GES technology can be used for both the discovery of insulin-sensitizing compounds and the characterization of patients into subtypes of insulin resistance according to GES scores, opening the possibility of developing a personalized medicine approach to type 2 diabetes.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty / Department / School: No Faculty
Date Deposited: 27 Feb 2017 02:27
Last Modified: 15 Mar 2017 04:27
Uncontrolled Keywords: microarray; screening; diabetes;personalized medicine; tumor-necrosis-factor;
Fields of Research : 11 Medical and Health Sciences > 1103 Clinical Sciences > 110306 Endocrinology
11 Medical and Health Sciences > 1103 Clinical Sciences > 110311 Medical Genetics (excl. Cancer Genetics)
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970111 Expanding Knowledge in the Medical and Health Sciences
Identification Number or DOI: 10.1152/physiolgenomics.00115.2010
URI: http://eprints.usq.edu.au/id/eprint/30604

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