A gene expression signature for insulin resistance

Konstantopoulos, Nicky, Foletta, Victoria C., Segal, David H., Shields, Katherine A., Sanigorski, Andrew, Windmill, Kelly, Swinton, Courtney, Connor, Tim, Wanyonyi, Stephen, Dyer, Thomas D., Fahey, Richard P., Watt, Rose A., Curran, Joanne E., Molero, Juan-Carlos, Krippner, Guy, Collier, Greg R., James, David E., Blangero, John, Jowett, Jeremy B. and Walder, Ken R. 2011, A gene expression signature for insulin resistance, Physiological genomics, vol. 43, no. 3, pp. 110-120.

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Title A gene expression signature for insulin resistance
Author(s) Konstantopoulos, Nicky
Foletta, Victoria C.
Segal, David H.
Shields, Katherine A.
Sanigorski, Andrew
Windmill, Kelly
Swinton, Courtney
Connor, Tim
Wanyonyi, Stephen
Dyer, Thomas D.
Fahey, Richard P.
Watt, Rose A.
Curran, Joanne E.
Molero, Juan-Carlos
Krippner, Guy
Collier, Greg R.
James, David E.
Blangero, John
Jowett, Jeremy B.
Walder, Ken R.
Journal name Physiological genomics
Volume number 43
Issue number 3
Start page 110
End page 120
Total pages 11
Publisher American Physiological Society
Place of publication Bethesda, Md.
Publication date 2011-11-16
ISSN 1094-8341
1531-2267
Keyword(s) microarray
screening
insulin
diabetes
personalised medicine
Summary Insulin resistance is a heterogeneous disorder caused by a range of genetic and environmental factors, and we hypothesize that its aetiology 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 tumour necrosis factor-alpha (TNFα) and then reversed with aspirin and troglitazone (‘re-sensitized’). The GES consisted of five genes whose expression levels best discriminated between the insulin resistant and insulin re-sensitized 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 using aspirin and troglitazone. This screen identified both known and new insulin sensitizing compounds including non-steroidal anti inflammatory agents, β-adrenergic antagonists, beta-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.
Language eng
Field of Research 110306 Endocrinology
Socio Economic Objective 920104 Diabetes
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2010, American Physiological Society.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30032301

Document type: Journal Article
Collections: School of Medicine
Centre for Physical Activity and Nutrition Research
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