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 largescale 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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 110-120 |
| Number of pages | 11 |
| Journal | Physiological Genomics |
| Volume | 43 |
| Issue number | 3 |
| DOIs | |
| State | Published - Feb 2011 |
| Externally published | Yes |
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Keywords
- Diabetes
- Microarray
- Personalized medicine
- Screening
ASJC Scopus subject areas
- Physiology
- Genetics
Cite this
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.; Walder, Ken R.
In: Physiological Genomics, Vol. 43, No. 3, 02.2011, p. 110-120.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A gene expression signature for insulin resistance
AU - Konstantopoulos, Nicky
AU - Foletta, Victoria C.
AU - Segal, David H.
AU - Shields, Katherine A.
AU - Sanigorski, Andrew
AU - Windmill, Kelly
AU - Swinton, Courtney
AU - Connor, Tim
AU - Wanyonyi, Stephen
AU - Dyer, Thomas D.
AU - Fahey, Richard P.
AU - Watt, Rose A.
AU - Curran, Joanne E.
AU - Molero, Juan Carlos
AU - Krippner, Guy
AU - Collier, Greg R.
AU - James, David E.
AU - Blangero, John
AU - Jowett, Jeremy B.
AU - Walder, Ken R.
PY - 2011/2
Y1 - 2011/2
N2 - 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 largescale 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.
AB - 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 largescale 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.
KW - Diabetes
KW - Microarray
KW - Personalized medicine
KW - Screening
UR - http://www.scopus.com/inward/record.url?scp=79951870962&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79951870962&partnerID=8YFLogxK
U2 - 10.1152/physiolgenomics.00115.2010
DO - 10.1152/physiolgenomics.00115.2010
M3 - Article
C2 - 21081660
AN - SCOPUS:79951870962
VL - 43
SP - 110
EP - 120
JO - Physiological Genomics
JF - Physiological Genomics
SN - 1531-2267
IS - 3
ER -