Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Expression data from insulin-treated human primary fibroblasts and effects of U0126 on insulin-induced gene expression

ABSTRACT: We carried out a high throughput analysis of insulin-induced kinase signaling pathways in primary fibroblasts from 35 unrelated individuals. We found that extensive individual variation exists in induction of various signaling pathways. ERK signaling displayed the greatest variation, which led to extensive variation in expression of downstream target genes. Our results suggest that phenotypic variation in kinase signaling mediates variation in downstream processes of insulin response. Future study of such phenotypic variation is important to linking genetic variants to individual susceptibility to complex diseases such as diabetes. Passage-matched primary fibroblasts from 35 unrelated newborns (foreskin) were treated with 100 nM insulin. We measured gene expression levels in each of 35 individuals at baseline, and 1 hour and 6 hours after insulin treatment using expression arrays. In order to examine effects of ERK inhibition on insulin-induced gene expression, we treated fibroblasts from 4 individuals with DMSO or 10uM of U0126 for 1 hour, followed by insulin treatment for one hour and 6 hours. We then harvested cells and measured gene expression in each sample using expression arrays.

ORGANISM(S): Homo sapiens

SUBMITTER: Isabel Wang

PROVIDER: E-GEOD-21891 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Genetic variation in insulin-induced kinase signaling.

Wang Isabel Xiaorong IX Ramrattan Girish G Cheung Vivian G VG

Molecular systems biology 20150722 7

Individual differences in sensitivity to insulin contribute to disease susceptibility including diabetes and metabolic syndrome. Cellular responses to insulin are well studied. However, which steps in these response pathways differ across individuals remains largely unknown. Such knowledge is needed to guide more precise therapeutic interventions. Here, we studied insulin response and found extensive individual variation in the activation of key signaling factors, including ERK whose induction d ...[more]

PMID: 26202599

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Project description:Insulin resistance is a heterogeneous disorder caused by a range of genetic and environmental factors, and we hypothesise 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 M-bM-^@M-^\insulin resistantM-bM-^@M-^] by treatment with tumour necrosis factor (TNF-alpha and then reversed with aspirin and troglitazone (M-bM-^@M-^\re-sensitizedM-bM-^@M-^]). The GES consisted of 5 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 sensitising compounds including non-steroidal anti-inflammatory agents, beta-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 discovery of insulin sensitising compounds and characterisation of patients into subtypes of insulin resistance according to GES scores, opening the possibility of developing a personalised medicine approach to type 2 diabetes. Three-condition experiment, Vehicle, TNF and TNF+ASA+TGZ with biological replicates: 22 Vehicle, 21 TNF and 21 TNF+ASA+TGZ , independently grown and harvested. One replicate per array.

Dataset Information

Expression data from insulin-treated human primary fibroblasts and effects of U0126 on insulin-induced gene expression

Publications

Genetic variation in insulin-induced kinase signaling.

Similar Datasets

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