Dataset Information

P38 MAPK activation upregulates proinflammatory pathways in skeletal muscle cells from insulin-resistant type 2 diabetic patients.

ABSTRACT: Skeletal muscle is the key site of peripheral insulin resistance in type 2 diabetes. Insulin-stimulated glucose uptake is decreased in differentiated diabetic cultured myotubes, which is in keeping with a retained genetic/epigenetic defect of insulin action. We investigated differences in gene expression during differentiation between diabetic and control muscle cell cultures. Microarray analysis was performed using skeletal muscle cell cultures established from type 2 diabetic patients with a family history of type 2 diabetes and clinical evidence of marked insulin resistance and nondiabetic control subjects with no family history of diabetes. Genes and pathways upregulated with differentiation in the diabetic cultures, compared with controls, were identified using Gene Spring and Gene Set Enrichment Analysis. Gene sets upregulated in diabetic myotubes were associated predominantly with inflammation. p38 MAPK was identified as a key regulator of the expression of these proinflammatory gene sets, and p38 MAPK activation was found to be increased in the diabetic vs. control myotubes. Although inhibition of p38 MAPK activity decreased cytokine gene expression from the cultured diabetic myotubes significantly, it did not improve insulin-stimulated glucose uptake. Increased cytokine expression driven by increased p38 MAPK activation is a key feature of cultured myotubes derived from insulin-resistant type 2 diabetic patients. p38 MAPK inhibition decreased cytokine expression but did not affect the retained defect of impaired insulin action in the diabetic muscle cells.

SUBMITTER: Brown AE

PROVIDER: S-EPMC4281683 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

p38 MAPK activation upregulates proinflammatory pathways in skeletal muscle cells from insulin-resistant type 2 diabetic patients.

Brown Audrey E AE Palsgaard Jane J Borup Rehannah R Avery Peter P Gunn David A DA De Meyts Pierre P Yeaman Stephen J SJ Walker Mark M

American journal of physiology. Endocrinology and metabolism 20141104 1

Skeletal muscle is the key site of peripheral insulin resistance in type 2 diabetes. Insulin-stimulated glucose uptake is decreased in differentiated diabetic cultured myotubes, which is in keeping with a retained genetic/epigenetic defect of insulin action. We investigated differences in gene expression during differentiation between diabetic and control muscle cell cultures. Microarray analysis was performed using skeletal muscle cell cultures established from type 2 diabetic patients with a f ...[more]

PMID: 25370850

Similar Datasets

Project description:The obese people with abnormal BMI are predisposed to insulin resistance and diabetes. At the same time, human subjects with obesity and high BMI that are otherwise insulin sensitive are an interesting group to study the underlying gene expression patterns which provide them with such protective phenotype. Objective: Insulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavoured to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans. Methods: We integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single-gene, gene-set and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in 3 independent human datasets (n =115). Results: This GEM of 93 genes substantially improved diagnosis of IR compared to routine clinical measures across multiple independent datasets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between Î²-catenin and Jak1 and IR. Functional analyses using small molecule inhibitors showed an important role for these proteins in insulin action. Conclusions: This study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the Î²-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation. Gene expression from muscle biopsies of Lean, Obese insulin sensitive (OIS), Obese insulin resistant (OIR) and obese T2D patients (T2D) were compared for differential expression between the groups (n=7 in each group)

Project description:The obese people with abnormal BMI are predisposed to insulin resistance and diabetes. At the same time, human subjects with obesity and high BMI that are otherwise insulin sensitive are an interesting group to study the underlying gene expression patterns which provide them with such protective phenotype. Objective: Insulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavoured to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans. Methods: We integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single-gene, gene-set and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in 3 independent human datasets (n =115). Results: This GEM of 93 genes substantially improved diagnosis of IR compared to routine clinical measures across multiple independent datasets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between β-catenin and Jak1 and IR. Functional analyses using small molecule inhibitors showed an important role for these proteins in insulin action. Conclusions: This study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the β-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation.

Dataset Information

P38 MAPK activation upregulates proinflammatory pathways in skeletal muscle cells from insulin-resistant type 2 diabetic patients.

Publications

p38 MAPK activation upregulates proinflammatory pathways in skeletal muscle cells from insulin-resistant type 2 diabetic patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure