Dataset Information

Transcriptomic profiles of skeletal muscle tissue following an euglycemic-hyperinsulinemic clamp in insulin-resistant obese subjects.

ABSTRACT: Insulin resistance in skeletal muscle is an early phenomenon in the pathogenesis of type 2 diabetes. Muscle is mainly responsible for insulin-stimulated glucose clearance from the bloodstream. Thus, regulation of gene expression in muscle tissue may be involved in the pathogenesis of insulin resistance. The objective was to investigate gene expression and metabolic pathways alterations in skeletal muscle tissue following an euglycemic-hyperinsulinemic clamp in obese insulin-resistant subjects. We carried out a transcriptome comparison of skeletal muscle tissue before and after a 3-h euglycemic-hyperinsulinemic clamp following 8-week supplementation with n-3 polyunsaturated fatty acid (PUFA) (1.8 g/day) with or without a supplement of fish gelatin (FG) (25 % of daily protein intake) in 16 obese insulin-resistant subjects. Results indicate that approximately 5 % (1932) of expressed transcripts were significantly changed after the clamp in both n-3 PUFA and n-3 PUFA + FG supplementation periods. Of these differentially expressed transcripts, 1394 genes associated with enzymes, transcription and translation regulators, transporters, G protein-coupled receptors, cytokines, and ligand-dependent nuclear receptors were modified. Metabolic pathways that were significantly modified included liver X receptor/retinoid X receptors (RXR) activation, vitamin D receptor/RXR activation, interleukin (IL)-8, acute phase response, IL10, triggering receptor expressed on myeloid cells 1, peroxisome proliferator-activated receptor, G-beta/gamma and hepatocyte growth factor and IL6 signaling. Taken together, results suggest that mainly inflammatory and transcription factors are modified following clamp in obese insulin-resistant subjects. Overall, understanding the changes in metabolic pathways due to insulin may be a potential target for the management of insulin resistance.

SUBMITTER: Rudkowska I

PROVIDER: S-EPMC3534998 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Transcriptomic profiles of skeletal muscle tissue following an euglycemic-hyperinsulinemic clamp in insulin-resistant obese subjects.

Rudkowska Iwona I Jacques Hélène H Weisnagel S John SJ Marette André A Vohl Marie-Claude MC

Genes & nutrition 20120508 1

Insulin resistance in skeletal muscle is an early phenomenon in the pathogenesis of type 2 diabetes. Muscle is mainly responsible for insulin-stimulated glucose clearance from the bloodstream. Thus, regulation of gene expression in muscle tissue may be involved in the pathogenesis of insulin resistance. The objective was to investigate gene expression and metabolic pathways alterations in skeletal muscle tissue following an euglycemic-hyperinsulinemic clamp in obese insulin-resistant subjects. W ...[more]

PMID: 22566203

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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.

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)

Dataset Information

Transcriptomic profiles of skeletal muscle tissue following an euglycemic-hyperinsulinemic clamp in insulin-resistant obese subjects.

Publications

Transcriptomic profiles of skeletal muscle tissue following an euglycemic-hyperinsulinemic clamp in insulin-resistant obese subjects.

Similar Datasets

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