Dataset Information

Transcriptomic effects of metformin in skeletal muscle arteries of obese insulin-resistant rats.

ABSTRACT: We examined the effects of metformin, a commonly used antidiabetic drug, on gene expression in multiple arteries. Specifically, transcriptional profiles of feed arteries and second branch order arterioles in the soleus, gastrocnemius, and diaphragm muscles as well as aortic endothelial scrapes were examined from obese insulin-resistant Otsuka Long-Evans Tokushima Fatty rats treated with ( n?=?9) or without ( n?=?10) metformin from 20 to 32 weeks of age. Metformin-treated rats exhibited a reduction in body weight, adiposity, and HbA1c ( P?

SUBMITTER: Padilla J

PROVIDER: S-EPMC5685263 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Transcriptomic effects of metformin in skeletal muscle arteries of obese insulin-resistant rats.

Padilla Jaume J Thorne Pamela K PK Martin Jeffrey S JS Rector R Scott RS Akter Sadia S Davis J Wade JW Laughlin M Harold MH Jenkins Nathan T NT

Experimental biology and medicine (Maywood, N.J.) 20170123 6

We examined the effects of metformin, a commonly used antidiabetic drug, on gene expression in multiple arteries. Specifically, transcriptional profiles of feed arteries and second branch order arterioles in the soleus, gastrocnemius, and diaphragm muscles as well as aortic endothelial scrapes were examined from obese insulin-resistant Otsuka Long-Evans Tokushima Fatty rats treated with ( n = 9) or without ( n = 10) metformin from 20 to 32 weeks of age. Metformin-treated rats exhibited a reducti ...[more]

PMID: 28114814

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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 effects of metformin in skeletal muscle arteries of obese insulin-resistant rats.

Publications

Transcriptomic effects of metformin in skeletal muscle arteries of obese insulin-resistant rats.

Similar Datasets

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