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Integration of microRNA changes in vivo identifies novel molecular features of muscle insulin resistance in type 2 diabetes

Iain J Gallagher1, Camilla Scheele23, Pernille Keller12, Anders R Nielsen2, Judit Remenyi4, Christian P Fischer2, Karim Roder1, John Babraj1, Claes Wahlestedt5, Gyorgy Hutvagner4, Bente K Pedersen2 and James A Timmons1367*

Author Affiliations

1 Translational Biomedicine, Heriot-Watt University, Edinburgh, EH14 4AS, UK

2 Centre for Inflammation and Metabolism, Department of Infectious Diseases and CMRC, Rigshospitalet, University of Copenhagen, DK2100, Denmark

3 The Wenner-Gren Institute, Arrhenius Laboratories, Stockholm University, SE-106 91 Stockholm, Sweden

4 Wellcome Trust Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK

5 Department of Biochemistry, Scripps Research Institute, Jupiter, FL33458, USA

6 Royal Veterinary College, University of London, Royal College Street, London, NW1, UK

7 Centre for Healthy Ageing, Department of Biomedical Sciences, Panum Institutet, University of Copenhagen, Blegdamsvej 3B, DK-2200, Denmark

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Genome Medicine 2010, 2:9  doi:10.1186/gm130

Published: 1 February 2010

Abstract

Background

Skeletal muscle insulin resistance (IR) is considered a critical component of type II diabetes, yet to date IR has evaded characterization at the global gene expression level in humans. MicroRNAs (miRNAs) are considered fine-scale rheostats of protein-coding gene product abundance. The relative importance and mode of action of miRNAs in human complex diseases remains to be fully elucidated. We produce a global map of coding and non-coding RNAs in human muscle IR with the aim of identifying novel disease biomarkers.

Methods

We profiled >47,000 mRNA sequences and >500 human miRNAs using gene-chips and 118 subjects (n = 71 patients versus n = 47 controls). A tissue-specific gene-ranking system was developed to stratify thousands of miRNA target-genes, removing false positives, yielding a weighted inhibitor score, which integrated the net impact of both up- and down-regulated miRNAs. Both informatic and protein detection validation was used to verify the predictions of in vivo changes.

Results

The muscle mRNA transcriptome is invariant with respect to insulin or glucose homeostasis. In contrast, a third of miRNAs detected in muscle were altered in disease (n = 62), many changing prior to the onset of clinical diabetes. The novel ranking metric identified six canonical pathways with proven links to metabolic disease while the control data demonstrated no enrichment. The Benjamini-Hochberg adjusted Gene Ontology profile of the highest ranked targets was metabolic (P < 7.4 × 10-8), post-translational modification (P < 9.7 × 10-5) and developmental (P < 1.3 × 10-6) processes. Protein profiling of six development-related genes validated the predictions. Brain-derived neurotrophic factor protein was detectable only in muscle satellite cells and was increased in diabetes patients compared with controls, consistent with the observation that global miRNA changes were opposite from those found during myogenic differentiation.

Conclusions

We provide evidence that IR in humans may be related to coordinated changes in multiple microRNAs, which act to target relevant signaling pathways. It would appear that miRNAs can produce marked changes in target protein abundance in vivo by working in a combinatorial manner. Thus, miRNA detection represents a new molecular biomarker strategy for insulin resistance, where micrograms of patient material is needed to monitor efficacy during drug or life-style interventions.