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Integrative miRNA-mRNA Profiling of Adipose Tissue Unravels Transcriptional Circuits Induced by Sleep Fragmentation

Overview of attention for article published in PLOS ONE, May 2012
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Title
Integrative miRNA-mRNA Profiling of Adipose Tissue Unravels Transcriptional Circuits Induced by Sleep Fragmentation
Published in
PLOS ONE, May 2012
DOI 10.1371/journal.pone.0037669
Pubmed ID
Authors

Sina A. Gharib, Abdelnaby Khalyfa, Amal Abdelkarim, Bharat Bhushan, David Gozal

Abstract

Obstructive sleep apnea (OSA) is a prevalent condition and strongly associated with metabolic disorders. Sleep fragmentation (SF) is a major consequence of OSA, but its contribution to OSA-related morbidities is not known. We hypothesized that SF causes specific perturbations in transcriptional networks of visceral fat cells, leading to systemic metabolic disturbances. We simultaneously profiled visceral adipose tissue mRNA and miRNA expression in mice exposed to 6 hours of SF during sleep, and developed a new computational framework based on gene set enrichment and network analyses to merge these data. This approach leverages known gene product interactions and biologic pathways to interrogate large-scale gene expression profiling data. We found that SF induced the activation of several distinct pathways, including those involved in insulin regulation and diabetes. Our integrative methodology identified putative controllers and regulators of the metabolic response during SF. We functionally validated our findings by demonstrating altered glucose and lipid homeostasis in sleep-fragmented mice. This is the first study to link sleep fragmentation with widespread disruptions in visceral adipose tissue transcriptome, and presents a generalizable approach to integrate mRNA-miRNA information for systematic mapping of regulatory networks.

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Geographical breakdown

Country Count As %
United States 2 4%
France 1 2%
United Kingdom 1 2%
Finland 1 2%
Spain 1 2%
Canada 1 2%
Unknown 44 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 25%
Student > Ph. D. Student 11 22%
Professor > Associate Professor 7 14%
Student > Bachelor 5 10%
Student > Master 3 6%
Other 5 10%
Unknown 7 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 25%
Medicine and Dentistry 12 24%
Biochemistry, Genetics and Molecular Biology 6 12%
Neuroscience 3 6%
Mathematics 1 2%
Other 5 10%
Unknown 11 22%