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A Systems Biology Approach Identifies Molecular Networks Defining Skeletal Muscle Abnormalities in Chronic Obstructive Pulmonary Disease

Overview of attention for article published in PLoS Computational Biology, September 2011
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Title
A Systems Biology Approach Identifies Molecular Networks Defining Skeletal Muscle Abnormalities in Chronic Obstructive Pulmonary Disease
Published in
PLoS Computational Biology, September 2011
DOI 10.1371/journal.pcbi.1002129
Pubmed ID
Authors

Nil Turan, Susana Kalko, Anna Stincone, Kim Clarke, Ayesha Sabah, Katherine Howlett, S. John Curnow, Diego A. Rodriguez, Marta Cascante, Laura O'Neill, Stuart Egginton, Josep Roca, Francesco Falciani

Abstract

Chronic Obstructive Pulmonary Disease (COPD) is an inflammatory process of the lung inducing persistent airflow limitation. Extensive systemic effects, such as skeletal muscle dysfunction, often characterize these patients and severely limit life expectancy. Despite considerable research efforts, the molecular basis of muscle degeneration in COPD is still a matter of intense debate. In this study, we have applied a network biology approach to model the relationship between muscle molecular and physiological response to training and systemic inflammatory mediators. Our model shows that failure to co-ordinately activate expression of several tissue remodelling and bioenergetics pathways is a specific landmark of COPD diseased muscles. Our findings also suggest that this phenomenon may be linked to an abnormal expression of a number of histone modifiers, which we discovered correlate with oxygen utilization. These observations raised the interesting possibility that cell hypoxia may be a key factor driving skeletal muscle degeneration in COPD patients.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 128 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 7 5%
United Kingdom 5 4%
Hong Kong 1 <1%
Sweden 1 <1%
Italy 1 <1%
Denmark 1 <1%
Germany 1 <1%
Japan 1 <1%
United States 1 <1%
Other 0 0%
Unknown 109 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 25%
Student > Ph. D. Student 19 15%
Professor > Associate Professor 14 11%
Professor 13 10%
Student > Master 13 10%
Other 26 20%
Unknown 11 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 39 30%
Medicine and Dentistry 28 22%
Biochemistry, Genetics and Molecular Biology 22 17%
Computer Science 5 4%
Nursing and Health Professions 3 2%
Other 17 13%
Unknown 14 11%