Title |
A Systems Biology Approach Identifies Molecular Networks Defining Skeletal Muscle Abnormalities in Chronic Obstructive Pulmonary Disease
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Published in |
PLoS Computational Biology, September 2011
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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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 20% |
United States | 1 | 20% |
Unknown | 3 | 60% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 60% |
Scientists | 1 | 20% |
Science communicators (journalists, bloggers, editors) | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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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 % |
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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% |