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Network analysis of human muscle adaptation to aging and contraction
Department of Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, Exeter, UK.
Biosciences, University of Exeter, Exeter, UK.
Department of Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, Exeter, UK.
MRC-ARUK Centre for Musculoskeletal aging Research and National Institute of Health Research, Biomedical Research Centre, Royal Derby Hospital Centre, School of Medicine, University of Nottingham, Derby, UK.
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2020 (English)In: Aging, E-ISSN 1945-4589, Vol. 12, no 1, p. 740-755Article in journal (Refereed) Published
Abstract [en]

Resistance exercise (RE) remains a primary approach for minimising aging muscle decline. Understanding muscle adaptation to individual contractile components of RE (eccentric, concentric) might optimise RE-based intervention strategies. Herein, we employed a network-driven pipeline to identify putative molecular drivers of muscle aging and contraction mode responses. RNA-sequencing data was generated from young (21±1 y) and older (70±1 y) human skeletal muscle before and following acute unilateral concentric and contralateral eccentric contractions. Application of weighted gene co-expression network analysis identified 33 distinct gene clusters ('modules') with an expression profile regulated by aging, contraction and/or linked to muscle strength. These included two contraction 'responsive' modules (related to 'cell adhesion' and 'transcription factor' processes) that also correlated with the magnitude of post-exercise muscle strength decline. Module searches for 'hub' genes and enriched transcription factor binding sites established a refined set of candidate module-regulatory molecules (536 hub genes and 60 transcription factors) as possible contributors to muscle aging and/or contraction responses. Thus, network-driven analysis can identify new molecular candidates of functional relevance to muscle aging and contraction mode adaptations.

Place, publisher, year, edition, pages
Impact Journals LLC , 2020. Vol. 12, no 1, p. 740-755
Keywords [en]
Aging, candidate target discovery, contraction, network analysis, skeletal muscle
National Category
Geriatrics
Identifiers
URN: urn:nbn:se:oru:diva-78968DOI: 10.18632/aging.102653ISI: 000507233100042PubMedID: 31910159Scopus ID: 2-s2.0-85078574775OAI: oai:DiVA.org:oru-78968DiVA, id: diva2:1385572
Funder
Wellcome trust, WT105618MA
Note

Funding Agencies:

EPSRC/BBSRC Innovation Fellowship EP/S001352/1

Bournemouth University  

Medical Research Council UK (MRC) MR/P021220/1 MR/K00414X/1

Arthritis Research UK via the MRC-ARUK Centre for Musculoskeletal Aging Research 19891

Biotechnology and Biological Sciences Research Council (BBSRC) BB/J014400/1 BB/M009122/1 BB/N015894/1

Swedish Research Council for Sport Science  dnr 2016/125 dnr 2017/143

National Institute for Health Research, Nottingham Biomedical Research Centre  

Available from: 2020-01-14 Created: 2020-01-14 Last updated: 2024-07-04Bibliographically approved

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Kadi, Fawzi

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