Dr DANIEL WILKINSON DANIEL.WILKINSON@NOTTINGHAM.AC.UK
PRINCIPAL RESEARCH FELLOW
Untargeted metabolomics for uncovering biological markers of human skeletal muscle ageing
Wilkinson, Daniel J.; Rodriguez-Blanco, Giovanny; Dunn, Warwick B.; Phillips, Bethan E.; Williams, John P.; Greenhaff, Paul L.; Smith, Kenneth; Gallagher, Iain J.; Atherton, Philip J.
Authors
Giovanny Rodriguez-Blanco
Warwick B. Dunn
Professor BETH PHILLIPS beth.phillips@nottingham.ac.uk
PROFESSOR OF TRANSLATIONAL PHYSIOLOGY
John P. Williams
Professor PAUL GREENHAFF PAUL.GREENHAFF@NOTTINGHAM.AC.UK
PROFESSOR OF MUSCLE METABOLISM
Professor KENNETH SMITH KEN.SMITH@NOTTINGHAM.AC.UK
PROFESSOR OF METABOLIC MASS SPECTROMETRY
Iain J. Gallagher
Professor PHILIP ATHERTON philip.atherton@nottingham.ac.uk
PROFESSOR OF CLINICAL, METABOLIC & MOLECULAR PHYSIOLOGY
Abstract
Ageing compromises skeletal muscle mass and function through poorly defined molecular aetiology. Here we have used untargeted metabolomics using UHPLC-MS to profile muscle tissue from young (n=10, 25±4y), middle aged (n=18, 50±4y) and older (n=18, 70±3y) men and women (50:50). Random Forest was used to prioritise metabolite features most informative in stratifying older age, with potential biological context examined using the prize-collecting Steiner forest algorithm embedded in the PIUMet software, to identify metabolic pathways likely perturbed in ageing. This approach was able to filter a large dataset of several thousand metabolites down to subnetworks of age important metabolites. Identified networks included the common age-associated metabolites such as androgens, (poly)amines/amino acids and lipid metabolites, in addition to some potentially novel ageing related markers such as dihydrothymine and imidazolone-5-proprionic acid. The present study reveals that this approach is a potentially useful tool to identify processes underlying human tissue ageing, and could therefore be utilised in future studies to investigate the links between age predictive metabolites and common biomarkers linked to health and disease across age.
Citation
Wilkinson, D. J., Rodriguez-Blanco, G., Dunn, W. B., Phillips, B. E., Williams, J. P., Greenhaff, P. L., Smith, K., Gallagher, I. J., & Atherton, P. J. (2020). Untargeted metabolomics for uncovering biological markers of human skeletal muscle ageing. Aging, 12(13), 12517-12533. https://doi.org/10.18632/aging.103513
Journal Article Type | Article |
---|---|
Acceptance Date | Jun 4, 2020 |
Online Publication Date | Jun 24, 2020 |
Publication Date | Jun 24, 2020 |
Deposit Date | Jun 12, 2020 |
Publicly Available Date | Jun 24, 2020 |
Journal | Aging |
Electronic ISSN | 1945-4589 |
Publisher | Impact Journals |
Peer Reviewed | Peer Reviewed |
Volume | 12 |
Issue | 13 |
Pages | 12517-12533 |
DOI | https://doi.org/10.18632/aging.103513 |
Public URL | https://nottingham-repository.worktribe.com/output/4630996 |
Publisher URL | https://www.aging-us.com/article/103513/text |
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