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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.

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Authors

Giovanny Rodriguez-Blanco

Warwick B. Dunn

BETH PHILLIPS beth.phillips@nottingham.ac.uk
Professor of Translational Physiology

John P. Williams

PAUL GREENHAFF PAUL.GREENHAFF@NOTTINGHAM.AC.UK
Professor of Muscle Metabolism

KENNETH SMITH KEN.SMITH@NOTTINGHAM.AC.UK
Professor of Metabolic Mass Spectrometry

Iain J. Gallagher

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., …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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