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Appears in Collections:Faculty of Health Sciences and Sport Journal Articles
Peer Review Status: Refereed
Title: Untargeted metabolomics for uncovering biological markers of human skeletal muscle ageing
Author(s): 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
Issue Date: 15-Jul-2020
Citation: Wilkinson DJ, Rodriguez-Blanco G, Dunn WB, Phillips BE, Williams JP, Greenhaff PL, Smith K, Gallagher IJ & Atherton PJ (2020) Untargeted metabolomics for uncovering biological markers of human skeletal muscle ageing. Aging, 12 (13), p. 12517—12533.
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.
DOI Link: 10.18632/aging.103513
Rights: Copyright © 2020 Wilkinson et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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