Network analysis of human skeletal muscle adaptation under different loading states
Willis, C
Date: 29 November 2021
Publisher
University of Exeter
Degree Title
Doctor of Philosophy in Sport and Health Sciences (SWBio)
Abstract
Adequate maintenance of skeletal muscle remains vital towards maximising human health and performance. Mechanical load is a primary regulator of skeletal muscle tissue, with increased load favouring muscle maintenance/growth and decreased load favouring muscle loss. Nevertheless, the molecular mechanisms that underpin human muscle ...
Adequate maintenance of skeletal muscle remains vital towards maximising human health and performance. Mechanical load is a primary regulator of skeletal muscle tissue, with increased load favouring muscle maintenance/growth and decreased load favouring muscle loss. Nevertheless, the molecular mechanisms that underpin human muscle (mal)adaptation under notable physiological states of altered mechanical load (resistance exercise, eccentric versus concentric loading, muscle disuse) remain incompletely defined in youth and older age, hindering the development of optimal lifestyle/pharmacological interventions to ensure muscle mass and function are appropriately maintained throughout the life course. Data-driven network analysis offers a powerful tool to expedite understanding on the biological pathways and key molecular drivers that modulate phenotypic change. The overall focus of this thesis was therefore on using this approach to generate new insights into the molecular basis of human muscle adaptation under different loading states. Notably, network analysis was used to reveal new information on molecular networks and candidate regulatory molecules: (i) linked to muscle strength in the context of human muscle ageing and acute eccentric versus concentric exercise responses; (ii) of divergent muscle responses to eccentric versus concentric training habituation; (iii) linked to muscle mass or protein synthesis declines during short-term muscle disuse, and; (iv) of specific muscle responses during early- versus later-stage disuse, resistance exercise training versus muscle disuse, and younger versus older resistance exercise training. Methodologically, this work confirms network analysis as a judicious strategy to shed new light on possible molecular causes of human muscle adaptation under different loading states. Experimentally, the resultant data offer a promising benchmark to expedite mechanistic understanding on human muscle responses to exercise/disuse and, as such, may help to accelerate the development of targeted therapeutics for ensuring adequate maintenance of skeletal muscle throughout the life course.
Doctoral Theses
Doctoral College
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