ABSTRACT: The athletic horse, despite being over 50% muscle mass, remains understudied with regard to the effects of exercise and training on skeletal muscle metabolism. To begin to address this knowledge gap, we employed an untargeted metabolomics approach to characterize the exercise-induced and fitness-related changes in the skeletal muscle of eight unconditioned Standardbred horses (four male, four female) before and after a 12-week training period. Before training, unconditioned horses showed a high degree of individual variation in the skeletal muscle metabolome, resulting in very few differences basally and at 3 and 24 h after acute fatiguing exercise. Training did not alter body composition but did improve maximal aerobic and running capacities (p < 0.05), and significantly altered the skeletal muscle metabolome (p < 0.05, q < 0.1). While sex independently influenced body composition and distance run following training (p < 0.05), sex did not affect the skeletal muscle metabolome. Exercise-induced metabolomic alterations (p < 0.05, q < 0.1) largely centered on the branched-chain amino acids (BCAA), xenobiotics, and a variety of lipid and nucleotide-related metabolites, particularly in the conditioned state. Further, training increased (p < 0.05, q < 0.1) the relative abundance of almost every identified lipid species, and this was accompanied by increased plasma BCAAs (p < 0.0005), phenylalanine (p = 0.01), and tyrosine (p < 0.02). Acute exercise in the conditioned state decreased (p < 0.05, q < 0.1) the relative abundance of almost all lipid-related species in skeletal muscle by 24 h post-exercise, whereas plasma amino acids remained unaltered. These changes occurred alongside increased muscle gene expression (p < 0.05) related to lipid uptake (Cd36) and lipid (Cpt1b) and BCAA (Bckdk) utilization. This work suggests that metabolites related to amino acid, lipid, nucleotide and xenobiotic metabolism play pivotal roles in the response of equine skeletal muscle to vigorous exercise and training. Use of these and future data sets could be used to track the impact of training and fitness on equine health and may lead to novel predictors and/or diagnostic biomarkers.
