Alvarez Romero, Javier (2023) The molecular architecture of trainability explained by genetics. PhD thesis, Victoria University.

Abstract

Introduction There is a substantial interindividual variability in responses to exercise training, which is influenced by both environmental and genetic factors. However, the influence these genes is relatively unknown. Therefore, we have investigated the association between robust genetic variants, using the Exercise Polygenic Score (EPS), on mitochondrial and physiological response to four weeks of High-Intensity Interval Training (HIIT) in the Gene SMART (Skeletal Muscle Response to Training) study. Thus, the overarching aim of this thesis is to evaluate the association of robust genetic variants using the Exercise Polygenic Score (EPS) in mitochondrial and physiological response to exercise phenotypes to four weeks of High Intensity Interval Training (HIIT). Methods 116 adults from the Gene SMART cohort study completed four weeks of HIIT to measure physiological and mitochondrial responses. Maximal oxygen uptake (VO2max), lactate threshold (LT), and peak power output (Wpeak) were assessed before and after the exercise training. Muscle biopsies were collected before and after 4-weeks of HIIT to assess mitochondrial markers: citrate synthase (CS), cytochrome c oxidase (COX), succinate dehydrogenase (SDH), mitochondrial copy number (mtCN) and mitochondrial health index (MHI). DNA isolated from blood samples was genotyped using the Genome-Wide Genotyping Array, and genotype data was then used to derive exercise polygenic scores for each participant using exercise-related SNPs identified in Chapter 3. Results: We found significant increases in mitochondrial markers CS and COX after the HIIT intervention (p < 0.05), but no changes in mitochondrial health index (MHI). We also found that changes in LT were found to be positively correlated with changes in both CS (r = 0.2, p = 0.014,) and COX (r = 0.19, p = 0.019). Also, significant correlations were found between changes in VO2max and changes in two mitochondrial markers, CS (r = 0.24, p = 0.0025) and SDH (r = 0.20, p = 0.011). Finally, a significant correlation was found between changes in Wpeak and changes in CS (r= 0.24, p = 0.0024). We did not observe significant associations between MHI changes and changes in physiological measurements. We found no associations between EPSs and physiological and mitochondrial markers either before or after four weeks of HIIT. However, we found a significant association between baseline mtCN and the (PPARGC1A) rs8192678 SNP (p= 0.012). We further showed several associations between SNPs and mitochondria factors i) baseline mtCN and rs8192678 (p= 0.021), ii) 4-week change in mtCN and (BIRC) rs6090327 (p <0.001), iii) 4-week change in CS and (AGT) rs699 (p=0.0381), iv) 4-week change in SDH and rs609037 (p= 0.030) and (DAAM1) rs12891759 (p= 0.035) and v) baseline MHI and (PPARA) rs4253778 (p= 0.027), (RGS18) rs10921078 (p= 0.029), and (ACTN3) rs1815739 (p= 0.027). We found no significant differences between SNP genotypes and least square means of VO2max, LT, and Wpeak. Summary The experimental design of this study enables a better understanding of the roles of genes contributing to the complexity of exercise responses in humans. Future research should also integrate physiological molecular and omics (epigenomics, transcriptomics, metabolomics) to elucidate the mechanisms of exercise training in humans.

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