Harvey, Nicholas

English

Exercise remains the most efficient way to maintain health in normative states and increases the likelihood of recovery from a host of disease states. Even so the exact mechanisms by which these changes occur within skeletal muscle remain unknown. Whilst there have been many exercise studies that examine endurance exercise and consequently identified genes (PPARGC1A, HIF1A) and processes (mitochondrial biogenesis/function, lipid metabolism, angiogenesis) involved in adaptation, very few were centred around high intensity interval training (HIIT). Of these, even less included more than ten participants, analysed young/lean/healthy individuals, examined Omic level data, or aimed to tie together multiple molecular datasets to more robustly identify molecular processes involved in adaptation to HIIT. A recent meta-analysis aimed to address the limitations within the current literature and was able to discern many genes and molecular processes associated in various forms of training. However molecular changes regarding HIIT remained elusive with only two studies examining large-scale gene expression changes. As such, the Genes and Skeletal Muscle Adaptive Response to Training (Gene SMART) study was designed to alleviate these limitations and further the knowledge of molecular exercise adaptation processes. The Gene SMART study represents a moderately trained, longitudinal HIIT cohort. Briefly, ~60 participants completed the study, wherein muscle and blood samples were taken at baseline (PRE), immediately following a single bout of high intensity interval exercise (HIIE) (P0), three-hours post HIIE (P3), and four-week post HIIT (4WP). This thesis represents a collaborative research scheme between Bond University, Victoria University, and Queensland University of Technology. The aim of this thesis was to identify robust genetic and molecular determinants of acute response to HIIE and chronic responses to HIIT. For the purposes of this thesis, the study contains four related projects, each centred around a different molecular aspect of adaptation: nuclear genetics, mitochondrial genetics, transcriptomics, and epigenetics. Therefore, this thesis has achieved and reported key findings from each of these projects and is detailed in the following. Firstly, the study aimed to identify associations between implicated genetic variants with response phenotypes from the Gene SMART cohort. Further, we utilised a highly trained (2008 Konoha Ironman Triathlon) exercise population to identify variants solely associated with either moderate or highly trained individuals. A MassARRAY SNP genotyping plex was developed that contained 36 genetic variants within three categories; 1) previously strongly associated with training, 2) previously associated but with both positive and negative results, and 3) associated with exercise intolerant disorders,. The rs1474347 G allele within the IL6 gene was found to be associated with an increased aerobic exercise capacity following training. Further, there was no overlap between the nominally significant variants associated with the highly trained and moderately trained cohorts indicating a large difference in the adaptation processes between fitness levels. The next focus of the study was to sequence mitochondrial genomes to discover point variants and mitochondrial regional haplogroups associated with exercise response. As a secondary aim of this study, the design and optimisation of a high throughput method for the sequencing of mitochondrial genomes was utilised. Point variations were also examined within nuclear encoded mitochondrial related genes to ensure any additional variation in mitochondrial function was considered. Unfortunately, the study lacked significant statistical power to identify associations between mitochondrial variation and exercise response phenotypes. When regional haplogroups were examined, the number of individual haplogroups was identified to be equivalent to the number of participants and prevented association testing. Whilst this did not yield any significant results, this work highlighted the requirement for accurate representation of the mitochondrial genome, rather than just hypervariable region sequencing, to obtain haplogroup information. A total of 4,325 nuclear encoded variants were identified within mitochondrial related genes that passed a nominal threshold of α&lt;0.05. Nine nuclear encoded variants in eight separate genes were found to be associated with exercise responses (FDR&lt;0.05) (rs11061368: DIABLO, rs113400963: FAM185A, rs6062129 and rs6121949: MTG2, rs7231304: AFG3L2, rs2041840: NDUFAF7, rs7085433: TIMM23, rs1063271: SPTLC2, rs2275273: ALDH18A1). The data generated from this study suggest novel nuclear-encoded SNPs and mitochondrial pathways associated with exercise response phenotypes. The third aim of this project utilised Ion Proton transcriptome sequencing to identify genes and molecular pathways differentially regulated in skeletal muscle following a single bout of HIIE and four-weeks of HIIT. The transcription factors, MYC, FOS, and JUN, were identified to be highly upregulated immediately (P0) following a single bout of HIIE. Further, these remained upregulated after three-hours (P3), but the effect was attenuated following four-weeks of training (4WP). These findings replicated upregulation of previously discovered exercise inducible genes (PPARGC1a, MSTN, CKM). To discern biological meaning, Gene Set Enrichment Analysis (GSEA) was performed to determine enriched biological processes (BPs) within each exercise time point. Immune BPs were highly upregulated in immediate and three-hour acute HIIE. Interestingly, the number of differentially expressed genes did not correlate with the number of enriched BPs as chronic HIIT (4WP) contained the largest number of enriched gene ontology terms within the data. A longitudinal switch from immediate stress response inducible BPs to prolonged biological function was observed with protein targeting and energy metabolism more evident after HIIT (4WP). The next aim was to address the transcriptomic responses pertaining to previous chapters within this thesis framework. This analysis found that repression of IL6 at chronic HIIT may be prevalent within BPs corresponding to cytokine production. The MICAL-L2 gene was identified as an important mediator between insulin signalling, actin filament polymerisation, and glucose transport into skeletal muscle. Lastly, the METT-L12 and JMJD6 genes were identified as likely important mediators of epigenetic chromatin remodelling in chronic HIIT. To assess global DNA methylation levels in skeletal muscle, 850K EPIC arrays were utilised. A subset (n=19) of individuals from the Gene SMART study were used to assess the immediate (P0), and four-weeks of HIIT (4WP). The global DNA hypomethylation event commonly described at immediate exercise time points was observed and provided confidence in the findings. Following differential methylation analysis (delta Beta (Δβ) ±2%, FDR&lt;0.05), we identified a total of 1,138 (746 hypermethylated and 392 hypomethylated differentially methylated probes (DMPs)) for acute HIIE. For chronic HIIT, 7,470 probes were differentially methylated, of which, 2,371 DMPs were hypermethylated and 5,099 hypomethylated. To prevent the use of arbitrary thresholds that may induce false positive data, mCSEA (methylation set enrichment analysis) was performed to discern coordinated shifts in methylation within gene promoter regions and their negative correlation with gene expression (i.e. upregulated gene expression with corresponding promoter hypermethylation). We identified 122, and 276 significant (FDR&lt;0.05) genes that were likely to be regulated via promoter methylation for acute HIIE (P0) and chronic HIIT (4WP) respectively. The correlated genes for acute HIIE were extremely similar to the sole transcriptome data, where transcription factors such as JUNB, EGR1, FOS, FOSB, EGR3, and MYC, were upregulated with corresponding promoter hypomethylation. For chronic HIIT, the correlated genes seemed to suggest a more functional adaptive response involving upregulation/hypermethylation of RNA processing (BRUNOL6) and protein localisation genes (DZIP1L), and downregulation/hypermethylation of genes involved in calcium signalling (MYOZ1, HRC) and muscle differentiation and growth (SPEG, PPAPDC3). The methylation regulated BPs associated with acute HIIE overlapped with the clusters identified from the sole transcriptome analysis (response to reactive oxygen species, processes involved in development, skeletal muscle tissue development, and response to peptide). For chronic HIIT, both up and downregulated were identified. Upregulated/hypermethylated genes mapped to clusters contained similar immune BPs to the sole transcriptome data (regulation of leukocyte differentiation, regulation of myeloid differentiation, negative regulation of leukocyte differentiation, neutrophil activation, negative regulation of leukocyte apoptosis, and regulation of leukocyte proliferation). Whilst downregulated/hypomethylated genes mapped to clusters associated with muscle contraction and muscle tissue development, which represented a novel finding for response to sustained HIIT. Whilst this study generated large-scale datasets, there were several limitations within the study design and analysis methodologies utilised. 1) The sample size used for the study, while comparatively large, was too underpowered to gain genome wide significant findings from the genomic data generated. This was addressed through limiting the genomic data to the mitochondrial related genes of interest. 2) The Gene SMART sub-cohort utilised was all male, and as such, only representative of half of the available healthy population. 3) There were constraints within the analysis approaches utilised for the study. As yet, no robust analysis pipeline for MultiOmics applications has been developed. 4) Skeletal muscle cellular complexity, specifically myonucleation (more than one myonuclei per fibre) and microenvironment cell heterogeneity (endothelial cells, mesenchymal progenitors (fibro-adipogenic progenitors (FAP) cells), and resident immune cells). Future directions for this research study are as follows. 1) The results gained from Omics analysis must be replicated within other exercise cohorts. 2) Female populations should be analysed to discern biological differences in exercise response between sexes. 3) Novel data analysis techniques such as data imputation and deconvolution analysis should be employed to increase the statistical power of the studies, and ensure findings are representative of skeletal muscle myonuclei respectively. 4) Proteomic approaches should be utilised to functionally assess the findings within this thesis. The findings from this thesis represent novel and exciting results for the field of exercise science at large. Further, the results discussed within the framework of this thesis represent a summary of the overall findings from each of the projects. The Omic level data generated from this thesis will continue to be useful for the exercise genetics community and will assist with future meta-analyses that aim to delineate the adaptive response to training. The projects within this study were able to identify novel genes and biological processes, and replicate previous findings, emphasizing the requirement and statistical power gained from strong studies within the field of exercise science (longitudinal, paired design, healthy population, physiological and biochemical data)

Bond University Research Portal

Identifying the genomic predictors of exercise-induced adaptation.

https://pure.bond.edu.au/ws/files/111674278/Nicholas_Harvey_Thesis.pdf

Identifying the genomic predictors of exercise-induced adaptation.

Abstract

Similar works

Full text

Available Versions

Bond University Research Portal