We aimed to investigate the human skeletal muscle (SkM) DNA methylome after exercise in low carbohydrate (CHO) energy balance (with high fat) compared with exercise in low-CHO energy deficit (with low fat) conditions. The objective to identify novel epigenetically regulated genes and pathways associated with ‘train-low sleep-low’ paradigms. The sleep-low conditions included 9 males that cycled to deplete muscle glycogen while reaching a set energy expenditure. Post-exercise, low-CHO meals (protein-matched) completely replaced (using high-fat) or only partially replaced (low-fat) the energy expended. The following morning resting baseline biopsies were taken and the participants then undertook 75 minutes of cycling exercise, with skeletal muscle biopsies collected 30 minutes and 3.5 hours post exercise. Discovery of genome-wide DNA methylation was undertaken using Illumina EPIC arrays and targeted gene expression analysis was conducted by RT-qPCR. At baseline participants under energy balance (high fat) demonstrated a predominantly hypermethylated (60%) profile across the genome compared to energy deficit-low fat conditions. However, post exercise performed in energy balance (with high fat) elicited a more prominent hypomethylation signature 30 minutes post-exercise in gene regulatory regions important for transcription (CpG islands within promoter regions) compared with exercise in energy deficit (with low fat) conditions. Such hypomethylation was enriched within pathways related to: IL6-JAK-STAT signalling, metabolic processes, p53 / cell cycle and oxidative / fatty acid metabolism. Hypomethylation within the promoter regions of genes: HDAC2, MECR, IGF2 and c13orf16 were associated with significant increases in gene expression in the post-exercise period in energy balance compared with energy deficit. Furthermore, histone deacetylase, HDAC11 was oppositely regulated at the gene expression level compared with HDAC2, where HDAC11 was hypomethylated yet increased in energy deficit compared with energy balance conditions. Overall, we identify some novel epigenetically regulated genes associated with train-low sleep-low paradigms.
Overall design: Methods
Ethics
The study was approved by the Norwegian School of Sport Sciences (NIH) Ethics Committee (Application ID 01-020517) and conformed to the standards of the Declaration of Helsinki. The study was registered in the Norwegian Centre for Research Data (NSD) with reference number 54131/3/ASF. All subjects were informed about the nature of the study and possible risks involved and gave written consent prior to participating in the study.
Participants characteristics
Nine well-trained males (tier 2/3 athletes (1)) completed the study. Participants characteristics were: VO2max: 66 ± 6 ml/kg/min, height: 185 ± 5 cm, body mass: 81 ± 8 kg, body fat: 17 ± 5%.
Experimental protocol
Briefly, in a randomised, counterbalanced, crossover design, participants visited the laboratory on two separate occasions to undergo two different ‘sleep-low’ interventions which were only distinguished based on the dietary intervention. The intervention aimed at depleting SkM glycogen with cycle ergometer-based exercise in the evening of day 1 (~18:00), followed by a low-CHO diet (to avoid muscle glycogen resynthesis). The participants slept at the same premises as the laboratory and performed a second exercise session, completed with low muscle glycogen that took place in the morning of day 2 (~7:00). The glycogen depleting exercise elicited an energy expenditure of 30 kcal/kg fat-free mass (FFM) with alternating exercise of 2 mins at 85% of aerobic peak power output (PPO) and 2 min at 50% PPO (total duration ~2 h). Immediately following each glycogen-depleting session, participants consumed the low-CHO meals which either completely (energy balance, high-fat; EB-HF) or partially (energy deficit, low-fat; ED-LF) replaced the energy expended during glycogen depleting exercise (see ‘dietary interventions’ section below for details on the meals). In the morning of day 2, the structured cycle ergometer exercise session that lasted 75 mins was comprised of mostly low-intensity exercise (50% PPO) but included 4 x 30 seconds and 5 x 1 minute high-intensity intervals. Further details of the experimental protocol have been published elsewhere (2).
Dietary interventions
Diet was controlled and standardised for both interventions for the 24 hrs before visiting the laboratory. Specifically, the diets were pre-packaged and provided 40 kcal/kg FFM/day containing 1.2, 6.0 and 1.35 g/kg FFM/day of fat, CHO, and protein, respectively. Immediately after the exercise in the evening of Day 1, participants consumed one of two low-CHO diets: either a high-fat, energy balance diet (EB-HF), which provided 30 kcal/kg FFM, completely replacing the energy expended during exercise and was composed of 2.5 g/kg FFM (73% energy) fat, 1.2 g/kg FFM (16% energy) CHO and 0.84 g/kg FFM (11% Energy) protein, or a low-fat, energy deficit (ED-LF) diet which provided 9 kcal/kg FFM, partially replacing the energy expended during exercise and was composed of 0.1 g/kg FFM (10% energy) fat, 1.2 g/kg FFM (53% energy) CHO and 0.84 g/kg FFM (37% energy) protein. On day 2, both groups ingested a recovery drink 30 min after the morning exercise containing: 1.2 g/kg FFM CHO and 0.38 g/kg FFM of protein, as this nutrient composition is common practice for athletes to maximise training adaptation (3). Diets were designed to provide the same amount of CHO and protein while providing divergent amounts of energy (deficit and balance), with the energy difference depending solely on the difference in exogenous fat consumption.
Biopsies
Muscle biopsies were taken from the vastus lateralis using a 6 mm Bergström needle modified for manual suction, following local anaesthesia (1% lidocaine, AstraZeneca, Cambridge, UK). Muscle biopsies were taken on day 2 at rest immediately before the start of exercise (baseline), and at 30 mins and 3.5 h after the exercise bout. From the 9 subjects completing the study, we identified a random subpopulation of 4 participants biopsies from each condition and each time point to analyse genome-wide DNA methylation (detailed methods below). Based on the genes identified to possess alterations in DNA methylation, we then validated those changes with gene expression of the same genes across the entire cohort of 9 participants (see ´RNA isolation, primer design & gene expression analysis´ methods section below). This helped to determine whether the identified changes at the genome-wide DNA methylation level in the subpopulation were associated with changes in gene expression of the entire cohort. Baseline characteristics of subpopulation for DNA methylome analysis were: VO2max: 70 ± 5 ml/kg/min, height: 185 ± 6 cm, body mass: 77 ± 6 kg, body fat: 14 ± 4%.
Tissue homogenization and DNA isolation
Muscle samples were homogenized for 45 seconds at 6,000 rpm × 3 (5 min on ice in-between intervals) in lysis buffer (180 µl buffer ATL with 20 µl proteinase K) provided in the DNeasy spin column kit (Qiagen, UK) using a Roche Magnalyser instrument and homogenization tubes containing ceramic beads (Roche, UK). The DNA was then isolated using the DNeasy spin column kit (Qiagen, UK) bisulfite converted using the EZ DNA Methylation Kit (Zymo Research, CA, United States) as per the manufacturer’s instructions.
DNA methylation analysis
All DNA methylation experiments were performed in accordance with Illumina manufacturer instructions for the Infinium Methylation EPIC BeadChip Array. Methods for the amplification, fragmentation, precipitation and resuspension of amplified DNA, hybridisation to EPIC beadchip, extension and staining of the bisulfite converted DNA (BCD) can be found in detail in our open access methods paper (4, 5). EPIC BeadChips were imaged using the Illumina iScan System (Illumina, United States).
DNA methylome analysis, differentially methylated positions (DMPs), pathway enrichment analysis (KEGG and GO pathways) and differentially methylated region (DMR) analysis
Following MethylationEPIC BeadChip arrays, raw .IDAT files were processed using Partek Genomics Suite V.7 (Partek Inc. Missouri, USA) and annotated using the MethylationEPIC_v-1-0_B4 manifest file. The mean detection p-value for all samples was 0.0002, which was well below the recommended 0.01 (6). The difference between the average median methylated and average median unmethylated signal was 0.08, well below the recommended difference of less than 0.5 (6). Upon import of the data we filtered out probes located in known single nucleotide polymorphisms (SNPs) and any known cross-reactive probes using previously defined SNP and cross-reactive probe lists from EPIC BeadChip 850K validation studies (7). Although the average detection p-value for each sample across all probes was very low (on average 0.0002), we also excluded any individual probes with a detection p-value that was above 0.01 as recommended previously (6). Out of a total of 865,860 probes in the EPIC array, removal of known SNPs, cross-reactive probes, those with a detection p-value above 0.01 resulted in 809,832 probes being taken forward for downstream analysis. Following this, background normalisation was performed via functional normalisation (with noob background correction) as previously described (8). After functional normalisation, we also undertook quality control procedures via principal component analysis (PCA). One sample in the EB-HF trial was removed due to a larger variation than that expected within that condition (variation defined as values above 2.2 standard deviations for that condition). Following normalisation and quality control procedures, we undertook differentially methylated position (DMP) analysis by converting β-values to M-values (M-value = log2(β / (1 - β)), as M-values show distributions that are more statistically valid for the differential analysis of methylation levels (9). We then performed a two-way ANOVA for condition (high-fat, energy balance/EB-HF and low-fat energy deficit/ED-LF) and time (baseline, 30 minutes, 3.5 hrs) with planned contrast/pairwise comparisons of: EB-HF baseline vs. ED-LF baseline, EB-HF 30 min vs. ED-LF 30 min, EB-HF 3.5 hrs vs. ED-LF 3.5 hrs. For initial discovery of CpG sites that were deemed statistically significant, DMPs with an unadjusted P value of ≤ 0.01 were accepted for downstream analysis (Kyoto Encyclopedia of Genes and Genomes/KEGG pathway, Gene Ontology/GO and differentially methylated region/DMR analysis - see below). We then undertook CpG enrichment analysis on these DMPs within GO terms and KEGG pathways (10-12) using Partek Genomics Suite and Partek Pathway software at the significance level of FDR ≤ 0.05. Differentially methylated region (DMR) analysis was performed to identify where several CpGs were differentially methylated within a short chromosomal locations/regions, undertaken using the Bioconductor package DMRcate (DOI: 10.18129/B9.bioc.DMRcate). Finally, to plot and visualise temporal changes in methylation across the post-exercise period (baseline, 30 min and 3.5 hr) within each condition (EB-HF and ED-LF) we implemented Self Organising Map (SOM) profiling of the change in mean methylation within each condition using Partek Genomics Suite.
References
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