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It is unknown if adult human skeletal muscle has an epigenetic memory of earlier encounters with growth. We report, for the first time in humans, genome-wide DNA methylation (850,000 CpGs) and gene expression analysis after muscle hypertrophy (loading), return of muscle mass to baseline (unloading), followed by later hypertrophy (reloading). We discovered increased frequency of hypomethylation across the genome after reloading (18,816 CpGs) versus earlier loading (9,153 CpG sites). We also identified AXIN1, GRIK2, CAMK4, TRAF1 as hypomethylated genes with enhanced expression after loading that maintained their hypomethylated status even during unloading where muscle mass returned to control levels, indicating a memory of these genes methylation signatures following earlier hypertrophy. Further, UBR5, RPL35a, HEG1, PLA2G16, SETD3 displayed hypomethylation and enhanced gene expression following loading, and demonstrated the largest increases in hypomethylation, gene expression and muscle mass after later reloading, indicating an epigenetic memory in these genes. Finally, genes; GRIK2, TRAF1, BICC1, STAG1 were epigenetically sensitive to acute exercise demonstrating hypomethylation after a single bout of resistance exercise that was maintained 22 weeks later with the largest increase in gene expression and muscle mass after reloading. Overall, we identify an important epigenetic role for a number of largely unstudied genes in muscle hypertrophy/memory.
(A) Schematic representation of experimental conditions and types of analysis undertaken across the time-course. The image of a muscle represents the time point for analysis of muscle mass via (i) DEXA and strength via (ii) isometric quadriceps muscle torque using an isokinetic dynamometer. The images of muscle tissue also represent the time point of skeletal muscle biopsy of the Vastus Lateralis, muscle sample preparation for downstream analysis of (iii) Infinium MethylationEPIC BeadChip arrays (850 K CpG sites) methylome wide array (iv) and rt-qRT-PCR for gene expression analysis of important genes identified following methylome wide analysis. (B) Weekly total volume of resistance exercise undertaken by human participants (n = 7) during the first 7-week resistance exercise period (loading, weeks 1-7), followed by a 7 week cessation of resistance exercise (unloading, weeks 8-14) and the later second period of 7 weeks resistance exercise (reloading, weeks 15-21). Data represents volume load as calculated by ((load (Kg) x reps) x sets)) averaged across 3 resistance exercise sessions per week. Data presented mean ± SEM. (Ci) Lean lower limb mass changes in human subjects (n = 7) after a period of 7 weeks resistance exercise (loading), exercise cessation (unloading) and a subsequent second period of 7 weeks resistance exercise (reloading). Total limb lean mass normalised to baseline (percentage change). Significant change compared to baseline represented by * and significant difference to all other conditions represented by ** (Cii) Total lean mass percentage change when loading is normalised to baseline, and reloading normalised to unloading to account for starting lean mass in both conditions. Pairwise t-test of significance indicated by *. All data presented as mean ± SEM (n = 7).
… 
(A) Heat map depicting unsupervised hierarchical clustering of the top 500 statistically differentially regulated CpG loci (columns) and conditions (baseline, loading, unloading and reloading) in previously untrained male participants (n = 8). The heat-map colours correspond to standardised expression normalised β-values, with green representing hypomethylation, red hypermethylation and unchanged sites are represented in black. (4B and C) Relative gene expression (i) and schematic representation of CpG DNA methylation and gene expression relationship (ii) in two identified gene clusters from genome wide methylation analysis after a period of 7 weeks resistance exercise (loading), exercise cessation (unloading) and a subsequent secondary period of 7 weeks resistance exercise (reloading). (Bi) Expression of genes that displayed a significant increase compared to baseline (represented by *) upon earlier loading, that returned to baseline during unloading, and displayed enhanced expression after reloading (significantly different to all other conditions **). MANOVA analysis reported a significant effect over the entire time course of the experiment (P < 0.0001). (Bii) Representative schematic displaying the inverse relationship between mean gene expression (solid black lines) and CpG DNA methylation (dashed black lines) of grouped transcripts (RPL35a, C12orf50, BICC1, ZFP2, UBR5, HEG1, PLA2G16, SETD3 and ODF2). Data represented as fold change for DNA methylation (left y axis) and gene/mRNA expression (right y axis). (Ci) Clustering of genes that portrayed an accumulative increase in gene expression after loading, unloading and reloading. With the largest increase in gene expression after reloading. Culminating in significance in the unloading (baseline vs. unloading*), and reloading (reloading vs. baseline**). (Cii) Representative schematic displaying the inverse relationship between mean gene expression (solid black lines) and CpG DNA methylation (dashed black lines) of grouped transcripts (AXIN1, TRAF1, GRIK2, CAMK4). Data represented as fold change for methylation (left y axis) and mRNA expression (right y axis). All data represented as mean ± SEM for gene expression (n = 7 for UBR5, PLA2G16, AXIN1, GRIK2; n = 8 for all others) and CpG DNA methylation (n = 8 for baseline, loading and unloading; n = 7 for reloading).
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SCIENTIfIC REPORtS | (2018) 8:1898 | DOI:10.1038/s41598-018-20287-3
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Human Skeletal Muscle Possesses
an Epigenetic Memory of
Hypertrophy
Robert A. Seaborne1,2, Juliette Strauss2, Matthew Cocks2, Sam Shepherd2, Thomas D.
O’Brien2, Ken A. van Someren3, Phillip G. Bell3, Christopher Murgatroyd4, James P. Morton2,
Claire E. Stewart2 & Adam P. Sharples
1,2
It is unknown if adult human skeletal muscle has an epigenetic memory of earlier encounters with
growth. We report, for the rst time in humans, genome-wide DNA methylation (850,000 CpGs)
and gene expression analysis after muscle hypertrophy (loading), return of muscle mass to baseline
(unloading), followed by later hypertrophy (reloading). We discovered increased frequency of
hypomethylation across the genome after reloading (18,816 CpGs) versus earlier loading (9,153 CpG
sites). We also identied AXIN1, GRIK2, CAMK4, TRAF1 as hypomethylated genes with enhanced
expression after loading that maintained their hypomethylated status even during unloading
where muscle mass returned to control levels, indicating a memory of these genes methylation
signatures following earlier hypertrophy. Further, UBR5, RPL35a, HEG1, PLA2G16, SETD3 displayed
hypomethylation and enhanced gene expression following loading, and demonstrated the largest
increases in hypomethylation, gene expression and muscle mass after later reloading, indicating an
epigenetic memory in these genes. Finally, genes; GRIK2, TRAF1, BICC1, STAG1 were epigenetically
sensitive to acute exercise demonstrating hypomethylation after a single bout of resistance exercise
that was maintained 22 weeks later with the largest increase in gene expression and muscle mass after
reloading. Overall, we identify an important epigenetic role for a number of largely unstudied genes in
muscle hypertrophy/memory.
Numerous studies demonstrate that skeletal muscle can be programed, where early life exposure to environ-
mental stimuli lead to a sustained alteration of skeletal muscle phenotype in later life [reviewed in ref.1]. is has
been demonstrated in mammalian models in which reduced nutrient availability during gestation impairs skel-
etal muscle bre number, composition (fast/slow bre proportions) and size ofthe ospring1. Epidemiological
studies in human ageing cohorts also suggest that low birth weight and gestational malnutrition are strongly
associated with reduced skeletal muscle size, strength and gait speed in older age2,3. Driven by encounters with
the environment, foetal programming in skeletal muscle has been attributed in part to epigenetics4,5, which refers
to alterations in gene expression as a result of non-genetic structural modications of DNA and/or histones6.
Despite these compelling data, it is unknown if adult skeletal muscle possesses the capacity to respond dierently
to environmental stimuli in an adaptive or maladaptive manner if the stimuli have been encountered previously,
a concept recently dened as skeletal muscle memory1, or if this process is epigenetically regulated. Indeed, it is
known that skeletal muscle cells retain information or ‘remember’ the stem cell niche of the donor once derived
in-vitro from physically active7 obese8,9 and sarcopenic individuals [recently reviewed in ref.1]. Our group were
the rst to demonstrate this phenomenon, where human muscle stem cells derived from the skeletal muscle of
cancer patients exhibited overactive proliferation versus age matched control cells10. ese studies collectively
suggest that skeletal muscle cells could be epigenetically regulated, as they appear to not only retain information
from the environmental niche from which they originated, but also to pass this molecular ‘signature’ onto future
daughter cell progeny in-vitro. Furthermore, we have recently reported that mouse skeletal muscle cells (C2C12),
1Institute for Science and Technology in Medicine (ISTM), School of Medicine, Keele University, Staordshire, United
Kingdom. 2Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, United
Kingdom. 3Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United
Kingdom. 4School of Healthcare Science, Manchester Metropolitan University, Manchester, United Kingdom.
Correspondence and requests for materials should be addressed to A.P.S. (email: a.p.sharples@googlemail.com)
Received: 31 October 2017
Accepted: 16 January 2018
Published: xx xx xxxx
OPEN
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SCIENTIfIC REPORtS | (2018) 8:1898 | DOI:10.1038/s41598-018-20287-3
following an early-life inammatory stress, pass molecular information onto future generations (30 cellular divi-
sions), through a process of DNA methylation11. Importantly, the cells that encountered catabolic inammatory
stress in their earlier proliferative life had impaired dierentiation capacity when encountering the same inam-
matory stress in later proliferative life11. It has therefore been proposed that a memory and susceptibility of skele-
tal muscle to repeated encounters with inammation may be controlled by epigenetic modications such as DNA
methylation, a phenomenon we have termed skeletal muscle ‘epi-memory’1.
Mouse skeletal muscle in-vivo also appears to possess a memory from the anabolic growth steroid sex hor-
mone, testosterone. Where testosterone induced hypertrophy over a period of 3 months, resulted in enhanced
incorporation of myonuclei within muscle bres12,13. ese myonuclei were retained even following testosterone
withdrawal and the return of muscle mass to baseline12,13. Most notably the mice exposed to earlier life testoster-
one, exhibited a 31% increase in muscle cross-sectional area following mechanical loading versus control mice
that failed to grow in the same period of time12,13. is suggests an enhanced response to load induced mus-
cle hypertrophy when earlier life growth from testosterone had been encountered, and therefore corresponds
with the previously highlighted denition of muscle memory by Sharples et al.1. However, epigenetics has not
been studied in this model, and specically genome-wide DNA methylation has not been investigated aer adult
human skeletal muscle growth (hypertrophy) alone, or in skeletal muscle that has experienced later growth, to
investigate if skeletal muscle possesses an epigenetic memory from earlier life encounters with hypertrophy.
To provide parallel insights into the eect of the environment on genome-wide methylation changes in skeletal
muscle, recent studies have suggested that even an acute period of increased fat intake can alter the human DNA
methylome of CpGs in over 6,500 genes14. Like previous studies demonstrating rapid and dynamic alterations in
DNA methylation in skeletal muscle tissue aer acute metabolic stress (aerobic exercise)15,16 or disuse atrophy in
rats17, this study also suggested that large scale epigenetic modications can occur very rapidly in skeletal mus-
cle, aer only 5 days of high fat feeding. However, the authors also demonstrated a maintenance of methylation
following cessation of the high fat diet14. Where aer 8 weeks of returning to a normal diet, not all of the altered
methylation, particularly hypermethylation, was fully returned to baseline control levels14. is therefore suggests
that in response to an acute negative environmental stress, DNA methylation could be retained and accumulated
over time. Indeed, human skeletal muscle cells isolated from aged donors demonstrated a genome wide hyper-
methylated prole versus young adult tissue18. erefore, because DNA methylation, particularly within promoter
or enhancer regions of genes, generally leads to suppressed gene expression19, accumulation of high DNA methyl-
ation (hypermethylation) following a high fat diet and/or ageing could lead to universally suppressed gene expres-
sion. It may therefore be hypothesised that positive environmental encounters, such as muscle growth stimuli,
may induce a hypomethylated state (low DNA methylation) of important target transcripts or loci associated with
cellular growth and as a result, lead to enhanced gene expression when exposed to later life anabolic encounters.
To test this hypothesis, we aimed to investigate an epigenetic memory of earlier hypertrophy in adult human skel-
etal muscle using a within measures design, by investigating genome wide DNA methylation of over 850,000 CpG
sites aer: (1) Resistance exercise induced muscle growth (loading), followed by; (2) cessation of resistance exercise
to return muscle back towards baseline levels (unloading), and; (3) a subsequent later period of resistance exercise
induced muscle hypertrophy (reloading). is allowed us to assess the epigenetic regulation of skeletal muscle; (a)
hypertrophy, (b) a return of muscle back to baseline and, (c) memory of previous encounters with hypertrophy,
respectively. Importantly, these investigations for the rst time identied an increased frequency of hypomethyla-
tion across the genome during later reloading where lean muscle mass increases were enhanced compared to earlier
loading. We also detected genes; AXIN1, GRIK 2, CAMK4 and TRAF1 displayed increasing DNA hypomethylation
together with enhanced gene expression across loading, unloading and reloading. Where hypomethylation of these
genes was maintained even during unloading where muscle mass returned back to baseline, indicating an epigenetic
memory of earlier muscle growth. Furthermore, UBR5, RPL35a, HEG1 and PLA2G16 previously unstudied in skel-
etal muscle, together with SETD3 displayed hypomethylation and enhanced gene expression following loading ver-
sus baseline and displayed even larger increases in both hypomethylation and gene expression aer later reloading,
also indicating an epigenetically regulated memory leading to enhanced gene expression during reloading. Gene
expression of this cluster also strongly and positively correlated with increased muscle mass across all conditions,
conrming these transcripts to be novelresistance exercise induced- hypertrophy genes in skeletal muscle. Finally,
we identied genes GRIK2, TRAF1 (identied above), BICC1 and STAG1 were hypomethylated aer a single bout
of acute resistance exercise that were maintained as hypomethylated, and had enhanced gene expression aer later
reloading. Suggesting that these are epigenetically sensitive genes aer a single bout of resistance exercise and asso-
ciated with enhanced muscle hypertrophy 22 weeks later.
Methods
Human Participants and Ethical Approval. Eight healthy males gave written, informed consent to par-
ticipate in the study, following successful completion of a readiness to exercise questionnaire and a pre-biopsy
screening as approved by a physician. One participant withdrew from the study at experimental week 17 of 21,
for reasons unrelated to this investigation. However, consent allowed samples to be analysed prior to withdrawal,
therefore for this participant, this included all conditions excluding the nal reloading condition (for details see
below). Ethical approval was granted by the NHS West Midlands Black Country, UK, Research Ethics Committee
(NREC approval no. 16/WM/0103), all methods were performed in accordance with the relevant ethical guide-
lines and regulations.
Experimental Design. Using a within subject design, eight previously untrained male participants
(27.6 ± 2.4 yr, 82.5 ± 6.0 kg, 178.1 ± 2.8 cm, means ± SEM) completed an acute bout of resistance exercise (acute RE),
followed by 7 weeks (3d/week) of resistance exercise (loading), 7 weeks of exercise cessation (unloading) and a
further period of 7 weeks (3d/week) resistance exercise (re-loading). Graphical representation of experimental
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SCIENTIfIC REPORtS | (2018) 8:1898 | DOI:10.1038/s41598-018-20287-3
design is provided in Fig.1A. Whole-body fan beam dual-energy x-ray absorptiometry (DEXA), strength of the
quadriceps via dynamometry and muscle biopsies from the vastus lateralis for RNA and DNA isolation were
obtained at baseline, aer 7 weeks loading (beginning of week 8), 7 weeks unloading (end of week 14) and 7 weeks
reloading (beginning of week 22). A muscle biopsy was also obtained 30 minutes aer acute RE prior to 7 weeks
loading. Genome-wide analysis of DNA methylation was performed via Illumina EPIC array (850,000 CpG sites-
detailed below) for participants across all conditions (n = 8 baseline, acute, loading, unloading, n = 7 reloading).
Rt-qRT-PCR was used to investigate corresponding transcript expression of epigenetically altered genes identied
via the genome wide DNA methylation analysis.
Resistance exercise induced muscle hypertrophy: Loading, unloading and reloading.
Untrained male subjects initially performed an exercise familiarization week, in which participants performed
all exercises with no/low load to become familiar with the exercise type (detailed below). In the nal session of
the familiarzation week, the load that participants could perform 4 sets of 8–10 repetitions for each exercise was
assessed. Due to participants being uncustomized to resistance exercise, assessment was made on competence of
liing technique, range of exercise motion and verbal feedback (participant), and a starting load was set for each
participant on an individual basis (mean load for this starting load is included below). ree to four days later,
participants then undertook a single bout of lower limb resistance exercise (acute RE, exercises detailed below)
followed by biopsies 30 minutes post exercise. Following this single bout of acute RE they then began a chronic
resistance exercise program, completing 60-min training sessions (Monday-Wednesday-Friday), for 7 weeks,
Figure 1. (A) Schematic representation of experimental conditions and types of analysis undertaken across
the time-course. e image of a muscle represents the time point for analysis of muscle mass via (i) DEXA and
strength via (ii) isometric quadriceps muscle torque using an isokinetic dynamometer. e images of muscle
tissuealso represent the time point of skeletal muscle biopsy of the Vastus Lateralis, muscle sample preparation
for downstream analysis of (iii) Innium MethylationEPIC BeadChip arrays (850 K CpG sites) methylome wide
array (iv) and rt-qRT-PCR for gene expression analysis of important genes identied following methylome wide
analysis. (B) Weekly total volume of resistance exercise undertaken by human participants (n = 7) during the
rst 7-week resistance exercise period (loading, weeks 1–7), followed by a 7 week cessation of resistance exercise
(unloading, weeks 8–14) and the later second period of 7 weeks resistance exercise (reloading, weeks 15–21).
Data represents volume load as calculated by ((load (Kg) x reps) x sets)) averaged across 3 resistance exercise
sessions per week. Data presented mean ± SEM. (Ci) Lean lower limbmass changes in human subjects (n = 7)
aer a period of 7 weeks resistance exercise (loading), exercise cessation (unloading) and a subsequent second
period of 7 weeks resistance exercise (reloading). Total limb lean mass normalised to baseline (percentage
change). Signicant change compared to baseline represented by * and signicant dierence to all other
conditions represented by ** (Cii) Total lean mass percentage change when loading is normalised to baseline,
and reloading normalised to unloading to account for starting lean mass in both conditions. Pairwise t-test of
signicance indicated by *. All data presented as mean ± SEM (n = 7).
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SCIENTIfIC REPORtS | (2018) 8:1898 | DOI:10.1038/s41598-018-20287-3
with 2 sessions/week focusing on lower limb muscle groups (Monday and Friday) and the third session focusing
on upper body muscle groups (Wednesday). Lower limb exercises included, behind head squat, leg press, leg
extension, leg curl, Nordic curls, weighted lunges and calf raises. Upper limb exercises included, at barbell bench
press, shoulder press, latissimus pull down, dumbbell row and triceps cable extension. To ensure progression in
participants with no previous experience in resistance exercise, a progressive volume model was adopted20 in
which investigators regularly assessed competency of sets, reps and load of all exercises. Briey, exercises were
performed for 4 sets of 10 reps in each set, ~90–120 s in between sets and ~3 mins between exercises. When par-
ticipants could perform 3 sets of 10 repetitions without assistance and with the correct range of motion, load was
increased by ~5–10% in the subsequent set and participants continued on this new load until further modica-
tions wererequired, as similar to that previously described20. Where subjects failed to complete 10 full repetitions
(usually for their nal sets), they were instructed to reduce the load in order to complete a full repetition range
for the subsequent (usually nal)set. Total weekly volume load was calculated as the sum of all exercise loads;
..=∗Totalexercisevolumekgs Exercise load kgsNoofRepsNoSets()(())
e acute resistance exercise session resulted in a total load of 8,223 kg (±284kg). ereaer, the load-
ing and reloading phases resulted in a progressive increase in training volume (±SEM) of 2,257 ± 639 kg and
2,386 ± 222 kg respectively per week (Fig.1B), with the reloading phase displaying a signicant (P = 0.043)
increase in average load. Loading and reloading programs were conducted in an identical manner, with the same
exercises, program layout (same exercises on thesame day), sets and repetition pattern and rest between sets and
exercises. During the 7 week unloading phase, participants were instructed to return to habitual pre-intervention
exercise levels and not to perform any resistance training. Regular verbal communication between researcher and
participant ensured subjects followed these instructions. A trainer was present at all resistance exercise sessions
to enable continued monitoring, provide verbal encouragement and to ensure sucient progression. No injuries
were sustained throughout the exercise intervention.
Lean mass and strength of the lower limbs by dual-energy x-ray absorptiometry (DEXA) and
dynamometry. A whole-body fan beam dual-energy x-ray absorptiometry (DEXA; Hologic QDR Series,
Discovery A, Bedford, MA, USA) scan was performed after loading, unloading and reloading (depicted in
Fig.1A) to assess lower limb changes in lean mass. All scans were performed and analysed (QDR for Windows,
version 12:4:3) by the same trained operator, according to Hologic guidelines. e DEXA scan was automatically
analysed via the QDR soware and the operator conrmed areas of interest including lower limb positions. Lean
mass was calculated on absolute values for each condition, and presented as percentage change compared to
baseline. Furthermore, in addition, a separate analysis was undertakento assess whether later reloading altered
lean mass,where loading was normalised to baseline, and reloading was normalised to unloading to account for
anyresidual starting mass(even if non-signicant)following the earlier loading period. A pairwise t-test was
then used to analyse the percentage increase in lean mass as a consequence of reloading compared to loading.
To assess quadriceps muscle strength, in-vivo isometric knee extension maximal voluntary contractions (MVC)
were performed using an isokinetic dynamometer (IKD; Biodex, New York, USA) to measure peak joint torque.
Data presented as percentage increase to baseline (%) using absolute values (Nm), unless otherwise stated. A full
description of strength assessment can be found in Supplementary File1.
Muscle Biopsies and Sample Preparation. At baseline, 30 minutes post acute resistance exercise (RE)
and aer 7 weeks loading (beginning of week 8), 7 weeks unloading (end of week 14) and 7 weeks reloading
(beginning of week 22) (Fig.1A), a conchotome muscle biopsy was obtained from the vastus lateralis muscle of
the quadriceps from each participant, avoiding areas of immediate proximity to previous incisions, before being
carefully cleaned and dissected using a sterile scalpel on a sterile petri dish. In the unlikely event of any brous/fat
tissue, this was removed using a scapel, leaving only lean tissue. Separate samples were immediately snap frozen
in liquid nitrogen before being stored at 80 °C for RNA and DNA analysis.
DNA Isolation, Bisulte Conversion and Methylome Wide BeadChip Arrays. DNA was extracted
from frozen tissue samples using a commercially available DNA isolation kit (DNeasy Blood and Tissue Kit,
Qiagen, Manchester, UK) in accordance with manufacturer’s instructions, before being analysed (Nanodrop,
ThermoFisher Scientific, Paisley, UK) for yield (mean ± SDEV8.0 µg ± 4.2) and quality (260/280 ratio of
mean ± SDEV1.88 ± 0.09). Five-hundred ng of prepared DNA was bisulte converted using the EZ-96 DNA
Methylation Kit (Zymo Research Corp., CA, USA) following the manufacturer’s instructions for use of the DNA
in Illumina assays. Innium MethylationEPIC BeadChip array examined over 850,000 CpG sites of the human
epigenome (Innium MethylationEPIC BeadChip, Illumina, California, United States) and data was analysed
in Partek Genomics Suite V.6.6 (Partek Inc. Missouri, USA). Raw data les (.IDAT) were normalised via the
Subset-Quantile Within Array Normalisation (SWAN) method, as previously described21. Initial quality control
steps were undertaken to detect samples withinarrays that were identied as outliers. Principal component anal-
ysis (PCA) and normalisation histograms detected two observable outliers across all samples. ese samples were
removed from any further analysis (Supplementary Figure1A & B). While skeletal muscle tissue samples may
contain a small proportion of other non-muscle cells this analysis suggests sample homogeneity was consistent
in the experimental groups and therefore downstream analysis was representative of skeletal muscle tissue and
its niche. Data sets represent SWAN-normalised beta (β)-values which correspond to the percentage of methyl-
ation at each site and are calculated as a ratio of methylated to unmethylated probes22. Dierential methylation
was subsequently detected across all experimental conditions, and between conditions to identify statistically
dierentially regulated CpG sites. Fold change in CpG specic DNA methylation and statistical signicance
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SCIENTIfIC REPORtS | (2018) 8:1898 | DOI:10.1038/s41598-018-20287-3
was performed using Partek Genomic Suite V.6.6 soware, where statistical signicance was obtained following
ANOVA (with bonferroni correction) analysis.
Hierarchical Clustering Dendogram. Unadjusted p-value signicance (P < 0.05) was used to create a
CpG site marker list of standardized beta-values. A standardized expression normalisation was performed to
shi CpG sites to mean of zero and scale to a standard deviation of one. Unsupervised hierarchical clustering
was performed and dendograms wereconstructed to represent dierentially methylated CpG loci and statistical
clustering of experimental samples. Heatmaps represent expression of CpG loci, where reduced methylation at
DNA sites (hypomethylated) are represented in green, increased methylation at DNA sites (hypermethylated) in
red, and unchanged sites are represented in black.
Tissue Homogenisation, RNA Isolation and rt-qRT-PCR. Skeletal muscle tissue (~30 mg) was
immersed in Tri-Reagent (Sigma-Aldrich, MO, United States) in MagNA Lyser 1.4 mm beaded tubes (MagNA
Lyser Green Beads, Roche, Germany) and homogenised for 40 secs at 6 m/s in a MagNA Lyser Homogeniser
(Roche, Germany), before being stored on ice for 5 mins. is step was repeated three times to ensure complete
disruption of muscle tissue sample. RNA was extracted using standard Tri-Reagent procedure via chloroform/iso-
propanol extractions and 75% ethanol washing as per manufacturer’s instructions. RNA pellets were resuspended
in 30 μl of RNA storage solution (Ambion, Paisley, UK) and analysed (Nanodrop, ermoFisher Scientic, Paisley,
UK) for quantity (mean ±SDEV ; 6671 ± 3986 ng) andan indication of quality (260/280 ratio of mean ± SDEV,
1.95 ± 0.09). For rt-qRT-PCR using QuantiFastTM SYBR® Green RT-PCR one-step kit on a Rotorgene 3000Q,
reactions were setup as follows; 9.5 μl experimental sample (5.26 ng/μl totaling 50 ng per reaction), 0.15 μl of both
forward and reverse primer of the gene of interest (100 μM), 0.2 μl of QuantiFast RT Mix (Qiagen, Manchester,
UK) and 10 μl of QuantiFast SYBR Green RT-PCR Master Mix (Qiagen, Manchester, UK). Reverse transcrip-
tionwas initiated with a hold at 50 °C for 10 minutes (cDNA synthesis), followed by a 5-minute hold at 95 °C
(transcriptase inactivation and initial denaturation), before 40–45 PCR cycles of; 95 °C for 10 sec (denaturation)
followed by 60 °C for 30 secs (annealing and extension). Primer sequences are provided in Supplementary File7.
Gene expression analysis was performed on at least n = 7 for all genes, unless otherwise stated. All relative gene
expression was quantied using the comparative Ct (∆∆Ct) method. Individual participants own baseline Ct val-
ues were used in ∆∆Ct equation as the calibrator using RPL13a as the reference gene. e average Ct value for the
reference gene was consistent across all participants and experimental conditions (20.48 ± 0.64, SDEV) with low
variation of 3.17%.
Statistical Analysis. Analysis of exercise volume load was performed via a T-test (MiniTab Version 17.2.1)
of average participant load during the loading vs. reloading phases. DEXA and isometric peak torque; for
n = 7, as well as correlation analysis was analysed via a statistical package for the social sciences soware for
Microso (SPSS, version 23.0, SPSS Inc, Chicago, IL) using a one-way repeated measures ANOVA, where appli-
cable. Pearson correlation of coecient analysis (two tailed) was conducted for gene expression vs. percentage
change of leg lean mass. Methylome wide array data sets (n = 8 for baseline, acute RE, loading, unloading, n = 7
for reloading) were analysed for signicant epigenetically modied CpG sites in Partek Genome Suite (ver-
sion 6.6). All gene ontology and KEGG signalling pathway2325 analysis was performed in Partek Genomic Suite
and Partek Pathway, on generated CpG lists of statistical signicance (P < 0.05) across conditions (ANOVA) or
pairwise comparisonsbetween conditions. In MiniTab Statistical Soware (MiniTab Version 17.2.1) follow up
rt-qRT-PCR gene expression was analysed using both a MANOVA, to detect for signicant interactions across
time for identied clusters of genes, and an ANOVA for follow up of individual genes over time. A pairwise
t-test was used to analyse gene expression following acute RE vs. baseline. For follow up fold change in CpG
DNA methylation analysis was performedvia ANOVA in MiniTab Statistical Soware (MiniTab Version 17.2.1).
Statistical values were considered signicant at the level of P 0.05. All data represented as mean ± SEM unless
otherwise stated.
Results
Lean leg muscle mass is increased after loading, returns toward baseline during unloading and
is further increased after reloading. Analysis of lower limb lean mass via DEXA, identied a signicant
increase of 6.5% ( ± 1.0%; P = 0.013) in lean mass aer 7-wks of chronic loading compared to baseline (20.74 ± 1.11 kg
loading vs. 19.47 ± 1.01 kg baseline). Following 7-wks of unloading, lean mass signicantly reduced by 4.6% ± 0.6%
(P = 0.02) vs. the 7 weeks loading, back towards baseline levels (unloading, 19.83 ± 1.06 kg), conrmed by no signif-
icant dierence between unloading and baseline. Subsequently, a signicant increase in lean mass of the lower limbs
was accrued aer the reloading phase of 12.4 ± 1.3%, compared to baseline (reloading, 21.85 ± 2.78 kg, P = 0.001,
Fig.1Ci), resulting in an increase of 5.9 ± 1.0% compared to the earlier period of loading (P = 0.005). Pairwise t-test
analysis that corrected for any lean mass that was maintained during unloading demonstrated a signicant increase
in lean muscle mass in the reloading phase (unloading to reloading), compared to the loading phase (baseline to
loading) (P = 0.022; Fig.1Cii). Analysis of muscle strength suggested a similar trend. Isometric peak torque increased
by 9.3 ± 3.5% from 296.2 ± 22.1 Nm at baseline to 324.5 ± 27.3 Nm aer 7-wks of loading, this dierence was not
statistically signicant (Supplementary Figure2). Upon 7-wks of unloading, peak torque reduced by 8.3 ± 2.8% vs.
loading, back towards baseline levels. Upon subsequent reloading, a signicant 18 ± 3.6% increase in isometric peak
torque production (349.6 ± 27.7 Nm) was observed compared to baseline (P = 0.015; Supplementary Figure2A).
The largest DNA hypomethylation across the genome occurred following reloading. The
frequency of statistically (P < 0.05) differentially regulated CpGs in each condition was analysed (Fig.2A;
Supplementary File2B). 17,365 CpG sites were signicantly (P < 0.05) dierentially epigenetically modied fol-
lowing loading induced hypertrophy compared to baseline, with a larger number being hypomethylated (9,153)
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compared to hypermethylated (8,212) (Fig.2A; Supplementary File2A & B). e frequency of hypomethylated
epigenetic modications was similar to loading aer unloading (8,891) (Fig.2A; Supplementary File2A & C),
where we reported lean muscle mass returned back towards baseline. Importantly, following reloading induced
muscle growth we observed an increase in the number of epigenetically modied sites (27,155) and an enhanced
number of hypomethylated DNA sites (18,816, Fig.2A; Supplementary File2A & D). is increase in hypometh-
ylation coincided with the largest increase in skeletal muscle mass in reloading. By contrast, hypermethylation
remained stable (8,339) versus unloading (8,638) and initial loading (8,212). To further analyse the reported
increased frequency of hypomethylated genes across the genome following reloading, gene ontologies were ana-
lysed for the frequency of hypo and hypermethylated CpG sites. In agreement with our above frequency analysis,
the most statistically signicant enriched GO terms identied an increased number of hypomethylated CpG sites
compared to baseline (Fig.2Bi–iii). Indeed, the most statistically signicantly (FDR < 0.05) enriched GO terms
were: 1) molecular function GO:0005488 encoding for genes related to ‘binding’, that displayed 9,577 (68.71%)
CpG sites that were hypomethylated following reloading and 4,361 (31.29%) sites as hypermethylated compared
to baseline (Fig.2Bi), and: 2) Biological process GO:0044699 encoding for genes related to ‘single-organism pro-
cesses’ that displayed 7,586 (68.57%) hypomethylated CpG sites compared to 3,493 (31.43%) sites proled as
hypermethylated aer reloading compared to baseline (Fig.2Bii). Finally, 3) cellular component, GO:004326
encoding for genes related to ‘organelle’ reported 7,301 hypomethylated CpG sites following reloading and 3,311
hypermethylated sites, compared to baseline, therefore favouring a majority 68.88% hypomethylated prole
(Fig.2Biii).
Following conrmation that the largest alteration in CpG DNA methylation occurred upon laterreload-
ingevoked hypertrophy, we sought to elucidate how the serine/threonine AKT signaling pathway, a critical path-
way involved in mammalian growth, proliferation and protein synthesis26,27, was dierentially regulated across
experimental conditions (Fig.3, Supplementary Figure3A and B). Intuitively, we report that the PI3K/AKT path-
way was signicantly enriched upon all pairwise comparisons of baseline vs. loading, unloading and reloading,
respectively (P < 0.022; Supplementary Figure3A,B and Fig.3A), suggesting that the pathway was signicantly
epigenetically modied following periods of skeletal muscle perturbation. Importantly, frequency analysis of
statistically dierentially regulated transcripts (Fig.3B) attributed to this pathway, reported an enhanced number
of dierentially regulated CpG sites (444 CpG sites) following reloading (Fig.3A), compared to loading ( 264 CpG
sites; Supplementary Figure3A) and unloading (283 CpG sites; Supplementary Figure3B) alone. In accordance
with our previous ndings, the enhanced number of statistically dierentially regulated CpG sites in this pathway
upon reloading is attributed to an enhanced number of hypomethylated (299 CpG sites, 67.3%) compared to
hypermethylated (145 sites, 32.7%) CpG sites (Raw data: Supplementary File3).
Figure 2. (A) Innium MethylationEPIC BeadChip arrays (850 K CpG sites) identied an enhanced frequency
of hypomethylated CpG sites upon reloading (n = 7). (B) Gene ontology analysis using forest plot schematics
conrmed an enhanced hypomethylated prole aer reloading across various (i) molecular function, (ii)
biological processes and (iii) cellular components. Functional groups with a fold enrichment >3 (as indicated
via shaded blue region) represents statistically ‘over expressed’ (in this case epigenetically modied) KEGG
pathways FDR < 0.05 (n = 8).
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Genome-wide DNA methylation analysis identied two clusters of temporal DNA methylation
patterns that provide initial evidence of an epigenetic memory. Changes in genome-wide DNA meth-
ylation were analysed following loading, unloading and reloading induced muscle adaptation. A dendogram of
the top 500 most statistically epigenetically modied CpG sites across each experimental condition compared to
baseline, identied large alterations in DNA methylation proles (Fig.4A; Supplementary File4A). A ranked unsu-
pervised hierarchical clustering analysis demonstrated signicant dierences between the initial loading (weeks
1–7) vs. all other conditions (Fig.4A). Closer analysis of the top 500 CpG sites across experimental conditions high-
lighted a clear temporal trend occurring within dierent gene clusters. e rst cluster (named Cluster, A) displayed
enhanced hypomethylation with earlier loading-induced hypertrophy. is cluster was methylated at baseline and
became hypomethylated aer loading, re-methylated with unloading (Fig.4A) and hypomethylated aer reloading.
e second temporal trend (named Cluster B) also displayed an enhanced hypomethylated state across the top
500 CpG sites as a result of load induced hypertrophy. As with Cluster A, Cluster B genes were methylated at baseline
and became hypomethylated aer initial loading. In contrast to Cluster A, Cluster B remained hypomethylated with
unloading, even when muscle returned to baseline levels, and this hypomethylation was also maintained/‘remem-
bered’ aer reload induced hypertrophy (Cluster B, depicted Fig.4A). e third temporal trend, named Cluster C,
revealed genes as hypomethylated at both baseline and aer initial loading, suggesting no epigenetic modication
aer the rst period of hypertrophy in these genes (Cluster C, Fig.4A). During unloading, genes were hypermeth-
ylated and remained in this state during reloading. e nal cluster (Cluster D) of genes, were hypomethylated at
baseline, became hypermethylated aer loading (Cluster D, Fig.4A), reverted back to a hypomethylated state with
Figure 3. (A) Representation of the DNA methylation modications that occurred within the PI3K/AKT
KEGG pathway following 7 weeks of reloading in human subjects. Signalling analysis performed on statistically
dierentially regulated CpG sites compared to baseline, with green indicating a hypomethylated fold change and
red indicating a hypermethylated change, with strength of colour representing the intensity of fold change2325.
Figure3b. Venn diagram analysis of the statistically dierentially regulated CpG sites attributed to the PI3K/
AKT pathway following loading, unloading and reloading, compared to be baseline. Ellipsis reports number of
commonly statistically dierentially regulated CpG sites across each condition. Analysis conrms an enhanced
number of dierentially regulated CpG sites upon reloading condition.
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unloading and then maintained the hypomethylated state aer reloading, reecting the prole of the baseline targets
in the same cluster (Cluster D, Fig.4A). ese two clusters (C&D) did report a maintenance of the DNA methylation
prole from unloading to reloading conditions. Cluster C also reported a hypermethylated prole aer unloading
following a period of loading, that may therefore identify important CpG sites that are hypermethylated when mus-
cle mass is reduced (we therefore include a full list from cluster C that includes the CpG sites signicantly modied
in loading vs. unloading, Supplementary File4G). However, both Cluster C&D suggest no retention of epigenetic
modications from the rst loading period to the later reloading phase.
Identification of gene expression clusters inversely associated with DNA methylation. To
assess whether the changes in DNA methylation aected gene expression, the 100 most signicantly dierentially
modied CpG sites across all conditions were identied and cross referenced with the most frequently occurring
(Supplementary File4B) CpG modications in pairwise comparisons of all conditions (Supplementary File4C to H).
is identied 48 genes that were then analysed by rt-qRT-PCR to assess gene expression. Forty-six percent of the
Figure 4. (A) Heat map depicting unsupervised hierarchical clustering of the top 500 statistically dierentially
regulated CpG loci (columns) and conditions (baseline, loading, unloading and reloading) in previously
untrained male participants (n = 8). e heat-map colours correspond to standardised expression normalised
β-values, with green representinghypomethylation, red hypermethylation and unchanged sites are represented
in black. (4B and C) Relative gene expression (i) and schematic representation of CpG DNA methylation and
gene expression relationship (ii) in two identied gene clusters from genome wide methylation analysis aer
a period of 7 weeks resistance exercise (loading), exercise cessation (unloading) and a subsequent secondary
period of 7 weeks resistance exercise (reloading). (Bi) Expression of genes that displayed a signicant increase
compared to baseline (represented by *) upon earlier loading, that returned to baseline during unloading, and
displayed enhanced expression aer reloading (signicantly dierent to all other conditions **). MANOVA
analysis reported a signicant eect over theentire time course of the experiment (P < 0.0001). (Bii)
Representative schematic displaying the inverse relationship between mean gene expression (solid black lines)
and CpG DNA methylation (dashed black lines) of grouped transcripts (RPL35a, C12orf50, BICC1, ZFP2,
UBR5, HEG1, PLA2G16, SETD3 and ODF2). Data represented as fold change for DNAmethylation (le y axis)
and gene/mRNA expression (right y axis). (Ci) Clustering of genes that portrayed an accumulative increase
in gene expression aer loading, unloading and reloading. With the largest increase in gene expression aer
reloading. Culminating in signicance in the unloading (baseline vs. unloading*), and reloading (reloading vs.
baseline**). (Cii) Representative schematic displaying the inverse relationship between mean gene expression
(solid black lines) and CpG DNA methylation (dashed black lines) of grouped transcripts (AXIN1, TRAF1,
GRIK2, CAMK4). Data represented as fold change for methylation (le y axis) and mRNA expression (right
y axis). All data represented as mean ± SEM for gene expression (n = 7 for UBR5, PLA2G16, AXIN1, GRIK2;
n = 8 for all others) and CpG DNA methylation (n = 8 for baseline, loading and unloading; n = 7 for reloading).
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top 100 CpG sites were within gene promotor regions with 18%residing in intergenic regions (Supplementary File5).
Interestingly, gene expression analysis identied two distinct clusters of genes that had dierent transcript proles.
is rst cluster included RPL35a, C12orf50, BICC1, ZFP2, UBR5, HEG1, PLA2G16, SETD3 and ODF2 genes that
displayed a signicant main eect for time (P < 0.0001) aer MANOVA analysis (Fig.4Bi). Chromosome loca-
tions, reference sequence numbers and generegion section details for these genes can be found in Supplementary
File5. Importantly, this rst cluster displayed a mirrored (inverse) temporal pattern to those identied previously in
Cluster A above for CpG methylation (in the top 500 dierentially regulated CpG sites, Fig.4A). Where, upon 7-wks
of loading, gene expression of this cluster signicantly increased (1.22 ± 0.09, P = 0.004) and CpG methylation of
the same genes was non-signicantly reduced (hypomethylated) (0.95 ± 0.04 Fig.4Bii). During unloading, meth-
ylation returned to baseline (1.03 ± 0.07), which was met by a return to baseline in gene expression (0.93 ± 0.05),
as indicated by both CpG methylation and gene expression displaying no signicant dierence compared to base-
line (Fig.4Bii). Importantly, upon reloading, both CpG methylation and gene expression displayed an enhanced
response compared to the baseline and loading time point, respectively. Indeed, upon reloading, this cluster became
hypomethylated (0.91 ± 0.03, P = 0.05, Fig.4Bii). is was met with a signicant enhancement (1.61 ± 0.06) in gene
expression of the same cluster compared to baseline and loading (P < 0.001, Fig.4Bii).
A second separate gene cluster was identied and included: AXIN1, GRIK2, CAMK4, TRAF1, NR2F6 and
RSU1. Although together there was no signicant eect of time via MANOVA analysis. ANOVA analysis reported
that this cluster displayed increased gene expression aer loading (1.19 ± 0.08) that then further increased during
unloading (1.58 ± 0.13) resulting in statistical signicance (P = 0.001) compared to baseline alone. Gene expression
was then even further enhanced (1.79 ± 0.09) upon reload induced hypertrophy (P < 0.0001; Fig.4Ci; Chromosome
locations, reference sequence numbers, region section details for this cluster of genes can be found in Supplementary
File5). In this cluster we identied an accumulative increase in gene expression, attaining signicance at unloading
condition (ANOVA; P = 0.001) compared to baseline, gene expression was subsequently further increased follow-
ing reloading conditions (ANOVA; P < 0.0001). is temporal gene expression pattern was inversely associated
to CpG methylation observed in Cluster B (identied previously in the top 500 dierentially regulated CpG sites,
Fig.4A). Closer fold-change analysis of CpG DNA methylation of this gene cluster, identied a distinct inverse
relationship with methylation and gene expression of 4 out of 6 of the targets (AXIN1, GRIK2, CAMK4, TRAF1).
Where, upon loading, these genes became signicantly hypomethylated (0.78 ± 0.09; P = 0.036) compared to base-
line, with this prole being maintained during unloading (0.84 ± 0.09) and reloading (0.83 ± 0.05) conditions, albeit
non-signicantly. Collectively, we report that a sustained hypomethylated state in 4 out of 6 of the genes in this
cluster that correspond to an increased transcript expression of the same genes (Fig.4Cii).
Identication of a number of novel genes at the expression level associated with skeletal mus-
cle hypertrophy. To ascertain the relationship between skeletal muscle hypertrophy and gene expression, fold
change in gene expression was plotted against percentage changes (to baseline) in leg lean mass. Interestingly, in
our rst cluster of genes identied above (RPL35a, C12orf50, BICC1, ZFP2, UBR5, HEG1, PLA2G16, SETD3 and
ODF2), a signicant correlation between gene expression and lean mass was observed for genes RPL35a, UBR5,
SETD3, PLA2G16 and HEG1 (Fig.5A & BI–V). Following exposure to 7-wks of load induced hypertrophy, RPL35a
gene expression displayed a non-signicant increase compared to baseline (1.13 ± 0.23; Fig.5AI), that upon unload-
ing returned back to the baseline levels (1.01 ± 0.21). Upon reloading, the expression of RPL35a increased to 1.70
( ± 0.44; Fig.5AI) compared to baseline (P = 0.05). is expression pattern across loading, unloading and reload-
ing conditions corresponded to a signicant correlation with percentage changes in skeletal muscle mass (R = 0.6,
P = 0.014; Fig.5BI), with RPL35a accounting for 36% of the variation in muscle across experimental conditions.
Both UBR5 and SETD3 displayed similar percentage accountability for the change in skeletal muscle mass across
conditions. Indeed, UBR5 and SETD3 accounted for 33.64% and 32.49% of the variability in skeletal muscle mass,
respectively, both portraying strong correlations between their gene expression and the percentage change in lean
leg mass (UBR5, R = 0.58, P = 0.018, Fig.5BII; SETD3, R = 0.57, P = 0.013, Fig.5BII, respectively). Additionally,
UBR5 (1.65 ± 0.4; Fig.5BII) and SETD3 (1.16 ± 0.2; Fig.5AIII) both demonstrated non-signicant increases in
gene expression aer 7-wks of loading (P > 0.05), with the expression of both genes, UBR5 (0.82 ± 0.27) and SETD3
(0.90 ± 0.15), returning to baseline levels upon 7-wks of unloading (Fig.5AII and AIII, respectively). Furthermore,
upon reloading UBR5 displayed its greatest increase in expression (1.84 ± 0.5; Fig.5AII), demonstrating a trend
for significance compared to baseline condition (P = 0.07), and a significant increase compared to unloading
(P = 0.035). Whereas, SETD3 demonstrated a fold increase of 1.48 ( ± 0.25; Fig.5AIII) approaching signicance
compared to baseline (P = 0.072) and achieving signicance compared to unloading (P = 0.036). PLA2G16 also
demonstrated a signicant correlation between its fold change in gene expression and the percentage change in
skeletal muscle mass (R = 0.55; P = 0.027; Fig.5BIV), with PLA2G16 accounting for 30.25% of the change in skeletal
muscle. Interestingly, across conditions, PLA2G16 demonstrated the greatest signicant changes in gene expres-
sion. Indeed, loading induced hypertrophy, PLA2G16 displayed a non-signicant increase compared to baseline
in expression (1.09 ± 0.17; Fig. 5AIV), that upon unloading returned back to the baseline levels (1.04 ± 0.25).
Importantly, upon reloading, the expression of PLA2G16 signicantly increased (1.60 ± 0.18; Fig.5AIV) compared
to baseline (P = 0.026) and unloading conditions (P = 0.046), as well as approaching a signicant increase compared
to the initial loading stimulus (P = 0.067 compared to load; Fig.5AIV). HEG 1 gene expression exhibited a signif-
icant correlation with skeletal muscle mass (R = 0.53, P = 0.05) with HEG 1 accounting for 28.09% of the changes
in muscle mass. However, HEG1 did not demonstrate any signicant fold changes in gene expression across the
experimental conditions. Furthermore, no signicant correlation was observed for the other identied cluster of
genes (AXIN1, GRIK2, CAMK4, TRAF1, NR2F6 and RSU1; P > 0.05; Data not shown). Collectively, these data
suggest that RPL35a, UBR5, SETD3 and PLA2G16 all display a signicantly enhanced gene expression upon reload-
ing induced hypertrophy. is suggests, that these genes portray a memory of earlier load induced hypertrophy, by
displaying the largest fold increases in gene expression aer reload induced growth.
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The E3 Ubiquitin Ligase, UBR5, has enhanced hypomethylation and the largest increase in
gene expression during reloading. e HECT E3 ubiquitin ligase gene UBR5 (Fig.6), for which the CpG
identied is located on chromosome 8 (start 103424372) in the promoter region 546 bp from the transcription
start site, was identied as being within the top 100 most statistically dierentially regulated CpG sites across all
pair-wise conditions (loading, unloading and reloading; Fig.6); but also the transcript that displayed the most
distinctive mirrored-inverse relationship with gene expression (Fig.5CII), aer every condition. Following the
initial period of 7-weeks of load induced hypertrophy, there was a non-signicant increase in UBR5 gene expres-
sion (1.65 ± 0.4) versus baseline, which was met with a concomitant (albeit non-signicant) reduction in CpG
DNA methylation (0.87 ± 0.03). Gene expression returned to baseline control levels aer unloading (0.82 ± 0.27)
demonstrated by a signicant reduction vs. loading (P = 0.05) and non-signicance versus baseline (P = N.S;
Fig.5CII). Aer the same unloading condition, we observed a signicant increase in CpG DNA methylation
compared to baseline (1.27 ± 0.02; P = 0.013; Fig.5CII). Importantly, upon reloading, UBR5 displayed its larg-
est increase in transcript expression, signicantly greater compared to unloading (1.84 ± 0.5 vs. 0.82 ± 0.27,
P = 0.035) and versus baseline levels to the level of P = 0.07. Concomitantly, aer the reloading condition, we
observed the largest statistically signicant reduction in CpG DNA methylation (0.78 ± 0.02) compared to base-
line (P = 0.039), and unloading (P 0.05; Fig .5CII).
Dynamic changes in DNA methylation after a single acute bout of resistance exercise precede
changes in gene expression after loading and reloading. We next wished to ascertain how dynamic and
transient DNA methylation of the identied genes were, aer a single acute bout of resistance exercise (acute RE).
Figure 5. Relative fold changes in: (A) gene expression; (B) correlation between gene expression and lower
limb leanmass across experimental conditions, and; (C) schematic representation of relationship between fold
changes in CpG DNA methylation (dashed black line; le y axis) and fold change in gene/mRNA expression
(solid black line; right y axis) for identied genes: RPL35a (I), UBR5 (II), SETD3 (III), PLA2G16 (IV) and
HEG1 (V). Statistical signicance compared to baseline and unloading represented by* and** respectively.
All signicance taken as p less than or equal to 0.05 unless otherwise state on graph. All data presented as
mean ± SEM (n = 7/8).
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We wanted to identify methylation sensitive genes (to single acute resistanceloading stimuli) that were still
aected at the DNA methylation and gene expression levels aer laterchronic load and reload induced hyper-
trophyconditions. We identied that acute loading evoked a greater hypomethylation compared to hypermeth-
ylation response of the human methylome (10,284 hypomethylated sites vs. 7,600 hypermethylated DNA sites;
Fig.7A) with hierarchical clustering analyses displaying distinct dierences between statistically signicant CpG
sites at baseline and acute RE conditions (P < 0.05; total of 17884 CpG sites, Fig.7A). is occurred with a sim-
ilar frequency versus loading where we previously reported 9,153 hypomethylated vs. 8,212 hypermethylated
(8,212) CpG sites (Fig.2A). Overlapping the top 100 signicantly dierentially identied targets from the loading,
unloading and reloading analysis (Supplementary File 4A) together with the 17,884 sites from acute stimulus
analysis (Supplementary File6), identied 27 CpG targets that were signicantly dierentially regulated across
comparisons (Fig.7B). We subsequently removed9 CpG sites that did not map to gene transcripts andwere there-
fore unable to analysefor corresponding gene expression. We identied that the fold change in DNA methylation
pattern of the remaining 18 CpG sites was virtually identical across these conditions (Fig.7C), displaying a signif-
icant correlation across acute RE to loading and reloading conditions (R = 0.94, P < 0.0001; Fig.7D), with follow
up broader hierarchical clustering analysis of the top 500 genes signicantly modied within these conditions
(Fig.7E) also conrming that the majority of sites in were hypomethylated. Suggesting that even aer a single
bout of acuteresistance exercise that the DNA methylation remained the sameaer later load and reload induced
hypertrophy. Interestingly, we identied 4 of the 18 CpG sites identied above (BICC1, GRIK2, ODF2, TRAF1)
that were also identied in our earlier analyses of loading, unloading and reloading conditions (Figs.7A and B).
is suggested that these genes were immediately altered following acute RE, and hypomethylation was retained
Figure 6. Representation and characterisation of the DNA methylation modications that occurred withinthe
ubiquitin mediated proteolysis pathway across all conditions of loading, unloading and reloading compared to
baseline (ANOVA). Signalling analysis performed on statistically dierentially regulated CpG sites compared
to baseline, with green indicating a hypomethylated fold change and red indicating a hypermethylated change,
with strength of colour representing the intensity of fold change2325. Importantly, the novel HECT-type E3
ubiquitin ligase, UBR5, displays a signicantly hypomethylated state within this pathway.
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during chronic loading, unloading and subsequent reloading conditions. Finally, we analysed fold changes in
gene expression of a sub set of the 18 CpG sites identied as overlapping in both sets of methylome analysis exper-
iments (Supplementary Figure4a) and compared changes in gene expression to changes in CpG DNA methyla-
tion (Supplementary Figure4B). We identied that signicant hypomethylation upon acute resistance exercise
(Figure7Fi–vii) was not associated with signicant changes in gene expression (Figure7Fi–vii) in a sub-set of
analysed transcripts. However, upon continued loading (chronic loading and reloading conditions), changes in
CpG DNA methylation were associated with signicant changes in a number of these genes uponthe reloading
stimulus (Figure7Fi–vii).Suggesting that these newly identied epigenetically regulated genes (BICC1, GRIK2,
TRAF1 and STAG1) were acutely sensitive to hypomethylation aer a single bout of resistance exercise, that
enhanced gene expression 22 weeks aer a period of load induced hypertrophy, a return of muscle to baseline
and later reloading induced hypertrophy. erefore, the epigenetic regulation of these genes seems to be an early,
acute exercise biomarker of later muscle hypertrophy.
Discussion
Frequency of genome-wide hypomethylation is the largest after reloading induced hypertrophy
where lean muscle mass is enhanced. We aimed to investigate an epigenetic memory of earlier hyper-
trophy in adult human skeletal muscle using a within measures design, by undertaking: (1) resistance exercise
induced muscle growth (loading), followed by; (2) cessation of resistance exercise, to return muscle back towards
baseline levels (unloading), and; (3) a subsequent later period of resistance exercise induced muscle hypertrophy
(reloading). We rst conrmed that we were able to elicit an increase in lean mass of the lower limbs aer 7 weeks
loading, that returned back to baseline levels aer 7 weeks unloading, with 7 weeks reloading evoking the largest
Figure 7. Response of the methylome aer acute resistance loading stimulus compared to baseline, 7 weeks
loading and 7 weeks reloading: (A) Heat map depicting unsupervised hierarchical clustering of statistically
dierentially regulated (P = 0.05) CpG loci following exposure to acute RE compared to baseline; (B) a Venn
diagram depicting the number of CpG sites that were signicantly dierentially regulated in both methylome
analysis experiments (base, loading, unloading and reloading, blue circle; baseline and acute resistance
stimulus, red circle), and the amount of genes analysed for gene expression across acute, 7 weeks loading and 7
weeks reloading, respectively; (C) temporal pattern of fold change in DNA CpG methylation of the identied
overlapping CpG sites that mapped to relevant gene transcripts; (D) correlation of CpG DNA methylation of
acute RE vs. 7 weeks loading and reloading conditions, and(E)Heat map depicting unsupervised hierarchical
clustering of statistically dierentially regulated (P = 0.05) CpG loci following exposure to acute RE compared to
baseline,loading andreloading (F) representative schematic displaying the inverse relationship between mean
gene expression (solid black lines) and CpG DNA methylation (dashed black lines) of identied transcripts.
Signicance indicated in gene expression (*) and in CpG DNA methylation (§) when compared to baseline.
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SCIENTIfIC REPORtS | (2018) 8:1898 | DOI:10.1038/s41598-018-20287-3
increase in lean mass. Interestingly, aer DNA methylation analysis of over 850,000 CpG sites, we identied the
largest frequency of hypomethylation (18,816 CpG sites) occurred aer reloading where the largest lean mass
occurred. Previous studies have suggested that hypermethylation of over 6,500 genes are retained, aer an more
acute stress of high fat intake (for 5 days) 8 weeks later despite removal of the high fat diet14, and hypermethylation
occurs following early life inammatory stress in muscle cells and is maintained for over 30 cellular divisions11. e
present study also suggested hypomethylation was maintained during unloading (8,891 CpG sites) where muscle
mass returned to baseline having being subjected to an earlier period of load induced muscle growth (9,153 CpG
sites), then upon reloading the frequency of hypomethylation was enhanced in association with the largest
increases in lean mass. Furthermore, bioinformatic analysis of the PI3K/AKT pathway across loading, unloading
and reloading conditions, supports the ndings of an enhanced hypomethylated state upon secondary exposure to
resistance stimulus. Importantly, this pathway is identied as critical for cell proliferation/dierentiation, muscle
protein synthesis and therefore muscle hypertrophy27, and therefore, it is plausible that the enhanced hypometh-
ylated state of the genes in this pathways would lead to enhanced gene expression and protein levels. However,
further analysis is required to investigate the total protein or activity of these pathways in this model. Nonetheless,
collectively, these results provide initial evidence for a maintenance/memory of universal hypomethylation. e
only other study to demonstrate a memory of prior hypertrophy in skeletal muscle was in rodents following earlier
encounters with testosterone administration, where a retention of myonuclei occurred even during testosterone
withdrawal and a return of muscle to baseline levels13, suggesting a memory at the cellular level. However, these are
the rst studies to demonstrate that a memory occurs at the epigenetic level within skeletal muscle tissue.
Hypomethylation is maintained from earlier load induced hypertrophy even during unload-
ing where muscle mass returns back towards baseline and is inversely associated with gene
expression. Following the frequencyanalysis of hypo/hypermethylated sites mentionedabove, closer analysis
of the top 500 most signicantly dierentially modied CpG sites across all conditions, identied two epigenet-
ically modied clusters of interest (named Cluster A&B). Cluster B supported the frequency analysis above and
demonstrated hypomethylation aer load induced hypertrophy that was then maintained following unloading
where muscle returned to baseline levels and this hypomethylation was then also maintained aer reload induced
hypertrophy. is maintenance of hypomethylation during unloading, suggested that the muscle ‘remembered’
the epigenetic modications that occurred aer an earlier period of load induced muscle hypertrophy. As reduced
DNA methylation of genes generally leads to enhanced gene expression due to the removal of methylation allow-
ing improved access of the transcriptional machinery and RNA polymerase that enable transcription, and also
creating permissive euchromatin19,2830, this would be suggestive that the earlier period of hypertrophy leads
to increased gene expression of this cluster of genes that is then retained during unloading to enable enhanced
muscle growth in the later reloading period. To conrm this, in a separate analysis we identied the top 100 most
signicantly dierentially modied CpG sites across all conditions and cross referenced these with the most
frequently occurring CpG modications in all pairwise comparisons of experimental conditions. From this we
identied 48 genes that were frequently occurring in all pairwise comparisons and examined gene expression by
rt-qRT-PCR. Interestingly, we identied two clusters of genes with distinct temporal expression aer loading,
unloading and reloading. One of the clusters included AXIN1, GRIK2, CAMK4, TRAF1. Importantly, the major-
ity of these genes demonstrated a mirror/inverse relationship with DNA methylation of the CpG sites within the
same genes. Where DNA methylation reduced aer loading and remained low into unloading and reloading,
gene expression accumulated, demonstrating the highest expression aer reloading where the largest increase in
lean mass was also demonstrated. Overall, this suggested that these genes were hypomethylated and switched on
aer the earlier period of load induced hypertrophy, maintained during unloading due to methylation of these
genes remaining low, and then upon exposure to a later period of reload induced hypertrophy, these genes were
switched on to an even greater extent. Overall, this demonstrates that the methylation and collective responsive-
ness of these genes are important epigenetic regulators of skeletal muscle memory.
Interestingly, AXIN1 is a component of the beta-catenin destruction complex, where in skeletal muscle cells
AXIN1 has been shown to inhibit WNT/β-catenin signalling and enable differentiation31, where treatment
with the canonical WNT ligand suppresses dierentiation32. Other studies suggested that AXIN2 not AXIN1 is
increased aer dierentiation, however conrmed that the absence of AXIN1 reduced proliferation and myotube
formation32. erefore, together with the present data perhaps suggest an important epigenetic regulation of
AXIN1 involved in human skeletal muscle memory and hypertrophy at the tissue level, perhaps due to inhibition
of WNT/β-catenin signaling. GRIK2 (glutamate ionotropic receptor kainate type subunit 2, a.k.a. GluK2) belongs
to the kainate family of glutamate receptors, which are composed of four subunits and function as ligand-activated
ion channels33. Although reportedly expressed in skeletal muscle, its role in muscle growth or cellular function has
not been determined. CAMK4 is calcium/calmodulin-dependent protein kinase, that via phosphorylation, trig-
gers the CaMKK-CaMK4 signaling cascade and activates several transcription factors, such as MEF234. MEF2 has
been previously associated with a switch to slow bre types aer exercise35 and is hypomethylated aer 6 months
aerobic exercise36. While resistance exercise has been show to preferentially increase the size of type II faster bres,
chronic innervation even at higher loads can lead to an overall slowing in phenotype [reviewed in ref.37) and
therefore this epigenetically regulated gene, although not usallystudied during hypertrophy maybe important in
bre type changes in the present study. However, it is unknown how DNA methylation aects the protein levels
of CAMK4, and with its role in phosphorylation, would be important to ascertain in the future. Furthermore,
bre type properties were not analyzed in the present study and thereforerequire further investigation. TRAF1
is the TNF receptor-associated factor 1 and together with TRAF2 form the heterodimeric complex required for
TNF-α activation of MAPKs, JNK and NFκB38. In skeletal muscle, acute TNF exposure activates proliferation
via activation of MAPKs such as ERK and P38 MAPK3941. erefore, acutely elevated systemic TNF-α following
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SCIENTIfIC REPORtS | (2018) 8:1898 | DOI:10.1038/s41598-018-20287-3
damaging exercise such as resistance exercise correlates positively with satellite cell activation in-vivo aer dam-
aging exercise42,43, yet chronic administration in-vitro inhibits dierentiation, promotes myotube atrophy40,44 and
muscle wasting in-vivo44. Indeed, exposure to early life TNF-α during an early proliferative age in mouse C2C12s
results in maintenance of hypermethylation in the myoD promoter aer 30 divisions and an increased susceptibil-
ity to reduced dierentiation and myotube atrophy when muscle cells encounter TNF-α in later proliferative life11.
Suggesting a role for DNA methylation in retention of memory following earlier periods of high inammation.
Because resistance exercise evokes increases in TNF-α in the systemic circulation and has been shown increase
locally in muscle at the protein level (discussed above), these data collectively suggest an interesting epigenetic
role for TNF and TRAF1 in the epigenetic memory of earlier load induced muscle hypertrophy.
Identication of novel genes with the largest hypomethylation during reloading that are
associated with enhanced gene expression. e second DNA methylation cluster determined in the
top 500 dierentially modied CpG sites across all conditions, identied a cluster of genes (named Cluster A)
that was methylated at baseline and also became hypomethylated aer loading (similar to Cluster B above),
then, upon unloading, genes reverted back to a methylated state, and aer reloading switched back to hypo-
methylated. erefore, while not demonstrating an epigenetic memory per se, if hypomethylation was further
enhanced and was associated with enhanced gene expression in reloading versus loading would also sup-
port an epigenetic memory. Further gene expression analysis identied a cluster of genes that demonstrated
a mirror/inverse temporal pattern of gene expression versus their DNA methylation pattern. ese genes
included RPL35a, C12orf50, BICC1, ZFP2, UBR5, HEG1, PLA2G16, SETD3 and ODF2, that demonstrated
hypomethylation of DNA aer load induced growth and an increase in gene expression. Subsequently, then
both DNA methylation and gene expression returned back to baseline levels (in opposite directions) and aer
reload induced muscle growth DNA was hypomethylated again with an associated increase in gene expression.
Importantly, during reloading, gene expression was further enhanced versus loading, suggesting that an earlier
period of load induced growth was enough to produceenhanced gene expression when reload induced muscle
growth was encountered later, again suggesting a skeletal muscle memory at both the epigenetic andresultant
transcript level. Statistical analysis identied the genes RPL35a, UBR5, SETD3 and PLA2G16 as having signif-
icantly enhanced expression upon reloading. Importantly, these four genes, plus HEG1, displayed signicant
correlations between their gene expression and the percentage change in lean mass, suggesting for the rst time,
a role for these four genes in regulating adult human load induced skeletal muscle growth. Interestingly, SET
Domain Containing 3 (SETD3) is a H3K4/H3K36 methyltransferase, is abundant in skeletal muscle, and has
been shown to be recruited to the myogenin promoter, with MyoD, to promote its expression45. Furthermore,
overexpression of SETD3 in C2C12 murine myoblasts, evokes increases in myogenin, muscle creatine kinase,
and Myf6 (or MRF4) gene expression. Inhibition via shRNA in a myoblasts also impairs muscle cell dierentia-
tion45, suggesting a role for SETD3 in regulating skeletal muscle regeneration. However, less is known regarding
the role of PLA2G16 in skeletal muscle. PLA2G16 is a member of the superfamily of phospholipase A enzymes,
whose predominant localization is in adipose tissue. PLA2G16 is known to regulate adipocyte lipolysis in an
autocrine/paracrine manner, via interactions with prostaglandin and EP3 in a G-protein-mediated pathway46.
Indeed, ablation of PLA2G16 (referred to as Adpla), prevents obesity during periods high fat feeding in mouse
models, indicated via signicantly less adipose tissue and triglyceride content, compared to relevant controls46.
However, to date no known research has elucidated the role of PLA2G16 in skeletal muscle and therefore,
this requires future experimentation. Finally, HEG homology 1 (HEG1), initially reported as the heart of glass
gene, is recognised for its role in regulating the zebrash heart growth. HEG1 is a transmembrane receptor
that has been reported to be fundamental in the development of both the heart and blood vessels47. However,
a recent study reported a distinct role for HEG1 in regulating malignant cell growth48. Tsuji, et al.48 and col-
leagues reported that gene silencing of HEG1 in human MPM cell line, a cell linage that develop mesothelioma
tumours, signicantly reduced the survival and proliferation of mesothelioma cells, suggesting a role for HEG1
in regulating cellular growth. However, no known research has examined the role of HEG1 in regulating adult
skeletal muscle growth.
In the present study UBR5 displayed the most distinctive inverse relationship between DNA hypomethylation
and increased gene expressionfollowing loading and reloading. With the largest increase in hypomethylation and
gene expression aer reloading where the largest increase in lean mass was observed. UBR5 is a highly conserved
homologue of the drosophila tumour suppressor hyperplastic discs (HYD), and in the mammalian genome refers
to a protein that is a member of the HECT-domain E3 ubiquitin-ligase family49. E3 ubiquitin ligases play an
integral role in the ubiquitin - proteasome pathway, providing the majority of substrate recognition for the attach-
ment of ubiquitin molecules onto targeted proteins, preferentially modifying them for targeted autophagy/break-
down50. Indeed, extensive work has identied a distinct role of a number of E3 ubiquitin ligases such as MuRF1,
MAFbx and MUSA1 in muscle atrophy51,52. Furthermore, we have recently demonstrated that reduced DNA
methylation and increased gene expression of MuRF1 and MAFbx are associated with disuse atrophy in rats fol-
lowing nerve silencing of the hind limbs via tetrodotoxin exposure17. A process that is reversed upon a return to
habitual physical activity and a partial recovery of skeletal muscle mass17, suggesting a role for DNA methylation
in regulating the transcript behavior of a number of ubiquitinligases during periods of skeletal muscle atrophy
and recovery. However, there have been no studies that the authors are aware of, exmaining the role of UBR5 in
skeletal muscle atrophy or growth. Given the role of ubiquitin ligases in skeletal muscle, counterintuitively, we
report that the expression of the E3 ubiquitin ligase, UBR5, is increased during earlier periods of skeletal muscle
hypertrophy and are even further enhanced in later reload induced muscle growth. We further report that the
methylation prole of this E3 ubiquitin ligase portrays an inversed relationship with gene expression, support-
ing a role for DNA epigenetic modications in regulating its expression, as previously suggested17. However, in
support of its role in positively impacting on muscle, UBR5 has also been shown to promote smooth muscle
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SCIENTIfIC REPORtS | (2018) 8:1898 | DOI:10.1038/s41598-018-20287-3
dierentiation through its ability to stabilize myocardin proteins53. While myocardin is only expressed in smooth
and cardiac muscle, it is considered the master regulator of smooth muscle gene expression54 and a known tran-
scription factor that upregulates smooth muscle myosin heavy chains (MYHCs), actin and desmin. It therefore
possesses a similar role to the myogenic regulatory factors during early dierentiation (Mrf5 and MyoD), dur-
ing fusion (myogenin) and during myotube hypertrophy (adult MYHC’s). Interestingly, it has previously been
observed that myocardin-related transcription factors (MRTF) interact with the myogenic regulatory factor,
MyoD, to activate skeletal muscle specic gene expression55, suggesting a potential cross-talk between muscle
specic regulatory factors, enabling skeletal muscle adaptations55,56. erefore, UBR5s expression throughout
the time course of skeletal muscle cell dierentiation, its role in myotube hypertrophy are required in-vitro as well
as mammalian overexpression and knock-out of UBR5 to conrm its importance in-vivo. Further work is needed
to characterize UBR5, as well as other HECT-domain E3 ubiquitin ligase protein members identied in this work
via pathway analysis of the ubiquitin mediated proteolysis pathway, in the development of muscle growth to better
understand its role in facilitating skeletal muscle hypertrophy.
A single bout of acute resistance exercise evokes hypomethylation of genes that have
enhanced gene expression in later reload induced hypertrophy: Novel acutely exercise sensi-
tive DNA methylation biomarkers. Finally, we identied genes BICC1, STAG1, GRIK2 and TRAF1 were
hypomethylated aer a single bout of acute resistance exercise that were maintained as hypomethylated during
loading (as identied above) and reloading and demonstrated an enhanced gene expression aer later reloading.
Previous studies have suggested that acute aerobic exercise hypomethylates important genes in metabolic adapta-
tion and mitochondrial biogenesis such as PGC-1α, mitochondrial transcription factor A (TFAM) and pyruvate
dehydrogenase lipoamide kinase isozyme 4 (PDK4) post exercise, and reduces PPAR-δ methylation (hypometh-
ylates) 3 hours post exercise16, with corresponding increases in gene expression (3 hrs post exercise for PGC-1α,
PDK4 and PPAR-δ, immediately post for TFAM)16. Interestingly, hypermethylation of PGC 1α and reduced gene
expression, observed in skeletal muscle of the ospring of obese murine mothers, was reversed (hypomethylated)
by exercise in the mothers4. ese data support the role for aerobic exercise in hypomethylating candidate genes.
We also identify in the present study that hypomethylation (10,284 CpG sites) is favoured over hypermethylation
(7,600 CpG sites) across the genome 30 minutes post an acute bout of resistance exercise, yet without changes
in gene expression at this time point. Interestingly, however, hypomethylation of BICC1, STAG1, GRIK2 and
TRAF1 aer acute RE that was maintained aer 7 weeks loading and reloadinginduced hypertrophy, resulted in
signicantly enhanced gene expression 22 weeks later. is suggested that DNA methylation of these genes aer
a single bout of resistance exercise were more sensitive biomarkers than their acutely corresponding gene expres-
sion for later load induced hypertrophy. BICC1 is an RNA binding protein that has an undermined role in adult
skeletal muscle. It has been identied as dierentially expressed during prenatal muscle development between
two dierent pig breads57. RNA binding proteins in general are important in post transcriptional modications,
suggesting that perhaps reduced DNA methylation and increased gene expression may indicate an increase in
post-transcriptional modication aer reloading, however this requires further investigation to conrm. STAG1
(Cohesin subunit SA-1) isfundamental in cell division andpart of the cohesin complex, which is required for
the cohesion of sister chromatids aer DNA replication58. However, to the authors knowledge there is no spe-
cic rolefor STAG1 identied in adult skeletal muscle hypertrophy. GRIK2 and TRAF2 were also identied as
being hypomethylated aer loading and reloading together with enhanced gene expression. As suggested above,
GRIK2s role in skeletal muscle is not well dened. However, TRAF1 has been widely implicated in skeletal muscle
cell proliferation and dierentiation, as discussed above, and hypomethylation of TRAF1 appears to be both sensi-
tive to acute RE, as well as maintained following repeated loading and reloading induced hypertrophy that resulted
in the largest increase in gene expression aer reloading, 22 weeks aer being detected as hypomethylated aer
acute RE. Overall, suggesting an important role for TRAF2 in skeletal muscles epigenetic memory of hypertrophy.
Conclusion
We identify that human skeletal muscle possesses an epigenetic memory of earlier acute and chronic anabolic
stimuli when encountering later muscle hypertrophy.
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Acknowledgements
is work was funded by a PhD studentship for Robert A. Seaborne by the Doctoral Training Alliance UK/LJMU/
Keele University awarded via Adam P. Sharples (PI). Genome-wide methylation/gene expression studies were
funded by a GlaxoSmithKline grant awarded to Adam P. Sharples (PI).
Author Contributions
Sharples concieved experiments,Sharples and Seaborne designed experiments and research methodology,
performed the research and data collection, analysed all the data and wrote the manuscript. Sharples, Seaborne,
Strauss, Cocks, Shepherd, O’Brien, van Someren, Bell, Murgatroyd, Morton, Stewart provided expertise for
sample, data collection and analysis. All authors reviewed the manuscript drafts and inputted corrections,
amendments and their expertise.
Additional Information
Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-018-20287-3.
Competing Interests: e authors declare that they have no competing interests.
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Physical activity is a strong stimulus influencing the overall physiology of the human body. Exercises lead to biochemical changes in various tissues and exert an impact on gene expression. Exercise-induced changes in gene expression may be mediated by epigenetic modifications, which rearrange the chromatin structure and therefore modulate its accessibility for transcription factors. One of such epigenetic mark is DNA methylation that involves an attachment of a methyl group to the fifth carbon of cytosine residue present in CG dinucleotides (CpG). DNA methylation is catalyzed by a family of DNA methyltransferases. This reversible DNA modification results in the recruitment of proteins containing methyl binding domain and further transcriptional co-repressors leading to the silencing of gene expression. The accumulation of CpG dinucleotides, referred as CpG islands, occurs at the promoter regions in a great majority of human genes. Therefore, changes in DNA methylation profile affect the transcription of multiple genes. A growing body of evidence indicates that exercise training modulates DNA methylation in muscles and adipose tissue. Some of these epigenetic markers were associated with a reduced risk of chronic diseases. This review summarizes the current knowledge about the influence of physical activity on the DNA methylation status in humans.
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Polycystic ovary syndrome (PCOS) is characterised by a hormonal imbalance affecting the reproductive and metabolic health of reproductive‐aged women. Exercise is recommended as a first‐line therapy for women with PCOS to improve their overall health; however, women with PCOS are resistant to the metabolic benefits of exercise training. Here, we aimed to gain insight into the mechanisms responsible for such resistance to exercise in PCOS. We employed an in vitro approach with electrical pulse stimulation (EPS) of cultured skeletal muscle cells to explore whether myotubes from women with PCOS have an altered gene expression signature in response to contraction. Following EPS, 4719 genes were differentially expressed (false discovery rate <0.05) in myotubes from women with PCOS compared to 173 in healthy women. Both groups included genes involved in skeletal muscle contraction. We also determined the effect of two transforming growth factor β (TGFβ) ligands that are elevated in plasma of women with PCOS, TGFβ1 and anti‐Müllerian hormone (AMH), alone and on the EPS‐induced response. While AMH (30 ng/ml) had no effect, TGFβ1 (5 ng/ml) induced the expression of extracellular matrix genes and impaired the exercise‐like transcriptional signature in myotubes from women with and without PCOS in response to EPS by interfering with key processes related to muscle contraction, calcium transport and actin filament. Our findings suggest that while the fundamental gene expression responses of skeletal muscle to contraction is intact in PCOS, circulating factors like TGFβ1 may be responsible for the impaired adaptation to exercise in women with PCOS. Gene expression responses to in vitro contraction (electrical pulse stimulation, EPS) are altered in myotubes from women with polycystic ovary syndrome (PCOS) compared to healthy controls, with an increased expression of genes related to pro‐inflammatory pathways. Transforming growth factor β1 (TGFβ1) upregulates genes related to extracellular matrix remodelling and reduces the expression of contractile genes in myotubes, regardless of the donor's health status. TGFβ1 alters the gene expression response to EPS, providing a possible mechanism for the impaired exercise adaptations in women with PCOS. Abstract figure legend Women with polycystic ovary syndrome (PCOS) have elevated levels of circulating transforming growth factor β (TGFβ) ligands, in particular TGFβ1 and anti‐Müllerian hormone (AMH). Electrical pulse stimulation (EPS), a model of in vitro contraction, produced different transcriptomic responses in myotubes from healthy women and women with PCOS, as evidenced by changes in the number of genes and the associated Reactome pathways. EPS produced a pro‐inflammatory response in myotubes from women with PCOS and supressed genes related to DNA methylation and reproduction. Treatment of myotubes with TGFβ1 resulted in increased expression of genes related to ‘extracellular matrix organization’ and ‘collagen synthesis’, and a downregulation of genes involved in ‘muscle contraction’ and ‘NOTCH signalling’ in both groups. Conversely, AMH had no effect. TGFβ1 treatment altered the response to EPS, resulting in the activation of genes related to ‘extracellular matrix organization’ and ‘unfolded protein response’ with the suppression of genes related to contractile function in both groups, while only altered ‘DNA methylation’ and ‘GPCR signalling’ pathways in myotubes from women with PCOS. Collectively, this highlights that dysregulated TGFβ1 signalling may influence skeletal muscle signalling in response to contraction and subsequent adaptations in women with PCOS.
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KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an encyclopedia of genes and genomes. Assigning functional meanings to genes and genomes both at the molecular and higher levels is the primary objective of the KEGG database project. Molecular-level functions are stored in the KO (KEGG Orthology) database, where each KO is defined as a functional ortholog of genes and proteins. Higher-level functions are represented by networks of molecular interactions, reactions and relations in the forms of KEGG pathway maps, BRITE hierarchies and KEGG modules. In the past the KO database was developed for the purpose of defining nodes of molecular networks, but now the content has been expanded and the quality improved irrespective of whether or not the KOs appear in the three molecular network databases. The newly introduced addendum category of the GENES database is a collection of individual proteins whose functions are experimentally characterized and from which an increasing number of KOs are defined. Furthermore, the DISEASE and DRUG databases have been improved by systematic analysis of drug labels for better integration of diseases and drugs with the KEGG molecular networks. KEGG is moving towards becoming a comprehensive knowledge base for both functional interpretation and practical application of genomic information.
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Background In recent years the Illumina HumanMethylation450 (HM450) BeadChip has provided a user-friendly platform to profile DNA methylation in human samples. However, HM450 lacked coverage of distal regulatory elements. Illumina have now released the MethylationEPIC (EPIC) BeadChip, with new content specifically designed to target these regions. We have used HM450 and whole-genome bisulphite sequencing (WGBS) to perform a critical evaluation of the new EPIC array platform. Results EPIC covers over 850,000 CpG sites, including >90 % of the CpGs from the HM450 and an additional 413,743 CpGs. Even though the additional probes improve the coverage of regulatory elements, including 58 % of FANTOM5 enhancers, only 7 % distal and 27 % proximal ENCODE regulatory elements are represented. Detailed comparisons of regulatory elements from EPIC and WGBS show that a single EPIC probe is not always informative for those distal regulatory elements showing variable methylation across the region. However, overall data from the EPIC array at single loci are highly reproducible across technical and biological replicates and demonstrate high correlation with HM450 and WGBS data. We show that the HM450 and EPIC arrays distinguish differentially methylated probes, but the absolute agreement depends on the threshold set for each platform. Finally, we provide an annotated list of probes whose signal could be affected by cross-hybridisation or underlying genetic variation. Conclusion The EPIC array is a significant improvement over the HM450 array, with increased genome coverage of regulatory regions and high reproducibility and reliability, providing a valuable tool for high-throughput human methylome analyses from diverse clinical samples. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1066-1) contains supplementary material, which is available to authorized users.
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Canonical Wnt/β-catenin signaling plays an important role in myogenic differentiation, but its physiological role in muscle fibers remains elusive. Here, we studied activation of Wnt/β-catenin signaling in adult muscle fibers and muscle stem cells in an Axin2 reporter mouse. Axin2 is a negative regulator and a target of Wnt/βcatenin signaling. In adult muscle fibers, Wnt/β-catenin signaling is only detectable in a subset of fast fibers that have a significantly smaller diameter than other fast fibers. In the same fibers, immunofluorescence staining for YAP/Taz and Tead1 was detected. Wnt/β-catenin signaling was absent in quiescent and activated satellite cells. Upon injury, Wnt/β-catenin signaling was detected in muscle fibers with centrally located nuclei. During differentiation of myoblasts expression of Axin2, but not of Axin1, increased together with Tead1 target gene expression. Furthermore, absence of Axin1 and Axin2 interfered with myoblast proliferation and myotube formation, respectively. Treatment with the canonical Wnt3a ligand also inhibited myotube formation. Wnt3a activated TOPflash and Tead1 reporter activity, whereas neither reporter was activated in the presence of Dkk1, an inhibitor of canonical Wnt signaling. We propose that Axin2-dependent Wnt/β-catenin signaling is involved in myotube formation and, together with YAP/Taz/Tead1, associated with reduced muscle fiber diameter of a subset of fast fibers.
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The molecular mechanisms by which aging affects stem cell number and function are poorly understood. Murine data have implicated cellular senescence in the loss of muscle stem cells with aging. Here, using human cells and by carrying out experiments within a strictly pre-senescent division count, we demonstrate an impaired capacity for stem cell self-renewal in elderly muscle. We link aging to an increased methylation of the SPRY1 gene, a known regulator of muscle stem cell quiescence. Replenishment of the reserve cell pool was modulated experimentally by demethylation or siRNA knockdown of SPRY1. We propose that suppression of SPRY1 by age-associated methylation in humans inhibits the replenishment of the muscle stem cell pool, contributing to a decreased regenerative response in old age. We further show that aging does not affect muscle stem cell senescence in humans.
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