Content uploaded by Adam Philip Sharples
Author content
All content in this area was uploaded by Adam Philip Sharples on Aug 23, 2024
Content may be subject to copyright.
Citation: Sexton, C.L.; Godwin, J.S.;
McIntosh, M.C.; Ruple, B.A.; Osburn,
S.C.; Hollingsworth, B.R.; Kontos,
N.J.; Agostinelli, P.J.; Kavazis, A.N.;
Ziegenfuss, T.N.; et al. Skeletal
Muscle DNA Methylation and
mRNA Responses to a Bout of
Higher Versus Lower Load
Resistance Exercise in Previously
Trained Men. Cells 2023,12, 263.
https://doi.org/10.3390/
cells12020263
Academic Editors: Matt S. Stock
and Michael Deschenes
Received: 28 November 2022
Revised: 24 December 2022
Accepted: 4 January 2023
Published: 9 January 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
cells
Article
Skeletal Muscle DNA Methylation and mRNA Responses to a
Bout of Higher Versus Lower Load Resistance Exercise in
Previously Trained Men
Casey L. Sexton 1, Joshua S. Godwin 1, Mason C. McIntosh 1, Bradley A. Ruple 1, Shelby C. Osburn 1,
Blake R. Hollingsworth 1, Nicholas J. Kontos 1, Philip J. Agostinelli 1, Andreas N. Kavazis 1, Tim N. Ziegenfuss 2,
Hector L. Lopez 2, Ryan Smith 3, Kaelin C. Young 1,4, Varun B. Dwaraka 3, Andrew D. Frugé5,
Christopher B. Mobley 1, Adam P. Sharples 6 ,* and Michael D. Roberts 1, 4,*
1School of Kinesiology, Auburn University, Auburn, AL 36849, USA
2The Center for Applied Health Sciences, Canfield, OH 44406, USA
3TruDiagnostic, Lexington, KY 40503, USA
4Edward Via College of Osteopathic Medicine, Auburn, AL 24060, USA
5College of Nursing, Auburn University, Auburn, AL 36849, USA
6Institute for Physical Performance, Norwegian School of Sport Sciences, 0863 Oslo, Norway
*Correspondence: adams@nih.no (A.P.S.); mdr0024@auburn.edu (M.D.R.)
Abstract:
We sought to determine the skeletal muscle genome-wide DNA methylation and mRNA
responses to one bout of lower load (LL) versus higher load (HL) resistance exercise. Trained college-
aged males (n= 11, 23
±
4 years old, 4
±
3 years self-reported training) performed LL or HL bouts to
failure separated by one week. The HL bout (i.e., 80 Fail) consisted of four sets of back squats and
four sets of leg extensions to failure using 80% of participants estimated one-repetition maximum
(i.e., est. 1-RM). The LL bout (i.e., 30 Fail) implemented the same paradigm with 30% of est. 1-RM.
Vastus lateralis muscle biopsies were collected before, 3 h, and 6 h after each bout. Muscle DNA
and RNA were batch-isolated and analyzed using the 850k Illumina MethylationEPIC array and
Clariom S mRNA microarray, respectively. Performed repetitions were significantly greater during
the 30 Fail versus 80 Fail (p< 0.001), although total training volume (sets x reps x load) was not
significantly different between bouts (p= 0.571). Regardless of bout, more CpG site methylation
changes were observed at 3 h versus 6 h post exercise (239,951 versus 12,419, respectively; p< 0.01),
and nuclear global ten-eleven translocation (TET) activity, but not global DNA methyltransferase
activity, increased 3 h and 6 h following exercise regardless of bout. The percentage of genes
significantly altered at the mRNA level that demonstrated opposite DNA methylation patterns
was greater 3 h versus 6 h following exercise (~75% versus ~15%, respectively). Moreover, high
percentages of genes that were up- or downregulated 6 h following exercise also demonstrated
significantly inversed DNA methylation patterns across one or more CpG sites 3 h following exercise
(65% and 82%, respectively). While 30 Fail decreased DNA methylation across various promoter
regions versus 80 Fail, transcriptome-wide mRNA and bioinformatics indicated that gene expression
signatures were largely similar between bouts. Bioinformatics overlay of DNA methylation and
mRNA expression data indicated that genes related to “Focal adhesion,” “MAPK signaling,” and
“PI3K-Akt signaling” were significantly affected at the 3 h and 6 h time points, and again this
was regardless of bout. In conclusion, extensive molecular profiling suggests that post-exercise
alterations in the skeletal muscle DNA methylome and mRNA transcriptome elicited by LL and HL
training bouts to failure are largely similar, and this could be related to equal volumes performed
between bouts.
Keywords: resistance exercise; training volume; DNA methylation; transcriptomics
Cells 2023,12, 263. https://doi.org/10.3390/cells12020263 https://www.mdpi.com/journal/cells
Cells 2023,12, 263 2 of 22
1. Introduction
Resistance training increases myofiber hypertrophy, whole-tissue hypertrophy, and
strength [
1
]. Training with higher loads (e.g., ~80%+ of a person’s one repetition maximum,
or 1RM) and lower volumes (e.g., ~8–12 repetitions per set) generally increases force
production capabilities versus lower load training with higher volumes (e.g., 20+ repetitions
per set with 30–60% 1RM) [
2
–
5
], although equivocal evidence exists [
6
,
7
]. Notwithstanding,
most studies to date support that a wide range of loads
≥
30 1RM can promote a similar
magnitude of skeletal muscle hypertrophy if training is performed to failure [5–13].
Although the effects of volume-load manipulations on strength and hypertrophy have
been extensively investigated over recent years, much less attention has been given to the
potential divergent molecular responses that may occur between higher load and lower
load training. Initial findings by Mitchell et al. [
5
] and Haun et al. [
14
] suggested that one
bout of lower load and higher load failure training similarly elevated post-exercise anabolic
signaling events in skeletal muscle (i.e., mTORC1 phosphorylation markers). However,
more recent research provides evidence suggesting that different muscle-molecular adap-
tations may occur between training paradigms. For instance, Lim and colleagues [
10
]
reported that 10 weeks of 30% 1RM training to failure affected markers associated with mi-
tochondrial biogenesis and remodeling versus 80% 1RM training to failure. Haun et al. [
15
]
reported that a six-week lower load and higher volume resistance training program in
previously trained men elicited a significant upregulation in sarcoplasmic proteins associ-
ated with glycolysis measured via proteomics. In a separate cohort of previously trained
men, Vann et al. [
16
] reported that a 10-week higher load and moderate-to-low volume
training program did not alter the sarcoplasmic proteome or promote the glycolytic protein
adaptations observed by Haun and colleagues. Vann et al. [
4
] subsequently examined
how six weeks of unilateral lower load versus higher load unilateral leg resistance training
affected select molecular outcomes in a third cohort of previously trained men. While
shotgun proteomics were not performed, these authors reported that six-week integrated
non-myofibrillar protein synthesis rates were significantly greater in the lower load versus
higher load trained leg. This collective evidence has led our laboratory to hypothesize that
lower load and higher volume resistance training promotes metabolic adaptations relative
to higher load training [17].
Unique molecular signaling events induced by exercise training promote alterations in
mRNA expression [
18
], and several lines of evidence suggest that epigenetic modifications
to DNA is a mechanism involved in this process [
19
–
21
]. DNA methylation is one of the
most studied epigenetic mechanisms in humans, and recent research supports that a variety
of exercise modes, including resistance training, can alter DNA methylation status across
various gene promoters [
22
–
24
]. In mammalian species, DNA methylation primarily occurs
at cytosine and guanine dinucleotide-rich sites (CpGs). DNA methylation is catalyzed
by DNA methyltransferase (DNMT) enzymes, and DNA de-methylation is catalyzed by
ten-eleven translocation methylcytosine dioxygenases (TETs) [
20
,
25
]. Increased metabolite
flux through glycolysis and the citric acid cycle are thought to alter TET activity [
26
], and
affect the pool of methyl groups available for donation during the methylation process [
25
].
Current dogma indicates that decreased methylation (hypomethylation) of CpG sites,
particularly if occurring in promoter regions, contributes to increased gene expression
whereas increased methylation (hypermethylation) contributes to gene silencing [19,21].
It is plausible that divergent genome-wide DNA methylation and mRNA expression
signatures induced by higher and lower load resistance training could contribute to some
of the unique muscle-molecular phenotypes that have been previously observed between
these paradigms. However, neither phenomenon has been previously investigated. There-
fore, the purpose of this study was to examine how an acute bout of higher versus lower
load resistance exercise to failure affected genome-wide DNA methylation and mRNA
transcription profiles in skeletal muscle. Moreover, we sought to determine whether DNA
methylation events after each mode of training overlapped with mRNA responses. Herein,
lower load and higher load resistance exercise to failure utilized loads that were 30% 1-RM
Cells 2023,12, 263 3 of 22
(30 Fail) and 80% 1-RM (80 Fail), respectively. Given previous findings, we hypothesized
that 30 Fail training would elicit greater DNA hypomethylation relative to 80 Fail train-
ing, and this would correspond to an increased expression of mRNAs associated with
mitochondrial biogenesis, mitochondrial remodeling, and glycolysis.
2. Methods
2.1. Ethical Approval and Pre-Screening
This study was conducted with prior review and approval from the Auburn Institu-
tional Review Board (IRB approval #: 20-081 MR 2003), and in accordance with the most
recent revisions of the Declaration of Helsinki except not being pre-registered as a clinical
trial. The participants recruited for this study were males from the local community that
met the following criteria: (i) 18–35 years old, (ii) body mass index ([BMI] body mass in
kilograms/ height in meters
2
) not exceeding 35 kg/m
2
, (iii) no known cardio-metabolic dis-
ease (e.g., obesity, diabetes, hypertension, heart disease) or any condition contraindicating
participation in resistance training or donating muscle biopsies, (iv) have participated in
lower-body training at least once per week over the last six months, (v) have a self-reported
barbell back-squat one repetition maximum of
≥
1.5 times body mass. Following verbal
and written consent, participants proceeded with study procedures described below.
2.2. Study Design
Figure 1provides a schematic of the study design. Participants completed a within-
subject design that included a total of five visits to the laboratory. At Visit 1, an informed
consent and health history questionnaire were completed. This was followed by barbell
back squat and knee extension strength tests as well as body composition testing for
phenotyping purposes. At Visit 2 (~7 days following Visit 1), participants completed
a battery of baseline (PRE) tests that included measures of height, weight, and vastus
lateralis (VL) thickness. Participants also donated a muscle biopsy from the VL prior to the
completion of a single bout of resistance exercise using a randomly assigned experimental
load (i.e., 30% or 80% estimated 1RM). At both 3 h (3 h POST) and 6 h (6 h POST) following
the exercise bout, participants donated additional muscle biopsies from the VL. Seven
days following Visit 2, participants completed another bout of resistance exercise using
the experimental load not completed at Visit 2 (i.e., if Visit 2 was lifting to failure at 30%
1RM, then Visit 4 implemented 80% 1RM loads). As with Visit 2, VL biopsies were obtained
at PRE, 3 h POST, and 6 h POST. Visits 3 and 5 were follow-up visits to ensure biopsy
sites were properly healing. During each visit, participants were reminded not to alter
their typical diets given that various micronutrients (e.g., B vitamins) can alter methylation
status. However, food records were not collected during the intervention. The paragraphs
below provide more expanded details on testing procedures.
2.3. Testing Sessions
2.3.1. Estimated 1-RM Testing
During Visit 1, participants completed testing to determine their estimated 1-RM
(est. 1-RM) on the barbell back squat and leg extension exercises. Testing procedures
conformed to the National Strength and Conditioning Association (NSCA) guidelines for
strength testing and were performed by Certified Strength and Conditioning Specialists
(CSCS). Testing was performed as follows: (i) participants completed a general warm-
up of 25 jumping jacks and 10 body weight squats, (ii) for a specific warm-up, 50% of
participants’ most recent, self-reported back squat max was executed for 10 repetitions,
(iii) additional warm-up reps were completed at approximately 60% (8–10 repetitions), 70%
(5–8 repetitions), 80% (3–5 repetitions), 90% (1–3 repetitions) and 95% (1 repetition) of their
self-reported 1-RM. Thereafter, 3-RMs for each exercise were assessed within six attempts,
and NSCA guidelines were used to convert the 3-RM loads to est. 1-RM values [27].
Cells 2023,12, 263 4 of 22
Cells 2021, 10, x FOR PEER REVIEW 4 of 23
Figure 1. Study design. This schematic represents the timeline for participant consent, max testing,
PRE- phenotype testing, experimental resistance exercise bouts, cross-over washout period, and
time points for all biopsies. Abbreviations: 30 Fail, 30% one repetition maximum to failure; 80 Fail;
80% one repetition maximum; BIS, bioelectrical impedance spectroscopy (for body composition);
USG, urine-specific gravity testing; V1/V2/V4, Visits 1/2/4; VL, vastus lateralis.
2.3. Testing Sessions
2.3.1. Estimated 1-RM Testing.
During Visit 1, participants completed testing to determine their estimated 1-RM (est.
1-RM) on the barbell back squat and leg extension exercises. Testing procedures con-
formed to the National Strength and Conditioning Association (NSCA) guidelines for
strength testing and were performed by Certified Strength and Conditioning Specialists
(CSCS). Testing was performed as follows: i) participants completed a general warm-up
of 25 jumping jacks and 10 body weight squats, ii) for a specific warm-up, 50% of partici-
pants’ most recent, self-reported back squat max was executed for 10 repetitions, iii) ad-
ditional warm-up reps were completed at approximately 60% (8–10 repetitions), 70% (5–
8 repetitions), 80% (3–5 repetitions), 90% (1–3 repetitions) and 95% (1 repetition) of their
self-reported 1-RM. Thereafter, 3-RMs for each exercise were assessed within six attempts,
and NSCA guidelines were used to convert the 3-RM loads to est. 1-RM values [27].
2.3.2. Hydration Testing.
Whole-body hydration was assessed through urine specific gravity (USG) analysis.
During Visits 2 and 4, participants donated a urine sample (~5 mL) that was analyzed
using a handheld refractometer (ATAGO; Bellevue, WA, USA). All participants had a
USG level ≤ 1.020, and this was used as a threshold of sufficient hydration to continue
testing [28].
2.3.3. Body Composition Testing.
During Visit 2 only, height and body mass were measured using a digital column
scale (Seca 769; Hanover, MD, USA). Height was measured to the closest 0.5 cm and body
mass to the closest 0.1 kg. Bioelectrical impedance spectroscopy (SFB7; ImpediMed Lim-
ited, Queensland, AU) was used to estimate body composition for phenotyping purposes.
30 Fail bout
80 Fail bout
Consent,
Health history
Max testing
Biopsy
n = 5
n = 6
PRE 3h POST 6h POST PRE 3h POST 6h POST
~7 d
7-day
washout
V1 V2 V4
USG
Body mass
BIS
VL ultrasound
Legend
Figure 1.
Study design. This schematic represents the timeline for participant consent, max testing,
PRE- phenotype testing, experimental resistance exercise bouts, cross-over washout period, and time
points for all biopsies. Abbreviations: 30 Fail, 30% one repetition maximum to failure; 80 Fail; 80%
one repetition maximum; BIS, bioelectrical impedance spectroscopy (for body composition); USG,
urine-specific gravity testing; V1/V2/V4, Visits 1/2/4; VL, vastus lateralis.
2.3.2. Hydration Testing
Whole-body hydration was assessed through urine specific gravity (USG) analysis.
During Visits 2 and 4, participants donated a urine sample (~5 mL) that was analyzed using
a handheld refractometer (ATAGO; Bellevue, WA, USA). All participants had a USG level
≤1.020, and this was used as a threshold of sufficient hydration to continue testing [28].
2.3.3. Body Composition Testing
During Visit 2 only, height and body mass were measured using a digital column scale
(Seca 769; Hanover, MD, USA). Height was measured to the closest 0.5 cm and body mass
to the closest 0.1 kg. Bioelectrical impedance spectroscopy (SFB7; ImpediMed Limited,
Queensland, AU) was used to estimate body composition for phenotyping purposes. The
procedure was performed according to manufacturer’s instructions. The technique was
also explained in more depth by Esco et al. [
29
] and Moon et al. [
30
]. After participants
rested in a supine position for 5 min, two electrodes were placed above and below the
right wrist (5 cm apart) and two were placed above and below the anterior portion of the
ankle (
5 cm apart
). Impedance signals were then translated by the device and converted to
fat-free mass and fat mass.
2.3.4. Vastus Lateralis Thickness Assessments
During Visit 2 only, the ultrasound was used to measure VL muscle thickness as
reported in Sexton et al. [
31
] for phenotyping purposes. Participants were instructed
to lie supine, with knee and hip fully extended, for a minimum of 5 min before image
acquisition. Real-time B-mode ultrasonography (NextGen LOGIQe R8, GE Healthcare,
Chicago, IL, USA) and a multi-frequency linear-array transducer (L4-12T, 4–12 MHz, GE
Healthcare) was utilized to capture a VL image the transverse plane at approximately 50%
the distance between the mid-inguinal crease and the proximal patella. The image depth
was adjusted until the edge of the femur was in view, which allowed for a full-thickness
view of the VL. All ultrasound images were obtained using a generous amount of water-
Cells 2023,12, 263 5 of 22
soluble transmission gel. Associated software (LOGIQe R10.0.4; GE Healthcare) was used
to quantify VL thickness, which was defined as the superficial aponeurosis to the deep
aponeurosis. All ultrasound images were taken and analyzed by the same investigator.
2.3.5. Skeletal Muscle Biopsy and Processing
During Visits 2 and 4, VL biopsies were collected at PRE, 3 h POST, and 6 h POST
using a 5-gauge biopsy needle and local anesthesia. Approximately 50–80 mg of tissue was
collected in total. A portion of tissue (~20 mg) was adhered to a cork block via tragacanth
gum and preserved in an insulating layer of optimum cutting temperature (OCT) media
that was frozen in liquid nitrogen-cooled 2-methylbutane, then stored
−
80
◦
C to be later
used for histology. Remaining tissue was wrapped in prelabeled foils, flash frozen in liquid
nitrogen, and stored at
−
80
◦
C for later use. In total, tissue processing occurred within a
5-minute window.
2.4. Resistance Exercise Bouts
During Visit 2, participants completed a resistance exercise bout in the morning hours
(between 7:00 AM–10:00 AM) following an overnight fast. The bout format was randomly
assigned whereby participants performed 4 sets of the back squat and leg extension ex-
ercises at either 80% (80 Fail) or 30% (30 Fail) of their est. 1-RM until volitional failure.
At the beginning of the bout participants completed a general warm-up of 25 jumping
jacks and 10 body weighted squats, followed by a specific warm-up of 10 repetitions at
30–50% and 5–8 repetitions at 75% of the experimental load assigned to them (i.e., 80%
or 30% of est. 1-RM). Participants then initiated sets to failure and were provided 5 min
of rest between sets. Additionally, participants were given 5 min of rest between squats
and knee extensions. Failure was determined as either: (i) an inability to complete a full
repetition, (ii) technical errors that potentially compromised safety (e.g., difficulty keeping
balance or failure to maintain appropriate posture), or (iii) the participant spent longer than
4 s between repetitions. Participants reporting nausea, malaise, or light-headedness were
provided a cereal bar (calories: 170, total fat: 8 g, carbohydrates: 20 g, protein: 4 g) and a
sports drink (calories: 80, carbohydrates: 21 g) to reduce symptoms. After the exercise bout
was completed, participants were instructed to return to the laboratory 3 h and 6 h later for
post-exercise biopsies. Participants were also instructed to not perform rigorous exercise
during this time frame.
Seven days later, participants reported back to the laboratory within a two-hour time
window of their Visit 2 session and performed the bout format that was not performed
the week prior. The warm-up, lifting to failure and between-set recovery parameters, and
biopsy sampling times were kept identical to the week prior. Additionally, those that were
given post-exercise nutrition the week prior were then given the same items following the
second exercise bout.
2.5. Biochemical Laboratory Assays
2.5.1. DNA and RNA Isolation
Skeletal muscle samples were retrieved from
−
80
◦
C, crushed on a liquid nitrogen
cooled stage, weighed to ~10 mg using a laboratory scale (Mettler-Toledo; Columbus,
OH, USA), and added to 500
µ
L of Trizol in 1.7 mL polypropylene tubes. Muscle was
homogenized in Trizol (VWR; Radnor, PA, USA) with tight fitting plastic pestles for ap-
proximately 30 s and stored in
−
80
◦
C overnight to increase RNA yield. The following day
samples were removed from
−
80
◦
C and allowed to thaw completely at room tempera-
ture. Bromochloropropane (100
µ
L) was added to samples, samples were shaken for 15 s,
samples were incubated at room temperature for 2 min, and samples were centrifuged at
12,000 g for 15 min. Once removed from the centrifuge, samples were kept on ice for the
remainder of the procedures. Centrifugation yielded three distinct phases including the
top aqueous phase containing RNA, the center meniscus containing DNA, and the bottom
phase containing protein. Approximately 200
µ
L of the aqueous phase was removed and
Cells 2023,12, 263 6 of 22
placed in a new tube with 500
µ
L of 100% isopropanol, and the RNA was subsequently
pelleted via centrifugation and reconstituted with DEPC water. Reconstituted RNA was
then stored at
−
80
◦
C and shipped to a commercial laboratory for transcriptome-wide
analysis described below.
The remaining DNA and protein were separated by first adding 300
µ
L of 100%
ethanol. Samples were then shaken for 15 s, incubated for 3 min at room temperature, and
subsequently centrifuged at 5000 g for 10 min at room temperature. This produced a DNA
pellet and a protein supernatant. The supernatant (protein-Trizol-ethanol mixture) was
removed, and the DNA pellet was stored at
−
80
◦
C until purification was performed using
a commercial kit (DNeasy Blood and Tissue Kit, catalog #69504; Qiagen; Germantown,
MD, USA) according to manufacturer’s instructions with a minor modification to the
elution step. DNA quantity and quality was assessed in duplicate using an absorbance of
260/280 nm provided by a desktop spectrophotometer (NanoDrop Lite; Thermo Scientific;
Waltham, MA, USA). DNA was then stored at
−
80
◦
C until being shipped on dry ice to a
commercial laboratory (TruDiagnostic; Lexington, KY, USA) for the bisulfite conversion
and methylation analysis described below.
Transcriptome-wide mRNA analysis. RNA was shipped on dry ice to a commercial
laboratory (North American Genomics; Decatur, GA, USA) for transcriptomic analysis
using the Clariom S Assay Human mRNA array. Raw data were received as .CEL files
and analyzed using the Transcriptome Analysis Console v4.0.2 (Thermo Scientific). More
details on the statistical analysis are found later in the Methods section.
2.5.2. DNA Bisulfite Conversion and Methylome Analysis
Bisulfite conversion was performed using an Infinium HD Methylation Assay bisul-
fite conversion kit (EZ DNA Methylation Kit, Zymo Research, CA, USA). Bisulfite Con-
verted DNA (BCD) was stored at
−
80
◦
C until DNA methylation analysis. The Infinium
MethylationEpic BeadChip Array (Illumina; San Diego, CA, USA) was performed per
the manufacturer’s instructions, and BeadChips were imaged using the Illumina iScan
®
System (Illumina).
2.5.3. Nuclear Protein Isolations
Nuclear protein isolations from frozen muscle tissue were performed using a commer-
cial kit (Nuclear Extraction Kit, catalog# ab113474; Abcam; Waltham, MA, USA). Briefly,
skeletal muscle was retrieved from
−
80
◦
C, crushed on a liquid nitrogen cooled stage, and
~15–20 mg of tissue was added to kit pre-extraction buffer containing DTT. The tissue was
then homogenized using tight-fitting pestles, incubated on ice for 15 min, and centrifuged
for 10 min at 12,000 rpm at 4
◦
C. The supernatant was removed, 1x pre-extraction buffer
containing DTT and protease inhibitors was added to the pellet, and a 15 min incubation
ensued with 5 s vortexes every 3 min. The suspension was centrifuged for 10 min at
14,000 rpm at 4
◦
C. The supernatant was transferred to a new tube and protein concentra-
tions were assessed using a bicinchoninic acid (BCA) assay kit (Thermo Fisher Scientific;
Waltham, MA, USA) and microplate spectrophotometer (Synergy H1; Biotek; Winooski,
VT, USA).
2.5.4. Global DNMT and TET Activity Assays
A DNMT activity assay was performed on nuclear extracts using a commercial as-
say (Colorimetric DNMT Activity Quantification Kit, catalog# ab113467; Abcam) and
microplate spectrophotometer (Synergy H1; Biotek). DNMT activity was expressed as
absorbance units per microgram of nuclear protein. The average coefficient of variation for
duplicate absorbance values was 4.2%. A TET activity assay was also performed on nuclear
extracts using a fluorometric commercial assay (TET Hydroxylase Activity Quantification
Kit, catalog# ab156913; Abcam) and microplate fluorometer (Synergy H1; Biotek). TET
activity was expressed as fluorescence units per microgram nuclear protein. The average
coefficient of variation for duplicate fluorescence values was 7.7%.
Cells 2023,12, 263 7 of 22
2.5.5. Immunohistochemistry
Muscle samples that were collected and preserved in OCT as described above were
sectioned at a thickness of 10
µ
m in a cooled (
−
22
◦
C) cryostat (Leica Biosystems; Buffalo
Grove, IL, USA) and electrostatically adhered to positively charged histology slides. The
slides were stored at
−
80
◦
C until immunohistochemical staining whereby were removed
from the
−
80
◦
C, allowed to equilibrate and dry at room temperature for 1.5 h. Sections
were then outlined using a hydrophobic pen to retain solutions for the following incubation
steps. First, phosphate buffered saline (PBS) was applied for 10 min to rehydrate muscle
sections. PBS was then removed, and a 3% peroxide solution was added to sections for
15 min. The peroxide solution was removed, and the slides were washed in PBS for three
5-minute washes on a rocker. Sections were then incubated with TrueBlack Lipofuscin
Autofluorescence Quencher solution (biotium; Fremont, CA, USA) for 1 min, and slides
were washed in PBS for three 5-minute washes thereafter. The sections were then blocked
with a 5% normal goat serum and 2.5% normal horse serum for 1 h and washed in PBS for
5 min. Next, the primary antibody solution was applied (1:20 Mandra, 1:20 of BA_D5, 9:20
and 9:20 5% normal horse serum, all diluted in PBS; all antibodies from Developmental
Studies Hybridoma Bank; Iowa City, IA, USA), and slides were incubated overnight in
4◦C
.
The following day, slides were washed four times in PBS (5 min per wash) A secondary
antibody solution was applied for 1 h (1:100 goat anti-mouse IgG1 594 and 1:100 goat
anti-mouse IgG2B 488 diluted in PBS). Slides were then washed four times in PBS (5 min
per wash) and a DAPI fluorescent dye (1:10,000 DAPI, diluted in deionized water) was
then applied for 15 min. Thereafter two 5-minute PBS washes were applied to slides,
a 1:1 PBS-glycerol solution was applied around the sections, and glass coverslips were
applied thereafter. Immediately following mounting, digital images were captured using
a fluorescent microscope (Nikon Instruments, Melville, NY, USA) and a 10
×
objective.
Standardized measurements of type I and type II fiber cross-sectional areas (fCSA) were
performed using open-sourced software (MyoVision 2.0) [
32
,
33
]. A pixel conversion ratio
value of 0.964
µ
m/pixel was applied to account for the size and bit-depth of images, and
a detection range of detection from 500 to 12,000
µ
m
2
was used to ensure artifact was
removed (i.e., large myofibers which may have not been in transverse orientation, or
structures between dystrophin stains which were likely small vessels). Image analysis was
also visually inspected to ensure proper analytical fidelity.
2.6. Statistical Analyses
Statistical analyses on training repetition/volume and nuclear activity assays were
performed using SPSS v26.0 (IBM Corp, Armonk, NY, USA). Prior to analyses, Shapiro–Wilk
tests of normality were performed. Two-way ANOVAs were used to assess main effects
and potential interactions of condition (30 Fail vs. 80 Fail)
×
time (PRE, 3 h POST, and 6 h
POST). For any main effect that violated the assumption of sphericity, a Greenhouse–Geiser
correction was applied. Significant interactions were further interrogated using dependent
samples t-tests between conditions at each time point and within each condition over time.
2.6.1. DNA Methylation Analysis
The Partek Genomic Suite V.7 (Partek Inc., St. Louis, MO, USA) was used to process
the methylation data as described by Maasar et al. [
34
]. Briefly, an average detection p-value
was assessed for all samples to ensure values below 0.01 [
35
], and any probes outside of
this range were removed from analysis. Raw signals for the methylated and unmethylated
probes were assessed for the difference between average median methylated and average
median unmethylated probes to ensure the recommended difference of 0.5 or less [
35
].
After the data were imported to Partek Genomics Suite, single nucleotide polymorphism
(SNP) associated probes and cross-reactive probes, identified in validation studies [
36
],
were removed from analysis. Functional normalization using a noob background correction
was performed for normalization [
37
]. Then, principal component analyses (PCA), density
plots, and box and whisker plots were used for quality control (i.e., no samples exceeded
Cells 2023,12, 263 8 of 22
2.2 standard deviations or presented abnormal distributions). Following normalization
and quality control analysis, differentially methylated position analysis was performed.
β
-values were converted into M-values to represent a more valid distribution of data for
analysis of differential methylation [
38
]. Because both treatments were completed by each
participant, paired samples t-tests were used to assess 30 Fail versus 80 Fail at PRE, 3 h
POST, and 6 h POST, and significant DMPs at PRE were removed as confounding DMPs.
An ANOVA was also used to determine the main effects for condition (30 Fail and 80 Fail
over time) and for time (PRE vs. 3 h POST, PRE vs. 6 h POST, and 3 h POST vs. 6 h
POST). Differentially methylated CpG positions (DMPs) were subjected to a significance
threshold of p< 0.01. Additionally, an analysis to quantify differentially methylated regions
(DMRs) that contained 2 DMPs or more within a short genomic region was performed
using the Bioconductor package DMRcate (DOI: 10.18129/B9.bioc.DMRcate). Pathway
enrichment analysis of these DMPs was performed using KEGG (Kyoto Encyclopedia of
Genes and Genomes) pathways [
39
–
41
] via Partek Genomics Suite and Partek Pathway
software. All DNA methylation data can be accessed on Gene Expression Omnibus [
42
]
(URL: www.ncbi.nlm.nih.gov/geo/; GEO accession number: GSE220928 (uploaded on
14-12-2022)).
2.6.2. Transcriptome Analysis
Following the quantification of gene expression with the Clariom S microarray, raw
.CEL files were uploaded into the Transcriptome Analysis Console v4.0.2 (TAC) (Thermo
Scientific). The H. sapiens genome was used to generate the reference annotations. Two
analyses ensued. First, pairwise comparisons were performed to determine mRNAs that
were altered from PRE within each bout. A gene target was considered significant if gene
expression exceeded a
±
1.5-fold-change from PRE and the p-value was less than 0.01.
Second, two-way repeated measures (2
×
2) ANOVAs, with the eBayes correction factor
applied, were performed to detect genes that differed between bouts from PRE to 3 h POST
and PRE to 6 h POST. For this statistical analysis, gene expression was considered significant
if a fold-change over times between bouts of
±
1.5 was exceeded and the interaction p-value
was less than 0.01. Bioinformatics using gene lists from both analyses was performed using
PANTHER v17.0 [
43
,
44
]. All mRNA data can be accessed on Gene Expression Omnibus
(URL: www.ncbi.nlm.nih.gov/geo/; GEO accession number: GSE220899 (uploaded on
14-12-2022 and will be made public on 1-6-2023)).
2.6.3. Gene List Overlap Using KEGG Pathways
Gene lists that showed significant DNA methylation and mRNA time effects from PRE
to 3 h POST, and 6 h POST were entered into KEGG pathway analysis [
39
–
41
] to examine
significantly associated pathways. Thereafter, common pathways that were predicted
to be significantly affected from each dataset (p< 0.01) were assessed to determine if
pathways overlapped.
3. Results
3.1. Participant Characteristics
Table 1contains participant characteristics. The male participants that completed
the study (n= 11) were 23
±
4 years old with a body mass of 86
±
12 kg, a height of
180 ±7 cm
, and a BMI of 27
±
3 kg/m
2
. Resistance training experience (i.e., training age)
was
4±3 years
and the est. 1RM for the barbell back squat was 143
±
33 kg (relative to
body mass: 1.7
±
0.3 kg 1RM/ kg body mass). Average vastus lateralis thickness in all
participants was 2.99
±
0.36 cm, and mean fCSA values from biopsied VL tissue averaged
4259 ±882 µm2(type I fiber percent: 34.6 ±16.6, type II fiber percent: 65.4 ±16.6).
Cells 2023,12, 263 9 of 22
Table 1. Participant characteristics.
Variables Mean ±SD
Age (years) 23 ±4
Training Age (years) 4 ±3
Body Mass (kg) 86 ±12
Height (cm) 180 ±7
BMI (kg/m2)27 ±3
Est. squat 1-RM (kg) 143 ±33
Squat (kg)/Body Mass (kg) 1.7 ±0.3
VL thickness (cm) 2.99 ±0.36
Mean fCSA (µm2)4259 ±882
Type I Fiber Percent 34.6 ±16.6
Type II Fiber Percent 65.4 ±16.6
Data are for the 11 participants and presented as mean
±
standard deviation (SD) values. Abbreviations: BMI,
body mass index; 1-RM, one repetition maximum; VL, vastus lateralis; fCSA, fiber cross-sectional area. All data
were collected prior to first experimental resistance training bout.
3.2. Training Volume Differences between the 30 Fail and 80 Fail Bouts
Training volume data are presented in Figure 2(panels A–C). Barbell back squat
training volume (Figure 2A) exhibited a condition by set interaction (p< 0.001) and a main
effect of condition (p< 0.001) whereby more volume was performed in the 30 Fail versus
80 Fail condition. A main effect of set was also evident (p< 0.001) whereby average volume
decreased across all sets for both conditions. On a per set basis, more back squat volume
was performed in the 30 Fail versus the 80 Fail condition (set 1 p< 0.01, set 2 p= 0.002, set
3p= 0.001, set 4 p= 0.005). Moreover, total back squat volume was greater in the 30 Fail
versus 80 Fail condition (p< 0.001). Leg extension training volume exhibited a main effect
of condition (p< 0.05, Figure 2B) where more volume was completed in the 80 Fail versus
30 Fail condition. A dependent samples t-test also indicated that significantly more total
leg extension volume was completed in the 80 Fail versus 30 Fail condition (p= 0.030).
However, total lower body training volume between bouts was not significantly different
(p= 0.570, Figure 2C).
Training repetition data are presented in Figure 2(panels D–F). The number of back
squat repetitions performed exhibited a condition by set interaction (p< 0.001, Figure 2D)
and a main effect of condition (p< 0.001) whereby more repetitions were performed
during the 30 Fail versus 80 Fail condition. Additionally, a main effect of set was evident
(p< 0.001)
whereby repetitions per set decreased in both conditions. Dependent samples
t-tests indicated there were significantly more back squat repetitions completed in the
30 Fail versus 80 Fail condition during each set (p< 0.001 for sets 1–4) and for all sets
combined (
p< 0.001
). The repetitions performed across sets during leg extensions showed
a main effect of condition (p< 0.001; Figure 2E) whereby significantly more repetitions
were completed per set in the 30 Fail versus 80 Fail condition. Total lower body repetitions
completed was also significantly greater in the 30 Fail versus 80 Fail bout (p< 0.001,
Figure 2F).
3.3. DNA Methylation Changes with the 30 Fail and 80 Fail Bouts
Figure 3presents differentially methylated position (DMP) and region (DMR) data for
significant targets (p< 0.01), and these data are presented wherein positive values indicate
increased methylation in the 30 Fail versus 80 Fail condition and negative values indicate
decreased methylation in the 30 Fail versus 80 Fail condition. There were 3958 significant
DMPs between conditions at baseline (p< 0.01). Of these 3958 DMPs, only 156 possessed a
differentially methylated status at 3 h POST and/or 6 h POST. Therefore, these 156 DMPs
were removed from analyses to ensure that changes in each condition were due to the
exercise stimuli and not due to altered variation at baseline.
Cells 2023,12, 263 10 of 22
Cells 2021, 10, x FOR PEER REVIEW 10 of 23
Training repetition data are presented in Figure 2 (panels D–F). The number of back
squat repetitions performed exhibited a condition by set interaction (p < 0.001, Figure 2D)
and a main effect of condition (p < 0.001) whereby more repetitions were performed dur-
ing the 30 Fail versus 80 Fail condition. Additionally, a main effect of set was evident (p <
0.001) whereby repetitions per set decreased in both conditions. Dependent samples t-
tests indicated there were significantly more back squat repetitions completed in the 30
Fail versus 80 Fail condition during each set (p < 0.001 for sets 1–4) and for all sets com-
bined (p < 0.001). The repetitions performed across sets during leg extensions showed a
main effect of condition (p < 0.001; Figure 2E) whereby significantly more repetitions were
completed per set in the 30 Fail versus 80 Fail condition. Total lower body repetitions
completed was also significantly greater in the 30 Fail versus 80 Fail bout (p < 0.001, Figure
2F).
Total Repetitions
Figure 2. Volume and repetition differences between 30 Fail and 80 Fail bouts. Data are presented
as mean ± SD for squat volume across sets and total squat volume (panel A), leg extension volume
across sets and total leg extension volume (panel B), total volume for both exercises (panel C), squat
repetitions across sets and total squat repetitions (panel D), leg extension repetitions across sets and
total leg extension repetitions (panel E), and total repetitions performed for both exercises (panel F).
Data are for n = 11 participants. Symbol: *, indicates significant difference between conditions per
set and in totality (p < 0.05). Abbreviations: 30 Fail, 30% one repetition maximum to failure bout; 80
Fail, 80% one repetition maximum to failure bout.
3.3. DNA Methylation Changes with the 30 Fail and 80 Fail Bouts.
Figure 3 presents differentially methylated position (DMP) and region (DMR) data
for significant targets (p < 0.01), and these data are presented wherein positive values in-
dicate increased methylation in the 30 Fail versus 80 Fail condition and negative values
indicate decreased methylation in the 30 Fail versus 80 Fail condition. There were 3958
significant DMPs between conditions at baseline (p < 0.01). Of these 3958 DMPs, only 156
possessed a differentially methylated status at 3 h POST and/or 6 h POST. Therefore, these
156 DMPs were removed from analyses to ensure that changes in each condition were due
to the exercise stimuli and not due to altered variation at baseline.
At PRE (following confounding target removal) there were 3802 DMPs (first bar in
Figure 3A), with 1468 DMPs (38.6%) being hypermethylated in 30 Fail versus 80 Fail and
1651 CpGs (61.4%) being hypomethylated in 30 Fail versus 80 Fail. Of these 3802 DMPs,
Figure 2.
Volume and repetition differences between 30 Fail and 80 Fail bouts. Data are presented
as mean
±
SD for squat volume across sets and total squat volume (panel
A
), leg extension volume
across sets and total leg extension volume (panel
B
), total volume for both exercises (panel
C
), squat
repetitions across sets and total squat repetitions (panel
D
), leg extension repetitions across sets and
total leg extension repetitions (panel E), and total repetitions performed for both exercises (panel F).
Data are for n= 11 participants. Symbol: *, indicates significant difference between conditions per set
and in totality (p< 0.05). Abbreviations: 30 Fail, 30% one repetition maximum to failure bout; 80 Fail,
80% one repetition maximum to failure bout.
Cells 2021, 10, x FOR PEER REVIEW 11 of 23
426 DMPs (11.2%) were in a CpG island within a promoter region (first bar in Figure 3B),
and of these 426 DMPs, 76 CpGs (17.8%) were hypermethylated in 30 Fail versus 80 Fail
and 772 CpGs (82.2%) were hypomethylated in 30 Fail versus 80 Fail.
At 3 h POST there were 4186 DMPs (middle bar in Figure 3A), with 2,535 DMPs
(60.6%) being hypermethylated in 30 Fail versus 80 Fail and 1651 CpGs (39.4%) being hy-
pomethylated in 30 Fail versus 80 Fail. Of these 4186 DMPs, 793 DMPs (18.9%) were in a
CpG island within a promoter region (middle bar in Figure 3B), and of these 793 DMPs,
21 CpGs (2.6%) were hypermethylated in 30 Fail versus 80 Fail and 772 CpGs (97.4%) were
hypomethylated in 30 Fail versus 80 Fail.
At 6 h POST there were 3488 DMPs (third bar in Figure 3A), with 1,608 DMPs (46.1%)
being hypermethylated in 30 Fail versus 80 Fail and 1880 DMPs (53.9%) being hypometh-
ylated in 30 Fail versus 80 Fail. Of these 3488 DMPs, 858 DMPs (24.6%) resided in CpG
islands within a promoter region (third bar in Figure 3B), and of these, 189 were hyper-
methylated in 30 Fail versus 80 Fail (22%) and 669 were hypomethylated in 30 Fail versus
80 Fail (88%).
Figure 3. Global and island/promoter methylation differences between bouts. Data are presented as
total number of significant differentially methylated positions (DMPs) in 30 Fail compared to 80 Fail
(panel A), and DMPs present in islands and promoters (panel B). Data are for n = 11 participants
and significance was established as p < 0.01 between conditions at each time point. Abbreviations:
30 Fail, 30% one repetition maximum to failure bout; 80 Fail, 80% one repetition maximum to failure
bout.
Regarding DMRs at PRE between the 30 Fail and 80 Fail bouts, there were 68 DMRs
that all contained 2 DMPs (p < 0.01). Regarding DMRs at 3 h POST between the 30 Fail and
80 Fail bouts, there were one hundred fifty-five DMRs with two or more significant DMPs
(p < 0.01), nine of these DMRs contained three DMPs (associated genes: DIP2B, AHCYL,
TMEM134, FAM216A, SLC8B1, SERF2, TOM1L2, TEX14, and TXN2), and only one DMR
contained four DMPs (associated gene: EIF4B). Further, forty-nine of these one hundred
fifty-five DMRs (32%) were in CpG islands within promoter regions, with two genes
Figure 3.
Global and island/promoter methylation differences between bouts. Data are presented as
total number of significant differentially methylated positions (DMPs) in 30 Fail compared to 80 Fail
(panel
A
), and DMPs present in islands and promoters (panel
B
). Data are for n= 11 participants and
significance was established as p< 0.01 between conditions at each time point. Abbreviations: 30 Fail,
30% one repetition maximum to failure bout; 80 Fail, 80% one repetition maximum to failure bout.
Cells 2023,12, 263 11 of 22
At PRE (following confounding target removal) there were 3802 DMPs (first bar in
Figure 3A), with 1468 DMPs (38.6%) being hypermethylated in 30 Fail versus 80 Fail and
1651 CpGs (61.4%) being hypomethylated in 30 Fail versus 80 Fail. Of these 3802 DMPs,
426 DMPs (11.2%) were in a CpG island within a promoter region (first bar in Figure 3B),
and of these 426 DMPs, 76 CpGs (17.8%) were hypermethylated in 30 Fail versus 80 Fail
and 772 CpGs (82.2%) were hypomethylated in 30 Fail versus 80 Fail.
At 3 h POST there were 4186 DMPs (middle bar in Figure 3A), with 2535 DMPs
(60.6%) being hypermethylated in 30 Fail versus 80 Fail and 1651 CpGs (39.4%) being
hypomethylated in 30 Fail versus 80 Fail. Of these 4186 DMPs, 793 DMPs (18.9%) were in a
CpG island within a promoter region (middle bar in Figure 3B), and of these 793 DMPs,
21 CpGs
(2.6%) were hypermethylated in 30 Fail versus 80 Fail and 772 CpGs (97.4%) were
hypomethylated in 30 Fail versus 80 Fail.
At 6 h POST there were 3488 DMPs (third bar in Figure 3A), with 1608 DMPs (46.1%)
being hypermethylated in 30 Fail versus 80 Fail and 1880 DMPs (53.9%) being hypomethy-
lated in 30 Fail versus 80 Fail. Of these 3488 DMPs, 858 DMPs (24.6%) resided in CpG
islands within a promoter region (third bar in Figure 3B), and of these, 189 were hyper-
methylated in 30 Fail versus 80 Fail (22%) and 669 were hypomethylated in 30 Fail versus
80 Fail (88%).
Regarding DMRs at PRE between the 30 Fail and 80 Fail bouts, there were 68 DMRs
that all contained 2 DMPs (p< 0.01). Regarding DMRs at 3 h POST between the 30 Fail
and 80 Fail bouts, there were one hundred fifty-five DMRs with two or more significant
DMPs (p< 0.01), nine of these DMRs contained three DMPs (associated genes: DIP2B,
AHCYL, TMEM134, FAM216A, SLC8B1, SERF2, TOM1L2, TEX14, and TXN2), and only
one DMR contained four DMPs (associated gene: EIF4B). Further, forty-nine of these one
hundred fifty-five DMRs (32%) were in CpG islands within promoter regions, with two
genes containing three DMPs (associated genes: FAM216 and TOM1L2) and one gene
containing four DMPs (associated gene: EIF4B).
At 6 h POST between the 30 Fail and 80 Fail bouts there were sixty-seven DMRs with at
least two significant DMPs. Three DMRs contained three DMPs (associated genes: SIN3A,
RBM39, and PEG10), and only one DMR contained four DMPs (associated gene: MAP3K3).
Of these sixty-seven DMRs, sixty-five DMRs (97%) were in CpG islands within promoter
regions, with these DMRs containing three or more DMPs.
3.4. Nuclear TET and DNMT Activity
Given robust alterations in DNA methylation with both training bouts, we opted to
investigate global TET (demethylating) and DMNT (methylating) activities from nuclear
lysates. Due to tissue limitations, n= 10 participants yielded enough nuclear lysate material
to perform the TET activity assay and n= 9 participants yielded enough nuclear lysate
material to perform the DNMT activity assay. There was a significant main effect of time
for TET activity (p= 0.023, Figure 4A) where average TET activity increased at both post-
exercise time points. However, there was no significant main effect of condition (p= 0.163)
or condition by time interaction (p= 0.190). For DNMT activity there were no significant
main effects of time (p= 0.271), condition (p= 0.096), or condition by time interaction
(p= 0.174; Figure 4B).
Cells 2023,12, 263 12 of 22
Cells 2021, 10, x FOR PEER REVIEW 12 of 23
containing three DMPs (associated genes: FAM216 and TOM1L2) and one gene contain-
ing four DMPs (associated gene: EIF4B).
At 6 h POST between the 30 Fail and 80 Fail bouts there were sixty-seven DMRs with
at least two significant DMPs. Three DMRs contained three DMPs (associated genes:
SIN3A, RBM39, and PEG10), and only one DMR contained four DMPs (associated gene:
MAP3K3). Of these sixty-seven DMRs, sixty-five DMRs (97%) were in CpG islands within
promoter regions, with these DMRs containing three or more DMPs.
3.4. Nuclear TET and DNMT Activity
Given robust alterations in DNA methylation with both training bouts, we opted to
investigate global TET (demethylating) and DMNT (methylating) activities from nuclear
lysates. Due to tissue limitations, n = 10 participants yielded enough nuclear lysate mate-
rial to perform the TET activity assay and n = 9 participants yielded enough nuclear lysate
material to perform the DNMT activity assay. There was a significant main effect of time
for TET activity (p = 0.023, Figure 4A) where average TET activity increased at both post-
exercise time points. However, there was no significant main effect of condition (p = 0.163)
or condition by time interaction (p = 0.190). For DNMT activity there were no significant
main effects of time (p = 0.271), condition (p = 0.096), or condition by time interaction (p =
0.174; Figure 4B).
Figure 4. Nuclear TET and DNMT activities between bouts. All data are presented as mean±SD for
global TET activity (n = 10) from PRE to 3 h POST and 6 h POST resistance exercise (panel A) and
DNMT activity (n = 9) from PRE to 3 h POST and 6 h POST resistance exercise (panel B); Symbol: *,
indicates a significant change from PRE regardless of bout. Abbreviations: 30 Fail, 30% one repeti-
tion maximum to failure bout; 80 Fail, 80% one repetition maximum to failure bout; DNMT, DNA
Methyltransferase; OD, optical density; RFU, relative fluorescent units; TET, Ten-Eleven Trans-
locase.
3.5. mRNA Expression Data and Bioinformatics
There were 21,488 genes probed to identify differentially expressed genes ( ± 1.5-fold,
p < 0.01; termed ‘DEGs’). There were 889 significant DEGs at PRE between conditions (p <
0.05); thus, these DEGs were removed from analyses to ensure that changes in each con-
dition were due to the exercise stimuli and not due to altered variation at baseline.
From PRE to 3 h POST with 30 Fail training, 1428 mRNAs were upregulated and
1,201 mRNAs were downregulated (Figure 5A). Bioinformatics indicated that mRNAs in
the following pathways were significantly enriched (p < 0.001, FDR < 0.05): i) Toll receptor
✱
✱
Figure 4.
Nuclear TET and DNMT activities between bouts. All data are presented as
mean ±SD
for
global TET activity (n= 10) from PRE to 3 h POST and 6 h POST resistance exercise (panel
A
) and
DNMT activity (n= 9) from PRE to 3 h POST and 6 h POST resistance exercise (panel
B
); Symbol:
*, indicates a significant change from PRE regardless of bout. Abbreviations: 30 Fail, 30% one repetition
maximum to failure bout; 80 Fail, 80% one repetition maximum to failure bout; DNMT, DNA
Methyltransferase; OD, optical density; RFU, relative fluorescent units; TET,
Ten-Eleven Translocase
.
3.5. mRNA Expression Data and Bioinformatics
There were 21,488 genes probed to identify differentially expressed genes (
±
1.5-fold,
p< 0.01; termed ‘DEGs’). There were 889 significant DEGs at PRE between conditions
(p< 0.05); thus, these DEGs were removed from analyses to ensure that changes in each
condition were due to the exercise stimuli and not due to altered variation at baseline.
From PRE to 3 h POST with 30 Fail training, 1428 mRNAs were upregulated and
1201 mRNAs were downregulated (Figure 5A). Bioinformatics indicated that mRNAs in
the following pathways were significantly enriched (p< 0.001, FDR < 0.05): (i) Toll receptor
signaling (fold-enrichment = 2.78), (ii) CCKR signaling (fold-enrichment 2.35), (iii) apopto-
sis signaling (fold-enrichment = 2.31), (iv) interleukin signaling (
fold-enrichment = 2.19
),
(v) gonadotropin-releasing hormone receptor signaling (fold-enrichment = 1.86), (vi) inte-
grin signaling (fold-enrichment = 1.82), (vii) inflammation mediated by chemokine and
cytokine signaling (fold-enrichment = 1.79). From PRE to 6 h POST with 30 Fail training
932 mRNAs were upregulated and 924 mRNAs were downregulated (Figure 5B). Bioin-
formatics indicated that mRNAs in the following pathways were significantly enriched:
(i) CCKR signaling (fold-enrichment 2.44), (ii) gonadotropin-releasing hormone receptor
signaling (fold-enrichment = 2.13).
Cells 2023,12, 263 13 of 22
Figure 5.
Time effect mRNAs between bouts with pathway enrichment data. Differentially expressed
genes (DEGs) within the 30 Fail and 80 Fail bouts viewed as volcano plots and PANTHER pathway
enrichment. More specifically, 30 Fail training DEGs from PRE to 3 h POST (panel
A
), 30 Fail training
DEGs from PRE to 6 h POST (panel
B
), DEGs from PRE to 3 h POST with 80 Fail training (panel
C
),
and DEGs from PRE to 3 h POST with 80 Fail training (panel
D
). Data are for n= 11 participants,
all DEGs have a fold-change from PRE of
±
1.5 (p< 0.01), and pathway analysis was significant
if False Discovery Rate (FDR) p< 0.05. Abbreviations: 30 Fail, 30% one repetition maximum to
failure bout; 80 Fail, 80% one repetition maximum to failure bout; REFLIST, reference gene list across
human genome.
From PRE to 3 h POST with 80 Fail training 1492 mRNAs were upregulated and
1353 mRNAs
were downregulated (Figure 5C). Bioinformatics indicated that mRNAs in the following
pathways were significantly enriched: (i) Toll receptor signaling (
fold-enrichment = 2.78
),
(ii) VEGF signaling (
fold-enrichment = 2.30
), (iii) apoptosis signaling (
fold-enrichment = 2.00
),
(iv) CCKR signaling (fold-enrichment = 2.35), (v) angiogenesis (
fold-enrichment = 1.77
),
(vi) integrin signaling (fold-enrichment = 1.76), (vi) gonadotropin-releasing hormone re-
ceptor signaling (fold-enrichment = 1.75), (vii) inflammation mediated by chemokine and
cytokine signaling (fold-enrichment = 1.68). From PRE to 6 h POST with 80 Fail training,
974 mRNAs were upregulated and 577 mRNAs were downregulated (Figure 5D). Bioinfor-
matics indicated that mRNAs in the following pathways were significantly enriched: (i) p38
MAPK signaling (fold-enrichment 2.44), (ii) CCKR signaling (fold-enrichment = 2.57),
(iii) TGF-beta signaling (fold-enrichment = 2.53), (iv) gonadotropin-releasing hormone
receptor signaling (fold-enrichment = 2.30).
Regarding condition by time interactions (delta-delta fold-change
±
1.5, p< 0.01), there
were only 11 significant DEGs from PRE to 3 h POST (Figure 6A,B), and 17 significant DEGs
between bouts from PRE to 6 h POST (Figure 6C,D). Given the low number of differentially
expressed genes between bouts over time, no pathways were predicted to differ between
bouts at the 3 h POST or 6 h POST time points.
Cells 2023,12, 263 14 of 22
Cells 2021, 10, x FOR PEER REVIEW 14 of 23
Regarding condition by time interactions (delta-delta fold-change ±1.5, p < 0.01), there
were only 11 significant DEGs from PRE to 3 h POST (Figure 6A,B), and 17 significant
DEGs between bouts from PRE to 6 h POST (Figure 6C,D). Given the low number of dif-
ferentially expressed genes between bouts over time, no pathways were predicted to dif-
fer between bouts at the 3 h POST or 6 h POST time points.
Figure 6. Differentially expressed mRNAs between bouts at both post-exercise time points. All data
are significant differentially-expressed genes (DEGs) following 30 Fail versus 80 Fail for the ΔFold-
change in gene expression plotted against p-value (-log
10
transformed) at 3 h POST versus PRE
(panel A), the ΔFold-change over time for significant DEGs at 3 h POST versus PRE (panel B), the
Figure 6.
Differentially expressed mRNAs between bouts at both post-exercise time points. All
data are significant differentially-expressed genes (DEGs) following 30 Fail versus 80 Fail for the
∆
Fold-change in gene expression plotted against p-value (
−
log
10
transformed) at 3 h POST versus
PRE (panel
A
), the
∆
Fold-change over time for significant DEGs at 3 h POST versus PRE (
panel B
),
the
∆
Fold-change in gene expression plotted against p-value (
−
log
10
transformed) at 6 h POST
versus PRE (panel
C
), and the
∆
Fold-change over time for significant DEGs at 6 h POST versus
PRE (panel
D
). Data are for n= 11 participants and the significance threshold was established as
±1.5 ∆Fold-change
,p< 0.01. Abbreviations: 30 Fail, 30% one repetition maximum to failure bout;
80 Fail, 80% one repetition maximum to failure bout.
Cells 2023,12, 263 15 of 22
3.6. Overlapping Genome-wide Methylome and Transcriptome Results
An overlay of methylome and transcriptome data can be found in Figure 7. No-
tably, these analyses only considered mRNAs and DNA methylation values that showed
significant main effects of time from PRE.
Cells 2021, 10, x FOR PEER REVIEW 15 of 23
ΔFold-change in gene expression plotted against p-value (-log10 transformed) at 6 h POST versus
PRE (panel C), and the ΔFold-change over time for significant DEGs at 6 h POST versus PRE (panel
D). Data are for n = 11 participants and the significance threshold was established as ±1.5 ΔFold-
change, p < 0.01. Abbreviations: 30 Fail, 30% one repetition maximum to failure bout; 80 Fail, 80%
one repetition maximum to failure bout.
3.6. Overlapping Genome-wide Methylome and Transcriptome Results
An overlay of methylome and transcriptome data can be found in Figure 7. Notably,
these analyses only considered mRNAs and DNA methylation values that showed signif-
icant main effects of time from PRE.
A relatively high level of DNA methylation events occurred from PRE to 3 h POST,
as there was a significant decrease in the methylation status of 69,696 CpG sites and sig-
nificant increase in 170,255 CpG sites (Figure 7A). Of the 1640 mRNAs that were signifi-
cantly up-regulated at 3 h POST, 1,144 (or 68.8%) of these mRNAs had one or more asso-
ciated CpG site that was hypomethylated at this time point. Of the 1482 mRNAs that were
significantly down-regulated 3 h POST, 1156 (or 78.8%) of these mRNAs had one or more
associated CpG site that was hypermethylated at this time point.
Compared to 3 h POST there were fewer DNA methylation events that occurred 6 h
POST, as there was a significant decrease in the methylation status of 3578 CpG sites and
significant increase in 8841 CpG sites (Figure 7B). Of the 1177 mRNAs that were signifi-
cantly up-regulated 6 h POST, 128 (or 10.8%) of these mRNAs had one or more associated
CpG site that was hypomethylated at this time point. Of the 861 mRNAs that were signif-
icantly down-regulated 6 h POST, 175 (or 20.3%) of these mRNAs had one or more asso-
ciated CpG site that was hypermethylated at this time point.
Because DNA methylation precedes alterations in gene transcription, we also inves-
tigated DNA methylation changes at 3 h POST relative to 6 h POST mRNA expression
changes (Figure 7C). Of the 1177 mRNAs that were significantly up-regulated at 6 h POST,
763 (or 64.8%) of these mRNAs had one or more associated CpG site that was hypometh-
ylated at 3 h POST. Of the 861 mRNAs that were significantly down-regulated 6 h POST,
703 (or 81.6%) of these mRNAs had one or more associated CpG site that was hypermeth-
ylated at 3 h POST.
Figure 7.
Transcriptome and methylation signatures overlayed. Venn diagrams illustrating mRNAs
and DNA methylation values that showed significant main effects of time from PRE where red circles
indicate targets that were up-regulated from PRE to 3-/6-h and green circles indicate targets that
were down-regulated from PRE to 3-/6-h. Panel
A
contains significantly altered targets from PRE to
3 h post-exercise only. Panel
B
contains significantly altered targets from PRE to 6 h post-exercise
only. Panel
C
contains DNA methylation alterations that occurred from PRE to 3 h post-exercise
and mRNA expression alterations from PRE to 6 h post-exercise. Data are from n= 11 participants,
differentially methylated CpG sites were considered significantly altered from PRE if p< 0.01, and
mRNAs were considered significantly altered from PRE if ±1.5 fold-change (p< 0.01).
A relatively high level of DNA methylation events occurred from PRE to 3 h POST, as
there was a significant decrease in the methylation status of 69,696 CpG sites and significant
increase in 170,255 CpG sites (Figure 7A). Of the 1640 mRNAs that were significantly
up-regulated at 3 h POST, 1144 (or 68.8%) of these mRNAs had one or more associated CpG
site that was hypomethylated at this time point. Of the 1482 mRNAs that were significantly
down-regulated 3 h POST, 1156 (or 78.8%) of these mRNAs had one or more associated
CpG site that was hypermethylated at this time point.
Compared to 3 h POST there were fewer DNA methylation events that occurred 6 h
POST, as there was a significant decrease in the methylation status of 3578 CpG sites and
significant increase in 8841 CpG sites (Figure 7B). Of the 1177 mRNAs that were significantly
up-regulated 6 h POST, 128 (or 10.8%) of these mRNAs had one or more associated CpG
site that was hypomethylated at this time point. Of the 861 mRNAs that were significantly
down-regulated 6 h POST, 175 (or 20.3%) of these mRNAs had one or more associated CpG
site that was hypermethylated at this time point.
Because DNA methylation precedes alterations in gene transcription, we also inves-
tigated DNA methylation changes at 3 h POST relative to 6 h POST mRNA expression
changes (Figure 7C). Of the 1177 mRNAs that were significantly up-regulated at 6 h POST,
Cells 2023,12, 263 16 of 22
763 (or 64.8%) of these mRNAs had one or more associated CpG site that was hypomethy-
lated at 3 h POST. Of the 861 mRNAs that were significantly down-regulated 6 h POST,
703 (or 81.6%) of these mRNAs had one or more associated CpG site that was hypermethy-
lated at 3 h POST.
Finally, Table 2contains KEGG analysis results of significantly altered pathways
predicted to be affected following exercise, regardless of bout, according both the DNA
methylation and mRNA expression signatures. When removing disease-related pathways
from the 3 h POST analysis (e.g., “Yersina infection,” “Salmonella infection,” “Cushing
syndrome,” etc.), 50 KEGG pathways were significantly enriched (p< 0.01) according
to the DNA methylation data, 22 KEGG pathways were significantly enriched (p< 0.01)
according to the mRNA expression data, and 14 (64%) of these pathways overlapped. When
removing disease-related pathways from the 6 h POST analysis, 40 KEGG pathways were
significantly enriched (p< 0.01) according to the DNA methylation data, 16 KEGG pathways
were significantly enriched (p< 0.01) according to the mRNA expression data, and four
(25%) of these pathways overlapped. Of the 50 KEGG pathways were significantly enriched
(p< 0.01) according to the 3 h POST DNA methylation data and 16 KEGG pathways were
significantly enriched (p< 0.01) according to the 6 h POST mRNA data, 13 (81%) of these
pathways overlapped.
Table 2. KEGG pathway analysis and overlap of DNA methylation and mRNA expression data.
Pathways that Overlapped Fold-Enrichment
(DNA Meth., mRNA)
% Genes Affected
in Pathway
(DNA Meth., mRNA)
Pathway p-Values
(DNA Meth., mRNA)
3 h post-exercise DNA meth. and mRNA *
TNF signaling pathway 6.9, 2.4 97.3, 1.5 0.001, 2.9 ×10−9
MAPK signaling pathway 12.6, 1.6 96.3, 2.7 3.3 ×10−6, 3.4 ×10−6
HIF-1 signaling pathway 5.3, 2.0 96.3, 1.2 0.005, 2.4 ×10−5
FOXO signaling pathway 4.7, 1.9 95.4, 1.9 0.007, 6.6 ×10−5
TGF-beta signaling pathway 8.6, 2.0 98.9, 1.0 0.0002, 8.9 ×10−5
Other KEGG pathways that overlapped at 3 h post-exercise (n= 9 additional): Apoptosis, Chemokine signaling pathway, PI3K-Akt
signaling pathway, Rap1 signaling pathway, Focal adhesion, Leukocyte transendothelial migration, Hippo signaling pathway,
Insulin signaling pathway, C-type lectin receptor signaling pathway
6 h post-exercise DNA meth. and mRNA †
MAPK signaling pathway 17.9, 1.9 40.1, 2.8 1.7 ×10−8, 5.1 ×10−6
PI3K-Akt signaling pathway 7.1, 1.5 33.0, 2.7 0.0008, 0.0004
Focal Adhesion 25.0, 1.6 47.5, 1.7 1.4 ×10−11, 0.006
Wnt signaling pathway 8.2, 1.6 38.1, 1.6 0.0002, 0.001
3 h post-exercise DNA meth. and 6 h mRNA *
MAPK signaling pathway 12.6, 1.9 96.3, 2.8 3.3 ×10−6, 5.1 ×10−6
Autophagy 7.7, 2.1 97.1, 1.5 0.004, 1.1 ×10−4
TNF signaling pathway 6.9, 2.2 97.3, 1.3 0.001, 1.8 ×10−4
HIF-1 signaling pathway 5.3, 2.2 96.3, 1.2 0.005, 3.0 ×10−4
Th17 cell differentiation 6.3, 2.0 97.2, 1.1 0.0017, 0.0016
Other KEGG pathways that overlapped using 3 h post-exercise DNA methylation and 6 h post-exercise mRNA data (n= 8
additional): Apoptosis, FoxO signaling pathway, TGF-beta signaling pathway, Insulin signaling pathway, PI3K-Akt signaling
pathway, Adipocytokine signaling pathway, Focal Adhesion, Wnt signaling pathway
Legend:
These data were derived from entering gene lists into the KEGG pathway analysis queue, and gene
lists were derived from genes that showed significant (p< 0.01) time effects for DNA methylation (DNA meth.)
and mRNA expression changes from pre-exercise to 3 h and 6 h following exercise regardless of bout (i.e., those
showing significant time effects). Symbols: *, indicates these lists were sorted by lowest p-value according to
mRNA bioinformatics; †, indicates only 4 pathways overlapped at the 6 h post-exercise time point.
While not highlighted in Table 2, it is notable that three pathways were predicted to
be significantly altered according to the DNA methylation and mRNA signatures in all 3 h
and 6 h post exercise comparisons included “MAPK signaling,” “PI3K-Akt signaling,” and
“Focal adhesion.”
Cells 2023,12, 263 17 of 22
4. Discussion
Research interest into how exercise affects genome-wide skeletal muscle DNA methy-
lation has increased in recent years, and multiple associated reviews have been pub-
lished [
25
,
45
–
47
]. Barres et al. [
19
] were the first to report that the hypomethylation of
various metabolic genes occurs in skeletal muscle following a single high-intensity aerobic
exercise session in humans, and that these events corresponded with an alteration in the
mRNA expression levels of these genes. A 2021 study from Maasar and colleagues com-
pared the skeletal muscle DNA methylome responses to two bouts of running exercise [
34
].
The authors reported that CpG sites associated with several metabolic genes exhibited
more robust demethylation responses to the higher intensity bout of change-of-direction
running versus straight line running. Telles et al. [
48
] more recently examined how re-
sistance exercise, high-intensity interval exercise, or the combination of both affected the
mRNA expression and DNA methylation of select myogenic regulatory factors (MYOD1,
MYF5, and MYF6). All exercise protocols were reported to promote DNA demethylation
of these genes 4 h and 8 h post-exercise, and the mRNA expression of MYOD1 and MYF6
were elevated 4 h following exercise. Finally, acute resistance exercise in untrained males
has been demonstrated to promote DNA hypomethylation and increased expression of
mRNAs associated with matrix/actin structure and remodeling, mechano-transduction,
and TGF-beta signaling and protein synthesis [
22
,
24
]. While these investigations have been
insightful, it is difficult to compare our findings to data from these aforementioned studies
given the differences in exercise modes and muscle biopsy sampling time points. The cur-
rent study adds meaningful insight to this body of literature. First, the lower load (30 Fail)
resistance exercise bout promoted a more robust CpG island/promoter hypomethylation
response at both post-exercise time points relative to higher load (80 Fail) resistance exercise.
Further, a gene related to translation initiation and protein synthesis (EIF4B) possessed
four DMRs in the promoter region that were hypomethylated in the 30 Fail versus 80 Fail
condition 3 h following exercise. Whether these acute responses eventually translate to dif-
ferential training adaptations remains to be determined. Further, whether this differential
methylation signature in one gene between conditions (EIF4B) led to differences in muscle
protein synthesis was not examined. Notwithstanding, and in agreement with the data
from Massar and colleagues, this finding supports that implementing different training
styles elicits modest but unique post-exercise DNA methylation signatures.
Another novel implication from these data is that most DNA methylation events
occurred 3 h versus 6 h post-exercise regardless of bout (239,951 versus 12,419 CpG sites
significantly hyper- or hypomethylated at these respective time points, p< 0.01). Indeed,
long-lived alterations in skeletal muscle DNA methylation have been reported to occur in
response to weeks of resistance training [
23
], and this phenomenon has been posited to
serve as an “epigenetic memory” mechanism to promote more streamlined gene expression
responses to subsequent training bouts. However, the data herein also illustrate that a bout
of resistance exercise can lead to appreciably rapid and robust genome-wide alterations in
skeletal muscle DNA methylation patterns within a 3-h time frame following an exercise
bout (and regardless of condition). Accordingly, data in Figure 7show that the percentage
of significantly altered mRNAs that also demonstrated significantly inversed methylation
patterns across one or more CpG sites was appreciably higher at 3 h versus
6 h
following
exercise (~75% versus ~15%, respectively). Moreover, high percentages (65% and 82%
respectively) of genes that were up- or downregulated 6 h following exercise also demon-
strated significantly inversed DNA methylation patterns across one or more CpG sites 3 h
following exercise. Only a handful of studies have sought to compare DNA methylation
events to corresponding mRNA expression patterns following one or multiple bouts of
resistance exercise. For instance, Laker and colleagues [
49
] reported that only 2% of differ-
entially expressed genes from a one-week (three-bout) resistance training intervention were
also differentially methylated. However, these data were confounded by participants being
assigned to a high fat diet and the post-intervention biopsy was collected one day following
the last exercise bout. Seaborne and colleagues reported that an acute bout of resistance
Cells 2023,12, 263 18 of 22
exercise in eight untrained males led to ~10,000 CpG sites becoming hypomethylated
and ~7500 becoming hypermethylated [
23
]. Interestingly, DNA methylation signatures
were not associated with changes in mRNA expression until participants had undergone
a chronic training paradigm [
23
]. Additionally, when acute and chronic methylome and
transcriptome data were overlapped, ~40% of differentially expressed genes were shown
to be associated with altered DNA methylation signatures [
24
]. Telles et al. [
48
] more re-
cently reported that an interrelated, but not time-aligned response, of myogenic regulatory
factor gene demethylation and mRNA expression occurred up to 8 h following resistance
exercise and other exercise modalities. However, their analyses were only limited to three
genes, and it is notable that all these prior studies examined untrained participants. When
considering our data in lieu of this prior evidence, it is becoming increasingly evident that
resistance exercise-induced alterations in skeletal muscle DNA methylation and mRNA
expression patterns are interrelated, albeit this relationship appears to be more coupled
rapidly following exercise (i.e., within a 3-hour window) rather than 6+ hours or days
following an exercise bout.
Bioinformatics overlay analysis on DNA methylation and mRNA expression changes
from PRE (regardless of bout) was also insightful. As seen in Table 2, the three pathways
were predicted to be significantly altered whereby genes showed overlap at the DNA
methylation and mRNA expression levels 3 h and 6 h following exercise included “MAPK
signaling,” “PI3K-Akt signaling,” and “Focal adhesion.” Indeed, several studies examining
the acute signaling responses to a bout of resistance exercise have implicated that proteins
encoded from genes of these pathways show altered phosphoprotein statuses [
50
–
54
]. How-
ever, data regarding how resistance exercise affects the DNA methylation status and mRNA
responses of genes associated with these pathways are sparse. Seaborne and colleagues
utilized KEGG pathway analysis with acute and chronic resistance training to report that
the skeletal muscle DNA methylation statuses of genes related to “MAPK signaling” and
“Focal adhesion” are significantly altered [
22
]. Turner et al. [
24
] have overlapped methy-
lome and pooled transcriptome data to report that chronic resistance training affects genes
associated with “matrix/actin structure and remodeling,” “Focal adhesion,” and “TGF-beta
signaling and protein synthesis.” Hence, the current data continue to provide evidence that
post-exercise alterations in DNA methylation and mRNA expression patterns are predicted
to affect select genes associated with these cellular processes. Also notable, the pathways
predicted to be affected according to 3 h post-exercise DNA methylation changes and 6 h
post-exercise mRNA expression changes showed the greatest overlap (81%) compared
to 3 h overlay data (64%) and 6 h overlay data (25%). As has been discussed above, this
finding continues to indicate that earlier DNA methylation events better align with mRNA
expression events at later time points.
A final novel finding resulting from our nuclear lysate assays is the observed changes
in nuclear TET, but not DNMT, activity. Comparing our data to other research findings is
not possible since this is the first investigation to determine how nuclear DNMT and TET
activities are altered by exercise. Indeed, it is plausible that the exercise-induced increase
in TET activity observed herein promoted a reduction in DNA methylation. However,
there are limitations to this interpretation. First, we were unable to determine which TET
isoforms were operative in affecting global TET activity, and it is also possible that the
activities of specific DNMT isoforms were differentially affected to result in a nullified
post-exercise global DNMT activity change. Second, data presented in Figure 7indicate that
appreciably more CpG sites showed increased methylation relative to CpG sites showing
decreased methylation, which indicates that the activities of one or multiple DNMTs were
likely elevated during or within the first 3 h of exercise. Given the novelty and elusiveness
of these findings, more refined analytical approaches are needed to determine how exercise
affects the activities of various TET and DNMT isoforms. As an aside, there are interesting
data linking TET activity to muscle function. In an elegant series of experiments by
Wang et al. [
55
], the authors reported that TET2 knockout mice experienced severe muscle
dysfunction. The authors used these data as well as
in vitro
experiments to conclude that
Cells 2023,12, 263 19 of 22
TET2 activity is essential for muscle regeneration as well as myoblast differentiation and
fusion. Although the implications of these preclinical data are difficult to relate to the
current study, the data from Wang and colleagues reiterate the need to examine nuclear
TET activity in human skeletal muscle during periods of exercise training and various
disease states.
Experimental Limitations
The present study possesses limitations including a small sample size, the younger
healthy male sample population, and muscle sample collection time points. A larger num-
ber of more diverse participants and/or muscle samples being collected at additional time
points across a 24 to 72 h time frame may have yielded more insight. Moreover, although
participants were instructed to not deviate from their typical diets throughout the inter-
vention, dietary recalls were not obtained to account for how self-reported micronutrient
statuses may have affected results. This is an unresolved limitation and, while dietary
effects were not the emphasis of the current study, we acknowledge that inter-individual
differences in macro- and micronutrient intakes likely played a role in some of the ob-
served methylation outcomes. Finally, our significance thresholds in relation to the DNA
methylation data warrant discussion. Indeed, applying a p< 0.01 threshold to methylation
array data can be interpreted by some as being a rather liberal threshold cutoff; refer to
Mansell et al. [56]
who suggest a significance threshold of p< 9
×
10
−8
should be consid-
ered. However, when applying such stringent thresholds to our current dataset we find,
for instance, that (in referral to Figure 3data) there are no DMPs that differ (p< 9
×
10
−8
)
between 30 Fail and 80 Fail at PRE, 3 h, or 6 h post-exercise. Importantly, with our approach
we demonstrated a degree of overlap between the pathway analysis at the methylation
level (using FDR) and the mRNA level, with enrichment in the same pathways of “Focal
adhesion,” “MAPK signaling,” and “PI3K-Akt signaling.” These pathways have also been
shown to be differentially methylated and differentially methylated and expressed after
resistance exercise in independent cohorts [
22
,
24
]. Therefore, we surmise that the current
findings, whereby methylation data were deemed significant at p< 0.01, demonstrate
biological relevance given the degree of overlap discovered at both the methylation level
and transcriptome level as well as exhibiting agreement with previous findings in the
literature. Notwithstanding, we do realize that applying p< 0.01 is a liberal approach and
want to make the reader aware of this aspect of the study.
5. Conclusions
In conclusion, 30 Fail and 80 Fail resistance exercise bouts produced unique DNA
methylation responses across various gene promoters, albeit largely similar transcriptomic
responses. These data continue to add insightful information to the body of literature
comparing the muscle-molecular responses of higher load versus lower load resistance
training paradigms.
Author Contributions:
Conceptualization, C.L.S., A.N.K., T.N.Z., H.L.L., R.S., K.C.Y., V.B.D., A.D.F.,
C.B.M., A.P.S. and M.D.R.; methodology, C.L.S., J.S.G., M.C.M., B.A.R., S.C.O., B.R.H., N.J.K., P.J.A.,
R.S., V.B.D., A.P.S. and M.D.R.; software, R.S., V.B.D., A.P.S. and M.D.R.; validation, R.S., V.B.D.,
A.P.S. and M.D.R.; formal analysis, C.L.S., J.S.G. and M.D.R.; investigation, all co-authors; resources,
R.S., V.B.D., A.P.S. and M.D.R.; data curation, C.L.S., J.S.G., R.S., V.B.D., A.P.S. and M.D.R.; writ-
ing—original draft preparation, C.L.S., A.P.S. and M.D.R.; writing—review and editing, all co-authors;
visualization, all co-authors; supervision, C.L.S., J.S.G., M.C.M., B.A.R., S.C.O., B.R.H., N.J.K., P.J.A.
and M.D.R.; project administration, C.L.S., J.S.G., M.C.M. and M.D.R.; funding acquisition, T.N.Z.,
H.L.L. and M.D.R. All authors have read and agreed to the published version of the manuscript.
Funding:
M.C. McIntosh was fully supported through a T32 NIH grant (T32GM141739). MDR
discretionary laboratory funds (unrestricted donations, indirect cost recoveries from non-related
grants) were used to fund assay and participant compensation costs. Article processing charges were
covered using a MDPI waiver.
Cells 2023,12, 263 20 of 22
Data Availability Statement:
Queries not related to DNA methylation analysis can be addressed
by M.D.R. (mdr0024@auburn.edu). Questions related to DNA methylation can be addressed by
A.P.S. (adams@nih.no). All mRNA data can be accessed on Gene Expression Omnibus (URL: www.
ncbi.nlm.nih.gov/geo/; GEO accession number: GSE220899 (uploaded on 14-12-2022 and will
be made public on 1-6-2023)). All DNA methylation data can also be accessed on GEO (URL:
www.ncbi.nlm.nih.gov/geo/; GEO accession number: GSE220928 (uploaded on 14-12-2022).
Acknowledgments:
The authors would like to thank the participants for devoting their time and
willingness to engage in this study. The authors would also like to thank Christopher Vann (Duke
University) for his assistance in uploading mRNA data into a public repository.
Conflicts of Interest:
None of the authors declare financial or other potential conflicts of interest in
relation to the data presented in this manuscript.
References
1.
Schoenfeld, B.J. The Mechanisms of Muscle Hypertrophy and Their Application to Resistance Training. J. Strength Cond. Res.
2010,24, 2857–2872. [CrossRef] [PubMed]
2.
Jenkins, N.D.M.; Miramonti, A.A.; Hill, E.C.; Smith, C.M.; Cochrane-Snyman, K.C.; Housh, T.J.; Cramer, J.T. Greater Neural
Adaptations following High- vs. Low-Load Resistance Training. Front. Physiol. 2017,8, 331. [CrossRef] [PubMed]
3.
Jenkins, N.D.; Housh, T.J.; Buckner, S.L.; Bergstrom, H.C.; Cochrane, K.C.; Hill, E.C.; Smith, C.M.; Schmidt, R.J.; Johnson, G.O.;
Cramer, J.T. Neuromuscular Adaptations After 2 and 4 Weeks of 80% Versus 30% 1 Repetition Maximum Resistance Training to
Failure. J. Strength Cond. Res. 2016,30, 2174–2185. [CrossRef]
4.
Vann, C.G.; Sexton, C.L.; Osburn, S.C.; Smith, M.A.; Haun, C.T.; Rumbley, M.N.; Mumford, P.W.; Montgomery, N.T.; Ruple, B.A.;
McKendry, J.; et al. Effects of High-Volume Versus High-Load Resistance Training on Skeletal Muscle Growth and Molecular
Adaptations. Front. Physiol. 2022,13, 857555. [CrossRef]
5.
Mitchell, C.J.; Churchward-Venne, T.A.; West, D.W.; Burd, N.A.; Breen, L.; Baker, S.K.; Phillips, S.M. Resistance exercise load does
not determine training-mediated hypertrophic gains in young men. J. Appl. Physiol. (1985)
2012
,113, 71–77. [CrossRef] [PubMed]
6.
Cholewa, J.M.; Rossi, F.E.; MacDonald, C.; Hewins, A.; Gallo, S.; Micenski, A.; Norton, L.; Campbell, B.I. The Effects of Moderate-
Versus High-Load Resistance Training on Muscle Growth, Body Composition, and Performance in Collegiate Women. J. Strength
Cond. Res. 2018,32, 1511–1524. [CrossRef]
7.
Low-Load vs. High-Load Resistance Training to Failure on One Repetition Maximum Strength and Body Composition in
Untrained Women: Addendum. J. Strength Cond. Res. 2019,33, e227. [CrossRef]
8.
Grgic, J.; Schoenfeld, B.J. Are the Hypertrophic Adaptations to High and Low-Load Resistance Training Muscle Fiber Type
Specific? Front. Physiol. 2018,9, 402. [CrossRef]
9.
Lasevicius, T.; Ugrinowitsch, C.; Schoenfeld, B.J.; Roschel, H.; Tavares, L.D.; De Souza, E.O.; Laurentino, G.; Tricoli, V. Effects of
different intensities of resistance training with equated volume load on muscle strength and hypertrophy. Eur. J. Sport Sci.
2018
,
18, 772–780. [CrossRef]
10.
Lim, C.; Kim, H.J.; Morton, R.W.; Harris, R.; Phillips, S.M.; Jeong, T.S.; Kim, C.K. Resistance Exercise-induced Changes in Muscle
Phenotype Are Load Dependent. Med. Sci. Sport. Exerc. 2019,51, 2578–2585. [CrossRef]
11.
Lopez, P.; Radaelli, R.; Taaffe, D.R.; Newton, R.U.; Galvao, D.A.; Trajano, G.S.; Teodoro, J.L.; Kraemer, W.J.; Hakkinen, K.; Pinto,
R.S. Resistance Training Load Effects on Muscle Hypertrophy and Strength Gain: Systematic Review and Network Meta-analysis.
Med. Sci. Sport. Exerc. 2021,53, 1206–1216. [CrossRef] [PubMed]
12.
Schoenfeld, B.J.; Grgic, J.; Ogborn, D.; Krieger, J.W. Strength and Hypertrophy Adaptations Between Low- vs. High-Load
Resistance Training: A Systematic Review and Meta-analysis. J. Strength Cond. Res. 2017,31, 3508–3523. [CrossRef] [PubMed]
13.
Morton, R.W.; Oikawa, S.Y.; Wavell, C.G.; Mazara, N.; McGlory, C.; Quadrilatero, J.; Baechler, B.L.; Baker, S.K.; Phillips, S.M.
Neither load nor systemic hormones determine resistance training-mediated hypertrophy or strength gains in resistance-trained
young men. J. Appl. Physiol. (1985) 2016,121, 129–138. [CrossRef] [PubMed]
14.
Haun, C.T.; Mumford, P.W.; Roberson, P.A.; Romero, M.A.; Mobley, C.B.; Kephart, W.C.; Anderson, R.G.; Colquhoun, R.J.;
Muddle, T.W.D.; Luera, M.J.; et al. Molecular, neuromuscular, and recovery responses to light versus heavy resistance exercise in
young men. Physiol. Rep. 2017,5, e13457. [CrossRef] [PubMed]
15.
Haun, C.T.; Vann, C.G.; Osburn, S.C.; Mumford, P.W.; Roberson, P.A.; Romero, M.A.; Fox, C.D.; Johnson, C.A.; Parry, H.A.;
Kavazis, A.N.; et al. Muscle fiber hypertrophy in response to 6 weeks of high-volume resistance training in trained young men is
largely attributed to sarcoplasmic hypertrophy. PLoS ONE 2019,14, e0215267. [CrossRef]
16.
Vann, C.G.; Osburn, S.C.; Mumford, P.W.; Roberson, P.A.; Fox, C.D.; Sexton, C.L.; Johnson, M.R.; Johnson, J.S.; Shake, J.;
Moore, J.H.; et al
. Skeletal Muscle Protein Composition Adaptations to 10 Weeks of High-Load Resistance Training in Previously-
Trained Males. Front. Physiol. 2020,11, 259. [CrossRef]
17.
Roberts, M.D.; Haun, C.T.; Vann, C.G.; Osburn, S.C.; Young, K.C. Sarcoplasmic Hypertrophy in Skeletal Muscle: A Scientific
“Unicorn” or Resistance Training Adaptation? Front. Physiol. 2020,11, 816. [CrossRef]
Cells 2023,12, 263 21 of 22
18.
Damas, F.; Ugrinowitsch, C.; Libardi, C.A.; Jannig, P.R.; Hector, A.J.; McGlory, C.; Lixandrao, M.E.; Vechin, F.C.; Montenegro, H.;
Tricoli, V.; et al. Resistance training in young men induces muscle transcriptome-wide changes associated with muscle structure
and metabolism refining the response to exercise-induced stress. Eur. J. Appl. Physiol. 2018,118, 2607–2616. [CrossRef]
19.
Barres, R.; Yan, J.; Egan, B.; Treebak, J.T.; Rasmussen, M.; Fritz, T.; Caidahl, K.; Krook, A.; O’Gorman, D.J.; Zierath, J.R. Acute
exercise remodels promoter methylation in human skeletal muscle. Cell Metab. 2012,15, 405–411. [CrossRef] [PubMed]
20. Bird, A.P. CpG-rich islands and the function of DNA methylation. Nature 1986,321, 209–213. [CrossRef]
21.
Voisin, S.; Eynon, N.; Yan, X.; Bishop, D.J. Exercise training and DNA methylation in humans. Acta Physiol.
2015
,213, 39–59.
[CrossRef] [PubMed]
22.
Seaborne, R.A.; Strauss, J.; Cocks, M.; Shepherd, S.; O’Brien, T.D.; Someren, K.A.V.; Bell, P.G.; Murgatroyd, C.; Morton, J.P.;
Stewart, C.E.; et al. Methylome of human skeletal muscle after acute & chronic resistance exercise training, detraining & retraining.
Sci. Data 2018,5, 180213. [CrossRef] [PubMed]
23.
Seaborne, R.A.; Strauss, J.; Cocks, M.; Shepherd, S.; O’Brien, T.D.; van Someren, K.A.; Bell, P.G.; Murgatroyd, C.; Morton, J.P.;
Stewart, C.E.; et al. Human Skeletal Muscle Possesses an Epigenetic Memory of Hypertrophy. Sci. Rep.
2018
,8, 1898. [CrossRef]
[PubMed]
24.
Turner, D.C.; Seaborne, R.A.; Sharples, A.P. Comparative Transcriptome and Methylome Analysis in Human Skeletal Muscle
Anabolism, Hypertrophy and Epigenetic Memory. Sci. Rep. 2019,9, 4251. [CrossRef]
25.
Seaborne, R.A.; Sharples, A.P. The Interplay Between Exercise Metabolism, Epigenetics, and Skeletal Muscle Remodeling. Exerc.
Sport Sci. Rev. 2020,48, 188–200. [CrossRef]
26.
Wu, X.; Zhang, Y. TET-mediated active DNA demethylation: Mechanism, function and beyond. Nat. Rev. Genet.
2017
,18, 517–534.
[CrossRef]
27.
Haff, G.; Triplett, N.T.; National, S.; Conditioning, A. Essentials of Strength Training and Conditioning; Human Kinetics: Champaign,
IL, USA, 2016.
28.
American College of Sports Medicine; Sawka, M.N.; Burke, L.M.; Eichner, E.R.; Maughan, R.J.; Montain, S.J.; Stachenfeld, N.S.
American College of Sports Medicine position stand. Exercise and fluid replacement. Med. Sci. Sport. Exerc.
2007
,39, 377–390.
[CrossRef]
29.
Esco, M.R.; Fedewa, M.V.; Freeborn, T.J.; Moon, J.R.; Wingo, J.E.; Cicone, Z.; Holmes, C.J.; Hornikel, B.; Welborn, B. Agreement
between supine and standing bioimpedance spectroscopy devices and dual-energy X-ray absorptiometry for body composition
determination. Clin. Physiol. Funct. Imaging 2019,39, 355–361. [CrossRef]
30.
Moon, J.R.; Eckerson, J.M.; Tobkin, S.E.; Smith, A.E.; Lockwood, C.M.; Walter, A.A.; Cramer, J.T.; Beck, T.W.; Stout, J.R. Estimating
body fat in NCAA Division I female athletes: A five-compartment model validation of laboratory methods. Eur. J. Appl. Physiol.
2009,105, 119–130. [CrossRef]
31.
Sexton, C.L.; Smith, M.A.; Smith, K.S.; Osburn, S.C.; Godwin, J.S.; Ruple, B.A.; Hendricks, A.M.; Mobley, C.B.; Goodlett, M.D.;
Fruge, A.D.; et al. Effects of Peanut Protein Supplementation on Resistance Training Adaptations in Younger Adults. Nutrients
2021,13, 3981. [CrossRef]
32.
Wen, Y.; Murach, K.A.; Vechetti, I.J., Jr.; Fry, C.S.; Vickery, C.; Peterson, C.A.; McCarthy, J.J.; Campbell, K.S. MyoVision: Software
for automated high-content analysis of skeletal muscle immunohistochemistry. J. Appl. Physiol. (1985)
2018
,124, 40–51. [CrossRef]
33.
Viggars, M.R.; Wen, Y.; Peterson, C.A.; Jarvis, J.C. Automated cross-sectional analysis of trained, severely atrophied, and
recovering rat skeletal muscles using MyoVision 2.0. J. Appl. Physiol. (1985) 2022,132, 593–610. [CrossRef] [PubMed]
34.
Maasar, M.F.; Turner, D.C.; Gorski, P.P.; Seaborne, R.A.; Strauss, J.A.; Shepherd, S.O.; Cocks, M.; Pillon, N.J.; Zierath, J.R.;
Hulton, A.T.; et al
. The Comparative Methylome and Transcriptome After Change of Direction Compared to Straight Line
Running Exercise in Human Skeletal Muscle. Front. Physiol. 2021,12, 619447. [CrossRef] [PubMed]
35.
Maksimovic, J.; Phipson, B.; Oshlack, A. A cross-package Bioconductor workflow for analysing methylation array data.
F1000Research 2016,5, 1281. [CrossRef] [PubMed]
36.
Pidsley, R.; Zotenko, E.; Peters, T.J.; Lawrence, M.G.; Risbridger, G.P.; Molloy, P.; Van Djik, S.; Muhlhausler, B.; Stirzaker, C.;
Clark, S.J
. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation
profiling. Genome Biol. 2016,17, 208. [CrossRef] [PubMed]
37.
Maksimovic, J.; Gordon, L.; Oshlack, A. SWAN: Subset-quantile within array normalization for illumina infinium HumanMethy-
lation450 BeadChips. Genome Biol. 2012,13, R44. [CrossRef] [PubMed]
38.
Du, P.; Zhang, X.; Huang, C.C.; Jafari, N.; Kibbe, W.A.; Hou, L.; Lin, S.M. Comparison of Beta-value and M-value methods for
quantifying methylation levels by microarray analysis. BMC Bioinform. 2010,11, 587. [CrossRef]
39. Kanehisa, M.; Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000,28, 27–30. [CrossRef]
40.
Kanehisa, M.; Furumichi, M.; Tanabe, M.; Sato, Y.; Morishima, K. KEGG: New perspectives on genomes, pathways, diseases and
drugs. Nucleic Acids Res. 2017,45, D353–D361. [CrossRef]
41.
Kanehisa, M.; Sato, Y.; Kawashima, M.; Furumichi, M.; Tanabe, M. KEGG as a reference resource for gene and protein annotation.
Nucleic Acids Res. 2016,44, D457–D462. [CrossRef]
42.
Barrett, T.; Wilhite, S.E.; Ledoux, P.; Evangelista, C.; Kim, I.F.; Tomashevsky, M.; Marshall, K.A.; Phillippy, K.H.;
Sherman, P.M.
;
Holko, M.; et al. NCBI GEO: Archive for functional genomics data sets—Update. Nucleic Acids Res.
2013
,41, D991–D995.
[CrossRef] [PubMed]
Cells 2023,12, 263 22 of 22
43.
Mi, H.; Ebert, D.; Muruganujan, A.; Mills, C.; Albou, L.P.; Mushayamaha, T.; Thomas, P.D. PANTHER version 16: A revised
family classification, tree-based classification tool, enhancer regions and extensive API. Nucleic Acids Res.
2021
,49, D394–D403.
[CrossRef] [PubMed]
44.
Mi, H.; Thomas, P.