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Cited by 458Muscle Physiology and DisordersCardiovascular and exercise physiologyGenetics and Physical Performance

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Transcriptomic profiling of skeletal muscle adaptations to exercise and inactivity

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Pillon, Nicolas J., Gabriel, Brendan M., Dollet, Lucile, Smith, Jonathon A. B., Sardón Puig, Laura, Botella, Javier, Bishop, David J., Krook, Anna, Zierath, Juleen R.·2020·Nature Communications
DOI 10.1038/s41467-019-13869-wPMC PMC6981202

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Abstract

The molecular mechanisms underlying the response to exercise and inactivity are not fully understood. We propose an innovative approach to profile the skeletal muscle transcriptome to exercise and inactivity using 66 published datasets. Data collected from human studies of aerobic and resistance exercise, including acute and chronic exercise training, were integrated using meta-analysis methods ( www.metamex.eu ). Here we use gene ontology and pathway analyses to reveal selective pathways activated by inactivity, aerobic versus resistance and acute versus chronic exercise training. We identify NR4A3 as one of the most exercise- and inactivity-responsive genes, and establish a role for this nuclear receptor in mediating the metabolic responses to exercise-like stimuli in vitro. The meta-analysis (MetaMEx) also highlights the differential response to exercise in individuals with metabolic impairments. MetaMEx provides the most extensive dataset of skeletal muscle transcriptional responses to different modes of exercise and an online interface to readily interrogate the database.

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Fig. 1 MetaMEx reveals the behavior of PPARGC1A across 66 transcriptomic studies. The online tool MetaMEx (www.metamex.eu) allows for the quick interrogation of all published exercise and inactivity studies for a single gene. The analysis provides annotations of each study with respect to skeletal muscle type obtained, sex, age, fitness, weight, and metabolic status of the participants studied. The forest plot of individual statistics (fold-change, FDR, 95% confidence intervals), as well as the meta-analysis score is provided. In the case of HIIT training and combined exercise training protocols, the number of studies is insufficient to calculate meaningful meta-analysis statistics. NA: not available.
Fig. 1 MetaMEx reveals the behavior of PPARGC1A across 66 transcriptomic studies. The online tool MetaMEx (www.metamex.eu) allows for the quick interrogation of all published exercise and inactivity studies for a single gene. The analysis provides annotations of each study with respect to skeletal muscle type obtained, sex, age, fitness, weight, and metabolic status of the participants studied. The forest plot of individual statistics (fold-change, FDR, 95% confidence intervals), as well as the meta-analysis score is provided. In the case of HIIT training and combined exercise training protocols, the number of studies is insufficient to calculate meaningful meta-analysis statistics. NA: not available.CC BY · PubMed Central
Fig. 2 Inter-array comparisons separate acute exercise from training and inactivity. All datasets of healthy individuals were compared with each other using a principle component analysis (a), a chord plot (b) and a correlation matrix of fold-changes (c). A Venn Diagram presents the overlap of the significantly (FDR < 1%) expressed genes (d). All genes are presented in M-plots (e–i) with significantly changed genes (FDR < 1%) represented with colored dots.
Fig. 2 Inter-array comparisons separate acute exercise from training and inactivity. All datasets of healthy individuals were compared with each other using a principle component analysis (a), a chord plot (b) and a correlation matrix of fold-changes (c). A Venn Diagram presents the overlap of the significantly (FDR < 1%) expressed genes (d). All genes are presented in M-plots (e–i) with significantly changed genes (FDR < 1%) represented with colored dots.CC BY · PubMed Central
Fig. 3 Genes and pathways altered by exercise and inactivity. The top 5 up- and downregulated genes in each protocol and their overlap was calculated (a) and gene ontology analysis was calculated based on genes with FDR < 0.01 (b). Genes corresponding to the proteins of interest were collected from the KEGG database and fold-changes were added to present the overall modification of enzymes involved in pathways. The behavior of lipid metabolism signaling (c), mitochondrial respiration (d), inflammation (e), and muscle fiber composition (f) are presented.
Fig. 3 Genes and pathways altered by exercise and inactivity. The top 5 up- and downregulated genes in each protocol and their overlap was calculated (a) and gene ontology analysis was calculated based on genes with FDR < 0.01 (b). Genes corresponding to the proteins of interest were collected from the KEGG database and fold-changes were added to present the overall modification of enzymes involved in pathways. The behavior of lipid metabolism signaling (c), mitochondrial respiration (d), inflammation (e), and muscle fiber composition (f) are presented.CC BY · PubMed Central
Fig. 4 DNAJA4, KLHL40, NR4A3, and VGLL2 respond to exercise and inactivity. a Genes significantly modified by acute aerobic and resistance exercise and inactivity were overlapped in a Venn Diagram. b DNAJA4, KLHL40, NR4A3, and VGLL2 were validated in an independent cohort of pre- and post-acute aerobic exercise. Individual paired t-tests vs pre, n = 8 biologically independent volunteers, **p < 0.01. c DNAJA4, NR4A3, KLHL40, and VGLL2 gene expression following electrical pulse stimulation in primary human myotubes. Individual paired t-tests vs basal, n = 8 biologically independent primary cells from different donors, **p < 0.01.
Fig. 4 DNAJA4, KLHL40, NR4A3, and VGLL2 respond to exercise and inactivity. a Genes significantly modified by acute aerobic and resistance exercise and inactivity were overlapped in a Venn Diagram. b DNAJA4, KLHL40, NR4A3, and VGLL2 were validated in an independent cohort of pre- and post-acute aerobic exercise. Individual paired t-tests vs pre, n = 8 biologically independent volunteers, **p < 0.01. c DNAJA4, NR4A3, KLHL40, and VGLL2 gene expression following electrical pulse stimulation in primary human myotubes. Individual paired t-tests vs basal, n = 8 biologically independent primary cells from different donors, **p < 0.01.CC BY · PubMed Central
Fig. 5 NR4A3 regulates the metabolic response to in vitro exercise. a NR4A3 responds in a time- and intensity-dependent manner to electrical pulse stimulation. Data are mean ± SEM, n = 3 biologically independent primary cells from different donors, two-way ANOVA (time, intensity), *overall effect p < 0.05. b Silencing efficiency using siRNA against NR4A3, n = 4 biologically independent primary cells from different donors, individual paired t-test vs scramble, ***p < 0.001. c Electrical pulse stimulation-induced glucose uptake is abolished after NR4A3 silencing. Two-way ANOVA (siNR4A3, EPS), n = 6 biologically independent primary cells from different donors, *p < 0.05. d Silencing NR4A3 using siRNA modifies the mRNA levels of exercise- and inactivity-responsive genes. Data are mean ± SEM, n = 4 biologically independent primary cells from different donors, individual paired t-test vs scramble. e Silencing of NR4A3 correlates with inactivity observed in MetaMEx. f Reduction of NR4A3 level impairs basal and maximal oxygen consumption measured by Seahorse XF analysis. Individual paired t-tests vs scramble, n = 5 biologically independent primary cells from different donors, *p < 0.05, **p < 0.01. g Silencing of NR4A3 leads to a drift of muscle cells towards a more quiescent phenotype. Data are mean ± SEM. h Silencing of NR4A3 decreases the abundance of mitochondrial complexes. Representative blot and quantification, n = 6 biologically independent primary cells from different donors, two-way ANOVA, significant effect of silencing (p = 0.018), *p < 0.05 uncorrected Fisher’s LSD post-test. i The increase in glycolysis (ECAR) induced by beta-adrenergic stimulation (20 µM salbutamol for 3 h) is impaired in the absence of NR4A3. SeaHorse XF experiment, two-way ANOVA (siNR4A3, Salbutamol), n = 7 biologically independent primary cells from different donors, **p < 0.01. AA: acute aerobic, AR: acute resistance, IN: inactivity, TA: training aerobic, TR: training resistance.
Fig. 5 NR4A3 regulates the metabolic response to in vitro exercise. a NR4A3 responds in a time- and intensity-dependent manner to electrical pulse stimulation. Data are mean ± SEM, n = 3 biologically independent primary cells from different donors, two-way ANOVA (time, intensity), *overall effect p < 0.05. b Silencing efficiency using siRNA against NR4A3, n = 4 biologically independent primary cells from different donors, individual paired t-test vs scramble, ***p < 0.001. c Electrical pulse stimulation-induced glucose uptake is abolished after NR4A3 silencing. Two-way ANOVA (siNR4A3, EPS), n = 6 biologically independent primary cells from different donors, *p < 0.05. d Silencing NR4A3 using siRNA modifies the mRNA levels of exercise- and inactivity-responsive genes. Data are mean ± SEM, n = 4 biologically independent primary cells from different donors, individual paired t-test vs scramble. e Silencing of NR4A3 correlates with inactivity observed in MetaMEx. f Reduction of NR4A3 level impairs basal and maximal oxygen consumption measured by Seahorse XF analysis. Individual paired t-tests vs scramble, n = 5 biologically independent primary cells from different donors, *p < 0.05, **p < 0.01. g Silencing of NR4A3 leads to a drift of muscle cells towards a more quiescent phenotype. Data are mean ± SEM. h Silencing of NR4A3 decreases the abundance of mitochondrial complexes. Representative blot and quantification, n = 6 biologically independent primary cells from different donors, two-way ANOVA, significant effect of silencing (p = 0.018), *p < 0.05 uncorrected Fisher’s LSD post-test. i The increase in glycolysis (ECAR) induced by beta-adrenergic stimulation (20 µM salbutamol for 3 h) is impaired in the absence of NR4A3. SeaHorse XF experiment, two-way ANOVA (siNR4A3, Salbutamol), n = 7 biologically independent primary cells from different donors, **p < 0.01. AA: acute aerobic, AR: acute resistance, IN: inactivity, TA: training aerobic, TR: training resistance.CC BY · PubMed Central
Fig. 6 Differential response to exercise training in metabolically impaired individuals. To compare healthy (HLY) to metabolically impaired (MTI) individuals, a principle component analysis was performed (a) and the significantly regulated genes (FDR < 0.1) overlapped in a Venn diagram (b). Gene ontology analysis calculated based on genes with FDR < 0.1 demonstrated a differential response of metabolically impaired individual to both aerobic and resistance training protocols (c).
Fig. 6 Differential response to exercise training in metabolically impaired individuals. To compare healthy (HLY) to metabolically impaired (MTI) individuals, a principle component analysis was performed (a) and the significantly regulated genes (FDR < 0.1) overlapped in a Venn diagram (b). Gene ontology analysis calculated based on genes with FDR < 0.1 demonstrated a differential response of metabolically impaired individual to both aerobic and resistance training protocols (c).CC BY · PubMed Central

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Metadata

Authors
Pillon, N. J., Gabriel, B. M., Dollet, L., Smith, J. A. B., Sardón Puig, L., Botella, J., Bishop, D. J., Krook, A., & Zierath, J. R.
Affiliations
  • Karolinska Institutet (SE)
  • Victoria University (AU)
  • University of Copenhagen (DK)
Corresponding author
Zierath, Juleen R.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2020-01-24
Volume
11
Issue
1
Pages
470-470
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-019-13869-w
PMC
PMC6981202
OpenAlex
W3002380642
Cited by
457
References
96
Citations by year
2026: 77 · 2025: 74 · 2024: 84 · 2023: 54 · 2022: 74 · 2021: 60 · 2020: 33
FWCI
5.29
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
  • Victoria University Research Repository (Victoria University) (submittedVersion, cc-by)
  • Aberdeen University Research Archive (Aberdeen University) (submittedVersion, other-oa)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Deakin Research Online (Deakin University) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
Funders
  • Wenner-Gren Foundation
  • Judith Jane Mason and Harold Stannett Williams Memorial Foundation
  • European Commission (675610, 704978, 2015-00165)
  • European Foundation for the Study of Diabetes
  • Karolinska Institutet (2009-1068)
  • Novo Nordisk (NNF18CC0034900)
  • Stockholms Läns Landsting (SLL20170159, NNF14OC0011493, SLL20150517)
  • Vetenskapsrådet (2015-00165, 2018-02389, 2018-02389, P2018-0097, 2015-00165, 2018-, NNF14OC0011493, 2009–1068)
  • Centrum för idrottsforskning (P2018-0097)
  • Novo Nordisk Fonden (NNF17OC0030088, NNF19OC0055072, NNF15SA0018346, NNF 18 CC0034900, NNF14OC0011493, NNF14OC0009941)
  • Novo Nordisk Foundation Center for Basic Metabolic Research (NNF14OC0011493, NNF 18 CC0034900, NNF17OC0030088)
  • Australian Research Council (DP140104165)
  • H2020 Marie Skłodowska-Curie Actions (675610, NNF18CC0034900, 704978)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Muscle Physiology and Disorders
  • Health Sciences › Medicine › Physiology (medical) › Cardiovascular and exercise physiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Physical Performance
Keywords
skeletal muscle transcriptome, exercise training, physical inactivity, aerobic exercise, resistance training, NR4A3, nuclear receptors, metabolic response, metabolic dysfunction
MeSH terms
Adaptation, Physiological, DNA-Binding Proteins, Humans, Receptors, Steroid, Receptors, Thyroid Hormone, Exercise, Muscle, Skeletal, Gene Expression Profiling, Gene Regulatory Networks, Resistance Training, Sedentary Behavior, Gene Ontology

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