Remodeling of the human skeletal muscle proteome found after long-term endurance training but not after strength training

doi:10.1016/j.isci.2023.108638

. 2023 Dec 5;27(1):108638.

doi: 10.1016/j.isci.2023.108638. eCollection 2024 Jan 19.

Remodeling of the human skeletal muscle proteome found after long-term endurance training but not after strength training

Eric B Emanuelsson¹, Muhammad Arif², Stefan M Reitzner^{1

3}, Sean Perez⁴, Maléne E Lindholm^{1

5}, Adil Mardinoglu^{2

6}, Carsten Daub^{7

8}, Carl Johan Sundberg^{1

9

10}, Mark A Chapman^{1

11}

Affiliations

¹ Department of Physiology and Pharmacology, Karolinska Institutet, 171 77 Stockholm, Sweden.
² Science for Life Laboratory, KTH - Royal Institute of Technology, 171 77 Stockholm, Sweden.
³ Department of Women's and Children's Health, Karolinska Institutet, 171 77 Stockholm, Sweden.
⁴ Department of Biology, Pomona College, Claremont, CA 91711, USA.
⁵ Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
⁶ Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London WC2R 2LS, UK.
⁷ Department of Biosciences and Nutrition, Karolinska Institutet, 171 77 Stockholm, Sweden.
⁸ Science for Life Laboratory, 171 65 Solna, Sweden.
⁹ Department of Laboratory Medicine, Karolinska Institutet, 141 52 Huddinge, Sweden.
¹⁰ Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, 171 77 Stockholm, Sweden.
¹¹ Department of Integrated Engineering, University of San Diego, San Diego, CA 92110, USA.

PMID: 38213622
PMCID: PMC10783619
DOI: 10.1016/j.isci.2023.108638

Remodeling of the human skeletal muscle proteome found after long-term endurance training but not after strength training

Eric B Emanuelsson et al. iScience. 2023.

. 2023 Dec 5;27(1):108638.

doi: 10.1016/j.isci.2023.108638. eCollection 2024 Jan 19.

Authors

Affiliations

¹ Department of Physiology and Pharmacology, Karolinska Institutet, 171 77 Stockholm, Sweden.
² Science for Life Laboratory, KTH - Royal Institute of Technology, 171 77 Stockholm, Sweden.
³ Department of Women's and Children's Health, Karolinska Institutet, 171 77 Stockholm, Sweden.
⁴ Department of Biology, Pomona College, Claremont, CA 91711, USA.
⁵ Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
⁶ Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London WC2R 2LS, UK.
⁷ Department of Biosciences and Nutrition, Karolinska Institutet, 171 77 Stockholm, Sweden.
⁸ Science for Life Laboratory, 171 65 Solna, Sweden.
⁹ Department of Laboratory Medicine, Karolinska Institutet, 141 52 Huddinge, Sweden.
¹⁰ Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, 171 77 Stockholm, Sweden.
¹¹ Department of Integrated Engineering, University of San Diego, San Diego, CA 92110, USA.

PMID: 38213622
PMCID: PMC10783619
DOI: 10.1016/j.isci.2023.108638

Abstract

Exercise training has tremendous systemic tissue-specific health benefits, but the molecular adaptations to long-term exercise training are not completely understood. We investigated the skeletal muscle proteome of highly endurance-trained, strength-trained, and untrained individuals and performed exercise- and sex-specific analyses. Of the 6,000+ proteins identified, >650 were differentially expressed in endurance-trained individuals compared with controls. Strikingly, 92% of the shared proteins with higher expression in both the male and female endurance groups were known mitochondrial. In contrast to the findings in endurance-trained individuals, minimal differences were found in strength-trained individuals and between females and males. Lastly, a co-expression network and comparative literature analysis revealed key proteins and pathways related to the health benefits of exercise, which were primarily related to differences in mitochondrial proteins. This network is available as an interactive database resource where investigators can correlate clinical data with global gene and protein expression data for hypothesis generation.

Keywords: Biological sciences; Health sciences; Medicine; Omics.

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Conflict of interest statement

The authors declare no competing interests.

Figures

**Figure 1**
Experimental workflow and summary of differential protein expression between experimental groups (A) Flowchart of the study design. (B) Subject characteristics. (C) Principal component analysis. (D) Bar chart of statistically significant differentially expressed proteins between groups (5% false discovery rate). The color indicates how many proteins that are unique to one or more group comparisons. See also Table S1. PA, physical activity; LC-MS, liquid chromatography–mass spectrometry; PC, principal component; ME, male endurance; MC, male control; FE, female endurance; FC, female control; MS, male strength; DEP, differentially expressed proteins.

**Figure 2**
Skeletal muscle proteomic differences with long-term training and sex (A and B) Volcano plot of all DEPs between ME vs. MC and FE vs. FC, respectively. The color of each dot indicates if the protein is unique to one group comparison or shared between more group comparisons. (C and D) Overexpression analysis of the up- (C) and downregulated (D) shared DEPs in both male and female subjects versus controls using KEGG pathways. The number next to each individual bar indicates the number of DEPs within each pathway. (E) Heatmap of all DEP between MS vs. MC. Red boxes in all heatmaps indicate a significant upregulation; blue indicates a significant downregulation. (F) Gene set analysis of KEGG pathways comparing ME vs. MC, FE vs. FC, and MS vs. MC. (G) Gene set analysis of KEGG pathways comparing FC vs. MC and FE vs. ME. (H) Schematic representation of differentially expressed mitochondrial proteins with endurance training. A selection of differentially expressed mitochondrial metabolic proteins between endurance-trained males and females versus the corresponding control group. Specific proteins are displayed in white boxes. The number of differentially expressed subunits of the mitochondrial complexes (CI-V) in electron transport chain, based on MitoPathways3.0, is displayed. Yellow text = ME vs. MC. Orange text = FE vs. FC. Black text = same in both ME and FE vs. controls. Red boxes in all heatmaps indicate a significant upregulation; blue indicates a significant downregulation. LogFC, log fold change; FDR, false discovery rate. See also Figures S1–S3 and Table S1.

**Figure 3**
Correlation between global gene and protein analysis (A) Density plot of the correlation (Spearman rank correlation) of all gene-protein pairs, with the statistically significant (5% false discovery rate, FDR) pairs highlighted in red (upregulated) and blue (downregulated). (B) Functional analysis of the significantly correlated pairs, based on (A). The number of gene-protein pairs within each pathway is included next to each pathway. (C) A Venn diagram of all differentially expressed proteins and the differentially expressed genes that were detected also at the protein level, between both endurance groups and the corresponding controls. See also Figure S4 and Table S2.

**Figure 4**
Network analysis of global gene and protein expression data (A) Correlation matrix of “clinical data,” including subject characteristics, MRI data, and exercise performance data. (B) Centrality analysis of the most central analytes (proteins in green, genes in blue, and clinical outcome measures in pink circles). (C) Sub-network of the strongest correlating differentially expressed proteins and genes compared with maximal oxygen uptake (VO2-peak) and citrate synthase activity (CS activity). The circle border color in B & C indicates the direction of gene and protein expression, blue = significantly downregulated, red = significantly upregulated (5% false discovery rate) in endurance versus control. (D) Sub-network of the strongest correlating proteins and genes compared with anterior thigh volume and leg strength. (E) The three main clusters found from community detection of all genes, proteins, and clinical data and the proportion of analytes within each cluster. The five most enriched pathways based on analytes within each cluster are displayed, bold = pathways from proteins, italic = pathways from genes, bold italic = enriched in both proteins and genes within the cluster. Red lines = significant positive correlation. Blue lines = significant negative correlation. See also Figures S3 and S4.

**Figure 5**
Correlations between trained muscle proteomes from trained males and individuals with various metabolic conditions (A–F) Correlations of protein expression of DEPs in the current study with (A and B) an HIIT intervention in young males, (C and D) aging including young adults (20–49 years), middle-aged (50–64 years) and old adults (65+ yrs), and (E and F) a type 2 diabetes (T2D) study, including men with normal glucose tolerance (NGT), impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and T2D. (B), (D), and (F) represent comparisons of directionality of the differentially expressed proteins in the current compared with the external datasets in (A, C, and E), respectively. For clarity, significant correlations between study groups from the current study are only depicted in 5A. ∗ = FDR <0.05. See also Figure S5.

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References

1. Booth F.W., Roberts C.K., Laye M.J. Lack of exercise is a major cause of chronic diseases. Compr. Physiol. 2012;2:1143–1211. - PMC - PubMed
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[1] Booth F.W., Roberts C.K., Laye M.J. Lack of exercise is a major cause of chronic diseases. Compr. Physiol. 2012;2:1143–1211. - PMC - PubMed

[2] Booth F.W., Roberts C.K., Laye M.J. Lack of exercise is a major cause of chronic diseases. Compr. Physiol. 2012;2:1143–1211. - PMC - PubMed

[3] Neufer P.D., Bamman M.M., Muoio D.M., Bouchard C., Cooper D.M., Goodpaster B.H., Booth F.W., Kohrt W.M., Gerszten R.E., Mattson M.P., et al. Understanding the Cellular and Molecular Mechanisms of Physical Activity-Induced Health Benefits. Cell Metab. 2015;22:4–11. - PubMed

[4] Neufer P.D., Bamman M.M., Muoio D.M., Bouchard C., Cooper D.M., Goodpaster B.H., Booth F.W., Kohrt W.M., Gerszten R.E., Mattson M.P., et al. Understanding the Cellular and Molecular Mechanisms of Physical Activity-Induced Health Benefits. Cell Metab. 2015;22:4–11. - PubMed

[5] Egan B., Zierath J.R. Exercise Metabolism and the Molecular Regulation of Skeletal Muscle Adaptation. Cell Metab. 2013;17:162–184. - PubMed

[6] Egan B., Zierath J.R. Exercise Metabolism and the Molecular Regulation of Skeletal Muscle Adaptation. Cell Metab. 2013;17:162–184. - PubMed

[7] Baar K., Esser K. Phosphorylation of p70(S6k) correlates with increased skeletal muscle mass following resistance exercise. Am. J. Physiol. 1999;276:C120–C127. - PubMed

[8] Baar K., Esser K. Phosphorylation of p70(S6k) correlates with increased skeletal muscle mass following resistance exercise. Am. J. Physiol. 1999;276:C120–C127. - PubMed

[9] Coffey V.G., Zhong Z., Shield A., Canny B.J., Chibalin A.V., Zierath J.R., Hawley J.A. Early signaling responses to divergent exercise stimuli in skeletal muscle from well-trained humans. FASEB J. 2006;20:190–192. - PubMed

[10] Coffey V.G., Zhong Z., Shield A., Canny B.J., Chibalin A.V., Zierath J.R., Hawley J.A. Early signaling responses to divergent exercise stimuli in skeletal muscle from well-trained humans. FASEB J. 2006;20:190–192. - PubMed

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Remodeling of the human skeletal muscle proteome found after long-term endurance training but not after strength training

Affiliations

Remodeling of the human skeletal muscle proteome found after long-term endurance training but not after strength training

Authors

Affiliations

Abstract

Conflict of interest statement

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References

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Abstract

Conflict of interest statement

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