Molecular profiling of high-level athlete skeletal muscle after acute endurance or resistance exercise - A systems biology approach

Abstract

Objective: Long-term high-level exercise training leads to improvements in physical performance and multi-tissue adaptation following changes in molecular pathways. While skeletal muscle baseline differences between exercise-trained and untrained individuals have been previously investigated, it remains unclear how training history influences human multi-omics responses to acute exercise.

Methods: We recruited and extensively characterized 24 individuals categorized as endurance athletes with >15 years of training history, strength athletes or control subjects. Timeseries skeletal muscle biopsies were taken from M. vastus lateralis at three time-points after endurance or resistance exercise was performed and multi-omics molecular analysis performed.

Results: Our analyses revealed distinct activation differences of molecular processes such as fatty- and amino acid metabolism and transcription factors such as HIF1A and the MYF-family. We show that endurance athletes have an increased abundance of carnitine-derivates while strength athletes increase specific phospholipid metabolites compared to control subjects. Additionally, for the first time, we show the metabolite sorbitol to be substantially increased with acute exercise. On transcriptional level, we show that acute resistance exercise stimulates more gene expression than acute endurance exercise. This follows a specific pattern, with endurance athletes uniquely down-regulating pathways related to mitochondria, translation and ribosomes. Finally, both forms of exercise training specialize in diverging transcriptional directions, differentiating themselves from the transcriptome of the untrained control group.

Conclusions: We identify a "transcriptional specialization effect" by transcriptional narrowing and intensification, and molecular specialization effects on metabolomic level Additionally, we performed multi-omics network and cluster analysis, providing a novel resource of skeletal muscle transcriptomic and metabolomic profiling in highly trained and untrained individuals.

Keywords: Athletes; Human; Metabolomics; Molecular exercise effects; Multi-omics; Systems biology.

Figure 1

Study Design, Molecular Response to Exercise and overlap of DEGs, (A) Overview over the study design. Two bouts of endurance (EE) or resistance exercise (RE) were performed in a randomized cross-over design and skeletal muscle biopsies taken at 4 timepoints. (B) Cardiopulmonary (CPX) and strength testing and analyses performed. (C) Multi-omics response to acute exercise by subject group with the percentage of total significantly affected by EE or RE. (D) Overlap of differentially expressed genes compared to the respective pre timepoint. Widths of arches and segment lengths are proportional to the number of DEGs. (E) Within subject group coefficient of variation of analytes (transcripts, directly measured metabolites and transcription factor motifs) at baseline and in response to acute endurance or resistance exercise (mean of time course).

Figure 2

Functional analysis of the transcriptomic response to acute endurance and resistance exercise. (A) Gene set enrichment analysis (GSEA) of DEGs for acute endurance (EE) and resistance exercise (RE). Individual pathways are summarized by supergroups. (B) Ridge plot of mitochondrial and cellular respiration pathway molecular signatures from A. (C) Gene pattern identity from unsupervised clustering of genes of the endurance group performing EE (left) and the strength group performing RE (right) are used to compare the same sets of genes in all groups. Corresponding GSEA of patterns are shown on the far right in (C). Colors represent gene ontology collection of origin, labelled large dots are exercise-relevant pathways while small dots represent other pathways. (D) Projection (plotting of DEGs from one timepoint across the remaining timepoints; origin comparison timepoint with grey background) of DEGs comparing groups at individual timepoints to the remaining timepoints (mean ± confidence interval).

Figure 3

Clustering and classification of metabolomics. (A) Clustering (mean ± confidence interval) and classification of metabolites of endurance (EG) and strength group (SG) in response to acute endurance (EE) and resistance exercise (RE). Numbers in donuts represent the cluster size. (B) Clustering of lipid-associated metabolites in EG in response to EE and RE and (C) of amino acid-associated metabolites in SG in response to RE. (D) Analysis of carnitines in EG and amino acids in SG in response to acute exercise. Size and color of dots represent effect size and direction. Black circles represent statistical significance compared to pre.

Figure 4

Correlation of physiological parameters with molecular analytes. (A) Proportion of analyte response to EE and RE based on area under curve (AUC) significantly correlating to baseline $\dot{\mathrm{V}}$O2peak and peak torque respectively. Total number of analytes each in legend parenthesis. (B) Top and bottom 10 genes correlating with $\dot{\mathrm{V}}$O2peak and peak torque; top 3 genes shown individually. (C) Modelling of top genes explaining variation of $\dot{\mathrm{V}}$O2peak and peak torque. (D) Gene set enrichment analysis of genes (expressed as normalized enrichment score, NES) sorted by correlation for $\dot{\mathrm{V}}$O2peak and peak torque.

Figure 5

Omics-network overview and highlights. (A) Baseline -omics network showing the up to top 10 correlating genes for each physiological variable and all correlating transcription factor motifs and metabolites. (B) Overview over the acute -omics network with functional annotations of the three main clusters. (C) Distribution of analytes within the individual clusters from C. (D) Network of the top 10 % of metabolites, transcription factor motifs and physiological variables from each cluster. Edge colors represent direction and strength of connections. Red = positive, blue = negative. (E) Top 10 genes from each cluster, edges follow the same logic as in D. (fill colors in D and E represent cluster identity as in B; ∗: FDR<0.01 for Spearman correlation).

Figure 6

Multi-omics network of acute exercise. Multi-omics network of acute exercise showing genes, metabolites and transcription factor motifs significantly changed with acute exercise and positive or negative connection to selected physiological variables grouped by form of acute exercise and subject groups untrained control, and endurance and strength athletes with >15 years of training history.