Whole blood transcriptomics and urinary metabolomics to define adaptive biochemical pathways of high-intensity exercise in 50-60 year old masters athletes

Kamalika Mukherjee, Brittany A Edgett, Harrison W Burrows, Cecilia Castro, Julian L Griffin, Adel Giaid Schwertani, Brendon J Gurd, Colin D Funk, Kamalika Mukherjee, Brittany A Edgett, Harrison W Burrows, Cecilia Castro, Julian L Griffin, Adel Giaid Schwertani, Brendon J Gurd, Colin D Funk

Abstract

Exercise is beneficial for a variety of age-related disorders. However, the molecular mechanisms mediating the beneficial adaptations to exercise in older adults are not well understood. The aim of the current study was to utilize a dual approach to characterize the genetic and metabolic adaptive pathways altered by exercise in veteran athletes and age-matched untrained individuals. Two groups of 50-60 year old males: competitive cyclists (athletes, n = 9; VO2peak 59.1±5.2 ml·kg(-1)·min(-1); peak aerobic power 383±39 W) and untrained, minimally active individuals (controls, n = 8; VO2peak 35.9±9.7 ml·kg(-1)·min(-1); peak aerobic power 230±57 W) were examined. All participants completed an acute bout of submaximal endurance exercise, and blood and urine samples pre- and post-exercise were analyzed for gene expression and metabolic changes utilizing genome-wide DNA microarray analysis and NMR spectroscopy-based metabolomics, respectively. Our results indicate distinct differences in gene and metabolite expression involving energy metabolism, lipids, insulin signaling and cardiovascular function between the two groups. These findings may lead to new insights into beneficial signaling pathways of healthy aging and help identify surrogate markers for monitoring exercise and training load.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1. Gene expression in control and…
Figure 1. Gene expression in control and athlete subjects.
(A) Heatmap of the entire genome including all normalized and filtered probes in athlete and control groups across all time-points (T1: 24 hrs before exercise; T2: immediately after exercise; T3: 24 hrs after exercise). Red represents an up-regulation, and green a downregulation in gene expression. The probes were clustered by k-means clustering into 5 broad clusters. Representative Gene Ontology (GO) biological processes of each cluster are shown in their respective colours on the left. (B) Representative heatmap of probes found significantly different between athlete and control groups across all time-points (T1: 24 hrs before exercise; T2: immediately after exercise; T3: 24 hrs after exercise). The probes were clustered by hierarchical clustering. Fold change>1.5; False Discovery Rate (FDR)<0.1. (C) Top 10 networks representing genes differentially expressed between athlete and control groups across all time points by Metacore analysis, with corresponding p-values and FDR.
Figure 2. Differential gene expression between control…
Figure 2. Differential gene expression between control and athlete subjects after exercise.
(A) Venn diagram representing genes differentially expressed between control and athlete groups at time points T1 (blue; 24 hrs before exercise; n = 4 for each group); T2 (yellow; immediately after exercise; n = 7 for controls and n = 8 for athlete group); and T3 (green; 24 hrs after exercise; n = 4 for each group). Fold change>1.5; False Discovery Rate (FDR)<0.1. Genes differentially regulated at two or three time points are shown in the corresponding intersecting segments. (B) Heatmap representing genes differentially expressed between individual control and athlete samples at time point T2 (immediately after exercise). Each column represents an individual subject. Red represents an up-regulation, and green a down-regulation in gene expression. Probes and samples are clustered by hierarchical clustering Fold change>1.5; FDR<0.1. (C) Top 10 canonical pathway maps representing genes differentially expressed between athlete and control groups at time-point T2 by Metacore analysis with corresponding p-values and FDR.
Figure 3. Validation of microarray data.
Figure 3. Validation of microarray data.
(A) Fold-change in expression of genes UTS2, HSD11B1, OCLN, IGF1R and INSIG2 between athlete (n = 8) and control (n = 7) groups at time point T2, as evident by microarray analysis (Nexus Gene Expression and Genespring), and validated by quantitative real-time PCR (qRT-PCR). qRT-PCR data are normalized to the housekeeping gene and were statistically significant with p<0.05 (n = 6 in each group). (B) Concentration of urotensin (pg/ml) in the plasma of control (n = 8) and athlete (n = 6) subjects at time-point T1.
Figure 4. 1 H NMR spectra of…
Figure 4. 1H NMR spectra of control and athlete urine samples.
1H NMR spectra of a representative pre- (T1) and post-exercise (T2) urine sample from an athlete (A) and control (B) subject highlighting visible differences between the two spectra and the major resonances. Numbered biomolecules correspond to Table 3.
Figure 5. Statistical analysis of 1 H…
Figure 5. Statistical analysis of 1H NMR spectra.
(A) OPLS-DA component plot derived from spectra of urine before (▴, athlete and •, control) and after execise (Δ, athlete and ○, control). R2X = 0.50; Q2 = 0.43. (B) OPLS coefficient plot showing the most significant contributors to the separation. Numbered biomolecules correspond to Table 3.
Figure 6. Pathway analysis of urinary metabolites.
Figure 6. Pathway analysis of urinary metabolites.
Top 10 networks representing metabolites differentially expressed between athlete and control groups pre- and post-exercise by Metacore analysis, with corresponding p-values and FDR.

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