Metabolomics reveals mouse plasma metabolite responses to acute exercise and effects of disrupting AMPK-glycogen interactions

Abstract

Introduction: The AMP-activated protein kinase (AMPK) is a master regulator of energy homeostasis that becomes activated by exercise and binds glycogen, an important energy store required to meet exercise-induced energy demands. Disruption of AMPK-glycogen interactions in mice reduces exercise capacity and impairs whole-body metabolism. However, the mechanisms underlying these phenotypic effects at rest and following exercise are unknown. Furthermore, the plasma metabolite responses to an acute exercise challenge in mice remain largely uncharacterized. Methods: Plasma samples were collected from wild type (WT) and AMPK double knock-in (DKI) mice with disrupted AMPK-glycogen binding at rest and following 30-min submaximal treadmill running. An untargeted metabolomics approach was utilized to determine the breadth of plasma metabolite changes occurring in response to acute exercise and the effects of disrupting AMPK-glycogen binding. Results: Relative to WT mice, DKI mice had reduced maximal running speed (p < 0.0001) concomitant with increased body mass (p < 0.01) and adiposity (p < 0.001). A total of 83 plasma metabolites were identified/annotated, with 17 metabolites significantly different (p < 0.05; FDR<0.1) in exercised (↑6; ↓11) versus rested mice, including amino acids, acylcarnitines and steroid hormones. Pantothenic acid was reduced in DKI mice versus WT. Distinct plasma metabolite profiles were observed between the rest and exercise conditions and between WT and DKI mice at rest, while metabolite profiles of both genotypes converged following exercise. These differences in metabolite profiles were primarily explained by exercise-associated increases in acylcarnitines and steroid hormones as well as decreases in amino acids and derivatives following exercise. DKI plasma showed greater decreases in amino acids following exercise versus WT. Conclusion: This is the first study to map mouse plasma metabolomic changes following a bout of acute exercise in WT mice and the effects of disrupting AMPK-glycogen interactions in DKI mice. Untargeted metabolomics revealed alterations in metabolite profiles between rested and exercised mice in both genotypes, and between genotypes at rest. This study has uncovered known and previously unreported plasma metabolite responses to acute exercise in WT mice, as well as greater decreases in amino acids following exercise in DKI plasma. Reduced pantothenic acid levels may contribute to differences in fuel utilization in DKI mice.

Keywords: AMP-activated protein kinase; acylcarnitines; amino acids; exercise metabolism; glycogen; metabolomics; pantothenic acid; plasma metabolite.

FIGURE 1

AMPK DKI mouse model and phenotypic effects of disrupting whole-body AMPK-glycogen interactions on maximal running speed and body composition. (A) Schematic of WT mice with intact AMPK-glycogen binding and AMPK DKI mice in which critical tryptophan residues within the AMPK β subunit isoforms that mediate glycogen binding (β1; predominantly expressed in mouse liver; and β2 predominantly expressed in mouse skeletal muscle) have been mutated to alanine (β1 W100A and β2 W98A, respectively), resulting in whole-body disruption of AMPK-glycogen binding; (B) Maximal running speed (m/min); (C) Total body mass (g); (D) Total lean mass (g); (E) Total fat mass (g). *: p < 0.05, **: p < 0.01, ****p < 0.0001; values are represented as mean ± SEM; n = 21–23 mice per group.

FIGURE 2

Hierarchical Cluster Analysis (HCA) dendrogram of identified/annotated metabolites. (A) Agglomerative clustering of individual metabolites based on pairwise correlation is shown. The lowest linkages within the HCA dendrogram indicate metabolites that display similar relative responses between the experimental groups. Six clusters were observed. Metabolite labels are colored to reflect the results of the two-way ANOVA after filtering using a false discovery rate (FDR) of 0.1 (red = significant effect of condition only; magenta = significant effect of genotype and condition; black = no significance or FDR >0.1). *: Metabolites that significantly (p < 0.05) contributed to the model along canonical variate 1 (CV1, Figure 3A); #: Metabolites that significantly (p < 0.05) contributed to the model along CV2. (B) Z-scores plot of the mean responses for each metabolite cluster. After conversion of individual metabolite log₁₀ responses to a z-score, the average response of each cluster was calculated and presented here as a group error bar plot. Error bars indicate the standard error for each group mean (Red = WT; Blue = DKI). Following two-way ANOVA, only Clusters C and F showed significant differences in the averaged group metabolite response. Cluster C showed a significant effect of exercise for both genotypes (p = 2 × 10⁻⁵), and Cluster F showed a significant interaction between genotype and exercise (p = 4 × 10⁻⁴) such that the WT metabolite levels remained constant and the DKI metabolite levels significantly decreased in response to exercise.

FIGURE 3

Principal Component-Canonical Variates Analysis (PC-CVA) showing system-wide metabolite profile differences between genotype at rest and after exercise. (A) Scores plot of Canonical Variate 1 (CV1) vs. Canonical Variate 2 (CV2). Each point (circle, square or triangle) represents a single sample [WT-Rest (n = 8), WT-Ex (n = 9), DKI-Rest (n = 10), DKI-Ex (n = 9)]. The mean (x) of each group is surrounded by a 95% confidence interval of the mean (full-line circles) and 95% confidence interval of membership in each sample group (dashed-line circles). Sample group means are considered significantly different when the 95% confidence interval of the means do not overlap. (B) The loading plot shows the influence (model coefficient value) of each metabolite that significantly (p < 0.05) contributes to the separation observed in the scores plot. The direction of the coefficient vector maps directly to the direction of the data points in the scores plot relative to the origin.