GW501516 increased the running distance of sedentary and trained KM mice on endurance tolerance tests

To examine whether GW501516 can enhance the running endurance capacity of mice, we treated age-matched wild-type KM cohorts with either vehicle or GW501516 for 3 weeks. Trained mice increased their exhaustive running distance by 744.4 m (31.2%) after 3 weeks of GW501516 treatment (TG versus TN) (Fig. 1a). GW501516 treatment for 3 weeks in sedentary KM mice also significantly enhanced running distance by 750.3 m (68.6%) compared to untrained control mice (p < 0.01 for NG versus NN). Similarly, even GW501516 treatment alone (NG group) for only 1 week improved the running distance by 671.8 m (48.6%) compared to the vehicle control group (NN group) (Supplementary Fig. S1a online).

Figure 1 The effects of GW501516 on running performance, blood glucose and blood lactate in sedentary and trained KM mice. Forced wheel running endurance tests were performed following 3 weeks of treatment. (a) shows the total distance ran by mice in each group. Blood glucose (b) and serum lactate (c) levels are shown for all groups both before and after running tests (n = 10–11 per group).*p < 0.05, **p < 0.01 compared to the NN group; #p < 0.05 compared to the TN group. Full size image

Effects of GW501516 on blood glucose and lactate levels in sedentary and trained mice

Before endurance tolerance tests, blood glucose levels in the trained and untrained GW501516-treated mice (TG or NG) and trained control mice (TN) tended to be higher than the untrained control mice (NN), but no statistically significant differences were observed (Fig. 1b). Blood lactate was slightly decreased in treated sedentary mice and untreated trained mice compared to untrained sedentary mice (p < 0.05 for NG or TN versus NN) (Fig. 1c). Training and GW501516 treatment further decreased blood lactate levels compared to untrained sedentary mice (p < 0.01 for TG versus NN). Exhaustive endurance test in sedentary untreated mice reduced blood glucose levels by 69% (p < 0.01) and increased blood lactate levels 52% (p < 0.01) compared to before testing. Exercise training alone for 3 weeks significantly reduced the elevation of blood lactate (0.65 mM increase in TN mice versus 1.02 mM increase in NN mice) after exhaustive running. Additionally, GW501516 treatment reduced utilization of blood glucose during endurance running (p < 0.01 for TG versus TN and NG versus NN). Blood lactate did not increase in untrained GW501516-treated mice (NG) after running (change of −0.06 mM) and was modestly, but significantly increased in trained GW501516-treated mice (TG) (change of 0.42 mM, p < 0.05) after running. Blood glucose levels of untrained GW501516-treated mice were higher than untrained controls (p < 0.01 for NG versus NN). Post-exercise blood lactate was markedly lower in trained GW501516-treated mice compared trained controls (p < 0.05 for TG versus TN) and lower in sedentary GW501516-treated mice compared to sedentary controls (p < 0.01 for NG versus NN). Furthermore, similar results were obtained after only 1 week of GW501516 administration (Supplementary Fig. S1 online).

Serum fatty acid metabolomics

Fatty acid metabolomic analyses were performed on serum from exhausted mice by GC × GC–TOFMS. After data alignment, normalization and unit variance scaling, principle component analysis (PCA) score plots revealed a trend of intergroup separation and intragroup aggregation (Fig. 2). Results from a PLS-DA model by using two predictive components (R2X cum = 0.353, R2Y cum = 0.877 and Q2Y cum = 0.803 for sedentary groups; R2X cum = 0.306, R2Y cum = 0.960, Q2Y cum = 0.687 for trained groups) showed separations between GW501516-treated groups (TG and NG) and their respective control groups (TN and NN) (Fig. 3a). The metabolites with variable importance in the projection (VIP) values >1.5 and pvalues <0.05 were considered different between groups (Supplementary Table 1 online). These metabolites were subjected to computational systems analyses to investigate their roles in training and GW501516 treatment. The parallel PCA score trajectory plots (Fig. 2a) and heat map visualization (Fig. 2b) showed obvious differences resulting from training and/or GW501516. Relative to the distribution of metabolites in the untreated sedentary group (NN group) metabolites clustered along PC2 after GW501516 treatment alone (NG group) and above and to the right of the first component after training alone (TN group) (Fig. 2a). Combined GW501516 treatment and training generated a unique metabolic profile that that did not substantially overlap with the profiles of the individual treatments (training or GW501516 treatment alone). Scatter in the PCA plot for the trained and GW501516-treated group (TG) moved toward the training-only group (TN) along PC1, but the groups were distinct along PC2 (Fig. 2a).

Figure 2 Metabolomic analysis of serum fatty acids. (a) shows PCA scores trajectory plots, (b) shows the representative heat map of untrained (NN and NG) and trained (TN and TG) mice. In the heat map, samples are sorted by row and metabolites are sorted by column. Green diamond, NN; Red triangle, NG; Black circle, TN; Blue square, TG; Full size image

Figure 3 Differences in metabolite concentrations between groups. (a) shows PLS-DA score plots of groups based on serum spectral data from GC × GC–TOFMS. The score plots distinctly cluster the GW501516 treatment groups (NG and TG) compared to either control group (NN and NG). Furthermore, the trained groups (TN and TG) are distinctly clustered compared to their respective controls (NN and TG). (b) and (c) show changes in the relative concentrations of metabolites in the four groups. Two-tailed parametric t tests were used to compare concentration changes in each metabolite between groups (n = 10–11 per group). *p < 0.05, **p < 0.01 compared to the NN group, #p < 0.05, ##p < 0.01 versus TN; †p < 0.05, ††p < 0.01 compared to the NG group. Full size image

There were 10 types of fatty acids that differed between untreated trained mice and untreated sedentary mice (TN versus NN) (Fig. 3b,c, Supplementary Table 1 online). There were 15 fatty acids that were different between GW501516-treated sedentary mice and untreated sedentary mice (NG versus NN). There were 10 fatty acids that differed between trained mice receiving GW501516 treatment and untreated trained mice (TG versus TN). There were 20 fatty acids that were different in trained GW501516-treated mice compared to sedentary GW501516-treated mice (TG versus NG).Metabolites whose levels were different between groups could roughly be classified into 3 groups (constituents of fatty acid oxidation pathways, saturated fatty acid [SFA] and unsaturated fatty acid [UFA]). Palmitic acid, the predominate fatty acid in vivo, was increased substantially by training (143% greater in TN than NN) or GW501516 treatment (92% greater in NG than NN) and was further increased by their combination (282% greater in TG than NN) (Fig. 3b). Stearic acid was significantly decreased by training or GW501516 treatment alone, but which was reversed to level of NN group when training and GW501516 were combined (TG group), who had 60% more stearic acid than the trained untreated mice (TN group) and 208% more than untrained treated mice (NG group). The 5 kinds of fatty acids increased by training were all intermediates for β oxidation of long-chain fatty acids (Figs. 3b and 4).Compared to sedentary controls (NN group), 6 out of 10 metabolites that were markedly reduced in the sedentary GW501516-treated group are long-chain saturated fatty acids (15-methyl-hexadecanoic acid, pentadecanoic acid, octadecanoic acid, eicosanoic acid, dodecanoic acid and tetradecanoicacid) (Fig. 3, Supplementary Table 1 online). However, levels of the 6 saturated long-chain fatty acids reduced by GW501516 alone (NG compared to NN) were restored in trained mice that were treated with GW501516 (TG group) (Fig. 3b), suggesting higher rates of triglyceride hydrolysis after training and GW501516 treatment.

Figure 4 Pathway analysis of fatty acid metabolomics data Serum metabolites that were altered between groups are shown in red. Full size image

Another difference between training and GW501516 treatment is observed regarding UFA. GW501516 treatment alone markedly increased UFA and additional increases could be observed when combined with training (Fig. 3c). Relative to control mice (NN), only 1 polyunsaturated fatty acid (PUFA), 8,11,14-eicosatrienoic acid, was increased by training alone (TN) (79% greater in TN than NN), but 3 PUFAs, includingα-linolenic acid, 8,11,14-Eicosatrienoic acid and arachidonic acid, were increased by single GW501516 treatment (NG) (143%, 217% and 27% greater respectively in NG than NN). In mice that were trained, 7 of the metabolites (oleic acid, α-linolenic acid, 11,14-Eicosadienoic acid, 8,11,14-Eicosatrienoic acid, arachidonic acid, 8,11,14,17-eicosapentaenoic acid and docosahexaenoic acid) that were found to be different between GW501516-treated mice (TG) and trained controls (TN) were UFA and 6 of the UFA were PUFA (α-linolenic acid, 11,14-Eicosadienoic acid, 8,11,14-Eicosatrienoic acid, arachidonic acid, 8,11,14,17-eicosapentaenoic acid and docosahexaenoic acid). The above results indicated that exercise and GW501516 act synergistically to promote the mobilization of triglycerides and fatty acids oxidation.

Whole serum metabolomics

To further define the metabolic response to GW501516 and/or exercise, a non-selective metabolomic study of serum was performed. A total of 122 peaks were obtained from the GC-TOFMS spectra (Fig. 5a) and 43 of those peaks were confirmed to be endogenous metabolites based on comparison with NIST02 libraries and commercially available reference compounds. The orthogonal partial least squares-discriminant analysis (OPLS-DA) cross-validated scores plots of whole metabolites in a way that was similar to those of the serum fatty acids metabolomics and showed clear segregations between groups (Fig. 5b). The distribution of the metabolites after GW501516 treatment (NG group) moved toward the second component relative to untrained sedentary mice (NN group). Training alone (TN group) caused metabolites to cluster to the right of those in the NN group, while training and GW501516 treatment (TG group) were directly above the NN group on the plot (Fig. 5b). Among the 43 metabolites, 14 were significantly different between groups and are involved in several key metabolic pathways such as fatty acid metabolism, galactose metabolism, glycogenic amino acid (alanine, serine and threonine) metabolism, branched chain amino acid (BCAA) (valine, leucine and isoleucine) metabolism and metabolism of ketone bodies (Fig. 6). Changes in serum glucose and palmitic acid were in agreement with the other analyses performed in this study (Fig. 6a). Serum galactose and hydroxybutyrate were increased by GW501516 treatment regardless of training (TG and NG). Inositol however, was increased only after training alone (TN). Alanine, serine, threonine, valine and isoleucine were increased by training, but leucine was not (Fig. 6a and c). Moreover, the elevated levels of inositol and glycogenic amino acids seen with training alone (TN)were reversed by combining GW501516 treatment with training (TG). Additionally, BCAA were found to be consistently and significantly elevated by GW501516 treatment (Fig. 6c).

Figure 5 Changes in metabolites from whole serum. (a) shows the total ion chromatogram (TIC) of serum samples following GC-TOFMS. (b) shows the OPLS-DA score plot of serum samples for cross-validation. Full size image

Figure 6 Metabolites that were significantly different between groups (a) shows serum glucose, galactose, hydroxybutyrate and inositol. (b) shows serum glucogenic amino acid levels and (c) shows levels of branched chain amino acids (n = 10–11 per group). *p < 0.05, **p < 0.01 compared to the NN group; #p < 0.05 compared to the TN group. Full size image

Gene expression analysis

mRNA expression levels of PPARδ and PPARα were analysed by quantitative polymerase chain reaction (qPCR) (Fig. 7). qPCR analysis revealed that GW501516 treatment alone or in combination with training upregulated expression of PPARγ coactivator 1α (PGC1α) and pyruvate dehydrogenase kinase 4 (PDK4). Corresponding with this, more robust upregulating to genes involved in glucose supplementation (phosphoenolpyruvate carboxykinase1 (PEPCK1), fructose bisphosphatase 2 (Fbp2) and glycogen phosphorylase (Pygm)), glucose utilization and glycolysis (glucose transporters (GLUT4), hexokinase2 (HK2), phosphofructokinase (Pfkm), pyruvate kinase 2 (Pkm2) and lactate dehydrogenase (Ldh)) were found in untreated trained mice. Training and GW501516 together upregulated PPARα, muscle carnitine palmitoyltransferase Ib (CPT Ib), uncoupling protein 3 (UCP3) and cytochrome c (CYT-C). Upregulated PPARδ expression was observed only in trained mice (TG and TN), but greater expression was observed in trained mice that were treated with GW501516 than untreated trained mice. Similar expression patterns were also observed for PPARα and CPT Ib. Additionally, expression of the major histocompatibility complex I (MHC I) was down-regulated after GW501516 treatment alone (NG). Expression levels of MHCIIa and IIb were increased after training alone (TN), but these changes were reversed when training was combined with GW501516 treatment (TG).

Figure 7 The effects of GW501516 treatment on skeletal muscle gene expression in trained and sedentary KM mice Gene expression was measured by QPCR (n = 4 per group). *p < 0.05, **p < 0.01 compared to the NN group; #p < 0.05, ##p < 0.01 compared to the TN group. Full size image

GW501516 increases the quantity of succinate dehydrogenase (SDH)-positive muscle fibres in both sedentary and trained mice

SDH staining of gastrocnemius muscle sections revealed that there was a statistically insignificant trend for the proportion of SDH-positive fibres to increase following training alone compared to sedentary mice (TN versus NN) (Fig. 8). In contrast, GW501516 alone significantly increased the proportion of SDH-positive fibres by 72% compared to untreated sedentary mice (NG versus NN). Trained mice that were treated with GW501516 had 113% more SDH-positive fibres than untrained sedentary mice (TG versus NN) (Fig. 8b). Moreover the increase in SDH-positive fibres in trained mice treated with GW501516 occurred in a time dependent manner. Compared to the NN group, TG mice had 36% more fibres after 1 week and 82% after 2 weeks (Supplementary Fig. S2 online).