The benefits of endurance exercise on general health make it desirable to identify orally active agents that would mimic or potentiate the effects of exercise to treat metabolic diseases. Although certain natural compounds, such as reseveratrol, have endurance-enhancing activities, their exact metabolic targets remain elusive. We therefore tested the effect of pathway-specific drugs on endurance capacities of mice in a treadmill running test. We found that PPARβ/δ agonist and exercise training synergistically increase oxidative myofibers and running endurance in adult mice. Because training activates AMPK and PGC1α, we then tested whether the orally active AMPK agonist AICAR might be sufficient to overcome the exercise requirement. Unexpectedly, even in sedentary mice, 4 weeks of AICAR treatment alone induced metabolic genes and enhanced running endurance by 44%. These results demonstrate that AMPK-PPARδ pathway can be targeted by orally active drugs to enhance training adaptation or even to increase endurance without exercise.

Exercise training activates a number of transcriptional regulators and serine-threonine kinases in skeletal muscles that contribute to metabolic reprogramming (). We and others previously identified a critical role for PPARβ/δ (henceforth referred to as PPARδ) in transcriptional regulation of skeletal muscle metabolism (). Overexpression of a constitutively active PPARδ (VP16-PPARδ) in skeletal muscles of transgenic mice preprograms an increase in oxidative muscle fibers, enhancing running endurance by nearly 100% in untrained adult mice (). One of the best understood serine-threonine kinases is AMP-activated protein kinase (AMPK), a master regulator of cellular and organismal metabolism whose function is conserved in all eukaryotes (). In mammals, AMPK has been shown to contribute to glucose homeostasis, appetite, and exercise physiology (). These observations raise the question as to whether synthetic PPARδ or AMPK agonists can reprogram established fiber specification in adult muscle toward an overt endurance phenotype. We have found that the PPARδ agonist GW1516 (shown to be bioactive in humans;) enables mice to run 60%–75% longer and further than the nontreated controls only when combined with exercise training. This “super-endurance phenotype” is linked to a transcriptional boost provided by exercise-activated AMPK resulting in a novel endurance gene signature. A more critical role of AMPK in the super-endurance phenotype is revealed in our unexpected finding that the orally active AMPK agonist AICAR is sufficient as a single agent to improve running endurance by nearly 45% in nonexercised mice. Together, these results provide new insights into the pharmacological malleability of muscle performance.

Given the numerous benefits of exercise on general health, identification of orally active agents that mimic or potentiate the genetic effects of endurance exercise is a long-standing, albeit elusive, medical goal. High doses of certain natural extracts such as resveratrol can improve endurance (). The aerobic effects of resveratrol are thought to dependent on activation of SIRT1-PGC1α coactivator complex in skeletal muscle. However, the downstream transcriptional factor(s) targeted by SIRT1/PGC1α in mediating these effects are not known. More importantly, both SIRT1/PGC1α and resveratrol activate multiple targets, and thus whether there is a specific signaling pathway that can be selectively activated by a synthetic drug to improve endurance is not known.

Skeletal muscle is an adaptive tissue composed of multiple myofibers that differ in their metabolic and contractile properties, including oxidative slow-twitch (type I), mixed oxidative-glycolytic fast-twitch (type IIa) and glycolytic fast-twitch (type IIb) myofibers (). Type I fibers preferentially express enzymes that oxidize fatty acids, contain slow isoforms of contractile proteins, and are more resistant to fatigue than are glycolytic fibers. Type II fibers preferentially metabolize glucose and express the fast isoforms of contractile proteins. Endurance exercise training triggers a remodeling program in skeletal muscle that progressively enhances performance in athletes such as marathon runners, mountain climbers, and cyclists. This involves change in metabolic programs and structural proteins within the myofiber that alter the energy substrate utilization and contractile properties that act to reduce muscle fatigue (). Training-based adaptations in the muscle are linked to increases in the expression of genes involved in the slow-twitch contractile apparatus, mitochondrial respiration, and fatty acid oxidation (). These adaptations that improve performance can also protect against obesity and related metabolic disorders (). Moreover, skeletal muscles rich in oxidative slow-twitch fibers are resistant to muscle wasting ().

To test this idea we treated C57B/6J mice with AICAR (500 mg/kg/day) for 4 weeks. AICAR increased phosphorylation of AMPK α subunit and acetyl CoA carboxylase (ACC) and increased expression of UCP3 in quadriceps, confirming effective activation of AMPK signaling ( Figure 6 A). Interestingly, 4 weeks of drug treatment decreased the ratio of epididymal fat mass to body weight and increased oxygen consumption without changing body weight ( Figures 6 B–6E), supporting the speculation that AICAR may positively regulate endurance. Indeed, in a treadmill endurance test, ACIAR-treated mice ran longer (∼23%) and further (∼44%) than did vehicle-treated mice, revealing that increase in endurance can be achieved without exercise ( Figure 6 F). Furthermore, global gene expression analysis of quadriceps revealed that AICAR treatment alone upregulated a set of 32 genes linked to oxidative metabolism ( Figure 6 G and Table S5 ). Notably, 30 of these 32 genes were also upregulated in VP16-PPARδ transgenic mice, suggesting that stimulation of oxidative genes by AMPK may depend on PPARδ ( Table S5 ). To test this possibility, we utilized wild-type and PPARδ null primary muscle cells. Treatment of wild-type primary cells with AICAR (for 72 hr) increased expression of key oxidative biomarker genes (Scd1, fasn [FAS], Ppargc 1a, Pdk4) ( Figure 6 H). In contrast, AICAR failed to increase the expression of the above genes in PPARδ null cells, demonstrating the requirement of the receptor for transcriptional effects of AMPK on oxidative genes.

Data in (B) and (C) (n = 10), (D) and (E) (n = 4), (F) (n = 15–20), and (H) (n = 9) are presented as mean ± SEM, and ∗ indicates statistical significance (p < 0.05, unpaired student's t test).

Our findings show that pharmacologic activation of PPARδ in adult mice can increase running endurance only in conjunction with exercise signals. The central role for AMPK in this process is especially underscored by the observations that it is both robustly stimulated by exercise as well as constitutively active in muscles of VP16-PPARδ transgenic mice that exhibit endurance without exercise. Further, AMPK can integrate multiple transcriptional programs by interacting not only with PPARδ but also other transcriptional regulators of metabolism (e.g., PGC1α, PPARα) (). This raises the interesting question as to whether chemical activation of AMPK is sufficient to increase running endurance without exercise.

To more directly examine this connection, we utilized reporter gene expression assays. Cotransfection of either catalytic AMPK α1 or α2 subunits but not control vector with PPARδ increased the basal ( Figure 5 E) and GW1516-dependent transcriptional activity ( Figure 5 F) of PPARδ in inducing a PPRE-driven reporter gene in AD293 cells. It should be noted that AMPK overexpression or GW1516 treatment did not change reporter activity in transfections excluding the PPARδ expression vector (data not shown), negating the possibility of an effect via RXR. Additionally, in AD293 cells cotransfected with Flag-PPARδ and with either catalytic AMPK α1 or α2 subunits, we discovered that each of the AMPK subunits coimmunoprecipitated as a complex with Flag-PPARδ ( Figure 5 G). Furthermore, Flag-PPARδ coimmunoprecipitated endogenous AMPKα subunits from AD293 cells, confirming a tight physical interaction between the nuclear receptor and the kinase ( Figure 5 H). Despite this association, AMPK failed to increase PPARδ phosphorylation. In vivo orthophosphate labeling of PPARδ in AD 293 cells in the presence or absence of either AMPK alpha isoform under the same conditions where AMPK promotes PPARδ-dependent transcription revealed no change in overall PPARδ phosphorylation ( Figure 5 I). These data suggest that PPARδ phosphorylation is not increased by AMPK in vivo. However, cotransfection of AMPKα2 and coactivator PGC1α (a previously reported direct substrate of AMPK) cooperatively interact to further induce both the basal and ligand-dependent transcriptional activity of PPARδ ( Figure 5 J). Strikingly, we did not detect physical interaction between Flag-PGC1α and AMPK ( Figure 5 K), though both independently interacted with PPARδ. Collectively, these observations suggest that AMPK may be present in a transcriptional complex with PPARδ, where it can potentiate receptor activity via direct protein-protein interaction and/or by phosphorylating coactivators such as PGC1α.

The above described pathway crosstalk raised the possibility that AMPK directly regulates the transcriptional activity of PPARδ in skeletal muscles. An analysis of the effects of GW1516 and AICAR on gene expression in primary muscle cells isolated from wild-type and PPARδ null mice revealed that synergism is completely dependent on PPARδ and lost in the null cells ( Figures 5 A–5D). These observations show that AMPK enhances a subset of ligand-dependent PPARδ transcriptional targets in a cell-autonomous fashion.

Data in (A)–(D) (n = 6), (E), and (J) (n = 3–4) are presented as mean ± SEM, and ∗ indicates statistical significance (p < 0.05, one-way ANOVA; post hoc: Dunnett's multiple comparison test).

(E–F and J) AD293 cells were transfected with PPARδ+RXRα+Tk-PPRE along with control vector, AMPK α1, α2, and/or PGC1α as indicated.

According to this hypothesis, selective coactivation of AMPK and PPARδ would induce gene expression changes that mimic those triggered by combined exercise and PPARδ as well as VP16-PPARδ overexpression. To investigate this possibility, we compared the transcriptional changes induced in skeletal muscle by combined exercise and GW1516 treatment with that of combined AMPK activator (the cell-permeable AMP analog AICAR) and GW1516 treatment. It is noteworthy that simultaneous GW1516 and AICAR treatment created a unique gene expression signature in the quadriceps of untrained C57Bl/6J mice ( Figure S2 ) that shares 40% of the genes with that of combined GW1516 treatment and exercise ( Figure 4 C). Classification of the 52 genes common to the two signatures ( Figure 4 D, listed in Table S4 ) revealed that the majority of the targets were linked to oxidative metabolism. Quantitative expression analysis of selective oxidative genes by QPCR showed that several of these biomarkers, including Scd1, ATP citrate lyase (Acly), hormone sensitive lipase (HSL) (Lipe), muscle fatty acid binding protein (mFABP, Fabp3), Lpl, and Pdk4, were induced in a synergistic fashion by GW1516 and AICAR in the quadriceps ( Figures 4 E–4J). It is also noteworthy that all of the above genes were induced in quadriceps of untrained VP16-PPARδ mice, where AMPK is constitutively active ( Figure S1 G). Collectively, these results show that interaction between AMPK and PPARδ substantially contributes to reprogramming of the skeletal muscle transcriptome during exercise.

What might be the molecular interface between mechanical exercise and PPARδ transcription? Exercise training is known to activate multiple kinases, among which AMPK has profound effects on skeletal muscle gene expression and oxidative metabolism (). Indeed, mice defective for AMPK signaling in muscle exhibit reduced capacity for voluntary running (). As previously observed (), we found increased AMPK activation in the quadriceps of exercised mice relative to the sedentary controls ( Figure 4 A). Furthermore and unexpectedly, AMPK is constitutively activated in muscles of VP16-PPARδ transgenic mice in absence of exercise or drug ( Figure 4 B). In contrast, in our experiments, GW1516 treatment alone does not activate AMPK in either sedentary or exercise trained muscles, as previously suggested by some () but not by others (). Taken together, these results strongly suggest that the ability to promote endurance in mice is associated with activation of both AMPK and PPARδ.

(E–J) Expression of Scd1 (E), ATP citrate lyase (Acly) (F), HSL (Lipe) (G), Fabp3 (H), Lpl (I), and Pdk4 (J) transcripts in quadriceps of mice treated with vehicle (V), GW1516 (GW, 5 mg/kg/day), AICAR (AI, 250 mg/kg/day), and the combination of the two drugs (GW+AI) for 6 days. Data are presented as mean ± SEM (n = 6). ∗ indicates statistically significant difference between V and indicated groups (p < 0.05, one-way ANOVA; post hoc: Dunnett's multiple comparison test).

(C) Comparison of Tr+GW and AI+GW dependent gene signatures in quadriceps (N = 3). The selection criteria used is similar to one used in Figure 3 C.

Role of AMP kinase and PPARdelta in the regulation of lipid and glucose metabolism in human skeletal muscle.

To dissect the mechanism underlying the super-endurance phenotype, we conducted a comprehensive study of the muscle transcriptome induced by ligand, exercise, or the combination, which produced three overlapping networks of 96, 113, and 130 genes, respectively ( Figure 3 C). Approximately 50% of the target genes were common between GW1516 and exercise, demonstrating that PPARδ activation partially mimics exercise. To our surprise, combined GW1516 treatment and exercise established a unique gene expression pattern that was neither an amalgamation nor a complete overlap of the individual interventions ( Figure 3 C). This signature included 48 new target genes ( Table S1 ) not regulated by either GW1516 or exercise alone while excluding 74 genes regulated by GW1516 or exercise (selective genes are listed in Table S3 ). The majority of the genes in the GW1516-exercise signature were induced (108/130), the components of which are described in Figure 3 D. Although the largest gene subclass (32% of genes) was linked to positive regulation of aerobic capacity, additional pathways implicated in muscle remodeling and endurance were also represented in the signature (see Table S2 for detailed description). It is noteworthy that comparative expression analysis of the 48 exclusive genes of the endurance signature (but not of either intervention alone) revealed a striking similarity to “untrained” VP16-PPARδ transgenic mice ( Figure 3 E). This observation confirms the primary dependence of the 48 genes on PPARδ and points to the possibility that exercise-generated signals may function to synergize PPARδ transcriptional activity to levels comparable to transgenic overexpression.

As described above, although GW1516 treatment alone induces widespread genomic changes associated with oxidative metabolism, it fails to increase running endurance. On the other hand, drug treatment in conjunction with exercise produces an enriched remodeling program that includes a series of transcriptional and posttranslational adaptations in the skeletal muscle. This suggests that exercise training serves as a key trigger to unmask a cryptic set of PPARδ target genes, leading us to re-examine the ability of the drug to modulate endurance. Indeed, the same dose and duration of GW1516 treatment that previously failed to alter performance, when paired with 4 weeks of exercise training, increases running time by 68% and running distance by 70% over vehicle-treated trained mice ( Figures 3 A and 3B, compare week 5). It is also important to note that comparison of running time and distance before (week 0) and after (week 5) exercise and drug treatment revealed a 100% increment in endurance capacity for individual mice, underscoring the robustness of the combination paradigm ( Figures 3 A and 3B). Finally, it is noteworthy that the combined effects of GW1516 and exercise reduces the ratio of epididymal fat to body weight and fat cross-sectional area in these mice ( Figures S1 E and S1F), suggesting the broader systemic effects of this protocol.

We also measured the protein levels of selective oxidative biomarkers including myoglobin, UCP3, cytochrome c (CYCS), and SCD1. In each case, a more robust upregulation of protein expression was found by combining exercise and GW1516 treatment relative to either drug or exercise alone ( Figure 2 D). Altered triglycerides are one way to assess changes in muscle oxidative capacity. Triglyceride levels were unchanged in vehicle- or GW1516-treated sedentary mice but showed a striking increase with exercise. In contrast, this increase was completely reversed by GW1516 treatment, presumably because of enhanced fat utilization ( Figure S1 D).

The effects of GW1516 treatment and exercise, singly or in combination, on components of the oxidative metabolism of fatty acids were further analyzed by measurement of the gene expression levels of selective biomarkers for fatty acid β oxidation. As expected, we found that previously examined genes such as Ucp3, Cpt 1b, and Pdk4 were upregulated by GW1516 but showed no further induction with exercise ( Figure 2 A). Unexpectedly, we discovered a second set of genes that show no response to exercise or drug alone but are robustly induced by the combination. This intriguing response profile includes a series of genes involved in the regulation of fatty acid storage (such as steroyl-CoA-desaturase [Scd1], fatty acyl coenzyme A synthase [FAS, Fasn] and serum response element binding protein 1c [SREBP1c, Srebf1c]) and fatty acid uptake (such as the fatty acid transporter [FAT, Cd36] and lipoprotein lipase [Lpl]) (Figures 2 B, 2C, and 3 ).

(E) Relative expression of 48 unique TR+GW target genes in GW, TR, TR+GW, and VP16-PPARδ muscles. Each condition is represented by data from two samples (each sample is pooled from three mice). (Color scheme for fold change is provided.)

(C) Venn diagram comparing GW, Tr, and Tr+GW target genes identified in microarray analysis of quadriceps (n = 3). The selection criteria used a p < 0.05 on Bonferroni's multiple comparison test.

(A and B) Running endurance was tested in V- (open bars) and GW- (black bars) treated mice before (Week 0) and after (Week 5) exercise training. Running endurance is depicted as time (A) and distance (B) that animals in each group ran. Data are represented as mean ± SD (n = 6). ∗∗∗ indicates statistically significant difference between V- and GW-treated exercised mice (p < 0.001) (one-way ANOVA; post hoc: Tukey's multiple comparison test).

(A–C) Relative gene expression levels of FAO (Ucp3, Cpt 1b, Pdk4) (A), fatty acid storage (Scd1, Fasn, Srebf1c) (B), and fatty acid uptake (Cd36, Lpl) (C) biomarkers in quadriceps from V, GW, Tr, and Tr+GW groups. Data are presented as mean ± SEM (n = 9). ∗ indicates statistically significant difference between V and indicated groups (p < 0.05, one-way ANOVA; post hoc: Dunnett's multiple comparison test).

Since endurance exercise remodels the skeletal muscle to progressively alter performance (), we speculated whether coadministration of GW1516 in the context of exercise training might enhance anticipated changes in fiber type composition and mitochondrial biogenesis. The effect of GW1516 and exercise on fiber type composition was determined via metachromatic staining of cryosections of the gastrocnemius (). As expected from the results of the running performance in Figure 1 B, there was no significant difference in the proportion of type I fibers between vehicle- and GW1516-treated sedentary mice ( Figure 1 C). In contrast, in trained mice, GW1516 increased the proportion of type I fibers (by ∼38%) compared to the vehicle-treated sedentary mice ( Figures 1 C and 1D). In addition to its effects on the fiber type, exercise training increases skeletal muscle mitochondrial biogenesis, which was measured as a function of mitochondrial DNA expression levels via quantitative real-time PCR (QPCR). Similar to type I fiber changes, mitochondrial DNA expression was not changed by drug alone but was increased by approximately 50% with the combination of exercise and GW1516 treatment ( Figure 1 E).

To examine whether treatment with PPARδ ligands alone can reprogram the muscle transcriptome and endurance capacity, we treated wild-type C57Bl/6J age matched cohorts with vehicle or GW1516 for 4 weeks. QPCR analysis of selective target genes confirmed that drug treatment induced oxidative metabolic biomarkers such as uncoupling protein 3 (Ucp3), muscle carnitine palmitoyl transferase I (mCPT I, Cpt 1b), and pyruvate dehydrogenase kinase 4 (Pdk4) ( Figure 1 A). These changes in gene expression were detected as early as 4 days after treatment, as well as with drug concentrations ranging from 2–5 mg/kg/day. Moreover, in all our gene expression studies, maximal effects of PPARδ activation were detected in predominantly fast-twitch (quadricep and gastrocnemius) but not slow-twitch (soleus) muscles (data not shown). In primary muscle cells cultured from wild-type and PPARδ null mice (), we confirmed that the induction of oxidative genes by GW1516 is mediated via selective activation of PPARδ in skeletal muscles ( Figures S1 A–S1C available online). Moreover, this is similar to the expression changes found in the same genes in muscles expressing the constitutively active VP16-PPARδ transgene () ( Figure 1 A), supporting the concept that pharmacological activation of PPARδ is sufficient to initiate an oxidative response in adult skeletal muscle. To determine the functional effects of ligand, age- and weight-matched cohorts of treated and control mice were subjected to an endurance treadmill performance test before (week 0) and after (week 5) treatment. Curiously, running performance was unchanged by GW1516 treatment ( Figure 1 B). Furthermore, long-term drug treatment of up to 5 months also did not change running endurance (data not shown). These results indicate that pharmacologic activation of the PPARδ genetic program in adult C57Bl/6J mice is insufficient to promote a measurable enhancement of treadmill endurance.

Data in (D) and (E) are presented as mean ± SEM. ∗ indicates statistical differences between V and indicated groups (p < 0.05, one-way ANOVA; post hoc: Dunnett's multiple comparison test).

(B) Running endurance in sedentary mice. Endurance was tested in V- (open bars) and GW- (black bars) treated wild-type mice before (Week 0) and after (Week 5) treatment. Data are represented as mean ± SD (n = 6).

(A) Relative gene expression levels of Ucp3, Cpt1b, and Pdk4 in quadriceps isolated from vehicle (V)- and GW1516 (GW)-treated wild-type mice, as well as from muscle VP16-PPARδ transgenic () (TG) and nontransgenic (WT) littermates. Data are presented as mean ± SEM (n = 4–9).indicates statistically significant differences between GW and V groups or TG and WT groups (p < 0.05, unpaired student's t test).

Discussion

In this study, we show that the AMP-mimetic AICAR can increase endurance in sedentary mice by genetically reprogramming muscle metabolism in a PPARδ-dependent manner. We also found that a PPARδ agonist in combination with exercise synergistically induces fatigue-resistant type I fiber specification and mitochondrial biogenesis, ultimately enhancing physical performance. These changes correlate with an unexpected but interesting establishment of a muscle endurance gene signature that is unique to the drug-exercise paradigm. Such a signature is an outcome of molecular crosstalk and perhaps a physical association between exercise-activated AMPK and PPARδ. These findings identify a novel pharmacologic strategy to reprogram muscle endurance by targeting AMPK-PPARδ signaling axis with orally active ligands.

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et al. Resveratrol improves mitochondrial function and protects against metabolic disease by activating SIRT1 and PGC-1alpha. Although not all genes regulated by either exercise (data not shown) or exercise-PPARδ interaction (nonoverlapping signature, Figure 4 D) are AMPK dependent, two key findings assign a critical role for the kinase in promoting endurance compared to other known exercise signals (). First, AMPK is constitutively active in VP16-PPARδ transgenic muscles that exhibit endurance without exercise. Second, AMPK activation by AICAR was sufficient to increase running endurance without additional exercise signals. Strikingly, the majority of the oxidative genes (30 out of 32) upregulated by AICAR are active in super-endurance VP16-PPARδ mice and perhaps are the core set of genes required to improve muscle performance. Interestingly, AICAR failed to induce oxidative gene expression in PPARδ null muscle cells, indicting the requirement of PPARδ, at least for regulation of oxidative metabolism by AMPK. Collectively, these findings demonstrate a molecular partnership between AMPK and PPARδ in reprogramming skeletal muscle transcriptome and endurance ( Figure 6 I) that can be readily exploited by orally active AMPK drugs to replace exercise.