ToxSci Advance Access originally published online on May 17, 2006
Toxicological Sciences 2006 92(2):578-586; doi:10.1093/toxsci/kfl019
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Agonists of the Peroxisome Proliferator–Activated Receptor Alpha Induce a Fiber-Type–Selective Transcriptional Response in Rat Skeletal Muscle
Rosetta Inpharmatics LLC, Merck & Co, Inc, Seattle, Washington 98109
1 To whom correspondence should be addressed at 3605, 241st Avenue SE, Issaquah, WA 98029. Fax: 206-802-6377. E-mail: angus_desouza{at}w-link.net.
Received January 20, 2006; accepted April 11, 2006
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ABSTRACT |
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In rodents, treatment with peroxisome proliferator–activated receptor alpha (PPAR
) agonists results in peroxisome proliferation, hepatocellular hypertrophy, and hepatomegaly. Drugs in the fibrate class of PPAR agonists have also been reported to produce rare skeletal muscle toxicity. Although target-driven hepatic effects of PPAR treatment have been extensively studied, a characterization of the transcriptional effects of this nuclear receptor/transcription factor on skeletal muscle responses has not been reported. In this study we investigated the effects of PPAR agonists on skeletal muscle gene transcription in rats. Further, since statins have been reported to preferentially effect type II muscle fibers, we compared PPAR signaling effects between type I and type II muscles. By comparing the transcriptional responses of agonists that signal through different nuclear receptors and using a selection/deselection analytical strategy based on ANOVA, we identified a PPAR activation signature that is evident in type I (soleus), but not type II (quadriceps femoris), skeletal muscle fibers. The fiber-type–selective nature of this response is consistent with increased fatty acid uptake and ß-oxidation, which represent the major clinical benefits of the hypolipidemic compounds used in this study, but does not reveal any obvious off-target pathways that may drive adverse effects.
Key Words: peroxisome proliferator–activated receptor alpha (PPAR); microarray; fenofibrate; Wy-14,643; liver.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The peroxisome proliferator–activated receptors (PPARs) represent a subfamily of nuclear hormone receptors of which three isoforms, PPAR
, PPAR
, and PPAR
, have been identified that are encoded by separate genes. Each receptor is a ligand-dependent transcription factor that functions via heterodimerization with the retinoid X receptor (RXR) prior to binding to the promoter of genes containing a peroxisome proliferator response element (PPRE) (Berger and Moller, 2002
; Desvergne and Wahli, 1999; Francis et al., 2003). Both natural and synthetic ligands activate PPARs. Natural ligands include fatty acids and eicosanoids (e.g., linoleic acid, arachidonic acid, palmitic acid, and eicosapentanoic acid) (Forman et al., 1997; Kliewer et al., 1997; Xu et al., 1999), while synthetic ligands include drugs (fibrates, thiazolidinediones, leukotriene antagonists, and acetylsalicylic acid) and chemicals (partially hydrogenated fish oils, phthalate ester plasticizers, and herbicides) (Gonzalez et al., 1998).
PPAR agonists of the fibrate class, such as fenofibrate, clofibrate, and gemfibrozil, are used clinically to control dyslipidemia primarily by transcriptionally regulating genes involved in both peroxisomal and mitochondrial fatty acid ß-oxidation (Reddy and Hashimoto, 2001; Schoonjans et al., 1996) and glucose metabolism. Although target-driven hepatic effects of PPAR treatment have been extensively studied, responses in skeletal muscle are less well understood especially in light of the fact that lipid-lowering drugs such as fibrates have been implicated in the occurrence of myalgias (Bannwarth, 2002; Hodel, 2002; Rosenson, 2004) and rhabdomyolysis especially when coadministered with other drugs (Jones and Davidson, 2005; Roca et al., 2002). In order to identify potential mechanisms behind adverse muscle effects, it is important to identify those pathways within skeletal muscle that are impacted by PPAR agonists and the muscle fiber type in which these changes occur. This is important, since the statin class of hypocholesterolemic drugs have been reported to selectively impact type II fibers in rats (Westwood et al., 2005).
Skeletal muscle is made up predominantly of two types of fiber. Type I fibers are rich in mitochondria, use cellular respiration for ATP production (oxidative), are rich in myoglobin (and hence appear red in color), and are responsible for posture. Type II fibers have fewer mitochondria, depend on glycolysis for ATP production, are low in myoglobin, appear white in color, and are the principal muscles used for rapid movement. In a recent study investigating glucose homeostasis in relation to intramuscular lipid content, it was shown that fenofibrate improves insulin sensitivity in fructose-fed Sprague Dawley rats (Furuhashi et al., 2002). In addition, triglyceride levels in soleus (type I) and extensor digitorum longus (EDL) (a mixed fiber type) (Ariano et al., 1973) were reduced to control levels with treatment, but levels of fatty acid–binding protein and expression levels and activity of the ß-oxidation enzyme 3-hydroxyacyl–coenzyme A (CoA) dehydrogenase (Hadh) were elevated only in soleus muscle. To better understand the muscle fiber-type–selective nature of PPAR activation, we have used a systematic approach that utilizes multiple nuclear receptor agonists to characterize the specificity of skeletal muscle responses in soleus (type I) and quadriceps femoris (type II) (Ariano et al., 1973) muscle fibers.
We recently reported on the hepatic gene expression in rats treated with six fibric acid analogues (Cornwell et al., 2004); however, because all six study compounds were also PPAR agonists, it was not possible to attribute observations to a PPAR-selective transcriptional response relative to that of other PPAR-related nuclear receptor agonists (such as PPAR, PPAR, and the non-PPAR retinoic acid receptor [RAR]). Using a novel selection/deselection strategy based on ANOVA between muscle fiber types, we report on the identification of a transcriptional signature associated with fat metabolism in type I, but not type II, skeletal muscle fibers. A similar but more pronounced response is observed in the liver, and in both tissues it appears to be predominantly a consequence of PPAR agonism related to glucose and fatty acid ß-oxidation.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Animal husbandry.
Female CD (Crl: CD (SD) IGS BR) rats (Charles River Laboratory, Wilmington, MA) were obtained at 6–7 weeks of age and acclimatized for at least 1 week (to 8 weeks old) prior to study onset. Animals were housed individually in suspended wire mesh cages on a 12-h light/dark cycle, given water ad libitum, and restricted to 17 g of Lab Diet Certified Rodent Diet #5002 (PMI International, Inc, St Louis, MO) daily.
Treatments.
Test materials were dissolved/suspended in 0.25% methylcellulose (400 centipoise: Sigma-Aldrich Co, St Louis, MO) at a concentration that would allow daily doses of 10 ml suspended compound/kg body mass by oral gavage. Animals were dosed daily for 2, 3, 4, 5, 6, 9, and 16 days with fenofibrate (400 mg/kg/day: Sigma-Aldrich Co), Wy-14,643 (100 mg/kg/day: ChemSyn Sciences, Lenexa, KS), bezafibrate (250 mg/kg/day: Sigma-Aldrich Co), rosiglitazone (100 mg/kg/day), and all-trans retinoic acid (25 mg/kg/day: Sigma-Aldrich Co). After an overnight fast, animals were euthanized 6–9 h after the last dose (i.e., the last dose occurred during fasting) by isoflurane inhalation followed by blood collection from the vena cava and exsanguination.
Tissue collection.
At necropsy, tissues were collected for RNA extraction (snap frozen in liquid nitrogen) and histological examination (fixed in 10% neutral buffered formalin). The skin was reflected from a ventral midline incision and the abdominal-thoracic cavity opened, and the organs were removed. Tissues were divided for subsequent examination and collected in the following order. First, the left quadriceps femoris (type II skeletal fiber minus the vastus intermedius, see below), EDL (a mixed fiber–type skeletal muscle) (Ariano et al., 1973), and soleus (type I skeletal fiber) muscles were snap frozen separately. The vastus intermedius was separated (removed) from the other three bundles of the quadriceps femoris (rectus femoris, vastus medialis, and vastus lateralis) and discarded. Muscle samples for histology were collected by transecting the pubis and, with scissors, removing the entire right leg at the sacroiliac joint. The leg was gently skinned to the foot, cutting the fascia with scissors. The entire leg was put into formalin. For liver, the entire left lateral lobe was snap frozen while the remaining tissue was retained and fixed for histology. Tissues for RNA extraction were collected and frozen within 12 min (but no later than 15 min) of euthanasia.
RNA extraction.
RNA was extracted from tissues using a combination of TRIzol RNA extraction (Invitrogen Life Technologies, Carlsbad, CA) with the RNeasy RNA extraction kit (Qiagen, Valencia, CA). Briefly, tissue was incubated in TRIzol reagent (1 ml/100 mg tissue) for 15 s at room temperature and homogenized with a Polytron homogenizer followed by an 
5-min room temperature incubation. After the addition of 100 µl chloroform, 500 µl of homogenate was mixed by shaking for 15 s, incubated at room temperature for 2–3 min, and centrifuged at 10,000 x g for 10 min at 2–8°C. The supernatant was used as the input material for the RNeasy RNA extraction kit and RNA isolated according to the manufacturer's protocol. Following isolation, RNA quantity, purity, and quality were determined using a SpectraMax Plus384 (Molecular Devices, Sunnyvale, CA) spectrophotometer and a 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA).
Expression profiling.
Expression profiling was carried out using custom arrays consisting of 22,500 60mer oligonucleotides (plus control sequences) representing rat genes. The arrays were synthesized by Agilent Technologies using an inkjet printing method (Hughes et al., 2001). Cy3- or Cy5-labeled cRNA was created from total RNA using reverse transcription followed by in vitro transcription and a two-step label incorporation method (Hughes et al., 2001). All treated individual samples were hybridized against a pool of RNA from time-matched (concurrent) control animals (e.g., animals treated for 3 days were hybridized against a pool of animals dosed for 3 days with vehicle). The ratio of individual animal expression to control pool expression was used for all data analysis. All hybridizations (Hughes et al., 2001) were performed in duplicate, with fluor reversal (Cy3 or Cy5) in the second hybridization. The resultant fluor-reversed pairs were combined to give a single ratio measurement for each gene for each treated liver. In addition, a subset of the vehicle-treated animals was hybridized against the control pool in a manner identical to the compound-treated samples. Arrays were scanned using a DNA microarray scanner (Model G2565AA; Agilent Technologies), and feature intensities (background subtracted) were determined using feature extraction software developed at Rosetta (Qhyb, Seattle, WA; Marton et al., 1998). All hybridizations described herein passed a set of quality control criteria that evaluate feature quality/spot success rate (at least 90% of spots must pass metrics assessing handling or array synthesis problems), ratio reproducibility (intrachip standard deviation of the log10(ratio) < 0.0607 and the standard deviation of the log10(ratio) < 0.0792 within chip pairs), ratio accuracy (mean observed intrachip ratio not biased by more than 50% from expected ratio), and ratio sensitivity (at least 50% of transcripts spiked in at 1 part in 100,000 and at 1.5 copies per cell vs. 0.5 copies per cell [1:3] are within 50% of the expected ratio) based on the performance of 10 control transcripts spiked in at known concentrations and ratios and present on each chip in approximately 30 locations dispersed evenly across the chip. Expression profiling was carried out only from animals dosed for 2, 3, 4, 5, and 6 days. Microarray data for this study are available at the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/), ref: GSM107713, GSM107791.
The in-life portion of this study and the RNA extraction procedure were completed at MPI Research (Mattawan, MI). MPI Research is fully accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International. The animal study protocol was reviewed and approved by the MPI Institutional Animal Care and Use Committee and conducted according to The Guide for the Care and Use of Laboratory Animals (National Research Council, 1996).
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RESULTS |
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Pathology
For fenofibrate and Wy-14,643, the expected increase in liver weight was observed with time (Fig. 1a). The liver weight increases with Wy-14,643 treatment were greater than those with fenofibrate (Willson et al., 2000
) and significant (p < 0.01) from day 2. Moderate but significant (p 
0.05) liver weight increases were also observed with bezafibrate and retinoic acid treatments across most time points, whereas rosiglitazone effects were apparent only at days 9 and 16. The incidence and histopathological grade (minimal to moderate) of panlobular hypertrophy increased with time for fenofibrate and Wy-14,643 treatments (data not shown). There was significant, but modest (1.3- to 1.6-fold at day 16), time-dependent increases in the plasma aspartate aminotransferase concentration for fenofibrate (p 0.01), Wy-14,643 (p 0.05), bezafibrate (p 0.01), rosiglitazone (p 0.05), and retinoic acid (p 0.01) (Fig. 1b), and for fenofibrate, only a slight increase in plasma alanine aminotransferase concentration (Fig. 1c). These data are consistent with the lack of gross hepatic damage observed histopathologically with fibrates (Cornwell et al., 2004).
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For fenofibrate, Wy-14,643, and bezafibrate, plasma creatine kinase (CK) concentrations increased significantly over time (p
0.01 at day 16) with a maximum fold change of approximately 2.2 with fenofibrate treatment (Fig. 1d). Significant changes in plasma CK concentrations were not observed with rosiglitazone and retinoic acid treatments. Gross myopathy was not observed with either muscle fiber type. However, in soleus (type I) muscle fibers, minimal myofiber degeneration was observed only at day 9 in 1/4 animals treated with fenofibrate and Wy-14,643. In vehicle-treated animals, the background incidence was 1/16 animals at day 4 and day 9. In quadriceps femoris (type II) muscle fibers, myofiber degeneration (minimal) was only observed with bezafibrate treatment in 1/4 animals at day 5 and day 6. In vehicle-treated animals, the background incidence was 2/16 animals at day 5 and 1/16 animals at day 9 and day 16, respectively.
Gene Expression Analysis
All data were parsed using ANOVA as the primary statistical tool, comparing treatment groups to their concurrent controls. ANOVA was performed in Resolver v3.2.2.0 gene expression data analysis system (using the error-weighted model), and the results were analyzed in Excel. Analysis comprised two phases, selection and deselection, with the latter phase consisting of two steps. In the skeletal muscles, the selection and deselection strategies were based on fiber type to identify tissue-selective responses, whereas in the liver, compound-specific responses were ranked to identify genes specifically responsive to PPAR agonism. The selection strategy was used to identify a tissue- (muscle) or compound (liver)-specific response while minimizing inclusion of false positives and the deselection strategy used to rank the specificity of selected genes by minimizing false negatives observed with other tissues (muscle) or compound treatments (liver) in the study. As an example of the data-processing methods employed, first the selection and ranking of genes specific for PPAR signaling in the liver is described followed by muscle fiber-type–selective strategies.
Both fenofibrate and Wy-14,643 are PPAR agonists with minimal PPAR and negligible PPAR activities. Fenofibrate has been shown to activate PPAR with a 10-fold selectivity over PPAR (Willson et al., 2000). Consequently, genes that were significantly and commonly regulated between these compounds were regarded as strong candidates for characterizing a treatment-independent signature for PPAR. The signature for bezafibrate was ignored with regard to rank ordering the PPAR response given that bezafibrate is a panagonist with similar potencies for PPAR, PPAR, and PPAR in murine and human transactivation assays (Willson et al., 2000); thus, the PPAR component of the bezafibrate response was assumed to segregate with the fenofibrate and Wy-14,643 signatures.
For the selection strategy, gene expression data were available for each compound across five contiguous time points from day 2 to day 6. For PPAR agonism, a gene was considered if it was significantly regulated (p 0.001, regardless of direction) across three contiguous time points in both fenofibrate- and Wy-14,643-treated animals. Using these selection criteria, a PPAR set comprising 908 genes (data not shown) was identified and rank ordered in terms of specificity of response using the deselection strategy.
For the 908 genes, the responses of two compounds, rosiglitazone (a PPAR agonist) and retinoic acid (an RAR agonist), were used in the deselection strategy to rank PPAR activation specificity in a two-step process (A and B) as follows. A: For rosiglitazone and retinoic acid a significance threshold of p 0.05 was used to identify a subset of genes whose expression was different from control animals at no more than one time point out of five. These genes were regarded as not expressed relative to their control pool. B: The genes identified in step A were then subjected to a treatment (PPAR agonist) versus treatment (non-PPAR agonist) ANOVA comparison (e.g., fenofibrate vs. retinoic acid at days 2–6 at a minimum of two contiguous time points at a significance threshold of p 0.001) to eliminate genes in the PPAR activation set whose expression was not significantly different from non-PPAR agonist responses. Thus, genes that are not only differentially regulated by fenofibrate and Wy-14,643 treatments relative to their concurrent controls (selection) but also differentially regulated from non-PPAR agonist (rosiglitazone and retinoic acid) treatments (deselection) were identified. The resulting set of 116 genes was regarded as having the highest specificity for a PPAR transcriptional response relative to other compound treatments in the study (Fig. 2, section a). Deselection on the remaining 792 (908 – 116) genes that were significantly (p 0.001) regulated at three contiguous time points was repeated at progressively lower p values (p 0.01 and p 0.001) that represent progressively decreasing deselection stringencies, respectively. Thus, in total, the shared PPAR response between fenofibrate and Wy-14,643 was ranked into three subsets (comprising in total 211 genes) of high, but progressively decreasing, PPAR specificity (based on deselection criterion, see Fig. 2 legend) relative to other nuclear receptor responses (Fig. 2, sections a–c) and a fourth subset (697 genes) of lower PPAR specificity (data not shown). Differences in PPAR specificity for genes contained in sections a, b, and c are most evident when compared to the retinoic acid response signature (Fig. 2). Fenofibrate, Wy-14,643, and, to a lesser extent, bezafibrate treatments result in a marked modulation in expression of the genes in sections a, b, and c. However, the nuclear receptor specificity of these responses is greatest for PPAR in section a and least in section c, given the converse gene expression response pattern observed with retinoic acid treatment (Fig. 2, sections a–c).
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3ctp & d:p In skeletal muscle, a comparison between fenofibrate and Wy-14,643 treatments revealed a much greater transcriptional response in soleus muscle fibers with the former compound. At a minimum threshold of three significant (p
0.001) time points out of a possible five, the Wy-14,643 signature was 35% smaller than that for fenofibrate. This is in contrast to the signature sizes observed in the liver, where Wy-14,643 treatment resulted in a 42% greater signature size than fenofibrate treatment (data not shown). Consequently, given the reduced effect with Wy-14,643 treatment in skeletal muscle, fenofibrate treatment alone was selected to identify PPAR-responsive genes in type I versus type II muscle fibers. Application of the selection and deselection strategies between fenofibrate-treated soleus (type I) and quadriceps femoris (type II) tissues, respectively, identified 26 genes that were significantly (p 0.001 for three significant time points out of five) modulated in soleus (type I), but not in quadriceps femoris (type II), muscle fibers (Table 1 and Fig. 3). EDL (a mixed fiber type; Ariano et al., 1973) showed a response that was intermediate between soleus and quadriceps femoris (data not shown) and was not included in further analysis. All except one of the 26 sequences (96%) were modulated in the same direction as the liver transcriptional response, where regulation was found to be predominantly through PPAR (Fig. 3). A transcriptional signature specific for quadriceps femoris relative to soleus could not be identified, suggesting that PPAR activation in skeletal muscle is fiber-type specific.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Though the effects of PPAR
agonists on liver have been extensively studied, the transcriptional responses to this nuclear receptor in skeletal muscle and the fiber-type–selective nature of PPAR agonism have not been previously reported. And, although the liver is typically thought of as the therapeutic target tissue for this class of agents, concerns about adverse effects in muscle indicate that fibrates can have substantial effects on other tissue types. We, therefore, set out to determine what effects these drugs have on skeletal muscle, and in particular what effects are driven by the therapeutic target. In this study, we have used a variety of compounds that signal through different nuclear hormone receptors (though all share RXR as an obligate heterodimer-binding partner) to characterize the PPAR transcriptional response in type I (soleus) and type II (quadriceps femoris) skeletal muscle fibers and liver. Fenofibrate and Wy-14,643 are PPAR agonists, and bezafibrate is a panagonist (PPAR, , ), rosiglitazone is a PPAR agonist, and all-trans retinoic acid is an RAR agonist (Berger and Wagner, 2002; Willson et al., 2000). In the liver, by comparing gene expression over time (days 2, 3, 4, 5, and 6) between compounds that target different receptor subtypes, we have identified gene sets that represent ligand-specific transcriptional responses, and using an ANOVA selection/deselection strategy, we have ranked the receptor-selective nature of each response. The PPAR-specific signature represents a combination of the shared gene expression responses between fenofibrate and Wy-14,643 treatments, and the selective characteristics are based relative to those of rosiglitazone and retinoic acid responses (Fig. 2). In the skeletal muscle, using a similar, but tissue-oriented, selection/deselection strategy for fenofibrate treatment, we identified 26 genes that are specifically regulated in type I, but not type II, muscle fibers (Fig. 3). The specificity of the transcriptional response is overwhelmingly PPAR selective, given that rosiglitazone (PPAR agonist) and retinoic acid (RAR agonist) treatments had minimal transcriptional impact on gene regulation in the response signature. In addition, of the 26 genes identified as specifically regulated in type I (soleus) muscle fibers, the regulation of 11 (42%) was ranked in the liver as specifically responsive to PPAR agonists using the selection/deselection strategy based on ANOVA (Fig. 3). All except one of the 26 transcriptional responses in the soleus were directionally concordant with hepatic regulation. Thus, the PPAR transcriptional response in type I skeletal muscle, although weaker, is largely consistent with hepatic observations.
When all 26 genes were annotated with Gene Ontology Biological Process terms, the most significantly enriched term was fatty acid oxidation (p = 3.8 x 10–7, computed using an implementation of the hypergeometric cumulative distribution function). Ingenuity Pathways analysis tools (Ingenuity Systems, http://www.ingenuity.com/) also revealed that the most significant global function represented in the gene set was lipid metabolism (p = 4.4 x 10–22 to 8.9 x 10–6, computed using the right-tailed Fisher exact test) (data not shown). Mitochondria and peroxisomes play different roles in fatty acid metabolism: mitochondria oxidize most of the long-chain fatty acids in the normal diet, whereas peroxisomes are essential for the oxidation of very long-chain fatty acids and 2-methyl branched-chain fatty acids, such as pristanic acid and di- and trihydroxycholestanoic acid (Reddy and Hashimoto, 2001; Wanders et al., 2001). Genes, many of which contain functional PPREs (Desvergne and Wahli, 1999), coding for proteins associated with fatty acid ß-oxidation (Reddy and Hashimoto, 2001) in both organelles were upregulated, including acyl-CoA oxidase; 
3,2-enoyl-CoA isomerase (Geisbrecht et al., 1999); long-chain enoyl-CoA hydratase/3-hydroxyacyl-CoA dehydrogenase; carnitine/acylcarnitine carrier protein; carnitine palmitoyltransferase II (CptII); and long-chain acyl-CoA dehydrogenase. Pantothenate kinase 1 (PanK1) (Ramaswamy et al., 2004) and fatty acid translocase/CD36 antigen (FAT/CD36) (Abumrad et al., 1993; Endemann et al., 1993), both of which are indirectly involved in metabolizing fats, were also upregulated.
PanK1 is involved in regulating the intracellular concentration of CoA, an activated carrier of acyl groups and an important constituent of both catabolism (oxidation of fatty acids) and anabolism (synthesis of membrane lipids). Alterations of intracellular CoA concentrations during starvation or feeding (Smith et al., 1978), diabetes (Reibel et al., 1981a,b), and treatment with hypolipidemic drugs (Halvorsen, 1983; Skrede and Halvorsen, 1979) are likely mediated through PPAR regulation of PanK1. For example, treatment of HepG2 cells with the hypolipidemic drug and PPAR agonist bezafibrate results in enhanced PanK1 promoter activity, in which four putative PPREs reside (Ramaswamy et al., 2004). PPAR-regulated PanK1 induction in soleus is consistent with the need to increase the intracellular pool of CoA to support elevated fatty acid ß-oxidation that occurs in type I (Furuhashi et al., 2002), but not type II, skeletal muscle (Table 1 and Fig. 3).
The overexpression of FAT/CD36 in soleus in this study provides further support for enhanced muscle fiber-type–selective fatty acid oxidation induced by PPAR agonists. There is strong evidence implicating FAT/CD36 in binding and transport of long-chain fatty acids. In tissues that are highly active in fatty acid metabolism, such as skeletal muscle, heart, and fat, FAT/CD36 mRNA is abundant (Abumrad et al., 1993; Van Nieuwenhoven et al., 1995) and is modulated by high fat feeding and diabetes mellitus, conditions that alter lipid metabolism (Greenwalt et al., 1995). Consistent with our observations, FAT/CD36 expression in rat and in human has been shown to be higher in oxidative (type I) versus glycolytic (type II) skeletal muscle fibers (Bonen et al., 1999; Keizer et al., 2004), and the relative rates of fatty acid transport have been shown to correlate well with levels of FAT/CD36 (Luiken et al., 1999). In mice overexpressing FAT/CD36 in soleus muscle, plasma triglyceride and fatty acid concentrations were reduced accompanied by an increase in the rate of fatty acid oxidation in contracting muscle, indicating that fatty acid uptake at the plasma membrane is the rate-limiting step in the oxidation pathway (Ibrahimi et al., 1999). These workers also showed that FAT/CD36 overexpression resulted in significant increases in plasma glucose and insulin concentrations.
PPAR is a phosphoprotein, and its phosphorylation (and presumably its transcriptional activation) is increased by insulin (Shalev et al., 1996). PPAR activation by fenofibrate (Furuhashi et al., 2002), Wy-14,643 (Ide et al., 2004; Ye et al., 2001), and bezafibrate (Matsui et al., 1997) has been shown to improve insulin sensitivity in muscle tissues of fructose-fed rats and insulin-resistant mice and rats. Although 9 of the 26 transcriptional modulations in the type I (soleus)–selective signature can be directly attributed to alterations in fat metabolism, one gene, phosphatidylinositol 3-kinase (PI3K), has also been implicated in glucose homeostasis (Bhanot et al., 1999; Kruszynska et al., 2002; Terauchi et al., 1999; Yu et al., 2002). The mechanism by which fatty acids induce insulin resistance is complex, but recent studies in skeletal muscle support the hypothesis that an increase in plasma fatty acid concentration leads to an increase in the intracellular concentrations of fatty acyl-CoA and diacylglycerol, resulting in the activation of protein kinase C-
and increased insulin receptor substrate (IRS)-1 Ser307 phosphorylation. As a consequence, there is decreased IRS tyrosine phosphorylation, decreased activation of IRS-1–associated PI3K, and decreased insulin-stimulated glucose transport activity (Anai et al., 1999; Griffin et al., 1999; Kruszynska et al., 2002; Yu et al., 2002). Consistent with these observations, we found that PI3K gene expression was significantly upregulated in the soleus muscle by hypolipidemic compounds (that lower plasma-free fatty acids and have been shown to improve insulin sensitivity) and rosiglitazone (a PPAR agonist). Interestingly, in the liver, PI3K gene expression was downregulated in contrast to muscle (Fig. 3, gene #26). Opposite regulation of insulin-stimulated activation of PI3K activity in high fat fed rats between liver and muscle has been reported, suggesting that high fat feeding may cause insulin resistance in the liver by a mechanism different to that observed in hind limb muscle (Anai et al., 1999).
The fibrate and statin classes of drugs have been implicated in the occurrence of myalgias (Bannwarth, 2002; Hodel, 2002; Rosenson, 2004) and rhabdomyolysis, particularly when coadministered with each other or with other drugs (Roca et al., 2002). In this study, treatment with PPAR agonists, but not PPAR or RAR agonists, resulted in elevated plasma concentrations of CK, indicating muscle effects (Fig. 1), but this was not associated with a histopathological incidence of myopathy. The type I–selective nature of PPAR activation of transcription in skeletal muscle is particularly interesting given that statin-induced muscle degeneration occurs preferentially in type II skeletal muscle fibers with a reduced effect observed in type I fibers (Reijneveld et al., 1996; Smith et al., 1991; Waclawik et al., 1993; Westwood et al., 2005). And while our data do not provide any particular insight into the mechanism of muscle toxicity and we cannot rule out the possibility that differences in PPAR expression between fiber types could explain the observed difference in gene expression by the PPAR agonist, they do indicate that the major effects in muscle are target driven, and given that fibrates and statins have different fiber-type targets, there is less likelihood for adverse interactions at the cellular level.
In conclusion, by comparing the transcriptional responses of compounds that signal through different nuclear receptor agonists, using a selection/deselection analytical strategy based on ANOVA, we have identified a PPAR activation signature that is evident in type I (soleus), but not type II (quadriceps femoris), skeletal muscle fibers. The fiber-type–selective nature of this response is consistent with increased fatty acid uptake and ß-oxidation, which in muscle is reported to be associated with a lowered concentration in plasma triglycerides and increased insulin sensitivity, both of which represent the major clinical benefits of the hypolipidemic compounds used in this study. Given the difference in fiber-type selectivity observed for PPAR agonists in this study and for statins reported by others, we think that adverse interactions are not likely to occur at the cellular level. The mechanism by which fibrates may produce muscle toxicity remains elusive.
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ACKNOWLEDGMENTS |
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The authors would like to thank Ron Lindahl, Dr Dennis Nelson, and the technical staff at MPI Research for their work on the in-life portion of the study and on RNA production. The authors also acknowledge the invaluable contributions of the technical staff at the Rosetta Gene Expression Laboratory and Michelle Mullholland for her contribution to the in-life experimental design.
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