Agonists of the Peroxisome Proliferator–Activated Receptor Alpha Induce a Fiber-Type–Selective Transcriptional Response in Rat Skeletal Muscle

Souza, Angus T. De; Cornwell, Paul D.; Dai, Xudong; Caguyong, M.; Ulrich, Roger G.

doi:10.1093/toxsci/kfl019

Cited by 42 publications

(27 citation statements)

References 49 publications

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“…Although there are some reports showing that fast-twitch muscles are primarily affected in statin myopathy [34], whereas type I muscle fibers are mainly affected by fibrates [35], we focused the proteomic analysis on the fast-twitch EDL muscle because we previously showed that this muscle is a toxicological target of both statins and fibrates [20]. The results reveal various metabolic pathways involved in the response to antilipidemic drugs, which may concur to myotoxicity.…”

Section: Page 5 Of 47mentioning

confidence: 99%

Statin or fibrate chronic treatment modifies the proteomic profile of rat skeletal muscle

Camerino

Pellegrino

Brocca

et al. 2011

Biochemical Pharmacology

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Statins and fibrates can cause myopathy. To further understand the causes of the damage we performed a proteome analysis in fast-twitch skeletal muscle of rats chronically treated with different hypolipidemic drugs. The proteomic maps were obtained from extensor digitorum longus (EDL) muscles of rats treated for 2-months with 10mg/kg atorvastatin, 20mg/kg fluvastatin, 60mg/kg fenofibrate and control rats. The proteins differentially expressed were identified by mass spectrometry and further analysed by immunoblot analysis. We found a significant modification in 40 out of 417 total spots analysed in atorvastatin treated rats, 15 out of 436 total spots in fluvastatin treated rats and 21 out of 439 total spots in fenofibrate treated rats in comparison to controls. All treatments induced a general tendency to a down-regulation of protein expression; in particular, atorvastatin affected the protein pattern more extensively with respect to the other treatments.Energy production systems, both oxidative and glycolytic enzymes and creatine kinase, were downregulated following atorvastatin administration, whereas fenofibrate determined mostly alterations in glycolytic enzymes and creatine kinase, oxidative enzymes being relatively spared. Additionally, all treatments resulted in some modifications of proteins involved in cellular defenses against oxidative stress, such as heat shock proteins, and of myofibrillar proteins. These results were confirmed by immunoblot analysis. In conclusions, the proteomic analysis showed that either statin or fibrate administration can modify the expression of proteins essential for skeletal muscle function suggesting potential mechanisms for statin myopathy.

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Section: Page 5 Of 47mentioning

confidence: 99%

Statin or fibrate chronic treatment modifies the proteomic profile of rat skeletal muscle

Camerino

Pellegrino

Brocca

et al. 2011

Biochemical Pharmacology

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“…In muscle homogenates, fenofibrate induces mitochondrial dysfunction predominantly by inhibition of complex I of the respiratory chain (Brunmair et al, 2004). A fiber type-selective response caused by PPAR␣ activation was consistent with increased fatty acid uptake and ␤-oxidation, which represent the major pathways responsible for the clinical benefits of fibrate drugs (De Souza et al, 2006). In rat skeletal muscle cultures, PPAR␣ activation was found to mediate in part the toxic response to PPAR␣ agonists (Johnson et al, 2005).…”

Section: Discussionmentioning

confidence: 74%

Fenofibrate Metabolism in the Cynomolgus Monkey using Ultraperformance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry-Based Metabolomics

Liu¹,

Patterson²,

Yang³

et al. 2009

Drug Metab Dispos

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ABSTRACT:Fenofibrate, widely used for the treatment of dyslipidemia, activates the nuclear receptor, peroxisome proliferator-activated receptor ␣. However, liver toxicity, including liver cancer, occurs in rodents treated with fibrate drugs. Marked species differences occur in response to fibrate drugs, especially between rodents and humans, the latter of which are resistant to fibrate-induced cancer. Fenofibrate metabolism, which also shows species differences, has not been fully determined in humans and surrogate primates. In the present study, the metabolism of fenofibrate was investigated in cynomolgus monkeys by ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOFMS)-based metabolomics. Urine samples were collected before and after oral doses of fenofibrate. The samples were analyzed in both positive-ion and negative-ion modes by UPLC-QTOFMS, and after data deconvolution, the resulting data matrices were subjected to multivariate data analysis. Pattern recognition was performed on the retention time, mass/charge ratio, and other metabolite-related variables. Synthesized or purchased authentic compounds were used for metabolite identification and structure elucidation by liquid chromatographytandem mass spectrometry. Several metabolites were identified, including fenofibric acid, reduced fenofibric acid, fenofibric acid ester glucuronide, reduced fenofibric acid ester glucuronide, and compound X. Another two metabolites (compound B and compound AR), not previously reported in other species, were characterized in cynomolgus monkeys. More importantly, previously unknown metabolites, fenofibric acid taurine conjugate and reduced fenofibric acid taurine conjugate were identified, revealing a previously unrecognized conjugation pathway for fenofibrate.

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“…Four animals per group were treated 48 hours before livers were collected. RNAs were extracted and profiled on a custom-designed mouse oligonucleotide microarray with approximately 25,000 genes according to a published procedure [29]. In total, the Ppara siRNA mouse expression profiles included eight profiles for the Ppara siRNAs targeted to two different fragments of the Ppara mRNA sequence, four profiles for the SEAP control siRNA, four profiles for the GL3 control siRNA, and eight profiles for a vehicle control (Ringer solution).…”

Section: Methodsmentioning

confidence: 99%

“…Liver weight and body weight were measured for all animals, and the ratio of liver to body weight was used as the index for liver hypertrophy. RNAs were extracted from liver and profiled on a custom-designed rat oligonucleotide microarray with approximately 25,000 genes according to a published protocol [29]. The errorweighted ratios for the 2,569 transcripts used in our study are summarized in Table S2.…”

Section: Methodsmentioning

confidence: 99%

“…Assuming that the regulatory relationships derived in the mouse liver hold in the rat liver, we selected gene pairs only if there existed conditional independency between gene X and hypertrophy given gene Y in the PPAR minicompendium [29] and not conditional independency between gene X and gene Y given hypertrophy ( Figure 3D-3F). Specifically, for all of the selected gene pairs, the dependency between the top genes and liver hypertrophy was abolished when conditioned on the bottom genes (p , 0.01) ( Figure 3D, red dots).…”

Section: Author Summarymentioning

confidence: 99%

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PPARα siRNA-Treated Expression Profiles Uncover the Causal Sufficiency Network for Compound-Induced Liver Hypertrophy

Dai¹,

Souza²,

Dai³

et al. 2005

PLoS Comput Biol

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Uncovering pathways underlying drug-induced toxicity is a fundamental objective in the field of toxicogenomics. Developing mechanism-based toxicity biomarkers requires the identification of such novel pathways and the order of their sufficiency in causing a phenotypic response. Genome-wide RNA interference (RNAi) phenotypic screening has emerged as an effective tool in unveiling the genes essential for specific cellular functions and biological activities. However, eliciting the relative contribution of and sufficiency relationships among the genes identified remains challenging. In the rodent, the most widely used animal model in preclinical studies, it is unrealistic to exhaustively examine all potential interactions by RNAi screening. Application of existing computational approaches to infer regulatory networks with biological outcomes in the rodent is limited by the requirements for a large number of targeted permutations. Therefore, we developed a two-step relay method that requires only one targeted perturbation for genome-wide de novo pathway discovery. Using expression profiles in response to small interfering RNAs (siRNAs) against the gene for peroxisome proliferator-activated receptor a (Ppara), our method unveiled the potential causal sufficiency order network for liver hypertrophy in the rodent. The validity of the inferred 16 causal transcripts or 15 known genes for PPARa-induced liver hypertrophy is supported by their ability to predict non-PPARa-induced liver hypertrophy with 84% sensitivity and 76% specificity. Simulation shows that the probability of achieving such predictive accuracy without the inferred causal relationship is exceedingly small (p , 0.005). Five of the most sufficient causal genes have been previously disrupted in mouse models; the resulting phenotypic changes in the liver support the inferred causal roles in liver hypertrophy. Our results demonstrate the feasibility of defining pathways mediating drug-induced toxicity from siRNA-treated expression profiles. When combined with phenotypic evaluation, our approach should help to unleash the full potential of siRNAs in systematically unveiling the molecular mechanism of biological events.

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Agonists of the Peroxisome Proliferator–Activated Receptor Alpha Induce a Fiber-Type–Selective Transcriptional Response in Rat Skeletal Muscle

Cited by 42 publications

References 49 publications

Statin or fibrate chronic treatment modifies the proteomic profile of rat skeletal muscle

Statin or fibrate chronic treatment modifies the proteomic profile of rat skeletal muscle

Fenofibrate Metabolism in the Cynomolgus Monkey using Ultraperformance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry-Based Metabolomics

PPARα siRNA-Treated Expression Profiles Uncover the Causal Sufficiency Network for Compound-Induced Liver Hypertrophy

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