Human skeletal muscle PPAR{alpha} expression correlates with fat metabolism gene expression but not BMI or insulin sensitivity

Junlong Zhang,1 D. I. W. Phillips,2 Chunli Wang,1 and Christopher D. Byrne1

1Endocrinology and Metabolism Unit, Fetal Origins of Adult Disease Division, School of Medicine, University of Southampton, Southampton General Hospital, Southampton SO16 6YD; and 2Medical Research Council Environmental Epidemiology Unit, Southampton General Hospital, Southampton SO16 5YD, United Kingdom

Submitted 22 May 2003 ; accepted in final form 27 September 2003


    ABSTRACT
 TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Peroxisome proliferator-activated receptor-{alpha} (PPAR{alpha}) is a key regulator of fatty acid oxidation in skeletal muscle, but few data exist from humans in vivo. To investigate whether insulin sensitivity in skeletal muscle and body mass index (BMI) were associated with skeletal muscle expression of PPAR{alpha} and with important genes regulating lipid metabolism in humans in vivo, we undertook hyperinsulinemic-euglycemic clamps and measured PPAR{alpha} mRNA levels and mRNA levels of lipid regulating PPAR{alpha} response genes in skeletal muscle biopsies. mRNA levels were measured in 16 men, using a novel highly sensitive and specific medium throughput quantitative competitive PCR that allows reproducible measurement of multiple candidate mRNAs simultaneously. mRNA levels of PPAR{alpha} were positively correlated with mRNA levels of CD36 (r = 0.77, P = 0.001), lipoprotein lipase (r = 0.54, P = 0.024), muscle-type carnitine palmitoyltransferase-I (r = 0.54, P = 0.024), uncoupling protein-2 (r = 0.63, P = 0.008), and uncoupling protein-3 (r = 0.53, P = 0.026), but not with measures of insulin sensitivity, BMI, or GLUT4, which plays an important role in insulin-mediated glucose uptake. Thus our data suggest that in humans skeletal muscle PPAR{alpha} expression and genes regulating lipid metabolism are tightly linked, but there was no association between both insulin sensitivity and BMI with PPAR{alpha} expression in skeletal muscle.

peroxisome proliferator-activated receptor-{alpha}; messenger ribonucleic acid; insulin resistance; quantitative competitive polymerase chain reaction; skeletal muscle; body mass index


INSULIN SENSITIVITY IN HUMANS is classically measured by hyperinsulinemic-euglycemic clamp, and the principal site of insulin-mediated glucose uptake is skeletal muscle. Factors regulating insulin sensitivity are poorly understood, but obesity and fat metabolism are linked to insulin resistance and the metabolic syndrome, although in humans the relationship between each of these features in vivo and tissue-specific gene expression is not well defined. The concept that reduced capacity for skeletal muscle oxidation may be linked to insulin resistance is supported by some human and animal studies, i.e., inefficient fat oxidation may play a role in insulin resistance and be linked to obesity (16, 23, 29); therefore, it is important to study the relationship between the genes regulating fat oxidation in skeletal muscle and insulin sensitivity.

Peroxisomal proliferator-activated receptor-{alpha} (PPAR{alpha}) is a transcription factor that belongs to a family of transcription factors that form heterodimers with retinoid X receptors. PPARs also include PPAR{gamma} and -{delta} (34), and fatty acids or their metabolites are probably their natural ligands (34). PPAR{alpha} regulates fatty acid oxidation (33) and is well expressed in tissues active in lipid metabolism, including liver, skeletal muscle, and adipose and kidney tissues (34). For example, expression of muscle carnitine palmitoyltransferase-I (mCPT-I), a rate-limiting enzyme in skeletal muscle fatty acid oxidation (22), is regulated by PPAR{alpha} in vitro (21). Expression of CD36, an important transporter of long-chain fatty acid, is regulated by PPAR{alpha} (26, 31) and -{gamma} in the liver (24). Expression of lipoprotein lipase (LPL) is regulated by PPAR{alpha} in the liver (26, 32) and PPAR{gamma} in adipocytes (32). LPL hydrolyses lipoprotein triglyceride (TG) to free fatty acid and monoglycerides, permitting their uptake into muscle for oxidation (11). Therefore, activity of PPAR{alpha} plays a key role in regulating expression of key genes in lipid metabolism, but much of the data to date have been obtained in animal models and the relationships have not been defined for individual insulin-sensitive tissues and are therefore not fully understood in humans in vivo.

PPAR{alpha} activity can be affected by PPAR{alpha} mRNA levels. For example, in PPAR{alpha} transgenic overexpressing mice, PPAR{alpha} protein level is increased (10). mRNA levels of PPAR{alpha} response genes (e.g., acyl-CoA oxidase and CPT-I), and CPT-I activity are also increased in overexpressing PPAR{alpha} transgenic mice (10). Importantly, the magnitude of increase in mRNA levels of the PPAR{alpha} response gene (e.g., acyl-CoA oxidase) and in CPT-I activity is markedly greater in PPAR{alpha} transgenic mice than those from nontransgenic controls (10), whereas in PPAR{alpha} knockout mice mRNA and protein levels from PPAR{alpha} response genes (e.g., long-chain and very long-chain acyl-CoA dehydrogenase; see Ref. 1) are all reduced. Therefore, useful information on PPAR{alpha} activity can be obtained by measuring changes in PPAR{alpha} mRNA levels.

In fasting conditions, lipid oxidation is the predominant metabolic activity of skeletal muscle (6), and the majority of the energy requirement of skeletal muscle is obtained from fatty acid oxidation (6). The aim of this study was to investigate in humans in vivo the relationship between insulin sensitivity and body mass index (BMI), with fasting PPAR{alpha} expression in skeletal muscle. We measured PPAR{alpha} mRNA levels and mRNA levels of known PPAR{alpha} response genes, because for many years inhibition of glucose metabolism by fat metabolism has been proposed as a factor responsible for reducing insulin sensitivity (28).


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Subjects. Out of 36 subjects recruited, 16 adult men not known to have diabetes agreed to undergo skeletal muscle biopsy for this study (Table 1). On investigation, 14 subjects had normal glucose tolerance, whereas 2 subjects had glucose levels compatible with (undiagnosed) diabetes mellitus by World Health Organization criteria. No subjects were receiving medication. The experimental protocol was approved by the combined ethical committee of Southampton and Southwest Hampshire National Health Service Trust, and written informed consent was obtained from each subject. All of the nondiabetic subjects had normal glucose tolerance defined by a fasting glucose <126 mg/dl and a 2-h glucose level after a standard 75-g oral glucose tolerance test of <140 mg/dl.


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Table 1. Study subjects (males only)

 

Experimental design. All subjects were admitted to the Clinical Research Facility at Southampton General Hospital, where they consumed a standard weight maintenance diet comprising (calories) 55% of carbohydrate, 30% fat, and 15% protein for at least 24 h before the studies. Subjects were told not to engage in exercise the day before the study. Next, all subjects were fasted for 12 h overnight before the hyperinsulinemic-euglycemic clamp study.

Hyperinsulinemic-euglycemic clamp. Insulin sensitivity for all subjects was measured using the hyperinsulinemic-euglycemic clamp technique. Subjects fasted for 12 h overnight and received insulin and glucose infusions via an antecubital vein. Blood sampling was performed from a dorsal vein on the opposite hand prewarmed to enable sampling of arterialized blood. After a priming infusion of insulin, a continuous infusion of insulin was started at a rate of 60 mU·m–2·min–1. The infusion was continued for 2 h. Plasma glucose was maintained at 5 mmol/l by variable glucose infusion. The amount of glucose to maintain euglycemia was taken as the amount of glucose metabolized. The mean plasma insulin during steady-state euglycemia (60–120 min) was calculated. The ratio of metabolized glucose to mean plasma insulin (mg·m–2·min–1·µU–1·ml–1) was used as the measure of tissue sensitivity to insulin.

Muscle biopsies. Tissue was obtained before the clamp by Bergstrom needle biopsy (26–143 mg) of the vastus lateralis muscle in the thigh of the subjects. Tissue biopsies were microdissected free of any fat or connective tissue at 4°C and immediately snap-frozen in liquid nitrogen and stored at –80°C for RNA preparation.

PCR primers. The PCR primers were designed as described previously (35). The size of PCR products for all genes is ~450 bp (see Table 2 for primer sequences). All primer sequences were searched against existing sequences submitted to the Genebank using blastn software at the National Center for Biological Information to ensure that each of the primer sequences did not match sequences of other genes. Stock solutions (200 µM) of synthesized primers were prepared in 1x 10 mM Tris·HCl and 1 mM EDTA, pH 8.0.


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Table 2. A list of PCR primers and sizes of amplified PCR products

 

Multiplex standard DNA. A fragment of multiplex standard DNA (msDNA) containing PCR primer binding sequences for 11 genes (Fig. 1) was constructed using a technique of oligonucleotide overlap extension followed by PCR amplification (14). The size of PCR products from standard DNA for all genes is ~380 bp, so that the PCR products from standard DNA can be differentiated from target DNA by gel electrophoresis. The msDNA fragment (~800 bp in length) was cloned into a pGEM-T easy vector (Promega, Southampton, UK) in Escherichia coli JM 109 strain according to the manufacturer's protocol. Colonies harboring msDNA were identified. msDNA fragment was excised from plasmids (MH01), prepared from a single colony grown overnight, by digesting with EcoR I enzyme (Fig. 1). msDNA fragment was gel purified, dissolved in H2O, and quantified by spectrophotometry.



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Fig. 1. Multiplex standard DNA. The multiplex standard DNA fragment contains 11 genes. Data from 7 genes are presented and shown. The sequences of primers for the 7 genes are presented in Table 2. The size for PCR products of each of these genes is similar. CPT, carnitine palmitoyltransferase; LPL, lipoprotein lipase; PPAR, peroxisome proliferator-activated receptor; UCP, uncoupling protein; GR{alpha}, glucocorticoid receptor-{alpha}; M.GS, muscle glycogen synthase; IR, insulin receptor; GHR, growth hormone receptor.

 

Total RNA preparation. Total RNA was prepared as described previously (5) from frozen tissue biopsies. About 30 mg of skeletal muscle were placed in 400 µl of solution containing 4 M guanidinium thiocyanate, 25 mM sodium citrate (pH 7), 0.5% sarcosyl, and 0.1 M 2-mercaptoethanol in a 1.5-ml Eppendorf tube on ice. The tissue was homogenized, and 40 µl of 2 M sodium acetate (pH 4.0) were added and mixed to precipitate genenomic DNA. The protein and lipids in the preparation were extracted by adding 400 µl phenol (pH 4.3, saturated with 0.1 M citrate buffer) and 100 µl cloroform-isoamyl alcohol (49:1 vol/vol); they were then mixed well and left in ice for 5–10 min. The samples were centrifuged at 12,000 g for 15 min to separate the aqueous phase with phenol phase. The upper aqueous phase was carefully transferred to a fresh 1.5-ml Eppendorf tube. The extraction with phenol and chloroform-isoamyl alcohol (49:1 vol/vol) was repeated one or two times, until no protein was visible in the layer between the aqueous and phenol phase. After the upper aqueous phase was transferred to a fresh 1.5-ml Eppendorf tube, three volumes of absolute ethanol were added, and the samples were left on dry ice or at –70°C for 20 min to precipitate the RNA. The precipitated RNA was collected by centrifugation at 12,000 g at 4°C for 20 min. The ethanol was discarded, and the pellet was washed with 0.5 ml of 70% ethanol one time. The 70% ethanol was carefully discarded after centrifugation at 12,000 g at room temperature for 5 min. The pellet was dried at 37°C for 10–15 min and dissolved in ~30 µl diethyl pyrocarbonate-treated H2O. The yield of total RNA was measured by spectrophotometry.

cDNA synthesis. Total RNA (5 µg) in 50 µl of RNase-free H2O was denatured at 70°C for 5 min and then chilled to 4°C. Denatured total RNA was added with 0.5 mM dNTP mix, 5 µM random hexamers, 10 U/µl Moloney murine leukemia virus (MMLV) RT (Promega), and 1 U/µl RNasin (Promega) in 1x MMLV RT buffer [Promega, containing 50 mM Tris·HCl (pH 8.3 at 25°C), 75 mM KCl, 3 mM MgCl2, and 10 mM DTT] and H2O to make a final volume of 100 µl. The cDNA reaction was incubated for 1 h at 42°C and stopped by heating at 95°C for 5 min.

mRNA quantification from multiple genes. mRNA levels were quantified using a medium-throughput quantitative competitive PCR (MT qcPCR) developed in our laboratory that can detect an ~15% difference in mRNA levels (35). Briefly, cDNA (containing ~ 25 ng total RNA), msDNA (e.g., 0.17 pM for PCR amplification by 27 cycles, 0.084 pM for 28 cycles, etc; the msDNA concentrations were reduced by 1.9-fold as the number of PCR amplification cycles increased by 1), 0.4 µM gene-specific PCR primers for one gene, and 1x PCR Ready-Mix (containing 0.3 units Taq polymerase, 10 mM Tris-Cl, 50 mM KCl, 1.5 mM MgCl2, 0.001% gelatin, 0.2 mM dNTP, and stabilizers) in a total of 10 µl reaction volume were added to each well in a 96-well plate. Reactions in the 96-well plate were amplified by a given PCR cycle number under the following conditions: 4 min of preliminary heating at 94°C followed by 50 s denaturation at 94°C, 50 s annealing at 57°C and 1-min extension at 72°C, and a final 5-min extension at 72°C. For each gene analyzed, data were obtained from PCR reactions with 4 different cycle numbers, i.e., for simultaneous analysis of mRNA levels from 8 genes x 11 samples, 4 different PCR plates were amplified by 27, 28, 29, or 30 cycles, respectively. The PCR reaction conditions between these plates were identical except that the concentrations of msDNA were reduced sequentially by 1.9-fold in relation to increasing PCR cycle numbers (Fig. 2). Mean values of mRNA levels for each single gene were calculated from mRNA levels obtained from four different cycle numbers. The assay was repeated one time to verify the reproducibility of data. Amplified PCR products from different genes were confirmed by sequencing.



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Fig. 2. Gel analysis of mRNA levels from multiple genes. Equal amount of cDNA from 8 samples was added to each well of each column (e.g., sample 1 was added to A1–A11, sample 2 was added to B1–B11, etc). Each well in one row (rows 1–11) contains specific primer for one gene (e.g., row 1 from A1 to H1 contain primers for GR{alpha}, row 2 from A2 to H2 for M.GS, etc). The list of target genes is marked on the gel (d). Each well in the four plates (a–d) contains an identical amount of cDNA/primers/PCR reagents, but the concentration of standard DNA (sDNA) was reduced by a factor of 1.9-fold sequentially. Thus the number of PCR amplification cycles was increased by 1 cycle sequentially, i.e., 27 (plate a), 28 (plate b), 29 (plate c), and 30 (plate d) cycles. An arrow at A1 on the gel in d indicates the direction of electrophoresis. Two PCR products of different size were obtained; the upper bands represent target DNA, and the lower bands represent sDNA. The ratio of band intensities of target over sDNA increases with increasing cycle number (for example, comparing products of A1 from d with A1 from c, or A1 from b or A1 from a). The assay was repeated one time for each gene analyzed. Mean values of mRNA levels for each of these genes were calculated according to methods described previously (35). Data for mRNA levels of GR{alpha}, M.GS, IR, and GHR were not presented, as this study focused on genes regulating lipid metabolism.

 

Data analysis. Five microliters of each PCR product (up to 96 samples) were analyzed with a microplate diagonal gel electrophoresis containing 5% polyacrylamide, as described previously (35), stained in ethidium bromide, and destained in H2O for 30 min. The picture was taken with a digital camera (UVP, Cambridge, UK), and the image of the gel was saved on a floppy disk. The fluorescence of DNA bands was analyzed using Phoretix 1 D advanced v. 4.01 (Newcastle upon Tyne, UK) software. The detailed procedure for data analysis was described previously (35). mRNA levels were calculated and presented as arbitrary units. Values of mRNA levels from target genes were normalized to mRNA levels of 18S rRNA, measured with quantitative competitive PCR using a synthetic standard DNA for 18S rRNA.

Statistical analysis. Pearson correlation analysis was performed using SPSS software (version 10.1.0). Correlation is significant at a P value < 0.05.


    RESULTS
 TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Subjects were recruited from the local community with a wide variation in BMI (23.6–39 kg/m2) to allow us to examine relationships between BMI, insulin sensitivity, and PPAR{alpha} expression. Values of BMI were negatively correlated with the insulin sensitivity index (r = –0.73, P = 0.001, n = 16). The insulin sensitivity index values (ratio of metabolized glucose to mean plasma insulin), as a measure of whole body insulin-mediated glucose disposal, ranged between 2.6 and 10.1 (mg·m–2·min–1·µU–1·ml–1) in this cohort (Table 1).

To determine whether steady-state PPAR{alpha} mRNA levels correlate with mRNA levels of genes regulating fat metabolism, mRNA levels from PPAR{alpha} and key genes in lipid metabolism were measured in skeletal muscle biopsies obtained after overnight fasting, before hyperinsulinemic-euglycemic clamp study, using MT qcPCR (Fig. 2). A strong, significant and positive correlation was observed between mRNA levels of PPAR{alpha} and CD36 (r = 0.77, P = 0.001, n = 14; Fig. 3A), PPAR{alpha} and LPL (r = 0.54, P = 0.024; Fig. 3B), PPAR{alpha} and uncoupling protein (UCP)-3 (r = 0.53, P = 0.026; Fig. 3C), PPAR{alpha} and UCP-2 (r = 0.63, P = 0.008; Fig. 3E), and PPAR{alpha} and mCPT-I (r = 0.54, P = 0.024; Fig. 3D). In contrast, the association between mRNA levels of PPAR{alpha} and GLUT4 was not significant (r = 0.38, P = 0.09, Fig. 4A), and no association was observed between mRNA levels of PPAR{alpha} and insulin sensitivity index values (r = –0.04, P = 0.44, Fig. 4B) or BMI (r = 0.3, P = 0.15, Fig. 4C).



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Fig. 3. Correlation between mRNA levels of PPAR{alpha} with mRNA levels of CD36 (A), LPL (B), UCP-3 (C), muscle CPT-I (mCPT-I; D), and UCP-2 (E). Each point represents the mean value of 2 determinations (for each determination, mean values were taken from 4 different PCR cycles). AU, arbitrary units.

 


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Fig. 4. Correlation between mRNA levels (AU) of PPAR{alpha} with mRNA levels of GLUT4 (A), insulin sensitivity index (ratio of the amount of glucose metabolized to the mean plasma insulin during steady state; B), and body mass index (BMI; C). Each point represents the mean value of 2 determinations (for each determination, mean values were taken from 4 different PCR cycles).

 

Because the study recruited subjects with a wide range of BMI (23–39 kg/m2), we also analyzed data by stratifying nonobese subjects (BMI <30 kg/m2) and obese subjects (BMI >30 kg/m2, Table 3). PPAR{alpha} strongly correlated with CD36 (r = 0.99, P < 0.001) and LPL (r = 0.80, P = 0.01) in nonobese subjects (Table 3). There was a trend toward a weaker correlation in subjects with increasing BMI (r = 0.68, P = 0.07 for CD36 and r = 0.36, P = 0.28 for LPL). PPAR{alpha} correlated well with mCPT-I in all subjects. Interestingly, there was a trend toward a stronger correlation in obese subjects (r = 0.98, P = 0.01, Table 3). PPAR{alpha} correlated with UCP-3 (r = 0.74, P = 0.02) and GLUT4 (r = 0.64, P = 0.05) mainly in nonobese subjects, and the significant correlation was not present in obese subjects. There was no correlation between PPAR{alpha} and BMI or insulin sensitivity index values in either group.


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Table 3. Correlations between mRNA levels of PPAR{alpha} and mRNA levels of genes in lipid metabolism and BMI or M-to-I ratios stratified by BMI

 


    DISCUSSION
 TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Existing data show that there is a differential response to PPAR{alpha} between humans and rodents (19). For example, acyl-CoA oxidase, a PPAR{alpha} response gene in liver in rodents, is not responsive to PPAR{alpha} in human liver (19, 30); a similar phenomenon exists with peroxisomal enoyl-CoA hydratase/3-hydroxyacyl-CoA dehydrogenase (19). Thus data obtained from rodents may not be directly applied to humans; therefore, it is important to obtain data in humans in vivo.

Here we show that expression of mRNA levels of PPAR{alpha} is closely correlated with mRNA levels of CD36, LPL, UCP-2, UCP-3, and mCPT-I (Fig. 5) but not with a key gene in glucose metabolism such as GLUT4 nor BMI or insulin sensitivity index. Importantly, correlations were not observed between PPAR{alpha} and the housekeeping gene 18S rRNA. The novelty of this study is that 1) expression of PPAR{alpha} and genes controlling fat metabolism are tightly linked in human skeletal muscle in vivo; 2) no relationships are observed between PPAR{alpha} expression in skeletal muscle tissue and insulin sensitivity, as measured by the hyperinsulinemic-euglycemic clamp; 3) no relationships occur between PPAR{alpha} expression in skeletal muscle tissue and BMI as a measure of obesity; and 4) our novel mRNA methodology (35) has made this study possible in limited human biopsy material. The technique is particularly valuable in situations where quantity of tissue is limited, because it allows reproducible simultaneous multiple measurements with high sensitivity and specificity of several candidate mRNAs.



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Fig. 5. Correlation between mRNA levels of PPAR{alpha} and mRNA levels of important genes in lipid metabolism. No correlation between mRNA levels of PPAR{alpha} and mRNA levels of GLUT4 (r = 0.38, P = 0.09), BMI (r = 0.3, P = 0.15), and insulin sensitivity index (r = –0.04, P = 0.44) was observed. The 2-headed arrows between PPAR{alpha} and each gene represent that expression of PPAR{alpha} correlated with expression of those genes.

 

The mechanism underlying activation of PPAR{alpha} and improved insulin sensitivity is not fully understood. In animal studies, administration of PPAR{alpha} agonist reduces body insulin resistance induced by high-fat feeding (13) or high-fructose feeding (27). In human studies, administration of PPAR{alpha} agonist also improves insulin sensitivity and lipid profile (15). The underlying mechanisms may be explained by 1) activation of PPAR{alpha}, redirecting fatty acids from peripheral tissues (e.g., skeletal muscle) to the liver, because hepatic PPAR{alpha} CPT-I and CD36 expression is increased (13) by PPAR{alpha} agonist administration; and/or 2) increased glucose utilization in adipose tissue because GLUT4 mRNA levels are increased in visceral adipose tissue by lipid infusion (7). Thus activation of PPAR{alpha} in other tissues such as liver might reduce the fatty acid-mediated inhibition of insulin-stimulated glucose disposal in skeletal muscle (2, 13). These data and our results suggest that it is unlikely that activation of PPAR{alpha} directly increases glucose utilization in skeletal muscle although skeletal muscle plays a major role in whole body glucose utilization.

Our data show that PPAR{alpha} mRNA levels in skeletal muscle do not correlate with body insulin sensitivity (Fig. 4B, in particular, in obese subjects, Table 3), i.e., there was no marked difference in PPAR{alpha} mRNA levels in subjects with different insulin sensitivity indexes (Fig. 4B). Our data seem therefore to be also consistent with an earlier report showing that skeletal muscle PPAR{alpha} protein levels were similar between nondiabetic and diabetic subjects (20). We consider that these data do not contradict the concept that activation of PPAR{alpha} improves insulin sensitivity, as discussed above. In our nonobese subjects, for example, PPAR{alpha} positively correlates with GLUT4 mRNA levels (r = 0.64, P = 0.05, Table 3), and a similar trend is also shown between PPAR{alpha} and the insulin sensitivity index (r = 0.52, P = 0.09, Table 3). These relationships were weaker in obese subjects (Table 3). Thus the evidence suggests that increased fatty acid metabolism in skeletal muscle does not increase skeletal muscle glucose metabolism. The mechanism probably involves increased fatty acid metabolism, antagonizing insulin-induced glucose utilization and oxidation (28). This notion is consistent with an earlier report in humans showing that a lipid-heparin infusion reduces glucose uptake (9) and in animal studies that lipid-heparin infusion increases skeletal muscle CD36 mRNA levels but reduces skeletal muscle GLUT4 mRNA levels and skeletal muscle glucose utilization (7).

CD36 is a key transporter for lipid uptake in skeletal muscle (3) and LPL hydrolyzes TG to release fatty acids for oxidation. Expression of CD36 and LPL is mainly regulated by PPAR{alpha} in liver (26, 31) and by PPAR{gamma} in adipocytes (26, 31). To date, it has been uncertain whether expression of CD36 or LPL is regulated by PPAR{alpha} or -{gamma} in human skeletal muscle in vivo. Although both PPAR{alpha} (20) and PPAR{gamma} are expressed (8, 20), the levels of PPAR{gamma} mRNA are barely detectable in our experiments (data not shown), consistent with a previous report (8). Our data showing a positive and significant correlation between mRNA levels of PPAR{alpha} and those of CD36 or LPL suggest that PPAR{alpha} may be an important regulator of CD36 and LPL in vivo in humans in skeletal muscle, although such a correlation is not a direct proof that PPAR{alpha} is a regulator of CD36 and LPL in humans in vivo.

mCPT-I plays a key role in mitochondrial fatty acid oxidation. Administration of PPAR{alpha} agonists upregulates muscle CPT-I in skeletal muscle in hamster and stimulates fatty acid {beta}-oxidation in human skeletal muscle cells (25). Human mCPT-I is stimulated by fatty acids, a response that is thought to be mediated by a peroxisome proliferator response element to which PPAR{alpha} binds (21). Interestingly, PPAR{gamma} mRNA also correlates with mRNA levels of mCPT-I and LPL in human skeletal muscle (18). Thus it is possible that activation of either PPAR{alpha} or PPAR{gamma} might affect expression of mCPT-I.

Interestingly, there is a suggestion (albeit the numbers are small) that the correlation between PPAR{alpha} and mCPT-I is stronger in obese subjects than in nonobese subjects in our study (Table 3). These data suggest that, in obese subjects, fatty acid oxidation in skeletal muscle might also be increased, probably because of increased circulating concentrations of free fatty acids often seen in obesity (12).

UCP-2 and UCP-3 are proteins that uncouple substrate oxidation from ATP synthesis, converting fuel into heat energy (17). Whether expression of UCPs is regulated in humans by PPARs in vivo has not been determined. PPAR{alpha} agonists upregulate expression of UCP-3 in rat skeletal muscle (4). Our data showing a positive correlation between PPAR{alpha} and UCP-2 and UCP-3 suggest that, in human skeletal muscle in vivo, PPAR{alpha} regulates expression of UCP-2 and UCP-3.

Insulin sensitivity measured by the hyperinsulinemic-euglycemic clamp represents insulin-mediated whole body glucose disposal, and insulin sensitivity is affected by tissue insulin sensitivity, mainly in liver, skeletal muscle, and adipose tissues. Our data do not show any correlation between PPAR{alpha} mRNA levels in skeletal muscle with hyperinsulinemic-euglycemic clamp data, suggesting that, in our subjects, skeletal muscle glucose disposal is not affected by PPAR{alpha} mRNA levels, since the majority of insulin-induced glucose uptake during the clamp occurs in skeletal muscle.

It would have been ideal if we could have taken a second biopsy from these volunteers after the clamp study to investigate changes in gene expression. However, it was neither practical nor ethical in unpaid volunteers to do so given the painful procedure these volunteers had to experience. Only 16 of our original 36 recruited subjects agreed to undergo muscle biopsy. Because the amount of tissue biopsy obtained was very small (the smallest amount was ~26 mg), we were unable to analyze protein expression.

In summary, we have shown for the first time in humans in vivo, using unique mRNA measurement methodology for a range of relevant genes, that mRNA levels of PPAR{alpha} strongly correlate with mRNA levels of several key genes regulating fat metabolism in skeletal muscle. A measure of insulin sensitivity and BMI did not correlate with PPAR{alpha} expression in skeletal muscle tissue. Although not proof of causality, these novel results suggest that, in humans in vivo, PPAR{alpha} expression plays an important role in coordinate regulation of key genes in lipid but not glucose metabolism.


    ACKNOWLEDGMENTS
 
GRANTS

We are grateful to the Wellcome Trust for grant support. J. Zhang is currently supported by the School of Medicine, University of Southampton. C. D. Byrne is Director of the Wellcome Trust Clinical Research Facility at Southampton University Trust.


    FOOTNOTES
 

Address for reprint requests and other correspondence: C. D. Byrne, Endocrinology & Metabolism Unit, FOAD, Univ. of Southampton, CF96, Level F, Central Block, Southampton General Hospital, Southampton SO16 6YD, UK (E-mail: cdtb{at}soton.ac.uk).

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.


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