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
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ABSTRACT |
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peroxisome proliferator-activated receptor-; messenger ribonucleic acid; insulin resistance; quantitative competitive polymerase chain reaction; skeletal muscle; body mass index
Peroxisomal proliferator-activated receptor- (PPAR
) is a transcription factor that belongs to a family of transcription factors that form heterodimers with retinoid X receptors. PPARs also include PPAR
and -
(34), and fatty acids or their metabolites are probably their natural ligands (34). PPAR
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
in vitro (21). Expression of CD36, an important transporter of long-chain fatty acid, is regulated by PPAR
(26, 31) and -
in the liver (24). Expression of lipoprotein lipase (LPL) is regulated by PPAR
in the liver (26, 32) and PPAR
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
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 activity can be affected by PPAR
mRNA levels. For example, in PPAR
transgenic overexpressing mice, PPAR
protein level is increased (10). mRNA levels of PPAR
response genes (e.g., acyl-CoA oxidase and CPT-I), and CPT-I activity are also increased in overexpressing PPAR
transgenic mice (10). Importantly, the magnitude of increase in mRNA levels of the PPAR
response gene (e.g., acyl-CoA oxidase) and in CPT-I activity is markedly greater in PPAR
transgenic mice than those from nontransgenic controls (10), whereas in PPAR
knockout mice mRNA and protein levels from PPAR
response genes (e.g., long-chain and very long-chain acyl-CoA dehydrogenase; see Ref. 1) are all reduced. Therefore, useful information on PPAR
activity can be obtained by measuring changes in PPAR
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 expression in skeletal muscle. We measured PPAR
mRNA levels and mRNA levels of known PPAR
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).
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MATERIALS AND METHODS |
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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·m2·min1. 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 (60120 min) was calculated. The ratio of metabolized glucose to mean plasma insulin (mg·m2·min1·µU1·ml1) was used as the measure of tissue sensitivity to insulin.
Muscle biopsies. Tissue was obtained before the clamp by Bergstrom needle biopsy (26143 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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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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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 510 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 1015 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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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.
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RESULTS |
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To determine whether steady-state PPAR mRNA levels correlate with mRNA levels of genes regulating fat metabolism, mRNA levels from PPAR
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
and CD36 (r = 0.77, P = 0.001, n = 14; Fig. 3A), PPAR
and LPL (r = 0.54, P = 0.024; Fig. 3B), PPAR
and uncoupling protein (UCP)-3 (r = 0.53, P = 0.026; Fig. 3C), PPAR
and UCP-2 (r = 0.63, P = 0.008; Fig. 3E), and PPAR
and mCPT-I (r = 0.54, P = 0.024; Fig. 3D). In contrast, the association between mRNA levels of PPAR
and GLUT4 was not significant (r = 0.38, P = 0.09, Fig. 4A), and no association was observed between mRNA levels of PPAR
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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Because the study recruited subjects with a wide range of BMI (2339 kg/m2), we also analyzed data by stratifying nonobese subjects (BMI <30 kg/m2) and obese subjects (BMI >30 kg/m2, Table 3). PPAR 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
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
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
and BMI or insulin sensitivity index values in either group.
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DISCUSSION |
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Here we show that expression of mRNA levels of PPAR 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
and the housekeeping gene 18S rRNA. The novelty of this study is that 1) expression of PPAR
and genes controlling fat metabolism are tightly linked in human skeletal muscle in vivo; 2) no relationships are observed between PPAR
expression in skeletal muscle tissue and insulin sensitivity, as measured by the hyperinsulinemic-euglycemic clamp; 3) no relationships occur between PPAR
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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The mechanism underlying activation of PPAR and improved insulin sensitivity is not fully understood. In animal studies, administration of PPAR
agonist reduces body insulin resistance induced by high-fat feeding (13) or high-fructose feeding (27). In human studies, administration of PPAR
agonist also improves insulin sensitivity and lipid profile (15). The underlying mechanisms may be explained by 1) activation of PPAR
, redirecting fatty acids from peripheral tissues (e.g., skeletal muscle) to the liver, because hepatic PPAR
CPT-I and CD36 expression is increased (13) by PPAR
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
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
directly increases glucose utilization in skeletal muscle although skeletal muscle plays a major role in whole body glucose utilization.
Our data show that PPAR 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
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
protein levels were similar between nondiabetic and diabetic subjects (20). We consider that these data do not contradict the concept that activation of PPAR
improves insulin sensitivity, as discussed above. In our nonobese subjects, for example, PPAR
positively correlates with GLUT4 mRNA levels (r = 0.64, P = 0.05, Table 3), and a similar trend is also shown between PPAR
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 in liver (26, 31) and by PPAR
in adipocytes (26, 31). To date, it has been uncertain whether expression of CD36 or LPL is regulated by PPAR
or -
in human skeletal muscle in vivo. Although both PPAR
(20) and PPAR
are expressed (8, 20), the levels of PPAR
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
and those of CD36 or LPL suggest that PPAR
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
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 agonists upregulates muscle CPT-I in skeletal muscle in hamster and stimulates fatty acid
-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
binds (21). Interestingly, PPAR
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
or PPAR
might affect expression of mCPT-I.
Interestingly, there is a suggestion (albeit the numbers are small) that the correlation between PPAR 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 agonists upregulate expression of UCP-3 in rat skeletal muscle (4). Our data showing a positive correlation between PPAR
and UCP-2 and UCP-3 suggest that, in human skeletal muscle in vivo, PPAR
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 mRNA levels in skeletal muscle with hyperinsulinemic-euglycemic clamp data, suggesting that, in our subjects, skeletal muscle glucose disposal is not affected by PPAR
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 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
expression in skeletal muscle tissue. Although not proof of causality, these novel results suggest that, in humans in vivo, PPAR
expression plays an important role in coordinate regulation of key genes in lipid but not glucose metabolism.
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ACKNOWLEDGMENTS |
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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.
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FOOTNOTES |
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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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REFERENCES |
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