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Annals of Clinical & Laboratory Science 34:47-56 (2004)
© 2004 Association of Clinical Scientists

Quantification of Human PPAR{gamma}1 Gene Expression by Competitive PCR Using an Homologous Internal Standard

Nik Soriani Yaacob1, Ruzilawati Abu Bakar1 and Mohd-Nor Norazmi2
Schools of 1 Medicine and 2 Health Sciences, Universiti Sains Malaysia, Kelantan, Malaysia

Address correspondence to Nik Soriani Yaacob Ph.D., Department of Chemical Pathology, School of Medical Sciences, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia; tel 609 766 4750; fax 609 765 3370; e-mail soriani{at}kb.usm.my.


   Abstract
Top
 Abstract
Introduction
 Methods and Materials
 Results
 Discussion
 Conclusions
 Acknowledgements
 References
 
The polymerase chain reaction (PCR) is useful for amplifying specific mRNAs, particularly those present in low copy numbers. However, due to the exponential nature of the amplification process, PCR cannot readily be used to quantify gene expression. A competitive PCR technique was developed to address this shortcoming. An internal standard that is 100% homologous to, but shorter than, the target gene was constructed. The practicality of the method was demonstrated by determining the expression levels of a human transcription factor, peroxisome proliferator-activated receptor gamma 1 (hPPAR{gamma}1) which is normally present in low copy numbers in selected cells. A mock system was used to test the accuracy and sensitivity of the method, which was subsequently used to determine the expression of this receptor in lipopolysaccharide (LPS)-activated monocytes, which are known to express hPPAR{gamma}1 differentially during cellular activation. Densitometric analysis showed that the competitive PCR method reliably estimated the expression levels of hPPAR{gamma}1 at the attomole (10-18) level in monocytes.

(received 20 February 2003; accepted 22 July 2003)

Keywords: competitive PCR, monocyte, PPAR{gamma}1


   Introduction
Top
 Abstract
 Introduction
Methods and Materials
 Results
 Discussion
 Conclusions
 Acknowledgements
 References
 
Gene quantification is important for the detailed understanding of transcriptional activities of genes that are believed to play important roles in disease processes. Previous methods of gene quantification were either semi-quantitative or required an appreciable amount of starting material for analysis [1–4]. Since the advent of the polymerase chain reaction (PCR), many researchers have attempted to utilize this technique for gene quantification. Many problems arose during the early trials due to the exponential nature of the amplification dynamics [5,6]. Attempts were then made to include a standard that could be used to compare the amplification of the target with a known concentration of the standard. This included the use of housekeeping genes to provide relative quantification under the assumption that such housekeeping genes do not change under the experimental conditions [7]. That assumption, however was not always the case [8,9]. To overcome this problem, we and other workers have used standards such as the "PCR MIMIC," ie, a standard or competitor that has primer-annealing regions that are homologous to the target gene, flanking an unrelated DNA fragment [10]. However, the non-homologous region within the competitor raises the possibility of different amplification efficiencies of the target and the competitor, particularly if the fragments are long. An alternative approach is to use genes containing a short intron as an internal competitor to the target gene [2]. This does not address the problem of different kinetics of amplification, as described above, and limits the number of genes that can be quantified efficiently and uniformly, since the length of introns for the genes of interest are highly variable. Hence, a more reliable technique needs to be developed to increase the reproducibility and accuracy of gene quantification.

The objective of this study was to develop a PCR technique to quantify gene expression reliably. To achieve accuracy and consistency of the assay, an internal standard or competitor that is 100% homologous to, but shorter than, the target gene was used. The accuracy of the method was tested by estimating the expression levels of a transcription factor, human peroxisome proliferator-activated receptor gamma 1 (hPPAR{gamma}1) in a mock system, as well as in monocytes, a cell type that is known to express PPAR{gamma}1 differentially during cellular activation [11].


   Methods and Materials
Top
 Abstract
 Introduction
 Methods and Materials
Results
 Discussion
 Conclusions
 Acknowledgements
 References
 
Construction of recombinant plasmids: pNSY004, pNSY005, and pNSY006.  The cloning protocol is shown in Fig. 1Go. A region of the hPPAR{gamma}1 gene was amplified from cDNA of human adipose tissue (Maxim Biotech, USA) using the primers: sense: tctctccgtaatggaagacc; antisense: gcattatgagacatccccac. The amplicon (474 bp) was then cloned into the plasmid vector, pCR.TOPO2.1 (Invitrogen, USA) to produce the recombinant plasmid, pNSY004. The insert was verified by DNA sequencing (Bio-Syntech, Malaysia). To facilitate construction of the homologous standard as a competitor, the hPPAR{gamma}1 insert was excised from pNSY004 and re-cloned into the plasmid vector, pSG5 (Stratagene, USA) to produce pNSY005. A 70 bp portion was removed from the mid-part of the insert by HindIII digestion and the plasmid was re-ligated to produce pNSY006. This plasmid serves as the competitor for hPPAR{gamma}1.



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  		Fig. 1. Construction of the recombinant plasmids: pNSY004, pNSY005 and pNSY006. A region of the hPPAR{gamma}1 gene was amplified from the cDNA of human adipose tissue and cloned into the plasmid vector, pCR.TOPO2.1 to produce the recombinant plasmid, pNSY004. The hPPAR{gamma}1 gene was then excised from pNSY004 and re-cloned into the plasmid vector, pSG5 to facilitate the construction of the homologous competitor. A 70 bp portion was then removed from the middle part of the insert by HindIII digestion and the plasmid re-ligated to produce pNSY006. This plasmid forms the competitor for hPPAR{gamma}1.

 
Competitive PCR: mock experiment.  Concentrations of pNSY005 and pNSY006 were determined by spectrophotometry using a standard assay according to the manufacturer’s protocol (Biophotometer, Eppendorf, Germany). A serial, 10-fold dilution of competitor, pNSY006 (ranging from 10-1 to 10-5 picomole (pM)/ml), was used for competitive PCR. A PCR mastermix (0.2 mM dNTPs, 1.5 mM MgCl2, 1.0 U Taq DNA polymerase, buffer and 0.2 µM primers) containing a constant amount (1 µl) of the mock target, pNSY005 (10-3 pM/ml), and a constant volume (1 µl) of each serial 10-fold dilution of the competitor (pNSY006) in a series of 25 µl reaction mix was prepared. PCR was carried out for 30 cycles, using the following optimum reaction conditions: 94°C denaturation for 1 min; 59°C annealing for 1 min; 72°C extension for 1 min. The amplification products were then subjected to 1.2% agarose gel electrophoresis, stained with ethidium bromide, and visualized using a gel documentation system (Amersham Phamacia Biotech, USA).

The experiment was repeated using a 2-fold serial dilution of the competitor, based on the staining intensities of the bands obtained in the 10-fold dilution experiment, for accurate determination of the target concentration. Densitometric analyses were carried out using the gel documentation system and the standard curve was plotted using the Excel program (Microsoft Corp, USA) after correcting for the size difference between the target and competitor. The amount of target molecule was calculated using the formula, y = mx + c, where x represents the concentration of the target when y = 1 (a molar ratio of 1:1). These calculations are shown in the Results section.

Separation of peripheral blood mononuclear cells (PBMC).  Five ml samples of venous blood were collected from healthy volunteers and diluted 1:1 (v/v) in phosphate-buffered saline (PBS). The diluted blood was carefully layered onto 3 ml of Ficoll-Hypaque (Sigma, USA) and subjected to centrifugation (800 g, 20 min). The PBMC layer was collected, washed once in PBS (800 g, 5 min), and counted in a hemocytometer after staining with trypan blue (0.4%). The concentration of cells was adjusted to 2·106/ ml in PBS or culture media

Flow cytometrry.  Combinations of two fluorescent-conjugated monoclonal antibodies (anti-CD14, anti-CD4, anti-CD8, anti-CD19, and isotype controls) (BD Pharmingen, USA) were used separately to stain 2·105 cells per reaction tube. Briefly, 100 µl of cell suspension was incubated with 5 µl of relevant monoclonal antibodies for 30 min at room temperature in the dark. The cells were then washed 3 times in PBS (800 g, 5 min), fixed in 1% paraformaldehyde, and analysed using the FacScan analyzer (Becton-Dickinson, USA) to determine the percentages of specifically stained cell types.

Monocyte isolation and activation.  Five ml of PBMC (2·106 cells/ml) in complete RPMI media (containing 2.0 mM glutamine, 10% fetal bovine serum, and 50 µg/ml gentamycin) was plated in 25 cm2 tissue culture flasks (Costar, USA) and incubated in 5% CO2 at 37°C. After 1 hr, the non-adherent cells (non-monocytes) were collected and the percentages of cell subsets assessed by flow cytometry. The efficiency of monocyte adherence was determined by comparing the percentages of CD14-positive cells prior to and after adherence to the tissue culture flask. Subsequent experiments were carried out, provided the efficiency of monocyte isolation was >=85%. The adherent cells were cultured in 10 ml of complete RPMI media containing 10 ng/ml lipopolysaccharide (LPS) (Sigma, USA) and incubated for 7 days in 5% CO2 at 37°C, as previously described [12]. A control flask containing adherent cells, but without addition of LPS, was also set up.

Total RNA isolation.  Total RNA was isolated from the cultured monocytes using the RNeasy RNA Extraction Kit (Qiagen, USA), according to the manufacturer’s instructions. Briefly, the culture medium was collected and the detached cells were washed once in PBS and collected by centrifugation. The cell monolayer was rinsed once in PBS and 2 ml of lysis buffer was added. The adherent cells were scraped using a spatula and collected into microcentrifuge tubes. The cells collected from the culture medium above were combined with the scraped cells to ensure collection and lysis of all cells in the flask. The lysate was passed through a QIA-shredder column (Qiagen) to collect total RNA, which was then eluted and collected in a microcentrifuge tube. Purity of total RNA was determined by spectrophotometry (ie, the 260:280 ratio was at least 1.8) and integrity of total RNA was ascertained by gel electrophoresis.

cDNA synthesis.  Complementary DNA (cDNA) was synthesized from total RNA using the RevertAid H Minus First Strand cDNA Synthesis Kit (Maxim Bio, USA) according to the manufacturer’s instructions. Briefly, 200 units of MMLV reverse transcriptase, 0.5 µg oligo(dT)18 primer, 20 units ribonuclease inhibitor, and 1 mM dNTP mix were used to reverse transcribe 1 µg of total RNA as determined by spectrophotometry. Nuclease-free deionized water was added to a final volume of 100 µl. One µl of cDNA (equivalent to 10 ng of starting total RNA) was later used in each of the competitive PCR reaction tubes. The success of cDNA synthesis was assessed by amplifying it using primers against a housekeeping gene, rRNA, (results not shown).

Competitive PCR–hPPAR{gamma}1 in activated monocytes.  Expression of hPPAR{gamma}1 was determined in activated and non-activated monocytes in a competitive PCR using 10-fold and subsequently 2-fold serial dilution of pNSY006 as the competitor, as described above. The amount of hPPAR{gamma}1 expression in the starting material (1 µg total RNA) was calculated as shown in the Results section.


   Results
Top
 Abstract
 Introduction
 Methods and Materials
 Results
Discussion
 Conclusions
 Acknowledgements
 References
 
Competitive PCR: mock experiment.  For the mock experiment, the concentration of target was adjusted to 1-10-3 picomole/ml and spiked into the reaction tubes for competitive PCR. In Fig. 2Go, panels a and b show the results of a typical competitive PCR experiment using 10-fold and 2-fold serial dilutions of the competitor, pNSY006, respectively. As shown in Fig 2aGo, competitive PCR with 10-3 picomole pNSY006 resulted in target and competitor bands being of almost equal intensities, or at a molar ratio of about 1:1 (ie, the concentration of target approximately equals that of competitor).



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  		Fig. 2. Competitive PCR: mock experiment. Competitive PCR was performed in a single tube containing a constant amount (1 µl) of mock target, pNSY005 and a constant volume (1 µl) of (a) aserial, 10-fold dilution of pNSY006 (ranging between 10-1 to 10-5 picomole/ml), followed by (b) a 2-fold serial dilution of pNSY006 (ranging from 4.0 to 0.25 ·10-3 picomole/ml) based on the staining intensities of the bands obtained with the 10-fold dilution experiment. (c) A standard curve was plotted using the ratio of the intensities of the target-to-competitor electrophoretic bands, as determined by densitometry, against the reciprocal of competitor concentrations. The amount of target molecules was then calculated using the formula y = mx + c, where x represents the concentration of the target when y = 1 (molar ratio of 1:1). In this example, the concentration of the mock target was found to be 9.32·10-4 picomole/ml, which was close to its spiked concentration of 1.0·10-3 picomole/ml as determined by spectrophotometry.

 
Based on this concentration, a 2-fold dilution series of the competitor was prepared and used to perform another set of competitive PCR assays to delineate the concentration of the target more accurately (Fig. 2bGo). A standard curve was plotted based on the reciprocal of the competitor concentration against the ratio of target to competitor intensities (as measured by densitometry). The standard curve was used to calculate the concentration of the target molecule (Fig. 2cGo).

Based on the equation of the standard curve (ie, y = mx + c), the concentration of the target molecule was calculated as follows:

Given, y = (3·10-4)x + 0.6781

When, y equals 1, the molar ratio equals 1:1

Hence, 1 = (3·10-4)x + 0.6781

Thus, x = (1 - 0.6781) divided by (3·10-4) = 1073

The concentration of target = 1/1073 picomole/ml, = 9.32·10-4 picomole/ml.

As indicated by the calculation, the concentration of the target was similar to the original concentration of the spiked target in the competitive PCR reaction. This experiment was repeated several times using other samples with almost identical findings (results not shown).

Competitive PCR in a biological system.  Figs. 3Go and 4Go show the results of competitive PCR assays for the determination of hPPAR{gamma}1 expression in non-activated and LPS-activated human monocytes, respectively. Several preliminary experiments were carried out to determine the range of hPPAR{gamma}1 expression levels in these cells that was obtained from 1 µg of starting total RNA. As shown in Fig. 3aGo, the concentration of competitor for a target:competitor molar ratio of 1:1 was 10-4 to 10-5 attomole/ml. A 2-fold dilution series of the competitor ranging from 16·10-5 to 0.5·10-5 attomole/ml was therefore used in the second set of competitive PCR assays (Fig. 3bGo). Based on the graph equation in Fig. 3cGo, the concentration of hPPAR{gamma}1 in non-activated monocytes was 1.07·10-3 attomoles/mg of starting total RNA.



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  		Fig. 3. Competitive PCR: hPPAR{gamma}1 in non-activated monocytes. Competitive PCR was performed in a single tube containing a constant amount (1 µl) of cDNA from non-activated monocytes and a constant volume (1 µl) of (a) a serial, 10-fold dilution of pNSY006 (ranging between 10-2 to 10-7 attomole/ml), followed by (b) a 2-fold serial dilution of pNSY006 (ranging from 16 to 0.5·10-5 attomole/ml) based on the staining intensities of the bands obtained with the 10-fold dilution experiment. (c) From the equation of the standard curve, the concentration of hPPAR{gamma}1 expression by non-activated monocytes was 1.07·10-3 attomoles per mg of starting total RNA.

 


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  		Fig. 4. Competitive PCR – hPPAR{gamma}1 in activated monocytes. Competitive PCR was performed in a single tube containing a constant amount (1 µl) of cDNA obtained from LPS-activated monocytes and a constant volume (1 µl) of (a) a serial, 10-fold dilution of pNSY006 (ranging between 10-2 to 10-6 attomole/ml), followed by (b) a 2-fold serial dilution of pNSY006 (ranging from 16 to 1·10-4 attomole/ml) based on the staining intensities of the bands obtained with the 10-fold dilution experiment. (c) From the equation of the standard curve, the concentration of hPPAR{gamma}1 expression by LPS-activated monocytes was 1.73·10-2 attomoles/mg of starting total RNA.

 
From Fig. 4aGo, it can be deduced that the concentration of competitor that would give a molar ratio of 1:1 was between 10-3 and 10-4 attomole/ ml. Hence, a second series of competitive PCR was carried out using 2-fold serial dilutions of the competitor ranging from 16·10-4 to 1·10-4 attomole/ml (Fig. 4bGo). Based on the graph equation in Fig. 4cGo, the concentration of hPPAR{gamma}1 in LPS-activated monocytes was 1.73·10-2 attomole/mg of starting total RNA. In this example, the expression level of hPPAR{gamma}1 was thus increased 16-fold after the activation of monocytes by LPS.


   Discussion
Top
 Abstract
 Introduction
 Methods and Materials
 Results
 Discussion
Conclusions
 Acknowledgements
 References
 
PCR is a powerful tool for the detection of low levels of mRNA [1]. However, accurate quantification of mRNA present in the starting material requires several adaptations to overcome the non-linearity of PCR dynamics. Because the amplification reaction is an exponential process, minor differences in any of the variables could greatly affect the results of a PCR assay and thus render quantification unreliable. Tube-to-tube variations, eg, when using unrelated standards with different amplification conditions, may also impair the accuracy of quantification [2]. This problem is compounded by the uncertainty of mRNA levels in biological systems, particularly when such systems are subjected to varying stimuli and manipulations as in experimental situations. Hence, an accurate, reproducible, and robust PCR-based method of gene quantification needs to be developed.

There have been many previous reports of competitive PCR techniques [3,5,7]. The present report shows that quantification of very low copy numbers of mRNA can be determined in a biological system. To our knowledge, the detection levels (ie, < attomole levels) attained in the present study are the most sensitive that have been reported to date.

All of the present experiments were initially carried out with a 10-fold dilution series of the competitor to determine the approximate concentration range of the target. This was followed by competitive PCR assays using a 2-fold dilution series of competitor from which a standard curve of the reciprocal of competitor concentration was plotted versus the target-to-competitor ratio. Mathematical transformation of the data (ie, using the reciprocal of the competitor concentration) was necessary to derive a simple regression line with the formula, y = mx + c. A stringent cut-off of 0.95 was set for the correlation coefficient, R2, and calculation of the concentration was based on the equation above, whereby y = 1 when the target:competitor molar ratio is 1:1. The validity of this equation can be deduced from the fact that the R2 value of the regression line was consistently >0.95.

A previous study by Siebert [13] showed that quantitative PCR using PCR MIMIC as an internal standard was highly reliable in estimating small changes in the levels of specific mRNA. Briefly, 2 aliquots of IL-1ß mRNA, differing 4-fold in their starting concentrations, were reverse-transcribed and the levels of cDNA synthesized were measured using the competitive PCR method. The results showed a 4.3-fold difference between the samples, suggesting the reliability of this method even when cDNA was used for the determination of gene expression instead of mRNA. This may indicate that there exists an appreciable range of linearity for the relationship between the amount of starting template and the amount of amplicon obtained.

In our experience, it was possible to quantify reliably the amount of transcript from the same total RNA sample on 3 different occasions during a 6-mo period. Hence, although it would be desirable to run a competitive reverse transcription (RT) PCR on the template RNA and use RNA internal standards to control for the RT reaction, the unstable nature of the RNA standards would compromise the reproducibility and robustness of this technique. The commercial availability of highly efficient RNA extraction and cDNA synthesis kits would reduce the discrepancies of using the template RNA or cDNA synthesized from it for the quantification of gene expression, while ensuring more reproducible and probably more reliable gene quantification.

Mock experiments were conducted to establish that the competitive PCR worked efficiently and reproducibly. This was performed by determining the concentration of a spiked target. The concentration of the spiked target as determined by this method was almost identical to its actual amount as determined by spectrophotometry. This observation was reproducible and consistent, since repeat measurements of the same sample gave the same results after being stored for several months and compared again with repeat readings by spectrophotometry.

Another advantage of this technique was that the target concentration can easily be discerned visually, facilitating its estimation or semi-quantification. The concentration of hPPAR{gamma}1 expressed in monocytes was also readily estimated visually. This was particularly important when the concentration range of the target was being determined. This indicates that the method can easily be adapted for quick determination of the approximate concentration of a target gene without need for densitometry and additional calculations. In this visual method, when the concentration of target band approximates that of the competitor, the intensities of the bands would be almost equal, excepting for their size difference. Since the size difference was kept to a minimum (70 bp), this variable was reduced to a point where the bands could be separately visualized with minimal differences in their concentrations at approximate 1:1 molar ratio of target-to-competitor.

The developed method was successfully tested in a biological system, ie, quantification of the expression of a transcription factor, PPAR{gamma}1, in human monocytes. Although these cells have been reported to express PPAR{gamma}1 upon activation [11,14], its expression in non-activated monocytes was reported to be negligible [11], suggesting that accurate quantification of PPAR{gamma}1 in resting monocytes either has not been performed or has been impossible to perform. Macrophages, which are mature monocytes, are important in the defense against microbes; their main functions are in phagocytosis and antigen presentation. LPS, which forms part of the membrane of many microbes, can bind to and activate these cells. The level of PPAR{gamma}1 expression could therefore play a functional role in monocyte/macrophage activity, as previously suggested [11,14].

Using cDNA prepared from mRNA of non-activated and LPS-activated monocytes, we were able to demonstrate that the expression levels of PPAR{gamma}1 were at least 100-fold below the attomole level, and, consistent with previous findings [11,14], there was at least a 10-fold increase in PPAR{gamma}1 expression upon monocyte activation. Moreover, we were able to show that resting or unactivated monocytes express very low levels of PPAR{gamma}1.


   Conclusions
Top
 Abstract
 Introduction
 Methods and Materials
 Results
 Discussion
 Conclusions
Acknowledgements
 References
 
The competitive PCR technique reported herein is a reliable, reproducible, and robust method for quantification of gene expression. Since subtle changes in gene expression (particularly of transcription factors) result in significant changes in cell behaviour and function [15], this method could aid in studying the pathology of various diseases. In fact, PPAR{gamma}1 has been suggested to play an important role in arteriosclerosis [16] and the level of its expression may influence the outcome of this disease.

With the recent development of real-time PCR [17], the manual quantitative PCR technique described in the present study might be regarded as obsolete. However, real-time PCR is an expensive technique that requires sophisticated equipment and costly reagents and that presents unique technical problems [18]. Unless a high throughput for quantitative PCR assays is required, the competitive PCR technique described herein, or variations of this technique, will remain useful in many experimental situations [19].


   Acknowledgements
Top
 Abstract
 Introduction
 Methods and Materials
 Results
 Discussion
 Conclusions
 Acknowledgements
References
 
This study was supported by Universiti Sains Malaysia Short Term Grant (304/PPSP/6131140). RAB was supported by the Universiti Sains Malaysia Academic Staff Training Scheme.


   References
Top
 Abstract
 Introduction
 Methods and Materials
 Results
 Discussion
 Conclusions
 Acknowledgements
 References
 

  1. Wang AM, Doyle MV, Mark DF. Quantification of mRNA by the polymerase chain reaction. PNAS USA 1989;86:9717–9721.[Abstract/Free Full Text]
  2. Gilliland G, Perrin S, Blanchard K, Bunn HF. Analysis of cytokine mRNA and DNA: detection and quantification by competitive polymerase chain reaction. PNAS USA 1990;87:2725–2729.[Abstract/Free Full Text]
  3. Riedy MC, Timm EA Jr, Stewart CC. Quantitative RT-PCR for measuring gene expression. Biotechniques 1995;18:70–76.[Medline]
  4. Bishop GA, Rokahr KL, Lowes M, McGuinness PH, Napoli J, DeCruz, DJ, Wong WY, McCaughan GW. Quantitative reverse transcriptase-PCR amplification of cytokine RNA in liver biopsy specimens using a non-competitive method. Immunol Cell Biol 1997;75:142–147.[Medline]
  5. Anderson KM, Cheung PH, Kelly MD. Rapid generation of homologous internal standards and evaluation of data for quantitation of messenger RNA by competitive polymerase chain reaction. J Pharmacol Toxicol Meth 1997:38:133–140.[Medline]
  6. Jung R, Soondrum K, Neumaier M. Quantitative PCR. Clin Chem Lab Med 2000;38:833–836.[Medline]
  7. Ke LD, Chen Z, Yung WKA. A reliability test of standard-based quantitative PCR: exogenous vs endogenous standards. Mol Cell Probes 2000;14:127–135.[Medline]
  8. Rumsby PC, Davies MJ, Price RJ, Lake BJ. Effect of some peroxisome proliferators on transforming growth factor-ß1 gene expression and insulin-like growth factor II/mannose-6-phosphate receptor gene expression in rat liver. Carcinogenesis 1994;15:419–421.[Abstract/Free Full Text]
  9. Beier K, Volkl A, Fahimi, HD. TNF{alpha} downregulates the peroxisome proliferator-activated receptor {alpha} and the mRNAs encoding perosixomal proteins in rat liver. FEBS Lett 1997;412:385–387.[Medline]
  10. Yaacob NS, Norazmi MN, Kass GE, Gibson GG. Use of competitive RT-PCR in the molecular analysis of peroxi-some proliferation. Eur J Drug Metab Pharmacokinet 1997;22:321–324.[Medline]
  11. Ricote M, Huang JT, Welch JS, Glass CK. The peroxi-some proliferator-activated receptor-{gamma} (PPAR{gamma}) as a regulator of monocyte/macrophage function. J Leuko Biol 1999;66:733–739.[Abstract]
  12. Marchant A, Duchow J, Delville J-P, Goldman M. Lipopolysaccharide induces up-regulation of CD14 molecule on monocytes in human whole blood. Eur J Immunol 1992;22:1663–1665.[Medline]
  13. Siebert PD. Quantitative RT-PCR. In: Methods and Applications, Vol 3, BD-Biosciences Clontech, Palo Alto, 1993.
  14. Jiang C, Ting AT, Seed B. PPAR-{gamma} agonists inhibit production of monocyte inflammatory cytokines. Nature 1998;391:82–86.[Medline]
  15. Germain RN. The art of the probable: system control in the adaptive immune system. Science 2001;293: 240–245.[Abstract/Free Full Text]
  16. Lee CH, Evans RM. Peroxisome proliferator-activated receptor-gamma in macrophage lipid homeostasis. Trends Endocrinol Metab. 2002;13:331–335[Medline]
  17. Bustin SA. Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol. 2000;25:169–193.[Abstract]
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