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Physiol. Genomics 15: 9-19, 2003. First published July 8, 2003; doi:10.1152/physiolgenomics.00064.2003
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Received 15 April 2003; accepted in final form 30 June 2003.
Physiological Genomics 15:9-19 (2003)
1094-8341/03 $5.00 © 2003 American Physiological Society

Liver gene expression in rats in response to the peroxisome proliferator-activated receptor-{alpha} agonist ciprofibrate

Fekadu Yadetie1, Astrid Laegreid1, Ingunn Bakke1, Waclaw Kusnierczyk2, Jan Komorowski3, Helge L. Waldum1 and Arne K. Sandvik1

1 Department of Cancer Research and Molecular Medicine, Faculty of Medicine, Norwegian University of Science and Technology, N-7489 Trondheim, Norway
2 Department of Computer and Information Science, Norwegian University of Science and Technology, N-7491 Trondheim, Norway
3 The Linnaeus Center for Bioinformatics, Uppsala University, SE-7551 24 Uppsala, Sweden


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 DISCLOSURES
 References
 
Fibrate class hypolipidemic drugs such as ciprofibrate activate the peroxisome proliferator-activated receptor-{alpha} (PPAR{alpha}), which is involved in processes including lipid metabolism and hepatocyte proliferation in rodents. We examined the effects of ciprofibrate (50 mg/kg body wt per day for 60 days) on liver gene expression in rats using cDNA microarrays. The 60-day dosing period was chosen to elucidate both the metabolic and proliferative actions of this substance, while avoiding confounding effects from the hepatic carcinogenesis seen during more long-term stimulation. Ciprofibrate changed the expression of many genes including previously known PPAR{alpha} agonist-responsive genes involved in processes such as lipid metabolism and inflammatory responses. In addition, many novel candidate genes involved in sugar metabolism, transcription, signal transduction, cell proliferation, and stress responses appeared to be differentially regulated in ciprofibrate-dosed rats. Ciprofibrate also resulted in significant increases in liver weight and hepatocyte proliferation. The cDNA microarray results were confirmed by Northern blot analysis for selected genes. This study thus identifies many genes that appear to be differentially regulated in ciprofibrate-dosed rats, and some of these are potential targets of PPAR{alpha}. The functional diversity of these candidate genes suggests that most of them are likely to be differentially regulated as indirect consequence of the many processes affected by ciprofibrate in rodent liver. Although caution is advisable in the interpretation of genome-wide expression data, the genes identified in the present study provide candidates for further studies that may give new insight into the mechanisms of action of peroxisome proliferators.

DNA microarray; lipid metabolism; hepatocarcinogenesis


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 DISCLOSURES
 References
 
CIPROFIBRATE belongs to fibrate class hypolipidemic drugs that are categorized as peroxisome proliferators (PPs). PPs are a group of compounds that include fibrate hypolipidemic drugs and industrial chemicals such as phthalate ester plasticizers and herbicides (25). In rodents, PPs have numerous effects including induction of peroxisome proliferation, ß-oxidation, hepatocyte proliferation, and hepatocarcinogenesis (16, 38, 42). These effects of PPs are mediated by peroxisome proliferator-activated receptor-{alpha} (PPAR{alpha}) (18, 40, 66). PPAR{alpha} is one of the three structurally related transcription factors, which are members of the steroid hormone receptor superfamily of ligand-activated intracellular receptors (23, 31). PPARs form heterodimers with retinoid receptor (RXR) and modulate transcription by binding to PPAR response elements (PPRE) in the promoters of their target genes (66). The three PPAR isoforms PPAR{alpha}, PPARß/{delta}, and PPAR{gamma} show distinct tissue expressions and ligand specificities (16, 20). Ciprofibrate and other fibrate hypolipidemic drugs are selective PPAR{alpha} ligands (22). The role of PPAR{alpha} in mediating the effects of PPs has been well illustrated by the absence of the classic pleiotropic effects such as hepatomegaly, peroxisome proliferation, and transcriptional-activation of target genes in PPAR{alpha} knockout mice exposed to the compounds (40). Several studies have shown that PPAR{alpha} activators are important in the transcriptional regulation of several genes involved in metabolism, hepatocyte proliferation, and immune modulation (16, 38). Particularly well studied is the PPAR{alpha}-mediated transcriptional modulation of genes encoding enzymes of peroxisomal and mitochondrial fatty acid ß-oxidation as well as genes encoding fatty acid transport proteins in rodent liver (16, 38).

PPAR{alpha} agonists have also been shown to cause liver cancer in long-term exposures in rodents (53). Although PPs are carcinogenic in rats and mice, there are marked species differences in responses, and these compounds have not been found to induce peroxisome proliferation and hepatocarcinogenesis in humans (29). The mechanism of the hepatocarcinogenic effect of PPAR{alpha} activators in rats and mice is not well understood; however, it is thought to involve increased proliferation and oxidative stress induced by these compounds (4, 47). For example, PPAR{alpha} agonists such as ciprofibrate have been implicated in modulating the expression of some genes involved in cell proliferation such as cyclin-dependent kinases (CDKs) and cyclins (51, 56). A previous study from our group has shown that 60-day dosing of rats with ciprofibrate induces a significant hepatocyte proliferation with increased liver weight, without established hepatocellular tumors (68).

Recent studies using DNA microarray analysis have identified many novel genes regulated by PPAR{alpha} in mice given short-term doses (for up to 2 wk) with PPAR{alpha} agonists (10, 34). The aim of the present study was to identify ciprofibrate-regulated genes that may be important for liver growth and to examine ciprofibrate effects on the diverse metabolic systems of the liver, in rats dosed subchronically (for 8 wk) with ciprofibrate. This dosing period was chosen to elucidate both the metabolic and proliferative actions of ciprofibrate, while avoiding confounding effects from the hepatic carcinogenesis seen during more long-term stimulation. Using cDNAs microarrays of about 4,900 genes, we show that ciprofibrate modulates many genes previously known to respond to PPAR{alpha} agonists. Moreover, we identify novel potential targets of PPAR{alpha} agonists including genes involved in metabolism, cell growth and proliferation, inflammatory responses, and oxidative stress responses.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 DISCLOSURES
 References
 
Reagents and chemicals.
Ciprofibrate (2-[4-(6–2,2 dichlorocyclopropyl)phenoloxyl]-2-methyl propionic acid) (Modalim) was purchased from Sanofi-Synthelabo (Alnwick, UK). TRIzol reagent was purchased from GIBCO-BRL (Life Technologies, New York, NY). Radioisotope [{alpha}-32P]dCTP (3,000 Ci/mmol) was from Amersham Biosciences (Little Chalfont, UK), and DNA polymerase (Klenow fragment) was from Promega (Madison, WI). SpotReport Array Validation System was purchased from Stratagene (La Jolla, CA). Vectastain peroxidase-rabbit avidin-biotin complex (PK-4001) and a peroxidase substrate kit (AEC SK4200) were purchased from Vector Laboratories (Burlingame, CA).

Animals and drug administration.
Fischer rats were purchased from Møllegaard Breeding Center (Skensved, Denmark). Dosing of the animals with ciprofibrate was as described previously (5). Briefly, female Fischer rats (200–250 g) were given tap water (control group) or ciprofibrate (50 mg/kg body wt), by gastric intubation, once daily for 60 days. The animals were then weighed, anesthetized (0.3 ml/100 g body wt of 2.5 mg/ml fluanison, 0.05 mg/ml fentanyl, and 1.25 mg/ml midazolam), and killed by decapitation. The liver was removed in toto and weighed, and samples were fixed on 4% phosphate-buffered formalin or homogenized for subsequent preparation of total RNA.

The study was approved by the Animal Welfare Committee at the University Hospital of Trondheim.

Histology and immunohistochemistry.
Formalin-fixed tissue was embedded in paraffin and sectioned using routine histopathological methods. Histopathological examination was done on five randomly selected areas with hematoxylin-eosin-stained sections. Immunostaining was performed using an antibody against the proliferation marker proliferating cell nuclear antigen (PCNA), avidin-biotin complex, and peroxidase staining as previously described (68). The number of proliferating hepatocytes and the total number of hepatocytes were determined in liver sections from five rats in each group, using a 250 x 250-µm ocular grid. Five randomly selected areas were counted on each section, and the results are presented as the means ± SE of PCNA immunoreactive hepatocytes in percent of the total number of hepatocytes counted.

RNA preparation.
Fresh liver tissue was homogenized in a guanidinium-isothiocyanate buffer using an Ultra-Turrax rotating-knife homogenizer. Total RNA was isolated by ultracentrifugation on a cesium chloride cushion, then ethanol precipitated before further purification using TRIzol reagent according to the manufacturer’s protocols. The RNA quality and concentration was assessed by spectrophotometry and agarose gel electrophoresis.

Preparation of cDNA microarrays.
A collection of bacterial cultures containing plasmid clones of sequence-verified rat cDNAs (http://image.llnl.gov) was obtained from Research Genetics (Huntsville, AL). The complete list of the cDNA probes (5k rat clone list) is provided in the web site (http://nova.idi.ntnu.no/MCF/products.html). Plasmids were isolated from overnight bacterial cultures prepared in 96-well plates. The cDNA inserts were amplified by polymerase chain reaction (PCR) using M13 primers 5'-CTG CAA GGC GAT TAA GTT GGG TAA C and 5'-GTG AGC GGA TAA CAA TTT CAC ACA GGA AAG A. The PCR products were purified with Montage PCR purification kit (Millipore, Oslo, Norway), and quality and concentration was assessed by agarose gel electrophoresis. The PCR products were dissolved in 50% DMSO and printed onto aminosilane-coated CMT-GAPS glass slides (Corning, NY) using a high-precision printing robot constructed in collaboration with Nemko (Trondheim, Norway), after a prototype developed at the National Human Genome Research Institute (NHGRI; http://research.nhgri.nih.gov/microarray/index.html). The probes were fixed to the slide surface by UV irradiation with 200 mJ using a UV cross-linker (Hoefer Scientific Instruments, San Francisco, CA). Arrays used in the present study were printed with cDNA probes representing 4,908 unique UniGene clusters, of which 2,511 cDNAs (51%) were printed in duplicates. Ten different control PCR products from SpotReport Array Validation System (Stratagene) were also printed in duplicates on the arrays. Totally, each array contained 7,688 elements including duplicates and various controls.

Microarray hybridization.
Hybridization experiments were performed using a pool of liver RNA from five control rats and RNA from each of five ciprofibrate-dosed rats. One microgram of total RNA from the control pool and from each ciprofibrate-dosed rat was reverse transcribed and labeled with Cy3- and Cy5-attached dendrimer, respectively, using the 3DNA Array Detection Submicro kit as described in the manufacturer’s protocols (Genisphere, Montvale, NJ). Briefly, microarray slides were prehybridized in 1% BSA, 3.5x SSC, 0.1% SDS at 65°C for 20 min, then washed by dipping in deionized sterile filtered H2O (5 times) and finally dipped in isopropanol (2 times) and air dried. The Cy3- and Cy5-labeled samples (combined volume 5 µl) were mixed with 30 µl of the hybridization solution containing 0.25 M NaPO4, 1 mM EDTA, 4.5% SDS, 1x SSC, 2x Denhardt’s solution, 0.25 µl antifade reagent (Genisphere), and 0.2 µg mouse Cot1 DNA (GIBCO-BRL, Life Technologies), added onto the array, and covered with a 25 x 50-mm coverslip. The array was assembled in a humidified hybridization chamber (Corning) and hybridized submerged in a water bath at 65°C for 18 h. Posthybridization washes were done once in 2x SSC, 0.2% SDS at 55°C for 15 min, then in 2x SSC at room temperature for 15 min, and finally with 0.2x SSC at room temperature for 15 min. Arrays were scanned at 10-µm resolution with a confocal laser scanner constructed in collaboration with Nemko according to a prototype developed at NHGRI (http://research.nhgri.nih.gov/microarray/index.html).

Microarray data analysis.
Four of the five hybridizations performed were further analyzed. One array was excluded from the analysis because of experimental artifacts. Microarray image analysis was done with the MicroArray Suite software version 2.1 (Scanalytics, Fairfax, VA). Each array was globally normalized to balance intensity differences between the two channels. Data were then further analyzed using Microsoft Excel and Spotfire (Spotfire, Gothenburg, Sweden). First, spots with undetected fluorescence signals in either channel in at least one array were excluded. Further filtering to remove unreliable measurements based on fluorescence intensity values was performed by excluding spots with fluorescence signal intensity (averages of the 4 arrays) less than 300 in both channels. About 3,000 genes passed these criteria. The log ratios (Cy5/Cy3) of duplicate spots on each array showed good correlation (r = 0.82–0.94). Thus ratios for genes measured from probes spotted in duplicate were averaged for each slide. Then, mean ratios of the four hybridizations were calculated for each gene, and differentially regulated genes were determined based on selected fold change cutoff values.

Database submission of microarray data.
The microarray data was prepared according to "minimum information about a microarray experiment" (MIAME) recommendations (6), has been deposited in the Gene Expression Omnibus (GEO) database (19), and can be accessed at http://www.ncbi.nlm.nih.gov/geo/. The GEO accession for the platform is GPL264. The four samples can be retrieved with GEO accessions GSM4676, GSM4677, GSM4678, and GSM4679, which constitute the series with GEO accession GSE335.

Northern blot analysis.
DNA probes were made by amplifying cDNA inserts by PCR from plasmid clones using flanking M13 forward and M13 reverse primers. The PCR products were separated on agarose gel and purified using QIAQuick Gel Extraction kit (Qiagen, Max-Volmer-Strasse, Germany). The purified cDNA fragment was labeled using [{alpha}-32P]dCTP (3,000 Ci/mmol) (Amersham Biosciences) by random priming method and separated on Sephadex G-50 columns. Total RNA samples (15 µg/lane) were separated on a 1.1% formaldehyde-agarose gel and blotted onto nylon membranes (Roche, Mannheim, Germany). After UV cross-linking, the membrane was prehybridized at 42°C for at least 3 h in 50% formamide, 5x SSPE (1x SSPE is 0.18 M NaCl, 10 mM NaH2PO4, 1 mM EDTA, pH 7.4), 5x Denhardt’s solution, 0.5% SDS, and 200 µg/ml salmon sperm DNA. After prehybridization, the membrane was hybridized under identical conditions to cDNA probes for 12–18 h. Posthybridization washes were done twice in 2x SSPE, 0.1% SDS at room temperature for 20 min, then once in 0.1x SSPE, 0.1% SDS at 65°C for 20 min. The membrane was exposed to a phosphor storage screen for 2–24 h depending on signal intensity, scanned using PhosphorImager (Molecular Dynamics, Sevenoaks, UK), and image analyzed with ImageQuant software (Molecular Dynamics). The membrane was stripped by boiling in 0.1% SDS before rehybridization.

Statistical analysis.
Group means were compared using t-test. P < 0.05 was considered statistically significant.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 DISCLOSURES
 References
 
Effects of ciprofibrate on the animals, liver weight, and proliferation parameters.
Ciprofibrate dosing apparently did not influence the well-being of the animals. Body weight at the end of the experimental period was for controls 208.6 ± 2.0 g (n = 5) and for ciprofibrate-dosed rats 211.4 ± 1.6 g (n = 5) (mean ± SE) (P = 0.31). Ciprofibrate induced a significant (P < 0.001) 2.6-fold increase in total liver weight compared with controls, with no significant changes in liver architecture as assessed by routine light microscopy in hematoxylin-eosin-stained sections. A corresponding significant (p < 0.01) 2.1-fold increase in hepatocyte proliferation rate (% of PCNA-positive hepatocytes) was also observed in ciprofibrate-dosed rats. These results are in agreement with previous reports (51, 68).

Genes differentially regulated in ciprofibrate-dosed rats.
Genes differentially expressed in response to ciprofibrate were identified by microarray analysis by hybridizing RNA from liver of dosed rats against a pool of RNA from liver of control rats. RNA from control rats was pooled, because our goal was to identify genes differentially regulated in ciprofibrate-dosed rats at levels over the background biological variability in the controls. The use of a common reference or control is also the most commonly used design of microarray experiments, and it has the advantage of allowing efficient comparison of samples (11). Tables 1–3 show 90 known (named) genes annotated with processes that were considered to be of most interest in discussion of the pharmacological effects of PPAR{alpha} ligands. The full list of the differentially regulated genes is available as Supplementary Material (Supplemental Table 4) at the Physiological Genomics web site.1 A total of 382 genes [222 named genes and 160 expressed sequence tags (ESTs)], representing about 8% of the genes on the array, showed an average fold change in mRNA level of at least 1.4 (ratio >= 1.4 for upregulated or <=0.7 for downregulated genes) (Supplemental Table 4). Of these, 249 (191 named genes and 58 ESTs) were upregulated and 133 (31 named genes and 102 ESTs) were downregulated. The majority of the downregulated genes (77%) are ESTs, whereas only 23% of the upregulated genes are ESTs.


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  		Table 1. Differentially regulated genes in ciprofibrate-dosed rats: lipid metabolism and sugar metabolism

 

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  		Table 3. Differentially regulated genes in ciprofibrate-dosed rats: immune and inflammatory responses, and transcription

 
To facilitate interpretation of the results, the genes were grouped according to cellular processes in which they are likely to be involved, based on information from the literature, Swiss-Prot, and LocusLink databases. Most of the 268 genes, representing 222 named genes and 46 ESTs, annotated as "highly similar" or "moderately similar" to named genes could be grouped by cellular processes in this manner (Supplemental Table 4). Even though many genes may be involved in more than one process, each gene was grouped into only one category. Attempts were made to annotate a gene with the cellular process where it was considered most likely to belong in the context of pharmacological effects of PPAR{alpha} agonists.

To choose fold change cutoff for differentially regulated genes, we compared fold changes obtained with the microarray method and Northern blot analysis (Fig. 1B), and evaluated fold changes obtained for known PPAR{alpha}-regulated genes. The microarray protocol we used tends to underestimate fold changes for differentially regulated genes compared with Northern blot analysis (Fig. 1B). For example, 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 1 (Pfkfb1) with microarray ratio of 1.2 ± 0.2 was found to be significantly (P < 0.01) upregulated with ratio 2.4 ± 0.7, using Northern blot analysis (Fig. 1B). Fold changes obtained by both methods were qualitatively similar, but microarray fold changes were lower than fold changes obtained by Northern blot for all the genes compared except Crmp1 (Fig. 1). Since the fold changes obtained by the microarray protocol we used were generally underestimated, we set the cutoff for differentially regulated genes to >=1.4-fold. Although this threshold value may be considered low and some false positives are expected, our Northern blot analysis indicates that also false negatives occur and that a more stringent threshold would result in loss of valuable information.



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  		Fig. 1. Validation of microarray results using Northern blot analysis. A: liver total RNA from control or ciprofibrate (Cipro)-dosed rats (n = 5 per group) was subjected to Northern blot analysis using cDNA probes for the following genes: Crmp1 (collapsin response mediator protein 1), Pfkfb1 (6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 1), Hmgcs1 (3-hydroxy-3-methylglutaryl-coenzyme A synthase 1), Crot (carnitine O-octanoyltransferase), Cyp4a10 (cytochrome P450 4a10), Ldha (lactate dehydrogenase A), Cte1 (cytosolic acyl-CoA thioesterase 1), Atpi (ATPase inhibitor), Mug1 ({alpha}1-inhibitor 3, variant I), and 18S (18S rRNA). B: comparison of expression levels for selected genes by microarray and Northern blot hybridizations in ciprofibrate-dosed rats and controls. Ratios were calculated for Northern blot by quantifying radioactivity in corresponding bands shown in A, as detailed in MATERIALS AND METHODS. Ratios for microarray analysis represent mean values from 4 ciprofibrate-dosed rats compared with control pool of 5 rats. Data are means ± SD. *P < 0.05. **P < 0.01. ***P < 0.001.

 
Validation of microarray results by Northern blot hybridization and exogenous RNA spikes.
In this study, we used a microarray labeling and hybridization protocol that enables the use of very small amounts of RNA (63). We validated the microarray results using Northern blot analysis and by spiking labeling reactions with external RNA controls at predetermined ratios. For validation using Northern blotting, 11 genes with various levels of expression were selected and hybridized. Nine of the 11 probes tested by Northern hybridization gave detectable signals (Fig. 1A). Two probes, bcl-2 associated death agonist (Bad) and bone morphogenetic protein-6 (Bmp6), did not give detectable signals by Northern hybridization (data not shown). All genes detected by Northern hybridization showed qualitatively similar expression changes in Northern blot and microarray analysis except one (Crmp1), which appeared to be upregulated by microarray analysis but was found to be unchanged by Northern hybridization (Fig. 1). However, for most genes, expression changes detected by microarray were underestimated by a factor of 1.5–5 compared with Northern blot analysis (Fig. 1). One gene (Cte1) showed very high quantitative discrepancy (about 30-fold) between the two methods (Fig. 1B). The reason for the large deviation of Cte1 is not clear but may be due to cross-hybridization of other genes with underestimation of the fold change in cDNA microarray analysis. Importantly, although the fold changes are compressed in the microarray analysis, comparison with Northern blot analysis shows good qualitative agreement between the two methods for eight of nine genes that could be detected. For example, even genes that changed less than twofold by Northern blotting could be detected as upregulated by microarray (Fig. 1). One gene (Pfkfb1) with fold change 1.2, which is less than the threshold used for differentially regulated genes, showed significant (P <= 0.01) change with Northern blotting. However, two other genes with microarray ratio of 1.2 (Cyp4a10) and 1.4 (Hmgcs1) were not significantly upregulated by Northern blot analysis (Fig. 1). Overall, about 70% of the genes (8 of 11) tested by Northern blot analysis were confirmed, suggesting a false-positive rate of up to 30% of the genes identified as differentially regulated by the microarray analysis. This is within the range of false positives commonly found in microarray experiments (8, 61) and emphasizes that microarray results should be interpreted with some caution.

Further validation of our microarray protocol was done using the SpotReport Array Validation System (Stratagene). Analysis of exogenous (Arabidopsis thaliana) poly(A)+ RNAs spiked into labeling reactions at predetermined ratios showed that the observed ratios highly correlate with the expected ratios (r = 0.987) (Fig. 2). The external controls also showed that a twofold change could be reliably detected and correlated well with expected fold changes (Fig. 2). However, similar to Northern blot analysis, the observed fold changes for the external controls were also underestimated at both ends of the expected range (Fig. 2).



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  		Fig. 2. Validation of microarray protocol using spike controls. Comparison of expected and observed ratios for exogenous control poly(A)+ RNAs. Ten different control RNAs (C1–C10) were spiked into labeling reactions at predetermined ratios covering up to 8-fold changes and hybridized to the arrays on which corresponding PCR products were printed in duplicates. Data are the theoretical (expected) folds and means ± SD of the experimentally determined (observed) folds on 4 arrays. A positive ratio ("upregulation") indicates Cy5/Cy3 ratio (fold change). "Downregulation" is indicated by a negative ratio (reciprocal of Cy5/Cy3 ratio multiplied by -1).

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
DISCLOSURES
 References
 
In the present study, liver gene expression in rats subchronically dosed with the PPAR{alpha} agonist ciprofibrate was analyzed using cDNA microarrays with probes representing 4,908 genes. This drug increased liver weight and hepatocyte proliferation. Several known PPAR{alpha}-regulated genes and some potentially new targets, as well as indirectly affected nontarget genes, were differentially regulated in ciprofibrate-dosed rats.

To validate microarray data, three procedures were performed: 1) Northern blot analysis of selected genes with various levels of differential expression as detected by microarray analysis, 2) inclusion of exogenous RNA controls, and 3) validation of differentially regulated genes by literature search. Northern blot analysis of selected genes and analysis of external controls suggest that the microarray fold changes obtained are generally underestimated. The narrow window of change of expression observed in the microarray analysis appears to be a result of low dynamic range of fluorescence signals obtained using the dendrimer labeling and hybridization protocol, compared with other labeling methods (our own unpublished results). A recent study comparing the dendrimer labeling method with two other labeling methods using dilution series experiments also concluded that the dendrimer method had lower dynamic range (45). Despite these limitations, the method can identify differentially regulated genes with reasonable accuracy, as we showed using Northern blotting and spiked external RNA controls. Literature search also showed that about 20% of the named genes differentially regulated by >=1.5-fold are known to respond to PPAR{alpha} agonists in previous studies (data not shown). The corresponding figure falls to about 12–15% for the 222 named genes that changed by >=1.4-fold. This suggests that the large majority of the genes with 1.5-fold cutoff are true positives, whereas the genes with 1.4- to 1.5-fold change would have a higher rate of false positives. For example, hydroxysteroid dehydrogenase 11ß type 1 (Hsd11b1), known to be downregulated by PPs (28) but detected as upregulated by microarray analysis here (Supplemental Table 4), is most likely a false positive. Northern blotting results also suggest that both false positives and false negatives would be expected among the genes identified here to be differentially regulated. Thus the genes identified here, particularly those with fold changes <1.5, should be seen as candidates for differential regulation in ciprofibrate-dosed rats. Further validation of the results should be done before firm conclusions are drawn regarding the biological mechanisms of the ciprofibrate effect on the rat liver.

Several studies have shown that PPAR{alpha} regulates the expression of a number of genes involved in metabolism of lipids, glucose, and amino acids (10, 16, 33, 34, 38). In rodents, PPAR{alpha} agonists such as the hypolipidemic drug ciprofibrate are also known to induce hepatocyte proliferation, hepatocarcinogenesis (in long-term exposures), and immune modulation (16, 38). The genes found to be differentially regulated here in ciprofibrate-dosed rats are involved in a number of different cellular processes. In the following, some of these processes and the genes involved in them will be discussed.

Lipid metabolism.
Nearly all of the ciprofibrate-induced genes grouped under lipid metabolism (Table 1) have been previously reported to be modulated by PPAR{alpha} (10, 16, 38, 71). Most of these genes are involved in peroxisomal and mitochondrial ß-oxidation or fatty acid transport. For example, ciprofibrate increased the mRNA abundance of the rate-limiting peroxisomal ß-oxidation enzyme acyl-coA oxidase (Acoa1), which is known to be activated by PPAR{alpha} ligands (49, 66). Other upregulated genes involved in fatty acid metabolism which are known to respond to PPAR{alpha} activators include carnitine O-octanoyltransferase (Crot) (9), cytosolic acyl-CoA thioesterase 1 (Cte1) (70), and the microsomal cytochrome P450 4A3 (Cyp4a3) that catalyzes {omega}-hydroxylation of fatty acids (32). Genes for peroxisomal and mitochondrial enzymes involved in ß-oxidation are known to be regulated by PPAR{alpha}, and PPRE sequences have been identified in the promoters of many of them such as Acoa1 (49, 66) and Cte1 (70).

Sugar metabolism.
Ciprofibrate-upregulated genes encoding enzymes that seem to be involved in sugar metabolism include lactate dehydrogenase A (Ldha) (Table 1, Fig. 1) and Pfkfb1 (Fig. 1). Pfkfb1 regulates phosphofructokinase, which constitutes an important control point in glycolysis (52). The glucagon receptor (Gcgr), which is involved in regulation of blood glucose levels, was also upregulated (Table 1). Fasted PPAR{alpha} knockout mice have been found to develop severe hypoglycemia, suggesting the role of PPAR{alpha} in glucose metabolism (35, 41). A recent study has also indicated the involvement of PPAR{alpha} in controlling hepatic glucose production through mechanisms involving regulation of glycerol and lactate levels (69). Hypoglycemia in starving PPAR{alpha} knockout mice is thought to be secondary to reduced fatty acid oxidation, which affects substrate levels for hepatic gluconeogenesis (35, 41, 69). Whether this upregulation of the genes involved in sugar metabolism is related to the role of PPAR{alpha} in energy homeostasis should be clarified in future studies.

Growth and proliferation.
Several genes that appear to be involved in cell proliferation and related processes were found to be differentially expressed in ciprofibrate-dosed rats (Table 2). This group represents genes not previously associated with responses to PPAR{alpha} ligands. Some of these genes also seem to have known roles in cell growth, proliferation, and carcinogenesis. For example, nuclear protein 1 (Nupr1), which was upregulated by ciprofibrate in the present study, has been shown to promote cell growth (44). Nupr1 is reported to be overexpressed in human pancreatic cancers and metastatic breast cancer cells (55, 64) and has been found to be involved in induction of tumor development in transformed mouse embryo fibroblasts (67). Other upregulated genes in this category include the retinoblastoma-related gene (Rb2), involved in cell cycle regulation (39), ornithine decarboxylase (Odc1), the first rate-limiting enzyme in the synthesis of polyamines and often associated with increased proliferation and carcinogenesis (50), and insulin-like growth factor 2 receptor (Igf2r), which is implicated in mitogenic and carcinogenic processes through its regulation of insulin-like growth factor 2 (60). H-ras-revertant gene 107 (Hrev107), which was downregulated in the present study, is a tumor suppressor and growth inhibitory gene reported to be upregulated in fibroblasts that are resistant to transformation by H-ras oncogenes (27, 62). The changed expression patterns of a number of genes associated with cell growth and proliferation seem to agree with ciprofibrate-induced hepatocyte proliferation and hepatomegaly observed here and in a previous study in our laboratory (68). Although the mechanisms of the hepatocarcinogenic effects (in rodents) of PPAR{alpha} ligands such as ciprofibrate are incompletely understood, induced alterations in expression levels of genes related to cell proliferation are thought to be contributing factors (26, 48, 51). Thus the cell growth- and proliferation-related genes represent good candidates for further confirmation and follow-up studies that may shed light on the mechanisms of hepatocarcinogenesis induced by PPs in rodents. It should be noted, however, that although the mechanism of the carcinogenic effects of PPs is not well understood, humans appear to be resistant to peroxisome proliferation and hepatocarcinogenic effects of these compounds (29).


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  		Table 2. Differentially regulated genes in ciprofibrate-dosed rats: growth and proliferation, and stress response

 
Stress response.
Several genes that appear to be related to stress responses were differentially regulated in the present study (Table 2). Fibrates and other PPs are known to increase H2O2 levels, leading to oxidative stress (21). It is therefore interesting to note that several genes known to be involved in the cell response to oxidative stress are upregulated in ciprofibrate-dosed rats [e.g., superoxide dismutase 3 (Sod3), copper-zinc-containing superoxide dismutase (Sod1), glutathione peroxidase 4 (Gpx4), and glutathione S-transferase, mu type 3 (Gstm3)] (Table 2). Another upregulated gene in this category includes DNA-damage inducible transcript 3 (Ddit3), previously known to be induced by oxidative stress (43). In agreement with our results, Sod1 has been reported to be induced by bezafibrate in rat liver (30). Clofibrate and other PPs were recently shown to increase the expression of base excision repair enzymes including N-methylpurine-DNA glycosylase, DNA ligase I, and DNA polymerase ß in rat and mouse liver (58). In the present study, DNA polymerase-ß (Polb), replication factor C (Recc1), and the DNA repair protein O6-methylguanine-DNA methyltranferase (Mgmt) were upregulated (Table 2). Induction of these genes may be related both to increased proliferation and DNA damage repair. PPAR{alpha} ligand-induced elevation in H2O2 levels leading to oxidative stress and DNA damage is thought to be one possible mechanism contributing to hepatocarcinogenesis in rodents (13, 26, 72). Therefore, the differential expression of some of the stress-related genes presented under this category may be related to oxidative stress responses.

Immune and inflammatory response.
A role for PPAR{alpha} in control of inflammatory responses has been suggested by the prolonged inflammatory responses observed in PPAR{alpha} knockout mice (17). Furthermore, modulation of acute-phase inflammatory response genes appears to be a PPAR{alpha}-dependent process (3). Thus, ciprofibrate-regulated genes associated with immune and inflammatory responses (Table 3) are of interest in further understanding of the molecular basis of immune modulatory effects of PPAR{alpha}. Notably, we observed downregulation of several genes encoding proteins involved in acute-phase inflammatory responses [e.g., serine (or cysteine) proteinase inhibitor, clade A, member 1 (Serpina1/{alpha}1-PI), fetuin-ß (Fetub), transferrin (Tf), and fibrinogen ß (Fgb) (Table 3)]. During an acute-phase inflammatory response, the hepatic expressions of a number of plasma proteins genes such as Tf, {alpha}1-PI, Fgb, {alpha}1-inhibitor 3, variant I (Mug1), and {alpha}2-macroglobulin (a2M) have been reported to be positively or negatively regulated in response to pro-inflammatory cytokines (57). Tf, {alpha}1-PI, and Fgb are considered to be positive (upregulated) acute-phase inflammatory response proteins in rats (57). Thus their downregulation observed here and in other studies (12) appears to agree with the anti-inflammatory effects of PPAR{alpha} (15). However, a2M, which was upregulated here (Table 3), and another {alpha}-macroglobulin family member, Mug1, downregulated here (Fig. 1), are thought to be positive and negative acute-phase proteins, respectively (1, 57). Adenosine A2a-receptor (Adora2a) and high-mobility group 1 (Hmgb1), which were upregulated in the present study (Table 3), also appear to be involved in inflammatory responses (36, 37, 59). The results presented here provide many candidate genes that may increase further understanding of the immune modulatory effects of PPAR{alpha} agonists.

Transcription.
Transcription factors upregulated in ciprofibrate-dosed rats include hepatic nuclear factor-1 (Hnf1) and dimerization cofactor of Hnf1 (Dcoh) (Table 3). Hnf1 is known to participate in regulation of genes involved in lipid metabolism (2). Hnf1 and its transcriptional coactivator, Dcoh, are also involved in the regulation of a number of other genes including some acute-phase inflammatory response genes described above (7, 14). Two of the upregulated transcription factors are known to be involved in downregulation of immune and inflammatory responses: nuclease-sensitive element binding protein (Nsep1) represses HLA class II gene expression (65), and glucocorticoid modulatory element binding protein 2 (Gmeb2) modulates transcriptional activity of the glucocorticoid receptor (73). Thus, these transcription factors may mediate immune modulatory effects of ciprofibrate.

In addition to the group of genes discussed above, several genes that may be involved in processes such as cytoskeletal organization, extracellular matrix formation, cell adhesion, signal transduction, steroid metabolism, protein metabolism, and ion transport were differentially regulated in ciprofibrate-treated rats (Supplemental Table 4). The complex transcriptional responses to ciprofibrate suggest the involvement of PPAR{alpha} in several processes either directly or indirectly. It is more likely that most of the differentially regulated genes involved in the diverse cellular processes such as signal transduction, steroid metabolism, cytoskeletal organization, extracellular matrix formation, and cell adhesion are indirect consequences of the known PPAR{alpha} responses such as lipid homeostasis and hepatocyte proliferation. Some of the genes involved in tissue remodeling (e.g., extracellular matrix proteins, cell adhesion molecules and proteases) have been recently found to be differentially regulated in regenerating liver after partial hepatectomy (24) and thus are likely to be involved in the hepatocyte proliferation observed here.

Recently, DNA microarrays have facilitated the identification of a number of genes modulated by PPAR{alpha} agonists under various experimental conditions in mice (10, 71). Many genes differentially regulated in these studies were also identified in our study. A number of genes not previously known to respond to PPAR{alpha} agonists were also identified, and although direct PPAR{alpha} target genes may be found among them, the effects on most of the candidate genes presented here are likely to be through indirect mechanisms. Further confirmation and characterization of some of the candidate genes may increase the understanding of PPAR{alpha}-mediated processes, such as mechanisms leading to rodent hepatic tumor development observed after long-term exposure of these species to PPs (46, 53, 54). Although there are marked species differences in response to PPs, and there is no evidence of carcinogenic effects of these compounds in humans, understanding the mechanisms of hepatocarcinogenesis in rodents would provide better risk assessment of PPs such as fibrate class hypolipidemic agents.


   DISCLOSURES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 DISCLOSURES
References
 
This work was supported by the Norwegian Cancer Association, EXTRA funds from the Norwegian Foundation for Health and Rehabilitation, the Norwegian Research Council, and the Cancer Fund at the University Hospital of Trondheim.


   ACKNOWLEDGMENTS
 
We thank Sylvia Nome Kvam, Bruno Monterotti, Ole Støren, Randi Røsbak, Anne Kristensen, Vladimir Yankovski, and Hallgeir Bergum for technical assistance.


   FOOTNOTES
 
Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).

Address for reprint requests and other correspondence: F. Yadetie, Dept. of Cancer Research and Molecular Medicine, Faculty of Medicine, Norwegian Univ. of Science and Technology, N-7489 Trondheim, Norway (E-mail: fekadu.yadetie{at}medisin.ntnu.no).

10.1152/physiolgenomics.00064.2003.

1 The Supplementary Material for this article (Supplemental Table 4) is available online at http://physiolgenomics.physiology.org/cgi/content/full/00064.2003/DC1. Back


   References
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 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 DISCLOSURES
 References
 

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