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An Evaluation of a Low-Density DNA Microarray Using Cytochrome P450 Inducers Georgina Meneses-Lorente,*,† Franc¸ oise de Longueville,‡ Sofia Dos Santos-Mendes,‡ Timothy P. Bonnert,† Andrew Jack,† Ste´phanie Evrard,‡ Vincent Bertholet,§ Andrew Pike,† Paul Scott-Stevens,† Jose´ Remacle,‡ and Bindi Sohal† Merck Sharp & Dohme Neuroscience Research Center, Terling Park, Harlow, Essex, CM20 2QR, United Kingdom, Laboratory of Biochemistry and Cellular Biology, University of Namur, Belgium, and Department of Mathematics, Statistical Unit, University of Namur, Belgium Received June 11, 2003

The aim of this study was to validate a low-density DNA microarray “Rat HepatoChip”, which contains 59 genes from a range of potential toxic markers and drug metabolism-related genes. Liver mRNA was isolated from rats dosed with six different chemicals, dexamethasone, troleandomycin, miconazole, clotrimazole, and methylclofanapate, which are all known to induce different cytochrome P450 genes, and isoniazid, which does not cause histopathological changes. Replicate microarrays were used to measure the variability in the chips and in the process. The average variability in signal between different chips observed in triplicate experiments was 33% ranging from 21 to 39% depending on genes. We also demonstrated a strong correlation between the liver histopathology and the gene expression profiles indicating that the gene expression profile reflects histopathological changes. These results suggest that the Rat HepatoChip microarray may provide a fast and effective tool for assessing the toxicity profile of developmental drug candidates during the drug discovery process.

Introduction One of the main reasons for the failure of drug candidates during the development process is unforeseen toxicity in preclinical and clinical studies. The ability to assess potential toxicity of drug candidates early in the drug discovery process could save time and money (1). An emerging approach to achieve this is the use of DNA microarrays to identify drug-induced changes in gene expression. DNA microarrays typically consist of thousands (highdensity microarrays) or selected groups (low-density microarrays) of genes. High-density microarrays have the advantage that thousands of genes can be studied at the same time, giving a global view of the changes occurring in cells. There are several papers that describe their application in drug toxicity (2-4). The main drawbacks of the technique are cost, analysis time, and data interpretation. Low-density microarrays also allow the study of the gene expression changes associated with chemical exposure. The low-density approach is fast, reliable, and cost effective, although few studies have been carried out using this approach (5). Although several studies have shown a good correlation of microarray data with other mRNA profiling technologies such as Northern blotting (6) and quantitative PCR (7), it is still questionable whether the microarray technology is consistent, reproducible, and reliable enough for use as a screening method. * To whom correspondence should be addressed. Email: georgina_ [email protected]. † Merck Sharp & Dohme Neuroscience Research Center. ‡ Laboratory of Biochemistry and Cellular Biology, University of Namur. § Department of Mathematics, Statistical Unit, University of Namur.

Recently, a low-density DNA microarray consisting of 59 potential toxicity and drug metabolism-related genes was used to analyze gene changes in rat liver following treatment with phenobarbital and pregnenolone-16Rcarbonitrile (5). In this study, we have carried out further validation of this “Rat HepatoChip” by investigating the effects of six different chemicals, which show differential effects at the level of the liver weight and histological changes (8). This included dexamethasone (DEX), clotrimazole (CLOT), troleandomycin (TAO), miconazole (MIC), methylclofanapate (MCP), and isoniazid (ISN). All of these compounds are also well-known cytochrome P450 inducers both in vivo and in vitro (9-12). ISN was included in this study to show whether a drug that does not cause histological changes in the liver would cause changes in gene expression levels determined by microarray analysis. The main objectives were to validate the DNA array technology and establish the degree of correlation between any compound-induced effects on gene expression with those at the level of histological examination.

Materials and Methods Experimental Design. Groups of three rats were dosed orally with DEX, CLOT, MIC, TAO, MCP, and ISN for 4 days to study sources of variability on a microarray experiment and to compare changes in gene expression between control and treated animals following the experimental design described in Figure 1. Microarray, interanimal variability, and reproducibility were investigated using the experimental design described in Figure 1. Groups of three rats were dosed with vehicle or DEX for 4 days. Transcription reactions were carried out in triplicate for the same control and treated samples and then pooled to

10.1021/tx034117n CCC: $25.00 © 2003 American Chemical Society Published on Web 08/07/2003

Gene Expression Profiling Using a DNA Microarray

Figure 1. Experimental design: Rats were dosed with vehicle or compound for 4 days. After 4 days of treatment, the liver was removed. The transcription reaction was carried out in triplicate for the same control and treated samples. Transcription reactions were pooled to minimize any variability. minimize any variability. Hybridizations for the same control and treated animals were carried out in triplicate on the same day on three separate chips with a double array (Figure 2A). Interanimal variability and reproducibility was investigated with DEX treatment following the same experimental design shown in Figure 1. Reverse transcription was carried out in triplicate for the three control and treated mRNA samples on the same day, and RT reactions from the same control and treated animals were pooled to minimize any variability. Hybridizations for the three different pairs of animals (control/ treated) were carried out on the same day in three consecutive days (Figure 2B). Samples Used, Extract Preparation, and Labeling. Rat liver mRNA samples were obtained from female SpragueDawley CD rats (aged 10-12 weeks), obtained from Charles River. Three rats per group (control or treatment) were used in this study. Rats were dosed orally for 4 days with either CLOT (100 mg/kg per day in corn oil), MIC (100 mg/kg/day in gum tragacanth), ISN (100 mg/kg/day in gum tragacanth), DEX (50 mg/kg/day in gum tragacanth), MCP (75 mg/kg/day in corn oil), or TAO (500 mg/kg/day in corn oil). Control animals received corresponding quantities (5 mL/kg body weight) of the 0.56% (w/v) gum tragacanth or corn oil vehicles. Following the treatment period, the animals were euthanized by decapitation and the livers were immediately removed, snap-frozen in liquid nitrogen, and stored at -80 °C for further mRNA isolation. Poly(A)+ RNA was isolated using the FastTrack 2.0 mRNA Isolation Kit (Invitrogen, Leek, Netherlands) using the manufacturer’s protocol for isolating mRNA from 1 g of snap-frozen liver tissue. Denaturing agarose gel electrophoresis was used to assess the integrity and relative contamination of mRNA with ribosomal RNA. Synthesis of cDNA from the mRNA samples was performed in triplicate using the procedure provided by the manufacturer’s of the Rat HepatoChip (5). Two micrograms of poly(A)+ RNA was converted to biotinylated cDNA using oligodT (Gibco BRL,

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Figure 2. Experimental design: (A) Hybridizations for the same control and treated animal were carried out in triplicate and on the same day. (B) Hybridizations for the three control and treated animals were carried out in triplicate on three different chips and on the same day. Life Technologies), biotin-11-dCTP (NEN Life Science Product Inc.), and SuperScript II Rnase H- Reverse Transcriptase (Gibco BRL, Life Technologies). A synthetic poly(A)+ tailed RNA sample was spiked into the purified mRNA as an internal standard to assist in quantification and estimation of experimental variation introduced during labeling and analysis (5). No further cDNA purification was necessary. The three cDNA replicates were then pooled prior to hybridization to reduce the reverse transcription reaction variability. Hybridization Procedures and Parameters. Hybridization of the cDNA to the Rat HepatoChip was also carried out according to the microarray manufacturer’s instructions (5). In brief, hybridization was performed in a hybridization chamber (Biozym, Landgraaf, The Netherlands) containing buffer, the total biotinylated cDNA (from 2 µg of poly(A)+ RNA), and a positive hybridization control (biotinylated amplicon, 25 nM). Hybridization was carried out overnight at 60 °C in a custom slide cassette under humidity maintained by a small reservoir of 3X SSC buffer. Following buffer washes, the presence of biotinylated hybrids on the microarray was detected using a fluorescent streptavidin conjugate. The arrays were then incubated with a 1:500 dilution of cyanin 3 streptavidin conjugate in a blocking solution. Slides were then washed and dried prior to scanning. The Rat HepatoChips are double array so that hybridization for samples from the treated animals was carried out on the same chip as samples from the appropriate control animals. Hybridizations for control samples were always carried out on the left-hand side array whereas hybridizations for treated samples were always carried out on the right-hand side array. Measurement Data and Specifications. Hybridized arrays were scanned using a ScanArray 4000 laser confocal scanner (GSI Lumonics, Farmington Hills, MI) at a resolution of 10 µm. After image acquisition, the scanned 16-bit images were imported into ImaGene4.1 software (BioDiscovery, Los Angeles, CA) to quantify the signal intensities.

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Table 1. Sequences Presented on the HepatoChipsa gene

function

Bax, Bcl-2 c-jun, c-myc, Elk-1 Cox-2, IL6 Cyp 1A1, Cyp 1B1, Cyp 2B, Cyp 3A, Cyp 4A1 enoyl CoA hydratase, PPAR R ACO ferritine fibronectin GADD153, GADD45 MGMT GSH S-transferase subunit Ya, subunit θ5 GSH reductase, heme oxygenase 2, HSP70, MnSOD, ApoJ, cytochrome c oxidase subunit 1 hepatocyte GF histone D-acetylase (Hdac1) HMG CoA synthetase JNK-1, telomerase, cyclin D1 NFκB, p38, erk-1, c/EBP, IκβR

apoptosis oncogene inflammation cytochrome P450

U49729, L14680 X17163, Y00396, X87257 L20085, M26744 X00469, U09540, M34452, M10161, X07259 K03249, M88592 J02752 U58829 X15096 U30186, L32591 M76704 K01931, X67654

PP PP acyl CoA oxidase iron stock extracellular matrix DNA damage DNA repair oxidative stress oxidative stress

U73174, J05405, L16764, Y00497, M16975, M27315 D90102 NM008228 X52625 L27129, U89282, D14014 L26267, U73142, M61177, X12752, U66479 J04791 X13058 Y00047

growth factor DNA transcription cholesterol metabolism cell cycle activation transcription factor

ornithine carboxylase (odc) p53 PCNA

arginine synthesis tumor suppressor proliferation cellular nuclear antigen transporters senescence marker tumor necrosis factor TGF-β receptor glucuronyl transferase R2-macroglobulin

rmdr-1b, transferrin, albumin SMP30 TNF transforming growth factor-b type II UDPGT1A, UDPGT1A6 liver + ve control a

Genbank accesion no.

M81855, D38380, V01222 X69021 X66539 L09653 J05132, D83796 J02635

Shown is the known function and the Genbank accession number of each cDNA. Table 2. Housekeeping Genes Included on the HepatoChipsa housekeeping gene

function

abundance level

Genbank accession no.

R-tubulin ribosomal protein S29 myosin heavy chain 1 (myr) hypoxanthine guanine phosphoribosyl transferase glyceraldehyde-3-phopshate dehydrogenase (G3PDH) polyubiquitin

cytoskeletal protein protein synthesis muscle contraction nucleotide synthesis

high medium low medium

V01227 X59051 X68199 M86443

glycolysis

high

D16554

cellular metabolism, development lipid metabolism cytoskeletal protein

high

D00036

low high

X02231 V01217

phospholipase A2 β-actin a

Shown is the known function and the abundance level and Genbank accession number of each housekeeping gene. To maximize the dynamic range of microarrays, the same Microarray to microarray variability was calculated by taking arrays were scanned at different photomultiplier settings of the the CV over three identical chips on day 1 and then averaging scanner. Using different gains will allow us to quantify both that CV with similarly obtained CVs for days 2 and 3. In the high and the low copy expressed genes. After image contrast, animal to animal variability was calculated by averagacquisition, the scanned 16-bit images were imported into ing ratio values over three chips for each animal and taking ImaGene4.1 software (BioDiscovery) to quantify the signal the CV across the three animal averages. This was also the intensities. The fluorescent intensity of each DNA spot (average method used for day to day variability. Differences due to drug of intensity of each pixel present within the spot) was calculated treatment were determined using One sample t-test where the using local mean background subtraction. A signal was accepted mean of the signal intensity was compared with the hypothetical if the average intensity after background subtraction was at value 1. least 2.5-fold higher than their local background. The two Array Design. Genes on the Rat HepatoChips (AAT, Namur, intensity values of the duplicate DNA spots were averaged and Belgium) are presented in Table 1. To evaluate the reliability used to calculate the intensity ratio between the reference and of the experimental hybridization, several controls of positive the test samples. Very bright element intensities (saturated and negative hybridization and positive and negative detection signals, highly expressed genes) were deemed unsuitable for were included on the Rat HepatoChips. For normalization, an accurate quantification because they underestimated the ininternal standard control and eight housekeeping genes were tensity ratios and were excluded from further analysis. Data arrayed on the slides (Table 2). The Rat HepatoChip was were normalized in two steps. First, the values were corrected composed of single-stranded DNA probes attached to the glass using a factor calculated from the intensity ratios of the internal support by a covalent link. Each DNA probe was present in standard control and the treated sample. The second normalizaduplicate (Figure 3). The length of the DNA probes was tion step was performed based on expression levels of the optimized. The lengths were the same for all genes and were housekeeping genes. Variability between the microarray to located near the 3′ end of the transcript. All probes were microarray, the animal to animal, and the day to day variability designed to be gene specific and were prepared using rat cDNAs. was estimated by the percent coefficient of variation (%CV).



Figure 3. Design of the Rat HepatoChips with 59 genes (including eight housekeeping genes). Several controls were included on the microarray: positive hybridization controls (red), negative hybridization controls (dark green), negative detection controls (light green), and positive detection control (blue). The internal standard control is included on six different locations. The housekeeping genes are also located (gray).

Figure 4. Chip to chip variability. Each bar represents the CV of the mean of fluorescence intensity ratio for three hybridization experiments (control sample vs treated sample). Only the genes that were statistically different using a One sample t-test are shown.

Results Groups of three rats were dosed orally with DEX, CLOT, MIC, TAO, MCP, and ISN for 4 days to study sources of variability on a microarray experiment and to characterize changes in gene expression in response to drug treatment. Microarray to Microarray Hybridization Variability. To investigate the variability of the microarray hybridization, groups of three rats were dosed with vehicle or DEX for 4 days. After 4 days of treatment, the liver was removed and mRNA was extracted. Transcription reactions were carried out in triplicate for the same control and treated samples and then pooled to minimize any variability. Hybridizations for the same control and treated animal were carried out in triplicate on the same day on three separate chips. Results show that there is variation in expression levels between different chips (Figure 4). The average variability (CV) in signal between

different chips observed in triplicate experiments was 33% (calculated as the mean of variation coefficient) ranging from 21 to 39% depending on genes (Figure 4). Animal to Animal and Day to Day Variability. Animal to animal and day to day variability was also investigated in response to DEX treatment in this study. Reverse transcription was carried out in triplicate for the three control and treated mRNA samples on the same day, and RT reactions from the same control and treated animals were pooled to minimize any variability. To investigate animal to animal variability, hybridization on the microarray was performed for the three control and treated cDNA samples in three consecutive days. The average animal to animal variability (CV) for DEX was found to be 28% ranging from 8 to 53% (Figure 5). Day to day variability was also investigated by analyzing the data obtained from the same control and treated



Figure 5. Animal to animal variability. Each bar represents the CV of the mean of the fluorescence intensity ratio for the three pairs of animals (control sample vs treated sample) from three experiments. Only the genes that were statistically different using a One sample t-test are shown.

Figure 6. Day to day variability. Each bar represents the CV of the mean of the fluorescence intensity ratio for one pair of animals (control sample vs treated sample) from three experiments. Only the genes that were statistically different using a One sample t-test are shown.

animals in three different days. The average day to day variability (CV) for DEX was found to be 20% (Figure 6). Changes in Gene Expression Due to Drug Treatment. Gene expression changes were investigated in response to all of the different treatments used in this study: DEX, CLOT, MIC, TAO, MCP, and ISN. The results are summarized in Figure 7 for all treatments. The expression of seven genes was shown to be significantly different after DEX treatment. One of the genes (CYP3A) was found to be induced, and the other six genes [macroglobulin, cyclin D1, senesce marker protein (SMP30), GSH-θ5, acyl-CoA oxidase, and HMG] were decreased due to DEX treatment. Examples of changes in gene expression levels are shown in the Rat HepatoChip image in Figure 8. A total of five genes were shown to be significantly different after 4 days of treatment with one of the N-substituted imidazole antimycotics, CLOT. The five induced genes were CYP3A, CYP2B, UPDGT1a, GSHYa, and ornithine decarboxylase (ODC), and the one found to be decreased was SMP30. The other N-substituted imidazole antimycotic, MIC, increased only CYP3A and GSHYa mRNA levels after 4 days of treatment. TAO treatment also showed an increase in CYP3A and GHS-Ya mRNA levels after 4 days of treatment. However, TAO was shown to alter the mRNA levels of two other genes. Fibronectin gene expression levels were found to be induced whereas CYP4A1 mRNA levels were shown to be decreased after 4 days of TAO treatment.

A total of 12 genes were shown to be altered in rat liver after 4 days of MCP treatment. Eleven of these 12 genes were found to be induced whereas only one was shown to be decreased. The 11 induced genes were CYP4A, CYP3A, ACO, enoylCoA, UDPGT1a, ApoJ, ferritin, GSHYa, O6-methylguanine DNA methyltransferase (MGMT), ODC, and transferrin whereas the one decreased gene was found to be SMP30. Gene expression levels were also studied for the antituberculosis drug, ISN; however, no gene was shown to be changed on the Rat HepatoChip after 4 days of treatment with ISN.

Discussion In this study, we validated and evaluated the “Rat HepatoChip” as a means of identifying genes associated with drug toxicity. The main focus of these studies was to investigate the accuracy of the method by assessing any variations due to the technology (array to array), interanimal differences, and poor reproducibility. Examination of control and DEX-treated samples was used to study the array to array, reproducibility, and interanimal variability, for which the CVs were found to be 33, 20, and 28%, respectively (calculated as the mean of variation coefficient for differentially expressed genes). Some of the day to day and interanimal variability was due to microarray technology; however, this has been minimized by averaging across microarrays before calculating the CV. The variability inherent in the microarray technology has different sources such as different amounts of capture



Figure 7. Gene expression changes due to treatment with 50 mg/kg of DEX, 100 mg/kg of CLOT, 100 and 500 mg/kg of TAO, 75 mg/kg of MCP, and 100 and 100 mg/kg of ISN. The red color means that the gene has been up-regulated due to drug treatment. The green color means that the gene has been down-regulated due to drug treatment. The different color intensity denotes the degree of induction/repression. The gray color means that there was no significant change due to treatment. Genes shown in the table have fold changes, which were statistically different from 1 (p < 0.05) when using a One sample t-test.

Figure 8. Rat hepatocyte chips hybridized with cDNA obtained from mRNA extracted from control and DEX-treated rats. Fluorescence is represented in the pseudocolor scale and corresponds to the expression levels of genes. The boxes show examples of changes in gene expression levels. The red box corresponds to CYP3A, the yellow box corresponds to microglobulin, and the green box corresponds to GSH-θ5.

probe, spotting quality, slide preparation, experimental handling of the different steps of the process, and the environment for slide processing (13). It is also likely that the signal to noise ratios will vary when comparing experiments over different days giving rise to subtle variations in the net signal intensities. Another important source of variation occurs when a gene changes from undetectable to high levels of expression or vice versa. This could lead to gross distortion in ratiometric quantitation of inducted/repressed genes. This was illustrated in the present study on the SMP30 gene, which was drastically suppressed due to the treatment of three of

the tested drugs. In addition, the use of housekeeping genes for data normalization could potentially add more noise to the experiment, as it is not always known how these genes can also be affected under the experimental conditions. Previous studies indicate that the expression levels of selected housekeeping genes can vary in many situations (14, 15). We attempted to validate the data obtained with the Rat HepatoChips by comparison with that of more traditional approaches of measuring mRNA levels. All of the compounds used in this study are well-known inducers of cytochrome P450 such CYP3A (DEX, CLOT,


MIC, and TAO) and CYP4A (MCP). All of these inductions were confirmed by DNA array analysis using the Rat HepatoChip. However, the use of the DNA array approach also revealed changes in additional genes following treatment with these drugs, some of which have been previously reported. Treatment of rats with DEX produced alterations in regulatory cell cycle, oxidative stress, and lipid metabolism-related genes suchs as cyclin D1, GSH S-transferase, 3-hydroxy-3-methylglutaryl coenzyme A (HMG CoA) reductase, and acyl CoA oxidase (ACO). These gene changes are consistent with previous studies and explain the histological effects observed in DEX-treated animals (16-18). The treatment of rats with the two N-substituted imidazole antimycotics, CLOT and MIC, produced an induction of CYP3A mRNA levels and GSH-Ya as previously described (11, 19). Notable differences in gene expression were found between these two drugs, which could explain their differential effects at the histological level (8). CLOT also induced expression levels of UDPGT1a and CYP2B, which was consistent with previous papers (20, 21). CLOT also induced ODC mRNA levels, and previous papers have linked such an effect with sustained maintenance of hepatomegaly and increased liver cell proliferation (22). Previous studies have shown that TAO induces changes in gene expression levels other than the CYP3A gene (8, 11). In this study, we showed that TAO induces GSH-Ya and fibronectin mRNA levels and decreases the expression of the CYP4A1 gene. TAO has also been reported to produce a significant increase in liver weight accompanied by a mild increase in mitosis, although this still needs to be established (8). Chronic administration of peroxisome proliferators such as MCP is known to increase the incidence of liver tumors (23). MCP treatment has been shown to induce CYP4A, ACO, enoyl-CoA hydratase, CYP3A, UDPGT1a, and ODC mRNA levels in this study, which is consistent with previous papers (22, 24, 25). In this study, we showed that MCP treatment of rats produced alterations in oxidative and DNA repair-related genes such as GSHYa, ApoJ, and MGMT. The mRNA levels of reactive ion binding proteins such as transferrin and ferritin were also increased. This increase in oxidative-related genes suggests that hepatocytes may be undergoing oxidative stress as previously described (26). The increase in MGMT, a DNA repair protein (27), could be an indication that DNA damage is occurring due to MCP treatment and tissues are responding to DNA damage by increasing the levels of DNA repair enzymes as previously reported for other peroxisome proliferators (28). DEX, CLOT, and MCP treatments have been shown to decrease mRNA levels of SMP30. This protein has been suggested to play a role as a Ca2+ binding protein (29). The significance of the SMP30 down-regulation after DEX, CLOT, and MCP treatment in this study is not known. However, it is possible that this could lead to a dysregulation of Ca2+ homeostasis, which may compromise the cell status or affect signal transduction mechanisms. Several issues still need to be addressed with respect to reproducibility of DNA microarray technology if it is to be used for quantitative measurement of gene expression. However, one of the major advantages of DNA microarray technology is their ability to determine the


pattern of significantly altered gene expression changes (up-regulation/down-regulation) regardless of the fold change value. The fold changes parameter tends to vary depending on a range of factors such as the technology, probe specificity, and experimental conditions. The relevance of the specific fold change value may not be critical as compared to the qualitative gene expression profile changes. In this study, we showed good qualitative correlation of the data with that from previous studies when using the same treatments. The gene expression profiles also showed good correlation with the histopathology observations such that compounds inducing the major histopathological changes also induced gene changes, whereas the agents causing no histological changes had little or no effect on gene expression (Figure 7). This work also provides evidence that a designed lowdensity microarray has the potential to be used earlier in the drug development process once the initial signature has been established for the different types of toxicity caused by xenobiotics.

Acknowledgment. We thank Alan Watt, Sandra Cervino, and Paul Guest for their critical review of the manuscript.

References (1) Waring, J. F., and Ulrich, R. (2000) The impact of genomics-based technologies on drug safety evaluation. Annu. Rev. Pharmacol. Toxicol. 40, 335-352. (2) Gerhold, D., Lu, M., Xu, J., Austin, C., Caskey, C. T., and Rushmore, T. (2001) Monitoring expression of genes involved in drug metabolism and toxicology using DNA microarrays. Physiol. Genomics 5, 161-170. (3) Bartosiewickz, J. M., Jenkins, D., Penn, S., Emery, J., and Buckpitt, A. (2001) Unique gene expression patterns in liver and kidney associated with exposure to chemical toxicants. J. Pharmacol. Exp. Ther. 297, 895-905. (4) Waring, J. F., Jolly, R. A., Ciurlionos, R., Lum, P. Y., Praestgaard, J. T., Morfitt, D. C., Buratto, B., Roberts, C., Schadt, E., and Ulrich, R. (2001) Clustering of hepatotoxins based on mechanism of toxicity using gene expression profiles. Toxicol. Appl. Pharmacol. 175, 28-42. (5) De Longueville, F., Surry, D., Meneses-Lorente, G., Bertholet, V., Talbot, V., Evrard, S., Chandelier, N., Pike, A., Worboys, P., Rasson, J. P., Le Boudelles, B., and Remacle, J. Gene expression profiling of drug metabolism and toxicology markers using a lowdensity DNA microarray. Biochem. Pharmacol. 64, 137-149. (6) Chen, J. J. W., Wu, R., Yang, P.-C., Huang, J.-Y., Sher, Y.-P., Han, M.-H., Kao, W.-C., Lee, P.-J., Chiu, T. F., Chang, F., Chu, Y.-W., Wu, C.-W., and Peck, K. (1998) Profiling Expression Patterns and Isolating Differentially Expressed Gene by cDNA Microarray System with Colorimetry Detection. Genomics 51, 313-324. (7) Vishwanath, R., Iyer, M. B., Eisen, D. T., Ross, G. S., Troy, M., Jeffrey, C. F., Lee, J. M., Trent, L. M., Staudt, J. H., Jr., Mark, S. B., Deval, L., Dari, S., David, B., and Patrick, O. B. (1999) The Transcriptional Program in the Response of Human Fibroblasts to Serum. Science 283, 83-87. (8) Lake, B. G., Renwick, A. B., Morag, E. C., Price, R. J., Surry, D., and Evans, D. C (1998) Comparison of the effects of some CYP3A and other enzyme inducers on replicative DNA synthesis and cytochrome P450 isoforms in rat liver. Toxicology 131, 9-20. (9) Kocarek, T. A., Schuetz, E. G., Strom, S. C., Fisher, R. A., and Guzelian, P. S. (1995) Comparative analysis of cytochrome P4503A induction in primary cultures of rat, rabbit, and human hepatocytes. Drug Metab. Dispos. 23, 415-421. (10) Surry, D. D., Meneses-Lorente, G., Heavens, R., Jack, A., and Evans, D. C. (2000) Rapid determination of rat hepatocyte mRNA induction potential using oligonucleotide probes for CYP1A1, 1A2, 3A and 4A1. Xenobiotica 30, 441-456. (11) Suzuki, S., Kurata, N., Nishimura, Y., Yasuhara, H., and Satoh, T. (2000) Effects of imidazole antimycotics on the liver microsomal cytochrome P450 isoforms in rats: comparison of in vitro and ex vivo studies. Eur. J. Drug Metab. Pharmacokinet. 25, 121-126.

Gene Expression Profiling Using a DNA Microarray (12) Bell, D. R., and Elcombe, C. R. (1991) Induction of acyl-CoA oxidase and cytochrome P450IVA1 RNA in rat primary hepatocyte culture by peroxisome proliferators. Biochem. J. 280, 249-253. (13) Bartosiewicz, M., Trounstine, M., Barker, D., Johnston, R., and Buckpitt, A. (2000) Development of a toxicological gene array and quantitative assessment of this technology. Arch. Biochem. Biophys. 376, 66-73. (14) Eickhoff, B., Korn, B., Schick, M., Poustka, A., and van der Bosch, J. (1999) Normalization of array hybridization experiments in differential gene expression analysis. Nucleic Acids Res. 27, e33. (15) Savonet, V., Maenhaut, C., Miot, F., and Pirson, I. (1997) Pitfalls in the use of several “housekeeping” genes as standards for quantitation of mRNA: the example of thyroid cells. Anal. Biochem. 247, 165-167. (16) Fernandes, D., Guida, E., Koutsoubos, V., Harris, T., Vadiveloo, P., Wilson, J. W., and Stewart, A. G. (1999) Glucocorticoids inhibit proliferation, cyclin D1 expression, and retinoblastoma protein phosphorylation, but not activity of the extracellular-regulated kinases in human cultured airway smooth muscle. Am. J. Respir. Cell Mol. Biol. 21, 77-88. (17) Scott Simonet, W., and Ness, G. C. (1989) Posttranscriptional regulation of 3-hydroxy-3-methylglutaryl-CoA reductase mRNA in rat liver. J. Biol. Chem. 264, 569-573. (18) Letteron, P., Brahimi-Bourouina, N., Robin, M. A., Moreau, A., Feldmann, G., and Pessayre, D. (1997) Glucocorticoids inhibit mitochondrial matrix acyl-CoA dehydrogenase and fatty acid betaoxidation. Am. J. Physiol. 272, 1141-1150. (19) Gonzalez, P., Tunon, M. J., Manrique, V., Garcia-Pardo, L. A., and Gonzalez, J. (1989) Changes in hepatic cytosolic glutathione S-transferase enzymes induced by clotrimazole treatment in rats. Clin. Exp. Pharmacol. Physiol. 16, 867-871. (20) Ritter, J. K., and Franklin, M. R. (1987) Clotrimazole induction of cytochrome P-450: dose-differentiated isozyme induction. Mol. Pharmacol. 31, 135-139. (21) Ronis, M. J. J., Ingelman-Sundberg, M., Badger, and Thomas, M. (1994) Induction, suppression and inhibition of multiple


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hepatic cytochrome P450 isoenzymes in the male rat and bobwhite quail (Colinus virginianus) by ergosterol biosynthesis inhibiting fungicides (EBIFs). Biochem. Pharmacol. 48, 1953-1965. Izumi, K., Reddy, J. K., and Oyasu, R. (1981). Induction of hepatic ornithine decarboxylase by hypolipidemic drugs with hepatic peroxisome proliferative activity. Carcinogenesis (London) 2, 623627. Popp, J. A., and Cattley, R. C. (1993) Peroxisome proliferators as initiators and promoters of rodent hepatocarcinogenesis. In Peroxisomes: Biology and Importance in Toxicology Medicine (Gibson, G., and Lake, B., Eds.) pp 653-65, Taylor and Francis, London. Bell, D. R., and Elcombe, C. R. (1991) Induction of acyl-CoA oxidase and cytochrome P450IVA1 RNA in rat primary hepatocyte culture by peroxisome proliferators. Biochem. J. 280, 249-253. McQuaid, S., Russell, S. E. H., Withe, S. A., Pearson, C. M., Elcombe, C. R., and Humphries, P. (1987) Analysis of transcripts homologous to acyl-CoA oxidase and enoyl-CoA hydratase 3-hydroxyacyl-CoA dehydrogenase induced in rat liver by methylclofenapate. Cancer Lett. (Shannon, Ireland) 37, 115-121. Reddy, J. K., and Rao, M. S. (1989) Oxidative DNA damage caused by persistent peroxisome proliferation: its role in hepatocarcinogenesis. Mutat. Res. 214, 63-68. Pegg, A. E., Dolan, M. E., and Moschel, R. C. (1995) Structure, function, and inhibition of O6-alkylguanine-DNA alkyltransferase. Prog. Nucleic Acid Res. Mol. Biol. 51, 167-223. Rusyn, I., and et al. (2000) Expression of base excision repair enzymes in rat and mouse liver is induced by peroxisome proliferators and is dependent upon carcinogenic potency. Carcinogenesis 21, 2141-2145. Fujita, T. (1999) Senescence marker protein-30 (SMP30): structure and biological function. Biochem. Biophys. Res. Commun. 254, 1-4.

TX034117N

An Evaluation of a Low-Density DNA Microarray Using Cytochrome

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