Interlaboratory evaluation of rat hepatic gene expression changes induced by methapyrilene.

Several studies using microarrays have shown that changes in gene expression provide information about the mechanism of toxicity induced by xenobiotic agents. Nevertheless, the issue of whether gene expression profiles are reproducible across different laboratories remains to be determined. To address this question, several members of the Hepatotoxicity Working Group of the International Life Sciences Institute Health and Environmental Sciences Institute evaluated the liver gene expression profiles of rats treated with methapyrilene (MP). Animals were treated at one facility, and RNA was distributed to five different sites for gene expression analysis. A preliminary evaluation of the number of modulated genes uncovered striking differences between the five different sites. However, additional data analysis demonstrated that these differences had an effect on the absolute gone expression results but not on the outcome of the study. For all users, unsupervised algorithms showed that gene expression allows the distinction of the high dose of MP from controls and low dose. In addition, the use of a supervised analysis method (support vector machines) made it possible to correctly classify samples. In conclusion, the results show that, despite some variability, robust gene expression changes were consistent between sites. In addition, key expression changes related to the mechanism of MP-induced hepatotoxicity were identified. These results provide critical information regarding the consistency of microarray results across different laboratories and shed light on the strengths and limitations of expression profiling in drug safety analysis. Key words: methapyrilene, microarray, support vector machine, toxicogenomics, unsupervised algorithms, variability. Environ Health Perspect 112:439-448(2004). doi:10.1289/txg.6643 available via http://dx.doi.org/[Online 15 January 2004]

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In recent years the field of toxicology has begun the process of integrating genomic technologies into drug safety evaluation to understand and possibly predict adverse drug side effects. New technologies allow for the identification and quantification of thousands of gene changes occurring in a cell in a single experiment. Currently, microarrays are state of the art technology for evaluation of global gene expression changes. Several studies using microarrays have shown that changes in gene expression provide crucial information regarding the mechanism of toxicity induced by xenobiotic agents, including methapyrilene (MP), Aroclor 1254, and acetaminophen (Hamadeh et al. 2002; Reilly et al. 2001; Waring et al. 2002; Waring and Halbert 2002). In addition several studies have shown that compounds associated with a particular mechanism of toxicity, such as DNA-damaging agents, Aryl hydoxylase (Ah)-receptor ligands, and peroxisome proliferators, yield similar gene expression profiles (Burczynski et al. 2000; Thomas et al. 2001; Waring et al, 2001). Despite the potential that microarray analysis offers to toxicology, many questions remain concerning the reliability and reproducibility of these assays. Perhaps of primary importance is the issue of whether gene expression profiles for a given compound will reproduce consistently from study to study and across different laboratories.

To begin to address this issue, the Health and Environmental Sciences Institute (HESI) of the International Life Sciences Institute (ILSI) (http://www.ILSI.org) formed a consortium. Over thirty pharmaceutical companies participate in this effort that focuses on three categories of toxicants, hepatotoxins, nephrotoxins, and genotoxins.

In the HESI Hepatotoxicity Working Group, one of the compounds used in the evaluation was MP. MP is a known hepatotoxin that causes periportal cell necrosis and carcinomas in rats (Cunningham et al. 1995; Ratra et al. 2000). MP is metabolized in liver mainly by phase I enzymes; it does not show mutagenic properties and does not induce DNA synthesis (Budroe et al. 1984). Hence, MP is believed to act through a nonmutagenic mechanism. In the current study we have investigated the effects of a low (presumably nontoxic) and a high (toxic) dose of this compound on hepatic gene expression, with the goal of evaluating changes across different time points and concentrations. Most important, we designed the study to assess the reproducibility of the gene changes across different laboratories. To minimize variation due to the performance of the in vivo studies that might be reflected on the gene expression results, animals were treated at one facility (Abbott Laboratories) and RNA was distributed to five different users of the Affymetrix GeneChip system: Boehringer Ingelheim Pharmaceuticals (BI), Novartis Pharma AG (Nov), Pfizer Inc (Pfi), F. Hoffmann-La Roche AG (RO), and Schering AG (Sch). The results showed that, despite some variability, robust gene expression changes were consistent between sites. In addition, key gene expression changes related to the mechanism of methapyrilene-induced hepatotoxicity were identified.

Materials and Methods

Test article and formulation. Methapyrilene hydrochloride (MP) (CAS no. 135-23-9, lot number 037F0929) was obtained from Sigma Chemical Corporation (St. Louis, MO). MP was formulated in water and prepared fresh daily.

Animals and treatments. The rationale for dose selection was based on prior knowledge. Acute garage of 225 mg/kg MP caused hepatic necrosis, increased mitotic figures, and elevated serum enzyme levels characteristic of hepatotoxicity (Lijinsky et al. 1980). In a 3-day oral gavage study, 150 mg/kg/day MP resulted in periportal hepatic damage (Steinmetz et al. 1988). Previous results also showed that 50 mg/kg/day during 3 days resulted in minimal expression of single-cell necrosis with minimal mononuclear infiltrate without associated changes in clinical chemistry parameters (Waring et al. 2001). Thus, in the present study we chose 100 mg/kg/day as the high dose expected to elicit hepatotoxicity. A dose of 10 mg/kg/day was selected as the low dose with the expectation that no hepatotoxic effect would be observed.

Male Sprague-Dawley rats were obtained from Charles River Laboratories, Inc. (Wilmington, MA). Rats were 57 days old and weighed 233.4-274.0 g at the start of the treatment. Upon arrival to Abbott Laboratories (Abbott Park, IL), all rats were acclimated for 6 days before treatment began. The two treatment groups comprising four rats each received the test compound at a concentration of 10 or 100 mg/kg, respectively. Animals in the equally sized control group received vehicle only.

Rats were dosed once daily by gavage for 7 days. The dose volume was 10 mL/kg. Doses were milligram salt per kilogram per day and were calculated for each rat on the basis of the most recent body weight data available. Rats were fasted overnight after their last treatment, euthanized under halothane anesthesia and submitted for necropsy. Each rat received its last treatment approximately 24 hr before scheduled necropsy.

In vivo observations, pathology, and sampling. All rats were observed twice each day during the pretreatment and treatment periods for survival and general condition. Blood samples were drawn from all rats, and clinical chemistry parameters were obtained for alanine aminotransferase (ALT), aspartate aminotransferase (AST), sorbitol dehydrogenase (SDH), alkaline phosphatase (ALKPHOS), total bilirubin (TBIL), glucose (GLU), and triglycerides (TRIG). At necropsy, liver was weighed and the percent of body weight of each organ was calculated. One part of the liver (left lateral lobe) was fixed for potential histopathology in 10% formalin and subsequently sectioned and stained with hematoxylin and eosin, while the rest of the organ was rinsed in phosphate-buffered saline, immediately flash-frozen in liquid nitrogen, and kept frozen for subsequent RNA isolation.

RNA Isolation and Distribution

Approximately 100 mg of tissue from each liver was placed into TRIzol reagent (Invitrogen Corp., Carlsbad, CA) and homogenized. Total RNA isolation was performed exactly according to the TRIzol reagent protocol. The remaining portion of the liver was retained frozen. Following isolation, the RNA was quantitated using a BioRad SmartSpec 3000 spectrophotometer (BioRad, Hercules, CA), and the integrity of the RNA was determined using an Agilent 2100 bioanalyzer (Agilent Technologies, Palo Alto, CA). The RNA from the four animals in each treatment group was then pooled using equivalent amounts from each sample. The RNA was aliquoted and precipitated in ethanol and ammonium acetate for shipment to the participating DNA microarray users. In addition, RNA from individual animals was shipped to some of the DNA microarray analysis laboratories.

DNA microarray analysis. RNA samples were analyzed independently by five different Affymetrix users: Boehringer-Ingelheim Pharmaceuticals, Novartis, Pfizer Inc, F. Hoffmann-La Roche AG, and Schering AG using rat RGU34A expression probe arrays (Affymetrix, Santa Clara, CA), containing 8,799 probe sets interrogating primarily annotated genes. The rat sequences used for the design of the RGU34A expression probe array were derived from Build 34 of the UniGene database (http://www.ncbi. nih.gov/UniGene/; created from Genbank 107/dbEST 11/18/98) and supplemented with additional annotated gene sequences from Genbank 110 (http://www.ncbi. nih.gov/GenBank/). UniGene clusters are represented by an example sequence that is the most complete and most 3' sequence in the cluster. The oligonucleotide probes are 25mers and 16 probe pairs per sequence are used. Processing of RNA and GeneChip experiments was carried out basically as recommended by Affymetrix, with some user-specific variations (Table 1) (Lockhart et al. 1996). An initial amount of 5-20 [micro]g total RNA was used for the synthesis of double-stranded cDNA with a commercially available kit (Superscript Choice System; Invitrogen Life Technologies or Roche Molecular Biochemicals, Mannheim, Germany) in the presence of a T7-[(dT).sub.24] DNA oligonucleotide primer. After synthesis, the cDNA was purified by phenol/chloroform/isoamylalcohol extraction and ethanol precipitation. The purified cDNA was then transcribed in vitro [Enzo Diagnostics, Inc. (Farmingdale, NY) or Ambion, Inc. (Austin, TX)] in the presence of biotinylated ribonucleotides to form biotin labeled cRNA. The labeled cRNA was then purified on an affinity resin (Rneasy; Qiagen, Inc., Valencia, CA), quantified and fragmented. An amount of 10-20 [micro]g labeled cRNA was hybridized for approximately 16 hr at 45[degrees]C to an expression probe array. The array was then washed, stained with streptavidin-R-phycoerythrin (SAPE; Molecular Probes, Eugene, OR), and the signal amplified using a biotinylated goat anti-streptavidin antibody (Vector Laboratories, Burlingame, CA) followed by a final staining with SAPE. Arrays were stained using the GeneChip Fluidics Workstation 400 (Affymetrix) and then scanned twice using a confocal laser scanner [GeneArray Scanner 2500; Hewlett Packard (Palo Alto, CA) or Agilent Technologies], resulting in one average scanned image.

Data analysis. Tab-delimited files obtained from the Affymetrix Microarray Suite software, version 4.0, (*.chp files) and containing data on signal intensity [average difference (Avg Diff)] and categorical expression-level measurement (Absolute Call) were used for analysis. Data were normalized and further analyzed using Roche in-house developed software (RACE-A; F. Hoffmann-La Roche AG, Mannheim, Germany). Briefly, this software performs a normalization step on the signal intensities based on the average signal (Mean Avg Diff) of each microarray before calculating additional parameters. In the cases where biological replicates were included, RACE-A was also used to calculate the average signal (arithmetic mean), and SD for each probe set. Also, comparative analysis between control and treated was performed including fold change (Avg Diff Treated/Avg Diff Control) and a significance value (p-value), calculated using a two-tailed, unpaired t-test. Once the required statistical parameters were calculated, data were filtered and exported to MS-Excel 2002 (Microsoft, Corp., Bellevue, WA) or additional software for visualization and further analysis.

In addition, methods comprising more sophisticated algorithms and designed specifically for multivariate data analysis such as microarray data were employed. These methods share the characteristics of reducing the dimensionality of the data to a number of dimensions (components or vectors) that explain most of the variability in the data set. They are better suited to microarray analysis and generally superior in performance than gene-by-gene analysis with conventional statistical tests because they take into account the complex data structure. Such methods are known as unsupervised [hierarchical clustering and principal component analysis (PCA)] or supervised [support vector machines (SVMs)] multivariate analysis methods. Supervised methods such as SVMs are based on algorithms that learn from a selected training data set and use this previously acquired knowledge about classes to classify unknown data. The algorithm solves the classification problem while aiming to minimize the probability of false classifications for initially unknown test data. The basic idea of the SVM method and detailed explanations are described elsewhere (Cristianini and Shawe-Taylor 2000; Scholkopf et al. 1999).

Unsupervised methods such as clustering algorithms and PCA are commonly used to determine if gene expression patterns allow the discrimination of natural subpopulations that might bear a biological meaning such as treated/untreated or healthy/diseased. PCA is a mathematical technique that reduces the dimensionality of highly multivariate data. The reduced dimensions (or components) actually describe the major part of the variation in the samples and separate natural subpopulations without a priori knowledge (Liu et al. 2002).

Cluster analysis is a method used to organize primary data. Pairwise average-linkage cluster is a form of unsupervised hierarchical clustering commonly used for the analysis of microarray data. Relationships among objects such as experimental conditions or genes are represented by a tree whose branch lengths reflect the degree of similarity between the objects as assessed by a pairwise similarity function based on correlation coefficients (Eisen et al. 1998). The clustering tools and SVM used in this analysis are modules of RACE-A, whereas PCA was performed using SIMCA-P (Umetrics, Umea, Sweden).

The complete data set is currently being submitted to ArrayExpress (EMBL-European Bioinformatics Institute, Hinxton, UK; http://www.ebi.ac.uk/arrayexpress) and will be available for public download by the second quarter of 2004. Accession numbers referencing this data set will be available on the HESI website (http://hesi.ilsi.org/ index.cfm?pubentityid=120).

Results

Clinical chemistry and histopathology. A significant change in both body weight and food consumption compared to that of control groups was seen in the high-dose, but not in the low-dose group (data not shown). Clinical chemistry values confirmed liver toxicity occurred in the high-dose rats (Table 2). There were no significant changes at the low dose. Significant increases in leakage enzyme (AST, SDH and ALKPHOS) indicate both hepatocellular and cholangiolat injury. The dose-dependent decline in serum glucose and a trend toward a decrease in triglyceride levels might indicate compromise of hepatocellular metabolic function but may also have been influenced by reductions in food consumption.

No compound-related histopathological changes were found for the low-dose group, whereas several compound-related changes were seen in livers from rats treated at the high-dose level. These included cytoplasmic vacuolation of periportal hepatocytes, minimal to mild necrosis of periportal hepatocytes, increased infiltration of portal tracts by mononuclear inflammatory cells, and hyperplasia of oval cells along portal tracts.

Comparisons across users. After microarray analysis of the RNA, it was determined that different users obtained comparable results despite possible variation in the sample processing [from total RNA up to fragmented IVT (in vitro transcript)] and microarray hybridization protocols. In a preliminary round of analysis using rigid cutoff values to assess which genes were modulated, the number of genes detected as regulated in the pooled samples (2-fold increase or decrease) by each user were strikingly different (Table 3). All five users analyzing the pooled RNAs detected 254 genes that were regulated simultaneously, while each user recognized aa excess of 1,000 genes as up-or downregulated. The data set generated at RO appeared to be a clear outlier with nearly twice the amount of modulated genes as the other users. This may be due to the modifications introduced in the sample preparation (Table 1), but no direct evidence is available in support of this.

Further microarray results demonstrated that when individual animals were analyzed, as opposed to pooled samples, the number of genes detected as induced/ repressed was generally reduced. Table 3 shows the results from microarray analysis on pooled samples and individual animals conducted at RO and Sch. The inclusion of replicates very likely diminishes the influence of false signals. Nevertheless, there is still much disagreement among users when performing simple data analysis methods and defined cutoffvalues.

Whereas the gene expression analysis was not concordant between different laboratories, a critical question to be addressed is whether microarray results from all users reflected the observations from traditional toxicology markers and yielded similar mechanistic outcomes. When methapyrilene effects on the liver are examined, both histopathology and clinical chemistry analysis distinguished the high-dose animals from the low-dose and control animals. To determine if microarray analysis also distinguished between high and low dose, more sophisticated methods usually better suited to the analysis of highly multivariate microarray data were used. Among these methods, we chose to employ unsupervised as well as supervised approaches. Conversely, unsupervised methods are well suited to separate natural subpopulations in an unbiased manner. On the other hand, supervised methods allow incorporating knowledge obtained from the data (training set) to distinguish classes in the test data set. We analyzed the data using two unsupervised methods, namely PCA and hierarchical clustering. Using all expressed genes (4,846 probe sets), PCA analysis revealed a clear separation of the high-dose samples from controls and low-dose samples, despite the fact that the site differences are responsible for a large amount of variance. In this analysis, the second principal component (PC2; accounting for 15% of the variance) drives the treatment-related difference, as indicated by the arrows, whereas PC1 (accounting for 33% of the variance) showed a separation by site (Figure 1A, B). Excluding PC1 and relying exclusively on PC2 and PC3, a clear separation between high-dose-treated animals and the other two groups was achieved regardless of the site in which the sample processing was performed. Thus, the site-related differences do not mask the outcome of the classification. The low-dose samples could not be confidently distinguished from the vehicle-treated controls, a conclusion that accords with the clinical chemistry and histopathology findings.

[FIGURE 1 OMITTED]

To verify this latter conclusion, we grouped the data using another unsupervised clustering method, agglomerative hierarchical clustering. When we used the expressed genes employed for the PCA analysis, there was a tendency toward clustering by the site performing the microarray analysis (Figure 2A). An increase in statistical power can be achieved by including the confidence information obtained from the analysis of biological replicates (Lee et al. 2000). For most users conducting these experiments, individual replicates were not available, as the RNA had been pooled. However, replicates were available from the two sites that performed microarray analysis on individual animals. Thus, we performed hierarchical agglomerative clustering using the probe sets that were regulated in common by the high dose of MP from the individual replicates obtained by Sch and RO, With this smaller subset of genes, hierarchical clustering of the treatment groups allowed the high dose to be discriminated from the controls and low-dose-treated animals (Figure 2B). Similar to the PCA analysis, the low-dose samples could not be distinguished from the control samples.

[FIGURE 2 OMITTED]

To improve the discrimination between the groups, further analysis was performed using supervised methods. Because results from biological replicates were provided by two sites (RO and Sch), it was possible to generate a training set using the profiles obtained from the individual animals. This training set consisted of two analyses (one per site) that included 4 animals in each treatment group, amounting to 24 microarrays generated from 12 animals. This training set has the limitations of being rather small and of including in each group four biological replicates (independent) and for each of these independent replicates, two processing replicates from different sites (nonindependent). The data from individual animals were analyzed using the SVM to identify probe sets that were distinct for the three different classes of treatment (vehicle, low-dose, high-dose). The training of the SVMs and the subsequent classification were performed using all probe sets on the chip (8,799). Once the SVM was thus trained, the 15 microarrays obtained from the analysis of the pooled samples (5 controls, 5 low-dose, 5 high-dose) were used as test samples and classified. In this case, samples obtained from animals treated with low or high doses of MP were correctly classified. Classification of the control animals was relatively ambiguous, as only 2 animals were correctly classified as controls, whereas the other 3 showed no similarity to either group (Figure 3). An example of some of the genes that allow the distinction between control and treated animals (low- and high-dose) is shown in Figure 4. Thus, using supervised clustering, together with biological replicates, it was possible overall to distinguish not only the high-dose-treated group, but also the low-dose-treated group from the controls. This was not unequivocally possible using clinical chemistry, histopathology, or unsupervised clustering methods.

[FIGURES 3-4 OMITTED]

Genes affected by MP. More important than the number of regulated genes is the determination of the identity of the regulated genes, the affected cellular pathways, and their biological significance. Some genes described previously as regulated by MP or that are associated with the histopathology findings were consistently detected by all involved users. Genes associated with cell stress, cell damage or apoptosis, and oncogenesis were shown to be upregulated by all users at the high dose (Table 4). Among these genes were the GADD family members GADD153 and GADD45 (Stokes et al. 2002), the proto-oncogenes c-myc and c-jun (Hernandez et al. 1991), the antiproliferative protein PC3 (BTG2) (Tirone 2001), bax (Brunelle and Chandel 2002), and heme oxygenase-1 (Bauer et al. 2000). In addition, cyclin D1 and cyclin G1 were also induced by MP (Afshari and Barrett 1993). Genes coding for proteins involved in cholesterol biosynthesis and [beta]-oxidation appeared consistently regulated by the treatment (i.e., mitochondrial CPT1, acyl-CoA thioester hydrolase, 3-hydroxy-3-methylglutamyl-CoA (HMG-CoA) reductase, squalene epoxidase, farnesyl diphosphate (FPP)-transferase, FPP-synthase were upregulated, while acyl-CoA desaturase, acyl-CoA synthetase, and acyl-CoA oxidase 2 were downregulated) (Ratra et al 1998b). The induction of several enzymes in the sterol metabolic pathway by the high dose was accompanied by downregulation of retinol dehydrogenase and retinol-binding protein 1 in the related retinol metabolic pathway and of the androgen/estrogen metabolic pathways (Figure 5). In addition, MP produced a marked effect in some metabolic enzymes such as the upregulation of cytochrome P-450 (CYP)4B1, CYP2C12, and aflatoxin reductase and the downregulation of CYP1A1, CYP2A2, CYP2C11 and sulfotransferases (Ratra et al. 1998a). Additional genes involved in redox processes were affected by the treatment: glutathione S-transferase (GSTyc2), glutathione peroxidase, and glutathione synthetase were induced, whereas superoxide dismutase was repressed. A consistent induction of UDP-glucuronosyltransferase (UDPGT) 1-6 and a concomitant downregulation of UDPGT2B (3-hydroxyandrogen specific) were also observed. MP also seemed to have an effect on the expression levels of several transporters; MDR (P-glycoprotein), cMOAT1 (MRP2) and cMOAT2 (MRP3) were upregulated, whereas the expression of the sodium/taurocholate transporter was transcriptionally repressed.

[FIGURE 5 OMITTED]

As can be deduced from the cluster and PCA analyses (Figures 1 and 2), the effect of the low dose of MP is rather subtle, involving a small amount of regulated genes and moderate fold changes. This makes the distinction between low-dose treated animals and controls relatively difficult in a rather heterogeneous (different users, different protocols) set of samples comprising very few replicates to support statistical analysis (four biological replicates for individual sample analyses and five replicates for pooled samples). Nevertheless, some genes could be identified that are consistently modulated by the low dose of the compound. Among these genes, a dose-dependent decrease in acyl-CoA desaturase (EC 1.14.99.5) and in caltrin calcium transport inhibitor) were observed, together with a very slight decrease in betaine-homocysteine S-methyltransferase (EC 2.1.1.5) and an increase in insulin-like growth factor binding protein 1 precursor (IGFBP-1) (Mohn et al. 1991).

Discussion

In this study, we were able to examine differences and similarities of results from microarray analysis obtained from a common source of RNA by several users. A preliminary evaluation of the number and identity of modulated genes uncovered striking differences between the five different Affymetrix users. This is in contrast to previous studies that have shown high reproducibility, with microarray chips from the same RNA source (Waring et al. 2001). In addition, our unpublished data show that the correlation between gene expression in the liver and in the kidney from samples obtained from the same animal and prepared by the same operator following a standardized protocol is only 44%, whereas two different liver samples show a correlation of 98%. In this study some of the differences between users are likely because of different user protocols. In support of this, RO shows the most striking difference in the absolute values of regulated genes and is also the user introducing the most modifications of the sample processing and hybridization protocol (different cDNA kit, different blocking solution, etc.). Other users employing the protocols recommended by Affymetrix show less variability among them. The remaining differences are probably attributable to minor protocol deviations as well as to an expected amount of false positives. In this case the number of false positives was undoubtedly high because the samples were pooled and thus the number of replicates was low.

Further data analysis with additional tools corroborated the finding that differences between users, sample processing, and hybridization protocols affected the absolute results, but that this did not distort the major conclusion of the study. Indeed, the PCA results showed that the PC1 (accounting for 33% of the variance) was mainly site driven (Figure 1A), but this variability did not mask the effects elicited by the high dose of MP. Despite the observed differences, all users obtained similar overall results that correlated with histopathology and clinical chemistry analysis. A clear differentiation between high-dose (toxic)-treated animals and controls and low-dose-treated animals was obtained by all users, as shown using unsupervised data analysis methods (cluster analysis and PCA). Moreover, in a supervised approach it was possible to identify animals treated with high and low doses in all the pooled samples analyzed by five different users on the basis of SVM trained with data obtained from the samples processed individually. Surprisingly, three of the pooled control samples were not clearly assigned to any treatment group. There are two possible factors that could have led to this misclassification. On one hand, the training set is very small, and this type of model has an optimal performance with large data sets. Alternatively, the effects of 10 mg/kg MP are very subtle, thus making distinction of control and low-dose-treated animals rather difficult. This is even more pronounced in a heterogeneous set of data. For very slight effects a larger number of replicates might be required for optimal performance.

As stated by Hamadeh et al. (2002), the use of unsupervised analysis tools is essential to ensure that the data contain natural subpopulations and that no preconceived bias is introduced when classes of compounds are being identified. The results obtained using cluster analysis and PCA show that gene expression profiles allow the natural classification of the high dose of MP regardless of the variation introduced by the different users. Nevertheless, these unsupervised tools mainly allow the distinction of samples showing definitive histopathological findings (high-dose) from samples without findings (controls and low-dose samples). It might be argued that this is not sensitive enough for predictive toxicogenomics studies. However, using a supervised analysis method like SVM, it was possible to correctly categorize samples into vehicle, low- and high-dose classes, which was not possible with clinical chemistry or histopathology. Thus, similar to results obtained by Burczynski et al. (2000) and Thomas et al. (2001), the ability to correctly classify compounds using toxicogenomics can be greatly improved by selecting a smaller subset of the most predictive gene sets.

A number of the genes and pathways regulated by MP toxicity were similar across users. This is particularly true for the high-dose-treated animals in which the effects were more pronounced. The genes detected as transcriptionally induced or repressed are in good agreement with results from a similarly designed study by Hamadeh et al. (2002) using cDNA spotted arrays. In-depth analysis of the genes modulated by MP sheds light on the variety of cellular processes affected.

Our results provide ample proof that gene expression analysis is a suitable method to detect effects produced by a high dose (100 mg/kg) of MP. The results presented in this article are generally in good agreement with a similar study performed by Hamadeh et al. (2002) and also show signals characteristic of the compound under investigation. The decrease in cytochrome P450 after a high dose of MP was in agreement with results of previous studies that showed this compound decreased the content of CYP2C11, CYP3A and CY2A, possibly due to suicide substrate activation (Graichen et al. 1985, Ratra et al. 1998a). MP is also known to transcriptionally induce CYP2C12 and CYP4B1 (Hamadeh et al. 2002), as was also detected by all users analyzing the samples. In addition, several of the genes detected as modulated, including the GADD family, heme oxygenase and genes related to glutathione homeostasis are indicative of the oxidative stress known to be produced by MP (Ratra et al. 1998b). Also, several of the modulated genes indicate an effect of MP on lipid metabolism, which is one of the pathways affected by MP as shown in studies using in vitro approaches (Iype et al. 1985) and protein analysis (Man et al. 2002). Moreover, events indicative of lipid peroxidation were observed as previously published (Hamadeh et al. 2002). The induction of mitochondrial genes (i.e., CPT1 and acyl-CoA thioester hydrolase) is also indicative of the mitochondrial proliferation that has been previously related to MP (Iype et al. 1985).

In animals treated with a low dose of MP, some genes could be identified as already being modulated after 1-week treatment with 10 mg/kg/day MP. Among these, an induction of IGFBP-1 (Affymetrix probe set ID M58634_at) and aflatoxin B1 aldehyde reductase (Affymetrix ID AF045464_s_at), as well as the downregulation of retinol dehydrogenase type 2 (Affymetrix ID U33500_g_at) were observed. As depicted in Figure 4C, the observed downregulation of retinol dehydrogenase 2 at both the low and high doses was accompanied by the downregulation of retinol-binding protein 1 (Affymetrix ID M19257_at; Figure 4D). IGFBP-1 (Affymetrix ID M58634_at) appeared upregulated at both doses, whereas the growth-promoting insulin-like growth factor 1 (IGF1, Affymetrix ID M15481_g_at) appeared downregulated only after rats were exposed to a high dose of MP (Figure 4A, B). The upregulation of IGFBP-1 might be a protective mechanism for the known carcinogenic effect of MP because the levels of IGFBP-1 regulate the mitogenic effects of IGFs (Kelley et al. 1996). In fact, IGFBP-1 has been shown to inhibit hepatic preneoplasia in mice (Lu and Archer 2003). An additional cell protection mechanism that appears stimulated after treatment with 10 mg/kg/day of MP is aflatoxin B1 aldehyde reductase (Affymetrix ID AF045464_s_at; Figure 4E). This detoxifying enzyme shows only a slight induction at the low dose and an extensive induction at the high dose, which is in agreement with its previously reported induction by a high dose (100 mg/kg/day) of MP (Hamadeh et al. 2002). 17[beta]-Hydroxysteroid dehydrogenase type 2 (17bHSD2; Affymetrix ID X91234_at) shows an interesting regulation pattern, as it appears upregulated by the low dose of MP and downregulated by the high dose (Figure 4F). This enzyme is involved in the steroid conversion pathway (Akinola et al. 1996), which is one of the pathways affected by the treatment with MP (Figure 5), but the biological meaning of this finding remains unclear.

In conclusion, a high degree of user/site variability was observed with microarray analysis using the same RNA processed at different sites. Despite this, all the microarray results showed that it was nonetheless possible to distinguish toxic (i.e., histopathological findings) versus nontoxic dose levels of MP. Moreover, regardless of the user, gene expression analysis using supervised data analysis tools allowed the correct identification of the samples treated with the low dose of MP, a distinction that was not apparent from clinical chemistry or histopathology analysis.

The observed site-to-site variability did not impair the detection of molecular effects elicited by MP. In addition, crucial gene expression changes, which most likely reflect the mechanism of toxicity for MP, were observed across all user groups. These results provide critical information regarding the consistency of microarray results across different laboratories and shed light on the strengths and limitations of expression profiling in drug safety analysis.

Table 1. Sample preparation methods used by the contributing companies.

Gene expression
analysis site           Acronym    Sample type

Boehringer-Ingelheim      BI       Pool
Pharmaceuticals
Novartis Pharma AG        Nov      Pool
Pfizer Inc                Pfi      Pool
F. Hoffmann-La Roche      RO       Pool
F. Hoffmann-La Roche      RO       Individual
Schering AG               Sch      Pool
Schering AG               Sch      Individual

Gene expression
analysis site                   cDNA                  IVT

Boehringer-Ingelheim    SSII, Invitrogen        Enzo-Affymetrix
Pharmaceuticals
Novartis Pharma AG      SSII, Invitrogen        Enzo-Affymetrix
Pfizer Inc              SSII, Invitrogen        Enzo-Affymetrix
F. Hoffmann-La Roche    AMV, Roche Molecular    Ambion, Inc.
                          Biochemicals
F. Hoffmann-La Roche    AMV, Roche Molecular    Ambion, Inc.
                          Biochemicals
Schering AG             SSII, Invitrogen        Enzo-Affymetrix
Schering AG             SSII, Invitrogen        Enzo-Affymetrix

Abbreviations: AMV, avian myaloblestosis virus; Enzo-Affymetrix, Enzo
Diagnostics, Inc. and Affymetrix, Inc.; IVT, in vitro transcription;
SSII, Superscript II.

Table 2. Clinical chemistry values for methapyrilene-treated rats.

           Dosage      ALT        AST        SDH
Rat no.    (mg/kg)    (IU/L)     (IU/L)     (IU/L)

1001           0       42        91         10.9
1003           0       25       106          6.6
1005           0       28       103          6.7
1007           0       30        90          7.3
Average        0       31.3      97.5        7.9
SD             0        7.46      8.2        2.0
2001          10       51       168         17.4
2003          10       24        97          9.5
2005          10       23        90          9.1
2007          10       30        92         12.1
Average       10       32.0     111.8       12.0
SD            10       13.0      37.6        3.8
3001         100       36       162         12.3
3003         100       56       179         12.9
3005         100      193      >410         24.2
3007         100       51       200         19.4
Average      100       84.0     180.3 *     17.2 *
SD           100       73.2      19.0        5.7

              ALKPHOS     TBIL        GLU       TRIG
Rat no.       (IU/L)     (mg/dL)    (mg/dL)    (mg/dL)

1001           79          0.1      145         53
1003          170          0.1      136         19
1005          292          0.1      132         60
1007          197          0.1      121         48
Average        21.0        0.1      133.5       45.0
SD             56.1        0.0       10.0       18.0
2001          275          0.1      102         28
2003          217          0.1      111         50
2005          235          0.1      138         29
2007          255          0.1       97         19
Average       245.5        0.1      112         31.5
SD             25.1        0.1       18.3       13.1
3001          220          0.1      123         18
3003          283          0.4      100         28
3005          460          0.9      105         24
3007          417          0.3       88         15
Average       345.0 *      0.4      104.0 *     21.3
SD            112.4        0.3       14.5        5.9

Abbreviations: ALKPHOS, alkaline phosphatase; ALT, alanine
aminotransferase; AST, aspartate aminotransferase; GLU, glucose; SDH,
sorbitol dehydrogenese; TBIL, total bilirubin; TRIG, triglycerides.

* Significantly different from the control group using two-tailed
t-test (p-value < 0.05).

Table 3. Number of genes regulated by methapyrilene across the
different companies in the pooled and individual samples at the high
dose. (a)

                             Pooled RNA samples

                                                       Common
                  BI     Nov    Pfi     RO      Sch    RO/Sch

Upregulation      691    785    621    1,325    692     441
Downregulation    480    283    728      292    405     130

                  Pooled RNA
                   samples     Individual RNA samples

                  Common                     Common
                  All Users    RO     Sch    RO/Sch

Upregulation         179       282    352     120
Downregulation        75       262    157      73

Abbreviations: BI, Boehringer-Ingelheim Pharmaceuticals; Nov, Novartis
Pharma AG, Pfi, Pfizer Inc; RO, F. Hoffmann-La Roche AG; Sch, Schering
AG.

(a) Cut-off values: 2-fold change, p-value [less than or equal to]
0.05.

Table 4. Genes regulated by a high dose of methapyrilene.

Affymetrix
probe set ID (a)          Class                    Gene

X75207_s_at         Cell cycle            Cyclin D1
D14014_g_at         Cell cycle            Cyclin D1
D14014_at           Cell cycle            Cyclin D1
X70871_at           Cell cycle            Cyclin D1
E01184cds_s_at      Cyt P450              Cyt P450
M21208mRNA_s_at     Cyt P450              CYP17
K03241cds_s_at      Cyt P450              CYP1A2
J04187_at           Cyt P450              CYP2A2
J02657_s_at         Cyt P450              CYP2C11
M18363cds_s_at      Cyt P450              CYP2C11
X79081mRNA_f_at     Cyt P450              CYP2C11
J03786_s_at         Cyt P450              CYP2C12
M33550cds_s_at      Cyt P450              CYP2C12
rc_AA945573_f_at    Cyt P450              CYP2C39
M31031mRN_f_at      Cyt P450              CYP2C39
M14775_s_at         Cyt P450              CYP2C7
AB008424_s_at       Cyt P450              CYP2D3
U46118_at           Cyt P450              CYP3A9
M29853_at           Cyt P450              CYP4B1
D00680_at           Glutathione           Glutathione
                                          peridoxidase
L38615_g_at         Glutathione           Glutathione
                                          synthetase
rc_AA945082_at      Glutathione           GSTa2
S72506_s_at         Glutathione           GSTyc2
S82820mRNA_s_at     Glutathione           GSTyc2
X95189_at           Lipid metabolism      Acyl-CoA
                                          oxidase
AB010428_s_at       Lipid metabolism      Acyl-CoA
                                          thioesterase 1
Y09333_g_at         Lipid metabolism      Acyl-CoA
                                          thioesterase 1
Y09333_at           Lipid metabolism      Acyl-CoA
                                          thioesterase 1
D43623_g_at         Lipid metabolism      Carnitine palmitoyl-
                                          transferase
M26125_at           Lipid metabolism      Epoxide
                                          hydrolase 1
rc_AA893242_g_at    Lipid metabolism      Fatty acid-CoA
                                          ligase
M29249cds_at        Lipid metabolism      HMG-CoA
                                          reductase
X55286_g_at         Lipid metabolism      HMG-CoA
                                          Reductase
J02585_at           Lipid metabolism      Stearoyl-CoA
                                          desaturase 1
AB010429_s_at       Lipid metabolism      Very long chain acyl--
                                          CoA thioesterase
L07114_at           Lipid transport       Apolipoprotein B
                                          binding protein
AF072411_at         Lipid transport       CD36
rc_AA925752_at      Lipid transport       CD36
AB005743_g_at       Lipid transport       CD36
AF072411_g_at       Lipid transport       CD36
AB005743_at         Lipid transport       C036
K01180_at           Lipid transport       Fatty acid
                                          binding protein 2
U02096_at           Lipid transport       Fatty acid
                                          binding protein 7
L34049_g_at         Lipid transport       Megalin (LRP2)
U89280_at           Phase 2 metabolism    17-b Hydroxysteroid
                                          dehydrogenase
AF045464_s_at       Phase 2 metabolism    Aflatoxin b1
                                          aldehyde reductase
D38061exon_s_at     Phase 2 metabolism    UGT1-6
S56936_s_at         Phase 2 metabolism    UGT1-6
D38062exon_s_at     Phase 2 metabolism    UGT1-7
J02589mRNA#2_at     Phase 2 metabolism    UGT2B2
rc_Al169708_s_at    Phase 2 metabolism    UGT2B2
rc_Al180442_at      Steroid metabolism    Farensyl diphosphate
                                          synthase
M95591_g_at         Steroid metabolism    Farensyl diphosphate
                                          farnesyltransferase 1
M95591_at           Steroid metabolism    Farensyl diphosphate
                                          farnesyltransferase 1
M89945mRNA_g_at     Steroid metabolism    Farensyl diphosphate
                                          synthase
M89945mRNA_at       Steroid metabolism    Farensyl diphosphate
                                          synthase
M81225_at           Steroid metabolism    Farensyltransferase
U33500_at           Steroid metabolism    Retinol dehydro-
                                          genase type II
U33500_g_at         Steroid metabolism    Retinol dehydro-
                                          genase type II
M19257_at           Steroid metabolism    Retinol-binding
                                          protein 1
D37920_at           Steroid metabolism    Squalene epoxidase
U30186_at           Stress/damage         GADD153
L32591mRNA_at       Stress/damage         GADD45a
L32591mRNA_g_at     Stress/damage         GADD45a
rc_Al070295_g_at    Stress/damage         GADD45a
rc_Al175959_at      Stress/damage         c-jun
Y00396mRNA_at       Stress/damage         c-myc
Y00396mRNA_g_at     Stress/damage         c-myc
J02722cds_at        Stress/damage         Home oxygenase 1
M25157mRNA_i_at     Stress/damage         Superoxide
                                          dismutase
S76511_s_at         Stress/damage         BAX
M60921_g_at         Stress/damage         B-cell translocation
                                          gene 2
rc_AA944156_s_at    Stress/damage         B-cell translocation
                                          gene 2
U49729_at           Stress/damage         bcl12-associated X
                                          protein
M33329_f_at         Sulfotransferase      Alcohol
                                          sulfotransferase
X63410cds_f_at      Sulfotransferase      Alcohol
                                          sulfotransferase
S76489_s_at         Sulfotransferase      Estrogen
                                          sulfotransferase
D14989_f_at         Sulfotransferase      Hydroxysteroid
                                          sulfotransferase
rc_Al169695_f_at    Sulfotransferase      Hydroxysteroid
                                          sulfotransferase
D14988_f_at         Sulfotransferase      Hydroxysteroid
                                          sulfotransferase
D14987_f_at         Sulfotransferase      Hydroxysteroid
                                          sulfotransferase
rc_AA817987_f_at    Sulfotransferase      Hydroxysteroid
                                          sulfotransferase
M31363mRNA_f_at     Sulfotransferase      Hydroxysteroid
                                          sulfotransferase
rc_AA818122_f_at    Sulfotransferase      Hydroxysteroid
                                          sulfotransferase
rc_Al028836_at      Sulfotransferase      Hydroxysteroid
                                          sulfotransferase
L22339_at           Sulfotransferase      Phenol-preferring
                                          sulfotransferase
L22339_g_at         Sulfotransferase      Phenol-preferring
                                          sulfotransferase
AB010467_s_at       Transporter           Myosin-like protein 2
D86086_s_at         Transporter           Myosin-like protein 2
M81855_at           Transporter           P-glycoprotein/multi-
                                          drug resistance 1
M77479_at           Transporter           Sodium/bile acid con-
                                          transporter family
rc_Al235631_at      Unknown               Expressed sequence
                                            tag
rc_Al172452_at      Unknown               Expressed sequence
                                            tag
rc_AA866240_f_at    Unknown               Expressed sequence
                                            tag

                                     RO
                                 (individual)

Affymetrix           Max       Fold       t-test
probe set ID (a)    Signal    change     (p-value)

X75207_s_at            686      5.180      0.023
D14014_g_at          1,474     15.770      0.120
D14014_at            1,363      6.370      0.017
X70871_at              939      5.830      0.067
E01184cds_s_at       4,296      0.258      0.001
M21208mRNA_s_at      2,204      2.330      0.044
K03241cds_s_at        2576      0.119      0.005
J04187_at           11,191      0.383      0.007
J02657_s_at         24,686      0.053      0.001
M18363cds_s_at       7,563      0.013      0.012
X79081mRNA_f_at      3,775      0.005      0.008
J03786_s_at          7,156      8.350      0.021
M33550cds_s_at       7,758      3.590      0.093
rc_AA945573_f_at    16,174      0.317      0.009
M31031mRN_f_at      15,025      0.212      0.001
M14775_s_at         17,548      0.043      0.000
AB008424_s_at       15,558      0.376      0.003
U46118_at            1,234      0.239      0.039
M29853_at            2,149     13.830      0.128
D00680_at              539      9.210      0.039
L38615_g_at          1,169      2.440      0.085
rc_AA945082_at       1,049     13.460      0.019
S72506_s_at          2,022     43.900      0.004
S82820mRNA_s_at      7,696     19.750      0.000
X95189_at            2,963      0.249      0.093
AB010428_s_at        4,242     43.870      0.011
Y09333_g_at          5,696     14.130      0.024
Y09333_at            5,411      8.370      0.062
D43623_g_at            879     18.040      0.053
M26125_at           18,841      4.800      0.023
rc_AA893242_g_at     2,968      0.306      0.012
M29249cds_at           667      3.590      0.047
X55286_g_at            235      2.440      0.010
J02585_at            3,208      0.135      0.017
AB010429_s_at        1,772      2.700      0.015
L07114_at              287      2.480      0.022
AF072411_at          1,077      3.240      0.005
rc_AA925752_at       1,446      3.140      0.003
AB005743_g_at          307      3.060      0.005
AF072411_g_at        1,979      2.680      0.004
AB005743_at            289      2.400      0.003
K01180_at              321      6.730      0.100
U02096_at            3,443      0.172      0.005
L34049_g_at            977      0.330      0.001
U89280_at            6,406      0.249      0.020
AF045464_s_at        9,840      4.810      0.016
D38061exon_s_at      1,026     30.420      0.003
S56936_s_at          1,166      5.560      0.022
D38062exon_s_at        507      2.860      0.093
J02589mRNA#2_at      1,299      0.256      0.099
rc_Al169708_s_at    21,663      0.002      0.000
rc_Al180442_at       1,378      2.630      0.008
M95591_g_at          1,040      5.280      0.040
M95591_at            2,011      3.650      0.004
M89945mRNA_g_at      3,421      2.380      0.082
M89945mRNA_at        4,531      2.370      0.010
M81225_at            1,063      2.360      0.013
U33500_at            1,256      0.315      0.008
U33500_g_at          1,856      0.173      0.006
M19257_at            3,941      0.327      0.001
D37920_at              843      1.650      0.112
U30186_at            1,670     13.310      0.000
L32591mRNA_at        1,015      5.200      0.062
L32591mRNA_g_at      1,154      3.550      0.109
rc_Al070295_g_at       829      1.260      0.642
rc_Al175959_at       1,172      3.130      0.210
Y00396mRNA_at          643      4.640      0.061
Y00396mRNA_g_at        988      4.300      0.094
J02722cds_at           464      1.460      0.233
M25157mRNA_i_at      4,406      0.606      0.180
S76511_s_at            586      3.630      0.153
M60921_g_at            551     12.960      0.143
rc_AA944156_s_at     1,934      2.990      0.053
U49729_at              285      1.010      0.144
M33329_f_at          6,794      0.500      0.241
X63410cds_f_at       9,853      0.364      0.006
S76489_s_at         10,020      0.259      0.034
D14989_f_at          5,378      0.136      0.012
rc_Al169695_f_at     5,636      0.100      0.004
D14988_f_at         13,597      0.206      0.000
D14987_f_at         10,861      0.176      0.001
rc_AA817987_f_at     8,115      0.150      0.002
M31363mRNA_f_at     14,611      0.126      0.001
rc_AA818122_f_at    12,159      0.089      0.052
rc_Al028836_at         602      0.086      0.193
L22339_at            2,105      0.212      0.001
L22339_g_at         15,648      0.398      0.012
AB010467_s_at          985      6.620      0.032
D86086_s_at          4,083      2.860      0.053
M81855_at            5,082    117.530      0.000
M77479_at            6,110      0.198      0.010
rc_Al235631_at         795      4.700      0.005
rc_Al172452_at       1,603      2.390      0.001
rc_AA866240_f_at     8,131      0.164      0.000

                           Sch
                       (individual)

Affymetrix           Fold      t-test        BI       Nov,
probe set ID (a)    change    (p-value)    (pool)    (pool)

X75207_s_at          3.640      0.024       3.490      5.980
D14014_g_at          6.090      0.082       3.570     12.830
D14014_at            2.780      0.103       4.310      3.370
X70871_at            3.530      0.038       5.840      7.480
E01184cds_s_at       0.379      0.003       0.293      0.400
M21208mRNA_s_at      4.010      0.001       3.560      3.120
K03241cds_s_at       0.317      0.004       0.275      0.437
J04187_at            0.450      0.001       0.307      0.405
J02657_s_at          0.162      0.001       0.026      0.051
M18363cds_s_at       0.048      0.000       0.017      0.019
X79081mRNA_f_at      0.008      0.011       0.016      0.009
J03786_s_at          2.610      0.000       4.030      2.880
M33550cds_s_at       2.510      0.000       3.640      3.760
rc_AA945573_f_at     0.704      0.061       0.154      0.426
M31031mRN_f_at       0.578      0.100       0.249      0.395
M14775_s_at          0.448      0.108       0.210      0.275
AB008424_s_at        0.763      0.036       0.276      0.439
U46118_at            0.253      0.019       0.143      0.159
M29853_at            9.990      0.042      13.160     14.880
D00680_at            9.050      0.044       3.290      5.170
L38615_g_at         26.890      0.023       7.180      2.080
rc_AA945082_at      12.420      0.010      12.120     26.630
S72506_s_at         39.430      0.001      24.860     71.750
S82820mRNA_s_at     16.650      0.000      38.580     27.070
X95189_at            0.476      0.152       0.269      0.323
AB010428_s_at       94.300      0.002      23.730    105.300
Y09333_g_at         12.750      0.000      18.920     25.520
Y09333_at            4.810      0.004       5.620      8.580
D43623_g_at         33.410      0.004      18.160     11.040
M26125_at            1.400      0.009       3.170      2.340
rc_AA893242_g_at     0.412      0.017       0.431      0.667
M29249cds_at         2.020      0.033       9.100     20.360
X55286_g_at          3.650      0.009       3.500     10.540
J02585_at            0.130      0.008       0.126      0.087
AB010429_s_at        4.350      0.026       9.470      7.930
L07114_at            6.740      0.035       1.000      2.430
AF072411_at          2.660      0.030       3.400      4.650
rc_AA925752_at       2.520      0.035       3.060      4.700
AB005743_g_at        5.140      0.022       1.000     15.340
AF072411_g_at        3.120      0.017       3.700      4.560
AB005743_at          2.250      0.109       2.270     10.080
K01180_at            4.400      0.047      13.200     15.150
U02096_at            0.267      0.004       0.190      0.175
L34049_g_at          0.420      0.027       0.503      0.488
U89280_at            0.385      0.025       0.424      0.433
AF045464_s_at        4.000      0.000       8.260      4.840
D38061exon_s_at     37.320      0.012      24.130     42.050
S56936_s_at          7.810      0.005      10.630     13.400
D38062exon_s_at     13.190      0.049      11.030     25,340
J02589mRNA#2_at      0.015      0.011       0.157      0.258
rc_Al169708_s_at     0.033      0.000       0.578      0.746
rc_Al180442_at       3.220      0.013       2.560      3.100
M95591_g_at          2.770      0.033       1.520      3.660
M95591_at           26.720      0.021      14.210      4.820
M89945mRNA_g_at      2.710      0.004       3.250      3.380
M89945mRNA_at        2.830      0.002       4.030      2.980
M81225_at            2.380      0.019       2.340      2.240
U33500_at            0.510      0.005       0.437      0.420
U33500_g_at          0.228      0.013       0.190      0.193
M19257_at            0.420      0.000       0.352      0.353
D37920_at           31.010      0.007      20.470      7.760
U30186_at           11.470      0.026      17.950      7.680
L32591mRNA_at        8.590      0.027       2.800      4.370
L32591mRNA_g_at      3.230      0.033       2.600      5.050
rc_Al070295_g_at     4.160      0.024       2.270      7.050
rc_Al175959_at       2.840      0.154       3.170      2.830
Y00396mRNA_at       28.430      0.011      10.070     32.140
Y00396mRNA_g_at      6.090      0.029       3.600      8.890
J02722cds_at         2.570      0.056       2.210      7.580
M25157mRNA_i_at      0.450      0.042       0.265      0.315
S76511_s_at          8.910      0.040      13.530      4.800
M60921_g_at         15.090      0.056       7.420     22.640
rc_AA944156_s_at     1.570      0.046       3.950      2.730
U49729_at           14.080      0.010       6.840     14.270
M33329_f_at          0.571      0.048       0.298      0.280
X63410cds_f_at       0.610      0.034       0.254      0.324
S76489_s_at          0.552      0.001       0.270      0.439
D14989_f_at          0.327      0.003       0.194      0.254
rc_Al169695_f_at     0.293      0.001       0.146      0.236
D14988_f_at          0.405      0.005       0.189      0.244
D14987_f_at          0.562      0.054       0.118      0.227
rc_AA817987_f_at     0.340      0.008       0.077      0.166
M31363mRNA_f_at      0.236      0.000       0.101      0.086
rc_AA818122_f_at     0.380      0.007       0.065      0.129
rc_Al028836_at       0.041      0.007       0.108      0.104
L22339_at            0.181      0.000       0.155      0.155
L22339_g_at          0.424      0.001       0.201      0.229
AB010467_s_at        7.220      0.004       3.460      7.960
D86086_s_at          2.520      0.014       2.740      2.770
M81855_at           77.730      0.023      77.520    254.090
M77479_at            0.472      0.022       0.394      0.529
rc_Al235631_at       4.650      0.014       1.890      3.710
rc_Al172452_at       2.010      0.001       1.360      1.930
rc_AA866240_f_at     0.524      0.009       0.181      0.329

Affymetrix           Pfi,        RO,       Sch,      Direction
probe set ID (a)    (pool)     (pool)     (pool)     of change

X75207_s_at           3.100      4.770      3.780      Up
D14014_g_at           6.860     14.980      4.750      Up
D14014_at             2.010      4.660      4.370      Up
X70871_at             6.470      5.940      5.150      Up
E01184cds_s_at        0.265      0.444      0.266      Down
M21208mRNA_s_at       2.890      3.590      3.060      Up
K03241cds_s_at        0.171      0.493      0.291      Down
J04187_at             0.392      0.319      0.319      Down
J02657_s_at           0.007      0.038      0.024      Down
M18363cds_s_at        0.013      0.015      0.020      Down
X79081mRNA_f_at       0.013      0.055      0.011      Down
J03786_s_at           6.240      6.510      5.890      Up
M33550cds_s_at        5.900      3.090      6.390      Up
rc_AA945573_f_at      0.253      0.427      0.342      Down
M31031mRN_f_at        0.265      0.361      0.337      Down
M14775_s_at           0.129      0.121      0.152      Down
AB008424_s_at         0.204      0.260      0.350      Down
U46118_at             0.175      0.266      0.181      Down
M29853_at            18.980     28.490      8.740      Up
D00680_at             3.300     19.890      9.760      Up
L38615_g_at           3.450      6.280      2.430      Up
rc_AA945082_at        5.610     19.470     20.090      Up
S72506_s_at          39.040     67.230     67.540      Up
S82820mRNA_s_at      22.930     33.060     28.740      Up
X95189_at             0.120      0.407      0.452      Down
AB010428_s_at        86.490     91.700     56.760      Up
Y09333_g_at          10.810    190.190     16.820      Up
Y09333_at             7.210     15.250      8.570      Up
D43623_g_at           9.780      9.040     10.300      Up
M26125_at             3.980      5.450      3.300      Up
rc_AA893242_g_at      0.307      0.424      0.422      Down
M29249cds_at          1.000      7.110      5.930      Up
X55286_g_at           1.810      3.130      4.630      Up
J02585_at             0.172      0.135      0.116      Down
AB010429_s_at         1.120      6.790     11.520      Up
L07114_at             5.130      3.360      5.550      Up
AF072411_at           1.910      8.820      3.160      Up
rc_AA925752_at        2.090      5.500      3.040      Up
AB005743_g_at         1.000      1.880      5.790      Up
AF072411_g_at         2.190      7.150      4.180      Up
AB005743_at           4.140      4.240      2.920      Up
K01180_at             5.800      5.200      4.220      Up
U02096_at             0.163      0.108      0.175      Down
L34049_g_at           0.217      0.500      0.405      Down
U89280_at             0.345      0.391      0.348      Down
AF045464_s_at         9.310     10.710      9.340      Up
D38061exon_s_at      10.100     39.590     34.930      Up
S56936_s_at           3.950     13.050      9.200      Up
D38062exon_s_at      11.000      6.520     16.140      Up
J02589mRNA#2_at       0.115      0.272      0.146      Down
rc_Al169708_s_at      0.543      0.599      0.526      Down
rc_Al180442_at        4.690      3.280      3.380      Up
M95591_g_at           0.613     20.130      1.280      Up
M95591_at            10.490      9.020     26.960      Up
M89945mRNA_g_at       2.860      3.350      2.770      Up
M89945mRNA_at         3.820      4.540      2.710      Up
M81225_at             1.930      3.430      2.100      Up
U33500_at             0.324      0.402      0.495      Down
U33500_g_at           0.127      0.141      0.269      Down
M19257_at             0.394      0.331      0.382      Down
D37920_at            13.250     42.140      3.240      Up
U30186_at            32.030     10.800     14.340      Up
L32591mRNA_at        21.570      8.090     14.310      Up
L32591mRNA_g_at       3.150      2.820      7.010      Up
rc_Al070295_g_at      2.350      8.010      4.250      Up
rc_Al175959_at        2.060      4.260      6.690      Up
Y00396mRNA_at         5.320     16.280      9.300      Up
Y00396mRNA_g_at       2.610      8.420      9.070      Up
J02722cds_at          4.240      6.270      3.070      Up
M25157mRNA_i_at       0.110      0.455      0.247      Down
S76511_s_at          11.590      7.210      5.550      Up
M60921_g_at           5.250     22.590     11.820      Up
rc_AA944156_s_at      3.890      2.120      2.670      Up
U49729_at             6.630      2.700     12.100      Up
M33329_f_at           0.225      0.375      0.377      Down
X63410cds_f_at        0.253      0.233      0.431      Down
S76489_s_at           0.277      0.353      0.408      Down
D14989_f_at           0.142      0.121      0.256      Down
rc_Al169695_f_at      0.116      0.139      0.161      Down
D14988_f_at           0.139      0.178      0.187      Down
D14987_f_at           0.114      0.199      0.148      Down
rc_AA817987_f_at      0.124      0.126      0.139      Down
M31363mRNA_f_at       0.079      0.112      0.136      Down
rc_AA818122_f_at      0.083      0.165      0.127      Down
rc_Al028836_at        0.211      0.258      0.088      Down
L22339_at             0.156      0.076      0.190      Down
L22339_g_at           0.230      0.282      0.307      Down
AB010467_s_at         8.370     21.110     11.270      Up
D86086_s_at           2.570      3.870      2.990      Up
M81855_at           121.190    150.700    226.980      Up
M77479_at             0.269      0.322      0.515      Down
rc_Al235631_at        2.380      2.730      2.060      Up
rc_Al172452_at        1.800      2.530      2.010      Up
rc_AA866240_f_at      0.253      0.383      0.417      Down

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This article is part of the mini-monograph "Application of Genomics to Mechanism-Based Risk Assessment."

Address correspondence to J.F. Waring, 100 Abbott Park Rd., Department R463, Abbott Park, IL 60064-6123 USA. Telephone: [phone]. Fax: [phone]. E-mail: [e-mail]

We thank the International Life Sciences Institute for providing the framework that allowed the performance of this collaboration within the Health and Environmental Sciences Institute Hepatotoxicity Working Group. We also thank the pharmaceutical companies (Abbott Laboratories, Boehringer-Ingelheim Pharmaceuticals, Novartis Pharma AG, Pfizer Inc, F. Hoffman-La Roche AG, and Schering AG) that provided the data included in this article for coveting the costs of the experiments. Our special thanks go to G. Gibson for the critical reading of the manuscript and his constructive comments.

Jeffrey F. Waring, (1) Roger G. Ulrich, (1) Nick Flint, (2) David Morfitt, (1) Arno Kalkuhl, (3) Frank Staedtler, (4) Michael Lawton, (5) Johanna M. Beekman, (6) and Laura Suter (2)

(1) Abbott Laboratories, Abbott Park, Illinois, USA (2) F. Hoffmann-La Roche Ltd., Basel, Switzerland; (3) Boehringer-Ingelheim Pharmaceuticals, Ridgefield, Connecticut, USA; (4) Novartis Pharma AG, Basel, Switzerland; (5) Pfizer Inc, Groton, Connecticut, USA; (6) Schering AG, Berlin, Germany

The authors declare they have no competing financial interests.

Received 6 August 2003; accepted 6 January 2004.