Development of a DNA microarray for toxicology based on hepatotoxin-regulated sequences. (Article).

Toxicogenomics is an emerging field combining genomics and bioinformatics to identify and characterize mechanisms of toxicity of compounds. One of the main tools used in toxicogenomics is DNA microarrays. We have used a novel strategy to create a library highly enriched for genes expressed in the liver under hepatotoxic conditions. Using this library, we have created a new oligonucleotide microarray dedicated to the study of rat liver function. Oligonucleotide probes for these genes were designed and used in experimental hybridization studies to deduce the correct sequence orientation and to determine those sequences exhibiting differential regulation under a variety of toxicity-related treatments and conditions. The final array was benchmarked on treatments with 3-methylcholanthrene, Aroclor 1254, and cyclopropane carboxytic acid. Our results showed that the subtractive hybridization greatly enriched for genes regulated during a hepatotoxic response. Overall, our strategy for design of a new rat toxicology microarray can be applied to other systems as well and should aid greatly in the development of microarrays targeted for specific scientific areas. Key words: Aroclor, cyclopropane carboxylic acid, hybridization kinetics, microarray, toxicogenomics, toxicology, 3-methylcholanthrene. Environ Health Perspect 111:863-870 (2003). doi:10.1289/txg. 5998 available via http://dx.doi.org/[Online 18 November 2002]

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High-density microarrays offer a powerful approach to detecting changes in gene expression. Such analysis allows one to identify transcriptional changes in thousands of genes simultaneously. The application of microarrays as well as bioinformatics toward toxicology has resulted in the creation of a new science termed toxicogenomics. The field of toxicogenomics uses gene expression changes to identify mechanisms of toxicity and to predict potential toxicity on the basis of similarities of expression profiles to known toxicants. Several recent publications have described the use of microarray analysis to identify discrete gene changes associated with a toxic response (Gerhold et al. 1999, Holden et al. 2000, Reilly et al. 2001). In addition, our research, as well as the research of others, has shown that compounds associated with a particular mechanism of toxicity yield similar gene expression profiles (Bulera et al. 2001; Hamadeh et al. 2002a, 2002b; Thomas et al. 2001; Waring et al. 2001a, 2001b). Furthermore, these gene expression profiles can be used to build a predictive database encapsulating biological responses to toxic insult. To date, the field of toxicogenomics has largely concentrated on hepatotoxicity, since the liver is the primary target of most toxic responses and liver injury is the principle reason for new drugs being withdrawn from the market.

The question of which genes to analyze in order to build an expression database for hepatotoxicity is of paramount importance. There are commercially available microarrays specifically designed for use in toxicology, yet the gene sequences on these microarrays are derived mainly from DNA libraries from normal or disease tissues and not from tissues that have been challenged with xenobiotics. Ideally, one would like to analyze all genes that have the potential to be transcriptionally regulated during a hepatotoxic response. Although there have been some genes identified that are linked with hepatotoxicity, the identification of all genes regulated by the liver in response to toxic insult has yet to be resolved. This issue becomes more complex in the rat, which is the model often used in traditional toxicology because much of the rat genome remains unsequenced to date. To address this issue, we have constructed a microarray containing sequences from a rat library highly enriched for genes regulated by toxic compounds in the liver. We demonstrate the utility of this microarray by testing it against three hepatotoxins: 3-methylcholanthrene (3-MC), Aroclor 1254, and cyclopropane carboxylic acid (CPCA).

Materials and Methods

Construction of subtractive hybridization library. All rat liver RNA samples used in sequence screening experiments were prepared at Abbott Laboratories from 3-day toxicity studies. Rats were treated with 52 different compounds previously associated with hepatotoxicity (Table 1). Male Sprague-Dawley rats (Charles River Laboratories, Portage, MI, USA) approximately 6-12 weeks of age and weighing 225-275 g were treated with vehicle (corn oil or saline) or with compound. There were three rats per treatment group. The dosing methods used were oral, interperitoneal, or intravenous, depending on the preferred method of delivery for the compound. Two dose levels were chosen for each compound. Rats were dosed daily for 3 days and sacrificed on day 4. Blood samples drawn from the animals at necropsy were used to measure serum concentrations or activities of blood urea nitrogen, creatinine, alanine amino transferase, aspartate aminotransferase, gamma glutamyltransferase, alkaline phosphatase (Abbott Laboratories Diagnostic Division), cholesterol, triglycerides, bilirubin, glucose, total protein, albumin, and globulins using an Abbott Aeroset clinical chemistry analyzer (Abbott Laboratories Diagnostic Division, Abbott Park, IL, USA). The terminal body weights and liver weights were recorded. The left lateral lobe of the liver was processed for histopathologic evaluation. Approximately 100 mg from each liver was placed into TRIzola reagent (Invitrogen Life Technologies, Carlsbad, CA, USA) and immediately homogenized using a Turrax tissue grinder (Diagger, Vernon Hills, IL, USA). The remaining portion of the liver was retained frozen for future study.

Subtractive hybridization was performed using the method of suppressive subtractive hybridization (Diatchenko et al. 1996). A pool of liver RNA containing 10 mg from all rats treated with hepatotoxin was made and subtracted against a pool of liver RNA containing 150 mg from three naive rats. Subtraction was performed in the reverse direction as well. Subtraction efficiency was measured by quantitating the levels of D-glyceraldehyde-3-phosphate dehydrogenase (GAPDH) before and after subtraction.

The two libraries were cloned into the vector pCR2.1 (Invitrogen). The libraries were sequenced by Incyte Genomics (Palo Alto, CA, USA). A total of 16,128 reads were sequenced, corresponding to 8,064 attempted reads for each library. Each library was sequenced with M13 reverse ET-terminator in one direction with a sequencing efficiency of 92%. The average length read was 350 bp.

Sequence similarity searching. Individual sequence reads from the subtractive hybridization libraries were compared to the National Center for Biotechnology Information nr and nt collections (Altschul et al. 1994) using BLAST 2 (Altschul et al. 1997). Hits with E < 1e-8 were considered significant. Contiguous sequences (contigs) were compared to a comprehensive collection of 364,170 nucleotide sequences for Rattus norvegicus taken from GenBank (http://www.ncbi.nlm.nih.gov), dbEST (database of expressed sequence tags), and RefSeq, also using BLAST 2. The threshold for considering an alignment significant required that it be at least 100 bp in length, have at least 95% identity, and cover at least 20% of the length of the shorter of query and subject sequences.

Oligonucleotide probe design. A nonredundant set of rat gene transcripts was determined by clustering and assembly of cDNA sequences from the subtractive hybridization libraries and publicly available sequences. Sequences were assigned into clusters and assembled into contigs using a derivative of the Smith-Waterman algorithm (Smith and Waterman 1981). From this, 60-mer oligonucleotides were selected for the microarray. The process of probe selection is described in "Results."

Expression profiling experiments. Rats were treated with Aroclor 1254 (Monsanto, St. Louis, MO, USA), 3-MC (Sigma Chemicals, St. Louis, MO, USA), or CPCA (Sigma) at levels previously shown to be hepatotoxic but not lethal. Male Sprague-Dawley rats approximately 6-12 weeks of age and weighing 225-275 g were injected intraperitoneally with vehicle (corn oil or saline) or with Aroclor 1254 (400 mg/kg/ day) (Wolfgang et al. 1990), 3-MC (100 mg/kg/day) (Kleeberg et al. 1975), or CPCA (500 mg/kg/day) (Ulrich et al. 1998) for 3 days, and rats were sacrificed on day 4. Preparation of RNA was as described above. Preparation of labeled cDNA from the RNA, hybridization and scanning of the microarray were performed at Rosetta Inpharmatics (Kirkland, WA, USA) as described previously (Marton et al. 1998). For microarray analysis, tRNA from individual treated animals was hybridized against RNA from a pool of three vehicle-treated animals. Data analysis was performed using the Rosetta Resolver system (Rosetta). The fold change and p-value calculations have been previously described (Hughes et al. 2000).

Results

Subtractive hybridization. All rat liver RNA samples used in sequence screening experiments were prepared at Abbott Laboratories from 3-day toxicity studies. Rats were treated with 52 different compounds that have been previously associated with hepatotoxicity (Table 1). The selected compounds induced a hepatotoxic response by a variety of observed mechanisms, including necrosis, steatosis, DNA damage, cirrhosis, hypertrophy, phospholipidosis, and hepatic carcinoma. A 3-day time point was chosen to accommodate induction of liver genes without complication of a significant secondary inflammatory response (cellular infiltration) or fibrosis likely to be encountered over longer periods. Also, an earlier time point would likely have included immediate early response genes.

Using suppressive subtractive hybridization (Diatchenko et al. 1996), we performed a subtractive hybridization in both directions using pooled RNA from all hepatotoxin-treated rats against liver RNA from naive rats to enrich for genes regulated during a hepatotoxic response. By performing the subtractive hybridization in both directions, genes that are both upregulated (higher in treated animals) and downregulated (higher in naive animals) should be enriched. We used polymerase chain reaction to analyze the efficiency of the subtractive hybridization by measuring the levels of GAPDH before and after subtraction. The results indicated that we obtained between 10- and 15-fold subtraction efficiency (unpublished data). Sequences were obtained for 14,523 clones. Of these sequences, 16% had no significant similarity to known public domain sequences from any organism.

Oligonucleotide probe design. The first step in microarray design was to select a nonredundant set of rat gene transcripts by clustering and assembly of cDNA sequences. The input to clustering included 14,523 sequences from subtractive hybridization of hepatotoxin-treated rats (described above) and 92,319 public domain sequences. Sequences were assigned into clusters and assembled into contigs on the basis of sequence similarities identified by a derivative of the Smith-Waterman algorithm (Smith and Waterman 1981). This process resulted in 43,954 clusters and 54,437 contigs. For purposes of probe design, each duster was represented by its longest contig sequence. Some genes of interest, notably those encoding cytochrome P450 enzymes (CYP genes), are difficult to cluster accurately with automated methods because of high levels of homology between distinct family members. CYP sequences were curated and clustered manually on the basis of their annotation and sequence similarity. Sequences were prioritized and ordered in terms of their biological interest and quality of annotation.

The 14,523 sequences from the subtractive hybridization libraries fell into 2,728 clusters, suggesting that they represent approximately this number of genes. Comparison of representative contig sequences for these clusters against public domain rat sequences showed no significant hits for 555 sequences (20%). This suggests that around 20% of the genes represented in the subtractive hybridization libraries have not previously been described in the rat.

A single 60-mer oligonucleotide probe was selected to represent each input sequence. All possible probes within 900 bp of the 3' end of each sequence were considered candidates for selection. Probes closer to the 3' end were favored because the polyA-dependent sample preparation results in higher hybridization intensities closer to the 3' end of each transcript. Up to 12 probes (6 from each strand) were selected for each of 1,342 sequences for purposes of sequence orientation and empirical selection of the best probes. Of these sequences, 1,227 were of biological interest (e.g., because they arose from the subtractive hybridization libraries) but were of unknown orientation. A further 115 sequences of known orientation were included to provide training data for the orientation algorithms. For the selection of multiple probes, all probes within 900 bp of both ends were considered. Candidate probes were passed through a series of filters. First, probes containing repeat elements, vector contamination, or low-complexity sequences were eliminated. The best probes were then selected on the basis of a combination of sequence complexity, base composition, predicted thermodynamic properties, predicted cross-hybridization, and position within the transcript (Hughes et al. 2001). In total, 49,554 probes were selected and printed on microarrays. These microarrays were used in screening experiments to determine which probes should be included on the final 25,000-probe microarray.

Empirical probe evaluation. Empirical selection of probes on the basis of hybridization data is useful for refining microarray design in a number of ways. It enables the identification of genes of interest for a specific area of biological study, and it can lead to improved annotation of those genes (e.g., by revealing their orientation). It can also provide enhanced selection of optimal probes from a short list designed by computational methods, for example, by identifying probes subject to cross-hybridization. These advantages are particularly relevant when, as in this case, a microarray needs to be focused on a particular area of biology and when many input sequences are incomplete and poorly annotated.

The microarrays designed as described above were hybridized with samples relevant to hepatotoxicity. A total of 52 drugs and environmental toxins with cited liver toxicities were assembled and used in dedicated rat studies (Table 1). In these studies, RNA samples derived from compound treatments were pooled into nine groups, each comprising three to seven compound-treated rats. In total, 22 competitive hybridization experiments with fluor reversal were performed: naive liver versus each sample pool for compound-treated liver (including differing hybridization times) and naive liver versus isolated hepatocytes. To aid subsequent analysis of kinetic differences in hybridization intensity, one sample pool was also hybridized at two time points: 4 hr and 48 hr.

An important aspect of empirical probe selection was to orient sequences for which direction could not be determined reliably by computation alone. The direction of the sequences was thus inferred experimentally by consideration of a combination of three factors: hybridization intensity, differential expression, and kinetics of hybridization (48-hr compared with 4-hr intensity). These criteria were used because probes detecting the transcribed strand should a) give brighter signal intensity, b) be more likely to show differential regulation between the labeled samples, and c) show a brighter hybridization signal at 48 hr compared with 4 hr (Dai et al. 2002). A total of 1,227 unoriented sequences were screened in this way by testing 12 oligonucleotide probes, 6 in the forward direction and 6 in the reverse (relative to the input sequence). A further 115 training set gene sequences with known orientation were also tested to assess accuracy and train parameters.

Regarding hybridization kinetics for determining the orientation of the sequences, the significance of specific hybridization for each probe was calculated by comparing the intensity differences from two time points (4 hr and 48 hr) to their errors. For each sequence, six probes from each direction were grouped together, and the direction was predicted based on significance of forward versus reverse direction hybridization (Figure 1). Using parameters optimized for the training set, orientation was predicted correctly for 74 of 115 (64%) of training sequences, with 2 incorrect (2.7% of predictions). The orientations of 67% of the unoriented sequences were predicted using the same parameters.

[FIGURE 1 OMITTED]

Motivation for using differential regulation to determine sequence orientation is dictated by two considerations. First, differential regulation is evidence of sequence-specific hybridization since cross-hybridization of DNA sequences to oligonucleotide probes in most cases tends to suppress differential regulation. Second, significant regulation leads to the selection of oligonucleotide probes with intensity above background levels. In this study, six probes in each direction were averaged together to get an estimation of the significance of regulation over 10 experiments. The correct orientation was determined by the strand that gave lower p-values for differential regulation. Using parameters optimized for the training set, orientation was predicted correctly for 53 of 115 (46%) of training sequences, with 4 incorrect (7.5% of predictions). The orientations of 44% of the unoriented sequences were predicted using the same parameters.

Regarding intensity, it is clear that probes in the correct orientation should exhibit higher intensity for those sequences expressed. In this study the average intensity of forward strand probes for each sequence was compared with that of the reverse strand probes. The parameters for predicting the correct direction were determined again by optimizing for the training set sequences. The resulting parameters predicted orientation correctly for 62 of 115 (53%) training sequences, with 1 incorrect (1.6% of predictions). They yielded orientations for 76% of the unoriented sequences.

The error rates for all three types of evidence were reduced by applying filters on the basis of the observed trend in intensity along the length of a sequence. The polyT-primed RNA amplification protocol used in these experiments generally results in higher intensities closer to the 3' end of a transcript. A correctly oriented sequence should show a trend of increasing intensity toward the 3' end. A small subset of sequences for which predictions were initially made did not fit the expected pattern, and their predictions were discarded. The majority of these predictions were from the intensity-based analysis alone.

A final step in empirical orientation of sequences combined the predictions from the above analyses. Kinetics predictions, when available, took priority. Orientation for the remaining sequences was predicted by intensity or regulation, except that contradictory predictions from these two sources were discarded. With these criteria, 90 of 115 (78%) training sequences were correctly predicted, with just 3 sequences incorrect (3.3% of predictions). This translated into confident prediction for 78.5% of unoriented sequences.

The use of a large-format screening array made it possible to use 12 probes per sequence of unknown orientation. Using fewer probes would have yielded many of the same orientations, but any reduction would affect sensitivity. Simulations suggest that reducing the number of probes per sequence to 4 would have resulted in 50-90% of the sensitivity achieved with 12.

Selection of oligonucleotides for 25K array. The second data analysis step was to select approximately 25,000 oligonucleotides of those screened to fit the final 25K rat liver microarray. All genes on the 25K microarray were represented by a single probe. Single oligonucleotide probes on inkjet arrays have been shown to reliably report expression ratios in yeast and mammalian cells (Hughes et al. 2001).

Probes for genes of special interest were set aside for inclusion without any further preselection or analysis. In particular, 60 sequences representing cytochrome P450 genes and 2,475 sequences derived from subtracted hybridization libraries were automatically included in the selection for the 25K microarray. An additional 1,142 sequences were selected on the basis that they have known human orthologs (Makalowski et al. 1998). These are particularly useful in comparing and translating gene expression patterns between organisms. The remaining probes were prioritized in terms of their differential regulation compared with vehicle, by hybridization kinetics (as an indication of specific hybridization), and last, by their brightness ranking. These criteria produced mainly overlapping sets of probes. Application of a significance threshold in differential regulation (p < 0.2 in any experiments) produced 17,508 probes for potential inclusion on the final array. Kinetic comparisons, which help separate specific and nonspecific hybridization, resulted in 11,746 candidate probes. Combining these two criteria and eliminating probes already selected for inclusion enabled us to select 14,216 probes. Figure 2 summarizes the distribution of intensities for all candidate probes with known orientation and shows the average intensity distributions for the applied criteria.

[FIGURE 2 OMITTED]

Final specifications for microarray. Experimental screening of the approximate 50K probes provided valuable data to help prioritize sequences for representation on a 25K probe microarray. The majority of oligonucleotide probes (~14,000) selected for the microarray were derived from combination of significant differential regulation (p < 0.2 in any experiment) and favorable hybridization kinetics (lower probabilities of cross-hybridization) and biological interest. A smaller proportion of probes (~6,000) exhibiting lower (but significant) signal intensity in screening experiments were also included. The final breakdown for probe selection is shown in Table 2.

Transcriptional regulation by hepatotoxins. To determine the utility of the microarray toxicology, we examined gene changes in the liver induced by two aromatic hydrocarbons, Aroclor 1254 and 3-MC, and a short-chain fatty acid, CPCA. All these agents are well-described hepatotoxins, and previous research has identified some genes that are regulated by Aroclor 1254 and 3-MC (Borlak and Thum 2001; Jauregui et al. 1991). We wanted to determine if the subtractive hybridization enriched for genes that are regulated during a hepatotoxic response. Both Aroclor 1254 and 3-MC were used in the subtractive hybridization experiment, whereas CPCA was not. Rats were treated with Aroclor 1254 at 400 mg/kg, 3-MC at 100 mg/kg, or CPCA at 500 mg/kg for 3 days (Table 1). Significant gene regulation was defined as fold change > 1.5-fold over control with p [less than or equal to] 0.01.

Figure 3 shows a K-means clustering of genes regulated by treatment with CPCA, Aroclor 1254, 3-MC, or vehicle. When expression profiles from individual animals treated with vehicle control were compared with a pool, the results show that relatively few genes were significantly regulated (Figure 3). In contrast, treatment with the three hepatotoxins resulted in numerous gene changes that were highly reproducible across the three replicate animals for each different treatment. Rats treated with Aroclor 1254 and 3-MC yield similar patterns of gene expression; the two compounds significantly regulated 786 genes in common. Many of these genes have previously been shown to be regulated by aromatic hydrocarbons, including cytochromes P450 1A1, and 1B1, malic enzyme, glutathione-S-transferase, and UDP-glucuronosyltransferase 1 (Figure 3, group 1) (Borlak and Thum 2001; Jauregui et al. 1991; Lubet et al. 1991; Mehlman et al. 1975; Saarikoski et al. 1998). Other genes regulated by 3-MC and Aroclor 1254 were Erp29, liver tricarboxylate carrier, and cholesterol hydroxylase 7 [alpha] (Table 3).

[FIGURE 3 OMITTED]

CPCA is an inhibitor of [beta]-oxidation and has been shown to result in micro-vesicular steatosis (Ulrich et al. 1998). Therefore, it would be expected to result in regulation of mitochondrial and peroxisomal genes involved in [beta]-oxidation. Table 3 shows that genes involved in mitochondrial or peroxisomal [beta]-oxidation such as acyl-coenzyme A (acyl-CoA) dehydrogenase and enoyl-CoA hydratase were inhibited by treatment with CPCA. Treatment with CPCA also resulted in regulation of a number of genes in the cholesterol and fatty acid pathways, including an upregulation of adipocyte lipid binding protein, squalene epoxidase, 7-dehydrocholesterol reductase, and downregulation of cholesterol hydroxylase 7 [alpha], lecithin-cholesterol acyltransferase, fatty acid desaturase, fatty acid amide hydrolase, and fatty acid binding proteins 1 and 7 (Table 3).

Table 4 summarizes the gene regulation for the three compounds. The results show that the sequences from the subtractive hybridization contributed significantly to the expression profiles for the three compounds. While these sequences represented approximately 9% of the sequences on the chip, they represented approximately 17% of the signature profile for all three compounds. This is true for all three compounds despite the fact that CPCA was not included in the set of compounds used in the subtractive hybridization. This suggests that the generation of the subtractive hybridization library enriched for many genes involved in responses to a wide range of hepatotoxic compounds, not only those treatments that were used. In addition, roughly 5% of the regulated sequences were identified in the subtractive hybridization and have not been previously described.

Discussion

We have constructed a new rat toxicology microarray using a strategy to specifically represent genes that are regulated during a hepatotoxic response. The strategy we employed in constructing this array provided several advantages over other commercially available toxicology arrays. First, we attempted to represent every gene that can be regulated during a hepatotoxic response. Toward this end, we dosed rats with 52 known hepatotoxins using a combination of drugs and chemicals that have been associated with different mechanisms of hepatotoxicity including DNA damage, oxidative stress, peroxisomal proliferation, necrosis, cirrhosis, steatosis, and protein synthesis inhibitors. We sequenced approximately 16,000 clones, which collapsed down to roughly 2,700 contigs. Although oligonucleotides representing the subtractive hybridization clones made up only 9% of the final 25K array, these sequences represented approximately 17% of the signature profile for three different hepatotoxins. In addition, many of the sequences from the subtractive hybridization had not been described previously. Some of these sequences were regulated by the hepatotoxins tested and may be critical genes involved in the mechanism of toxicity. Further evidence of this is provided by experiments performed with two compounds considered to be nonhepatotoxic, spectinomycin and penicillin. Microarray results showed that these sequences were regulated by the nonhepatotoxins to a much lesser extent than by the three hepatotoxic compounds, suggesting that these sequences are relatively specific for a hepatotoxic reaction (unpublished data).

The strategy we employed for the subtractive hybridization does have inherent limitations. Because the RNA from all the treatment groups was pooled, it is possible a gene that may have been up- or downregulated by only one hepatotoxin may not be represented in the sequenced library. In addition, if a gene was upregulated by half the hepatotoxins and downregulated by the other half, this gene also would likely be missed. An alternative strategy would have been to perform subtractive hybridization on pooled RNA from compounds with similar mechanisms of toxicity rather than pooling all of the hepatotoxins. However, we believe that the strategy we employed identified sufficient sequences to allow for the classification of compounds into different mechanistic classes.

Our strategy for chip construction is similar to that employed by Thomas et al. (2001). In this study, the authors constructed an environmental database for gene expression (EDGE) composed of libraries from mice treated with various toxins, including dioxin and phenobarbitol. These libraries were than used to construct microarrays, which were used to identify gene changes in mice treated with compounds representing peroxisome proliferators, aryl hydrocarbon receptor agonists, and noncoplanar polychlorinated biphenyls. Similar to our results, the authors found the arrays they constructed were able to classify the toxicants into different categories on the basis of mechanism of toxicity. While our current microarray has 25,000 sequences represented, almost certainly this number can be reduced once a large number of structurally and mechanistically distinct hepatotoxins have been tested. However, having a large number of sequences represented has obvious advantages. For instance, on the basis of our results, it is quite clear that the aromatic hydrocarbons and CPCA have vastly different effects on the liver. CPCA regulated over 1,000 genes in the liver that were not regulated by either Aroclor 1254 or 3-MC (Figure 3). However, the compounds did regulate approximately 800 genes in common; the genes regulated in common were both high- and low-level expression genes. With a smaller array that might have only a subset of these genes present, the potential exists that it would be more difficult to distinguish between an aromatic hydrocarbon and an inhibitor of mitochondrial [beta]-oxidation. Clearly, if the concept of guilt by association is to prove accurate whereby compounds are considered to have toxic liabilities if they closely associate with a known toxin, it is extremely important to have the correct genes present that distinguish the mechanism of toxicity. This also applies toward understanding the mechanism behind the manifested toxicity. For instance, quite possibly the genes important for understanding the mechanism of toxicity of CPCA are represented in the unique set of genes for this compound. A smaller array, which might not have these genes present, would prove ineffective in aiding our understanding of the mechanism of toxicity of this compound.

With the 25K microarray, we observed regulation of genes that have been previously shown to be regulated by aromatic hydrocarbons and CPCA. This indicates that our strategy for optimizing oligonucleotide probe selection, using hybridization kinetics, differential regulation, and hybridization intensity, was a viable approach and should prove useful in future oligonucleotide probe design.

In addition, other genes were shown to be regulated that have not been previously associated with the hepatotoxins we tested. For instance, endoplasmic reticulum protein p29 (Erp29) was upregulated by both 3-MC and Aroclor 1254 treatment. Erp29 is a recently cloned gene shown to be a member of the endoplasmic reticulum stress-response machinery (Mkrtchian et al. 1998). Both 3-MC and Aroclor 1254 cause hypertrophy in the liver as a result of proliferation of the endoplasmic reticulum (Borlakoglu et al. 1991). Both 3-MC and Aroclor 1254 also downregulated the liver tricarboxylate carrier gene. The protein product of the liver tricarboxylate carrier gene functions to transport citrate and malate from the rat liver mitochondria. Previous research has shown that the function of the protein is reduced in conjuction with altered lipid accumulation in the liver, hepatocyte apoptosis, and hepatic hyperplasia, all of which occurred because of treatment with 3-MC and Aroclor 1254 (Dini et al. 1999, Waring et al. 2001b).

Although it has been shown that CPCA inhibits mitochondrial [beta]-oxidation, the mechanism by which this occurs is not well understood. Interestingly, peroxisomal proliferators, which also inhibit mitochondrial [beta]-oxidation, result in an increase in peroxisomal [beta]-oxidation and an upregulation of fatty acid binding protein (Kaikaus et al. 1993). Our results with CPCA show that it inhibits peroxisomal [beta]-oxidation and downregulates fatty acid binding protein, suggesting that CPCA inhibits mitochondrial [beta]-oxidation by a mechanism other than the peroxisomal proliferators. In addition, CPCA upregulated prohepcidin, which is a liver-specific gene that is overexpressed with iron overload and may be also upregulated during oxidative stress (Pigeon et al. 2001).

Overall, we believe our strategy for designing a rat toxicology microarray has great advantages. This type of strategy can be applied toward other systems as Well and should prove beneficial in the design of new microarrays.

Table 1. Hepatotoxins and dose levels used in the subtractive
hybridization study.

                                   Low dose      High dose
Compound                          (mg/kg/day)   (mg/kg/day)   Vehicle

3-Methylcholanthrene                10            100         Corn oil
Acetominophen                       70            700         Saline
Actinomycin D                        0.04           0.4       Saline
Adriamycin                           1             10         Saline
Aflatoxin B1                         0.05           0.5       Saline
Allopurinol                         10            100         Saline
Allyl alcohol                       10             40         Corn oil
Amiodarone                          10            100         Saline
ANIT                                10             40         Saline
Aroclor 1254                       100            400         Corn oil
Arsenic                              2             20         Saline
Aspirin                             15            150         Saline
Bezafibrate                         20            200         Saline
Carbamazepine                       50            250         Saline
Carbon tetrachloride               100          1,000         Corn oil
Chlorpromazine                      25            100         Saline
Cycloheximide                        0.1            1.0       Saline
Cyclophosphamide                    10            100         Saline
Dexamethasone                        1             10         Saline
Diclofenac                          10             40         Saline
Diethylnitrosamine                  10            100         Saline
Diethylstilbestrol                   2.5           25         Corn oil
Dimethyl formamide                 100          1,000         Saline
Dinitrophenol                        5             25         Saline
Diquat                              17.2           68.8 (a)   Saline
Erythromycin                        80            800         Saline
Estradiol glucuronide                1             10         Saline
Ethanol                             ND          3,000         Saline
Etoposide                            5             50         Saline
Galactosamine                       50            500         Saline
Ibuprofen                           20            200         Saline
Indomethacin                         2             20         Saline
Iodoacetic acid                      5             50         Saline
Iron dextran                        50            500         Saline
Ketoconazole                        30            120         Saline
L-Ethionine                          5             50         Saline
Methapyrilene                        5             50         Saline
Methotrexate                        50            250         Saline
Microcystin                          0.005          0.05      Saline
Monocrotaline                        5             50         Saline
Nicotinic acid                     200          2,000         Saline
Oligomycin                           0.1            1.0       Saline
Perhexilene                         32            320         Saline
Phenytoin                           45            450         Saline
Quinidine                            7.5           75         Saline
Retinol                             50            500         Saline
Tamoxifen                            5             50         Saline
Tetracycline                        50            500         Saline
Tumor necrosis factor-[alpha]        0.001          0.010     Saline
Trovafloxacin                      150            400         Saline
Valproic acid                       50            500         Saline
Verapamil                           40            200         Saline

                                   Method
Compound                           of delivery   Reference for dose

3-Methylcholanthrene               ip            Kleeberg et al. 1975
Acetominophen                      Oral          Smith et al. 1998
Actinomycin D                      iv            Bader et al. 1974
Adriamycin                         iv
Aflatoxin B1                       Oral          Root et al. 1997
Allopurinol                        Oral
Allyl alcohol                      ip            Ganey and Schultze
                                                   1995
Amiodarone                         ip            Reasor et al. 1996
ANIT                               Oral          Chisholm and
                                                   Dolphin 1996
Aroclor 1254                       ip            Wolfgang et al. 1990
Arsenic                            ip            Mahaffey et al. 1981
Aspirin                            Oral
Bezafibrate                        Oral          Beier et al. 1988
Carbamazepine                      ip            Regnaud et al. 1988
Carbon tetrachloride               ip            Wolfgang et al. 1990
Chlorpromazine                     Oral
Cycloheximide                      iv
Cyclophosphamide                   ip            Brown et al. 1985
Dexamethasone                      iv
Diclofenac                         Oral
Diethylnitrosamine                 ip            Williams et al. 1996
Diethylstilbestrol                 ip
Dimethyl formamide                 ip            Mathew et al. 1980
Dinitrophenol                      Oral
Diquat                             ip            Wolfgang et al. 1991
Erythromycin                       Oral          Kaltiala et al. 1967
Estradiol glucuronide              iv
Ethanol                            Oral          Speisky et al. 1985
Etoposide                          ip            Linden 1989
Galactosamine                      iv
Ibuprofen                          Oral          Adams et al. 1969
Indomethacin                       ip            Fracasso et al. 1987
Iodoacetic acid                    iv
Iron dextran                       ip            Younes et al. 1989
Ketoconazole                       Oral          Heel et al. 1982
L-Ethionine                        ip
Methapyrilene                      ip            Graichen et al. 1985
Methotrexate                       ip            Custer et al. 1977
Microcystin                        iv
Monocrotaline                      ip            Perazzo et al. 1999
Nicotinic acid                     Oral
Oligomycin                         iv            Kramar et al. 1984
Perhexilene                        Oral
Phenytoin                          Oral          Garzon De la Mora
                                                   et al. 1990
Quinidine                          iv
Retinol                            Oral          Lettinga et al. 1996
Tamoxifen                          Oral          Wogan 1997
Tetracycline                       Oral
Tumor necrosis factor-[alpha]      iv
Trovafloxacin                      Oral
Valproic acid                      Oral          Kesterson et al. 1984
Verapamil                          Oral          Landon et al. 1986

Compound                           Type of lesion

3-Methylcholanthrene               Hypertrophy
Acetominophen                      Necrosis
Actinomycin D                      Necrosis
Adriamycin                         Necrosis
Aflatoxin B1                       Necrosis
Allopurinol                        Phospholipidosis
Allyl alcohol                      Necrosis
Amiodarone                         Phospholipidosis
ANIT                               Cholestasis
Aroclor 1254                       Hypertrophy
Arsenic                            Cirrhosis
Aspirin                            Steatosis
Bezafibrate                        Cholestasis
Carbamazepine                      Necrosis
Carbon tetrachloride               Necrosis
Chlorpromazine                     Cholestasis
Cycloheximide                      Steatosis
Cyclophosphamide                   Necrosis
Dexamethasone                      Varying
Diclofenac                         Cirrhosis
Diethylnitrosamine                 Necrosis
Diethylstilbestrol                 Tumor formation
Dimethyl formamide                 Necrosis
Dinitrophenol                      Steatosis
Diquat                             Necrosis
Erythromycin                       Cholestasis
Estradiol glucuronide              Cholestasis
Ethanol                            Steatosis
Etoposide                          Necrosis
Galactosamine                      Steatosis
Ibuprofen                          Cholestasis
Indomethacin                       Cholestasis
Iodoacetic acid                    Varying
Iron dextran                       Necrosis
Ketoconazole                       Hepititis
L-Ethionine                        Steatosis
Methapyrilene                      Steatosis
Methotrexate                       Steatosis
Microcystin                        Necrosis
Monocrotaline                      Necrosis
Nicotinic acid                     Cholestasis
Oligomycin                         Steatosis
Perhexilene                        Phospholipidosis
Phenytoin                          Cholestasis
Quinidine                          Varying
Retinol                            Steatosis
Tamoxifen                          Cholestasis
Tetracycline                       Steatosis
Tumor necrosis factor-[alpha]      Hepititis
Trovafloxacin                      Necrosis
Valproic acid                      Steatosis
Verapamil                          Cholestasis

Abbreviations: ip, intraperitoneal; iv, intravenous; ND, not
done. (a) All animals in this treatment group died before
day 3 and were not used. Where no reference is provided,
dose levels were selected from unpublished results.

Table 2. Breakdown of probe selection for 25K
chip.

Subtractive hybridization
  Unoriented                                   927
  Oriented                                   1,548
CYP gene sequences                              60
RefSeq sequences (a)                            55
Sequences with human orthologs               1,144
Sequences selected by regulation
and/or kinetics                             14,216
Sequences selected by intensity              6,449
  Total                                     24,399

(a) Used in training set for sequence orientation.

Table 3. Some of the genes regulated by Aroclor 1254, 3-MC, or CPCA.

Accession number (a)      Gene

E00717                 CYP1A1
NM_012940              CYP1B1
NM_012600              Malic enzyme
NM_017014              Glutathione-S-transferase
D83796                 UDP-Glucuronosyltransferase 1
U36482                 Erp29
AA964229               Tricarboxylate carrier
J05030                 Acyl-CoA dehydrogenase
D16478                 Enoyl-CoA hydratase
K03249                 Peroxisomal bifunctional enzyme
J02749                 Acetyl-CoA acyltransferase,
                       3-Oxo-acyl-CoA thiolase A, peroxisomal
AI105060               Adipocyte lipid binding protein
D37920                 Squalene epoxidase
AB016800               7-Dehydrocholesterol reductase
J05509                 Cholesterol hydroxylase 7 [alpha]
NM_017024              Lecithin-cholesterol acyltransferase
AB021980               Fatty acid desaturase
NM_024132              Fatty acid amide hydrolase
NM_012556              Fatty acid binding protein 1
NM_030832              Fatty acid binding protein 7
AW534367               Prohepcidin

                                                            Cluster
Accession number (a)    Aroclor 1254    3-MC       CPCA     (Fig. 3)

E00717                     30.06       36.65         NC        1
NM_012940                  63.13         100         NC        1
NM_012600                   5.16        3.45       1.61        1
NM_017014                   5.91        5.72       2.06        3
D83796                      2.88        2.95         NC        3
U36482                       1.6        1.67         NC        3
AA964229                   -2.14       -2.68         NC        6
J05030                        NC          NC      -1.94       NS
D16478                        NC          NC      -1.35       NS
K03249                     -1.84       -1.97      -5.41        7
J02749                      4.23        2.93      -1.82        1
AI105060                      NC          NC       4.66        2
D37920                        NC          NC      10.21        2
AB016800                      NC          NC       1.83       NS
J05509                     -7.81        -1.87     -5.52        7
NM_017024                     NC          NC      -2.45        5
AB021980                    1.22        2.11      -3.40        5
NM_024132                     NC          NC      -2.11        5
NM_012556                     NC          NC      -3.92        7
NM_030832                     NC          NC      -3.79        7
AW534367                   -7.57        -3.42     10.00        2

Abbreviations: NC, no change in expression; NS, gene was not shown
in Figure 3 cluster.

(a) Accession numbers are from GenBank (http://www.ncbi.nlm.nih.gov).

Table 4. Summary of regulated genes for Aroclor 1254, 3-MC, and CPCA.

                                    Identified in   Previously unknown
Compound          Regulated genes     Sub. hyb.         sequences

Aroclor 1254           4,266             817               252
3-MC                   5,005             892               251
CPCA                   5,471             953               253

Sub. hyb., subtractive hybridization library.

REFERENCES

Adams, SS, McCullough KF, Nicholson JS. 1969. The pharmacological properties of ibuprofen, an anti-inflammatory, analgesic and antipyretic agent. Arch Int Pharmacodyn Ther 178:115-129.

Altschul SF, Boguski MS, Gish W, Wootton JC. 1994. Issues in searching molecular sequence databases. Nature Genet 6:119-129.

Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389-3402.

Bader M, Thrum H, Guttner J, Klinger W. 1974. Actinomycin 13: toxicity, liver functions and morphological findings in rats. Acta Biol Med Ger 32:91-98.

Beier K, Volkl A, Hashimoto T, Fahimi HD. 1988. Selective induction of peroxisomal enzymes by the hypolipidemic drug bezafibrate. Detection of modulations by automatic image analysis in conjunction with immunoelectron microscopy and immunoblotting. Eur J Cell Biol 46:383-393.

Borlak J, Thum T. 2001. Induction of nuclear transcription factors, cytochrome P450 monooxygenases, and glutathione S-transferase a gene expression in Aroclor 1254-treated rat hepatocyte cultures. Biochem Pharmacol 61:145-153.

Borlakoglu JT, Edwards-Webb JD, Dils RR. 1991. Evidence for the induction of fatty acid desaturation in proliferating hepatic endoplasmic reticulum in response to treatment with polychlorinated biphenyls. Are fatty acid desaturases cytochrome P-450-dependent monooxygenases? Int J Biochem 23:925-931.

Brown PA, Thomson AW, Whiting PH, Davidson RJ, Simpson JG. 1985. Immunosuppressive activity and toxicity of cyclosporin A in rats pretreated with high dose cyclophosphamide. Agents Actions 17:67-72.

Bulera SJ, Eddy SM, Ferguson E, Jatkoe TA, Reidel JF, Bleavins MR, et al. 2001. RNA expression in the early characterization of hepatotoxicants in Wistar rats by high-density DNA microarrays. Hepatology 33:1239-1258.

Chisholm JW, Dolphin PJ. 1996. Abnormal lipoproteins in the ANIT-treated rat: a transient and reversible animal model of intrahepatic cholestasis. J Lipid Res 37:1086-1098.

Custer RP, Freeman-Narrod M, Narrod SA. 1977. Hepatotoxicity in Wistar rats following chronic methotrexate administration: a model of human reaction. J Natl Cancer Inst 58:1011-1017.

Dai H, Meyer M, Stepaniants S, Ziman M, Stoughton R. 2002. Use of hybridization kinetics for differentiating specific from non-specific binding to oligonucleotide microarrays. Nucleic Acids Res 30:e86.

Diatchenko L, Lau YF, Campbell AP, Chenchik A, Moqadam F, Huang B, et al. 1996. Suppression subtractive hybridization: a method for generating differentially regulated or tissue-specific cDNA probes and libraries. Proc Natl Acad Sci U S A 93:6025-6030.

Dini L, Giudetti AM, Ruzittu M, Gnoni GV, Zara V. 1999. Citrate carrier and lipogenic enzyme activities in lead nitrate-induced proliferative and apoptotic phase in rat liver. Biochem Mol Biol Int 47:607-614.

Fracasso ME, Cuzzolin L, Soldato PD, Leone R, Velo GP, Benoni G. 1987. Multisystem toxicity of indomethacin: effects on kidney, liver and intestine in the rat. Agents Actions 22:310-313.

Ganey PE, Schultze AE. 1995. Depletion of neutrophils and modulation of Kupffer cell function in allyl alcohol-induced hepatotoxicity. Toxicology 1-2:99-106.

Garzon De la Mora P, Garcia-Estrada J, Navarro-Ruiz A, Roman-Maldonado S, Bastidas-Ramirez BE, Gonzalez-Hita M, et al. 1990. Oral administration of diphenylhydantoin sodium (DFH-Na or phenytoin) predictably affects the liver and kidney of Sprague Dawley rats. Arch Invest Med (Mex) 21:339-347.

Gerhold D, Rushmore T, Caskey CT. 1999. DNA chips: promising toys have become powerful tools. Trends Biochem 24:168-173.

Graichen ME, Neptun DA, Dent JG, Popp JA, Leonard TB. 1985. Effects of methapyrilene on rat hepatic xenobiotic metabolizing enzymes and liver morphology. Fundam Appl Toxicol 1:165-174.

Hamadeh HK, Bushel PR, Jayadev S, DiSorbo O, Bennett L, Li L, et al. 2002a. Prediction of compound signature using high density gene expression profiling. Toxicol Sci 67:232-240.

Hamadeh HK, Bushel PR, Jayadev S, Martin K, DiSorbo O, Sieber S, et al. 2002b. Gene expression analysis reveals chemical-specific profiles. Toxicol Sci 67:219-231.

Heel RC, Brogden RN, Carmine A, Morley PA, Speight TM, Avery GS. 1982. Ketoconazole: a review of its therapeutic efficacy in superficial and systemic fungal infections. Drugs 23:1-36.

Holden PR, James NH, Brooks AN, Roberts RA, Kimber I, Pennie WD. 2000. Identification of a possible association between carbon tetrachloride-induced hepatotoxicity and interleukin-8 expression. J Biochem Mol Toxicol 14:283-290.

Hughes TR, Mao M, Jones AR, Burchard J, Marton MJ, Shannon KW, et al. 2001. Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat Biotechnol 19:342-347.

Hughes TR, Marton MJ, Jones AR, Roberts CJ, Stoughton R, Armour CD, et al. 2000. Functional discovery via a compendium of expression profiles. Cell 102:109-126.

Jauregui HO, Ng SF, Gann KL, Waxman DJ. 1991. Xenobiotic induction of P-450 PB-4 (IIB1) and P-450c (IA1) and associated monooxygenase activities in primary cultures of adult rat hepatocytes. Xenobiotica 21:1091-1106.

Kaikaus RM, Chan WK, Montellano PR, Bass NM. 1993. Mechanisms of regulation of liver fatty acid-binding protein. Mol Cell Biochem 123:93-100.

Kaltiala EH, Rasanen O, Herva E, Karki NT. 1967. Subchronic toxicity of some erythromycin compounds in rats. Acta Pharmacol Toxicol (Copenh) 25:435-446.

Kesterson JW, Granneman GR, Machinist JM. 1984. The hepatotoxicity of valproic acid and its metabolites in rats. Toxicologic, biochemical and histopathologic studies. Hepatology 4:1143-1152.

Kleeberg U, Barth A, Roth J, Klinger W, Karge E. 1975. On the selectivity of aryl hydrocarbon hydroxylase induction after 3-methylcholanthrene pretreatment. Acta Biol Med Ger 710:1701-1705.

Kramar R, Hohenegger M, Srour AN, Khanakah G. 1984. Oligomycin toxicity in intact rats. Agents Actions 15:660-663.

Landon EJ, Naukam R J, Sastry BVR. 1986. Effects of calcium channel blocking agents on calcium and centrilobular necrosis in the liver of rats treated with hepatotoxic agents. Biochem Pharmacol 35:697-705.

Lettinga KD, Gutter W, VanNoorden CJ, Schellens JP, Frederiks WM. 1996. Early effects of high doses of retinol (vitamin A) on the in situ cellular metabolism in rat liver. Liver 16:1-11.

Linden CJ. 1989. Toxicity of interperitoneally administered antitumour drugs in athymic rats. In Vivo 3:259-262.

Lubet RA, Jones CR, Stockus DL, Fox SD, Nims RW. 1991. Induction of cytochrome P450 and other drug-metabolizing enzymes in rat liver following dietary exposure to Aroclor 1254. Toxicol Appl Pharmacol 108:355-365.

Mahaffey KR, Capar SC, Gladen BC, Fowler BA. 1981. Concurrent exposure to lead, cadmium, and arsenic. Effects on toxicity and tissue metal concentrations in the rat. J Lab Clin Med 98:463-481.

Makalowski W, Boguski MS. 1998. Evolutionary parameters of the transcribed mammalian genome: an analysis of 2,820 orthologous rodent and human sequences. Proc Natl Acad Sci U S A 95:9467-9412.

Marton MJ, DeRisi JL, Bennett HA, Iyer VR, Meyer MR, Roberts CJ, et al. 1998. Drug target validation and identification of secondary drug target effects using DNA microarrays. Nat Med 4:1293-1301.

Mathew T, Karunanithy R, Yee MH, Natarajan PN. 1980. Hepatotoxicity of dimethylformamide and dimethylsulfoxide at and above the levels used in some aflatoxin studies. Lab Invest 42:257-262.

Mehlman MA, Tobin RB, Friend B, Mackerer CR. 1975. The effects of a polychlorinated biphenyl mixture (Aroclor 1254) on liver gluconeogenic enzymes of normal and alloxan-diabetic rats. Toxicology 5:89-95.

Mkrtchian S, Fang C, Hellman U, Ingelman-Sundberg M. 1998. A stress-inducible rat liver endoplasmic reticulum protein, ERp29. Eur J Biochem 251:304-313.

Perazzo J, Eizayaga F, Romay S, Bengochea L, Pavese A, Lemberg A. 1999. An experimental model of liver damage and portal hypertension induced by a single dose of monocrotaline. Hepatogastroenterology 46:432-435.

Pigeon C, Ilyin G, Courselaud B, Leroyer P, Turlin B, Brissot P, et al. 2001. A new mouse liver-specific gene, encoding a protein homologous to human antimicrobial peptide hepcidin, is overexpressed during iron overload. J Biol Chem 276:7811-7819.

Reasor MJ, McCloud GM, Beard TL, Ebert DC, Kacew S, Gardner MF, et al. 1996. Comparative evaluation of amiodarone-induced phospholipidosis and drug accumulation in Fischer-344 and Sprague-Dawley rats. Toxicology 106:139-147.

Regnaud L, Sirois G, Chakrabarti S. 1988. Effect of four-day treatment with carbamazepine at different dose levels on microsomal enzyme induction, drug metabolism and drug toxicity. Pharmacol Toxicol 62:3-6.

Reilly TP, Bourdi M, Brady JN, Pise-Masison CA, Radonovich MF, George JW, et al. 2001. Expression profiling of acetaminophen liver toxicity in mice using microarray technology. Biochem Biophys Res Commun 282:321-328.

Root M, Lange T, Campbell TC. 1997. Dissimilarity in aflatoxin dose-response relationships between DNA adduct formation and development of preneoplastic foci in rat liver. Chem Biol Interact 106:213-227.

Saarikoski ST, Ikonen TS, Oinonen T, Lindros KO, Ulmanen I, Husgafvel-Pursiainen K. 1998. Induction of UDP-glycosyltransferase family 1 genes in rat liver: different patterns of mRNA expression with two inducers, 3-methylcholanthrene and [beta]-naphthoflavone. Biochem Pharmacol 56:569-575.

Smith GS, Nadig DE, Kokoska ER, Solomon H, Tiniakos DG, Miller TA. 1998. Role of neutrophils in hepatotoxicity induced by oral acetaminophen administration in rats. J Surg Res 80:252-258.

Smith TF, Waterman MS. 1981. Comparison of biosequences. Adv in App Math 2:482-489.

Speisky H, Bunout D, Orrego H, Giles HG, Gunasekara A, Israel Y. 1985. Lack of changes in diene conjugate levels following ethanol induced glutathione depletion or hepatic necrosis. Res Commun Chem Pharmacol 48:77-90.

Thomas RS, Rank DR, Penn SG, Zastrow GM, Hayes KR, Pande K, et al. 2001. Identification of toxicologically predictive gene sets using cDNA microarrays. Mol Pharmacol 60:1189-1194.

Ulrich RG, Bacon JA, Cramer CT, Petrella DK, Sun EL, Meglasson MD, et al. 1998. Disruption of mitochondrial activities in rabbit and human hepatocytes by a quinoxalinone anxiolytic and its carboxyic acid metabolite. Toxicology 131:33-47.

Waring JF, Ciurlionis R, Jolly RA, Heindel M, Ulrich RG. 2001a. Microarray analysis of hepatotoxins in vitro reveals a correlation between gene expression profiles and mechanisms of toxicity. Toxicol Lett 120:359-368.

Waring, JF, Jolly RA, Ciurlionis R, Lure PY, Praestgaard JT, Morfitt DC, et al. 2001b. Clustering of hepatotoxins based on mechanism of toxicity using gene expression profiles. Toxicol Appl Pharmacol 175:28-42.

Williams GM, Iatropoulos MJ, Wang CX, Ali N, Rivenson A, Peterson LA, et al. 1996. Diethylnitrosamine exposure-responses for DNA damage, centrilobular cytotoxicity, cell proliferation and carcinogenesis in rat liver exhibit some nonlinearities. Carcinogenesis 10:2253-2258.

Wogan GN. 1997. Review of the toxicology of tamoxifen. Semin Oncol 24:S1-S87.

Wolfgang GH, Donarski WJ, Petry TW. 1990. Effects of novel antioxidants on carbon tetrachloride-induced lipid peroxidation and toxicity in precision-cut rat liver slices. Toxicol Appl Pharmacol 106:63-70.

Wolfgang GH, Jolly RA, Petry TW. 1991. Diquat-induced oxidative damage in hepatic microsomes: effects of antioxidants. Free Radic Biol Med 10:403-411.

Younes M, Eberhardt I, Lemoine R. 1989. Effect of iron overload on spontaneous and xenobiotic-induced lipid peroxidation in vivo. J Appl Toxicol 9:103-108.

This article was previously published in the inaugural Toxicogenomics Section of EHP.

Address correspondence to J.F. Waring, Dept. of Cellular and Molecular Toxicology, Abbott Laboratories, D463, Abbott Park, IL 60064-6104 USA. Telephone: [phone]. Fax: [phone]. E-mail: [e-mail]

Received 17 September 2002; accepted 18 November 2002.

Jeffrey F. Waring, (1) Guy Cavet, (2) Robert A. Jolly, (1) Jeff McDowell, (1) Hongye Dai, (2) Rita Ciurlionis, (1) Chunsheng Zhang, (2) Roland Stoughton, (2) Pek Lum, (2) Allan Ferguson, (2) Christopher J. Roberts, (2) and Roger G. Ulrich (1)

(1) Abbott Laboratories, Abbott Park, Illinois, USA; (2) Rosetta Inpharmatics, Kirkland, Washington, USA