Phenotypic Anchoring of Global Gene Expression Profiles Induced by

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Phenotypic Anchoring of Global Gene Expression Profiles Induced by N-Hydroxy-4-acetylaminobiphenyl and Benzo[a]pyrene Diol Epoxide Reveals Correlations between Expression Profiles and Mechanism of Toxicity Wen Luo,†,‡,§ Wenhong Fan,‡ Hong Xie,† Lichen Jing,† Elaine Ricicki,§ Paul Vouros,§ Lue Ping Zhao,‡ and Helmut Zarbl*,†,‡ Divisions of Human Biology and Public Health Sciences, Fred Hutchison Cancer Research Center, 1100 Fairview Avenue North, Seattle, Washington 98109, and The Barnett Institute of Chemical and Biological Analysis, Northeastern University, 360 Huntington Avenue, Boston, Massachusetts 02115 Received June 29, 2004

The goal of this study was to compare changes in gene expression induced by exposure to different carcinogens and to anchor these changes to the induced levels of toxicity and mutagenesis. The human TK6 lymphoblastoid cell line was used as an in vitro model system, and reactive metabolites of two human carcinogens, benzo[a]pyrene and 4-aminobiphenyl, were used as model compounds. We first determined the toxicity of the model compounds N-hydroxy4-acetylaminobiphenyl (N-OH-AABP) and benzo[a]pyrene diol epoxide (BPDE) in TK6 cells. BPDE was about 1000-fold more toxic and mutagenic than N-OH-AABP in TK6 cells on a molar basis. We next treated cells with three doses of each compound that resulted in low, medium, and high toxicities (5, 15, and 40%) and harvested cells at different times after exposure. Using comparable levels of toxicity as the phenotypic anchor, we compared the patterns of gene expression induced by each reactive metabolite using printed cDNA microarrays comprising ∼18 000 human gene/EST sequences. The microarray data from the N-OH-AABP and BPDE treatment groups were compared using self-organizing map clustering algorithms, as well as a statistical regression modeling approach. While subsets of genes indicative of a generalized stress response [Hsp 40 homologue (DNAJ), Hsp70, Hsp105, and Hsp 125] were detected after exposure to both compounds at all concentrations, there were also many differentially regulated genes, including phase I xenobiotic metabolism [e.g., glutathione transferase ω (GSTTLp28) and antioxidant enzymes (Apxl)]. Other differentially regulated genes included those encoding proteins involved in all major DNA repair pathways, including excision repair (e.g., ERCC5), mismatch repair (e.g., MLH3), damage specific DNA binding protein (e.g., DDB2), and cisplatin resistance-associated overexpressed protein (LUC7A, CRA). Differences in the transcriptional response of TK6 cells to N-OH-AABP or BPDE exposure may explain the dramatic differences in the toxicity and mutagenicity of these human carcinogens.

Introduction Toxicogenomics, which combines classical toxicology with high-throughput genomic technologies, is focused on the integration of toxicant specific alterations in gene expression patterns with phenotypic responses of cells, organs, and organisms. The hope is that toxicant specific gene expression signatures will provide biomarkers for monitoring the effects of environmental exposure, predict the toxicity of untested compounds, detect toxicity at levels that do not yield clinical symptoms, and provide insights into mechanisms of toxicity (1). The prediction that classes of toxicants can produce overlapping patterns of gene expression is supported by a wealth of previous * To whom correspondence should be addressed. Tel: 206-667-4107. Fax: 206-667-5815. E-mail: [email protected]. † Division of Human Biology, Fred Hutchison Cancer Research Center. ‡ Division of Public Health Sciences, Fred Hutchison Cancer Research Center. § The Barnett Institute of Chemical and Biological Analysis, Northeastern University.

studies of candidate genes identified by classical biochemical or genetic approaches (2-5). However, the unprecedented capacity for highly parallel measurements of RNA expression using DNA microarrays affords the opportunity for the comprehensive and unrestricted discovery of gene expression signatures that are associated with complex biological processes, such as cellular responses to toxic insults from carcinogens (6-8). For example, one study used microarrays comprised of probes representing all of the ∼6200 Saccharomyces cerevisiae genes to profile the response of yeast cells to highly toxic doses of the alkylating agent, methyl methanesulfonate (9). In addition to the expected alterations in the expression of known DNA repair genes, these studies suggested that the cells’ response to alkylating damage also included the induction of pathways involved in the elimination and replacement of alkylated proteins from cells. Similarly, a recent study in mice successfully categorized toxicants according to class, to examine the effects of time, dose, and tissue on gene expression patterns in mice (10, 11). In another study, rat hepatocytes were treated

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with 15 known toxins and microarray technology was used to characterize the compounds based on gene expression changes (12). Studies in yeast and mammalian cells have also demonstrated that reproducible subsets of genes will show altered expression in response to specific environmental conditions such as anoxia, nutrient deprivation, exposure to alkylating agents, chemotherapeutic agents, etc. (9, 10, 13, 14). It is therefore reasonable to hypothesize that toxicant signatures will exist for individual classes of toxins and that expression signatures will provide the insight of toxicity and mutagenicity. Nonetheless, it is also clear that expression signatures for a given compound will vary with both dose and time after exposure. Moreover, the maximum internal dose of a compound can be limited by cellular uptake or metabolism. As a result acute toxicity may be limited, while effects remain cumulative under prolonged exposure conditions. Thus, to compare gene expression profiles elicited by different exposures, it is imperative that the experiments be performed under exposure conditions that produce comparable phenotypic changes within the target cells or organs (phenotypic anchoring). As a result, an increasing number of toxicogenomic studies are attempting to anchor specific gene expression profiles with commensurate phenotypes, including cell cycle arrest, cell death, and histopathology. A few studies, including our own studies in yeast, have attempted to relate expression profiles to specific measures of toxicity and genotoxicity (15) (Guo, Y., Breeden, L. L., Kelley, E., Eaton, D. L., and Zarbl H. Submitted for publication). The long-term goal of our studies is to integrate expression signatures induced by genotoxic agents with the genotype and phenotype. We posit that careful anchoring of expression profiles to biologically effective doses of known carcinogens will provide insight into the differential toxic, mutagenic, and carcinogenic potentials of different compounds. In the present study we compared two well-characterized human carcinogens, 4-aminobiphenyl (ABP)1 and benzo[a]pyrene (B[a]P), both of which have significant implications for public health. Chronic human exposures to either of these chemicals through ingestion of cooked food, smoking, and environment pollution have been associated with a variety of adverse health effect, including cancers (16, 17). Polycyclic aromatic hydrocarbons, such as B[a]P, are produced by incomplete combustion of organic material including gasoline in motor vehicles, heating, coal burning, cooking, industrial production activities, and tobacco smoke and are implicated in the etiology of human lung cancer (18). ABP, an aromatic amine, was among the first compounds present in cigarette smoke identified as human bladder carcinogen (19). These two carcinogens, which are also known to induce tumors in laboratory animals served as models for this study. Using the TK6 human lymphoblast cell line as model system, we investigated the carcinogen-induced changes in gene expression patterns as a function of toxicity and 1 Abbreviations: ABP, 4-aminobiphenyl; B[a]P, benzo[a]pyrene; BPDE, benzo[a]pyrene-r-7,t-8-dihydrodiol-t-9,10-epoxide(()(anti); CHAT, 10-5 M deoxycytidine + 2 × 10-4 M hypoxanthine + 2 × 10-7 M aminopterin + 1.75 × 10-5 M thymine; DMSO, dimethyl sulfoxide; HPRT, hypoxanthine-guanine phosphoribosyl transferase; RFU, relative fluorescent unit; SOM, self-organization map; 6TG, 6-thioguanine; N-OH-AABP, N-hydroxy-4-acetylaminobiphenyl; Z, standardized coefficient, defined as the regression coefficient divided by its standard error.

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mutagenesis. To eliminate the potential confounding effects of differential metabolic activation, we used the reactive metabolites of B[a]P and ABP as model compounds. To determine if the transcriptional responses of cells to toxicant exposures showed a dose response or become saturated at low doses, we compared expression profiles at doses of the compounds that give different levels of toxicity and mutagenicity. We observed that N-hydroxy-4-acetylaminobiphenyl (N-OH-AABP) is about 1000 times less toxic and mutagenic than benzo[a]pyrener-7,t-8-dihydrodiol-t-9,10-epoxide(()(anti) (BPDE) in this cell line. For the present study we phenotypically anchored the gene expression profiles to doses of each compound that resulted in low, medium, and high toxicity (5, 15, 40%) and harvested cells at different times after exposure. In addition to genes induced by both compounds, we identified a subset of genes whose expression levels were significantly decreased in BPDE treated cells but not in N-OH-AABP treated cells. The latter group included genes involved in DNA repair and replication, transcription factors, etc. The failure of N-OH-AABP exposure to decrease the expression of these genes may help to explain why N-OH-AABP is about 1000 times less toxic and mutagenic than BPDE in the TK6 cell line.

Materials and Methods Materials. BPDE and N-OH-AABP were purchased from National Cancer Institute Chemical Carcinogen Reference Standard Repository (Kansas City, MO). Anhydrous dimethyl sulfoxide (DMSO) (99.997% purity) sealed with N2 was purchased from Aldrich (Milwaukee, WI). 6-thioguanine (6TG), triflurothymidine (F3TdR), deoxycytidine, hypoxanthine, aminopterin, thymidine, and horse serum were obtained from Sigma (St. Louis, MO). RPMI medium 1640 with L-glutamine was obtained from Life Technologies, Inc. (Grand Island, NY). RNA Maxi Kits were purchased from Qiagen Inc. (Valencia, CA). Cell Culture. Human lymphoblastoid line TK6 (20) was derived from the parental human lymphoblastoid line HH4 and heterozygous for thymidine kinase (TK), an enzyme which phosphorylates thymidine and its toxic analogues in an ATPdependent reaction. The cells were grown in spinner flasks to allow for gentle stirring. Cells were maintained in exponential growth by daily dilution in RPMI 1640 medium supplemented with 5% donor horse serum and maintained in 37 °C incubators with a 5% CO2 atmosphere. Cell counts were taken daily using a Coulter Counter, and cultures were diluted to 4∼5 × 105 cells/ mL. Detailed growth records were maintained and used to ascertain any effects of the treatment on growth rate. Chemical Treatment, Mutagenesis, and Survival Measurements. N-OH-AABP and BPDE stock solutions were prepared in anhydrous DMSO (99.997% purity) and used immediately. The final DMSO concentration in the culture was less than 0.1%. Prior to mutagen treatment, the background Hypoxanthineguanine phosphoribosyl transferase (HPRT) mutant fraction was reduced. TK6 cells were first “CHAT” (10-5 M deoxycytidine + 2 × 10-4 M hypoxanthine + 2 × 10-7 M aminopterin + 1.75 × 10-5 M thymine) treated for 3 days, followed by 2 days recovery period in “THC” (21). Cells were then treated with various concentrations of test chemicals such as BPDE or N-OHAAB for up to 27 h. Duplicate cultures were completed at each condition. Untreated cells were used as negative controls. At the end of the treatment, cells were sampled for measurement of the mutant fractions, DNA adducts level and gene expression. At 1, 9 or 27 h after the treatment, cells were sampled for mutant fraction. At sampling, a 100 mL aliquot from each culture was centrifuged, and cells were resuspended in fresh RPMI medium to remove test chemicals. After cells had been growing exponentially for 7 days to allow for phenotypic expression (22),

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cultures were plated to determine mutant fractions. To determine mutant fraction at HPRT or TK locus, cells were plated in the presence and absence of 6TG or triflurothymidine (F3TdR), respectively. The mutant fraction is the ratio of the colonyforming efficiency under selective conditions to that of efficiency in the absence of selective conditions (23, 24). Survival of treated cultures was calculated from back extrapolation of growth curves by comparison of the growth of treated cultures to control cultures (25). For analysis of gene expression and DNA adducts, two 100 mL aliquots of cells (about 5 × 105 cells/mL) were removed from each culture at 1, 9 or 27 h after the treatment. Cells were collected by centrifugation, immediately frozen down in liquid nitrogen, and stored at -80 °C. DNA Microarrays. Human cDNA arrays were generated in the Genomics Facility at Fred Hutchinson Cancer Research Center (1, 26-28). Microarrays were constructed employing the first 17,569 clones from the sequence-verified ResGen Human Unigene Set comprising 31 105 clones (Research Genetics, Huntsville, ALsnow Invitrogen). Although each clone was originally representative of a unique UniGene Cluster, members of these clusters have changed over time. Our review of the annotations for 521 clones indicated that redundancy of probes was approximately less than 2.5% (12/521). Clones are representative of over 20 different human tissues/organs. The average length of the probe was 1200 ( 364 bases, with the minimum probe length being 251 bases and the maximum probe length being 2000+ bases. For array construction, each clone insert was individually amplified using PCR. Each PCR product was then verified on the basis of size using gel electrophoresis, and purified using the Millipore Multiscreen-PCR filtration system. In some instances, resequencing was performed to verify the identity of clones. Purified PCR products in 3X SSC (450 mM sodium chloride and 45 mM sodium citrate, pH 7.0) were each mechanically “spotted” onto polylysine coated microscope slides using an OmniGrid high-precision robotic gridder (GeneMachines, San Carlo, CA). Each clone was represented once per array. Preparation and Hybridization of cDNA Probes Labeled with Cy3 or Cy5. RNA was extracted using Qiagen RNeasyMaxi Kits according to the manufacturer’s instructions (Qiagen, Valencia, CA), followed by a quality test using Agilent BioAnalyzer chips. Synthesis of the labeled first strand cDNA was conducted using InVitrogen Superscript II reverse transcript system with starting material of 30 µg of total RNA plus oligo-dT. The amino-allyl labeled dUTP was added to the reaction to generate amino-allyl labeled second strand cDNA. Following the hydrolysis reaction, single-stranded cDNA probes were purified using a Microcon-30 concentrator. Probe mixtures were evaporated in a vacuum centrifuge, and the cDNA pellet was resuspended in 4.5 µL of water. The dye coupling reactions were performed by mixing the cDNA samples with Cy3 or Cy5 dyes and incubating for 1 h in the dark. After the reactions were quenched with hydroxylamine, Cy3 and Cy5 samples were combined for hybridization. To remove unincorporated/quenched Cy dye, a Qia-quick PCR purification kit (Qiagen) was used. The reactions were purified with Qiaquik PCR purification kits to remove the unincorporated/quenched dyes. The mixture of Cy3 and Cy5 labeled samples was dried down in a vacuum centrifuge and then brought to a volume of 20 µL with water. Millipore 0.45 µM spin columns were used to clean up after adding 20X SSC and Poly A to the mixture. Finally, the samples were stored at -20 °C until ready for hybridization. The labeled cDNAs were co-hybridized to cDNA microarrays in triplicate, including one dye swap. Slides were scanned on the GenePix 4000A Microarray Scanner at the optimal wavelength for the Cy3 and Cy5 using lasers. The fluorescence signals of each spot on the slide were analyzed and extracted with GenePix software to generate.gpr file, which was further formatted to plain.txt file for further data analysis. All the data mining steps including transformation, cleaning-up, normalization and hypothesis tests, were performed using the SAS statistical software.

Data Analysis. The mean log2 ratios of fluorescence intensities relative to untreated controls for each gene and each treatment were clustered using the J-Express Software (University of Bergen, Norway) unsupervised algorithm. Briefly, the fluorescence intensity of each spot was calculated using local median background subtraction. The relative fluorescent units (RFUs) were then normalized to the median signal (Cy3 and Cy5) for that slide. If both Cy3 and Cy5 signals were under the signal intensity of negative controls, genes were considered as not being expressed in either sample. The normalized microarray data were then analyzed at using two approaches, visualization and statistical analysis. The first of these involved a comparison of the normalized fluorescence intensity ratios, and was used only for the purpose of visualizing the data. In this approach, the fold change in gene expression for each array feature was determined as a ratio of normalized fluorescence intensities relative to the reference sample (i.e, RFU treated/ RFU untreated). We then selected a value of 2.0 in fold change in gene expression as the arbitrary cutoff for inclusion of data in clustering algorithms. The log2 gene expression ratios for all genes showing a 2-fold or greater change in gene expression at any concentration were used in unsupervised cluster analysis to generate the dendogram shown in Figure 3. The data were also analyzed by multidimensional scaling using Self-Organization Map techniques (32, 33). The arbitrary 2-fold cutoff was selected because it is the most frequently used value for preliminary comparison of expression profiles. As illustrated in Figure 3, this approach revealed significant differences in the expression profiles induced by the two carcinogens. Nonetheless, the data generated using this arbitrary cutoff value does not provide any indication as to the statistical significance of the differences in expression profiles. The latter requires that the data be subjected to rigorous statistical analyses. Statistical Modeling. To make statistical inference, we tested two null hypotheses for every gene: 1) gene expression does not change in ABP or BPDE treated samples compared to the untreated control samples; 2) gene expression does not change between ABP treated samples and BPDE treated samples. On a cDNA array, for each gene, the log ratio of signal from treated sample in one channel over the signal from control sample in the other channel was used as raw data, thereby eliminating some systemic variation. Thus, null hypothesis no. 1 becomes the following: the log ratio is equal to zero. After testing these hypotheses for every gene, the genes that rejected the hypothesis at a certain significance level were selected as candidate genessi.e., ABP or BPDE has an effect on the expression profiles of these genes. For this analysis we used software GenePlus (Enodar Biologic Corporation, http:// www.enodar.com, Seattle, WA), which implements a regression model using the estimating equation technique (29-31). Normalization was carried out simultaneously in the regression procedure. Regression coefficient and its standard error were estimated from normalized data. Standardized coefficient, Z, defined as regression coefficient divided by its standard error, was used to make the statistical inference. The higher the absolute value of standardized coefficient, the more significant the gene expression change is. The sign of the standardized coefficients reflects the directionality of change. To control false positives, the type I error was set equal to 5% in analyses. Since multiple hypothesis testing was carried out simultaneously for multiple genes, Bonferroni’s correction was used to control the inflated type I error.

Results Toxicity and Mutagenicity of N-OH-AABP and BPDE in TK6 Cells. TK6 cells treated with various levels of N-OH-AABP or BPDE were harvested at 1, 9, and 27 h after dosing. Toxicity and mutations analyses were conducted at each time point. BPDE survival decreased to 41% after 1 h of exposure at a dose of 0.185 µM BPDE (Figure 1). The HPRT mutant fraction in-

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Figure 1. Survival of TK6 cells as a function of carcinogen dose. Percent survival was determined by growth curve extrapolation relative to vehicle-treated control cells. Because a dose of 10 µM of N-OH-AABP yielded no toxicity at 1 h treatment (not shown), the survival curves shown were determined at 27 h after treatment with N-OH-AABP (O) and 1 h after treatment with BPDE (2).

creased from a background level of 2.31 × 10-6 to an induced mutant fraction of 104 × 10-6, an increase of ∼50-fold, after treatment with 0.185 µM BPDE. Similarly, BPDE increased the mutant fraction at the TK locus ∼50-fold, from a background of 1.63 × 10-5 to an induced mutant fraction of 93.9 × 10-5 under the same exposure conditions. By contrast, when TK6 cells were exposed to N-OHAABP at various concentrations ranging from 0.05 to 10 µM for 1 h, there was no detectable increase in toxicity or mutagenicity. To attain comparable levels of toxicity induced by BPDE after 1 h treatment, the cell exposure time for N-OH-AABP was increased from 1 to 27 h, and highest concentration was increased to 20 µM. After 27 h of exposure to N-OH-AABP cell survival was decreased to 57 and 16% at concentrations of 10 and 20 µM, respectively (Figure 1). After cell exposure to 10 µM N-OH-AABP for 27 h, HPRT mutant fraction increased from a background of 2.31 × 10-6 to 40.7 × 10-6, an increase of about 20-fold, and mutant fraction at the TK locus increased approximately 40-fold from a background of 1.63 × 10-5 to 63.4 × 10-5. Together, the results of the mutation assays (Figure 2) showed that mutation fraction at the TK and HPRT loci increased as a function of concentration and exposure times in both the OHAABP and BPDE treated cells, although the cells were more sensitive to BPDE by several orders of magnitude. For gene expression analyses we selected three concentration levels that resulted in low, medium and high (5, 15, and 40%) toxicity from the toxicity dose response curve (Figure 1). For N-OH-AABP, this corresponded to doses of 0.5, 1.0, and 10.0 µM while for BPDE the same toxicity levels were obtained at dosing of 0.017, 0.034, and 0.12 µM. The OH-AABP treated cells were harvested after 27 h, while those treated with BPDE were harvested after 1 h. The combined results of the toxicity and induced mutation fractions at HPRT and TK loci are presented in Table 1. The mutant fraction induced by lowest concentration of BPDE at 0.017 µM was 9.89 × 10-6 at HPRT locus, which was 4.3 times above the background detected in untreated cell culture. The same concentration of BPDE induced TK mutant fraction to 7.22 × 10-5, which was 4.4 times above the background level. The observed 4-fold increases in the induced mutant fractions were statistically significant (Table 1). Induction of a comparable increase in mutant fractions at the 5 and 15% toxicity

Figure 2. (a) Mutant fractions induced at the HPRT locus of TK6 cells as a function of carcinogen dose. Induced mutant fractions were determined by selection in medium containing 6TG as previously described (21-25). Mutant fractions were determined at 1 h exposure to BPDE (2) and at 27 h after exposure to N-OH-AABP (O). (b) Mutant fractions induced at the TK locus of TK6 cells as a function of carcinogen dose. Induced mutant fractions were determined by selection in medium containing triflurothymidine as previously described (21-25). Mutant fractions were determined at 1 h exposure to BPDE (2) and at 27 h after exposure to N-OH-AABP (O). Table 1. Mutation Fractions Induced by BPDE and N-OH-AABP under Experimental Conditions that Resulted in Low (5%), Medium (15%), and High (40%) Levels of Toxicity in TK6 Cellsa toxicity

control

5%

15%

40%

concn (µM) HPRT (× 10-6) TK (× 10-5)

BPDE (1 h) 0 0.017 9.89 2.31 × 10-6 7.22 1.63 × 10-5

0.034 21.5 16.3

0.12 62.2 89.5

concn (µM) HPRT (× 10-6) TK (× 10-5)

N-OH-AABP (27 h) 0 0.5 12.7 2.31 × 10-6 19.6 1.63 × 10-5

1 13.9 29.2

10 40.7 63.4

a Induced mutant fractions at HPRT and TK loci were determined as previously described (21-25).

levels required a 30-fold higher concentration of N-OHAABP than BPDE, while an 85-fold higher concentration of N-OH-AABP than BPDE was required at the 40% toxicity level. When considering both the higher doses and time of exposure, BPDE was about 800-2300-fold more toxic and mutagenic than N-OH-AABP in TK6 cells, depending on the dose. Moreover, the data indicated that at similar levels of toxicity, the mutation fractions induced by the two compounds were comparable. Gene Expression Profiling of Treated Cells. From each of the cell cultures conditions selected for toxicity and mutagenicity testing, we collected an aliquot for gene


expression profiling. We were thus able to compare patterns of gene expression induced by two different carcinogens at doses that yielded low, medium and high (5, 15, and 40%) toxicity, and comparable dose-dependent increases in the induced mutant fractions at the HPRT and TK6 loci (Table 1). Hierarchical Clustering. For the purpose of visualizing the data, the mean ratios of fluorescence intensities relative to untreated controls for each gene and each treatment were analyzed by hierarchical clustering and multidimensional scaling using the self-organization map (SOM) (32, 33) and the appropriate algorithms in the J-Express Software (University of Bergen, Norway). For these analyses, an arbitrary cutoff value of “two” was selected for the fold change in the gene expression ratio. At the three levels of toxicity (low, medium and high), the data analysis identified a total of 509 genes/EST (521array features) whose mean expression levels were altered at least 2-fold, by at least one metabolite, relative to the untreated, control cell cultures. The 509 genes showing a 2-fold or greater change in the mean level of gene expression under at least one condition were subjected to unsupervised, hierarchical clustering to generate the dendrogram shown in Figure 3. The results identified clusters of genes that were common among all treated cells, as well as clusters that differentiated the cellular responses to the different carcinogens. Using the SOM approach, we generated 25 clusters of genes showing similar patterns of regulation (Figures 4 and 5). For example, the genes comprising clusters A and B exhibited comparable decreases in expression following exposure to either BPDE or N-OHAABP. These preliminary analyses of the data suggested that the two carcinogens induced overlapping and unique changes in gene expression, the latter possibly defining their differential toxicity and mutagenicity. In addition, results indicated that for most of the genes there was probably no significant dose response in gene expression levels at 5, 15, and 40% cell toxicity. However, clustering algorithms do not infer any statistical significance, since the variance of the mean values is not taken into account, and the selection of a 2-fold change in gene expression as a cutoff was arbitrary. The data were therefore reanalyzed using a linear regression model that allows for the selection of genes whose changes in expression level after the treatments are statistically significant relative to the untreated controls. Statistical Analysis of Gene Expression Profiles. The SOM clustering analysis indicated that for most of the genes analyzed, there was probably no significant dose response over the range of toxicity used in the present study. Thus it was possible to combine the expression data from the low, medium and high toxicity conditions and compare the expression. Using this approach, each treatment group consisted of nine independent microarray experiments, three doses of each carcinogen done in triplicate. In addition, the SOM clustering results provided the direction of change in gene expression, and helped to frame the appropriate null hypotheses to be tested by statistical analysis. For each gene, first the log ratio of the gene expression values between treated and untreated control from the nine independent microarrays was calculated. Statistical regression was next used to test if the log ratio values were significantly different from zero. A log ratio of zero indicated there was no change in expression when


Figure 3. Hierachical clustering of the average fold change in gene expression levels induced in TK6 cells as a function of carcinogen-induced toxicity. TK6 cells were treated with N-OHAABP or BPDE under conditions previously determined to result in 5 (ABP1 and BPDE1), 15 (ABP2 and BPDE2), and 40% (ABP3 and BPDE3) toxicity (see Figure 2). After subtraction of local median background and normalization to the median signal for a given microarray, the relative fold change in expression for each feature was calculated as the mean log2 ratio of fluorescence intensities relative to untreated controls. A total of 521 array features were found to change at least 2-fold after treatment with at least one compound and under one treatment condition. The calculated mean log2 gene expression ratios for each of these 521 array features were then subjected to hierarchical clustering using the unsupervised algorithm in J-Express Software (University of Bergen, Norway). The log2 ratio expression for each these genes under each treatment is represented by a red and green scale, where saturated red denotes a log2 value of 3.784 and saturated green represents a log2 value of -3.784. Experimental conditions are on the horizontal axis and are specified at the bottom of the dendogram. Individual genes are represented along the vertical axis.

comparing the treated cells with the controls. If the log ratio was not equal to zero, expression changes were

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Figure 4. Clustering of mean log2 gene expression ratios of genes showing similar responses to different carcinogens using SOMs (32, 33). Panels A-D show representative examples of examples of clusters of genes that showed similar dose-dependent responses to both BPDE (9) and N-OH-AABP ([) at 5 (1), 15 (2), and 40% toxicities (3). The fold change in gene expression for each feature was determined as a ratio of fluorescence intensities relative to the reference sample as described in the legend to Figure 3. Table 2. Subset of Genes Whose Expression Ratios Were Identified as Significantly Decreased by Treatment with Both N-OH-AABP (A) and BPDE (B) Using Statistical Analysis with 95% Confidence

gene name

gene symbol

ESTs BCL2/adenovirus E1B 19kDa interacting protein 3 3-hydroxy-3-methylglutarylcoenzyme A synthase 1 serine (or cysteine) proteinase inhibitor a

gene bank accession ID

function

BNIP3

W45031 W96205 AA063521

HMGCS1

T56013

protects against cell death lipid biosynthesis

SERPINB1

AA486275

Signal transduction

fold change in A

Z in A

NFDa in A

fold change in B

Z in B

NFDa in B

2.0 1.7 2.8

-17.7 -10.0 -9.5

0.00 0.15 0.22

2.0 2.3 4.3

-12.8 -15.4 -21.2

0.02 0.01 0.00

2.3

-9.3

0.27

3.5

-15.4

0.01

1.7

-9.0

0.33

1.6

-7.8

0.94

NFD, number of falsely discovered genes in a list of candidates showing common patterns of expression.

sorted on the bases of direction, either up or up or down regulated relative to controls. Using this analytical approach we identified a total of 2250 genes with statistically significant changes in expression after treatment with N-OH-AABP treatment, and 1865 genes whose expression levels were significantly changed by treatment with BPDE (95% confidence). Among these was a subset of genes that showed a common pattern of regulation after treatment with any dose of N-OH-AABP and BPDE, albeit with variable fold changes (Figure 6 and Table 2 and Supporting Information, Table 1). Together, the subsets of commonly regulated genes were indicative of a general cellular response to toxicity. We next focused on the sets of genes whose expression levels were differentially affected by exposure to the BPDE and N-OH-AABP (95% confidence). These included 1731 genes whose expression was increased only by N-OH-AABP treatment relative to controls, and 1346 genes whose expression was changed solely by BPDE

treatment compared to untreated controls. Among these was a subset 703 genes, whose changes in expression level after exposure to one chemical was in the opposite direction in cells exposed to the other chemical. Among these, 403 genes had significantly lower expression in BPDE compared to N-OH-AABP treated cells, while 300 genes had significantly higher expression in BPDE compared to N-OH-AABP treated cells (Table 3). Among the nine genes whose expression levels were decreased in a dose dependent manner after both BPDE and N-OH-AABP treatments only two genes, 3-hydroxy3-methylglutaryl-coenzyme A synthase (HMGCS1) and BCL2/adenovirus E1B 19kDa interacting protein 3 (BNIP3), were confirmed by statistical analysis using a linear regression model (compare cluster A in Figure 3 with Table 2). Similarly, only two out of 87 genes that were decreased by both N-OH-AABP and BPDE treatments, SERPINB1, and an EST were confirmed by linear regression analysis (compare cluster B in Figure 3 with Table 2).



Figure 5. Clustering of mean log2 gene expression ratios of genes showing differential responses to carcinogen treatments using SOMs (32, 33). Panels E-M show representative examples of gene clusters that showed differential dose-dependent responses to BPDE (9) and N-OH-AABP ([) at 5 (1), 15 (2), and 40% toxicities (3). The fold change in gene expression for each feature was determined as a ratio of fluorescence intensities relative to the reference sample as described in the legend to Figure 3.

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Figure 6. Distribution and overlap of differentially expressed genes among treatment groups. Shown in the Venn diagram are the number of genes whose expression ratios were elevated (red font) or decreased (green font) in treatment vs control groups (95% confidence). The blue circle (left) indicates genes affected only by treatment with N-OH-AABP. The yellow circle (right) indicates genes affected only in BPDE-treated cells. The intersection represents the set of genes affected by both treatments. Genes changed in both N-OH-AABP and BPDE treatments; 514 genes were up-regulated, and five genes were downregulated after both exposures.

Similarly, only a fraction of the genes in the other clusters in Figure 4 were confirmed by statistical analysis. Moreover, there was no correlation between the calculated mean change in the level of gene expression and statistical significance. These findings attest to the fact that selecting an arbitrary cutoff in the observed fold change in gene expression as a measure of significance can lead to misleading interpretations of the data. In view of the above findings, only those genes identified as significant by regression were used for biochemical inference. We classified theses on the basis of gene ontology using publicly available bioinformatics tools and databases, to classify these differentially expressed genes based on ontology.

Discussion The biological response of cells to toxicants and stressors is complex and varies with dose, length and mode of exposure (34-36). It is thus imperative that gene expression profiles be anchored to biological endpoints that can be quantified. In the present study, we compared gene expression profiles in a defined in vitro model of human exposure to carcinogens, and correlated expression profiles to the induced levels of toxicity and mutagenicity. The compounds used in the present study dramatically illustrate this point. We found that BPDE was at least 1000 times more toxic and mutagenic than N-OH-AABP. To achieve levels of toxicity and mutagenicity comparable to those obtained after 1 h of exposure to BPDE, TK6 cells had to be exposed to concentration of OH-AABP dose about 30 times and for 27 h. Since gene expression profiles were compared to those in parallel cultures exposed only to the vehicle, differences resulting from the difference in exposure times were not a confounding factor. Using statistical regression, we identified a total of ∼2250 genes/ESTs with statistically significant changes in expression after treatment with N-OH-AABP treatment, and 1865 genes/ESTs whose expression levels were significantly changed by treatment with BPDE (95% confidence). Among these was a subset that showed a

Luo et al.

common pattern of regulation after treatment with any dose of N-OH-AABP and BPDE, albeit with variable fold changes (Figure 6, Table 2 and supplement Table 1). These included 514 genes that showed increased expression, and five that showed decreased expression after all chemical treatments. The latter subset of commonly decreased genes included BCL2/adenovirus E1B 19 kDa interacting protein 3 (BNIP3), serine (or cysteine) proteinase inhibitor (SERPINB1), and hydroxy-3-methylglutaryl-coenzyme A synthase (HMGCS1) (Table 2). These genes function in cell survival, cell growth and hence their decreased expression could be indicators of toxicity (37, 38). The subset of commonly induced genes included stress response genes, including major members of the heat shock class proteins [Hsp 40 homologue (DNAJ), Hsp70, Hsp105, and Hsp125] and metal regulatory transcription factor 1 (MTF1) (39). Another gene whose expression was consistently upregulated after both exposures was the XPA gene, which encodes a DNA damage recognition protein involved in excision repair. Another class of genes that was commonly induced in cells treated with N-OHAABP and BPDE included a number of genes involved in RNA transcription, among them Polymerases (e.g., Pol II), arginine/serine-rich five splicing factor (SFRS5), and transcription elongation factor (TCEB3). Also included were genes encoding proteins regulated by p53 (PIG11, TP53BP2), a number of transcription factors (zinc finger proteins), transmembrane proteins, solute carrier families, metabolism proteins and signaling molecules associated with alterations in cell growth (e.g., ID2, EDG1, CGR19, RANBP6, RANBP2, TOB2, H2BFQ, PPARAL, CDK9, CDC16, CCNT2, and MAP2K4) (Supporting Information, Table 1). Inflammatory response genes such as small inducible cytokine (SCYA4), interleukin receptors (IL18R1, IL13RA1) were induced 1.5-2.5-fold. Together, these subsets of commonly regulated genes were indicative of a general cellular response to toxicity. The previous studies of Bartosiewicz et al. (10, 11) used DNA arrays comprising hundreds of genes involved in xenobiotic metabolism, DNA repair enzymes, the stress response, cytokines, and house keeping genes compared gene expression patterns induced in the liver and kidney of mice exposed to B[a]P. Surprisingly, these studies did not report the induction of any of the stress response genes (e.g., Hsp 105, Hsp 25, and Hsp 86) detected in the our study. The observed differences could reflect differences in dose, toxicity or tissue specificity. However, since there is no common phenotypic anchor, it is not possible to determine the variables that contributed to differences in the transcription responses. The importance of phenotypic anchoring or expression data is even more imperative when comparing different compounds. Gene expression profiles induced by individual compounds varied significantly as a function of toxicity. Thus gene expression profiles generated by equivalent doses of compounds with dramatically different toxicities will be even less comparable. We attempted to mitigate the confounder by anchoring gene expression profiles to comparable levels of toxicity. At comparable levels of toxicity, differences in gene expression are presumably related to the biochemical mechanisms that determine the 1000-fold difference in the toxicity and mutagenicity of these two carcinogens. Thus the data obtained allowed us to generate testable hypotheses of underlying the mechanisms.



Table 3. Subset of Genes Whose Expression Ratios Were Identified as Significantly and Differentially Changed after Treatment with N-OH-AABP (A) and BPDE (B) Using Statistical Analysis with with 95% Confidence gene ontology gene name thymine-DNA glycosylase excision repair cross-complementing rodent repair deficiency, complementation group 5 mutL (E. coli) homologue 3 damage specific DNA binding protein 2 cisplatin resistance-associated overexpressed protein p53 inducible ribonucleotide reductase small subunit 2 homologue cisplatin resistance associated glutathione transferase omega HMT1 (methyltransferase)-like 1 apical protein, Xenopus laevis-like RAN, member RAS oncogene family TNF receptor-associated factor 1 tumor necrosis factor receptor superfamily, member 6 tumor necrosis factor (ligand) superfamily, member 13b suppression of tumorigenicity 7 BCL2-related protein A1 RBP1-like protein retinoblastoma-binding protein 1 prostate cancer overexpressed gene 1 MAX-interacting protein 1 inflammatory response CD83 CD86 CD58 interferon stimulated gene small inducible cytokine subfamily B small inducible cytokine A3-like 1 IKK-related kinase epsilon polymerase (RNA) III (DNA directed) (62 kDa) ELL-related RNA polymerase II, elongation factor

gene symbol

gene bank accession ID

Z score

fold change in A

fold change in B

repair TDG ERCC5

AA496947 N62586

5.65 -4.33

1.00 1.00

1.81 -1.22

MLH3 DDB2 LUC7A p53R2

AA682848 AA410404 AA412738 AA495950

-4.57 -4.69 -5.93 -4.97

1.00 1.00 1.00 1.49

-1.40 -1.78 -4.28 -1.25

CRA GSTTLp28 HRMT1L1 APXL

W72697 W81192 N52195 H49455

3.80 6.39 5.74 9.81

1.85 1.28 1.57 3.44

1.00 1.00 1.00 1.00

AA456636 R71691 AA293571 AA166695 T92561| AI821369 AA459263 AA046204 AA128328 T72067 AA705886

7.33 5.87 7.62 3.79 3.70

1.00 1.00 1.00 1.00 1.00

1.58 3.05 1.67 1.38 1.40

4.75 -5.26 -4.64 -4.40 -9.83

1.00 1.64 1.33 1.00 1.00

2.34 1.00 -1.18 -1.37 -2.33

5.82 5.16214 5.08508 4.875 8.60 5.52 6.27

1 1 1 1 1 1 1

2.95 1.92 1.90 11.62 10.05 3.42 1.77

suppressor genes RAN TRAF1 TNFRSF6 TNFSF13B ST7 BCL2A1 BCAA RBBP1 POV1 MXI1 CD83 CD86 CD58 ISG20 SCYB10 SCYA3L1 IKKE transcription RPC62 ELL2

AA111969 H16746 AA136271 AA150500 AA878880 R47893 AA022666 AA282063 W47105| AA284232 AA418832 T55835

4.96 4.37

1 1

1.32 1.53

3.80 -3.71

1 1

1.49 -1.40

cell division, growth, and apoptosis mitogen-activated protein kinase kinase kinase 7 MAP3K7 N46414 CDC14 (cell division cycle 14, S. cerevisiae) homologue B CDC14B AA417319 cell division cycle 25C CDC25C W95001 cyclin G2 CCNG2 AA489647 cyclin E2 CCNE2 AA425442 synaptojanin 1 SYNJ1 H05085 synaptojanin 2 SYNJ2 N47008 insulin induced gene 1 INSIG1 H59620 insulin receptor substrate 1 IRS1 AA460841 lymphoid-restricted membrane protein LRMP AA457051 growth differentiation factor 10 GDF10 R52085 myelin gene expression factor 2 MEF-2 H96671 cell division cycle 25A CDC25A AA071514

-5.94 -4.34 -4.58 -6.90 -3.90 -3.97 -7.33 -4.20 -4.01 -5.83 -5.68 -7.43 5.38

1 1.41 1 1 1.25 1.65 1.60 1 1 1 1.29 1.48 1

-1.38 -1.40 -1.53 -3.08 1.00 1.00 -1.27 -2.71 -1.21 -1.67 -1.43 -1.31 1.91

ribonuclease H1 histone deacetylase 6

RNASEH1 HDAC6

For example, the majority of genes that promote survival (e.g., MAP3K7), cell cycle progression (e.g., Cyclin E2), mitogenesis (e.g., several growth factors), and cell growth (MXI1) were expressed at higher levels in the N-OH-AABP treated cells. These findings are again consistent with the hypothesis that higher levels of DNA adducts in the BPDE treated cells lead to more pronounced cell cycle arrest, growth arrest, and apoptosis than treatment with ABP. Significantly, many of the differentially expressed genes encode proteins involved in the protection of DNA from mutagenic insult, including enzymes involved in detoxification and in DNA repair. By contrast the expression of all repair genes was unchanged in cells treated with N-OH-AABP as compared to untreated control cells.

Among the genes involved in detoxification of electrophiles, glutathione transferase ω (GSTTLp28) and Xenopus laevis-like apical protein (Apxl), the genes encoding cytosolic ascorbate peroxidase, were increased by 1.28 to 3.44-fold after N-OH-AABP treatment, while their expression levels in BPDE treated cells were unchanged relative to controls. Together, the observed differences in gene expression patterns generated testable hypotheses regarding the molecular basis for the differential sensitivity of TK6 cells to N-OH-AABP and BPDE. The decreased expression of genes involved in DNA repair by BPDE treatment, and an increased expression of gene function in antioxidation and detoxification by N-OH-AABP support the hypothesis that higher toxicity in BPDE than in OH-AABP treated

628


TK6 cells could be due to the less efficient repair and detoxification of BPDE, thereby increasing the internal effective dose. Whether this differential toxicity is indeed associated with a different biologically effective dose will be verified by comparing the steady state levels of DNA adducts among the treated cell populations. In preliminary studies using HPLC-MS, we have already demonstrated a linear relationship between adduct levels and toxicity in N-OH-AABP treated cells, and studies are in progress for BPDE (unpublished). Previous studies have demonstrated that TK6 have ability to repair BPDE DNA adduct with a half-life of 12.3 h (40). Measurements of DNA-carcinogen adducts formation and persistence will be used to validate the hypothesis that differential internal doses of these compounds, as determined by the set of genes whose expression they affect, are responsible for their differential toxicities. For example, BPDE or ABP adducts levels at low, medium and high toxicity levels can be compared. Thus mutation efficiency, the number of induced mutation at HPRT or TK locus by each DNA adduct, can be calculated. If DNA adduct levels of BPDE were the same as those of ABP, then the two kinds of adducts have similar mutation efficiency and therefore similar toxicity and mutagenicity. Since cells were exposed at 27 times longer and about 30-fold higher concentration of OH-AABP to yield the similar level of toxicity as BPDE, either ABP adducts were repaired much faster than BPDE adducts or OH-AABP was less permeable across the membrane than BPDE. However, if ABP DNA adduct levels were found to be much higher than those of BPDE, ABP adducts had much less mutation efficiency and were much less mutagenic and toxic compared to BPDE DNA adducts. Although this study has demonstrated the value of phenotypic anchoring and has generated testable hypotheses regarding the dramatic differences in the toxicity and mutagenicity, it has significant limitations. Since we used printed cDNA arrays comprised of only ∼18 000 gene and ESTs, a fraction of which are redundant, it is unlikely that we have captured all the gene or biochemical pathways that contributed to differential toxicity of these two compounds. Furthermore, the limited number of microarrays used in this study coupled to the relatively low levels of toxicity used, resulted in relatively small changes in gene expression levels. As a result, statistical analysis could only be performed if we combined the data from arrays generated with cells treated at different doses of carcinogen. Thus, the current analysis focused on the common set of genes that showed altered expression at all dose levels, and hence were maximally induced by the lowest dose. Clearly the analysis of genes whose expression levels changed at lower or higher dose levels would identify additional pathways involved in the differential toxicity of ABP and B[a]P. The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO Series accession number GSE2345.

Acknowledgment. This research was supported by Public Health Services Grants from the National Cancer Institute including Grant UO1 CA88361 (Cheryl L. Willman, P.I.) and Grant RO1 CA69390 (P.V.), and grants from the National Institute for Environmental Health Sciences, including Grant NIEHS U19ES011387 to the NIEHS sponsored Grant Toxicogenomics Research

Luo et al.

Consortium (H.Z., P.I.) and Grant P30ES07033 to NIEHS sponsored Center for Ecogenetics and Environmental Health (David Eaton, P.I.). This is publication No. 860 from the Barnett Institute. Supporting Information Available: Table of the subset of genes whose expression ratios were defined as significantly increased by treatment with both N-OH-AABP (A) and BPDE (B) using statistical analysis with 95% confidence. This material is available free of charge via the Internet at http://pubs.acs.org.

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Phenotypic Anchoring of Global Gene Expression Profiles Induced by

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