* Drug Safety Evaluation, Robert Wood Johnson Pharmaceutical Research Institute, P.O. Box 300, Route 202, Raritan, New Jersey 08869; and
Phase-1 Molecular Toxicology, Inc., Santa Fe, New Mexico 87505
Received May 18, 2000; accepted August 14, 2000
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ABSTRACT |
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Key Words: toxicogenomics; gene expression profiling; cDNA microarrays; non-steroidal anti-inflammatory agents; DNA damaging agents.
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INTRODUCTION |
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There is great interest in the application of this technology to toxicology, both as a powerful new tool for mechanistic studies and as a diagnostic parameter for toxicity screens that may far surpass traditional approaches in terms of sensitivity and speed. A main assumption in the use of toxicogenomics is that toxicity is accompanied by changes in gene expression that are either causally linked to the toxic outcome or are downstream sequelae of the toxic exposure. Monitoring gene expression profiles, induced directly or indirectly by different classes of toxicants, should eventually allow recognition of signature patterns that are representative of specific toxicities. Once recognized, these patterns could be used to evaluate new compounds (pharmaceutical candidates) possessing undefined toxicities. This is a compelling scenario that has received widespread attention, but to date there is little published data to support such a possibility (Afshari et al., 1999; Braxton and Bedilion, 1998
; Nuwaysir et al., 1999
).
The present studies were undertaken to evaluate the potential use of gene expression analysis to detect and distinguish toxicants with different mechanisms of action. HepG2 human hepatoma cells were selected as the model system in an attempt to minimize such complicating factors as cell type heterogeneity and interindividual differences. A single time point of 24 h was chosen to eliminate the potentially confusing contribution of nonspecific immediate early stress responses of cells exposed to toxic stimuli. To minimize the influence of potency differences, all compounds were first tested for cytotoxicity in HepG2 cells using a reductase activity assay at 72 h, and an ED30 dose was selected for monitoring gene expression at the earlier time point of 24 h.
The ability to detect reproducible gene expression patterns that are consistent within a class of toxicants and different across classes is key to the emerging field of toxicogenomics. These studies attempt to determine whether such patterns can be observed and as such are an early step towards proving principle.
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MATERIALS AND METHODS |
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Cell culture.
HepG2 human hepatoma cells were obtained from ATCC (catalog number HB-8065). Cells were maintained in log growth phase in minimal essential media (MEM) supplemented with 10% fetal bovine serum. Antibiotic or antifungal agents were not used, to avoid the potential effects of these agents on gene expression and cytotoxicity assays. Cultures were re-established after 20 passages.
Cytotoxicity assay.
HepG2 cells were grown in 96-well black, clear-bottom plates (Polyfiltronics, NUNC) at 37°C in a humidified cell culture incubator, in 5% CO2 and minimal essential media with 10% fetal bovine serum. Cells at 3050% confluence were treated with test compound (0.1 µM100 µM, in 0.5 log concentration increments) or vehicle (media or <1% DMSO) for 72 h and cellular reductase activity was measured using an Alamar Blue assay. One hundred µl of Alamar Blue (diluted 1:50 in Hanks Buffer) was added and fluorescence readings of the 96-well plate were immediately recorded using a Wallac Victor II plate reader with excitation at 535 nm and emission at 580 nm. The initial zero time-point readings (which were essentially equal to Alamar Blue readings from an empty culture plate) were subtracted from readings at 1 h to determine cell viability. Control-cell wells showed a pronounced increase in fluorescence at this time, whereas dead-cell wells were essentially equal to background. This assay is extremely sensitive and detects responses unaccompanied by cell death as measured morphologically or by assays such as LDH release. Thus concentrations producing 30% inhibition of fluorescence at 72 h were chosen to examine genomic effects of test compounds at the earlier, minimally cytotoxic time point of 24 h.
Data analysis overview.
Multiple replicate RNA samples were obtained for the genotoxic compound, cisplatin, its relatively inactive stereoisomer, transplatin, and the hepatotoxic nonsteroidal anti-inflammatory compounds diflunisal and flufenamic acid. Expression analysis was performed using a cDNA-based DNA microarray containing approximately 250 inducible genes (see Appendix) that respond during expression of various toxic endpoints. Their identification was based on extensive reviews of the scientific literature and on unpublished pilot studies that were conducted during the past several years as part of the core business activities at Phase-1 Molecular Toxicology (Farr and Dunn, 1999). Expression patterns from replicate experiments were examined for reproducibility and the subset of significantly regulated genes was used for subsequent similarity metric-based correlational analyses described below.
A second selection at the end of the study was performed using a computational algorithm that identified the reproducibly regulated genes that best clustered the class of compounds in question and also distinguished the class from different compound classes (see Figure 12 for a detailed description). To determine whether the discovered relationships could be generalized, the responses of this gene set for cisplatin, flufenamic acid, and diflunisal were then examined across a broader database of expression profiles, to approximately 100 toxic compounds in the Phase-1 toxicology database (http://www.phase1tox.com). To facilitate the comparisons, compounds were grouped by mechanism of toxicity and/or mechanism of action based upon extensive reviews of the scientific literature. From these analyses a battery of genes was discovered whose expression pattern accurately discriminates DNA damaging agents versus anti-inflammatory drugs.
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cDNA microarray hybridization and analysis.
Purified, labeled cDNA was boiled for 5 min in 30 µl of hybridization buffer (50% formamide, 5x SSC, 0.1% SDS), then cooled and maintained at 70°C. The solution was applied to the microarray slide and hybridized in a humidified custom hybridization chamber overnight at 42°C. Slides were washed in 2 x SSC, 0.2% SDS for 5 min, then 0.05 x SSC for 1 min. Slides were dried and then scanned using a confocal laser scanner, and fluorescence intensities were recorded.
Data normalization.
The data for each gene was normalized by dividing individual treated and untreated fluorescence values by the medians of the treated and untreated fluorescence values in each experiment, respectively. The expression ratio for each gene, determined by the ratio of treated to untreated values, was then log transformed.
Similarity matrix.
All statistical analysis was carried out using algorithms written in Oracle PL/SQL and Java. To measure the degree of similarity between the gene expression profiles produced by different toxicant treatments, the Pearson correlation coefficient was chosen. The Pearson correlation coefficient is a common similarity metric for hierarchical and other types of cluster analysis applied to gene expression patterns (Alizadeh et al., 2000; Ben-Dor et al., 1999
; Eisen et al., 1998
; Ross et al., 2000
; Scherf et al., 2000
; Weinstein et al., 1997
).
The Pearson correlation coefficient for experiment i and experiment j, rij, is given by:
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where xi and xj are the log-transformed expression profiles for experiment i and experiment j, (xig - i) is the deviation of gene g for experiment i from the mean expression value for experiment i, (xjg -
j) is the deviation of the same gene for experiment j from the mean for experiment j, sxi and sxj are the sample standard deviations for experiment i and experiment j, n is the number of pairs of genes, and the summation
is across the i = 1,2, ...., n pairs.
The correlation coefficient was calculated for all pairs of experiments. For m experiments, exactly m2 coefficients were calculated. Since certain genes were not examined across all experiments, missing gene expression values in each experiment were ignored in the calculation.
Graphical representation of data.
The similarity matrix was displayed using Spotfire Pro data visualization software (Spotfire Inc., http://www.spotfire.com). The toxicant expression profiles were ordered on the x and y axes by grouping together toxicants by assigned mechanisms of action. Others have used similar types of visual representations; however in those cases the order of items on the x and y axes were determined by the application of a clustering algorithm (Ben-Dor et al., 1999; Weinstein et al., 1997
).
Each of the 10,000 cells in a plot represents a comparison of the gene expression profile between 2 single toxicant treatments. The color in each cell of the plot reflects the similarity between the 2 experiments. The 2-color scale used to represent the correlation coefficient ranged from yellow for a perfect correlation coefficient of 1.0 to blue for an absolute negative correlation coefficient of 1.0. A grayish color, resulting from equal parts blue and yellow, signifies no correlation between the 2 samples (r = 0). Because of the nature of the plot, the similarity matrix is symmetric about the main diagonal and the correlation coefficients on the diagonal are unity, because each sample is 100% correlated with itself.
The initial correlational analysis was performed across the entire database of 100 toxic compounds, using all genes on the microarray ( 250). Subsequent correlations used smaller subsets of genes, which were found to be reproducibly and differentially expressed between a given set of treatments or based upon the computer optimization algorithm (see Fig. 12
).
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RESULTS |
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After RNA changes for each treatment were determined relative to control, the change in a gene's expression pattern following toxicant exposure (fold induction or repression) was compared to its change following exposure to every other compound in the database. Comparisons were performed, using the Pearson's correlation coefficient as described in Materials and Methods. This was repeated for 250 genes from all 100 toxicant samples (10,000 comparisons,
2.5 million data points), and a similarity score for the overall gene expression profile was then assigned pair-wise between compounds as described in Materials and Methods. By comparing each compound's correlation with all others, a mirror-image correlation plot was obtained that was symmetrical about a diagonal line of identity (Fig. 1
). This type of analysis revealed that inclusion of all genes on the array in the expression comparisons failed to yield significant correlations between database compounds having similar toxic or pharmacologic actions.
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This exercise showed quite clearly that certain gene induction events occurred consistently while others were highly variable. Although more than 200 genes were analyzed, only a small percentage appeared to respond similarly to cisplatin over all experiments (Fig. 2A), whereas most genes responded variably or with average fold inductions that were less than twice the standard error of the mean (Fig. 2B
). Approximately 20% of the genes analyzed in each of the cisplatin experiments were induced or repressed more than 2-fold by cisplatin exposure after 24 h. No relationship was apparent between the magnitude of a gene induction event and its reproducibility in subsequent experiments (Fig. 3
). Moreover, plotting intra-experimental coefficient of variation (COV) against inter-experimental SEM for each gene also failed to show a relationship (Fig. 4
).
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DISCUSSION |
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A variety of "toxicology arrays" are now available from several commercial vendors, consisting of genes with demonstrated or presumed relevance to toxic responses. These arrays can and will be used to produce large amounts of gene-expression data. However, utilization of the data for toxicity evaluation requires that the relevance of the gene expression patterns can be ascertained. Currently the application of toxicogenomics depends less on the availability of suitable gene arrays than on the existence of a reliable gene-expression database consisting of responses to prototypical toxic compounds. Indeed, the rate of application of toxicogenomics within the pharmaceutical industry will likely be directly related to the degree of complexity that is encountered in developing such a database. If expression patterns are found to be robust and reproducible, then meaningful and representative response patterns may be found rapidly. On the other hand, if expression patterns are found to be of lesser magnitude and reproducibility, or if they show high variability for a given biological system, depending on influences such as time or exposure conditions, then obtaining representative response patterns may become a long-term goal.
The studies described here were undertaken to determine whether toxicologically meaningful gene-expression responses could be detected in a model system. The key element was the availability of a database containing response patterns to various toxic compounds in a single-cell line under standardized conditions. It is encouraging that clustering can be observed so readily at this early stage of database development. Clusters of positive correlations were observed for compounds classified as DNA-damaging agents and for compounds classified as cytotoxic NSAIDs. For most compounds in the database n = 1, which may be suboptimal, as evidenced by the variability observed for the replicate treatments in this study. The subsets of genes used for the correlational analyses were selected using compounds for which replicate data were available (cisplatin, flufenamic acid, and diflunisal), but the strong clustering for other DNA-damaging agents and NSAIDs are based on single microarray determinations. It is interesting to speculate that the gene subsets identified in replicate analyses may represent those genes that demonstrate consistent responses for the toxicant class in question, which may explain why clustering could be observed for other compounds analyzed only once. However, until existing methodologies improve, replicate analyses may be necessary to identify significantly regulated genes with sufficient certainty for assignment of toxic mechanism. Even with improved methodology, the biological variability in the chosen model system may still require replicate analyses to determine genes that are significantly and reproducibly changed. The ability to observe correlations for mechanistic classes outside the learning set suggests that relatively small sets of genes may be sufficient to distinguish a variety of different toxic mechanisms. In the future, small "information-rich" gene chips may provide greater utility in assigning/discriminating toxic mechanisms. To identify the smaller subsets of genes useful for toxicant classification in any given system, it may be necessary to first perform transcriptional profiling using gene chips that span entire genomes. Once these genetic relationships are established, the use of gene microarrays may ultimately diminish and be replaced by more manageable and cost-effective platforms to identify unknown toxicants, based upon their effects on a limited number of predictively "valuable" genes.
The experiments in this study did detect numerous cisplatin-inducible genes that are in agreement with previous observations in the literature. These include induction of several p53-responsive genes (Aubrecht et al., 1999) including p21waf1/cip1 (Zamble et al., 1998
), GADD45 (Sun et al., 1995
) and PCNA (Shivakuvar et al., 1995). We also detected potent induction of Fas, which has been shown to mediate apoptosis in HepG2 cells exposed to cisplatin (Muller et al., 1997
). JNK activation and prolonged c-Jun induction have also been proposed to mediate apoptosis following cisplatin exposure (Nehme et al., 1997
; Sanchez-Perez and Perona, 1999
), and c-jun mRNA was robustly induced by cisplatin in the present studies, even after 24 h. Cisplatin also resulted in the repression of several genes, most notably a family of molecular chaperones which appear to be co-regulated by ATF-6 and have been implicated in preventing Ca2+-dependent cell death (Liu et al., 1998
; Yoshida et al., 1998
).
Taken together, these rather preliminary observations suggest that the ability of cisplatin to cause apoptosis in HepG2 cells may be determined by the relative levels of apoptotic- regulated versus anti-apoptoticregulated genes and activities. For instance, Fas causes apoptosis in HepG2 cells (Muller et al., 1997) but Fas-dependent apoptosis is blocked by the formation of an inactive complex between procaspase 3 and p21waf1/cip1 (Suzuki et al., 1998
), which was also induced by cisplatin in these studies. In addition, both pro-apoptotic (Bax, Bak) and anti-apoptotic (BAG-1) proteins were also induced by cisplatin in these studies (Adams and Cory, 1998
). In the future, more extensive microarray experiments could actually determine the ratios of different critical gene products that may decide cellular fate following exposure to DNA damaging agents. Although additional morphological and biochemical experiments to determine the nature of cell death were outside the scope of the present study, research correlating gene expression with cytotoxic mechanisms (for example, apoptosis vs. necrosis) could lead to improved understanding of these complex phenomena.
One illustration of the benefit of this type of microarray experiment is evidenced by the detection of cisplatin-inducible transcripts in these studies that had not been previously described, but which are, nevertheless, consistent with recent findings in the literature. For instance, these studies detected rather robust and consistent induction of a rad6 homolog in HepG2 cells exposed to cisplatin, suggesting that this mammalian homolog might play a role in the response to cisplatin-induced DNA damage. During the preparation of this manuscript, Simon et al. demonstrated that S. cerevisiae strains lacking the rad6 allele are extremely hypersensitive to the toxic effects of cisplatin compared with wild-type or other mutant rad strains (Simon et al., 2000). Thus, results from a purely functional genetic screen in yeast are consistent with our observation of mammalian rad6 homolog induction in HepG2 cells following exposure to cisplatin.
The supervised methods employed here (statistical brute force versus a computational algorithm for selecting gene sets that maximize/minimize Pearson's correlations) produced overlapping but distinct subsets of genes for discriminating between DNA-damaging agents and anti-inflammatory compounds. It is unclear at present whether either of these methods reflects the better approach until they are tested for assignment of mechanism to additional unknowns. Larger scale experiments on gene chips containing greater numbers of genes should allow the use of unsupervised clustering methods (hierarchical, K-means, neural networks) that will likely provide more powerful approaches to unbiased toxicant classification. Utilization of these types of learning methods would also allow the analysis of multiple time points to group toxicants by sets of genes that are similarly expressed in temporal fashion between treatments, likely improving pattern recognition in the future.
In the end, it will be important to compare these and other responses in HepG2 cells to other cellular and in vivo systems to determine whether response patterns are similar across different systems. The occurrence of characteristic responses across a variety of systems could allow the more rapid development of analytical capabilities. Because of an apparent deficiency in C/EBP, HepG2 cells lack several key CYPs and other enzymes responsible for metabolism (Jover et al., 1998
). This paucity of metabolic capability almost certainly affects the transcriptional responses observed in HepG2 cells and in other more metabolically competent systems. It should be possible to gauge the likely impact of this issue once a sufficient number of studies are published. In the same way it will be possible to compare the variability and magnitude of response patterns in different test systems. Additionally it may be worthwhile to monitor baseline variability using a reference set of mRNAs. Baseline variability may be an important consideration in selection of a test system, especially for methodologies using Cy3/Cy5 fluorescence, in which results are characteristically expressed relative to an untreated control.
Current efforts in this laboratory are underway to determine whether other biological systems may yield more reproducible and robust transcriptional profiles than the HepG2 model system. For instance, studying the effects of toxicants in primary rat hepatocytes will allow comparison with effects in rat liver following treatments in vivo. These types of studies will be crucial in determining whether transcriptional profiling in model systems is relevant to in vivo toxicity testing and thus useful for streamlining drug discovery. The predictive power of the rather limited gene sets identified in the present studies suggests that gene-expression-based approaches will continue to gain acceptance and application as powerful new tools for toxicity testing.
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ACKNOWLEDGMENTS |
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NOTES |
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2 To whom correspondence should be addressed. Fax: (908) 218-0668. E-mail: mjohnson{at}prius.jnj.com.
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