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Regulatory Toxicology and Pharmacology
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Assessment of genotoxic potential of Cr(VI) in the mouse duodenum: An in silico comparison with mutagenic and nonmutagenic carcinogens across tissues
Chad M. Thompson a'*, J. Gregory Hixonb, Deborah M. Proctorc, Laurie C. Hawsb, Mina Suhc, Jonathan D. Urban b, Mark A. Harris a
a ToxStrategies, Inc., Katy, TX 77494, USA b ToxStrategies, Inc., Austin, TX 78759, USA c ToxStrategies, Inc., Orange County, CA 92688, USA
ARTICLE INFO ABSTRACT
In vitro studies on hexavalent chromium [Cr(VI)] indicate that reduced forms of this metal can interact with DNA and cause mutations. Recently, Cr(VI) was shown to induce intestinal tumors in mice; however, Cr(VI) elicited redox changes, cytotoxicity and hyperplasia - suggesting involvement of tissue injury rather than direct mutagenesis. Moreover, toxicogenomic analyses indicated limited evidence for DNA damage responses. Herein, we extend these toxicogenomic analyses by comparing the gene expression patterns elicited by Cr(VI) with those of four mutagenic and four nonmutagenic carcinogens. To date, tox-icogenomic profiles for mutagenic and nonmutagenic duodenal carcinogens do not exist, thus duodenal gene changes in mice were compared to those elicited by hepatocarcinogens. Specifically, duodenal gene changes in mice following exposure to Cr(VI) in drinking water were compared to hepatic gene changes previously identified as potentially discriminating mutagenic and nonmutagenic hepatocarcinogens. Using multivariate statistical analyses (including logistic regression classification), the Cr(VI) gene responses clustered apart from mutagenic carcinogens and closely with nonmutagenic carcinogens. These findings are consistent with other intestinal data supporting a nonmutagenic mode of action (MOA). These findings may be useful as part of a full weight of evidence MOA evaluation for Cr(VI)-induced intestinal carcinogenesis. Limitations to this analysis will also be discussed.
© 2012 Elsevier Inc. All rights reserved.
Article history: Received 6 January 2012 Available online 15 June 2012
Keywords:
Hexavalent chromium (Cr(VI))
Toxicogenomics
Mutagens
Nonmutagens
Principal components analysis (PCA) Logistic regression classification
1. Introduction
Inhalation of hexavalent chromium [Cr(VI)] has long been recognized to pose a carcinogenic risk to the lung (IARC, 1990). Oral exposure to Cr(VI) at environmentally relevant levels has been widely considered not to pose a cancer risk due to reduction of Cr(VI) to poorly absorbed Cr(III) by bodily fluids and cellular constituents (De Flora et al., 1997; Proctor et al., 2002; US EPA, 1991). However, chronic exposure to very high levels of Cr(VI) in drinking water resulted in intestinal neoplasms in mice in a recent 2-year bioassay (NTP, 2008). Notably, the carcinogenic Cr(VI) concentrations in the drinking water were bright yellow and were associated with reduced water intake due to unpalatability (NTP, 2008; Thompson et al., 2011b). Considering that the intestinal carcinogenesis in mice occurred at unusually high Cr(VI)
* Corresponding author. Address: ToxStrategies, Inc., 23501 Cinco Ranch Blvd., Suite G265, Katy, TX, USA. Fax: +1 832 218 2756.
E-mail addresses: cthompson@toxstrategies.com (C.M. Thompson), ghixon@ toxstrategies.com (J. Gregory Hixon), dproctor@toxstrategies.com (D.M. Proctor), lhaws@toxstrategies.com (L.C. Haws), msuh@toxstrategies.com (M. Suh), jurban@ toxstrategies.com (J.D. Urban), mharris@toxstrategies.com (M.A. Harris).
0273-2300/$ - see front matter © 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.yrtph.2012.05.019
concentrations, it is critical to understand the mode of action (MOA) of the intestinal tumors in mice because it informs the relevance of these tumors to humans as well as the low-dose extrapolation methods employed for the derivation of Cr(VI) toxicity criteria in various media (e.g. drinking water). To this end, a comprehensive research program was conducted to gather critical data needed to inform the MOA underlying Cr(VI)-induced intestinal carcinogenesis (Kopec et al., 2012a; Thompson et al., 2011a,b).
An important consideration in these studies is whether Cr(VI) acts via a mutagenic or nonmutagenic MOA in the small intestine. Mutagens interact directly with DNA and are generally thought to exhibit a non-thresholded dose-response,1 whereas nonmutagenic (i.e. indirect) genotoxic carcinogens and nongenotoxic carcinogens exhibit thresholded behavior (Bolt et al., 2004; Eastmond, 2008). As part of our research into the MOA of Cr(VI)-induced intestinal carcinogenesis, in vivo micronucleus formation and k-ras mutations
1 However, according to US EPA (2005a), ''Special attention is important when the data support a nonlinear mode of action but there is also a suggestion of mutagenicity. Depending on the strength of the suggestion of mutagenicity, the assessment may justify a conclusion that mutagenicity is not operative at low doses and focus on a nonlinear approach...''.
were assessed in duodenal tissue sections of mice exposed to Cr(VI) up to 90 days, and were found to be negative (Harris et al., 2012; O'Brien et al., in preparation). Toxicogenomic evaluation of responses to Cr(VI) in the mouse small intestine indicated activation of Nrf2 signaling at relatively low exposure concentrations (Kopec et al., 2012a), consistent with clear alteration in cellular redox status in similarly treated mice (Thompson et al., 2011b). Several genes involved in DNA repair were elevated by day 8 of exposure to carcinogenic concentrations of Cr(VI), and functional analysis indicated enrichment of DNA repair pathways at the highest Cr(VI) concentrations (Kopec et al., 2012a). Beyond transcriptional and functional analyses, it is of interest to scientists and risk assessors to examine whether the genomic signature/profile of Cr(VI) is similar to that of known mutagens. Due in part to the low incidence of cancer in the small intestine (Greaves, 2007), there are insufficient toxicoge-nomic profile data from the rodent small intestine with which to compare the duodenal toxicogenomic data of mice treated with Cr(VI). There are, however, toxicogenomic comparisons for muta-genic and nonmutagenic carcinogens in rodent liver.
Ellinger-Ziegelbauer et al. (2005) exposed rats to carcinogenic concentrations of eight hepatocarcinogens (four mutagenic and four nonmutagenic) for up to 2 weeks, and identified a subset of genes that were useful for distinguishing between mutagenic and nonmutagenic hepatic carcinogens. In the absence of comparable intestinal data, we compared the differential expression of genes in the mouse duodenum following Cr(VI) treatment with the differential expression reported by Ellinger-Ziegelbauer and colleagues. To facilitate the comparison of the differential expression of genes across nine chemicals, we used data reduction techniques (e.g. principal components analysis, PCA) and multivariate analyses to make unbiased evaluation of whether Cr(VI) was similar to the mutagenic or nonmutagenic carcinogens. The results indicate that the expression pattern induced by Cr(VI) more closely follows that latter. These findings, notwithstanding the limitations discussed herein, may be useful as part of a weight of evidence to evaluate the MOA for Cr(VI)-induced intestinal carcinogenesis.
2. Material and methods
2.1. Animal treatments and tissue preparation
Test substance, animal husbandry, and study design have been described in detail elsewhere (Thompson et al., 2011b). Briefly, female B6C3F1 mice (Charles Rivers Laboratories International, Inc.) were provided ad libitum access to Cr(VI), as sodium dichromate dihydrate (SDD), in drinking water at concentrations ranging from 0.3-520 mg/L. After 7 and 90 days of exposure (referred to herein respectively as day 8 and 91), animals were euthanized using CO2. For toxicogenomic analyses, duodenal samples were scraped and processed as described previously (Kopec et al., 2012a).
2.2. Microarray analysis of Cr(VI) data
Details on mouse 4x44 K Agilent whole-genome oligonucleo-tide microarrays and data analysis for SDD-elicited duodenal gene expression at day 8 and 91 are described in Kopec et al. (2012a). In brief, total RNA was isolated according to the manufacturer's protocol with an additional acid phenol:chloroform extraction, resus-pended in RNA storage solution (Ambion Inc., Austin, TX), quantified (A260), and quality was assessed by evaluation of the A260/A280 ratio and by visual inspection of 1 ig total RNA on a denaturing gel. Dose-dependent changes in gene expression were examined using mouse 4 x 44 K Agilent whole-genome oligonucleotide microarrays (Agilent Technologies, Inc., Santa Clara, CA).
All experiments were performed with three biological replicates and independent labeling of each sample (Cy3 and Cy5, and dye swap) for every dose group at each time point. Microarray data were normalized using a semi-parametric approach (Eckel et al., 2004, 2005).
2.3. Gene expression data selection for comparisons
These Cr(Vl) gene expression data were compared to previously published gene expression data for four genotoxic and four non-genotoxic hepatic carcinogens (Ellinger-Ziegelbauer et al., 2005). The genotoxic carcinogens were 2-nitrofluorene (2-NF), dimethyl-nitrosamine (DMN), 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), and aflatoxin B1 (AB1); the nongenotoxic carcinogens were methapyrilene (MPy), diethylstilbestrol (DES), Wy-14643 (WY), and piperonylbutoxide (PBO). Importantly, the four "genotoxic" hepatic carcinogens were characterized as inducing DNA modification and causing mutations and physical distortion of DNA (Ellinger-Ziegelbauer et al., 2004, 2005), and have been characterized as mutagens by the U.S. EPA (US EPA, 2005b; US EPA, 2007). Thus, we will use the terminology mutagenic/non-mutagenic as opposed to the genotoxic/nongenotoxic terminology used by Ellinger-Ziegelbauer and colleagues. Moreover, the term genotoxic is somewhat ambiguous. For example, WY can induce oxidative DNA damage (i.e. genotoxicity), yet many scientists consider carcinogens that work primarily through oxidative stress and oxidative damage as nongenotoxic (Ellinger-Ziegelbauer et al., 2005; Klaunig et al., 1998).
Treatment of rats with these eight carcinogens resulted in significant expression of 651 probe sets, corresponding to 477 nonredundant genes, as measured by Affymetrix RG U34A arrays, which were further grouped into 23 toxicological categories (Ellinger-Ziegelbauer et al., 2005). Seven categories (comprised of 139 genes) were discussed in greater detail by Ellinger-Ziegelbauer and colleagues: oxidative stress/DNA response (13), oxidative stress/ protein damage response (25), oxidative stress response (13), stress response (9), regeneration (34), cell survival and/or proliferation (25), and cell cycle progression (20). Several (but not all) of the genes in these categories were differentially expression by the two classes of carcinogens. Gene expression data for all 477 genes were obtained from the Supplementary Material in Ellinger-Ziegelbauer et al. (2005). Data were available for 1, 3, 7 and 14 days of exposure; however, only the 7 day exposure data were averaged (3 replicates for DMN, NNK, AB1, MPy, DES, Wy, PBO and 2 replicates for 2-NF) in order to obtain a single day 8 expression value for each of the 139 genes for each of the 8 carcinogens.
The treatment doses employed in Ellinger-Ziegelbauer et al. (2005) were concentrations known to be carcinogenic to rats in longer-term bioassays; moreover, the doses also caused histopa-thological changes in the course of their short-term study. For consistency, we therefore selected the 520 mg/L SDD (182 mg/L Cr(Vl)) treatment group because it is carcinogenic in a 2-year bioassay (NTP, 2008), and elicited histopathological lesions in the mouse duodenum at day 8 (Thompson et al., 2011b).
To allow for direct comparison between our data and those of Ellinger-Ziegelbauer et al. (2005), 651 significant Affymetrix rat probes from Ellinger-Ziegelbauer et al. were converted to unique HomoloGenelDs using Database for Annotation, Visualization, and lntegrated Discovery and mapped to the mouse whole-genome 4 x 44 K Agilent array. Of the 477 unique genes, orthologous mapping identified 395 (82%) mouse genes for which Cr(Vl)-elic-ited gene expression changes at 520 mg/L SDD were available. Of the 139 unique genes in the 7 aforementioned categories, orthol-ogous mapping identified 116 (83%) mouse genes for which Cr(Vl)-elicited gene expression changes at 520 mg/L SDD were available.
2.4. Toxicogenomic data analysis
Expression data from Supplemental material in Ellinger-Ziegelbauer and the Cr(Vl) study were uploaded for transcription factor analysis using lngenuity Pathway Analysis (lPA) with filtering criteria of 1.5-fold change in expression. Hierarchical clustering was performed using MultiExperiment Viewer (MeV v. 4.6.0) of the TM4 microarray software suite (Saeed et al., 2003).
2.5. Data reduction via principal components analysis (PCA)
The objective of this analysis is to use differential gene expression data from 116 orthologs to investigate the relationship (if any) between those changes and genotoxicity for eight carcinogens with MOAs characterized as mutagenic or nonmutagenic, and then use that relationship as the basis for classifying a chemical for which the genotoxicity is uncertain, Cr(Vl). To reduce the dimension of the data from the 116 inputs to a more manageable number, unro-tated orthogonal PCA was employed - which is a standard technique for data reduction (Hair et al., 1998; Tabachnick and Fidell, 2007). The gene expression data, provided as ratios, were normalized by log10 transformation, which is the natural transform for ratio data (Keene, 1995). The Pearson correlation matrix amongst the transformed variables was used as the input to the PCA. Horn's parallel analysis as modified by Glorfeld (Glorfeld, 1995; Horn, 1965) -a simulation-based test in which eigenvalues for the first, second, third, etc. components from a series of simulated random datasets of the same size and number of variables as the actual data are compared with the eigenvalues from the actual data - was used to determine the number of components to retain. Only those components from the actual data whose eigenvalues exceed the 95th percentile of the values for the corresponding component from the simulations are retained. The PCA allows the bulk of the information from the entire set of 116 genes to be captured by a much smaller and more manageable number of retained components, which are themselves linear combinations of the original 116 gene expression changes. These retained component scores served as the inputs to the logistic regression and cluster analyses described below. The PCA and the subsequent analyses using the retained component scores were conducted in R (R Development Core Team).
2.6. Logistic regression classification analysis
The retained component scores of the four mutagenic and four nonmutagenic carcinogens were used as inputs to a logistic regression classification analysis. A logistic regression analysis was used because the research question involves a dichotomous outcome (mutagenic or nonmutagenic) and the logistic function is a bounded function ideally suited for problems involving dichoto-mous outcomes (Montgomery et al., 2006). The logistic regression analysis maps the retained component scores to the genotoxicity outcome (mutagenic vs. nonmutagenic) for the 8 carcinogens. The parameters from this analysis were then used to assess the retained component scores for Cr(Vl) to determine its most probable classification.2
2.7. Cluster analysis
To further explore the question of whether the gene changes associated with exposures to Cr(Vl) in drinking water more closely
2 For a general discussion of the use of a logistic-based classifier for this purpose, see Hastie et al. (2001). For a discussion of the mathematical superiority of a logistic-based classifier relative to another classical alternative, discriminant function analysis, see Press and Wilson (1978).
resembled those associated with known mutagenic or known nonmutagenic carcinogens, a cluster analysis was performed using the retained component scores for the four mutagenic and four nonmutagenic carcinogens. The general method involved maximum likelihood estimation via expectation maximization, with the quality of the candidate cluster solutions indexed by the Bayesian Information Criterion (BIC).3 Models where the clusters could have a variable size, variable shape (within the ellipse family), and variable orientation were considered. With only four mutagenic and four nonmutagenic carcinogens, the cluster analysis was limited to determining if the data comprised one cluster or two, as there were insufficient data to consider models with greater numbers of clusters. This was not a limitation because we were only interested in two outcomes (mutagenic and nonmutagenic). The cluster analysis was conducted using the mclust package for R (Fraley and Raftery, 2002, 2006). As with the logistic regression classifier analysis, after assessing the four mutagenic and four nonmutagenic carcinogens, the cluster analysis solution was then used to classify Cr(VI).
3. Results
3.1. Comparison of gene expression
Following treatment of rats with eight carcinogens, Ellinger-Ziegelbauer et al. (2005) identified 477 non-redundant genes that were significantly altered by at least one carcinogen p 1.7-fold. Of these 477 genes, expression values for 139 genes comprising seven toxicological categories were provided in Table 2 of Ellinger-Ziegelbauer et al. (2005). Two categories, Oxidative stress/DNA damage response and Cell cycle progression were especially noted as differentially affected by the two classes of carcinogens. Table 1 lists the expression values for genes in these two categories, viz. the maximal change in gene expression after either 1, 3, 7, or 14 days of exposure. Broadly, the genes listed for Cell cycle progression as well as Apexl were up-regulated by the nonmutagenic carcinogens, whereas the genes listed in their Oxidative stress/DNA damage response category were up-regulated by mutagenic carcinogens (Ellinger-Ziegelbauer et al., 2009, 2005). Listed in the center column of Table 1 are the fold-change values after 7 days of exposure to 520 mg/L SDD (182 mg/L Cr(Vl)). Generally, the magnitude and direction of change for each gene following Cr(Vl) exposure is more similar to nonmutagenic compounds.
To further compare the Cr(Vl) gene changes with those of the eight carcinogens in Ellinger-Ziegelbauer et al. (2005), we obtained the expression values for the 7-day treatment groups from the Supplemental material in Ellinger-Ziegelbauer et al. (2005). Of the 477 non-redundant rat genes, 395 (82%) orthologous mouse genes were identified (see Section 2.3). Expression data for these 395 orthologs were uploaded into lPA and subject to transcription factor analysis. As shown in Table 2, p53 activity was predicted to be activated in three of the four mutagenic carcinogens, but only one of four nonmutagenic carcinogens. Notably, p53 was not predicted to be activated using this set of 395 orthologs (Table 2) or when using the entire day 8 520 mg/L SDD microarray dataset (data not shown).
During lPA analysis, we noted that Mdm2 was increased p 1.5-fold by all mutagenic carcinogens but was unchanged by the nonmutagenic carcinogens at day 8. Other notable gene changes from Table 1 are Cdknla, CcngI and Mgmt, which are all regulated by p53 and purported to discriminate between muta-genic and nonmutagenic carcinogens (Boverhof and Gollapudi,
3 The BlC is a commonly used index of model quality that optimizes the trade-off between complexity and fit - in essence finding the most parsimonious model that fits the data well.
Table 1
Select Comparisons of Genes Involved in DNA Damage Response and Cell Cycle Progression.
Gene symbol
Mutagens3
Nonmutagensa
Cell cycle progression
2.0 1.8 1.7 1.7 -1.4 -1.1 1.3
Oxidative stress/DNA damage Mdm2 S.7
Mgmt 3.2
Cdknla 17.0
Ccngl 11.2
Apexl 2.6
2.2 2.2 1.7 1.6 1.0 1.1 1.7
3.3 2.7 23.1 6.1 1.2
-1.1 -1.3 1.1 1.1 -1.1 -1.2 1.1
2.7 14.S
7.8 1.2
1.2 1.1 1.2
1.4 1.3 1.1
4.9 13.0 12.8 -1.1
3.0 3.4
1.4 2.2
-1.3 1.2 -1.1
2.4 1.8
3.5 3.1
1.5 1.4 2.1 2.1
2.1 1.8
2.2 1.4 2.1 1.9 2.1
1.4 1.3
1.5 1.9 2.3
5.3 З.9
3.4 2.4 6.4 З.8
1.1 2.1 2.3
2.7 З.9
a Gene list and fold-change expression values were obtained from Fig. 2 and Table 2 of Ellinger-Zigelbauer et al. (2005). Mutagenic and nonmutagenic carcinogens were administered at known carcinogenic doses (based on longer-term studies) daily for up to 14 days. Italicized values represent maximum change from either 1 and/or 3 days of exposure, whereas non-italicized values represent a maximum change after 7 or 14 days of exposure as reported in Table 2 of Ellinger-Ziegelbauer et al. (2005). All nonmutagenic compounds elicited weak to moderate hyperplasia, whereas the mutagenic compounds produced variably hypertrophy, necrosis, and mitosis.
b Values for Cr(VI) represent changes in the duodenum after 7 days of exposure to 520 mg/L SDD (182 mg/L Cr(VI)), which induced histopathological lesions such as hyperplasia, atrophy, and cytoplasmic vacuolization (Thompson et al., 2011b).
Table 2
Transcription factor analysis.3
2-NF DMN NNK AB1 SDDb Mpy DES WY PBO
TP53 STAT3 TP53 RELA MYC MYCN MYCN PPARA NFE2L2
MYCN EZH2 MYCN STAT3 STATSB MYC RELA PPARG PPARA
CDKN2A J.PPARD MED1 TP53 FOXM1 PPARA YY1 RXRA J.NFYA
RELA J.CEBPD RELA J.CEBPB MYCN J.SREBF1 NFE2L2 MYCN J.MYOD1
EZH2 TP73 GFI1 J.SMARCB1 TPS3 ESRRA J.SMAD3
TP73 J.FOXO3 KEAP1 J.MYOD1 NFATC2 TFAM J.CREBBP
TRIM24 J.HNF4A ATF4 PPARGC1B ;EP300
J.SMARCA4 J.MLXIPL J.PPARGC1B TRIM24
J.PPARD J.TCF3 |MLX MYC
J.TCF3 J.CREBBP J.MLXIPL HSF1
|PXR J.RXRA Estrogen receptor
J.CEBPA J.SREBF1 ;spi1
|PML J.SREBF2 J.TP53
J.IRF7 |Rb
J.SMARCB1
J.MED1
J.PPARA
J.NR1I3
J.PPARGC1A
J.HNF4A
J.CREBBP J.STAT1 |IRF7 J.CTNNB1
PXR = PXR ligand-PXR-Retinoic acid-RXRa. a Based on 395 orthologs, with p1.5-fold filter, day 8 data only. ь 520 mg/L SDD.
2010; Ellinger-Ziegelbauer et al., 2009, 2005; Waters et al., 2010). The fold-change expression levels of these four genes in the mouse duodenum after 7 and 90 days of exposure to Cr(Vl) indicate that only Ccngl was elevated p 1.5-fold, and only at day 8 (Fig. 1A-B). In comparison to the eight carcinogens from Ellinger-Ziegelbauer et al. (2005), the gene changes for Cr(Vl) were on par with those of the four nonmutagenic carcinogens (Fig. 1C).
We expanded the gene comparisons for the nine carcinogens to the 139 genes discussed in Ellinger-Ziegelbauer et al. (2005). Of the 139 rat genes, 116 (83%) orthologous mouse genes were identified, and subjected to hierarchical clustering in order to better visualize the expression data, as well as address the question of whether the Cr(Vl)-induced gene changes were more similar to mutagenic or nonmutagenic carcinogens. The overall gene expression pattern induced by Cr(Vl) was more similar to the nonmutagenic carcinogens, however, Cr(Vl) also clustered separately from the four
nonmutagenic compounds as indicated by the dendrogram (Fig. 2). The most noticeable similarities with the nonmutagenic compounds can be seen in the categories of Oxidative Stress/Protein Damage Response and Regeneration, whereas the most noticeable dissimilarities with the mutagenic compounds can be seen in the category of Oxidative Stress/DNA Response (Fig. 2).
3.2. Principal components analysis
As explained in the Material and methods, the gene expression data following seven days of exposure to the eight carcinogens in Ellinger-Ziegelbauer et al. (2005) and for Cr(VI) were analyzed by PCA. Horn's parallel analysis (Glorfeld, 1995; Horn, 1965) of the PCA results showed that only the first three components were significant (i.e. they had eigenvalues exceeding 95% of the eigenvalues for the corresponding component in equivalently sized and
Fold change -2 1 2
Fig. 1. Fold change of select genes involved in DNA damage response. Fold change expression of Mgmt, Cdknla, Ccngl, and Mdm2 after 7 (A) and 90 (B) days of exposure to varying concentrations of Cr(Vl) in drinking water (Kopec et al., 2012a). (C) Expression of these four genes after 7 days of exposure to mutagenic (DMN, 2-NF, NNK, AB1) and nonmutagenic (WY, PBO, MPy, DES) carcinogens (Ellinger-Ziegelbauer et al., 2005). Also shown are expression values for Cr(Vl) in the 520 mg/ L treatment groups after 7 and 90 days of exposure. Note: dotted line indicates 1.5fold increase in expression.
Cell cycle progression
Cell survival/ proliferation
Oxidative stress response
Regeneration
Oxidative stress/ DNA response
Oxidative stress/ protein damage response
Stress response
Cstb Rb1
Cdkn1a
Gass45a Mdm2
Cgref1 Zmat3 Tmem47
de Ctsd
Psmb 1
Mt2 Gss Gclc Nqo1
Atf3 Fxc1 Tat
Eif4ebp
Rpl10a
Rps1 a
¿VVV^W
Fig. 2. Hierarchical clustering. Hierarchical clustering of 116 orthologs in seven categories previously reported to be differentially expressed by mutagenic (DMN, 2-NF, NNK, AB1) and nonmutagenic (WY, PBO, MPy, DES) carcinogens. The dendrogram indicates that Cr(VI) (in the form of SDD) more closely clusters with the nonmutagenic carcinogens.
structured random datasets). Thus, the set of 116 ortholog expression scores was reduced to three component scores. Those three components respectively accounted for 33.86%, 29.02%, 19.44% of the overall variance and had standard deviations of 1.52, 1.41, and 1.15. The fourth component had a standard deviation below
1 (0.68), which is another indication (beyond the Horn's parallel analysis) that the fourth and all subsequent components were
Classification via Cluster Analysis
-2-10 1 2 3
Principal Component 1
Fig. 3. Cluster analysis. Cluster analysis of 116 orthologs results in the 4 mutagenic carcinogens (triangles) clustering together and apart from the 4 nonmutagenic carcinogens (squares). The inner ellipse is the standard variance ellipse, and the outer ellipse is the 95% confidence boundary. The square with crosshair represents Cr(VI).
inconsequential. In aggregate, the three retained component scores represented over 80% of the variance contained in the entire set of 116 values.
3.3. Logistic regression classification analysis
Using the three component scores identified based on the PCA as independent variables and the known classification (i.e., mutagenic or nonmutagenic) of the eight carcinogens from Ellinger-Ziegelbauer et al. (2005) as the dependent variable, the logistic regression analysis showed that the first component was significantly related to the classification, XA2(1) = 11.09, p < 0.001. The second and third components were not significant. The overall analysis provided an excellent fit to the data, as a comparison of the predicted values from the analysis to the actual classification of the eight carcinogens as either mutagenic or nonmutagenic revealed a perfect correspondence. When the parameters from this analysis were used to assess the retained component scores for Cr(Vl) to determine its most probable classification, Cr(Vl) was classified as a nonmutagenic carcinogen with a probability of nearly one (deviating in the fifteenth decimal place).
four mutagenic carcinogens, and the group on the right is the four nonmutagenic carcinogens. With regard to Cr(Vl), the cluster analysis assigns a probability of nearly one (deviating in the ninth decimal place) that Cr(Vl) belongs to the nonmutagenic group (Fig. 3).
3.5. Validation of logistic classifier and cluster analysis performance
To validate the overall performance of the logistic regression classifier and cluster analysis techniques used to classify Cr(Vl), we first used these techniques to classify each of the eight known carcinogens. Specifically, we left out one of the carcinogens as a test case, used the remaining seven cases as inputs to the logistic regression and cluster analysis, and then used the results to classify the eighth case. We repeated this analysis leaving out each of the eight known cases in turn, and compared the classifications with that ascribed by Ellinger-Ziegelbauer et al. (2005). The logistic regression classifier correctly classified all eight of the left-out cases on the basis of the other seven, and the cluster analysis correctly classified six of the eight left-out cases. The overall performance across these two techniques, 14 correct classifications out of 16, is significantly better than chance, XA2(1) = 9, p <0 .01.
3.4. Cluster analysis
Because the logistic regression classification analysis indicated that the third principal component was not important in relation to the distinction between mutagenic and nonmutagenic carcinogens, the cluster analysis focused only on the first two component scores. (The third component was "significant" as per Horn's parallel analysis, but that significant variance is not related to the issue of classification as either mutagenic or nonmutagenic.) The cluster analysis showed that the BIC was maximized for a two cluster solution, indicating that the first two component scores for the four mutagenic and four nonmutagenic carcinogens formed two distinct clusters (Fig. 3). The inner ellipse is the standard variance ellipse, the outer ellipse is the 95% confidence boundary, and the differing symbols depict the groupings assigned by the cluster analysis. It should be noted that the cluster analysis routine was not supplied with information as to the classification of each carcinogen as either mutagenic or nonmutagenic, but only the first two component scores based on the PCA of the ortholog expression data. On that basis, the cluster analysis segregated the carcinogens into two visibly distinct groups. The group on the left is, in fact, the
4. Discussion
Kopec et al. (2012a) recently summarized the effects of Cr(Vl) on the duodenal transcriptome in mice following 7 or 90 days of exposure in drinking water. Therein, Cr(Vl) was shown to increase the expression of several genes involved in base excision repair related to oxidative DNA damage (Sedelnikova et al., 2010) including Apel, Parpl, Pcna, Fenl and Ligl. In addition, functional enrichment analysis indicated enrichment of DNA repair pathways related to mismatch repair (520 mg/L SDD), BRCA1 signaling (520 mg/L SDD), and nucleotide excision repair (170 and 520 mg/L SDD) at day 8 but not day 91. To further investigate genotoxic responses at the transcript level, we compared the gene expression changes induced by Cr(Vl) in the duodenum with gene changes induced by four mutagenic and four nonmutagenic hepatocarcinogens. Duodenal transcripts were compared to liver transcripts because the rarity of small intestinal cancer means transcript data for small intestinal carcinogens are not readily available. Therefore, gene expression patterns elicited by Cr(Vl) in the duodenum were compared to genes differentially effected by mutagenic and nonmuta-genic hepatocarcinogens (Ellinger-Ziegelbauer et al., 2005).
The comparison of a select set of genes in Table 1 and Fig. 1 suggested that the transcript changes elicited by Cr(Vl) in the mouse duodenum were not similar to the transcript changes induced by mutagenic carcinogens in the liver. Furthermore, transcription factor analysis (Table 2) suggested that p53 signaling was activated by 3 of 4 mutagens and only 1 of 4 nonmutagens. As p53 signaling is well known to be activated by DNA damage, it is somewhat surprising that p53 was not activated by all 4 mutagens. Nevertheless, the observation that p53 signaling was not predicted to be activated by Cr(Vl) at 520 mg/L SDD, suggests that Cr(Vl) was not acting like a mutagenic carcinogen. The multivariate analytical approaches (Figs. 2 and 3) further suggest that the toxicogenomic expression profile in the duodenal epithelium following 7 days of exposure to Cr(Vl) in drinking water more closely resembled the expression profiles of nonmutagenic hepatic carcinogens. ln total, these analyses lend support to the hypothesis that the carcinogenic MOA of Cr(Vl) in the small intestine does not involve a mutagenic MOA.
lt should be stressed that these analyses do not prove that Cr(Vl) acts via a nonmutagenic MOA; however the results herein are consistent with published evidence that Cr(Vl) induces redox changes at lower concentrations than those that caused cancer in a 2-year bioassay (Kopec et al., 2012a; Thompson et al., 2011b), as well as evidence that neither k-ras mutations or crypt micronu-cleus formation could be detected in duodenal tissues of mice exposed to Cr(Vl) for up to 90 days (Harris et al., 2012; O'Brien et al., in preparation). Although oxidative DNA damage can lead to mutation, this does not mean that compounds that induce oxidative stress (e.g. WY) have a mutagenic MOA (Ellinger-Ziegelbauer et al., 2005; Klaunig et al., 1998). lt should also be recognized that the term nonmutagenic is not synonymous with nongenotoxic. As discussed in several recent reviews on chromium (Holmes et al., 2008; Nickens et al., 2010; Zhitkovich, 2011), Cr(Vl) may work through epigenetic mechanisms such as altered DNA methylation. For example, lung biopsies from workers occupationally exposed to chromate exhibit fewer p53 point mutations than expected, but instead show signs of chromosomal instability including reduced expression and hypermethylation of MutL homolog 1 (MLH1) (Hirose et al., 2002; Kondo et al., 1997). More recently, Cr(Vl) has been shown to increase methylation of histone H3 lysine 9 (H3K9) and produced time- and dose-dependent decreases in MLH1 in vitro (Sun et al., 2009).
According to NTP, only two other chemicals tested by the NTP program have caused increased incidences of neoplasms in the mouse intestine, viz. captan and o-nitrotoluene (NTP, 2008). The latter caused tumors of the large intestine (NTP, 2002), and mechanistic studies suggest that o-nitrotoluene (2-nitrotoluene) carcin-ogenicity may involve oxidative DNA damage (Watanabe et al., 2010). Captan and the structurally similar folpet induce similar histopathological changes in the mouse duodenum as Cr(Vl), are reactive toward thiols, and have been determined to have a non-mutagenic MOA (Cohen et al., 2010; US EPA, 2004). More recently, it was reported that indium phosphide caused small intestinal tumors in mice (Chandra et al., 2010). However, NTP characterized the increased incidences of neoplasms of the small intestines of male mice (from inhalation studies) as marginal and ''may have been related to exposure to indium phosphide'' (emphases added) (NTP, 2001). Nevertheless, lung tumors induced by indium phosphide are thought to involve oxidative stress (Gottschling et al., 2001; Upham and Wagner, 2001). Thus, the chemicals inducing intestinal tumors in mice investigated to date generally appear to have oxidant properties that may be responsible for tumorigenesis. lmportantly, we have recently shown that Cr(Vl) exposure significantly decreases the GSH/GSSG ratio in the duodenal epithelium of mice and induces Nrf2 signaling (Kopec et al., 2012a; Thompson et al., 2011b). Moreover, Nrf2 activation at day 91 was recently confirmed by lPA transcription factor analysis (Kopec et al., 2012b).
Despite the aforementioned consistency with other findings from our studies into the MOA for Cr(Vl)-induced intestinal carcinogenesis (Harris et al., 2012; Kopec et al., 2012a; Thompson et al., 2011b; O'Brien et al., in preparation), the genomic profile comparisons described herein have several limitations. First, the microarray platforms (Affymetric vs. Agilent), specific gene probes (60 vs. 25 nucleotide oligomers), signal normalization, and statistical analyses differed between Kopec et al. (2012a) and Ellinger-Ziegelbauer et al. (2005). Notably, a basic premise/ assumption in our analysis is that, for example, a 2-fold change in gene expression in the Ellinger-Ziegelbauer et al. dataset is ''equivalent'' to a 2-fold change in the Kopec et al. dataset. Second, the gene changes were compared across species (mouse vs. rat). Notably, there is a federal effort to compare genomic responses to chemicals in yeast, C. elegans, zebrafish embryos, and mice in the pursuit of identifying ''expression signature profiles'' that can be used to predict toxicological responses in humans (NlEHS, 2011). Thus, it is anticipated that cellular responses to DNA damage, oxidative stress, and proliferation are likely to be, in part, similar across species. Recent studies indicate that basal gene expression is quite similar across species (Chan et al., 2009; Zheng-Bradley et al., 2010). Whether this species concordance holds for response to xenobiotics is less clear; however, our own studies conducted in mice and rats generally indicate similar gene expression changes in the intestines of both species in response to Cr(Vl) (Kopec et al., 2012b). The difference in tumor outcome in the intestine of rats and mice may arise from overall differences in dosimetry (pharmacokinetics) rather than pharmacodynamics (Proctor et al., in press).
While gene expression patterns appear to be similar across species at the tissue level, the basal gene expression patterns across tissues appear to be different - even within the same species (Chan et al., 2009; Jonker et al., 2009; Zheng-Bradley et al.,
2010). Thus, a third uncertainty in the analyses herein is whether differential gene responses in the liver should be compared to differential gene responses in the duodenum. Jonker et al. (2009) exposed mice to two genotoxic and nongenotoxic carcinogens and reported that there was little overlap in the genomic responses to the carcinogens in tissues examined (liver, spleen, bladder, blood, and lymph nodes) using a microarray of 8205 oligonucleotides. Nevertheless, it is well accepted that stress and DNA repair mechanisms are highly conserved across species, and even phyla, on a structural and pathway level (Robertson et al., 2009; Taylor and Lehmann, 1998). Again, the effort to compare genomic responses to chemicals across phyla (NlEHS,
2011) suggests that many scientists believe that some of the cellular responses governing oxidative stress and DNA damage are likely to be shared across cell types. lnterestingly, analyses by Zheng-Bradley et al. (2010) indicate that gene expression patterns from different cell lines were more homogeneous than their respective tissues of origin; the authors speculated that this might be due to immortalization or the lower variability in cell culture samples. Regardless of the reason(s), the fact that gene expression profiles differ between cultured cells and their tissues of origin suggest that extrapolation of gene changes from in vitro cell models to in vivo models (i.e. body organs) may be no less uncertain than extrapolating in vivo gene changes across tissues.
Finally, it is worth noting that the Cr(Vl) exposures associated with lung cancer are primarily of particulate form and are associated with inflammation (Nickens et al., 2010). ln contrast we observed relatively little histological, biochemical, or genomic evidence for inflammation in our studies (Kopec et al., 2012; Thompson et al., 2011b). Therefore, we draw no conclusions about the applicability of the findings herein pertaining to soluble Cr(Vl) in the duodenum to particulate Cr(Vl) in the lung.
5. Conclusions
This study does not attempt to identify gene changes that distinguish between mutagenic and nonmutagenic carcinogens, but rather compare the gene changes elicited by Cr(Vl) in the duodenum to a relatively small set of genes previously shown to be differentially expressed following exposure to mutagenic and nonmutagenic carcinogens. Data reduction and multivariate statistical analyses were used in order to remove subjectivity from these comparisons. Notwithstanding the limitations discussed previously, the observation that the gene changes elicited by Cr(Vl) are more similar to nonmutagenic than mutagenic carcinogens is consistent with evidence that Cr(Vl) induced redox changes in the intestine at both carcinogenic and noncarcinogenic concentrations, as well as lack of evidence for micronucleus formation and k-ras mutation after 90 days of exposure to Cr(Vl) concentrations in drinking water as high as 182 mg/L. Thus, while there is substantial data (mainly from in vitro studies) that indicate Cr(Vl) can interact directly with DNA, target tissue data do not support a mutagenic MOA for Cr(Vl) in the small intestine. As part of a weight of evidence evaluation of the MOA for Cr(Vl)-induced intestinal carcinogenesis, the analyses in this report add further support to the hypothesis that the intestinal tumors in the mouse duodenum are the result of a nonmutagenic MOA.
Conflict of interest
Acknowledgments
The authors would like to thank Dr. Anna Kopec for assistance with toxicogenomic data. We also thank Drs. Michael Dourson, David Gaylor, Lucy Anderson, Rebecca Fry and Timothy R. Zacharew-ski for critical review of an earlier version of portions of this manuscript. This work was funded by Cr(Vl) Panel of the American Chemistry Council.
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