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Review
. 2015 Jan;45(1):1-43.
doi: 10.3109/10408444.2014.973934.

Comparison of toxicogenomics and traditional approaches to inform mode of action and points of departure in human health risk assessment of benzo[a]pyrene in drinking water

Ivy Moffat #  1   2 , Nikolai Chepelev #  2 , Sarah Labib  2 , Julie Bourdon-Lacombe  1   2 , Byron Kuo  2 , Julie K Buick  2 , France Lemieux  1 , Andrew Williams  2 , Sabina Halappanavar  2 , Amal Malik  2 , Mirjam Luijten  3 , Jiri Aubrecht  4 , Daniel R Hyduke  5 , Albert J Fornace Jr  6 , Carol D Swartz  7 , Leslie Recio  7 , Carole L Yauk  2
Affiliations
Review

Comparison of toxicogenomics and traditional approaches to inform mode of action and points of departure in human health risk assessment of benzo[a]pyrene in drinking water

Ivy Moffat et al. Crit Rev Toxicol. 2015 Jan.

Abstract

Toxicogenomics is proposed to be a useful tool in human health risk assessment. However, a systematic comparison of traditional risk assessment approaches with those applying toxicogenomics has never been done. We conducted a case study to evaluate the utility of toxicogenomics in the risk assessment of benzo[a]pyrene (BaP), a well-studied carcinogen, for drinking water exposures. Our study was intended to compare methodologies, not to evaluate drinking water safety. We compared traditional (RA1), genomics-informed (RA2) and genomics-only (RA3) approaches. RA2 and RA3 applied toxicogenomics data from human cell cultures and mice exposed to BaP to determine if these data could provide insight into BaP's mode of action (MOA) and derive tissue-specific points of departure (POD). Our global gene expression analysis supported that BaP is genotoxic in mice and allowed the development of a detailed MOA. Toxicogenomics analysis in human lymphoblastoid TK6 cells demonstrated a high degree of consistency in perturbed pathways with animal tissues. Quantitatively, the PODs for traditional and transcriptional approaches were similar (liver 1.2 vs. 1.0 mg/kg-bw/day; lungs 0.8 vs. 3.7 mg/kg-bw/day; forestomach 0.5 vs. 7.4 mg/kg-bw/day). RA3, which applied toxicogenomics in the absence of apical toxicology data, demonstrates that this approach provides useful information in data-poor situations. Overall, our study supports the use of toxicogenomics as a relatively fast and cost-effective tool for hazard identification, preliminary evaluation of potential carcinogens, and carcinogenic potency, in addition to identifying current limitations and practical questions for future work.

Keywords: benchmark dose; carcinogens; dose–response; environmental pollutant; genomics; human health risk assessment; mode of action; point of departure; polycyclic aromatic hydrocarbon; transcriptomics.

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Figures

Figure 1
Figure 1
Comparison of traditional and genomics risk assessment approaches for determination of a point of departure (POD) for BaP in drinking water. RA1: Traditional risk assessment approach. A comprehensive literature review was performed and a POD was selected based on the most sensitive apical adverse effect reported (Section 2). RA2: Genomics-informed approach. Genomics information was included in the traditional assessment and used to inform the MOA and POD selection (Section 3.1 for general methods and Section 3.2 for RA2-specific details). RA3: Genomics-only approach. This approach assumed a data-poor chemical with little or no information other than genomics information. The definitions of “data-rich” and “data-poor” compounds relate to the amount of toxicity information available for a given compound and may be agency-specific. Only genomics information was used to select a POD for BaP (Section 3.1 for general methods and Section 3.3 for RA3-specific details).
Figure 2
Figure 2
DNA adduct formation (a) and lacZ mutant frequency (b) in the lungs, livers, and glandular stomach from Muta™Mouse exposed to 25, 50, and 75 mg/kg-bw per day BaP for 28 consecutive days and excised 3 days post-exposure. Levels of dG-N2-BPDE adducts were determined using the nuclease P1 enrichment version of the 32P-postlabeling method. Data are represented as average ± SEM (n = 5 mice/group). Average lacZ mutant frequency was determined using the P-Gal positive selection assay. Values shown are average frequencies × 10−5 ± SEM. Asterisk (*) indicates significance (p < 0.05) compared with controls. Please note, no adducts were detected in mice dosed with vehicle control. All data were previously published in Lemieux et al., 2011; Malik et al., 2012; and Labib et al., 2012.
Figure 3
Figure 3
General overview of BaP metabolism. Cytochrome P450s (CYPs) and other enzymes and cellular oxidants can convert BaP to radical cations. These can be further metabolized by CYPs to epoxide and then to diols by epoxide hydrolase (EH). BaP diols are converted to catechols by aldo-keto reductases (AKRs) or to BaP-7,8-diol-9,10-epoxide (BPDE) by CYPs. BPDE, BaP quinones, reactive oxygen species (ROS) (generated during BaP catechol–quinone redox cycling or from CYPs’ uncoupling) and BaP cations react with DNA (forming adducts) and leading to DNA mutations (predominantly G to T transversions). EH can further metabolize BPDE to tetraols that, along with diols, phenols (not shown) and quinines, are conjugated to glucuronosyl by uridine diphosphate–glucuronosyl transferases (UGTs). Diols, phenols and quinones can be also converted to sulfate esters by sulfotransferases (SULTs). Glutathione S-transferases (GSTs) conjugate BaP epoxides, diol epoxides and quinones to glutathione (Ramesh et al., 2004). Only representative BaP metabolites (i.e. modified at positions 7, 8, 9 and 10) are shown. In addition, BaP metabolism can yield many other hydroxy-, oxide-, dihydroxy- and quinone-related compounds at each of the 12 carbon atoms and it has been estimated that 709 oxygenated metabolites of BaP exist (reviewed in Nebert et al., 2013a); similar BaP metabolites are formed at other positions (Ramesh et al., 2004). See Section 2.3.3 for additional details.
Figure 4
Figure 4
Cytotoxicity and genotoxicity measurements in human TK6 cells following exposure to BaP using a flow cytometry–based assay (In Vitro Microflow kit; Litron Laboratories). Relative survival (shown in blue), percentage of apoptotic/necrotic cells (shown in green) and percentage of micronuclei (MN; shown in red) are depicted following 24 hours of exposure (4-hour exposure + 20-hour recovery). * denotes p < 0.05 compared with vehicle control (VC; + S9), and error bars are standard error.
Figure 5
Figure 5
Hazard identification of BaP as a genotoxic compound by comparison of its transcriptome profile with the genotoxicity biomarker following BaP exposure in human TK6 cells in the presence of metabolic activation (1% rat liver S9) at 4, 8 and 24 hours. (a) Hierarchical clustering of the expression levels of genes in a genotoxicity signature derived from a training set of twenty-eight genotoxic (pink) and non-genotoxic (blue) agents indicates that BaP clusters with genotoxic agents at the mid- and high concentrations at all three time points. The GenBank accession numbers for the 65 classifier genes contained within the predictive gene signature are indicated on the right hand-side of the heatmap. Gene expression fold-changes relative to control are shown by means of the colour scale: upregulated genes are shown in red, downregulated genes are shown in green and genes that are not regulated are shown in black. (b) BaP transcriptome profiles were analyzed using the 65-gene classifier to predict genotoxicity. Nearest shrunken centroids classification probabilities for BaP treatments are shown using the 65-gene classifier. The NSC method was employed to classify BaP transcriptome profiles by examining them for similarities with the transcriptome profiles of the reference chemicals in the database using statistical and bioinformatics tools. Briefly, the standardized centroid (SC) was computed by applying the NSC method for each class of training chemicals, in which SC is the mean expression level for each gene in a class divided by its within-class standard deviation. For each class, the SC is shrunken in the direction of the overall centroid to create the NSC. BaP was then classified through comparison of its gene expression profiles to the class of NSCs for each concentration and time point. Sample classification was achieved by assigning it to a class that is closest to it in squared distance [Tibshirani et al., 2002].
Figure 6
Figure 6
RA2: Postulated genotoxic MOA of BaP in animals (shaded rectangles). BaP binds to the AHR and activates transcription of AHR-controlled genes. These include xenobiotic metabolism enzymes that convert BaP to a variety of products that are subsequently conjugated to water-soluble moieties and excreted. BaP metabolites escaping detoxication (Section 2.3.3, e.g. BPDE) and reactive oxygen species (ROS) are genotoxic, leading to DNA adducts and oxidative damage. If unrepaired, genotoxic damage may cause mutations, leading to uncontrolled cell proliferation and tumor formation. In addition, BaP-mediated immunosuppression may provide a favorable environment for tumor growth, and activation of other signaling pathways that may favor tumorigenesis (See Section 3.2.1.7 for details). The double lined box illustrates the dual role of BaP metabolism by CYPs: their beneficial role in activating BaP for further conjugation and removal that becomes apparent from CYP knock-out studies of mice, exposed to BaP (e.g., Nebert et al., 2004). Metabolism by CYPs leads to efficient detoxication of BaP metabolites. However, a small portion of genotoxic metabolites escapes the detoxication to lead to DNA damage and mutations (Nebert et al., 2004).
Figure 7
Figure 7
RA3: Carcinogenic MOA of BaP in rodent liver, lung and forestomach developed using toxicogenomics data exclusively. In addition to the main genotoxic MOA (gray boxes), other factors are plausible based on the data. Redox reactions are unbalanced, showing conversion of molecular oxygen to reactive oxygen species (ROS; Section 2.3.3). Key events are numbered 1–4 and schematically represented.
Figure 8
Figure 8
RA3: Top eight canonical pathways affected by BaP in the livers of mice. The data shown are for mice treated with 300 mg/kg bw per day for 3 days and sacrificed 4 hours after the last exposure (Yauk et al., 2011). The left Y-axis represents the percentage of genes in each pathway, the numbers at the top of the graph indicate the number of genes in each pathway and the right Y-axis shows the negative log (p-value) of the Fisher's exact test performed by Ingenuity Systems Analysis software.
Figure 9
Figure 9
Heatmap analysis showing Ingenuity® Pathway Analysis (IPA) canonical pathways significantly affected in BaP-exposed TK6 cells and lungs, liver and forestomach of mice exposed to BaP. The left panel (A) compares in vitro TK6 pathways with lung and liver tissue from acute exposures (3 days + 4 hours), whereas the right panel (B) compares the in vitro TK6 pathways with lung, liver and forestomach tissue from the subchronic exposures (28 + 3 days). Pathways highlighted in yellow are consistent with the genotoxic MOA presented in this document. P-value scores (indicated by gradation of purple colors) are a measure of the significance of the pathway's association with the dataset.
Figure 10
Figure 10
Proposed role and integration of toxicogenomics in human health risk assessment.Toxicogenomics can benefit data-poor as well as data-rich chemicals by rapidly and inexpensively providing data that are useful for risk assessment. Toxicogenomics can be the first tier of toxicity testing to inform hazard identification and prioritization and identify relevant tests for further targeted testing. In addition, toxicogenomics can be integrated with existing data from empirical and alternative approaches (high-throughput screening [HTS], quantitative structure–activity relationship [QSAR]) to derive genomics PODs for data-poor chemicals. Knowledge of chemical's metabolism and kinetics are essential and should be developed utilizing in vitro systems in parallel with the genomics approach. For data-poor chemicals, toxicogenomics could rapidly generate genomics PODs as well as identify important endpoints for more thorough POD derivation by standard approaches, if applicable. Similarly, if no obvious toxicity is identified in toxicogenomics (e.g. no pathways relevant to toxicity are perturbed at certain doses), data-poor chemicals may be considered as “lower priority” for risk assessment. Based on the available toxicity information and other considerations, genomics PODs can be used either to inform targeted testing or to reach screening-level risk assessment and management decisions. For data-rich chemicals, toxicogenomics can provide mechanistic data to support the development of detailed MOAs and hence the risk assessment approach. The current approach is indicated in gray boxes, and the new approach is in red.
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