Progress in toxicogenomics to protect human health.

Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose-response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.

Unlocking the Power of Transcriptomic Biomarkers in Qualitative and Quantitative Genotoxicity Assessment of Chemicals.

To modernize genotoxicity assessment and reduce reliance on experimental animals, new approach methodologies (NAMs) that provide human-relevant dose-response data are needed. Two transcriptomic biomarkers, GENOMARK and TGx-DDI, have shown a high classification accuracy for genotoxicity. As these biomarkers were extracted from different training sets, we investigated whether combining the two biomarkers in a human-derived metabolically competent cell line (i.e., HepaRG) provides complementary information for the classification of genotoxic hazard identification and potency ranking. First, the applicability of GENOMARK to TempO-Seq, a high-throughput transcriptomic technology, was evaluated. HepaRG cells were exposed for 72 h to increasing concentrations of 10 chemicals (i.e., eight known in vivo genotoxicants and two in vivo nongenotoxicants). Gene expression data were generated using the TempO-Seq technology. We found a prediction performance of 100%, confirming the applicability of GENOMARK to TempO-Seq. Classification using TGx-DDI was then compared to GENOMARK. For the chemicals identified as genotoxic, benchmark concentration modeling was conducted to perform potency ranking. The high concordance observed for both hazard classification and potency ranking by GENOMARK and TGx-DDI highlights the value of integrating these NAMs in a weight of evidence evaluation of genotoxicity.

Duplex sequencing provides detailed characterization of mutation frequencies and spectra in the bone marrow of MutaMouse males exposed to procarbazine hydrochloride.

Mutagenicity testing is an essential component of health safety assessment. Duplex Sequencing (DS), an emerging high-accuracy DNA sequencing technology, may provide substantial advantages over conventional mutagenicity assays. DS could be used to eliminate reliance on standalone reporter assays and provide mechanistic information alongside mutation frequency (MF) data. However, the performance of DS must be thoroughly assessed before it can be routinely implemented for standard testing. We used DS to study spontaneous and procarbazine (PRC)-induced mutations in the bone marrow (BM) of MutaMouse males across a panel of 20 diverse genomic targets. Mice were exposed to 0, 6.25, 12.5, or 25 mg/kg-bw/day for 28 days by oral gavage and BM sampled 42 days post-exposure. Results were compared with those obtained using the conventional lacZ viral plaque assay on the same samples. DS detected significant increases in mutation frequencies and changes to mutation spectra at all PRC doses. Low intra-group variability within DS samples allowed for detection of increases at lower doses than the lacZ assay. While the lacZ assay initially yielded a higher fold-change in mutant frequency than DS, inclusion of clonal mutations in DS mutation frequencies reduced this discrepancy. Power analyses suggested that three animals per dose group and 500 million duplex base pairs per sample is sufficient to detect a 1.5-fold increase in mutations with > 80% power. Overall, we demonstrate several advantages of DS over classical mutagenicity assays and provide data to support efforts to identify optimal study designs for the application of DS as a regulatory test.

Duplex sequencing identifies genomic features that determine susceptibility to benzo(a)pyrene-induced in vivo mutations.

Exposure to environmental mutagens increases the risk of cancer and genetic disorders. We used Duplex Sequencing (DS), a high-accuracy error-corrected sequencing technology, to analyze mutation induction across twenty 2.4 kb intergenic and genic targets in the bone marrow of MutaMouse males exposed to benzo(a)pyrene (BaP), a widespread environmental pollutant. DS revealed a linear dose-related induction of mutations across all targets with low intra-group variability. Heterochromatic and intergenic regions exhibited the highest mutation frequencies (MF). C:G > A:T transversions at CCA, CCC and GCC trinucleotides were enriched in BaP-exposed mice consistent with the known etiology of BaP mutagenesis. However, GC-content had no effect on mutation susceptibility. A positive correlation was observed between DS and the "gold-standard" transgenic rodent gene mutation assay. Overall, we demonstrate that DS is a promising approach to study in vivo mutagenesis and yields critical insight into the genomic features governing mutation susceptibility, spectrum, and variability across the genome.

Comprehensive interpretation of in vitro micronucleus test results for 292 chemicals: from hazard identification to risk assessment application.

Risk assessments are increasingly reliant on information from in vitro assays. The in vitro micronucleus test (MNvit) is a genotoxicity test that detects chromosomal abnormalities, including chromosome breakage (clastogenicity) and/or whole chromosome loss (aneugenicity). In this study, MNvit datasets for 292 chemicals, generated by the US EPA's ToxCast program, were evaluated using a decision tree-based pipeline for hazard identification. Chemicals were tested with 19 concentrations (n = 1) up to 200 µM, in the presence and absence of Aroclor 1254-induced rat liver S9. To identify clastogenic chemicals, %MN values at each concentration were compared to a distribution of batch-specific solvent controls; this was followed by cytotoxicity assessment and benchmark concentration (BMC) analyses. The approach classified 157 substances as positives, 25 as negatives, and 110 as inconclusive. Using the approach described in Bryce et al. (Environ Mol Mutagen 52:280-286, 2011), we identified 15 (5%) aneugens. IVIVE (in vitro to in vivo extrapolation) was employed to convert BMCs into administered equivalent doses (AEDs). Where possible, AEDs were compared to points of departure (PODs) for traditional genotoxicity endpoints; AEDs were generally lower than PODs based on in vivo endpoints. To facilitate interpretation of in vitro MN assay concentration-response data for risk assessment, exposure estimates were utilized to calculate bioactivity exposure ratio (BER) values. BERs for 50 clastogens and two aneugens had AEDs that approached exposure estimates (i.e., BER < 100); these chemicals might be considered priorities for additional testing. This work provides a framework for the use of high-throughput in vitro genotoxicity testing for priority setting and chemical risk assessment.

R-ODAF: Omics data analysis framework for regulatory application.

Despite the widespread use of transcriptomics technologies in toxicology research, acceptance of the data by regulatory agencies to support the hazard assessment is still limited. Fundamental issues contributing to this are the lack of reproducibility in transcriptomics data analysis arising from variance in the methods used to generate data and differences in the data processing. While research applications are flexible in the way the data are generated and interpreted, this is not the case for regulatory applications where an unambiguous answer, possibly later subject to legal scrutiny, is required. A reference analysis framework would give greater credibility to the data and allow the practitioners to justify their use of an alternative bioinformatic process by referring to a standard. In this publication, we propose a method called omics data analysis framework for regulatory application (R-ODAF), which has been built as a user-friendly pipeline to analyze raw transcriptomics data from microarray and next-generation sequencing. In the R-ODAF, we also propose additional statistical steps to remove the number of false positives obtained from standard data analysis pipelines for RNA-sequencing. We illustrate the added value of R-ODAF, compared to a standard workflow, using a typical toxicogenomics dataset of hepatocytes exposed to paracetamol.

Development and validation of the TGx-HDACi transcriptomic biomarker to detect histone deacetylase inhibitors in human TK6 cells.

Transcriptomic biomarkers can be used to inform molecular initiating and key events involved in a toxicant's mode of action. To address the limited approaches available for identifying epigenotoxicants, we developed and assessed a transcriptomic biomarker of histone deacetylase inhibition (HDACi). First, we assembled a set of ten prototypical HDACi and ten non-HDACi reference compounds. Concentration-response experiments were performed for each chemical to collect TK6 human lymphoblastoid cell samples after 4 h of exposure and to assess cell viability following a 20-h recovery period in fresh media. One concentration was selected for each chemical for whole transcriptome profiling and transcriptomic signature derivation, based on cell viability at the 24-h time point and on maximal induction of HDACi-response genes (RGL1, NEU1, GPR183) or cellular stress-response genes (ATF3, CDKN1A, GADD45A) analyzed by TaqMan qPCR assays after 4 h of exposure. Whole transcriptomes were profiled after 4 h exposures by Templated Oligo-Sequencing (TempO-Seq). By applying the nearest shrunken centroid (NSC) method to the whole transcriptome profiles of the reference compounds, we derived an 81-gene toxicogenomic (TGx) signature, referred to as TGx-HDACi, that classified all 20 reference compounds correctly using NSC classification and the Running Fisher test. An additional 4 HDACi and 7 non-HDACi were profiled and analyzed using TGx-HDACi to further assess classification performance; the biomarker accurately classified all 11 compounds, including 3 non-HDACi epigenotoxicants, suggesting a promising specificity toward HDACi. The availability of TGx-HDACi increases the diversity of tools that can facilitate mode of action analysis of toxicants using gene expression profiling.

The 28 + 28 day design is an effective sampling time for analyzing mutant frequencies in rapidly proliferating tissues of MutaMouse animals.

The Organisation for Economic Co-Operation and Development Test Guideline 488 (TG 488) uses transgenic rodent models to generate in vivo mutagenesis data for regulatory submission. The recommended design in TG 488, 28 consecutive daily exposures with tissue sampling three days later (28 + 3d), is optimized for rapidly proliferating tissues such as bone marrow (BM). A sampling time of 28 days (28 + 28d) is considered more appropriate for slowly proliferating tissues (e.g., liver) and male germ cells. We evaluated the impact of the sampling time on mutant frequencies (MF) in the BM of MutaMouse males exposed for 28 days to benzo[a]pyrene (BaP), procarbazine (PRC), isopropyl methanesulfonate (iPMS), or triethylenemelamine (TEM) in dose-response studies. BM samples were collected + 3d, + 28d, + 42d or + 70d post exposure and MF quantified using the lacZ assay. All chemicals significantly increased MF with maximum fold increases at 28 + 3d of 162.9, 6.6, 4.7 and 2.8 for BaP, PRC, iPMS and TEM, respectively. MF were relatively stable over the time period investigated, although they were significantly increased only at 28 + 3d and 28 + 28d for TEM. Benchmark dose (BMD) modelling generated overlapping BMD confidence intervals among the four sampling times for each chemical. These results demonstrate that the sampling time does not affect the detection of mutations for strong mutagens. However, for mutagens that produce small increases in MF, sampling times greater than 28 days may produce false-negative results. Thus, the 28 + 28d protocol represents a unifying protocol for simultaneously assessing mutations in rapidly and slowly proliferating somatic tissues and male germ cells.

Progress towards an OECD reporting framework for transcriptomics and metabolomics in regulatory toxicology.

Omics methodologies are widely used in toxicological research to understand modes and mechanisms of toxicity. Increasingly, these methodologies are being applied to questions of regulatory interest such as molecular point-of-departure derivation and chemical grouping/read-across. Despite its value, widespread regulatory acceptance of omics data has not yet occurred. Barriers to the routine application of omics data in regulatory decision making have been: 1) lack of transparency for data processing methods used to convert raw data into an interpretable list of observations; and 2) lack of standardization in reporting to ensure that omics data, associated metadata and the methodologies used to generate results are available for review by stakeholders, including regulators. Thus, in 2017, the Organisation for Economic Co-operation and Development (OECD) Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) launched a project to develop guidance for the reporting of omics data aimed at fostering further regulatory use. Here, we report on the ongoing development of the first formal reporting framework describing the processing and analysis of both transcriptomic and metabolomic data for regulatory toxicology. We introduce the modular structure, content, harmonization and strategy for trialling this reporting framework prior to its publication by the OECD.

Transgenerational hypocortisolism and behavioral disruption are induced by the antidepressant fluoxetine in male zebrafish Danio rerio.

The global prevalence of depression is high during childbearing. Due to the associated risks to the mother and baby, the selective serotonin reuptake inhibitor fluoxetine (FLX) is often the first line of treatment. Given that FLX readily crosses the placenta, a fetus may be susceptible to the disruptive effects of FLX during this highly plastic stage of development. Here, we demonstrate that a 6-day FLX exposure to a fetus-relevant concentration at a critical developmental stage suppresses cortisol levels in the adult zebrafish (F0). This effect persists for three consecutive generations in the unexposed descendants (F1 to F3) without diminution and is more pronounced in males. We also show that the in vivo cortisol response of the interrenal (fish "adrenal") to an i.p. injection of adrenocorticotropic hormone was also reduced in the males from the F0 and F3 FLX lineages. Transcriptomic profiling of the whole kidney containing the interrenal cells revealed that early FLX exposure significantly modified numerous pathways closely associated with cortisol synthesis in the male adults from the F0 and F3 generations. We also show that the low cortisol levels are linked to significantly reduced exploratory behaviors in adult males from the F0 to F2 FLX lineages. This may be a cause for concern given the high prescription rates of FLX to pregnant women and the potential long-term negative impacts on humans exposed to these therapeutic drugs.

BMDExpress 2: enhanced transcriptomic dose-response analysis workflow.

SUMMARY: A new version (version 2) of the genomic dose-response analysis software, BMDExpress, has been created. The software addresses the increasing use of transcriptomic dose-response data in toxicology, drug design, risk assessment and translational research. In this new version, we have implemented additional statistical filtering options (e.g. Williams' trend test), curve fitting models, Linux and Macintosh compatibility and support for additional transcriptomic platforms with up-to-date gene annotations. Furthermore, we have implemented extensive data visualizations, on-the-fly data filtering, and a batch-wise analysis workflow. We have also significantly re-engineered the code base to reflect contemporary software engineering practices and streamline future development. The first version of BMDExpress was developed in 2007 to meet an unmet demand for easy-to-use transcriptomic dose-response analysis software. Since its original release, however, transcriptomic platforms, technologies, pathway annotations and quantitative methods for data analysis have undergone a large change necessitating a significant re-development of BMDExpress. To that end, as of 2016, the National Toxicology Program assumed stewardship of BMDExpress. The result is a modernized and updated BMDExpress 2 that addresses the needs of the growing toxicogenomics user community. AVAILABILITY AND IMPLEMENTATION: BMDExpress 2 is available at https://github.com/auerbachs/BMDExpress-2/releases. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A cross-sector call to improve carcinogenicity risk assessment through use of genomic methodologies.

Robust genomic approaches are now available to realize improvements in efficiencies and translational relevance of cancer risk assessments for drugs and chemicals. Mechanistic and pathway data generated via genomics provide opportunities to advance beyond historical reliance on apical endpoints of uncertain human relevance. Published research and regulatory evaluations include many examples for which genomic data have been applied to address cancer risk assessment as a health protection endpoint. The alignment of mature, robust, reproducible, and affordable technologies with increasing demands for reduced animal testing sets the stage for this important transition. We present our shared vision for change from leading scientists from academic, government, nonprofit, and industrial sectors and chemical and pharmaceutical safety applications. This call to action builds upon a 2017 workshop on "Advances and Roadblocks for Use of Genomics in Cancer Risk Assessment." The authors propose a path for implementation of innovative cancer risk assessment including incorporating genomic signatures to assess mechanistic relevance of carcinogenicity and enhanced use of genomics in benchmark dose and point of departure evaluations. Novel opportunities for the chemical and pharmaceutical sectors to combine expertise, resources, and objectives to achieve a common goal of improved human health protection are identified.

Development and validation of a high-throughput transcriptomic biomarker to address 21st century genetic toxicology needs.

Interpretation of positive genotoxicity findings using the current in vitro testing battery is a major challenge to industry and regulatory agencies. These tests, especially mammalian cell assays, have high sensitivity but suffer from low specificity, leading to high rates of irrelevant positive findings (i.e., positive results in vitro that are not relevant to human cancer hazard). We developed an in vitro transcriptomic biomarker-based approach that provides biological relevance to positive genotoxicity assay data, particularly for in vitro chromosome damage assays, and propose its application for assessing the relevance of the in vitro positive results to carcinogenic hazard. The transcriptomic biomarker TGx-DDI (previously known as TGx-28.65) readily distinguishes DNA damage-inducing (DDI) agents from non-DDI agents. In this study, we demonstrated the ability of the biomarker to classify 45 test agents across a broad set of chemical classes as DDI or non-DDI. Furthermore, we assessed the biomarker's utility in derisking known irrelevant positive agents and evaluated its performance across analytical platforms. We correctly classified 90% (9 of 10) of chemicals with irrelevant positive findings in in vitro chromosome damage assays as negative. We developed a standardized experimental and analytical protocol for our transcriptomics biomarker, as well as an enhanced application of TGx-DDI for high-throughput cell-based genotoxicity testing using nCounter technology. This biomarker can be integrated in genetic hazard assessment as a follow-up to positive chromosome damage findings. In addition, we propose how it might be used in chemical screening and assessment. This approach offers an opportunity to significantly improve risk assessment and reduce cost.

Identification of p53 Activators in a Human Microarray Compendium.

Biomarkers predictive of molecular and toxicological effects are needed to interpret emerging high-throughput transcriptomic data streams. The previously characterized 63 gene TGx-DDI biomarker that includes 20 genes known to be regulated by p53 was previously shown to accurately predict DNA damage in chemically treated cells. We comprehensively evaluated whether the molecular basis of the DDI predictions was based on a p53-dependent response. The biomarker was compared to microarray data in a compendium derived from human cells using the Running Fisher test, a nonparametric correlation test. Using the biomarker, we identified conditions that led to p53 activation, including exposure to the chemical nutlin-3 which disrupts interactions between p53 and the negative regulator MDM2 or by knockdown of MDM2. The expression of most of the genes in the biomarker (75%) were found to depend on p53 activation status based on gene behavior after TP53 overexpression or knockdown. The biomarker identified DDI chemicals that were strong inducers of p53 in wild-type cells; these p53 responses were decreased or abolished in cells after p53 knockdown by siRNAs. Using the biomarker, we screened ∼1950 chemicals in ∼9800 human cell line chemical vs control comparisons and identified ∼100 chemicals that caused p53 activation. Among the positive chemicals were many that are known to activate p53 through direct and indirect DNA damaging mechanisms. These results contribute to the evidence that the TGx-DDI biomarker is useful for identifying chemicals that cause DDI and activate p53.

Integrated in silico and in vitro genotoxicity assessment of thirteen data-poor substances.

The Canadian Domestic Substances List (DSL) contains chemicals that have not been tested for genotoxicity as their use pre-dates regulatory requirements. In the present study, (quantitative) structure-activity relationships ((Q)SAR) model predictions and in vitro tests were conducted for genotoxicity assessment of 13 data-poor chemicals from the DSL (i.e. CAS numbers 19286-75-0, 13676-91-0, 2478-20-8, 6408-20-8, 74499-36-8, 26694-69-9, 29036-02-0, 120-24-1, 84696-48-9, 4051-63-2, 5718-26-3, 632-51-9, and 600-14-6). First, chemicals were screened by (Q)SAR models in Leadscope® and OASIS TIMES; two chemicals were excluded from (Q)SAR as they are complex mixtures. Six were flagged by (Q)SAR as potentially mutagenic and were subsequently confirmed as mutagens using the Ames assay. Of nine chemicals with clastogenic (Q)SAR flags, eight induced micronuclei in TK6 cells. Benchmark dose analysis was used to evaluate the potency of the chemicals. Four chemicals were bacterial mutagens with similar potencies. Three chemicals were more potent in micronuclei induction than the prototype alkylating agent methyl methanesulfonate and three were equipotent to the mutagenic carcinogen benzo[a]pyrene in the presence of rat liver S9. Overall, 11 of the 13 DSL chemicals demonstrated at least one type of genotoxicity in vitro. This study demonstrates the application of genotoxic potency analysis for prioritizing further investigations.

Integration of sperm DNA damage assessment into OECD test guidelines for genotoxicity testing using the MutaMouse model.

The Organisation for Economic Co-operation and Development (OECD) endorses test guidelines (TG) for identifying chemicals that are genotoxic, such as the transgenic rodent gene mutation assay (TG 488). Current OECD TG do not include assays for sperm DNA damage resulting in a critical testing gap. We evaluated the performance of the Sperm Chromatin Structure Assay (SCSA) and the Terminal Deoxynucleotidyl Transferase-Mediated Deoxyuridine Triphosphate Nick end Labeling (TUNEL) assay to detect sperm DNA damage within the recommended TG 488 protocol. MutaMouse males received 0, 0.5, 1, or 2 mg/kg/day triethylenemelamine (TEM), a multifunctional alkylating agent, for 28 days orally and tissues were collected two (blood) and three (sperm and bone marrow) days later. TEM significantly increased the frequency of lacZ mutants in bone marrow, and of micronuclei (MN) in both reticulocytes (%MN-RET) and normochromatic erythrocytes (%MN-NCE) in a dose-dependent manner (P < 0.05). The percentage of DNA fragmentation index (%DFI) and %TUNEL positive cells demonstrated dose-related increases in sperm (P < 0.05), and the two assay results were strongly correlated (R = 0.9298). Within the same animal, a good correlation was observed between %MN-NCE and %DFI (R = 0.7189). Finally, benchmark dose modelling (BMD) showed comparable BMD10 values among the somatic and germ cell assays. Our results suggest that sperm DNA damage assays can be easily integrated into standard OECD designs investigating genotoxicity in somatic tissues to provide key information on whether a chemical is genotoxic in germ cells and impact its risk assessment.

Transcriptional profiling of male CD-1 mouse lungs and Harderian glands supports the involvement of calcium signaling in acrylamide-induced tumors.

Acrylamide (AA) exposure causes increased incidence of forestomach, lung, and Harderian gland tumors in male mice. One hypothesized mode of action (MOA) for AA-carcinogenicity includes genotoxicity/mutagenicity as a key event, possibly resulting from AA metabolism to the direct genotoxic metabolite glycidamide. Alternatively, altered calcium signaling (CS) has been proposed as a central key event in the MOA. To examine the plausibility of these proposed MOAs, RNA-sequencing was performed on tumor target tissues: Harderian glands (the most sensitive tumor target tissue in the rodent 2-year cancer bioassay) and lungs of AA-exposed male CD-1 mice. Animals were exposed to 0.0, 1.5, 3.0, 6.0, 12.0, or 24.0 mg AA/kg bw-day in drinking water for 5, 15, or 31 days. We observed a pronounced effect on genes involved in CS and cytoskeletal processes in both tissues, but no evidence supporting a genotoxic MOA. Benchmark dose modeling suggests transcriptional points of departure (PODs) of 0.54 and 2.21 mg/kg bw-day for the Harderian glands and lungs, respectively. These are concordant with PODs of 0.17 and 1.27 mg/kg bw-day derived from the cancer bioassay data for these tissues in male mice, respectively. Overall, this study supports the involvement of CS in AA-induced mouse carcinogenicity, which is consistent with a recently proposed CS-based MOA in rat thyroid, and with other published reports of aberrant CS in malignant tumors in rodents and humans.

Dose-response mutation and spectrum analyses reveal similar responses at two microsatellite loci in benzo(a)pyrene-exposed mouse spermatogonia.

Identifying chemical exposures that can cause germline mutations is important as these mutations can be inherited, impacting both individual and population health. However, germline mutations are extremely rare and difficult to detect. Chemically induced germline mutations can be detected through analysis of highly unstable tandem repeat DNA. We recently developed a single-molecule PCR (SM-PCR) approach to quantify mutations at a mouse microsatellite locus (Mm2.2.1) in sperm for such purposes. In this study, we refine this approach through the combined analysis of mouse microsatellites Mm2.2.1 and Mm19.2.3. Mice were exposed to 0, 25, 50 or 100 mg/kg/day benzo(a)pyrene (BaP) by oral gavage for 28 days and sperm sampled 42 days after the end of exposure to measure effects on dividing spermatogonia. DNA was diluted to a single genome per PCR well for amplification of microsatellites in singleplex and multiplex reactions, and alleles were sized to identify mutations using capillary electrophoresis. Analysis of ~300-500 molecules per animal at both microsatellite loci, when tested individually, showed a ~2-fold increase in mutations relative to the controls at both the 50 and 100 mg/kg/day BaP doses. Multiplex SM-PCR revealed similar increases in mutation frequencies in both microsatellites. Comparison with results from a previous lacZ mutation assay conducted on the same mice revealed that although microsatellite mutations are a sensitive endpoint for detecting changes in mutation frequencies at lower doses, they appear to be saturable and thus have a reduced dynamic range. These results confirm that BaP is a male germ cell mutagen that broadly impacts tandem repeat DNA. Likewise, addition of a second hypervariable microsatellite increases the sensitivity of this assay.

A framework for the use of single-chemical transcriptomics data in predicting the hazards associated with complex mixtures of polycyclic aromatic hydrocarbons.

The assumption of additivity applied in the risk assessment of environmental mixtures containing carcinogenic polycyclic aromatic hydrocarbons (PAHs) was investigated using transcriptomics. MutaTMMouse were gavaged for 28 days with three doses of eight individual PAHs, two defined mixtures of PAHs, or coal tar, an environmentally ubiquitous complex mixture of PAHs. Microarrays were used to identify differentially expressed genes (DEGs) in lung tissue collected 3 days post-exposure. Cancer-related pathways perturbed by the individual or mixtures of PAHs were identified, and dose-response modeling of the DEGs was conducted to calculate gene/pathway benchmark doses (BMDs). Individual PAH-induced pathway perturbations (the median gene expression changes for all genes in a pathway relative to controls) and pathway BMDs were applied to models of additivity [i.e., concentration addition (CA), generalized concentration addition (GCA), and independent action (IA)] to generate predicted pathway-specific dose-response curves for each PAH mixture. The predicted and observed pathway dose-response curves were compared to assess the sensitivity of different additivity models. Transcriptomics-based additivity calculation showed that IA accurately predicted the pathway perturbations induced by all mixtures of PAHs. CA did not support the additivity assumption for the defined mixtures; however, GCA improved the CA predictions. Moreover, pathway BMDs derived for coal tar were comparable to BMDs derived from previously published coal tar-induced mouse lung tumor incidence data. These results suggest that in the absence of tumor incidence data, individual chemical-induced transcriptomics changes associated with cancer can be used to investigate the assumption of additivity and to predict the carcinogenic potential of a mixture.

Recommended approaches in the application of toxicogenomics to derive points of departure for chemical risk assessment.

There is increasing interest in the use of quantitative transcriptomic data to determine benchmark dose (BMD) and estimate a point of departure (POD) for human health risk assessment. Although studies have shown that transcriptional PODs correlate with those derived from apical endpoint changes, there is no consensus on the process used to derive a transcriptional POD. Specifically, the subsets of informative genes that produce BMDs that best approximate the doses at which adverse apical effects occur have not been defined. To determine the best way to select predictive groups of genes, we used published microarray data from dose-response studies on six chemicals in rats exposed orally for 5, 14, 28, and 90 days. We evaluated eight approaches for selecting genes for POD derivation and three previously proposed approaches (the lowest pathway BMD, and the mean and median BMD of all genes). The relationship between transcriptional BMDs derived using these 11 approaches and PODs derived from apical data that might be used in chemical risk assessment was examined. Transcriptional BMD values for all 11 approaches were remarkably aligned with corresponding apical PODs, with the vast majority of toxicogenomics PODs being within tenfold of those derived from apical endpoints. We identified at least four approaches that produce BMDs that are effective estimates of apical PODs across multiple sampling time points. Our results support that a variety of approaches can be used to derive reproducible transcriptional PODs that are consistent with PODs produced from traditional methods for chemical risk assessment.