Benchmark dose modeling of transcriptional data: a systematic approach to identify best practices for study designs used in radiation research.

PURPOSE: Benchmark dose (BMD) modeling is a method commonly used in chemical toxicology to identify the point of departure (POD) from a dose-response curve linked to a health-related outcome. Recently, its application in the analysis of transcriptional data for quantitative adverse outcome pathway (AOP) development is being explored. As AOPs are informed by diverse data types, it is important to understand the impact of study parameters such as dose selection, the number of replicates and dose range on BMD outputs for radiation-induced genes and pathways. MATERIALS AND METHODS: Data were selected from the Gene Expression Omnibus (GSE52403) that featured gene expression profiles of peripheral blood samples from C57BL/6 mice 6 hours post-exposure to 137Cs gamma-radiation at 0, 1, 2, 3, 4.5, 6, 8 and 10.5 Gy. The dataset comprised a broad dose range over multiple dose points with consistent dose spacing and multiple biological replicates. This dataset was ideal for systematically transforming across three categories: (1) dose range, (2) dose-spacing and (3) number of controls/replicates. Across these categories, 29 transformed datasets were compared to the original dataset to determine the impact of each transformation on the BMD outputs. RESULTS: Most of the experimental changes did not impact the BMD outputs. The transformed datasets were largely consistent with the original dataset in terms of the number of reproduced genes modeled and absolute BMD values for genes and pathways. Variations in dose selection identified the importance of the absolute value of the lowest and second dose. It was determined that dose selection should include at least two doses <1 Gy and two >5 Gy to achieve meaningful BMD outputs. Changes to the number of biological replicates in the control and non-zero dose groups impacted the overall accuracy and precision of the BMD outputs as well as the ability to fit dose-response models consistent with the original dataset. CONCLUSION: Successful application of transcriptomic BMD modeling for radiation datasets requires considerations of the exposure dose and the number of biological replicates. Most important is the selection of the lowest doses and dose spacing. Reflections on these parameters in experimental design will provide meaningful BMD outputs that could correlate well to apical endpoints of relevance to radiation exposure assessment.

Evaluating the influences of confounding variables on benchmark dose using a case study in the field of ionizing radiation.

PURPOSE: A vast amount of data regarding the effects of radiation stressors on transcriptional changes has been produced over the past few decades. These data have shown remarkable consistency across platforms and experimental design, enabling increased understanding of early molecular effects of radiation exposure. However, the value of transcriptomic data in the context of risk assessment is not clear and represents a gap that is worthy of further consideration. Recently, benchmark dose (BMD) modeling has shown promise in correlating a transcriptional point of departure (POD) to that derived using phenotypic outcomes relevant to human health risk assessment. Although frequently applied in chemical toxicity evaluation, our group has recently demonstrated application within the field of radiation research. This approach allows the possibility to quantitatively compare radiation-induced gene and pathway alterations across various datasets using BMD values and derive meaningful biological effects. However, before BMD modeling can confidently be used, an understanding of the impact of confounding variables on BMD outputs is needed. METHODS: To this end, BMD modeling was applied to a publicly available microarray dataset (Gene Expression Omnibus #GSE23515) that used peripheral blood ex-vivo gamma-irradiated at 0.82 Gy/min, at doses of 0, 0.1, 0.5 or 2 Gy, and assessed 6 hours post-exposure. The dataset comprised six female smokers (F-S), six female nonsmokers (F-NS), six male smokers (M-S), and six male nonsmokers (M-NS). RESULTS: A combined total of 412 genes were fit to models and the BMD distribution was noted to be bi-modal across the four groups. A total of 74, 41, 62 and 62 genes were unique to the F-NS, M-NS, F-S and M-S groups. Sixty-two BMD modeled genes and nine pathways were common across all four groups. There were no differential sensitivity of BMD responses in the robust common genes and pathways. CONCLUSION: For radiation-responsive genes and pathways common across the study groups, the BMD distribution of transcriptional activity was unaltered by sex and smoking status. Although further validation of the data is needed, these initial findings suggest BMD values for radiation relevant genes and pathways are robust and could be explored further in future studies.

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.

Potency Ranking of Per- and Polyfluoroalkyl Substances Using High-Throughput Transcriptomic Analysis of Human Liver Spheroids.

Per- and polyfluoroalkyl substances (PFAS) are some of the most prominent organic contaminants in human blood. Although the toxicological implications of human exposure to perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA) are well established, data on lesser-understood PFAS are limited. New approach methodologies (NAMs) that apply bioinformatic tools to high-throughput data are being increasingly considered to inform risk assessment for data-poor chemicals. The aim of this study was to compare the potencies (ie, benchmark concentrations: BMCs) of PFAS in primary human liver microtissues (3D spheroids) using high-throughput transcriptional profiling. Gene expression changes were measured using TempO-seq, a templated, multiplexed RNA-sequencing platform. Spheroids were exposed for 1 or 10 days to increasing concentrations of 23 PFAS in 3 subgroups: carboxylates (PFCAs), sulfonates (PFSAs), and fluorotelomers and sulfonamides. PFCAs and PFSAs exhibited trends toward increased transcriptional potency with carbon chain-length. Specifically, longer-chain compounds (7-10 carbons) were more likely to induce changes in gene expression and have lower transcriptional BMCs. The combined high-throughput transcriptomic and bioinformatic analyses support the capability of NAMs to efficiently assess the effects of PFAS in liver microtissues. The data enable potency ranking of PFAS for human liver cell spheroid cytotoxicity and transcriptional changes, and assessment of in vitro transcriptomic points of departure. These data improve our understanding of the possible health effects of PFAS and will be used to inform read-across for human health risk assessment.

High-Throughput Transcriptomic Analysis of Human Primary Hepatocyte Spheroids Exposed to Per- and Polyfluoroalkyl Substances as a Platform for Relative Potency Characterization.

Per- and poly-fluoroalkyl substances (PFAS) are widely found in the environment because of their extensive use and persistence. Although several PFAS are well studied, most lack toxicity data to inform human health hazard and risk assessment. This study focused on 4 model PFAS: perfluorooctanoic acid (PFOA; 8 carbon), perfluorobutane sulfonate (PFBS; 4 carbon), perfluorooctane sulfonate (PFOS; 8 carbon), and perfluorodecane sulfonate (PFDS; 10 carbon). Human primary liver cell spheroids (pooled from 10 donors) were exposed to 10 concentrations of each PFAS and analyzed at 4 time points. The approach aimed to: (1) identify gene expression changes mediated by the PFAS, (2) identify similarities in biological responses, (3) compare PFAS potency through benchmark concentration analysis, and (4) derive bioactivity exposure ratios (ratio of the concentration at which biological responses occur, relative to daily human exposure). All PFAS induced transcriptional changes in cholesterol biosynthesis and lipid metabolism pathways, and predicted PPARα activation. PFOS exhibited the most transcriptional activity and had a highly similar gene expression profile to PFDS. PFBS induced the least transcriptional changes and the highest benchmark concentration (ie, was the least potent). The data indicate that these PFAS may have common molecular targets and toxicities, but that PFOS and PFDS are the most similar. The transcriptomic bioactivity exposure ratios derived here for PFOA and PFOS were comparable to those derived using rodent apical endpoints in risk assessments. These data provide a baseline level of toxicity for comparison with other known PFAS using this testing strategy.

Transcriptomic pathway and benchmark dose analysis of Bisphenol A, Bisphenol S, Bisphenol F, and 3,3',5,5'-Tetrabromobisphenol A in H9 human embryonic stem cells.

Bisphenol A (BPA) is a chemical used in the manufacturing of plastics to which human exposure is ubiquitous. Numerous studies have linked BPA exposure to many adverse health outcomes prompting the replacement of BPA with various analogues including bisphenol-F (BPF) and bisphenol S (BPS). Other bisphenols are used in various consumer applications, such as 3,3',5,5'-Tetrabromobisphenol A (TBBPA), which is used as a flame retardant. Few studies to date have examined the effects of BPA and its analogues in stem cells to explore potential developmental impacts. Here we used transcriptomics to investigate similarities and differences of BPA and three of its analogues in the estrogen receptor negative, human embryonic stem cell line H9 (WA09). H9 cells were exposed to increasing concentrations of the bisphenols and analyzed using RNA-sequencing. Our data indicate that BPA, BPF, and BPS have similar potencies in inducing transcriptional changes and perturb many of the same pathways. TBBPA, the least structurally similar bisphenol of the group, exhibited much lower potency. All bisphenols robustly impacted gene expression in these cells, albeit at concentrations well above those observed in estrogen-positive cells. Overall, we provide a foundational data set against which to explore the transcriptional similarities of other bisphenols in embryonic stem cells, which may be used to assess the suitability of chemical grouping for read-across and for preliminary potency evaluation.

Meta-analysis of transcriptomic datasets using benchmark dose modeling shows value in supporting radiation risk assessment.

PURPOSE: Benchmark dose (BMD) modeling is used to determine the dose of a stressor at which a predefined increase in any biological effect above background occurs (e.g. 10% increase from control values). BMD analytical tools have the capacity to model transcriptional dose-response data to derive BMDs for genes, pathways and gene ontologies. We recently demonstrated the value of this approach to support various areas of radiation research using predominately 'in-house' generated datasets. MATERIALS AND METHODS: As a continuation of this work, transcriptomic studies of relevance to ionizing radiation were retrieved through the Gene Expression Omnibus (GEO). The datasets were compiled and filtered, then analyzed using BMDExpress. The objective was to determine the reproducibility of BMD values in relation to pathways and genes across different exposure scenarios and compare to those derived using cytogenetic endpoints. A number of graphic visualization approaches were used to determine if BMD outputs could be correlated to parameters such as dose-rate, radiation quality and cell type. RESULTS: Curated studies were diverse and derived from experiments with varied design and intent. Despite this, common genes and pathways were identified with low and high dose thresholds. The higher BMD values were associated with immune response and cell death, while transcripts with lower BMD values were generally related to the classic DNA damage response/repair processes, centered on TP53 signaling. Analysis of datasets with relatively similar dose-ranges under comparable experimental conditions showed a bi-modal distribution with a high degree of consistency in BMD values across shared genes and pathways, particularly for those below the 25th percentile of total distribution by dose. The median BMD values were noted to be approximately 0.5 Gy for genes/pathways that comprised mode 1. Furthermore, transcriptional BMD values derived from a subset of genes using in vivo and in vitro datasets were in accord to those using cytogenetic endpoints. CONCLUSION: Overall, the results from this work highlight the value of the BMD methodology to derive meaningful outputs that are consistent across different models, provided the studies are conducted using a similar dose-range.

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.

The use of in vitro transcriptional data to identify thresholds of effects in a human lens epithelial cell-line exposed to ionizing radiation.

PURPOSE: The International Commission on Radiological Protection (ICRP) recently recommended reducing the occupational equivalent dose limit for the lens of the eye. Based primarily on a review of epidemiological data, the absorbed dose threshold is now considered to be 0.5 Gy independent of dose-rate and severity of opacification, reduced from the previous threshold of 2 Gy. However, direct mechanistic evidence to support an understanding of the underlying molecular mechanisms of damage is still lacking. To this end, we explored the effects of a broad dose-range of ionizing radiation exposure on gene expression changes in a human lens epithelial (HLE) cell-line in order to better understand the shape of the dose-response relationship and identify transcriptional thresholds of effects. METHODS: HLE cells were exposed to doses of 0, 0.01, 0.05, 0.25, 0.5, 2, and 5 Gy of X-ray radiation at two dose rates (1.62 cGy/min and 38.2 cGy/min). Cell culture lysates were collected 20 h post-exposure and analyzed using whole-genome RNA-sequencing. Pathways and dose-thresholds of biological effects were identified using benchmark dose (BMD) modeling. RESULTS: Transcriptional responses were minimal at doses less than 2 Gy. At higher doses, there were a significant number of differentially expressed genes (DEGs) (p≤.05, fold change≥|1.5|) at both dose rates, with 1308 DEGs for the low dose rate (LDR) and 840 DEGs for the high dose rate (HDR) exposure. Dose-response modeling showed that a number of genes exhibited non-linear bi-phasic responses, which was verified by digital droplet PCR. BMD analysis showed the majority of the pathways responded at BMD median values in the dose range of 1.5-2.5 Gy, with the lowest BMD median value being 0.6 Gy for the HDR exposure. The minimum pathway BMD median value for LDR exposure, however, was 2.5 Gy. Although the LDR and HDR exposures shared pathways involved in extracellular matrix reorganization and collagen production with BMD median value of 2.9 Gy, HDR exposures were more effective in activating pathways associated with DNA damage response, apoptosis, and cell cycling relative to LDR exposure. CONCLUSIONS: Overall, the results suggest that radiation induces complex non-linear transcriptional dose-response relationships that are dose-rate dependent. Pathways shared between the two dose rates may be important contributors to radiation-induced cataractogenesis. BMD analysis suggests that the majority of pathways are activated above 0.6 Gy, which supports current ICRP identified dose thresholds for deterministic effects to the lens of the eye of 0.5 Gy.

Hepatic transcriptional dose-response analysis of male and female Fischer rats exposed to hexabromocyclododecane.

Hexabromocyclododecane (HBCD) is a brominated flame retardant found in the environment and human tissues. The toxicological effects of HBCD exposure are not clearly understood. We employed whole-genome RNA-sequencing on liver samples from male and female Fischer rats exposed to 0, 250, 1250, and 5000 mg technical mixture of HBCD/kg diet for 28 days to gain further insight into HBCD toxicity. HBCD altered 428 and 250 gene transcripts in males and females, respectively, which were involved in metabolism of xenobiotics, oxidative stress, immune response, metabolism of glucose and lipids, circadian regulation, cell cycle, fibrotic activity, and hormonal balance. Signature analysis supported that HBCD operates through the constitutive androstane and pregnane X receptors. The median transcriptomic benchmark dose (BMD) for the lowest statistically significant pathway was within 1.5-fold of the BMD for increased liver weight, while the BMD for the lowest pathway with at least three modeled genes (minimum 5% of pathway) was similar to the lowest apical endpoint BMD. The results show how transcriptional analyses can inform mechanisms underlying chemical toxicity and the doses at which potentially adverse effects occur. This experiment is part of a larger study exploring the use of toxicogenomics and high-throughput screening for human health risk assessment.

The application of transcriptional benchmark dose modeling for deriving thresholds of effects associated with solar-simulated ultraviolet radiation exposure.

Considerable data has been generated to elucidate the transcriptional response of cells to ultraviolet radiation (UVR) exposure providing a mechanistic understanding of UVR-induced cellular responses. However, using these data to support standards development has been challenging. In this study, we apply benchmark dose (BMD) modeling of transcriptional data to derive thresholds of gene responsiveness following exposure to solar-simulated UVR. Human epidermal keratinocytes were exposed to three doses (10, 20, 150 kJ/m2 ) of solar simulated UVR and assessed for gene expression changes 6 and 24 hr postexposure. The dose-response curves for genes with p-fit values (≥ 0.1) were used to derive BMD values for genes and pathways. Gene BMDs were bi-modally distributed, with a peak at ∼16 kJ/m2 and ∼108 kJ/m2 UVR exposure. Genes/pathways within Mode 1 were involved in cell signaling and DNA damage response, while genes/pathways in the higher Mode 2 were associated with immune response and cancer development. The median value of each Mode coincides with the current human exposure limits for UVR and for the minimal erythemal dose, respectively. Such concordance implies that the use of transcriptional BMD data may represent a promising new approach for deriving thresholds of actinic effects. Environ. Mol. Mutagen. 59:502-515, 2018. © 2018 The Authors Environmental and Molecular Mutagenesis published by Wiley Periodicals, Inc. on behalf of Environmental Mutagen Society.

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.

Integration of the TGx-28.65 genomic biomarker with the flow cytometry micronucleus test to assess the genotoxicity of disperse orange and 1,2,4-benzenetriol in human TK6 cells.

In vitro gene expression signatures to predict toxicological responses can provide mechanistic context for regulatory testing. We previously developed the TGx-28.65 genomic biomarker from a database of gene expression profiles derived from human TK6 cells exposed to 28 well-known compounds. The biomarker comprises 65 genes that can classify chemicals as DNA damaging or non-DNA damaging. In this study, we applied the TGx-28.65 genomic biomarker in parallel with the in vitro micronucleus (MN) assay to determine if two chemicals of regulatory interest at Health Canada, disperse orange (DO: the orange azo dye 3-[[4-[(4-Nitrophenyl)azo]phenyl] benzylamino]propanenitrile) and 1,2,4-benzenetriol (BT: a metabolite of benzene) are genotoxic or non-genotoxic. Both chemicals caused dose-dependent declines in relative survival and increases in apoptosis. A strong significant increase in MN induction was observed for all concentrations of BT; the top two concentrations of DO also caused a statistically significant increase in MN, but these increases were <2-fold above controls. TGx-28.65 analysis classified BT as genotoxic at all three concentrations and DO as genotoxic at the mid and high concentrations. Thus, although DO only caused a small increase in MN, this response was sufficient to induce a cellular DNA damage response. Benchmark dose modeling confirmed that BT is much more potent than DO. The results strongly suggest that follow-up work is required to assess whether DO and BT are also genotoxic in vivo. This is particularly important for DO, which may require metabolic activation by bacterial gut flora to fully induce its genotoxic potential. Our previously published data and this proof of concept study suggest that the TGx-28.65 genomic biomarker has the potential to add significant value to existing approaches used to assess genotoxicity.

The TGx-28.65 biomarker online application for analysis of transcriptomics data to identify DNA damage-inducing chemicals in human cell cultures.

The TGx-28.65 biomarker is a 65-gene expression profile generated from testing 28 model chemicals (13 that cause DNA damage and 15 that do not) in human TK6 cells. It is used to predict whether a chemical induces DNA damage or not. We expanded availability to the biomarker by developing the online TGx-28.65 biomarker application for predicting the DNA damage inducing (DDI) potential of suspect toxicants tested in p53-proficient human cells and assessing putative mode(s) of action (MOA). Applications like this that analyse gene expression data to predict the hazard potential of test chemicals hold great promise for risk assessment paradigms. The TGx-28.65 biomarker interfaces with an analytical tool to predict the probability that a test chemical can directly or indirectly induce DNA damage. User submitted in vitro microarray data are compared to the 28-chemical x 65-gene signature profile and the probability that the data fit the profile for a DDI or a non-DDI (NDDI) chemical is calculated. The results are displayed in the Results Table, which includes the classification probability and hyperlinks to view heatmaps, hierarchical clustering, and principal component analyses of user-input data in the context of the reference profile. The heatmaps and cluster plots, along with the corresponding text data files of fold changes in gene expression and Euclidean distances can be downloaded. Review of the test chemical data in relationship to the biomarker allows rapid identification of key gene alterations associated with DNA damage as well as chemicals in the reference set that produced a similar response. Environ. Mol. Mutagen. 58:529-535, 2017. © 2017 Wiley Periodicals, Inc.

Transcriptional profiling of male F344 rats suggests the involvement of calcium signaling in the mode of action of acrylamide-induced thyroid cancer.

Acrylamide (AA) exposure in 2-year cancer bioassays leads to thyroid, but not liver, adenomas and adenocarcinomas in rats. Hypothesized modes of action (MOAs) include genotoxicity/mutagenicity, or thyroid hormone dysregulation. To examine the plausibility of these two or any alternative MOAs, RNA-sequencing was performed on the thyroids and livers of AA-exposed rats, in parallel with measurement of genotoxicity (blood micronucleus and Pig-a mutant frequency) and serum thyroid hormone levels, following the exposure of male Fischer 344/DuCrl rats to 0.0, 0.5, 1.5, 3.0, 6.0, or 12.0 mg AA/kg bw-day in drinking water for 5, 15, or 31 days. Differentially expressed genes in both tissues provided marginal support for hormonal and genotoxic MOAs, which was consistent with negative/equivocal genotoxicity assay and marginal changes in thyroid hormone levels. Instead, there was a pronounced effect on calcium signaling/cytoskeletal genes in the thyroid. Benchmark dose modeling of RNA-sequencing data for the calcium signaling pathway suggests a point of departure (POD) of 0.68 mg/kg bw-day, which is consistent with a POD of 0.82 mg/kg bw-day derived from the thyroid 2-year cancer bioassay data. Overall, this study suggests a novel MOA for AA-induced thyroid carcinogenicity in male rats centered around perturbation of calcium signaling.

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.

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.

Transcriptional benchmark dose modeling: Exploring how advances in chemical risk assessment may be applied to the radiation field.

Recent advances in "-omics" technologies have simplified capacity to concurrently assess expression profiles of thousands of targets in a cellular system. However, compilation and analysis of "omics" data in support of human health protection remains a challenge. Benchmark dose (BMD) modeling is currently being employed in chemical risk assessment to estimate acceptable levels of exposure. Although typically applied to conventional endpoints, newer software has enabled this application to be extended to transcriptomic datasets. BMD analytical tools now have the capacity to model transcriptional dose-response data to derive meaningful BMD values for genes, pathways and gene ontologies. In this report, radiation data obtained from the Gene Expression Omnibus (GEO) were analyzed to generate BMD values for transcriptional responses. The datasets comprised microarray analyses of human blood gamma-irradiated ex vivo (0-20 Gy) and human-derived cell lines exposed to alpha particle radiation (0.5-1.5 Gy). The distributions of BMDs for statistically significant genes and pathways in response to radiation exposure were examined and compared across studies. BMD modeling could identify pathway/gene sensitivities across wide radiation dose ranges, experimental conditions (time-points, cell types) and radiation qualities. BMD analysis offered a new approach to examine transcriptional data. The results were shown to provide information on transcriptional thresholds of effects to support refined risk assessments for low dose ionizing radiation exposures, derive gene-based values for relative biological effectiveness and identify pathways involved in radiation sensitivities across cell types which may extend to applications a clinical setting. Environ. Mol. Mutagen. 57:589-604, 2016. © 2016 Wiley Periodicals, Inc.

Transcriptional profiling of the mouse hippocampus supports an NMDAR-mediated neurotoxic mode of action for benzo[a]pyrene.

Benzo[a]pyrene (BaP) is a genotoxic carcinogen and a neurotoxicant. The neurotoxicity of BaP is proposed to arise from either genotoxicity leading to neuronal cell death, or perturbed expression of N-methyl-d-aspartate receptor (NMDAR) subunits. To explore these hypotheses, we profiled hippocampal gene expression of adult male Muta(™) Mouse administered 0, 1, 35, or 70 mg BaP/kg bw per day by oral gavage for 3 days. Transcriptional profiles were examined by RNA-sequencing (RNA-seq), DNA microarrays, and real-time quantitative reverse transcription polymerase chain reaction (RT-PCR). BaP-DNA adducts in the cerebellum were quantified by (32) P-post-labeling to measure genotoxicity. RNA-seq revealed altered expression of 0, 260, and 219 genes (P-value < 0.05, fold-change ≥ ± 1.5) following exposure to the low, medium, and high doses, respectively; 54 genes were confirmed by microarrays. Microarray and RT-PCR analysis showed increased expression of NMDAR subunits Grina and Grin2a. In contrast, no effects on DNA-damage response genes were observed despite comparable BaP-DNA adduct levels in the cerebellum and in the lungs and livers of mice at similar BaP doses in previous studies. The results suggest that DNA-damage response does not play a major role in BaP-induced adult neurotoxicity. Meta-analysis revealed that BaP-induced transcriptional profiles are highly correlated with those from the hippocampus of transgenic mice exhibiting similar neurotoxicity outcomes to BaP-exposed mice and rats (i.e., defects in learning and memory). Overall, we suggest that BaP-induced neurotoxicity is more likely to be a consequence of NMDAR perturbation than genotoxicity, and identify other important genes potentially mediating this adverse outcome. Environ. Mol. Mutagen. 57:350-363, 2016. © 2016 Her Majesty the Queen in Right of Canada. Environmental and Molecular Mutagenesis © 2016 Environmental Mutagen Society.

Transcriptional Profiling of Dibenzo[def,p]chrysene-induced Spleen Atrophy Provides Mechanistic Insights into its Immunotoxicity in MutaMouse.

Dibenzo[def,p]chrysene (DBC) is the most carcinogenic polycyclic aromatic hydrocarbon (PAH) examined to date. We investigated the immunotoxicity of DBC, manifested as spleen atrophy, following acute exposure of adult MutaMouse males by oral gavage. Mice were exposed to 0, 2.0, 6.2, or 20.0 mg DBC /kg-bw per day, for 3 days. Genotoxic endpoints (DBC-DNA adducts and lacZ mutant frequency in spleen and bone marrow, and red blood cell micronucleus frequency) and global gene expression changes were measured. All of the genotoxicity measures increased in a dose-dependent manner in spleen and bone marrow. Gene expression analysis showed that DBC activates p53 signaling pathways related to cellular growth and proliferation, which was evident even at the low dose. Strikingly, the expression profiles of DBC exposed mouse spleens were highly inversely correlated with the expression profiles of the only published toxicogenomics dataset of enlarged mouse spleen. This analysis suggested a central role for Bnip3l, a pro-apoptotic protein involved in negative regulation of erythroid maturation. RT-PCR confirmed expression changes in several genes related to apoptosis, iron metabolism, and aryl hydrocarbon receptor signaling that are regulated in the opposite direction during spleen atrophy versus benzo[a]pyrene-mediated splenomegaly. In addition, benchmark dose modeling of toxicogenomics data yielded toxicity estimates that are very close to traditional toxicity endpoints. This work illustrates the power of toxicogenomics to reveal rich mechanistic information for immunotoxic compounds and its ability to provide information that is quantitatively similar to that derived from standard toxicity methods in health risk assessment.