Increased Cell Proliferation as a Key Event in Chemical Carcinogenesis: Application in an Integrated Approach for the Testing and Assessment of Non-Genotoxic Carcinogenesis.

In contrast to genotoxic carcinogens, there are currently no internationally agreed upon regulatory tools for identifying non-genotoxic carcinogens of human relevance. The rodent cancer bioassay is only used in certain regulatory sectors and is criticized for its limited predictive power for human cancer risk. Cancer is due to genetic errors occurring in single cells. The risk of cancer is higher when there is an increase in the number of errors per replication (genotoxic agents) or in the number of replications (cell proliferation-inducing agents). The default regulatory approach for genotoxic agents whereby no threshold is set is reasonably conservative. However, non-genotoxic carcinogens cannot be regulated in the same way since increased cell proliferation has a clear threshold. An integrated approach for the testing and assessment (IATA) of non-genotoxic carcinogens is under development at the OECD, considering learnings from the regulatory assessment of data-rich substances such as agrochemicals. The aim is to achieve an endorsed IATA that predicts human cancer better than the rodent cancer bioassay, using methodologies that equally or better protect human health and are superior from the view of animal welfare/efficiency. This paper describes the technical opportunities available to assess cell proliferation as the central gateway of an IATA for non-genotoxic carcinogenicity.

Key Characteristics of Human Hepatotoxicants as a Basis for Identification and Characterization of the Causes of Liver Toxicity.

Hazard identification regarding adverse effects on the liver is a critical step in safety evaluations of drugs and other chemicals. Current testing paradigms for hepatotoxicity rely heavily on preclinical studies in animals and human data (epidemiology and clinical trials). Mechanistic understanding of the molecular and cellular pathways that may cause or exacerbate hepatotoxicity is well advanced and holds promise for identification of hepatotoxicants. One of the challenges in translating mechanistic evidence into robust decisions about potential hepatotoxicity is the lack of a systematic approach to integrate these data to help identify liver toxicity hazards. Recently, marked improvements were achieved in the practice of hazard identification of carcinogens, female and male reproductive toxicants, and endocrine disrupting chemicals using the key characteristics approach. Here, we describe the methods by which key characteristics of human hepatotoxicants were identified and provide examples for how they could be used to systematically identify, organize, and use mechanistic data when identifying hepatotoxicants.

Sex-, Age-, and Race/Ethnicity-Dependent Variations in Drug-Processing and NRF2-Regulated Genes in Human Livers.

Individual variations in xenobiotic metabolism affect the sensitivity to diseases. In this study, the impacts of sex, age, and race/ethnicity on drug-processing genes and nuclear factor erythroid 2-related factor 2 (NRF2) genes in human livers were examined via QuantiGene multiplex suspension array (226 samples) and quantitative polymerase chain reaction (qPCR) (247 samples) to profile the expression of nuclear receptors, cytochrome P450s, conjugation enzymes, transporters, bile acid metabolism, and NRF2-regulated genes. Sex differences were found in expression of about half of the genes, but in general the differences were not large. For example, females had higher transcript levels of catalase, glutamate-cysteine ligase catalytic subunit (GCLC), heme oxygenase 1 (HO-1), Kelch-like ECH-associated protein 1 (KEAP1), superoxide dismutase 1, and thioredoxin reductase-1 compared with males via qPCR. There were no apparent differences due to age, except children had higher glutamate-cysteine ligase modifier subunit (GCLM) and elderly had higher multidrug resistance protein 3. African Americans had lower expression of farnesoid X receptor (FXR) but higher expression of HO-1, Caucasians had higher expression of organic anion transporter 2, and Hispanics had higher expression of FXR, SULT2A1, small heterodimer partner, and bile salt export pump. An examination of 34 diseased and control human liver samples showed that compared with disease-free livers, fibrotic livers had higher NAD(P)H-quinone oxidoreductase 1 (NQO1), GCLC, GCLM, and NRF2; hepatocellular carcinoma had higher transcript levels of NQO1 and KEAP1; and steatotic livers had lower GCLC, GCLM, and HO-1 expression. In summary, in drug-processing gene and NRF2 genes, sex differences were the major findings, and there were no apparent age differences, and race/ethnicity differences occurred for a few genes. These descriptive findings could add to our understanding of the sex-, age-, and race/ethnicity-dependent differences in drug-processing genes as well as NRF2 genes in normal and diseased human livers. SIGNIFICANCE STATEMENT: In human liver drug-processing and nuclear factor erythroid 2-related factor 2 genes, sex differences were the main finding. There were no apparent differences due to age, except children had higher glutamate-cysteine ligase modifier subunit, and elderly had higher multidrug resistance protein 3. African Americans had lower expression of farnesoid X receptor (FXR) but higher expression of heme oxygenase 1, Caucasians had higher expression of organic anion transporter 2, and Hispanics had higher expression of FXR, small heterodimer partner, SULT2A1, and bile salt export pump.

A Gene Expression Biomarker Predicts Heat Shock Factor 1 Activation in a Gene Expression Compendium.

The United States Environmental Protection Agency (US EPA) recently developed a tiered testing strategy to use advances in high-throughput transcriptomics (HTTr) testing to identify molecular targets of thousands of environmental chemicals that can be linked to adverse outcomes. Here, we describe a method that uses a gene expression biomarker to predict chemical activation of heat shock factor 1 (HSF1), a transcription factor critical for proteome maintenance. The HSF1 biomarker was built from transcript profiles derived from A375 cells exposed to a HSF1-activating heat shock protein (HSP) 90 inhibitor in the presence or absence of HSF1 expression. The resultant 44 identified genes included those that (1) are dependent on HSF1 for regulation, (2) have direct interactions with HSF1 assessed by ChIP-Seq, and (3) are in the molecular chaperone family. To test for accuracy, the biomarker was compared in a pairwise manner to gene lists derived from treatments with known HSF1 activity (HSP and proteasomal inhibitors) using the correlation-based Running Fisher test; the balanced accuracy for prediction was 96%. A microarray compendium consisting of 12,092 microarray comparisons from human cells exposed to 2670 individual chemicals was screened using our approach; 112 and 19 chemicals were identified as putative HSF1 activators or suppressors, respectively, and most appear to be novel modulators. A large percentage of the chemical treatments that induced HSF1 also induced oxidant-activated NRF2 (∼46%). For five compounds or mixtures, we found that NRF2 activation occurred at lower concentrations or at earlier times than HSF1 activation, supporting the concept of a tiered cellular protection system dependent on the level of chemical-induced stress. The approach described here could be used to identify environmentally relevant chemical HSF1 activators in HTTr data sets.

A Gene Expression Biomarker Identifies Chemical Modulators of Estrogen Receptor α in an MCF-7 Microarray Compendium.

Identification of chemicals that affect hormone-regulated systems will help to predict endocrine disruption. In our previous study, a 46 gene biomarker was found to be an accurate predictor of estrogen receptor (ER) α modulation in chemically treated MCF-7 cells. Here, potential ERα modulators were identified using the biomarker by screening a microarray compendium consisting of ∼1600 gene expression comparisons representing exposure to ∼1200 chemicals. A total of ∼170 chemicals were identified as potential ERα modulators. In the Connectivity Map 2.0 collection, 75 and 39 chemicals were predicted to activate or suppress ERα, and they included 12 and six known ERα agonists and antagonists/selective ERα modulators, respectively. Nineteen and eight of the total number were also identified as active in an ERα transactivation assay carried out in an MCF-7-derived cell line used to screen the Tox21 10K chemical library in agonist or antagonist modes, respectively. Chemicals predicted to modulate ERα in MCF-7 cells were examined further using global and targeted gene expression in wild-type and ERα-null cells, transactivation assays, and cell-free ERα coregulator interaction assays. Environmental chemicals classified as weak and very weak agonists were confirmed to activate ERα including apigenin, kaempferol, and oxybenzone. Novel activators included digoxin, nabumetone, ivermectin, and six progestins. Novel suppressors included emetine, mifepristone, niclosamide, and proscillaridin. Our strategy will be useful to identify environmentally relevant ERα modulators in future high-throughput transcriptomic screens.

Expression of cytochrome P450 isozyme transcripts and activities in human livers.

Individual differences in cytochrome P450 (CYP) enzymes contribute to responses to drugs and environmental chemicals. The expression of CYPs is influenced by sex, age, and ethnicity. Human CYP studies are often conducted with human liver microsomes and liver cells to evaluate chemical induction and drug interactions. However, the basal or constitutive expression of CYP transcripts and enzyme activities in the intact liver are also important in our understanding of individual variation in CYPs. This study utilised 100 human liver samples to profile the constitutive expression of CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, 3A4, and 4A11 enzyme activity and transcript levels. The mRNA expression of the CYPs and xenobiotic receptors AhR, CAR, and PXR was examined via qPCR. Results showed that there was greater inter-individual variation in mRNA expression than in enzyme activities, except for CYP2C19. Females had higher CYP3A4 activity than males. Children had lower CYP4A14 activity, while elderly had lower P450 oxidoreductase activity. Compared to Caucasians, Hispanics had higher CYP2C8 activity and higher CYP2B6, CYP2C9, and CYP2C19 mRNA expression, whereas African Americans had lower CYP2D6 mRNA expression. These results add to our understanding of individual variations in xenobiotic metabolism and toxicology.

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.

Thresholds Derived From Common Measures in Rat Studies Are Predictive of Liver Tumorigenic Chemicals.

We hypothesized that typical tissue and clinical chemistry (ClinChem) end points measured in rat toxicity studies exhibit chemical-independent biological thresholds beyond which cancer occurs. Using the rat in vivo TG-GATES study, 75 chemicals were examined across chemical-dose-time comparisons that could be linked to liver tumor outcomes. Thresholds for liver weight to body weight (LW/BW) and 21 serum ClinChem end points were defined as the maximum and minimum values for those exposures that did not lead to liver tumors in rats. Upper thresholds were identified for LW/BW (117%), aspartate aminotransferase (195%), alanine aminotransferase (141%), alkaline phosphatase (152%), and total bilirubin (115%), and lower thresholds were identified for phospholipids (82%), relative albumin (93%), total cholesterol (82%), and total protein (94%). Thresholds derived from the TG-GATES data set were consistent across other acute and subchronic rat studies. A training set of ClinChem and LW/BW thresholds derived from a 38 chemical training set from TG-GATES was predictive of liver tumor outcomes for a test set of 37 independent TG-GATES chemicals (91%). The thresholds were most predictive when applied to 7d treatments (98%). These findings provide support that biological thresholds for common end points in rodent studies can be used to predict chemical tumorigenic potential.

Identification of potential endocrine disrupting chemicals using gene expression biomarkers.

Recent technological advances have moved the field of toxicogenomics from reliance on microarray platforms to high-throughput transcriptomic (HTTr) technologies that measure global gene expression. Gene expression biomarkers are emerging as useful tools for interpreting gene expression profiles to identify perturbations of targets of xenobiotic chemicals including those that act as endocrine disrupting chemicals (EDCs). Gene expression biomarkers are lists of similarly-regulated genes identified in global gene expression comparisons of cells or tissues 1) exposed to known agonists or antagonists of the transcription factor (TF) and 2) after expression of the TF itself is knocked down/knocked out or overexpressed. Estrogen receptor α (ERα) and androgen receptor (AR) biomarkers have been shown to be very accurate at identifying both agonists (94-97%) and antagonists (93-98%) in microarray data derived from human breast or prostate cancer cell lines. Importantly, the biomarkers have been shown to accurately replicate the results of computational models that predict ERα or AR modulation using multiple ToxCast HT screening assays. An integrated screening strategy using sets of biomarkers that simultaneously predict various EDC targets in relevant cell lines should simplify chemical screening without sacrificing accuracy. The biomarker predictions can be put into the context of the adverse outcome pathway framework to help prioritize chemicals with the greatest risk of potential adverse outcomes in the endocrine systems of animals and people.

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.

Adverse outcome pathway-driven identification of rat liver tumorigens in short-term assays.

Chemicals induce liver cancer in rodents through well characterized adverse outcome pathways (AOPs). We hypothesized that measurement of molecular initiating events (MIEs) and downstream key events (KEs) in liver cancer AOPs in short-term assays will allow early identification of chemicals and their associated doses that are likely to be tumorigenic in the liver in two-year bioassays. We tested this hypothesis using the rat in vivo TG-GATES study data to measure MIEs (genotoxicity, cytotoxicity, AhR, CAR, ER, PPARα) and associated KEs (oxidative stress, cell proliferation, liver to body weights) across 77 chemicals that could be linked to doses with previously established effects on rat liver tumor induction. Gene expression biomarkers for MIEs generally considered to be rodent specific and human irrelevant (CAR, PPARα) and for MIEs that would be considered of greater risk at human relevant exposures (ER, AhR) were built using microarray comparisons from the livers of rats treated with prototypical activators of the receptors. The genotoxicity biomarker, also a potentially human relevant MIE, was comprised of 7 p53-responsive genes known to be induced upon DNA damage. The ability of the biomarkers to accurately predict MIE activation ranged from 91% to 98%. The Toxicological Priority Index (ToxPi) was used to rank chemicals based on their ability to activate MIEs/KEs. Chemicals administered at tumorigenic doses clearly gave the highest ranked scores. Our AOP-directed approach could be used in short term assays to identify chemicals and their doses that would be predicted to cause liver tumors in rats.

The PPARα-dependent rodent liver tumor response is not relevant to humans: addressing misconceptions.

A number of industrial chemicals and therapeutic agents cause liver tumors in rats and mice by activating the nuclear receptor peroxisome proliferator-activated receptor α (PPARα). The molecular and cellular events by which PPARα activators induce rodent hepatocarcinogenesis have been extensively studied elucidating a number of consistent mechanistic changes linked to the increased incidence of liver neoplasms. The weight of evidence relevant to the hypothesized mode of action (MOA) for PPARα activator-induced rodent hepatocarcinogenesis is summarized here. Chemical-specific and mechanistic data support concordance of temporal and dose-response relationships for the key events associated with many PPARα activators. The key events (KE) identified in the MOA are PPARα activation (KE1), alteration in cell growth pathways (KE2), perturbation of hepatocyte growth and survival (KE3), and selective clonal expansion of preneoplastic foci cells (KE4), which leads to the apical event-increases in hepatocellular adenomas and carcinomas (KE5). In addition, a number of concurrent molecular and cellular events have been classified as modulating factors, because they potentially alter the ability of PPARα activators to increase rodent liver cancer while not being key events themselves. These modulating factors include increases in oxidative stress and activation of NF-kB. PPARα activators are unlikely to induce liver tumors in humans due to biological differences in the response of KEs downstream of PPARα activation. This conclusion is based on minimal or no effects observed on cell growth pathways and hepatocellular proliferation in human primary hepatocytes and absence of alteration in growth pathways, hepatocyte proliferation, and tumors in the livers of species (hamsters, guinea pigs and cynomolgus monkeys) that are more appropriate human surrogates than mice and rats at overlapping dose levels. Despite this overwhelming body of evidence and almost universal acceptance of the PPARα MOA and lack of human relevance, several reviews have selectively focused on specific studies that, as discussed, contradict the consensus opinion and suggest uncertainty. In the present review, we systematically address these most germane suggested weaknesses of the PPARα MOA.

Human relevance of rodent liver tumors: Key insights from a Toxicology Forum workshop on nongenotoxic modes of action.

The Toxicology Forum sponsored a workshop in October 2016, on the human relevance of rodent liver tumors occurring via nongenotoxic modes of action (MOAs). The workshop focused on two nuclear receptor-mediated MOAs (Constitutive Androstane Receptor (CAR) and Peroxisome Proliferator Activated Receptor-alpha (PPARα), and on cytotoxicity. The goal of the meeting was to review the state of the science to (1) identify areas of consensus and differences, data gaps and research needs; (2) identify reasons for inconsistencies in current regulatory positions; and (3) consider what data are needed to demonstrate a specific MOA, and when additional research is needed to rule out alternative possibilities. Implications for quantitative risk assessment approaches were discussed, as were implications of not considering MOA and dose in hazard characterization and labeling schemes. Most, but not all, participants considered the CAR and PPARα MOAs as not relevant to humans based on quantitative and qualitative differences. In contrast, cytotoxicity is clearly relevant to humans, but a threshold applies. Questions remain for all three MOAs concerning what data are necessary to determine the MOA and to what extent it is necessary to exclude other MOAs.

Mode of action framework analysis for receptor-mediated toxicity: The peroxisome proliferator-activated receptor alpha (PPARα) as a case study.

Several therapeutic agents and industrial chemicals induce liver tumors in rodents through the activation of the peroxisome proliferator-activated receptor alpha (PPARα). The cellular and molecular events by which PPARα activators induce rodent hepatocarcinogenesis has been extensively studied and elucidated. This review summarizes the weight of evidence relevant to the hypothesized mode of action (MOA) for PPARα activator-induced rodent hepatocarcinogenesis and identifies gaps in our knowledge of this MOA. Chemical-specific and mechanistic data support concordance of temporal and dose-response relationships for the key events associated with many PPARα activators including a phthalate ester plasticizer di(2-ethylhexyl) phthalate (DEHP) and the drug gemfibrozil. While biologically plausible in humans, the hypothesized key events in the rodent MOA, for PPARα activators, are unlikely to induce liver tumors in humans because of toxicodynamic and biological differences in responses. This conclusion is based on minimal or no effects observed on growth pathways, hepatocellular proliferation and liver tumors in humans and/or species (including hamsters, guinea pigs and cynomolgous monkeys) that are more appropriate human surrogates than mice and rats at overlapping dose levels. Overall, the panel concluded that significant quantitative differences in PPARα activator-induced effects related to liver cancer formation exist between rodents and humans. On the basis of these quantitative differences, most of the workgroup felt that the rodent MOA is "not relevant to humans" with the remaining members concluding that the MOA is "unlikely to be relevant to humans". The two groups differed in their level of confidence based on perceived limitations of the quantitative and mechanistic knowledge of the species differences, which for some panel members strongly supports but cannot preclude the absence of effects under unlikely exposure scenarios.

A transcriptomic biomarker predictive of cell proliferation for use in adverse outcome pathway-informed testing and assessment.

High-throughput transcriptomics (HTTr) is increasingly being used to identify molecular targets of chemicals that can be linked to adverse outcomes. Cell proliferation (CP) is an important key event in chemical carcinogenesis. Here, we describe the construction and characterization of a gene expression biomarker that is predictive of the CP status in human and rodent tissues. The biomarker was constructed from 30 genes known to be increased in expression in prostate cancers relative to surrounding tissues and in cycling human MCF-7 cells after estrogen receptor (ER) agonist exposure. Using a large compendium of gene expression profiles to test utility, the biomarker could identify increases in CP in (i) 308 out of 367 tumor vs. normal surrounding tissue comparisons from 6 human organs, (ii) MCF-7 cells after activation of ER, (iii) after partial hepatectomy in mice and rats, and (iv) the livers of mice and rats after exposure to nongenotoxic hepatocarcinogens. The biomarker identified suppression of CP (i) under conditions of p53 activation by DNA damaging agents in human cells, (ii) in human A549 lung cells exposed to therapeutic anticancer kinase inhibitors (dasatinib, nilotnib), and (iii) in the mouse liver when comparing high levels of CP at birth to the low background levels in the adult. The responses using the biomarker were similar to those observed using conventional markers of CP including PCNA, Ki67, and BrdU labeling. The CP biomarker will be a useful tool for interpretation of HTTr data streams to identify CP status after exposure to chemicals in human cells or in rodent tissues.

A 50-gene biomarker identifies estrogen receptor-modulating chemicals in a microarray compendium.

High throughput transcriptomics (HTTr) profiling has the potential to rapidly and comprehensively identify molecular targets of environmental chemicals that can be linked to adverse outcomes. We describe here the construction and characterization of a 50-gene expression biomarker designed to identify estrogen receptor (ER) active chemicals in HTTr datasets. Using microarray comparisons, the genes in the biomarker were identified as those that exhibited consistent directional changes when ER was activated (4 ER agonists; 4 ESR1 gene constitutively active mutants) and opposite directional changes when ER was suppressed (4 antagonist treatments; 4 ESR1 knockdown experiments). The biomarker was evaluated as a predictive tool using the Running Fisher algorithm by comparison to annotated gene expression microarray datasets including those evaluating the transcriptional effects of hormones and chemicals in MCF-7 cells. Depending on the reference dataset used, the biomarker had a predictive accuracy for activation of up to 96%. To demonstrate applicability for HTTr data analysis, the biomarker was used to identify ER activators in a set of 15 chemicals that are considered potential bisphenol A (BPA) alternatives examined at up to 10 concentrations in MCF-7 cells and analyzed by full-genome TempO-Seq. Using benchmark dose (BMD) modeling, the biomarker genes stratified the ER potency of BPA alternatives consistent with previous studies. These results demonstrate that the ER biomarker can be used to accurately identify ER activators in transcript profile data derived from MCF-7 cells.

A new approach methodology to identify tumorigenic chemicals using short-term exposures and transcript profiling.

Current methods for cancer risk assessment are resource-intensive and not feasible for most of the thousands of untested chemicals. In earlier studies, we developed a new approach methodology (NAM) to identify liver tumorigens using gene expression biomarkers and associated tumorigenic activation levels (TALs) after short-term exposures in rats. The biomarkers are used to predict the six most common rodent liver cancer molecular initiating events. In the present study, we wished to confirm that our approach could be used to identify liver tumorigens at only one time point/dose and if the approach could be applied to (targeted) RNA-Seq analyses. Male rats were exposed for 4 days by daily gavage to 15 chemicals at doses with known chronic outcomes and liver transcript profiles were generated using Affymetrix arrays. Our approach had 75% or 85% predictive accuracy using TALs derived from the TG-GATES or DrugMatrix studies, respectively. In a dataset generated from the livers of male rats exposed to 16 chemicals at up to 10 doses for 5 days, we found that our NAM coupled with targeted RNA-Seq (TempO-Seq) could be used to identify tumorigenic chemicals with predictive accuracies of up to 91%. Overall, these results demonstrate that our NAM can be applied to both microarray and (targeted) RNA-Seq data generated from short-term rat exposures to identify chemicals, their doses, and mode of action that would induce liver tumors, one of the most common endpoints in rodent bioassays.

Hepatic Transcriptome Comparative In Silico Analysis Reveals Similar Pathways and Targets Altered by Legacy and Alternative Per- and Polyfluoroalkyl Substances in Mice.

Per- and poly-fluoroalkyl substances (PFAS) are a large class of fluorinated carbon chains that include legacy PFAS, such as perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), and perfluorohexane sulfonate (PFHxS). These compounds induce adverse health effects, including hepatotoxicity. Potential alternatives to the legacy PFAS (HFPO-DA (GenX), HFPO4, HFPO-TA, F-53B, 6:2 FTSA, and 6:2 FTCA), as well as a byproduct of PFAS manufacturing (Nafion BP2), are increasingly being found in the environment. The potential hazards of these new alternatives are less well known. To better understand the diversity of molecular targets of the PFAS, we performed a comparative toxicogenomics analysis of the gene expression changes in the livers of mice exposed to these PFAS, and compared these to five activators of PPARα, a common target of many PFAS. Using hierarchical clustering, pathway analysis, and predictive biomarkers, we found that most of the alternative PFAS modulate molecular targets that overlap with legacy PFAS. Only three of the 11 PFAS tested did not appreciably activate PPARα (Nafion BP2, 6:2 FTSA, and 6:2 FTCA). Predictive biomarkers showed that most PFAS (PFHxS, PFOA, PFOS, PFNA, HFPO-TA, F-53B, HFPO4, Nafion BP2) activated CAR. PFNA, PFHxS, PFOA, PFOS, HFPO4, HFPO-TA, F-53B, Nafion BP2, and 6:2 FTSA suppressed STAT5b, activated NRF2, and activated SREBP. There was no apparent relationship between the length of the carbon chain, type of head group, or number of ether linkages and the transcriptomic changes. This work highlights the similarities in molecular targets between the legacy and alternative PFAS.