Screening a mouse liver gene expression compendium identifies modulators of the aryl hydrocarbon receptor (AhR)

Review and meta-analysis of gene expression biomarkers predictive of chemical-induced genotoxicity in vivo

There is growing recognition across broad sectors of the toxicology community that gene expression biomarkers have the potential to identify genotoxic and nongenotoxic carcinogens through a weight-of-evidence approach, providing opportunities to reduce reliance on the 2-year bioassay to identify carcinogens. In August 2022, a workshop within the International Workshops on Genotoxicity Testing (IWGT) was held to critically review current methods to identify genotoxicants using various 'omics profiling methods. Here, we describe the findings of a workshop subgroup focused on the state of the science regarding the use of biomarkers to identify chemicals that act as genotoxicants in vivo. A total of 1341 papers were screened to identify those that were most relevant. While six published biomarkers with characterized accuracy were initially examined, four of the six were not considered further, because they had not been tested for classification accuracy using additional sets of chemicals or other transcript profiling platforms. Two independently derived biomarkers used in conjunction with standard computational techniques can identify genotoxic chemicals in vivo (rat liver or both rat and mouse liver) on different gene expression profiling platforms. The biomarkers have predictive accuracies of ≥92%. These biomarkers have the potential to be used in conjunction with other biomarkers in integrated test strategies using short-term rodent exposures to identify genotoxic and nongenotoxic chemicals that cause cancer.

High-throughput transcriptomics toxicity assessment of eleven data-poor bisphenol A alternatives

Bisphenol A (BPA), a widely used chemical in the production of plastics and epoxy resins, has garnered significant attention due to its association with adverse health effects, particularly its endocrine-disrupting properties. Regulatory measures aimed at reducing human exposure to BPA have led to a proliferation of alternative chemicals used in various consumer and industrial products. While these alternatives serve to reduce BPA exposure, concerns have arisen regarding their safety and potential toxicity as regrettable substitutes. Previous efforts have demonstrated that in vitro high-throughput transcriptomics (HTTr) studies can be used to assess the endocrine-disrupting potential of BPA alternatives, and this strategy produces transcriptomic points-of-departure (tPODs) that are protective of human health when compared to the PODs from traditional rodent studies. In this study, we used in vitro HTTr to assess the potential for toxicity of eleven data-poor legacy chemicals sharing structural similarities to BPA. Human breast cancer MCF-7 cells were exposed to BPA and 11 alternatives at concentrations ranging from 0.1 to 25 μM to assess toxicity. Analysis of global transcriptomic changes and a previously characterized estrogen receptor alpha (ERα) transcriptomic biomarker signature revealed that 9 of 11 chemicals altered gene expression relative to controls. One of the chemicals (2,4'-Bisphenol A) activated the ERα biomarker at the same concentration as BPA (i.e., 4,4'-BPA) but was deemed to be more potent as it induced global transcriptomic changes at lower concentrations. These results address data gaps in support of ongoing screening assessments to identify BPA alternatives with hazard potential and help to identify potential candidates that may serve as safer alternatives.

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.

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.

Conjoint hepatobiliary-enterohepatic cycles for amyloid excretion and enhancing its drug-induced clearance: a systems biology approach to Alzheimer's disease

The liver is the major organ responsible for metabolism of amyloid-beta, the primary toxic misfolded protein responsible for Alzheimer's disease (AD). The present study focuses on the crucial role of liver in AD. We have developed a framework that formulates and integrates two reciprocal transport processes of amyloid, via hepato-biliary and enterohepatic circulations (EHC). Our system analysis approach shows that activating the liver X-receptor (LXR) can reduce amyloid-beta formation by increasing expression of the genes: ATP-binding-cassette-transporter (ABCA1) and Stearoyl-CoA-desaturase (SCD). Besides, activating the pregnane-X-receptor (PXR) can enhance the clearance of amyloid-beta by increasing the expression of the genes: ATP-Binding-Cassette-Superfamily-G-member-2 (ABCG2) and multidrug-resistance protein-1 (MDR1). We also identified receptor-like apical sodium-dependent bile-acid transporter (ASBT) of intestinal enterocyte, showing affinity towards amyloid-beta, suggesting amyloid-beta's possible reuptake from intestinal contents to the systemic circulation through this receptor. Further, we have performed protein-protein interaction to evaluate the binding affinity of amyloid-beta to these receptors. Moreover, we undertook molecular docking and molecular dynamic simulation of some repurposed drugs (rifampicin, 24-hydroxycholesterols, resveratrol, cilostazol) which can target the aforesaid receptors to enhance amyloid-beta's fecal clearance, reduce amyloid-beta formation, and prevent the reuptake of amyloid-beta from intestinal feces. Additionally, network pharmacology and synergism analysis were utilized to validate our hypothesis and identify the drug combinations, respectively. Gene-ontology investigation, network pharmacology, and consolidated pathway analysis validate the alteration of the above-mentioned gene expression profiles. Furthermore, our neuropharmacological synergism study identifies the optimal combination of the repurposed drugs. Finally, our findings on candidate drugs are substantiated by clinical-trial outcomes.Communicated by Ramaswamy H. Sarma.

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.

Determinants of gene expression in the human liver: Impact of aging and sex on xenobiotic metabolism

There is a need to characterize the potential susceptibility of older adults to toxicity from environmental chemical exposures. Liver xenobiotic metabolizing enzymes (XMEs) play important roles in detoxifying and eliminating xenobiotics. We examined global gene expression in the livers of young (21-45 years) and old (69+ years) men and women. Differentially expressed genes (DEG) were identified using two-way ANOVA (p ≤ 0.05). We identified 1437 and 1670 DEGs between young and old groups in men and women, respectively. Only a minor number of the total number of genes overlapped (146 genes). Aging increased or decreased pathways involved in inflammation and intermediary metabolism, respectively. Aging led to numerous changes in the expression of XME genes or genes known to control their expression (~90 genes). Out of 10 cytochrome P450s activities examined, there were increased activities of CYP1A2 and CYP2C9 enzymes in the old groups. We also identified sex-dependent genes that were more numerous in the young group (1065) than in the old group (202) and included changes in XMEs. These studies indicate that the livers from aging humans when compared to younger adults exhibit changes in XMEs that may lead to differences in the metabolism of xenobiotics.

A Collaborative Initiative to Establish Genomic Biomarkers for Assessing Tumorigenic Potential to Reduce Reliance on Conventional Rodent Carcinogenicity Studies

There is growing recognition across broad sectors of the scientific community that use of genomic biomarkers has the potential to reduce the need for conventional rodent carcinogenicity studies of industrial chemicals, agrochemicals, and pharmaceuticals through a weight-of-evidence approach. These biomarkers fall into 2 major categories: (1) sets of gene transcripts that can identify distinct tumorigenic mechanisms of action; and (2) cancer driver gene mutations indicative of rapidly expanding growth-advantaged clonal cell populations. This call-to-action article describes a collaborative approach launched to develop and qualify biomarker gene expression panels that measure widely accepted molecular pathways linked to tumorigenesis and their activation levels to predict tumorigenic doses of chemicals from short-term exposures. Growing evidence suggests that application of such biomarker panels in short-term exposure rodent studies can identify both tumorigenic hazard and tumorigenic activation levels for chemical-induced carcinogenicity. In the future, this approach will be expanded to include methodologies examining mutations in key cancer driver gene mutation hotspots as biomarkers of both genotoxic and nongenotoxic chemical tumor risk. Analytical, technical, and biological validation studies of these complementary genomic tools are being undertaken by multisector and multidisciplinary collaborative teams within the Health and Environmental Sciences Institute. Success from these efforts will facilitate the transition from current heavy reliance on conventional 2-year rodent carcinogenicity studies to more rapid animal- and resource-sparing approaches for mechanism-based carcinogenicity evaluation supporting internal and regulatory decision-making.

A gene expression biomarker identifies inhibitors of two classes of epigenome effectors in a human microarray compendium

Biomarkers predictive of molecular and toxicological effects are needed to interpret emerging high-throughput transcriptomics (HTTr) data streams. To address the limited approaches available for identifying epigenotoxicants, we previously developed and validated an 81-gene biomarker that accurately predicts histone deacetylase inhibition (HDACi) in transcript profiles derived from chemically-treated TK6 cells. In the present study, we sought to determine if this biomarker (TGx-HDACi) could be used to identify HDACi chemicals in other cell lines using the Running Fisher correlation test. Using microarray comparisons derived from human cells exposed to HDACi, we found considerable heterogeneity in correlation with the TGx-HDACi biomarker dependent on chemical exposure conditions and tissue from which the cell line was derived. Using a defined set of conditions that overlapped with our earlier study, the biomarker was able to accurately identify HDACi chemicals (90-100% balanced accuracy). In an in silico screen of 2427 chemicals in 9660 chemical versus control comparisons, the biomarker coupled with the Running Fisher test was able to identify 14 additional HDACi chemicals as well as other chemicals not previously associated with HDACi. Most notable were 12 inhibitors of bromodomain (BRD) and extraterminal (BET) family proteins including BRD4 that bind to acetylated histones. The BET protein inhibitors could be distinguished from the HDACi based on differences in the expression of a small set of biomarker genes. Our results indicate that the TGx-HDACi biomarker will be useful for identifying inhibitors of two classes of epigenome effectors in HTTr screening studies.

Towards replacement of animal tests with in vitro assays: a gene expression biomarker predicts in vitro and in vivo estrogen receptor activity

High-throughput transcriptomics (HTTr) has the potential to support efforts to reduce or replace some animal tests. In past studies, we described a computational approach utilizing a gene expression biomarker consisting of 46 genes to predict estrogen receptor (ER) activity after chemical exposure in ER-positive human breast cancer cells including the MCF-7 cell line. We hypothesized that the biomarker model could identify ER activities of chemicals examined by Endocrine Disruptor Screening Program (EDSP) Tier 1 screening assays in which transcript profiles of the same chemicals were examined in MCF-7 cells. For the 62 chemicals examined including 5 chemicals examined in this study using RNA-Seq, the ER biomarker model accuracy was 1) 97% for in vitro reference chemicals, 2) 76-85% for guideline uterotrophic assays, and 3) 87-88% for guideline and nonguideline uterotrophic assays. For the same chemicals, these accuracies were similar or slightly better than those of the ToxCast ER model based on 18 in vitro assays. The performance of the ER biomarker model indicates that HTTr interpreted using the ER biomarker correctly identifies active and inactive ER reference chemicals. As part of the HTTr screening program the approach could rapidly identify chemicals with potential ER bioactivities for additional screening and testing.

A gene expression biomarker for predictive toxicology to identify chemical modulators of NF-κB

The nuclear factor-kappa B (NF-κB) is a transcription factor with important roles in inflammation, immune response, and oncogenesis. Dysregulation of NF-κB signaling is associated with inflammation and certain cancers. We developed a gene expression biomarker predictive of NF-κB modulation and used the biomarker to screen a large compendia of gene expression data. The biomarker consists of 108 genes responsive to tumor necrosis factor α in the absence but not the presence of IκB, an inhibitor of NF-κB. Using a set of 450 profiles from cells treated with immunomodulatory factors with known NF-κB activity, the balanced accuracy for prediction of NF-κB activation was > 90%. The biomarker was used to screen a microarray compendium consisting of 12,061 microarray comparisons from human cells exposed to 2,672 individual chemicals to identify chemicals that could cause toxic effects through NF-κB. There were 215 and 49 chemicals that were identified as putative or known NF-κB activators or suppressors, respectively. NF-κB activators were also identified using two high-throughput screening assays; 165 out of the ~3,800 chemicals (ToxCast assay) and 55 out of ~7,500 unique compounds (Tox21 assay) were identified as potential activators. A set of 32 chemicals not previously associated with NF-κB activation and which partially overlapped between the different screens were selected for validation in wild-type and NFKB1-null HeLa cells. Using RT-qPCR and targeted RNA-Seq, 31 of the 32 chemicals were confirmed to be NF-κB activators. These results comprehensively identify a set of chemicals that could cause toxic effects through NF-κB.

Genomic comparisons between hepatocarcinogenic and non-hepatocarcinogenic organophosphate insecticides in the mouse liver

Short-term biomarkers of toxicity have an increasingly important role in the screening and prioritization of new chemicals. In this study, we examined early indicators of liver toxicity for three reference organophosphate (OP) chemicals, which are among the most widely used insecticides in the world. The OP methidathion was previously shown to increase the incidence of liver toxicity, including hepatocellular tumors, in male mice. To provide insights into the adverse outcome pathway (AOP) that underlies these tumors, effects of methidathion in the male mouse liver were examined after 7 and 28 day exposures and compared to those of two other OPs that either do not increase (fenthion) or possibly suppress liver cancer (parathion) in mice. None of the chemicals caused increases in liver weight/body weight or histopathological changes in the liver. Parathion decreased liver cell proliferation after 7 and 28 days while the other chemicals had no effects. There was no evidence for hepatotoxicity in any of the treatment groups. Full-genome microarray analysis of the livers from the 7 and 28 day treatments demonstrated that methidathion and fenthion regulated a large number of overlapping genes, while parathion regulated a unique set of genes. Examination of cytochrome P450 enzyme activities and use of predictive gene expression biomarkers found no consistent evidence for activation of AhR, CAR, PXR, or PPARα. Parathion suppressed the male-specific gene expression pattern through STAT5b, similar to genetic and dietary conditions that decrease liver tumor incidence in mice. Overall, these findings indicate that methidathion causes liver cancer by a mechanism that does not involve common mechanisms of liver cancer induction.

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.

Critical evaluation of the human relevance of the mode of action for rodent liver tumor formation by activators of the constitutive androstane receptor (CAR)

Many nongenotoxic chemicals have been shown to produce liver tumors in mice and/or rats by a mode of action (MOA) involving activation of the constitutive androstane receptor (CAR). Studies with phenobarbital (PB) and other compounds have identified the key events for this MOA: CAR activation; increased hepatocellular proliferation; altered foci formation; and ultimately the development of adenomas/carcinomas. In terms of human relevance, the pivotal species difference is that CAR activators are mitogenic agents in mouse and rat hepatocytes, but they do not stimulate increased hepatocellular proliferation in humans. This conclusion is supported by substantial in vitro studies with cultured rodent and human hepatocytes and also by in vivo studies with chimeric mice with human hepatocytes. Examination of the literature reveals many similarities in the hepatic effects and species differences between activators of rodent CAR and the peroxisome proliferator-activated receptor alpha (PPARα), with PPARα activators also not being mitogenic agents in human hepatocytes. Overall, a critical analysis of the available data demonstrates that the established MOA for rodent liver tumor formation by PB and other CAR activators is qualitatively not plausible for humans. This conclusion is supported by data from several human epidemiology studies.

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.

Improvement in the screening performance of potential aryl hydrocarbon receptor ligands by using supervised machine learning

The aryl hydrocarbon receptor (AhR), which is a ligand-dependent transcription factor, plays a crucial role in the regulation of xenobiotic metabolism. There are a large number of artificial or natural molecules in the environment that can activate AhR. In this study, we developed a virtual screening procedure to identify potential ligands of AhR. One structure-based method and two ligand-based methods were used for the virtual screening procedure. The results showed that the precision rate (0.96) and recall rate (0.64) of our procedure were significantly higher than those of a procedure used in a previous study, which suggests that supervised machine learning techniques can greatly improve the performance of virtual screening. Moreover, a pesticide dataset including 777 frequently used pesticides was screened. Seventy-seven pesticides were identified as potential AhR ligands by all three screening methods, among which 12 have never been previously reported as AhR agonists. Two non-agonist AhR ligands and 14 of the 77 pesticides were randomly selected for testing by in vitro and in vivo assays. All 14 pesticides showed different degrees of AhR agonistic activity, and none of the two non-agonist AhR ligand pesticides showed AhR agonistic activity, which suggests that our procedure had good robustness. Four of the pesticides were reported as AhR agonists for the first time, suggesting that these pesticides may need further toxicity assessment. In general, our procedure is a rapid, powerful and computationally inexpensive tool for predicting chemicals with AhR agonistic activity, which could be useful for environmental risk prediction and management.

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.

Mining a human transcriptome database for chemical modulators of NRF2

Nuclear factor erythroid-2 related factor 2 (NRF2) encoded by the NFE2L2 gene is a transcription factor critical for protecting cells from chemically-induced oxidative stress. We developed computational procedures to identify chemical modulators of NRF2 in a large database of human microarray data. A gene expression biomarker was built from statistically-filtered gene lists derived from microarray experiments in primary human hepatocytes and cancer cell lines exposed to NRF2-activating chemicals (oltipraz, sulforaphane, CDDO-Im) or in which the NRF2 suppressor Keap1 was knocked down by siRNA. Directionally consistent biomarker genes were further filtered for those dependent on NRF2 using a microarray dataset from cells after NFE2L2 siRNA knockdown. The resulting 143-gene biomarker was evaluated as a predictive tool using the correlation-based Running Fisher algorithm. Using 59 gene expression comparisons from chemically-treated cells with known NRF2 activating potential, the biomarker gave a balanced accuracy of 93%. The biomarker was comprised of many well-known NRF2 target genes (AKR1B10, AKR1C1, NQO1, TXNRD1, SRXN1, GCLC, GCLM), 69% of which were found to be bound directly by NRF2 using ChIP-Seq. NRF2 activity was assessed across ~9840 microarray comparisons from ~1460 studies examining the effects of ~2260 chemicals in human cell lines. A total of 260 and 43 chemicals were found to activate or suppress NRF2, respectively, most of which have not been previously reported to modulate NRF2 activity. Using a NRF2-responsive reporter gene in HepG2 cells, we confirmed the activity of a set of chemicals predicted using the biomarker. The biomarker will be useful for future gene expression screening studies of environmentally-relevant chemicals.

A Set of Six Gene Expression Biomarkers Identify Rat Liver Tumorigens in Short-term Assays

Chemical-induced liver cancer occurs in rodents through well-characterized adverse outcome pathways. We hypothesized that measurement of the 6 most common molecular initiating events (MIEs) in liver cancer adverse outcome pathways in short-term assays using only gene expression will allow early identification of chemicals and their associated doses that are likely to be tumorigenic in the liver in 2-year bioassays. We tested this hypothesis using transcript data from a rat liver microarray compendium consisting of 2013 comparisons of 146 chemicals administered at doses with previously established effects on rat liver tumor induction. Five MIEs were measured using previously characterized gene expression biomarkers composed of gene sets predictive for genotoxicity and activation of 1 or more xenobiotic receptors (aryl hydrocarbon receptor, constitutive activated receptor, estrogen receptor, and peroxisome proliferator-activated receptor α). Because chronic injury can be important in tumorigenesis, we also developed a biomarker for cytotoxicity that had a 96% balanced accuracy. Characterization of the genes in each biomarker set using the unsupervised TXG-MAP network model demonstrated that the genes were associated with distinct functional coexpression modules. Using the Toxicological Priority Index to rank chemicals based on their ability to activate the MIEs showed that chemicals administered at tumorigenic doses clearly gave the highest ranked scores. Balanced accuracies using thresholds derived from either TG-GATES or DrugMatrix data sets to predict tumorigenicity in independent sets of chemicals were up to 93%. These results show that a MIE-directed approach using only gene expression biomarkers could be used in short-term assays to identify chemicals and their doses that cause tumors.

The aryl hydrocarbon receptor mediates sex ratio distortion in the embryos sired by TCDD-exposed male mice

Many reports describe an association between preconceptional paternal exposure to environmental chemicals, including the persistent organic pollutant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) with an increased number of female offspring. We chronically treated wild-type C57BL/6 male mice with TCDD to investigate a role for the aryl hydrocarbon receptor (AHR) transcription factor. These mice had a 14 % lower male:female sex ratio than control mice, which was not observed in TCDD-treated Ahr knock out mice. AHR target genes Cyp1a1 and Ahrr were upregulated in the liver and testis of WT mice and Ahr expression was higher in the epididymis (2-fold) and liver (18-fold) than in whole testis tissue. The AHR protein was localized to round spermatids, elongating spermatids, and Leydig cells in the testis of WT mice. These studies demonstrate AHR involvement in the sex ratio distortion of TCDD-exposed males and the need for evaluating the molecular and genetic mechanism of this process.

Pathway-based assessment of single chemicals and mixtures by a high-throughput transcriptomics approach

The ever-increasing number of chemicals and complex mixtures demands a high-throughput and cost-effective approach for chemical safety assessment. High-throughput transcriptomics (HTT) is promising in investigating genome-scale perturbation of chemical exposure in concentration-dependent manner. However, the application of HTT has been limited due to lack of methodology for single chemicals and mixture assessment. This study aimed to evaluate the ability of a newly-developed human reduced transcriptomics (RHT) approach to assess pathway-based profiles of single chemicals, and to develop a biological pathway-based approach for benchmarking mixture potency using single chemical-based prediction model. First, concentration-dependent RHT were used to qualitatively and quantitatively differentiate pathway-based patterns of different chemicals, using three model toxicants, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), triclosan (TCS) and 5-Chloro-6-hydroxy-2,2',4,4'-tetrabromodiphenyl ether (5-Cl-6-OH-BDE-47). AHR-regulated genes and pathways were most sensitively induced by TCDD, while TCS and 5-Cl-6-OH-BDE-47 were much less potent in AHR-associated activation, which was concordant with known MoA of each single chemical. Second, two artificial mixtures and their components of twelve individual chemicals were performed with concentration-dependent RHT. Concentration addition (CA) and independent action (IA) models were used to predict transcriptional potency of mixtures from transcriptomics of individual chemicals. For overall bioactivity, CA and IA models can both predict potency of observed responses within 95% confidence interval. For specific biological processes, multiple biological processes such as hormone signaling and DNA damage can be predicted using CA models for mixtures. The concentration-dependent RHT can provide a powerful approach for qualitative and quantitative assessment of biological pathway perturbated by environment chemical and mixtures.

Chemical Activation of the Constitutive Androstane Receptor Leads to Activation of Oxidant-Induced Nrf2

Exposure to environmentally relevant chemicals that activate the xenobiotic receptors aryl hydrocarbon receptor (AhR), constitutive androstane receptor (CAR), and peroxisome proliferator-activated receptor alpha (PPARα) in rodent test systems often leads to increases in oxidative stress (OS) that contributes to liver cancer induction. We hypothesized that activation of the oxidant-induced transcription factor Nrf2 could be used as a surrogate endpoint for increases in OS. We examined the relationships between activation of xenobiotic receptors and Nrf2 using previously characterized gene expression biomarkers that accurately predict modulation. Using a correlation approach (Running Fisher Test), the biomarkers were compared with microarray profiles in a mouse liver gene expression compendium. Out of the 163 chemicals examined, 47% from 53 studies activated Nrf2. We found consistent coupling between CAR and Nrf2 activation. Out of the 41 chemicals from 32 studies that activated CAR, 90% also activated Nrf2. CAR was activated earlier and at lower doses than Nrf2, indicating CAR activation preceded Nrf2 activation. Nrf2 activation by 2 CAR activators was abolished in CAR-null mice. We hypothesized that Nrf2 is activated by reactive oxygen species from the increased activity of enzymes encoded by Cyp2b family members. However, Nrf2 was similarly activated in the livers of both TCPOBOP-treated wild-type and Cyp2b9/10/13-null mice. This study provides evidence that Nrf2 activation (1) often occurs after exposure to xenobiotic chemicals, (2) is tightly linked to activation of CAR, and (3) does not require induction of 3 Cyp2b genes secondary to CAR activation.

Identification of Androgen Receptor Modulators in a Prostate Cancer Cell Line Microarray Compendium

High-throughput transcriptomic (HTTr) technologies are increasingly being used to screen environmental chemicals in vitro to identify molecular targets and provide mechanistic context for regulatory testing. Here, we describe the development and validation of a novel gene expression biomarker to identify androgen receptor (AR)-modulating chemicals using a pattern matching method. Androgen receptor biomarker genes were identified by their consistent expression after exposure to 4 AR agonists and 4 AR antagonists and included only those genes that were regulated by AR. The 51 gene biomarker was evaluated as a predictive tool using the fold-change, rank-based Running Fisher algorithm. Using 158 comparisons from cells treated with 95 chemicals, the biomarker gave balanced accuracies for prediction of AR activation or AR suppression of 97% or 98%, respectively. The biomarker correctly classified 16 out of the 17 AR reference antagonists including those that are "weak" and "very weak". Predictions based on microarray profiles from AR-positive LAPC-4 cells treated with 28 chemicals in antagonist mode were compared with those from an AR pathway model which used 11 in vitro HT assays. The balanced accuracy for suppression was 93%. Using our approach, we identified conditions in which AR was modulated in a large collection of microarray profiles from prostate cancer cell lines including (1) constitutively active mutants or knockdown of AR, (2) decreases in availability of androgens by castration or removal from media, and (3) exposure to chemical modulators that work through indirect mechanisms including suppression of AR expression. These results demonstrate that the AR gene expression biomarker could be a useful tool in HTTr to identify AR modulators.

Cross-species comparison of CAR-mediated procarcinogenic key events in a 3D liver microtissue model

Characterisation of the mode of action (MOA) of constitutive androstane receptor (CAR)-mediated rodent liver tumours involves measurement 5 key events including activation of the CAR receptor, altered gene expression, hepatocellular proliferation, clonal expansion and increased hepatocellular adenomas/carcinomas. To test whether or not liver 3D microtissues (LiMTs) recapitulate CAR- mediated procarcinogenic key events in response to the prototypical CAR activator phenobarbital (PB) we performed hepatocyte proliferation (LI%) analysis in rat and human LiMTs using a microTMA technology in conjunction with integrated transcriptomics (microarray) and proteomics analysis. The rationale for this approach was that LiMTs containing parenchymal and non-parenchymal cells (NPCs) are more physiologically representative of liver and thus would generate data more relevant to the in vivo situation. Rat and human LiMTs were treated with PB over a range of concentrations (500 uM - 2000 uM) and times (24 h - 96 h) in a dose-response/time-course analysis. There was a dose-dependent induction of LI% in rat LiMTs, however there was little or no effect of PB on LI% in human LiMTs. ATP levels in the rat and human LiMTs were similar to control in all of the PB treatments. There was also a dose- and time-dependent PB-mediated RNA induction of CAR regulated genes CYP2B6/Cyp2b2, CYP3A7/Cyp3a9 and UGT1A6/Ugt1a6 in human and rat LiMTs, respectively. These CAR regulated genes were also upregulated at the protein level. Ingenuity pathways analysis (IPA) indicated that there was a significant (Z score >2.0;-log p value >) activation of CAR by PB in both human and rat LiMTs. These results indicate that human and rat LiMTs showed the expected responses at the level of PB-induced hepatocyte proliferation and enzyme induction with rat LiMTs showing significant dose-dependent effects while human LiMTs showed no proliferation response but did show dose-dependent enzyme induction at the RNA and protein levels. In conclusion LiMTs serve as a model to provide mechanistic data for 3 of the 5 key events considered necessary to establish a CAR-mediated MOA for liver tumourigenesis and thus can potentially reduce the use of animals when compiling mechanistic data packages.

Reviewing the mechanisms of natural product-drug interactions involving efflux transporters and metabolic enzymes

The World Health Organization (WHO) and other worldwide health agencies have recently taken initiatives to encourage the use of traditional medicine and/or complementary/alternative medicine in order to promote well-being and public health. In this way, one of the WHO's concerns is the safe use of these therapies. Phytotherapy is a strategy consisting of the use of medicinal plants (MP) and/or herbal medicinal products (HMP) for medicinal purposes. The use of phytotherapy concomitantly with drugs may cause interactions compromising the expected pharmacological action or generating toxic effects. These interactions are complex processes that may occur with multiple medications targeting different metabolic pathways, and involving different compounds present in MP and HMP. Thus, the aim of this review was to summarize the main MP- and HMP-drug interactions that involve specific transporters (P-glycoprotein and BCRP) and CYP450 enzymes (CYP3A4 and CYP2D6), which play relevant roles in the mechanisms of interactions. Firstly, multiple databases were used to search studies describing in vitro or in vivo MP and HMP-drug interactions and, after that, a systematic note-taking and appraisal of the literature was conducted. It was observed that several MP and HMP, metabolic pathways and transcription factors are involved in the transporters and enzymes expression or in the modulation of their activity having the potential to provide such interactions. Thus, the knowledge of MP- and HMP-drug interaction mechanisms could contribute to prevent harmful interactions and can ensure the safe use of these products to help the establishment of the therapeutic planning in order to certify the best treatment strategy to be used.

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.

Considerations for Strategic Use of High-Throughput Transcriptomics Chemical Screening Data in Regulatory Decisions

Recently, numerous organizations, including governmental regulatory agencies in the U.S. and abroad, have proposed using data from New Approach Methodologies (NAMs) for augmenting and increasing the pace of chemical assessments. NAMs are broadly defined as any technology, methodology, approach or combination thereof that can be used to provide information on chemical hazard and risk assessment that avoids the use of intact animals. High-throughput transcriptomics (HTTr) is a type of NAM that uses gene expression profiling as an endpoint for rapidly evaluating the effects of large numbers of chemicals on in vitro cell culture systems. As compared to targeted high-throughput screening (HTS) approaches that measure the effect of chemical X on target Y, HTTr is a non-targeted approach that allows researchers to more broadly characterize the integrated response of an intact biological system to chemicals that may affect a specific biological target or many biological targets under a defined set of treatment conditions (time, concentration, etc.). HTTr screening performed in concentration-response mode can provide potency estimates for the concentrations of chemicals that produce perturbations in cellular response pathways. Here, we discuss study design considerations for HTTr concentration-response screening and present a framework for the use of HTTr-based biological pathway-altering concentrations (BPACs) in a screening-level, risk-based chemical prioritization approach. The framework involves concentration-response modeling of HTTr data, mapping gene level responses to biological pathways, determination of BPACs, in vitro-to-in vivo extrapolation (IVIVE) and comparison to human exposure predictions.

Frequent Modulation of the Sterol Regulatory Element Binding Protein (SREBP) by Chemical Exposure in the Livers of Rats

Inappropriate activation of sterol regulatory element-binding proteins (SREBPs) can lead to non-alcoholic fatty liver disease (NAFLD). To link chemical exposure to SREBP activity, a previously characterized gene expression biomarker (Rooney et al., 2019) was used to identify microarray comparisons from rat liver that exhibited significant positive or negative correlation to the biomarker. The effects of 620 chemicals on SREBP activity were examined across 9305 chemical-dose-time microarray comparisons. SREBP was found to be frequently modulated by chemical exposure with 54% of the chemicals affecting SREBP activity. Activators included inhibitors of cholesterogenesis that act to inhibit HMG-CoA reductase (statins) or inhibit Cyp51 (conazoles). Most chemical effects were transient, lasting usually no more than 2-4 days. Modulation of SREBP in most cases led to coordinated increases or decreases in lipogenic and cholesterogenic genes. However, 570 chemical exposure conditions were identified in which regulation was uncoupled. Most of these conditions affected cholesterogenic genes in the absence of parallel effects on lipogenic genes. Together, these findings show that SREBP is a frequent target of chemical exposure and expression of lipogenic and cholesterogenic genes can be uncoupled.

Regulation of Hepatic Long Noncoding RNAs by Pregnane X Receptor and Constitutive Androstane Receptor Agonists in Mouse Liver

Altered expression of long noncoding RNAs (lncRNAs) by environmental chemicals modulates the expression of xenobiotic biotransformation-related genes and may serve as therapeutic targets and novel biomarkers of exposure. The pregnane X receptor (PXR/NR1I2) is a critical xenobiotic-sensing nuclear receptor that regulates the expression of many drug-processing genes, and it has similar target-gene profiles and DNA-binding motifs with another xenobiotic-sensing nuclear receptor, namely, constitutive andronstrane receptor (CAR/Nr1i3). To test our hypothesis that lncRNAs are regulated by PXR in concert with protein-coding genes (PCGs) and to compare the PXR-targeted lncRNAs with CAR-targeted lncRNAs, RNA-Seq was performed from livers of adult male C57BL/6 mice treated with corn oil, the PXR agonist PCN, or the CAR agonist 1, 4-bis[2-(3,5-dichloropyridyloxy)]benzene (TCPOBOP). Among 125,680 known lncRNAs, 3843 were expressed in liver, and 193 were differentially regulated by PXR (among which 40% were also regulated by CAR). Most PXR- or CAR-regulated lncRNAs were mapped to the introns and 3'-untranslated regions (UTRs) of PCGs, as well as intergenic regions. Combining the RNA-Seq data with a published PXR chromatin immunoprecipitation coupled with high-throughput sequencing; cytochrome P450 (P450; ChIP-Seq) data set, we identified 774 expressed lncRNAs with direct PXR-DNA binding sites, and 26.8% of differentially expressed lncRNAs had changes in PXR-DNA binding after PCN exposure. De novo motif analysis identified colocalization of PXR with liver receptor homolog (LRH-1), which regulates bile acid synthesis after PCN exposure. There was limited overlap of PXR binding with an epigenetic mark for transcriptional activation (histone-H3K4-di-methylation, H3K4me2) but no overlap with epigenetic marks for transcriptional silencing [H3 lysine 27 tri-methylation (H3K27me3) and DNA methylation]. Among differentially expressed lncRNAs, 264 were in proximity of PCGs, and the lncRNA-PCG pairs displayed a high coregulatory pattern by PXR and CAR activation. This study was among the first to demonstrate that lncRNAs are regulated by PXR and CAR activation and that they may be important regulators of PCGs involved in xenobiotic metabolism.

Sedaxane-Use of Nuclear Receptor Transactivation Assays, Toxicogenomics, and Toxicokinetics as Part of a Mode of Action Framework for Rodent Liver Tumors

Experimental data demonstrate a mode of action (MOA) for liver tumors in male rats and mice treated with sedaxane that starts with activation of CAR, followed by altered expression of CAR-responsive genes, increased cell proliferation, and eventually clonal expansion of preneoplastic cells, leading to the development of altered foci and tumors. This MOA is nonrelevant to human risk assessments. Methods and results in the MOA work for sedaxane illustrate promising directions that future MOA studies may be able to employ, in the spirit of "Tox21" and reduction of in vivo animal use: (1) currently available in vitro CAR and PXR reporter assays demonstrated that sedaxane is a direct CAR activator in mice and rats, and a weak PXR activator in rats; (2) mouse liver microarray results compared with a published CAR biomarker signature (based on 83 genes) showed a clear, statistical match, and a lack of correlation to similar biomarker signatures for AhR, PPARα, and STAT5B; (3) Ki67 immunohistochemistry and zonal image analysis showed significant increases in this marker of cell proliferation in mouse liver, without the need to dose a DNA labeling agent; and (4) toxicokinetic analysis of Cmax levels of sedaxane in blood showed a marked species difference between mice and rats that helps to explain differences in sensitivity to sedaxane. Incorporating these tools into the study plan for a new agrochemical or drug during development offers a promising alternative to the traditional need to conduct later, specialized MOA studies after the results of chronic bioassays are known.

Using a gene expression biomarker to identify DNA damage-inducing agents in microarray profiles

High-throughput transcriptomic technologies are increasingly being used to screen environmental chemicals in vitro to provide mechanistic context for regulatory testing. The TGx-DDI biomarker is a 64-gene expression profile generated from testing 28 model chemicals or treatments (13 that cause DNA damage and 15 that do not) in human TK6 cells. While the biomarker is very accurate at predicting DNA damage inducing (DDI) potential using the nearest shrunken centroid method, the broad utility of the biomarker using other computational methods is not fully known. Here, we determined the accuracy of the biomarker used with the Running Fisher test, a nonparametric correlation test. In TK6 cells, the methods could readily differentiate DDI and non-DDI compounds with balanced accuracies of 87-97%, depending on the threshold for determining DDI positives. The methods identified DDI agents in the metabolically competent hepatocyte cell line HepaRG (accuracy = 90%) but not in HepG2 cells or hepatocytes derived from embryonic stem cells (60 and 80%, respectively). DDI was also accurately classified when the gene expression changes were derived using the nCounter technology (accuracy = 89%). In addition, we found: (1) not all genes contributed equally to the correlations; (2) the minimal overlap in genes between the biomarker and the individual comparisons required for significant positive correlation was 10 genes, but usually was much higher; and (3) different sets of genes in the biomarker can by themselves contribute to the significant correlations. Overall, these results demonstrate the utility of the biomarker to accurately classify DDI agents. Environ. Mol. Mutagen. 59:772-784, 2018. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.

Characterisation of the NRF2 transcriptional network and its response to chemical insult in primary human hepatocytes: implications for prediction of drug-induced liver injury

The transcription factor NRF2, governed by its repressor KEAP1, protects cells against oxidative stress. There is interest in modelling the NRF2 response to improve the prediction of clinical toxicities such as drug-induced liver injury (DILI). However, very little is known about the makeup of the NRF2 transcriptional network and its response to chemical perturbation in primary human hepatocytes (PHH), which are often used as a translational model for investigating DILI. Here, microarray analysis identified 108 transcripts (including several putative novel NRF2-regulated genes) that were both downregulated by siRNA targeting NRF2 and upregulated by siRNA targeting KEAP1 in PHH. Applying weighted gene co-expression network analysis (WGCNA) to transcriptomic data from the Open TG-GATES toxicogenomics repository (representing PHH exposed to 158 compounds) revealed four co-expressed gene sets or ‘modules’ enriched for these and other NRF2-associated genes. By classifying the 158 TG-GATES compounds based on published evidence, and employing the four modules as network perturbation metrics, we found that the activation of NRF2 is a very good indicator of the intrinsic biochemical reactivity of a compound (i.e. its propensity to cause direct chemical stress), with relatively high sensitivity, specificity, accuracy and positive/negative predictive values. We also found that NRF2 activation has lower sensitivity for the prediction of clinical DILI risk, although relatively high specificity and positive predictive values indicate that false positive detection rates are likely to be low in this setting. Underpinned by our comprehensive analysis, activation of the NRF2 network is one of several mechanism-based components that can be incorporated into holistic systems toxicology models to improve mechanistic understanding and preclinical prediction of DILI in man.

Binding of Drug-Activated CAR/Nr1i3 Alters Metabolic Regulation in the Liver

The constitutive androstane receptor (CAR/Nr1i3) regulates detoxification of drugs and other xenobiotics by the liver. Binding of these compounds, activating ligands, causes CAR to translocate to the nucleus and stimulate genes of detoxification. However, CAR activation also changes metabolism and induces rapid liver growth. To explain this gene regulation, we characterized the genome-wide early binding of CAR; its binding partner, RXRα; and the acetylation that they induced on H4K5. CAR-linked genes showed either stimulation or inhibition and regulated lipid, carbohydrate, and energy metabolism, as well as detoxification. Stimulation of expression increased, but inhibition did not decrease, H4K5Ac. Transcriptional inhibition occurred when CAR bound with HNF4α, PPARα, or FXR on the same enhancers. Functional competition among these bound nuclear receptors normally coordinates transcriptional resources as metabolism shifts. However, binding of drug-activated CAR to the same enhancers adds a new competitor that constitutively alters the normal balance of metabolic gene regulation.

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CAR activation stimulates a massive liver transcriptional response

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Target genes control drug detoxification, general metabolism, and liver growth

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CAR stimulates genes through coactivation and histone acetylation

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CAR inhibits genes when it shares their enhancers with other nuclear receptors

Activation of Nrf2 in the liver is associated with stress resistance mediated by suppression of the growth hormone-regulated STAT5b transcription factor

The transcription factor Nrf2 (encoded by Nfe2l2) induces expression of numerous detoxifying and antioxidant genes in response to oxidative stress. The cytoplasmic protein Keap1 interacts with and represses Nrf2 function. Computational approaches were developed to identify factors that modulate Nrf2 in a mouse liver gene expression compendium. Forty-eight Nrf2 biomarker genes were identified using profiles from the livers of mice in which Nrf2 was activated genetically in Keap1-null mice or chemically by a potent activator of Nrf2 signaling. The rank-based Running Fisher statistical test was used to determine the correlation between the Nrf2 biomarker genes and a test set of 81 profiles with known Nrf2 activation status demonstrating a balanced accuracy of 96%. For a large number of factors examined in the compendium, we found consistent relationships between activation of Nrf2 and feminization of the liver transcriptome through suppression of the male-specific growth hormone (GH)-regulated transcription factor STAT5b. The livers of female mice exhibited higher Nrf2 activation than male mice in untreated or chemical-treated conditions. In male mice, Nrf2 was activated by treatment with ethinyl estradiol, whereas in female mice, Nrf2 was suppressed by treatment with testosterone. Nrf2 was activated in 5 models of disrupted GH signaling containing mutations in Pit1, Prop1, Ghrh, Ghrhr, and Ghr. Out of 59 chemical treatments that activated Nrf2, 36 exhibited STAT5b suppression in the male liver. The Nrf2-STAT5b coupling was absent in in vitro comparisons of chemical treatments. Treatment of male and female mice with 11 chemicals that induce oxidative stress led to activation of Nrf2 to greater extents in females than males. The enhanced basal and inducible levels of Nrf2 activation in females relative to males provides a molecular explanation for the greater resistance often seen in females vs. males to age-dependent diseases and chemical-induced toxicity.

Human relevance of rodent liver tumour formation by constitutive androstane receptor (CAR) activators

A large number of nongenotoxic chemicals have been shown to increase the incidence of liver tumours in rats and/or mice by a mode of action (MOA) involving activation of the constitutive androstane receptor (CAR). Studies with the model CAR activator phenobarbital (PB) and its sodium salt (sodium phenobarbital; NaPB) have demonstrated that the key and associative events for rat and mouse liver tumour formation include CAR activation, increased hepatocyte replicative DNA synthesis (RDS), induction of cytochrome P450 CYP2B subfamily enzymes, liver hypertrophy, increased altered hepatic foci and hepatocellular adenomas/carcinomas. The key species difference between the rat and mouse compared to humans, is that human hepatocytes are refractory to the mitogenic effects of PB/NaPB and other CAR activators. While PB/NaPB and other CAR activators stimulate RDS in rat and mouse hepatocytes in both in vitro and in vivo studies, such compounds do not stimulate RDS in cultured human hepatocytes and in in vivo studies performed in chimeric mice with humanised livers. In terms of species differences in RDS, unlike the rat and mouse, humans are similar to other species such as the Syrian hamster and guinea pig in being nonresponsive to the mitogenic effects of CAR activators. Overall, the MOA for rat and mouse liver tumour formation by PB/NaPB and other CAR activators is considered qualitatively not plausible for humans. This conclusion is supported by data from a number of epidemiological studies, which demonstrate that chronic treatment with PB does not increase the incidence of liver cancer in humans.

From the Cover: Genomic Effects of Androstenedione and Sex-Specific Liver Cancer Susceptibility in Mice

Current strategies for predicting carcinogenic mode of action for nongenotoxic chemicals are based on identification of early key events in toxicity pathways. The goal of this study was to evaluate short-term key event indicators resulting from exposure to androstenedione (A4), an androgen receptor agonist and known liver carcinogen in mice. Liver cancer is more prevalent in men compared with women, but androgen-related pathways underlying this sex difference have not been clearly identified. Short-term hepatic effects of A4 were compared with reference agonists of the estrogen receptor (ethinyl estradiol, EE) and glucocorticoid receptor (prednisone, PRED). Male B6C3F1 mice were exposed for 7 or 28 days to A4, EE, or PRED. EE increased and PRED suppressed hepatocyte proliferation, while A4 had no detectable effects. In a microarray analysis, EE and PRED altered >3000 and >670 genes, respectively, in a dose-dependent manner, whereas A4 did not significantly alter any genes. Gene expression was subsequently examined in archival liver samples from male and female B6C3F1 mice exposed to A4 for 90 days. A4 altered more genes in females than males and did not alter expression of genes linked to activation of the mitogenic xenobiotic receptors AhR, CAR, and PPARα in either sex. A gene expression biomarker was used to show that in female mice, the high dose of A4 activated the growth hormone-regulated transcription factor STAT5b, which controls sexually dimorphic gene expression in the liver. These findings suggest that A4 induces subtle age-related effects on STAT5b signaling that may contribute to the higher risk of liver cancer in males compared with females.

Activation of the Aryl Hydrocarbon Receptor Leads to Resistance to EGFR TKIs in Non-Small Cell Lung Cancer by Activating Src-mediated Bypass Signaling

Purpose: The aryl hydrocarbon receptor (AhR) has been generally recognized as a ligand-activated transcriptional factor that responds to xenobiotic chemicals. Recent studies have suggested that the expression of AhR varies widely across different cancer types and cancer cell lines, but its significance in cancer treatment has yet to be clarified.Experimental Design: AhR expression in non-small cell lung cancer (NSCLC) was determined by Western blotting and IHC staining. In vitro and in vivo functional experiments were performed to determine the effect of AhR on sensitivity to targeted therapeutics. A panel of biochemical assays was used to elucidate the underlying mechanisms.Results: A high AhR protein level indicated an unfavorable prognosis for lung adenocarcinoma. Inhibition of AhR signaling sensitized EGFR tyrosine kinase inhibitors (TKIs) in NSCLC cells that express high level of endogenous AhR protein. Notably, activation of AhR by pharmacologic and molecular approaches rendered EGFR-mutant cells resistant to TKIs by restoring PI3K/Akt and MEK/Erk signaling through activation of Src. In addition, we found that AhR acts as a protein adaptor to mediate Jak2-Src interaction, which does not require the canonical transcriptional activity of AhR.Conclusions: Our results reveal a transcription-independent function of AhR and indicate that AhR may act as a protein adaptor that recruits kinases bypassing EGFR and drives resistance to TKIs. Accordingly, targeting Src would be a strategy to overcome resistance to EGFR TKIs in AhR-activated NSCLC. Clin Cancer Res; 24(5); 1227-39. ©2017 AACR.

PPARα-independent transcriptional targets of perfluoroalkyl acids revealed by transcript profiling

Perfluoroalkyl acids (PFAAs) are ubiquitous and persistent environmental contaminants. Compounds such as perfluoroocanoic acid (PFOA), perfluorooctane sulfonate (PFOS), perfluorononanoic acid (PFNA), and perfluorohexane sulfonate (PFHxS) are readily found in the tissues of humans and wildlife. While PFOA and PFOS have been the subject of numerous studies since they were first described over a decade ago, less is known about the biological activity of PFHxS and PFNA. Most PFAAs are activators of peroxisome proliferator-activated receptor α (PPARα), although the biological effects of these compounds are likely mediated by other factors in addition to PPARα. To evaluate the effects of PFHxS and PFNA, male wild-type and Pparα-null mice were dosed by oral gavage with PFHxS (3 or 10mg/kg/day), PFNA (1 or 3mg/kg/day), or vehicle for 7days, and liver gene expression was evaluated by full-genome microarrays. Gene expression patterns were then compared to historical in-house data for PFOA and PFOS in addition to the experimental hypolipidemic agent, WY-14,643. While WY-14,643 altered most genes in a PPARα-dependent manner, approximately 11-24% of regulated genes in PFAA-treated mice were independent of PPARα. The possibility that PFAAs regulate gene expression through other molecular pathways was evaluated. Using data available through a microarray database, PFAA gene expression profiles were found to exhibit significant similarity to profiles from mouse tissues exposed to agonists of the constitutive activated receptor (CAR), estrogen receptor α (ERα), and PPARγ. Human PPARγ and ERα were activated by all four PFAAs in trans-activation assays from the ToxCast screening program. Predictive gene expression biomarkers showed that PFAAs activate CAR in both genotypes and cause feminization of the liver transcriptome through suppression of signal transducer and activator of transcription 5B (STAT5B). These results indicate that, in addition to activating PPARα as a primary target, PFAAs also have the potential to activate CAR, PPARγ, and ERα as well as suppress STAT5B.

Disruption of STAT5b-Regulated Sexual Dimorphism of the Liver Transcriptome by Diverse Factors Is a Common Event

Signal transducer and activator of transcription 5b (STAT5b) is a growth hormone (GH)-activated transcription factor and a master regulator of sexually dimorphic gene expression in the liver. Disruption of the GH hypothalamo-pituitary-liver axis controlling STAT5b activation can lead to metabolic dysregulation, steatosis, and liver cancer. Computational approaches were developed to identify factors that disrupt STAT5b function in a mouse liver gene expression compendium. A biomarker comprised of 144 STAT5b-dependent genes was derived using comparisons between wild-type male and wild-type female mice and between STAT5b-null and wild-type mice. Correlations between the STAT5b biomarker gene set and a test set comprised of expression datasets (biosets) with known effects on STAT5b function were evaluated using a rank-based test (the Running Fisher algorithm). Using a similarity p-value ≤ 10(-4), the test achieved a balanced accuracy of 99% and 97% for detection of STAT5b activation or STAT5b suppression, respectively. The STAT5b biomarker gene set was then used to identify factors that activate (masculinize) or suppress (feminize) STAT5b function in an annotated mouse liver and primary hepatocyte gene expression compendium of ~1,850 datasets. Disruption of GH-regulated STAT5b is a common phenomenon in liver in vivo, with 5% and 29% of the male datasets, and 11% and 13% of the female datasets, associated with masculinization or feminization, respectively. As expected, liver STAT5b activation/masculinization occurred at puberty and suppression/feminization occurred during aging and in mutant mice with defects in GH signaling. A total of 70 genes were identified that have effects on STAT5b activation in genetic models in which the gene was inactivated or overexpressed. Other factors that affected liver STAT5b function were shown to include fasting, caloric restriction and infections. Together, these findings identify diverse factors that perturb the hypothalamo-pituitary-liver GH axis and disrupt GH-dependent STAT5b activation in mouse liver.

Chemical and Hormonal Effects on STAT5b-Dependent Sexual Dimorphism of the Liver Transcriptome

The growth hormone (GH)-activated transcription factor signal transducer and activator of transcription 5b (STAT5b) is a key regulator of sexually dimorphic gene expression in the liver. Suppression of hepatic STAT5b signaling is associated with lipid metabolic dysfunction leading to steatosis and liver cancer. In the companion publication, a STAT5b biomarker gene set was identified and used in a rank-based test to predict both increases and decreases in liver STAT5b activation status/function with high (≥ 97%) accuracy. Here, this computational approach was used to identify chemicals and hormones that activate (masculinize) or suppress (feminize) STAT5b function in a large, annotated mouse liver and primary hepatocyte gene expression compendium. Exposure to dihydrotestosterone and thyroid hormone caused liver masculinization, whereas glucocorticoids, fibroblast growth factor 15, and angiotensin II caused liver feminization. In mouse models of diabetes and obesity, liver feminization was consistently observed and was at least partially reversed by leptin or resveratrol exposure. Chemical-induced feminization of male mouse liver gene expression profiles was a relatively frequent phenomenon: of 156 gene expression biosets from chemically-treated male mice, 29% showed feminization of liver STAT5b function, while <1% showed masculinization. Most (93%) of the biosets that exhibited feminization of male liver were also associated with activation of one or more xenobiotic-responsive receptors, most commonly constitutive activated receptor (CAR) or peroxisome proliferator-activated receptor alpha (PPARα). Feminization was consistently associated with increased expression of peroxisome proliferator-activated receptor gamma (Pparg) but not other lipogenic transcription factors linked to steatosis. GH-activated STAT5b signaling in mouse liver is thus commonly altered by diverse chemicals, and provides a linkage between chemical exposure and dysregulated gene expression associated with adverse effects on the liver.

Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium

Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including "very weak" agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells.