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

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.

Signature analysis of high-throughput transcriptomics screening data for mechanistic inference and chemical grouping

High-throughput transcriptomics (HTTr) uses gene expression profiling to characterize the biological activity of chemicals in in vitro cell-based test systems. As an extension of a previous study testing 44 chemicals, HTTr was used to screen an additional 1,751 unique chemicals from the EPA's ToxCast collection in MCF7 cells using 8 concentrations and an exposure duration of 6 h. We hypothesized that concentration-response modeling of signature scores could be used to identify putative molecular targets and cluster chemicals with similar bioactivity. Clustering and enrichment analyses were conducted based on signature catalog annotations and ToxPrint chemotypes to facilitate molecular target prediction and grouping of chemicals with similar bioactivity profiles. Enrichment analysis based on signature catalog annotation identified known mechanisms of action (MeOAs) associated with well-studied chemicals and generated putative MeOAs for other active chemicals. Chemicals with predicted MeOAs included those targeting estrogen receptor (ER), glucocorticoid receptor (GR), retinoic acid receptor (RAR), the NRF2/KEAP/ARE pathway, AP-1 activation, and others. Using reference chemicals for ER modulation, the study demonstrated that HTTr in MCF7 cells was able to stratify chemicals in terms of agonist potency, distinguish ER agonists from antagonists, and cluster chemicals with similar activities as predicted by the ToxCast ER Pathway model. Uniform manifold approximation and projection (UMAP) embedding of signature-level results identified novel ER modulators with no ToxCast ER Pathway model predictions. Finally, UMAP combined with ToxPrint chemotype enrichment was used to explore the biological activity of structurally related chemicals. The study demonstrates that HTTr can be used to inform chemical risk assessment by determining in vitro points of departure, predicting chemicals' MeOA and grouping chemicals with similar bioactivity profiles.

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.

High-Throughput Transcriptomics Screen of ToxCast Chemicals in U-2 OS Cells

New approach methodologies (NAMs) aim to accelerate the pace of chemical risk assessment while simultaneously reducing cost and dependency on animal studies. High Throughput Transcriptomics (HTTr) is an emerging NAM in the field of chemical hazard evaluation for establishing in vitro points-of-departure and providing mechanistic insight. In the current study, 1201 test chemicals were screened for bioactivity at eight concentrations using a 24-h exposure duration in the human- derived U-2 OS osteosarcoma cell line with HTTr. Assay reproducibility was assessed using three reference chemicals that were screened on every assay plate. The resulting transcriptomics data were analyzed by aggregating signal from genes into signature scores using gene set enrichment analysis, followed by concentration-response modeling of signatures scores. Signature scores were used to predict putative mechanisms of action, and to identify biological pathway altering concentrations (BPACs). BPACs were consistent across replicates for each reference chemical, with replicate BPAC standard deviations as low as 5.6 × 10-3 μM, demonstrating the internal reproducibility of HTTr-derived potency estimates. BPACs of test chemicals showed modest agreement (R2 = 0.55) with existing phenotype altering concentrations from high throughput phenotypic profiling using Cell Painting of the same chemicals in the same cell line. Altogether, this HTTr based chemical screen contributes to an accumulating pool of publicly available transcriptomic data relevant for chemical hazard evaluation and reinforces the utility of cell based molecular profiling methods in estimating chemical potency and predicting mechanism of action across a diverse set of chemicals.

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.

Exploring the effects of experimental parameters and data modeling approaches on in vitro transcriptomic point-of-departure estimates

Multiple new approach methods (NAMs) are being developed to rapidly screen large numbers of chemicals to aid in hazard evaluation and risk assessments. High-throughput transcriptomics (HTTr) in human cell lines has been proposed as a first-tier screening approach for determining the types of bioactivity a chemical can cause (activation of specific targets vs. generalized cell stress) and for calculating transcriptional points of departure (tPODs) based on changes in gene expression. In the present study, we examine a range of computational methods to calculate tPODs from HTTr data, using six data sets in which MCF7 cells cultured in two different media formulations were treated with a panel of 44 chemicals for 3 different exposure durations (6, 12, 24 hr). The tPOD calculation methods use data at the level of individual genes and gene set signatures, and compare data processed using the ToxCast Pipeline 2 (tcplfit2), BMDExpress and PLIER (Pathway Level Information ExtractoR). Methods were evaluated by comparing to in vitro PODs from a validated set of high-throughput screening (HTS) assays for a set of estrogenic compounds. Key findings include: (1) for a given chemical and set of experimental conditions, tPODs calculated by different methods can vary by several orders of magnitude; (2) tPODs are at least as sensitive to computational methods as to experimental conditions; (3) in comparison to an external reference set of PODs, some methods give generally higher values, principally PLIER and BMDExpress; and (4) the tPODs from HTTr in this one cell type are mostly higher than the overall PODs from a broad battery of targeted in vitro ToxCast assays, reflecting the need to test chemicals in multiple cell types and readout technologies for in vitro hazard screening.

In vitro transcriptomic analyses reveal pathway perturbations, estrogenic activities, and potencies of data-poor BPA alternative chemicals

Since initial regulatory action in 2010 in Canada, bisphenol A (BPA) has been progressively replaced by structurally related alternative chemicals. Unfortunately, many of these chemicals are data-poor, limiting toxicological risk assessment. We used high-throughput transcriptomics to evaluate potential hazards and compare potencies of BPA and 15 BPA alternative chemicals in cultured breast cancer cells. MCF-7 cells were exposed to BPA and 15 alternative chemicals (0.0005-100 µM) for 48 h. TempO-Seq (BioSpyder Inc) was used to examine global transcriptomic changes and estrogen receptor alpha (ERα)-associated transcriptional changes. Benchmark concentration (BMC) analysis was conducted to identify 2 global transcriptomic points of departure: (1) the lowest pathway median gene BMC and (2) the 25th lowest rank-ordered gene BMC. ERα activation was evaluated using a published transcriptomic biomarker and an ERα-specific transcriptomic point of departure was derived. Genes fitting BMC models were subjected to upstream regulator and canonical pathway analysis in Ingenuity Pathway Analysis. Biomarker analysis identified BPA and 8 alternative chemicals as ERα active. Global and ERα transcriptomic points of departure produced highly similar potency rankings with bisphenol AF as the most potent chemical tested, followed by BPA and bisphenol C. Further, BPA and transcriptionally active alternative chemicals enriched similar gene sets associated with increased cell division and cancer-related processes. These data provide support for future read-across applications of transcriptomic profiling for risk assessment of data-poor chemicals and suggest that several BPA alternative chemicals may cause hazards at similar concentrations to BPA.

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.

Epigenetic remodelling of enhancers in response to estrogen deprivation and re-stimulation

Estrogen hormones are implicated in a majority of breast cancers and estrogen receptor alpha (ER), the main nuclear factor mediating estrogen signaling, orchestrates a complex molecular circuitry that is not yet fully elucidated. Here, we investigated genome-wide DNA methylation, histone acetylation and transcription after estradiol (E2) deprivation and re-stimulation to better characterize the ability of ER to coordinate gene regulation. We found that E2 deprivation mostly resulted in DNA hypermethylation and histone deacetylation in enhancers. Transcriptome analysis revealed that E2 deprivation leads to a global down-regulation in gene expression, and more specifically of TET2 demethylase that may be involved in the DNA hypermethylation following short-term E2 deprivation. Further enrichment analysis of transcription factor (TF) binding and motif occurrence highlights the importance of ER connection mainly with two partner TF families, AP-1 and FOX. These interactions take place in the proximity of E2 deprivation-mediated differentially methylated and histone acetylated enhancers. Finally, while most deprivation-dependent epigenetic changes were reversed following E2 re-stimulation, DNA hypermethylation and H3K27 deacetylation at certain enhancers were partially retained. Overall, these results show that inactivation of ER mediates rapid and mostly reversible epigenetic changes at enhancers, and bring new insight into early events, which may ultimately lead to endocrine resistance.

Commentary: Novel strategies and new tools to curtail the health effects of pesticides

BACKGROUND

Flaws in the science supporting pesticide risk assessment and regulation stand in the way of progress in mitigating the human health impacts of pesticides. Critical problems include the scope of regulatory testing protocols, the near-total focus on pure active ingredients rather than formulated products, lack of publicly accessible information on co-formulants, excessive reliance on industry-supported studies coupled with reticence to incorporate published results in the risk assessment process, and failure to take advantage of new scientific opportunities and advances, e.g. biomonitoring and "omics" technologies.

RECOMMENDED ACTIONS

Problems in pesticide risk assessment are identified and linked to study design, data, and methodological shortcomings. Steps and strategies are presented that have potential to deepen scientific knowledge of pesticide toxicity, exposures, and risks. We propose four solutions: (1) End near-sole reliance in regulatory decision-making on industry-supported studies by supporting and relying more heavily on independent science, especially for core toxicology studies. The cost of conducting core toxicology studies at labs not affiliated with or funded directly by pesticide registrants should be covered via fees paid by manufacturers to public agencies. (2) Regulators should place more weight on mechanistic data and low-dose studies within the range of contemporary exposures. (3) Regulators, public health agencies, and funders should increase the share of exposure-assessment resources that produce direct measures of concentrations in bodily fluids and tissues. Human biomonitoring is vital in order to quickly identify rising exposures among vulnerable populations including applicators, pregnant women, and children. (4) Scientific tools across disciplines can accelerate progress in risk assessments if integrated more effectively. New genetic and metabolomic markers of adverse health impacts and heritable epigenetic impacts are emerging and should be included more routinely in risk assessment to effectively prevent disease.

CONCLUSIONS

Preventing adverse public health outcomes triggered or made worse by exposure to pesticides will require changes in policy and risk assessment procedures, more science free of industry influence, and innovative strategies that blend traditional methods with new tools and mechanistic insights.

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.

High-Throughput Transcriptomics Platform for Screening Environmental Chemicals

New approach methodologies (NAMs) that efficiently provide information about chemical hazard without using whole animals are needed to accelerate the pace of chemical risk assessments. Technological advancements in gene expression assays have made in vitro high-throughput transcriptomics (HTTr) a feasible option for NAMs-based hazard characterization of environmental chemicals. In this study, we evaluated the Templated Oligo with Sequencing Readout (TempO-Seq) assay for HTTr concentration-response screening of a small set of chemicals in the human-derived MCF7 cell model. Our experimental design included a variety of reference samples and reference chemical treatments in order to objectively evaluate TempO-Seq assay performance. To facilitate analysis of these data, we developed a robust and scalable bioinformatics pipeline using open-source tools. We also developed a novel gene expression signature-based concentration-response modeling approach and compared the results to a previously implemented workflow for concentration-response analysis of transcriptomics data using BMDExpress. Analysis of reference samples and reference chemical treatments demonstrated highly reproducible differential gene expression signatures. In addition, we found that aggregating signals from individual genes into gene signatures prior to concentration-response modeling yielded in vitro transcriptional biological pathway altering concentrations (BPACs) that were closely aligned with previous ToxCast high-throughput screening assays. Often these identified signatures were associated with the known molecular target of the chemicals in our test set as the most sensitive components of the overall transcriptional response. This work has resulted in a novel and scalable in vitro HTTr workflow that is suitable for high-throughput hazard evaluation of environmental chemicals.

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.

A set of six Gene expression biomarkers and their thresholds identify rat liver tumorigens in short-term assays

Traditional methods for cancer risk assessment are retrospective, resource-intensive, and not feasible for the vast majority of environmental chemicals. In earlier studies, we used a set of six biomarkers to accurately identify liver tumorigens in transcript profiles derived from chemically-treated rats using either a Toxicological Priority Index (ToxPi) approach or using derived biomarker thresholds for cancer. The biomarkers consisting of 7-113 genes are used to predict the most common liver cancer molecular initiating events: genotoxicity, cytotoxicity and activation of the xenobiotic receptors AhR, CAR, ER, and PPARα. In the present study, we apply and evaluate the performance of these methods for cancer prediction in an independent rat liver study of 44 chemicals (6 h-7d exposures) examined by Affymetrix arrays. In the first approach, ToxPi ranking of biomarker scores consistently gave the highest scores to tumorigenic chemical-dose pairs; balanced accuracies for identification of liver tumorigenic chemicals were up to 89 %. The second approach used tumorigenic thresholds derived in the present study or from our earlier study that were set at the maximum value for chemical-dose exposures without detectable liver tumor outcomes. Using these thresholds, balanced accuracies were up to 90 %. Both approaches identified all tumorigenic chemicals. Almost all of the tumorigenic chemicals activated more than one MIE. We also compared biomarker responses between two types of profiling platforms (Affymetrix full-genome array, TempO-Seq 1500+ array containing ∼2600 genes) and found that the lack of the full set of biomarker genes on the 1500+ array resulted in decreased ability to identify chemicals that activate the MIEs. Overall, these results demonstrate that predictive approaches based on the 6 biomarkers could be used in short-term assays to identify chemicals and their doses that induce liver tumors, the most common endpoint in rodent bioassays.

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.

Identification of novel activators of the metal responsive transcription factor (MTF-1) using a gene expression biomarker in a microarray compendium

Environmental exposure to metals is known to cause a number of human toxicities including cancer. Metal-responsive transcription factor 1 (MTF-1) is an important component of metal regulation systems in mammalian cells. Here, we describe a novel method to identify chemicals that activate MTF-1 based on microarray profiling data. MTF-1 biomarker genes were identified that exhibited consistent, robust expression across 10 microarray comparisons examining the effects of metals (zinc, nickel, lead, arsenic, mercury, and silver) on gene expression in human cells. A subset of the resulting 81 biomarker genes was shown to be altered by knockdown of the MTF1 gene including metallothionein family members and a zinc transporter. The ability to correctly identify treatment conditions that activate MTF-1 was determined by comparing the biomarker to microarray comparisons from cells exposed to reference metal activators of MTF-1 using the rank-based Running Fisher algorithm. The balanced accuracy for prediction was 93%. The biomarker was then used to identify organic chemicals that activate MTF-1 from a compendium of 11 725 human gene expression comparisons representing 2582 chemicals. There were 700 chemicals identified that included those known to interact with cellular metals, such as clioquinol and disulfiram, as well as a set of novel chemicals. All nine of the novel chemicals selected for validation were confirmed to activate MTF-1 biomarker genes in MCF-7 cells and to lesser extents in MTF1-null cells by qPCR and targeted RNA-Seq. Overall, our work demonstrates that the biomarker for MTF-1 coupled with the Running Fisher test is a reliable strategy to identify novel chemical modulators of metal homeostasis using gene expression profiling.

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.

Transcriptional changes caused by estrogenic endocrine disrupting chemicals in gonad-mesonephros complexes of genetic male Xenopus laevis: Multiple biomarkers for early detection of testis differentiation disruption

Our recent study revealed some early molecular and cellular events in which 17β-estradiol (E2) disrupted testis differentiation and resulted in feminization in Xenopus laevis (the African clawed frog), an ideal species for studying reproductive endocrine disruption by estrogenic endocrine disrupting chemicals (EDCs). On this basis, we aimed to develop multiple biomarkers for early detection of testis differentiation disruption by estrogenic EDCs in X. laevis. Tadpoles at stage 45/46 were exposed to four known estrogenic EDCs with different estrogenic activities, including E2, diethylstilbestrol (DES), mestranol (MES) and 4-n-nonyphenol (NP). At stage 53, gonadal morphological and histological changes as well as altered sex-dimorphic gene expression in gonad-mesonephros complexes (GMCs) showed that these estrogenic EDCs disrupted testis differentiation and caused feminization to different degrees. Then we measured transcriptional changes of 48 candidate genes, which are believed to be associated with E2-induced testis differentiation alterations, in GMCs at stage 50. As a result, 19 genes were found to be transcriptionally altered by all test chemicals and proposed as promising biomarkers for early detection of testis differentiation disruption by estrogenic EDCs. Finally, all biomarker responses were integrated as integrated biomarker response (IBR) index to characterize testis differentiation disruption by these estrogenic EDCs in X. laevis. Compared with the methods used in previous studies, the multiple biomarker test using X. laevis at early developmental stages largely shortens the exposure duration, thereby achieving the goal of rapid detection. Certainly, the biomarker test needs further validations in the future study.

Early microRNA indicators of PPARα pathway activation in the liver

MicroRNAs (miRNAs) are short non-coding RNA species that play key roles in post-transcriptional regulation of gene expression. MiRNAs also serve as a promising source of early biomarkers for different environmental exposures and health effects, although there is limited information linking miRNA changes to specific target pathways. In this study, we measured liver miRNAs in male B6C3F1 mice exposed to a known chemical activator of the peroxisome proliferator-activated receptor alpha (PPARα) pathway, di(2-ethylhexyl) phthalate (DEHP), for 7 and 28 days at concentrations of 0, 750, 1500, 3000, or 6000 ppm in feed. At the highest dose tested, DEHP altered 61 miRNAs after 7 days and 171 miRNAs after 28 days of exposure, with 48 overlapping miRNAs between timepoints. Analysis of these 48 common miRNAs indicated enrichment in PPARα–related targets and other pathways related to liver injury and cancer. Four of the 10 miRNAs exhibiting a clear dose trend were linked to the PPARα pathway: mmu-miRs-125a-5p, -182−5p, -20a−5p, and -378a−3p. mmu-miRs-182−5p and -378a−3p were subsequently measured using digital drop PCR across a dose range for DEHP and two related phthalates with weaker PPARα activity, di-n-octyl phthalate and n-butyl benzyl phthalate, following 7-day exposures. Analysis of mmu-miRs-182−5p and -378a−3p by transcriptional benchmark dose analysis correctly identified DEHP as having the greatest potency. However, benchmark dose estimates for DEHP based on these miRNAs (average 163; range 126−202 mg/kg-day) were higher on average than values for PPARα target genes (average 74; range 29−183 mg/kg-day). These findings identify putative miRNA biomarkers of PPARα pathway activity and suggest that early miRNA changes may be used to stratify chemical potency.

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.

Mining Public Toxicogenomic Data Reveals Insights and Challenges in Delineating Liver Steatosis Adverse Outcome Pathways

Exposure to chemicals contributes to the development and progression of fatty liver, or steatosis, a process characterized by abnormal accumulation of lipids within liver cells. However, lack of knowledge on how chemicals cause steatosis has prevented any large-scale assessment of the 80,000+ chemicals in current use. To address this gap, we mined a large, publicly available toxicogenomic dataset associated with 18 known steatogenic chemicals to assess responses across assays (in vitro and in vivo) and species (i.e., rats and humans). We identified genes that were differentially expressed (DEGs) in rat in vivo, rat in vitro, and human in vitro studies in which rats or in vitro primary cell lines were exposed to the chemicals at different doses and durations. Using these DEGs, we performed pathway enrichment analysis, analyzed the molecular initiating events (MIEs) of the steatosis adverse outcome pathway (AOP), and predicted metabolite changes using metabolic network analysis. Genes indicative of oxidative stress were among the DEGs most frequently observed in the rat in vivo studies. Nox4, a pro-fibrotic gene, was down-regulated across these chemical exposure conditions. We identified eight genes (Cyp1a1, Egr1, Ccnb1, Gdf15, Cdk1, Pdk4, Ccna2, and Ns5atp9) and one pathway (retinol metabolism), associated with steatogenic chemicals and whose response was conserved across the three in vitro and in vivo systems. Similarly, we found the predicted metabolite changes, such as increases of saturated and unsaturated fatty acids, conserved across the three systems. Analysis of the target genes associated with the MIEs of the current steatosis AOP did not provide a clear association between these 18 chemicals and the MIEs, underlining the multi-factorial nature of this disease. Notably, our overall analysis implicated mitochondrial toxicity as an important and overlooked MIE for chemical-induced steatosis. The integrated toxicogenomics approach to identify genes, pathways, and metabolites based on known steatogenic chemicals, provide an important mean to assess development of AOPs and gauging the relevance of new testing strategies.

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.

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.

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.

A Review of Drug Repositioning Based Chemical-induced Cell Line Expression Data

Drug repositioning is an important area of biomedical research. The drug repositioning studies have shifted to computational approaches. Large-scale perturbation databases, such as the Connectivity Map and the Library of Integrated Network-Based Cellular Signatures, contain a number of chemical-induced gene expression profiles and provide great opportunities for computational biology and drug repositioning. One reason is that the profiles provided by the Connectivity Map and the Library of Integrated Network-Based Cellular Signatures databases show an overall view of biological mechanism in drugs, diseases and genes. In this article, we provide a review of the two databases and their recent applications in drug repositioning.

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.

Editor's Highlight: Transcriptome Profiling Reveals Bisphenol A Alternatives Activate Estrogen Receptor Alpha in Human Breast Cancer Cells

Plasticizers with estrogenic activity, such as bisphenol A (BPA), have potential adverse health effects in humans. Due to mounting evidence of these health effects, BPA is being phased out and replaced by other bisphenol variants in “BPA-free” products. We have compared estrogenic activity of BPA with 6 bisphenol analogues [bisphenol S (BPS); bisphenol F (BPF); bisphenol AP (BPAP); bisphenol AF (BPAF); bisphenol Z (BPZ); bisphenol B (BPB)] in 3 human breast cancer cell lines. Estrogenicity was assessed (10⁻¹¹–10⁻⁴ M) by cell growth in an estrogen receptor (ER)-mediated cell proliferation assay, and by the induction of estrogen response element-mediated transcription in a luciferase assay. BPAF was the most potent bisphenol, followed by BPB > BPZ ∼ BPA > BPF ∼ BPAP > BPS. The addition of ICI 182,780 antagonized the activation of ERs. Data mining of ToxCast high-throughput screening assays confirm our results but also show divergence in the sensitivities of the assays. Gene expression profiles were determined in MCF-7 cells by microarray analysis. The comparison of transcriptome profile alterations resulting from BPA alternatives with an ERα gene expression biomarker further indicates that all BPA alternatives act as ERα agonists in MCF-7 cells. These results were confirmed by Illumina-based RNA sequencing. In conclusion, BPA alternatives are not necessarily less estrogenic than BPA in human breast cancer cells. BPAF, BPB, and BPZ were more estrogenic than BPA. These findings point to the importance of better understanding the risk of adverse effects from exposure to BPA alternatives, including hormone-dependent breast cancer.

BCScreen: A gene panel to test for breast carcinogenesis in chemical safety screening

Targeted gene lists have been used in clinical settings to specify breast tumor type, and to predict breast cancer prognosis and response to treatment. Separately, panels have been curated to predict systemic toxicity and xenoestrogen activity as a part of chemical screening strategies. However, currently available panels do not specifically target biological processes relevant to breast development and carcinogenesis. We have developed a gene panel called the Breast Carcinogen Screen (BCScreen) as a tool to identify potential breast carcinogens and characterize mechanisms of toxicity. First, we used four seminal reviews to identify 14 key characteristics of breast carcinogenesis, such as apoptosis, immunomodulation, and genotoxicity. Then, using a hybrid data and knowledge-driven framework, we systematically combined information from whole transcriptome data from genomic databases, biomedical literature, the CTD chemical-gene interaction database, and primary literature review to generate a panel of 500 genes relevant to breast carcinogenesis. We used normalized pointwise mutual information (NPMI) to rank genes that frequently co-occurred with key characteristics in biomedical literature. We found that many genes identified for BCScreen were not included in prognostic breast cancer or systemic toxicity panels. For example, more than half of BCScreen genes were not included in the Tox21 S1500+ general toxicity gene list. Of the 230 that did overlap between the two panels, representation varied across characteristics of carcinogenesis ranging from 21% for genes associated with epigenetics to 82% for genes associated with xenobiotic metabolism. Enrichment analysis of BCScreen identified pathways and processes including response to steroid hormones, cancer, cell cycle, apoptosis, DNA damage and breast cancer. The biologically-based systematic approach to gene prioritization demonstrated here provides a flexible framework for creating disease-focused gene panels to support discovery related to etiology. With validation, BCScreen may also be useful for toxicological screening relevant to breast carcinogenesis.

Evaluation of estrogen receptor alpha activation by glyphosate-based herbicide constituents

The safety, including the endocrine disruptive capability, of glyphosate-based herbicides (GBHs) is a matter of intense debate. We evaluated the estrogenic potential of glyphosate, commercial GBHs and polyethoxylated tallowamine adjuvants present as co-formulants in GBHs. Glyphosate (≥10,000 μg/L or 59 μM) promoted proliferation of estrogen-dependent MCF-7 human breast cancer cells. Glyphosate also increased the expression of an estrogen response element-luciferase reporter gene (ERE-luc) in T47D-KBluc cells, which was blocked by the estrogen antagonist ICI 182,780. Commercial GBH formulations or their adjuvants alone did not exhibit estrogenic effects in either assay. Transcriptomics analysis of MCF-7 cells treated with glyphosate revealed changes in gene expression reflective of hormone-induced cell proliferation but did not overlap with an ERα gene expression biomarker. Calculation of glyphosate binding energy to ERα predicts a weak and unstable interaction (-4.10 kcal mol-1) compared to estradiol (-25.79 kcal mol-1), which suggests that activation of this receptor by glyphosate is via a ligand-independent mechanism. Induction of ERE-luc expression by the PKA signalling activator IBMX shows that ERE-luc is responsive to ligand-independent activation, suggesting a possible mechanism of glyphosate-mediated activation. Our study reveals that glyphosate, but not other components present in GBHs, can activate ERα in vitro, albeit at relatively high concentrations.

Developing a Gene Biomarker at the Tipping Point of Adaptive and Adverse Responses in Human Bronchial Epithelial Cells

Determining mechanism-based biomarkers that distinguish adaptive and adverse cellular processes is critical to understanding the health effects of environmental exposures. Shifting from in vivo, low-throughput toxicity studies to high-throughput screening (HTS) paradigms and risk assessment based on in vitro and in silico testing requires utilizing toxicity pathway information to distinguish adverse outcomes from recoverable adaptive events. Little work has focused on oxidative stresses in human airway for the purposes of predicting adverse responses. We hypothesize that early gene expression-mediated molecular changes could be used to delineate adaptive and adverse responses to environmentally-based perturbations. Here, we examined cellular responses of the tracheobronchial airway to zinc (Zn) exposure, a model oxidant. Airway derived BEAS-2B cells exposed to 2–10 μM Zn²⁺ elicited concentration- and time-dependent cytotoxicity. Normal, adaptive, and cytotoxic Zn²⁺ exposure conditions were determined with traditional apical endpoints, and differences in global gene expression around the tipping point of the responses were used to delineate underlying molecular mechanisms. Bioinformatic analyses of differentially expressed genes indicate early enrichment of stress signaling pathways, including those mediated by the transcription factors p53 and NRF2. After 4 h, 154 genes were differentially expressed (p < 0.01) between the adaptive and cytotoxic Zn²⁺ concentrations. Nearly 40% of the biomarker genes were related to the p53 signaling pathway with 30 genes identified as likely direct targets using a database of p53 ChIP-seq studies. Despite similar p53 activation profiles, these data revealed widespread dampening of p53 and NRF2-related genes as early as 4 h after exposure at higher, unrecoverable Zn²⁺ exposures. Thus, in our model early increased activation of stress response pathways indicated a recoverable adaptive event. Overall, this study highlights the importance of characterizing molecular mechanisms around the tipping point of adverse responses to better inform HTS paradigms.