Identification of potential endocrine disrupting chemicals using gene expression biomarkers

Progress in toxicogenomics to protect human health

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

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.

Transcriptome analysis of the aged SAMP8 mouse model of Alzheimer's disease reveals novel molecular targets of formononetin protection

Background

Senescence-accelerated mouse prone 8 (SAMP8) and age-matched SAMR1 mice are used to study the pathogenesis and therapeutics of Alzheimer's disease (AD); however, the molecular mechanisms are not completely understood.

Objective

This study aimed to examine the effects of the 5-month administration of formononetin in SAMP8 mice and used RNA-seq to explore the molecular targets.

Methods

SAMP8 mice were orally administered formononetin (0, 8, and 16 mg/kg) from 4 months of age, and age-matched SAMR1 mice were used as controls. Behavioral tests were performed in 9-month-old mice, followed by histopathologic analysis. Total RNA from the hippocampus was isolated and subjected to RNA-seq, RT-qPCR, and bioinformatics analysis.

Results

The 9-month-old SAMP8 mice exhibited cognition deficits, evidenced by novel object recognition, open-field test, elevated plus maze, and passive avoidance. Nissl bodies in the cortex and hippocampus were decreased. Formononetin treatments ameliorated behavioral deficits and improved morphological changes, which were evidenced by Nissl and H&E staining. RNA-seq revealed distinct gene expression patterns between SAMP8 and SAMR1 mice. Differentially expressed genes in SAMP8 mice were attenuated or normalized by formononetin. Ingenuity pathway analysis (IPA) of canonical pathway and upstream regulators revealed increases in proinflammatory factors and immune dysfunction and decreases in NRF2 and SIRT-1 signaling pathways, leading to neuroinflammation. Formononetin treatment attenuated or reversed these molecular changes. The transcriptome of SAMP8 mice was correlated with transcriptomic profiles of other AD mouse models in the GEO database.

Conclusion

Neuroinflammation and decreased antioxidant and SIRT-1 signaling contributed to cognitive deficits in aged SAMP8 mice, which are potential therapeutic targets of formononetin in combination with other therapies.

Gene-environment interactions within a precision environmental health framework

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.

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

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

A novel co-culture model of human prostate epithelial and stromal cells for androgenic and antiandrogenic screening

The risk assessment of endocrine-disrupting chemicals (EDCs) greatly relies on in vitro screening. A 3-dimensional (3D) in vitro prostate model that can reflect physiologically-relevant prostate epithelial and stromal crosstalk can significantly advance the current androgen assessment. This study built a prostate epithelial and stromal co-culture microtissue model with BHPrE and BHPrS cells in scaffold-free hydrogels. The optimal 3D co-culture condition was defined, and responses of the microtissue to androgen (dihydrotestosterone, DHT) and anti-androgen (flutamide) exposure were characterized using molecular and image profiling techniques. The co-culture prostate microtissue maintained a stable structure for up to seven days and presented molecular and morphological features of the early developmental stage of the human prostate. The cytokeratin 5/6 (CK5/6) and cytokeratin 18 (CK18) immunohistochemical staining indicated epithelial heterogeneity and differentiation in these microtissues. The prostate-related gene expression profiling did not efficiently differentiate androgen and anti-androgen exposure. However, a cluster of distinctive 3D image features was identified and could be applied in the androgenic and anti-androgenic effect prediction. Overall, the current study established a co-culture prostate model that provided an alternative strategy for (anti-)androgenic EDC safety assessment and highlighted the potential and advantage of utilizing image features to predict endpoints in chemical screening.

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

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

Proteomic analysis of anti-aging effects of Dendrobium nobile Lindl. alkaloids in aging-accelerated SAMP8 mice

Senescence-accelerated mouse prone 8 (SAMP8) mice exhibit cognitive defects and neuron loss with aging, and were used to study anti-aging effects of Dendrobium nobile alkaloids (DNLA). DNLA (20 and 40 mg/kg) were orally administered to SAMP8 mice from 6 to 10 months of age. At 10-month of age, behavioral tests via Y-maze and Open-field and neuron damage via Nissl staining were evaluated. Protein was extracted and subjected to phosphorylated proteomic analysis followed by bioinformatic analysis. The cognitive deficits and neuron loss in hippocampus and cortex of aged SAMP8 mice were improved by DNLA. Hippocampal proteomic analysis revealed 196 differentially expressed protein/genes in SAMP8 compared to age-matched senescence-accelerated resistant SAMR1 mice. Gene Oncology enriched the tubulin binding, microtubule binding, and other activities. Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed endocytosis, mRNA surveillance, tight junction, protein processing in endoplasmic reticulum, aldosterone synthesis and secretion, and glucagon signaling pathway changes. Upregulated protein/genes in the hippocampus of SAMP8 mice, such as Lmtk3, Usp10, Dzip1, Csnk2b, and Rtn1, were attenuated by DNLA; whereas downregulated protein/genes, such as Kctd16, Psd3, Bsn, Atxn2l, and Kif1a, were rescued by DNLA. The aberrant protein/gene expressions of SAMP8 mice were correlated with transcriptome changes of Alzheimer's disease in the Gene Expression Omnibus (GEO) database, and the scores were attenuated by DNLA. Thus, DNLA improved cognitive dysfunction and ameliorated neuronal injury in aged SAMP8 mice, and attenuated aberrant protein/gene expressions.

In vitro toxicity assessment of haloacetamides via a toxicogenomics assay

It is important to study the stress effects and mechanisms of haloacetamide (HAcAm) disinfection byproducts to reveal their health hazards. In this context, toxicological g was applied to evaluate the effects of four HAcAms, revealing the status of gene expression on Escherichia coli in different stress response types (oxidative, protein, membrane, general, DNA). This study revealed that the main toxic action modes of these HAcAms were general and membrane stresses by high-resolution, real-time gene expression profiling combined with clustering analysis. The results of time-gene evaluation showed that the presence of chloroacetamide (CAcAm) and bromoacetamide (BAcAm) generated more reactive oxygen species, thus activating oxidative stress. Trichloroacetamide (tCAcAm) induced altered expression of glutathione marker genes and membrane stress-related genes, and iodoacetamide (IAcAm) caused severe DNA damage by damaging DNA strands and individual nucleotides mainly through damage to nucleic acids and bases. Furthermore, quantitative structure-activity relationship (QSAR) modelling results indicated that the biological activities of HAcAms were related to their quantum chemical and topological properties.

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.

Screening potential biomarkers of cholangiocarcinoma based on gene chip meta-analysis and small-sample experimental research

Cholangiocarcinoma (CCA) is a rare malignant tumor associated with poor prognosis. This study aimed to identify CCA biomarkers by investigating differentially expressed genes (DEGs) between CCA patients and healthy subjects obtained from the Gene Expression Omnibus database. Bioinformatics tools, including the Illumina BaseSpace Correlation Engine (BSCE) and Gene Expression Profiling Interactive Analysis (GEPIA), were used. The initial DEGs from GSE26566, GSE31370, and GSE77984 were analyzed using GEO2R and Venn, and protein–protein interaction networks were constructed using STRING. The BSCE was applied to assess curated CCA studies to select additional DEGs and them DEGs across the 10 biosets, which was supported by findings in the literature. The final 18 DEGs with clinical significance for CCA were further verified using GEPIA. These included CEACAM6, EPCAM, LAMC2, MMP11, KRT7, KRT17, KRT19, SFN, and SOX9, which were upregulated, and ADH1A, ALDOB, AOX1, CTH, FGA, FGB, FGG, GSTA1, and OTC, which were downregulated in CCA patients. Among these 18 genes, 56 groups of genes (two in each group) were significantly related, and none were independently and differentially expressed. The hub genes FGA, OTC, CTH, and MMP11, which were most correlated with the 18 DEGs, were screened using STRING. The significantly low expression of FGA, OTC, and CTH and significantly high expression of MMP11 were verified by immunohistochemical analysis. Overall, four CCA biomarkers were identified that might regulate the occurrence and development of this disease and affect the patient survival rate, and they have the potential to become diagnostic and therapeutic targets for patients with CCA.

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.

Towards regulation of Endocrine Disrupting chemicals (EDCs) in water resources using bioassays - A guide to developing a testing strategy

Endocrine disrupting chemicals (EDCs) are found in every environmental medium and are chemically diverse. Their presence in water resources can negatively impact the health of both human and wildlife. Currently, there are no mandatory screening mandates or regulations for EDC levels in complex water samples globally. Bioassays, which allow quantifying in vivo or in vitro biological effects of chemicals are used commonly to assess acute toxicity in water. The existing OECD framework to identify single-compound EDCs offers a set of bioassays that are validated for the Estrogen-, Androgen-, and Thyroid hormones, and for Steroidogenesis pathways (EATS). In this review, we discussed bioassays that could be potentially used to screen EDCs in water resources, including in vivo and in vitro bioassays using invertebrates, fish, amphibians, and/or mammalians species. Strengths and weaknesses of samples preparation for complex water samples are discussed. We also review how to calculate the Effect-Based Trigger values, which could serve as thresholds to determine if a given water sample poses a risk based on existing quality standards. This work aims to assist governments and regulatory agencies in developing a testing strategy towards regulation of EDCs in water resources worldwide. The main recommendations include 1) opting for internationally validated cell reporter in vitro bioassays to reduce animal use & cost; 2) testing for cell viability (a critical parameter) when using in vitro bioassays; and 3) evaluating the recovery of the water sample preparation method selected. This review also highlights future research avenues for the EDC screening revolution (e.g., 3D tissue culture, transgenic animals, OMICs, and Adverse Outcome Pathways (AOPs)).

GC-MS Profile of Hua-Feng-Dan and RNA-Seq Analysis of Induced Adaptive Responses in the Liver

Background: Hua-Feng-Dan is a patent Chinese medicine for stroke recovery and various diseases. This study used GC-MS to profile its ingredients and RNA-Seq to analyze the induced adaptive response in the liver. Methods: Hua-Feng-Dan was subjected to steam distillation and solvent extraction, followed by GC-MS analysis. Mice were orally administered Hua-Feng-Dan and its "Guide drug" Yaomu for 7 days. Liver pathology was examined, and total RNA isolated for RNA-Seq, followed by bioinformatic analysis and quantitative real-time PCR (qPCR). Results: Forty-four volatile and fifty liposoluble components in Hua-Feng-Dan were profiled and analyzed by the NIST library and their concentrations quantified. The major components (>1%) in volatile (5) and liposoluble (10) were highlighted. Hua-Feng-Dan and Yaomu at hepatoprotective doses did not produce liver toxicity as evidenced by histopathology and serum enzyme activities. GO Enrichment revealed that Hua-Feng-Dan affected lipid homeostasis, protein folding, and cell adhesion. KEGG showed activated cholesterol metabolism, bile secretion, and PPAR signaling pathways. Differentially expressed genes (DEGs) were identified by DESeq2 with p < 0.05 compared to controls. Hua-Feng-Dan produced more DEGs than Yaomu. qPCR on selected genes largely verified RNA-Seq results. Ingenuity Pathways Analysis of the upstream regulator revealed activation of MAPK and adaptive responses by Hua-Feng-Dan, and Yaomu was less effective. Hua-Feng-Dan-induced DEGs were highly correlated with the Gene Expression Omnibus database of chemical-induced adaptive transcriptome changes in the liver. Conclusion: GC-MS primarily profiled volatile and liposoluble components in Hua-Feng-Dan. Hua-Feng-Dan at the hepatoprotective dose did not produce liver pathological changes but induced metabolic and signaling pathway activations. The effects of Hua-Feng-Dan on liver transcriptome changes point toward induced adaptive responses to program the liver to produce hepatoprotective effects.

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.

Innovation in regulatory approaches for endocrine disrupting chemicals: The journey to risk assessment modernization in Canada

Globally, regulatory authorities grapple with the challenge of assessing the hazards and risks to human and ecosystem health that may result from exposure to chemicals that disrupt the normal functioning of endocrine systems. Rapidly increasing number of chemicals in commerce, coupled with the reliance on traditional, costly animal experiments for hazard characterization - often with limited sensitivity to many important mechanisms of endocrine disruption -, presents ongoing challenges for chemical regulation. The consequence is a limited number of chemicals for which there is sufficient data to assess if there is endocrine toxicity and hence few chemicals with thorough hazard characterization. To address this challenge, regulatory assessment of endocrine disrupting chemicals (EDCs) is benefiting from a revolution in toxicology that focuses on New Approach Methodologies (NAMs) to more rapidly identify, prioritize, and assess the potential risks from exposure to chemicals using novel, more efficient, and more mechanistically driven methodologies and tools. Incorporated into Integrated Approaches to Testing and Assessment (IATA) and guided by conceptual frameworks such as Adverse Outcome Pathways (AOPs), emerging approaches focus initially on molecular interactions between the test chemical and potentially vulnerable biological systems instead of the need for animal toxicity data. These new toxicity testing methods can be complemented with in silico and computational toxicology approaches, including those that predict chemical kinetics. Coupled with exposure data, these will inform risk-based decision-making approaches. Canada is part of a global network collaborating on building confidence in the use of NAMs for regulatory assessment of EDCs. Herein, we review the current approaches to EDC regulation globally (mainly from the perspective of human health), and provide a perspective on how the advances for regulatory testing and assessment can be applied and discuss the promises and challenges faced in adopting these novel approaches to minimize risks due to EDC exposure in Canada, and our world.

Variations of enzymatic activity and gene expression in zebrafish (Danio rerio) embryos co-exposed to zearalenone and fumonisin B1

The natural co-occurrence of multiple mycotoxins has been reported in cereals and cereal products worldwide. Even though the dietary exposure to mycotoxins constitutes a serious human health, most reports are limited to the toxic effect of individual mycotoxins. The purpose of the present study was to assess the combined toxic effects of zearalenone (ZEN) and fumonisin B1 (FB1) and the potential interaction of their mixture on zebrafish (Danio rerio) embryos. Our results showed that ZEN possessed the higher toxicity to embryonic zebrafish (7-day LC₅₀ value of 0.78 mg a.i. L^-1) compared with FB1 (7-day LC₅₀ value of 227.7 mg a.i. L^-1). The combination of ZEN and FB1 exerted an additive effect on zebrafish embryos. Meanwhile, the activities of antioxidant CAT, caspase-3, and detoxification enzyme CYP450, as well as the expressions of six genes (Mn-sod, cas9, bax, cc-chem, ERα, and crh) associated with oxidative stress, cellular apoptosis, immune system, and endocrine system were prominently altered in the mixture exposure compared with the corresponding single treatment group of ZEN or FB1. Taken together, the regulatory standards of mycotoxins in food and feed should be updated based on the mixture effects of mycotoxins, and there is an increased need on effective detoxification methods for controlling and reducing the toxicity of multiple mycotoxins in animal feed and throughout the food supply chain.

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.

Cross-Model Comparison of Transcriptomic Dose-Response of Short-Chain Chlorinated Paraffins

Short-chain chlorinated paraffins (SCCPs) have attracted attention because of their toxicological potential in humans and wildlife at environmentally relevant doses. However, limited information is available regarding mechanistic differences across species in terms of the biological pathways that are impacted by SCCP exposure. Here, a concentration-dependent reduced human transcriptome (RHT) approach was conducted to evaluate 15 SCCPs in HepG2 cells and compared with our previous results using a reduced zebrafish transcriptome (RZT) approach in zebrafish embryos (ZFEs). Generally, SCCPs induced a broader suite of biological pathways in ZFEs than HepG2 cells, and all of the 15 SCCPs were more potent in HepG2 cells compared to ZFEs. Despite these general differences, the transcriptional potency of SCCPs in both model systems showed a significant linear relationship (p = 0.0017, r² = 0.57), and the average ratios of transcriptional potency for each SCCP in RZT to that in RHT were ∼100,000. C₁₀H₁₄Cl₈ was the most potent SCCP, while C₁₀H₁₇Cl₅ was the least potent in both ZFEs and HepG2 cells. An adverse outcome pathway network-based analysis demonstrated model-specific responses, such as xenobiotic metabolism that may be mediated by different nuclear receptor-mediated pathways between HepG2 cells (e.g., CAR and AhR activation) and ZFEs (e.g., PXR activation). Moreover, induced transcriptional changes in ZFEs associated with pathways and molecular initiating events (e.g., activation of nicotinic acetylcholine receptor) suggest that SCCPs may disrupt neural development processes. The cross-model comparison of concentration-dependent transcriptomics represents a promising approach to assess and prioritize SCCPs.

The role of validation in establishing the scientific credibility of predictive toxicology approaches intended for regulatory application

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The role of validation in establishing the credibility of predictive methods is discussed.

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Various assessment frameworks for predictive methods have evolved independently, being developed by different communities.

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A set of seven credibility factors is proposed as a method-agnostic means of comparing the various assessment frameworks.

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It is hoped this will facilitate communication and cross-disciplinary collaboration between method developers and users.

New approaches in toxicology based on in vitro methods and computational modelling offer considerable potential to improve the efficiency and effectiveness of chemical hazard and risk assessment in a variety of regulatory contexts. However, this presents challenges both for developers and regulatory assessors because often these two communities do not share the same level of confidence in a new approach. To address this challenge, various assessment frameworks have been developed over the past 20 years with the aim of creating harmonised and systematic approaches for evaluating new methods. These frameworks typically focus on specific methodologies and technologies, which has proven useful for establishing the validity and credibility of individual methods. However, given the increasing need to compare methods and combine their use in integrated assessment strategies, the multiplicity of frameworks is arguably becoming a barrier to their acceptance. In this commentary, we explore the concepts of model validity and credibility, and we illustrate how a set of seven credibility factors provides a method-agnostic means of comparing different kinds of predictive toxicology approaches. It is hoped that this will facilitate communication and cross-disciplinarity among method developers and users, with the ultimate aim of increasing the acceptance and use of predictive approaches in toxicology.

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.

Towards a systematic use of effect biomarkers in population and occupational biomonitoring

Effect biomarkers can be used to elucidate relationships between exposure to environmental chemicals and their mixtures with associated health outcomes, but they are often underused, as underlying biological mechanisms are not understood. We aim to provide an overview of available effect biomarkers for monitoring chemical exposures in the general and occupational populations, and highlight their potential in monitoring humans exposed to chemical mixtures. We also discuss the role of the adverse outcome pathway (AOP) framework and physiologically based kinetic and dynamic (PBK/D) modelling to strengthen the understanding of the biological mechanism of effect biomarkers, and in particular for use in regulatory risk assessments. An interdisciplinary network of experts from the European chapter of the International Society for Exposure Science (ISES Europe) and the Organization for Economic Co-operation and Development (OECD) Occupational Biomonitoring activity of Working Parties of Hazard and Exposure Assessment group worked together to map the conventional framework of biomarkers and provided recommendations for their systematic use. We summarized the key aspects of this work here, and discussed these in three parts. Part I, we inventory available effect biomarkers and promising new biomarkers for the general population based on the H2020 Human Biomonitoring for Europe (HBM4EU) initiative. Part II, we provide an overview AOP and PBK/D modelling use that improved the selection and interpretation of effect biomarkers. Part III, we describe the collected expertise from the OECD Occupational Biomonitoring subtask effect biomarkers in prioritizing relevant mode of actions (MoAs) and suitable effect biomarkers. Furthermore, we propose a tiered risk assessment approach for occupational biomonitoring. Several effect biomarkers, especially for use in occupational settings, are validated. They offer a direct assessment of the overall health risks associated with exposure to chemicals, chemical mixtures and their transformation products. Promising novel effect biomarkers are emerging for biomonitoring of the general population. Efforts are being dedicated to prioritizing molecular and biochemical effect biomarkers that can provide a causal link in exposure-health outcome associations. This mechanistic approach has great potential in improving human health risk assessment. New techniques such as in silico methods (e.g. QSAR, PBK/D modelling) as well as 'omics data will aid this process. Our multidisciplinary review represents a starting point for enhancing the identification of effect biomarkers and their mechanistic pathways following the AOP framework. This may help in prioritizing the effect biomarker implementation as well as defining threshold limits for chemical mixtures in a more structured way. Several ex vivo biomarkers have been proposed to evaluate combined effects including genotoxicity and xeno-estrogenicity. There is a regulatory need to derive effect-based trigger values using the increasing mechanistic knowledge coming from the AOP framework to address adverse health effects due to exposure to chemical mixtures. Such a mechanistic strategy would reduce the fragmentation observed in different regulations. It could also stimulate a harmonized use of effect biomarkers in a more comparable way, in particular for risk assessments to chemical mixtures.

Potential Molecular Target Prediction and Docking Verification of Hua-Feng-Dan in Stroke Based on Network Pharmacology

Objective

Hua-Feng-Dan (HFD) is a Chinese medicine for stroke. This study is to predict and verify potential molecular targets and pathways of HFD against stroke using network pharmacology.

Methods

The TCMSP database and TCMID were used to search for the active ingredients of HFD, and GeneCards and DrugBank databases were used to search for stroke-related target genes to construct the “component-target-disease” by Cytoscape 3.7.1, which was further filtered by MCODE to build a core network. The STRING database was used to obtain interrelationships by topology and to construct a protein-protein interaction network. GO and KEGG were carried out through DAVID Bioinformatics. Autodock 4.2 was used for molecular docking. BaseSpace was used to correlate target genes with the GEO database.

Results

Based on OB ≥ 30% and DL ≥ 0.18, 42 active ingredients were extracted from HFD, and 107 associated targets were obtained. PPI network and Cytoscape analysis identified 22 key targets. GO analysis suggested 51 cellular biological processes, and KEGG suggested that 60 pathways were related to the antistroke mechanism of HFD, with p53, PI3K-Akt, and apoptosis signaling pathways being most important for HFD effects. Molecular docking verified interactions between the core target (CASP8, CASP9, MDM2, CYCS, RELA, and CCND1) and the active ingredients (beta-sitosterol, luteolin, baicalein, and wogonin). The identified gene targets were highly correlated with the GEO biosets, and the stroke-protection effects of Xuesaitong in the database were verified by identified targets.

Conclusion

HFD could regulate the symptoms of stroke through signaling pathways with core targets. This work provided a bioinformatic method to clarify the antistroke mechanism of HFD, and the identified core targets could be valuable to evaluate the antistroke effects of traditional Chinese medicines.

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.

Gene Expression Thresholds Derived From Short-term Exposures Identify Rat Liver Tumorigens

Traditional methods for cancer risk assessment are resource-intensive, retrospective, and not feasible for the vast majority of environmental chemicals. In this study, we investigated whether quantitative genomic data from short-term studies may be used to set protective thresholds for potential tumorigenic effects. We hypothesized that gene expression biomarkers measuring activation of the key early events in established pathways for rodent liver cancer exhibit cross-chemical thresholds for tumorigenesis predictive for liver cancer risk. We defined biomarker thresholds for 6 major liver cancer pathways using training sets of chemicals with short-term genomic data (3-29 days of exposure) from the TG-GATES (n = 77 chemicals) and DrugMatrix (n = 86 chemicals) databases and then tested these thresholds within and between datasets. The 6 pathway biomarkers represented genotoxicity, cytotoxicity, and activation of xenobiotic, steroid, and lipid receptors (aryl hydrocarbon receptor, constitutive activated receptor, estrogen receptor, and peroxisome proliferator-activated receptor α). Thresholds were calculated as the maximum values derived from exposures without detectable liver tumor outcomes. We identified clear response values that were consistent across training and test sets. Thresholds derived from the TG-GATES training set were highly predictive (97%) in a test set of independent chemicals, whereas thresholds derived from the DrugMatrix study were 96%-97% predictive for the TG-GATES study. Threshold values derived from an abridged gene list (2/biomarker) also exhibited high predictive accuracy (91%-94%). These findings support the idea that early genomic changes can be used to establish threshold estimates or "molecular tipping points" that are predictive of later-life health outcomes.