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

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.

Fluid shear stress affects the metabolic and toxicological response of the rainbow trout gill cell line RTgill-W1

The Rainbow trout gill cell-line (RTgill-W1) has been accepted by the Organisation for Economic Co-operation and Development (OECD TG249) as a replacement for fish in acute toxicity tests. In these tests cells are exposed under static conditions. In contrast, in vivo, water moves over fish gills generating fluid shear stress (FSS) that alters cell physiology and response to toxicants. The current study uses a specialised 3D printed chamber designed to house inserts and allows for the flow (0.2 dynes cm²) of water over the cells. This system was used to assess RTgill-W1 cell responses to FSS in the absence and presence of copper (Cu) over 24 h. FSS caused increased gene expression of mechanosensitive channel peizo1 and the Cu-transporter atp7a, elevated reactive oxygen species generation and increased expression of superoxidase dismutase. Cell metabolism was unaffected by Cu (0.163 μM to 2.6 μM Cu) under static conditions but significantly reduced by FSS + Cu above 1.3 μM. Differential expression of metallothionein (mt) a and b was observed with increased expression of mta under static conditions and mtb under FSS on exposure to Cu. These findings highlight toxicologically relevant mechanosensory responses by RTgill-W1 to FSS that may influence toxicological responses.

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.

Repurposing Disulfiram as an Antimicrobial Agent in Topical Infections

Antimicrobial drugs applied topically offer several advantages. However, the widespread use of antibiotics has led to increasing antimicrobial resistance. One interesting approach in the drug discovery process is drug repurposing. Disulfiram, which was originally approved as an anti-alcoholism drug, offers an attractive alternative to treat topical multidrug resistance bacteria in skin human infections. This study aimed to evaluate the biopharmaceutical characteristics of the drug and the effects arising from its topical application in detail. Microdilution susceptibility testing showed antibacterial activity against Gram-positive bacteria Staphylococcus aureus and Streptococcus pyogenes. Dermal absorption revealed no permeation in pig skin. The quantification of the drug retained in pig skin demonstrated concentrations in the stratum corneum and epidermis, enough to treat skin infections. Moreover, in vitro cytotoxicity and micro-array analyses were performed to better understand the mechanism of action and revealed the importance of the drug as a metal ion chelator. Together, our findings suggest that disulfiram has the potential to be repurposed as an effective antibiotic to treat superficial human skin infections.

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

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

A 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 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.

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.

Evaluating adaptive stress response gene signatures using transcriptomics

Stress response pathways (SRPs) mitigate the cellular effects of chemicals, but excessive perturbation can lead to adverse outcomes. Here, we investigated a computational approach to evaluate SRP activity from transcriptomic data using gene set enrichment analysis (GSEA). We extracted published gene signatures for DNA damage response (DDR), unfolded protein response (UPR), heat shock response (HSR), response to hypoxia (HPX), metal-associated response (MTL), and oxidative stress response (OSR) from the Molecular Signatures Database (MSigDB). Next, we used a gene-frequency approach to build consensus SRP signatures of varying lengths from 50 to 477 genes. We then prepared a reference dataset from perturbagens associated with SRPs from the literature with their transcriptomic profiles retrieved from public repositories. Lastly, we used receiver-operator characteristic analysis to evaluate the GSEA scores from matching transcriptomic reference profiles to SRP signatures. Our consensus signatures performed better than or as well as published signatures for 4 out of the 6 SRPs, with the best consensus signature area under the curve (% performance relative to median of published signatures) of 1.00 for DDR (109%), 0.86 for UPR (169%), 0.99 for HTS (103%), 1.00 for HPX (104%), 0.74 for MTL (150%) and 0.83 for OSR (148%). The best matches between transcriptomic profiles and SRP signatures correctly classified perturbagens in 78% and 88% of the cases by first and second rank, respectively. We believe this approach can characterize SRP activity for new chemicals using transcriptomics with further evaluation.

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.

Transcriptomic insights into the zinc homeostasis of MCF-7 breast cancer cells via next-generation RNA sequencing

A significant gap in the knowledge of zinc homeostasis exists for breast cancer cells. In this study, we investigated the transcriptomic response of the luminal breast cancer cells (MCF-7) to the exposure of extracellular zinc using next-generation RNA sequencing. The dataset was collected for three time points (T0, T30, and T120) in the time course of zinc treatment, which revealed the dramatic increase, up to 869-fold, of the gene expression for metallothioneins (MT1B, MT1F, MT1X, and MT2A) and the zinc exporter ZnT1 (SLC30A1) at T30, continuingly through to T120. The similar dynamic expression pattern was found for the autophagy-related gene (VMP1) and numerous genes for zinc finger proteins (e.g. RNF165, ZNF365, ZBTB2, SNAI1, ZNF442, ZNF547, ZNF563, and ZNF296). These findings point to the all-hands-on-deck strategy adopted by the cancer cells for maintaining zinc homeostasis. The stress responsive genes encoding heat shock proteins (HSPA1A, HSPA1B, HSPA1L, HSPA4L, HSPA6, HSPA8, HSPH1, HSP90AA1, and HSP90AB1) and the MTF-1 biomarker genes (AKR1C2, CLU, ATF3, GDF15, HMOX1, MAP1A, MAFG, SESN2, and UBC) were also differentially up-regulated at T120, suggesting a role of heat shock proteins and the MTF-1 related stress proteins in dealing with zinc exposure. It is for the first time that the gene encoding Polo-like kinase 2 (PLK2) was found to be involved in zinc-related response. The top differentially expressed genes were validated by qRT-PCR and further extended to the basal type breast cancer cells (MDA-MB-231). It was found that the expression level of SLC30A1 in MDA-MB-231 was higher than MCF-7 in response to zinc exposure. Taken together, the findings contribute to our knowledge and understanding of zinc homeostasis in breast cancer cells.