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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., (Published by Oxford University Press on behalf of the Society of Toxicology 2024.)

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

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.

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., Competing Interests: The authors have declared that no competing interests exist.

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., (Published 2018. This article is a U.S. Government work and is in the public domain in the USA.)

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.

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., (© The Author 2017. Published by Oxford University Press on behalf of the Society of Toxicology.)

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

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

Aging is associated with a loss of cellular homeostasis, a decline in physiological function and an increase in various pathologies. Employing a meta-analysis, hepatic gene expression profiles from four independent mouse aging studies were interrogated. There was little overlap in the number of genes or canonical pathways perturbed, suggesting that independent study-specific factors may play a significant role in determining age-dependent gene expression. However, 43 genes were consistently altered during aging in three or four of these studies, including those that (1) exhibited progressively increased expression starting from 12 months of age, (2) exhibited similar expression changes in models of progeria at young ages and dampened or no changes in old longevity mouse models, (3) were associated with inflammatory tertiary lymphoid neogenesis (TLN) associated with formation of ectopic lymphoid structures observed in chronically inflamed tissues, and (4) overlapped with genes perturbed by aging in brain, muscle, and lung. Surprisingly, around half of the genes altered by aging in wild-type mice exhibited similar expression changes in adult long-lived mice compared to wild-type controls, including those associated with intermediary metabolism and feminization of the male-dependent gene expression pattern. Genes unique to aging in wild-type mice included those linked to TLN., (Published by Elsevier Ireland Ltd.)