High-throughput profiling assays such as high-throughput phenotypic profiling (HTPP) with Cell Painting and high-throughput transcriptomics (HTTr) with TempO-Seq have been used to characterize the bioactivity and potential hazards associated with large inventories of chemicals. Although both methods offer broad coverage of molecular targets, a limitation is that the cell types used in these in vitro assays typically lack the xenobiotic metabolism capabilities of humans or laboratory animals used for in vivo testing. To address this limitation, this proof-of-concept study coupled the Alginate Immobilization of Metabolic Enzymes (AIME) platform to both assays and evaluated the impact of metabolism on chemical bioactivity in a breast cancer cell line, VM7Luc4E2. HTPP detected concentration-dependent increases in chemical bioactivity corresponding to increased estrogen receptor (ER) activation measured using an ER transactivation assay (ERTA) that had been previously coupled to the AIME platform in VM7Luc4E2 cells. Additionally, HTTr detected a greater number of active genes in the metabolic condition associated with increased ER activation. This corresponded to a greater number of active ER high-confidence (ERHC) gene signatures and/or metabolism-induced shifts in ERHC signature enrichment as a transcriptomic readout of ER activity. This study demonstrates that the high-throughput profiling assays can detect changes in chemical bioactivity between parent compounds and metabolites generated using the AIME platform in a reproducible way. Incorporating metabolic competence into high-throughput profiling assays will better inform next-generation risk assessment by capturing potential metabolite-based changes in bioactivity of test chemicals that may be missed by current screening approaches.

Previous high-throughput transcriptomic screening of over 1,300 chemicals in three different human cell lines relied on a whole transcriptome targeted RNA-seq assay that measures the expression of over 19,000 genes and a signature-based concentration-response analysis method to derive an overall transcriptomic point of departure (tPOD) for each chemical. To explore the impacts of switching to a reduced representation version of the assay ("S1500+") measuring only 2730 genes, we re-analyzed the existing data using only the S1500+ gene panel, and performed concentration-response modeling using the same methodology previously applied to the whole transcriptome data. The tPODs derived from the S1500+ genes were highly concordant with the tPODs derived from the whole transcriptome expression data regardless of cell line, with over 93 % of the corresponding tPODs falling within 1 order of magnitude of each other. The overall root mean squared deviation between tPODs derived from the two gene sets was less than what was observed between duplicate samples of the same chemical within screening studies. Importantly, the total number of active gene signatures shrunk by only 13-17 % (depending on cell line) when reducing the analysis to the S1500+ genes. However, examination of the individual active gene signatures showed systematic differences between the two TempO-Seq gene panels as a function of source database or associated protein target. Overall, our analysis suggests that switching to the reduced representation assay in the context of high-throughput transcriptomic screening would likely have minimal impacts on the inference of overall tPODs, but could impact inference of specific mechanisms-of-action.

Per- and polyfluoroalkyl substances (PFAS) are a diverse class of anthropogenic chemicals, and their widespread use in manufacturing and commerce has led to introduction of these chemicals into the environment. Owing to the lack of traditional toxicology data on the majority of PFAS, novel testing methods that provide supporting information to inform human health impacts in a relatively short time frame will be increasingly important. The US Environmental Protection Agency's (EPA) Transcriptomic Assessment Process (ETAP) was recently implemented by the Agency as an efficient and cost-effective method to begin assessing potential human health impacts of chemicals that lack traditional toxicity testing data. The method involves short-term oral dosing in male and female adult rats over a five-day interval, followed by transcriptomic dose-response assessment in twelve tissues to determine a point of departure. The ETAP point of departure identifies the dose at which there are no coordinated transcriptional changes that would indicate a potential toxicity of concern. However, this approach does not explore any specific association with hazard or mechanism. Reported here are ETAP results for three PFAS chemicals: 3:3 fluorotelomer carboxylic acid (3:3 FTCA), 7:3 fluorotelomer alcohol (7:3 FTOH), and perfluorohexanesulfonamide (PFHxSA). The transcriptomic points of departure associated with the tested chemicals, as assessed via ETAP and allometrically scaled to human equivalent doses, were 0.00235 (3:3 FTCA), 0.0152 (7:3 FTOH), and 0.00358 (PFHxSA) mg/kg-day.

BACKGROUND:With thousands of chemicals in commerce and the environment, rapid identification of potential hazards is a critical need. Combining broad molecular profiling with targeted in vitro assays, such as high-throughput transcriptomics (HTTr) and receptor screening assays, could improve identification of chemicals that perturb key molecular targets associated with adverse outcomes. OBJECTIVES:We aimed to link transcriptomic readouts to individual molecular targets and integrate transcriptomic predictions with orthogonal receptor-level assays in a proof-of-concept framework for chemical hazard prioritization. METHODS:Transcriptomic profiles generated via TempO-Seq in U-2 OS and HepaRG cell lines were used to develop signatures composed of genes uniquely responsive to reference chemicals for distinct molecular targets. These signatures were applied to 75 reference and 1,126 nonreference chemicals screened via HTTr in both cell lines. Selective bioactivity toward each signature was determined by comparing potency estimates against the bulk of transcriptomic bioactivity for each chemical. Chemicals predicted by transcriptomics were confirmed for target bioactivity and selectivity using available orthogonal assay data from the US Environmental Protection Agency ToxCast program. A subset of 37 selectively acting chemicals from HTTr that did not have sufficient orthogonal data were prospectively tested using one of five receptor-level assays. RESULTS:Of the 1,126 nonreference chemicals screened, 201 demonstrated selective bioactivity in at least one transcriptomic signature and 57 were confirmed as selective nuclear receptor agonists. Chemicals bioactive for each signature were significantly associated with orthogonal assay bioactivity, and signature-based points-of-departure were equally or more sensitive than biological pathway altering concentrations in 95.4% of signature-prioritized chemicals. Prospective profiling found that 18 of 37 (49%) chemicals without prior orthogonal assay data were bioactive against the predicted receptor. DISCUSSION:Our work demonstrates that integrating transcriptomics with targeted orthogonal assays in a tiered framework can support Next Generation Risk Assessment by informing putative molecular targets and prioritizing chemicals for further testing. https://doi.org/10.1289/EHP16024.

There is a pressing need to increase the efficiency and reliability of toxicological safety assessment for protecting human health and the environment. Although conventional toxicology tests rely on measuring apical changes in vertebrate models, there is increasing interest in the use of molecular information from animal and in vitro studies to inform safety assessment. One promising and pragmatic application of molecular information involves the derivation of transcriptomic points of departure (tPODs). Transcriptomic analyses provide a snapshot of global molecular changes that reflect cellular responses to stressors and progression toward disease. A tPOD identifies the dose level below which a concerted change in gene expression is not expected in a biological system in response to a chemical. A common approach to derive such a tPOD consists of modeling the dose-response behavior for each gene independently and then aggregating the gene-level data into a single tPOD. Although different implementations of this approach are possible, as discussed in this manuscript, research strongly supports the overall idea that reference doses produced using tPODs are health protective. An advantage of this approach is that tPODs can be generated in shorter term studies (e.g. days) compared with apical endpoints from conventional tests (e.g. 90-d subchronic rodent tests). Moreover, research strongly supports the idea that reference doses produced using tPODs are health protective. Given the potential application of tPODs in regulatory toxicology testing, rigorous and reproducible wet and dry laboratory methodologies for their derivation are required. This review summarizes the current state of the science regarding the study design and bioinformatics workflows for tPOD derivation. We identify standards of practice and sources of variability in tPOD generation, data gaps, and areas of uncertainty. We provide recommendations for research to address barriers and promote adoption in regulatory decision making.

Improved scalability of high-throughput RNA-sequencing technologies has contributed to their proposed use in regulatory contexts for chemical hazard identification. However, the high dimensionality and size of these transcriptomic data sets present a formidable obstacle for utilizing these data to address gaps in chemical safety information. New bioinformatic approaches for extracting mechanistic insight from transcriptomic data sets are a critical need. Here, we present a framework for predicting molecular initiating events (MIE) from transcriptomic chemical bioactivity screens. This framework trains models to predict MIE activation using Machine Learning (MIEML). Classifiers were trained by integrating gene expression profiles, derived from chemical exposures in MCF-7 cells, with chemical-MIE annotations from RefChemDB, a database that identifies reference chemicals for molecular targets by aggregating data across multiple sources. Classifiers were trained to predict the activation of nine distinct MIEs using profiles derived from reference chemical exposures. Of the nine MIEs modeled, three yielded classifiers that performed significantly better than classifiers trained on randomly selected profiles (p value ≤ 0.1) and correctly predicted MIEs of training-excluded reference chemicals. These classifiers were then used to generate MIE predictions for 1750 test chemicals. These predictions showed significant overlap with those derived from targeted molecular bioassay data for estrogen receptor agonism, aryl hydrocarbon receptor agonism, and glucocorticoid receptor agonism. These results demonstrate the utility of this framework in predicting the MIE activation from large transcriptomic chemical screens.

Per- and polyfluoroalkyl substances (PFAS) comprise a large class of human-made chemicals that are in widespread use and present concerns for persistence, bioaccumulation and toxicity. Whilst a handful of PFAS have been characterized for their hazard profiles, the vast majority of PFAS have not been extensively studied. A comprehensive evaluation to characterize the hazard profiles of the thousands of available PFAS would require extensive resources in terms of cost, number of animals and time. An alternative and more efficient approach is to develop a structural chemical categorization approach to prioritize which PFAS or categories of PFAS should be subject to additional study. To that end, the U.S. Environmental Protection Agency (EPA), in collaboration with the National Institute of Environmental Health Sciences (NIEHS) Division of Translational Toxicology (DTT), initiated a research project in 2018 to screen approximately 150 PFAS through a battery of alternative model organisms, in vitro cell and biochemical assays, and in vitro toxico kinetic (TK) assays in order to inform chemical category and read-across approaches. The aim of this review summarizes the experimental testing undertaken, how data were processed, what insights were derived from a category perspective and how these might potentially inform subsequent tiered testing.

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.

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.

The presence of numerous chemical contaminants from industrial, agricultural, and pharmaceutical sources in water supplies poses a potential risk to human and ecological health. Current chemical analyses suffer from limitations, including chemical coverage and high cost, and broad-coverage in vitro assays such as transcriptomics may further improve water quality monitoring by assessing a large range of possible effects. Here, we used high-throughput transcriptomics to assess the activity induced by field-derived water extracts in MCF7 breast carcinoma cells. Wastewater and surface water extracts induced the largest changes in expression among cell proliferation-related genes and neurological, estrogenic, and antibiotic pathways, whereas drinking and reclaimed water extracts that underwent advanced treatment showed substantially reduced bioactivity on both gene and pathway levels. Importantly, reclaimed water extracts induced fewer changes in gene expression than laboratory blanks, which reinforces previous conclusions based on targeted assays and improves confidence in bioassay-based monitoring of water quality.

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.

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.

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.

EDITORIAL article Front. Toxicol., 05 September 2023Sec. Toxicogenomics Volume 5 - 2023 | https://doi.org/10.3389/ftox.2023.1284932

‘Cell Painting’ is an imaging-based high-throughput phenotypic profiling (HTPP) method in which cultured cells are fluorescently labeled to visualize subcellular structures (i.e., nucleus, nucleoli, endoplasmic reticulum, cytoskeleton, Golgi apparatus / plasma membrane and mitochondria) and to quantify morphological changes in response to chemicals or other perturbagens. HTPP is a high-throughput and cost-effective bioactivity screening method that detects effects associated with many different molecular mechanisms in an untargeted manner, enabling rapid in vitro hazard assessment for thousands of chemicals. Here, 1201 chemicals from the ToxCast library were screened in concentration-response up to ∼100 μM in human U-2 OS cells using HTPP. A phenotype altering concentration (PAC) was estimated for chemicals active in the tested range. PACs tended to be higher than lower bound potency values estimated from a broad collection of targeted high-throughput assays, but lower than the threshold for cytotoxicity. In vitro to in vivo extrapolation (IVIVE) was used to estimate administered equivalent doses (AEDs) based on PACs for comparison to human exposure predictions. AEDs for 18/412 chemicals overlapped with predicted human exposures. Phenotypic profile information was also leveraged to identify putative mechanisms of action and group chemicals. Of 58 known nuclear receptor modulators, only glucocorticoids and retinoids produced characteristic profiles; and both receptor types are expressed in U-2 OS cells. Thirteen chemicals with profile similarity to glucocorticoids were tested in a secondary screen and one chemical, pyrene, was confirmed by an orthogonal gene expression assay as a novel putative GR modulating chemical. Most active chemicals demonstrated profiles not associated with a known mechanism-of-action. However, many structurally related chemicals produced similar profiles, with exceptions such as diniconazole, whose profile differed from other active conazoles. Overall, the present study demonstrates how HTPP can be applied in screening-level chemical assessments through a series of examples and brief case studies.

The growing number of chemicals in the current consumer and industrial markets presents a major challenge for regulatory programs faced with the need to assess the potential risks they pose to human and ecological health. The increasing demand for hazard and risk assessment of chemicals currently exceeds the capacity to produce the toxicity data necessary for regulatory decision making, and the applied data is commonly generated using traditional approaches with animal models that have limited context in terms of human relevance. This scenario provides the opportunity to implement novel, more efficient strategies for risk assessment purposes. This study aims to increase confidence in the implementation of new approach methods in a risk assessment context by using a parallel analysis to identify data gaps in current experimental designs, reveal the limitations of common approaches deriving transcriptomic points of departure, and demonstrate the strengths in using high-throughput transcriptomics (HTTr) to derive practical endpoints. A uniform workflow was applied across six curated gene expression datasets from concentration-response studies containing 117 diverse chemicals, three cell types, and a range of exposure durations, to determine tPODs based on gene expression profiles. After benchmark concentration modeling, a range of approaches was used to determine consistent and reliable tPODs. High-throughput toxicokinetics were employed to translate in vitro tPODs (µM) to human-relevant administered equivalent doses (AEDs, mg/kg-bw/day). The tPODs from most chemicals had AEDs that were lower (i.e., more conservative) than apical PODs in the US EPA CompTox chemical dashboard, suggesting in vitro tPODs would be protective of potential effects on human health. An assessment of multiple data points for single chemicals revealed that longer exposure duration and varied cell culture systems (e.g., 3D vs. 2D) lead to a decreased tPOD value that indicated increased chemical potency. Seven chemicals were flagged as outliers when comparing the ratio of tPOD to traditional POD, thus indicating they require further assessment to better understand their hazard potential. Our findings build confidence in the use of tPODs but also reveal data gaps that must be addressed prior to their adoption to support risk assessment applications.

Background The advent of high-throughput transcriptomic screening technologies has resulted in a wealth of publicly available gene expression data associated with chemical treatments. From a regulatory perspective, data sets that cover a large chemical space and contain reference chemicals offer utility for the prediction of molecular initiating events associated with chemical exposure. Here, we integrate data from a large compendium of transcriptomic responses to chemical exposure with a comprehensive database of chemical-protein associations to train binary classifiers that predict mechanism(s) of action from transcriptomic responses. First, we linked reference chemicals present in the LINCS L1000 gene expression data collection to chemical identifiers in RefChemDB, a database of chemical-protein interactions. Next, we trained binary classifiers on MCF7 human breast cancer cell line derived gene expression profiles and chemical-protein labels using six classification algorithms to identify optimal analysis parameters. To validate classifier accuracy, we used holdout data sets, training-excluded reference chemicals, and empirical significance testing of null models derived from permuted chemical-protein associations. To identify classifiers that have variable predicting performance across training data derived from different cellular contexts, we trained a separate set of binary classifiers on the PC3 human prostate cancer cell line. Results We trained classifiers using expression data associated with chemical treatments linked to 51 molecular initiating events. This analysis identified and validated 9 high-performing classifiers with empirical p-values lower than 0.05 and internal accuracies ranging from 0.73 to 0.94 and holdout accuracies of 0.68 to 0.92. High-ranking predictions for training-excluded reference chemicals demonstrating that predictive accuracy extends beyond the set of chemicals used in classifier training. To explore differences in classifier performance as a function of training data cellular context, MCF7-trained classifier accuracies were compared to classifiers trained on the PC3 gene expression data for the same molecular initiating events. Conclusions This methodology can offer insight in prioritizing candidate perturbagens of interest for targeted screens. This approach can also help guide the selection of relevant cellular contexts for screening classes of candidate perturbagens using cell line specific model performance.