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Assessing the Impact of Metabolic Competence on Estrogenic Chemical Bioactivity in High-Throughput Profiling Assays

ABSTRACT: High-throughput profiling assays such as high-throughput phenotypic profiling (HTPP) with Cell Painting and high-throughput transcriptomics (HTTr) with TempO-SeqTM are new approach methods that have been used to characterize the bioactivity and potential hazards of chemicals. To better account for potential in vivo hazards in screening, we previously coupled both assays to an in vitro metabolism platform, Alginate Immobilization of Metabolic Enzymes (AIME). In this study, we expanded upon our previous results in estrogenic reference chemicals by screening an additional 40 chemicals in VM7Luc4E2 breast cells anticipated to have varied activity and/or shifts in activity with metabolism in an estrogen receptor transactivation assay (ERTA) using the AIME platform. Our results demonstrate how HTTr could detect ER activity and the influence of metabolism on estrogenicity through the activation of ER high-confidence (ERHC) gene signatures and metabolism-induced shifts in ERHC signature enrichment. Additionally, HTPP could detect cases where metabolism impacted chemical cytotoxicity and detect generation of metabolites that were bioactive in a different way than their respective parent compounds when comparing the profiles of active features between AIME conditions. Notably, our findings highlighted examples of chemicals that had very different phenotypic and gene expression profiles between metabolic conditions that would not be observed in traditional chemical screening in most cell models. This study exhibited how high-throughput profiling assays could distinguish changes in bioactivity with metabolic competence using the AIME platform, which will ultimately better inform next generation risk assessment by providing more comprehensive hazard characterizations.

ORGANISM(S): Homo sapiens

PROVIDER: GSE296915 | GEO | 2025/06/10

REPOSITORIES: GEO

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Project description: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. 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. Transcriptomic profiles generated via TempO-Seq in U-2 OS and HepaRG cell lines were used to develop signatures comprised of genes uniquely responsive to reference chemicals for distinct molecular targets. These signatures were applied to 75 reference and 1,126 non-reference chemicals screened via HTTr in both cell lines. Selective bioactivity towards 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 US EPA’s 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. Of the 1,126 non-reference 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 81.2% of signature-prioritized chemicals. Prospective profiling found that 18 of 37 (49%) chemicals without prior orthogonal assay data were bioactive against the predicted receptor. 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. NOTE: this GEO entry only includes the HepaRG cell line data; the U-2 OS cell line data can be located via GEO accession number GSE274318.

Project description: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 1751 unique chemicals from the EPA’s ToxCast collection in MCF7 cells using eight concentrations and an exposure duration of 6 hours. 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’ molecular mechanism(s)-of-action (MeOA) and grouping chemicals with similar bioactivity profiles.

Project description:The United States Environmental Protection Agency has proposed a tiered testing strategy for chemical hazard evaluation based on new approach methods (NAMs). The first tier includes in vitro profiling assays applicable to many (human) cell types, such as high-throughput transcriptomics (HTTr) and high-throughput phenotypic profiling (HTPP). The goals of this study were to: (1) harmonize the seeding density of U-2 OS human osteosarcoma cells for use in both assays; (2) compare HTTr- versus HTPP-derived potency estimates for 11 mechanistically diverse chemicals; (3) identify candidate reference chemicals for monitoring assay performance in future screens; and (4) characterize the transcriptional and phenotypic changes in detail for all-trans retinoic acid (ATRA) as a model compound known for its adverse effects on osteoblast differentiation. The results of this evaluation showed that (1) HTPP conducted at low (400 cells/well) and high (3000 cells/well) seeding densities yielded comparable potency estimates and similar phenotypic profiles for the tested chemicals; (2) HTPP and HTTr resulted in comparable potency estimates for changes in cellular morphology and gene expression, respectively; (3) three test chemicals (etoposide, ATRA, dexamethasone) produced concentration-dependent effects on cellular morphology and gene expression that were consistent with known modes-of-action, demonstrating their suitability for use as reference chemicals for monitoring assay performance; and (4) ATRA produced phenotypic changes that were highly similar to other retinoic acid receptor activators (AM580, arotinoid acid) and some retinoid X receptor activators (bexarotene, methoprene acid). This phenotype was observed concurrently with autoregulation of the RARB gene. Both effects were prevented by pre-treating U-2 OS cells with pharmacological antagonists of their respective receptors. Thus, the observed phenotype could be considered characteristic of retinoic acid pathway activation in U-2 OS cells. These findings lay the groundwork for combinatorial screening of chemicals using HTTr and HTPP to generate complementary information for the first tier of a NAM-based chemical hazard evaluation strategy.

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Assessing the Impact of Metabolic Competence on Estrogenic Chemical Bioactivity in High-Throughput Profiling Assays

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