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Incorporating Metabolic Competence into High-Throughput Profiling Assays

ABSTRACT: One limitation of previous high-throughput transcriptomics (HTTr) chemical screening studies using TempO-Seq is that most of the in vitro models used on those studies lack instrinsic capabilites to metabolize xenobiotics. This study incorporated an in vitro metabolism platform, aliginate immobilization of metabolic enzymes (AIME), into the dosing procedures for HTTr screening to address this limitation. The 384-well, lid-based platform is incubated for 2 hours on standard microplates containing chemicals, metabolic co-factors and cell culture medium to make "conditioned" medium. This medium is used to treat cells rather than treated them with parent chemicals directly, and the AIME platform does not impact downstream procedures for preparation of cell lysates that are compatible with the TempO-Seq assay. The AIME platform was used concurrently in negative or "nomet" (water + alginate) and positive or "met" (hepatic S9 + alginate) metabolic conditions. This proof-of-concept experiment screened three previously studied estrogenic reference chemicals representing three "shifts" with metabolism in the VM7Luc4E2 breast cell line in an estrogen receptor transactivation assay: bioinactivation, bioactivation or no change with metabolism. Our results indicated that the TempO-Seq assay could detect changes in bioactivity with metabolism. The established workflow can be used in future studies to improve human relevance in our chemical screening efforts.

ORGANISM(S): Homo sapiens

PROVIDER: GSE292777 | GEO | 2025/04/29

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: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.

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:Introduction: Higher-throughput mode-of-action-based assays provide a valuable approach to expedite chemical evaluation for human health risk assessment. In this study, we combined the high-throughput alkaline CometChip® assay with the TGx-DDI transcriptomic biomarker (DDI = DNA damage-inducing) using high-throughput TempO-Seq® as an integrated genotoxicity testing approach. Objective: To determine if the high-throughput CometChip® assay and TGx-DDI biomarker analysis can be combined using human HepaRG™ cells to provide an efficient next-generation genotoxicity screening approach to identify DNA damage-inducing (DDI) chemicals. Methods: Metabolically competent human HepaRG™ cell cultures were exposed to increasing concentrations of 12 chemicals (9 DDI and 3 non-DDI) using a repeat exposure design (0 hr, 24 hr, 48 hr) in 96-well plates to measure and classify chemicals based on their ability to damage DNA. After a 4 hr recovery period, exposed cells were used to perform the Trevigen CometChip assay to assess DNA damage or used for TempO-Seq® S1500+ Targeted Transcriptome Sequencing for TGx-DDI classification purposes. Bioinformatics and statistical tools were used to classify chemicals as DDI or non-DDI using three separate analyses. Benchmark concentration analysis was also conducted for both assays for the purposes of potency ranking. Results: In combination, the CometChip® assay and the TGx-DDI were 100% accurate in identifying chemicals that induce DNA damage. Quantitative benchmark concentration (BMC) modeling was applied to evaluate chemical potencies for both assays. The BMCs for the CometChip® assay and the TGx-DDI for all 12 chemicals were highly concordant (within four-fold) and resulted in identical potency rankings. Conclusions: These results suggest that these two assays can be integrated for efficient, high-throughput identification of DNA damage in HepaRG™ cells. This study provides evidence for the complementarity of the high-throughput CometChip® assay with TGx-DDI biomarker analysis. Integration of these two tests provides an effective and high-throughput approach to identify DDI agents. This work is a first step in accomplishing a more integrated genotoxicity testing strategy to derive mechanistic information to better inform human health risk assessment in a high-throughput manner.

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Incorporating Metabolic Competence into High-Throughput Profiling Assays

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