ArticleToxicological sciences : an official journal of the Society of Toxicology2019
The Power of Resolution: Contextualized Understanding of Biological Responses to Liver Injury Chemicals Using High-throughput Transcriptomics and Benchmark Concentration Modeling.
Article in Toxicological sciences : an official journal of the Society of Toxicology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed, 81 citations in OpenAlex.
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- Deciphering per- and polyfluoroalkyl substances mode of action: comparative gene expression analysis in human liver spheroids.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
- Quantifying liver-toxic responses from dose-dependent chemical exposures using a rat genome-scale metabolic model.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
- Unraveling Human Hepatocellular Responses to PFAS and Aqueous Film-Forming Foams (AFFFs) for Molecular Hazard Prioritization and In Vivo Translation.Environmental science & technology · 2025Article
- Bioinformatic workflows for deriving transcriptomic points of departure: current status, data gaps, and research priorities.Toxicological sciences : an official journal of the Society of Toxicology · 2025Review
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- Bayesian gene set benchmark dose estimation for "omic" responses.Bioinformatics (Oxford, England) · 2024Article
- A hierarchical constrained density regression model for predicting cluster-level dose-response.Environmetrics · 2024Article
- Empirical Characterization of False Discovery Rates of Differentially Expressed Genes and Transcriptomic Benchmark Concentrations in Zebrafish Embryos.Environmental science & technology · 2024Article
- Unlocking the Power of Transcriptomic Biomarkers in Qualitative and Quantitative Genotoxicity Assessment of Chemicals.Chemical research in toxicology · 2024Article
- Investigating the dependency of in vitro benchmark concentrations on exposure time in transcriptomics experiments.Toxicology in vitro : an international journal published in association with BIBRA · 2024Article
- Article
- Definition of the Neurotoxicity-Associated Metabolic Signature Triggered by Berberine and Other Respiratory Chain Inhibitors.Antioxidants (Basel, Switzerland) · 2023Article
- High-Throughput Transcriptomics Differentiates Toxic versus Non-Toxic Chemical Exposures Using a Rat Liver Model.International journal of molecular sciences · 2023Article
- A tiered testing strategy based on in vitro phenotypic and transcriptomic data for selecting representative petroleum UVCBs for toxicity evaluation in vivo.Toxicological sciences : an official journal of the Society of Toxicology · 2023Article
- Determination of in vitro hepatotoxic potencies of a series of perfluoroalkyl substances (PFASs) based on gene expression changes in HepaRG liver cells.Archives of toxicology · 2023Article
- Derivation of metabolic point of departure using high-throughput in vitro metabolomics: investigating the importance of sampling time points on benchmark concentration values in the HepaRG cell line.Archives of toxicology · 2023Article
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
Abstract
Prediction of human response to chemical exposures is a major challenge in both pharmaceutical and toxicological research. Transcriptomics has been a powerful tool to explore chemical-biological interactions, however, limited throughput, high-costs, and complexity of transcriptomic interpretations have yielded numerous studies lacking sufficient experimental context for predictive application. To address these challenges, we have utilized a novel high-throughput transcriptomics (HTT) platform, TempO-Seq, to apply the interpretive power of concentration-response modeling with exposures to 24 reference compounds in both differentiated and non-differentiated human HepaRG cell cultures. Our goals were to (1) explore transcriptomic characteristics distinguishing liver injury compounds, (2) assess impacts of differentiation state of HepaRG cells on baseline and compound-induced responses (eg, metabolically-activated), and (3) identify and resolve reference biological-response pathways through benchmark concentration (BMC) modeling. Study data revealed the predictive utility of this approach to identify human liver injury compounds by their respective BMCs in relation to human internal exposure plasma concentrations, and effectively distinguished drug analogs with varied associations of human liver injury (eg, withdrawn therapeutics trovafloxacin and troglitazone). Impacts of cellular differentiation state (proliferated vs differentiated) were revealed on baseline drug metabolizing enzyme expression, hepatic receptor signaling, and responsiveness to metabolically-activated toxicants (eg, cyclophosphamide, benzo(a)pyrene, and aflatoxin B1). Finally, concentration-response modeling enabled efficient identification and resolution of plausibly-relevant biological-response pathways through their respective pathway-level BMCs. Taken together, these findings revealed HTT paired with differentiated in vitro liver models as an effective tool to model, explore, and interpret toxicological and pharmacological interactions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.