Datasets used in RD-023418: Adverse Outcome Pathway-Driven Identification of Rat Hepatocarcinogens in Short-Term Assays. This dataset is associated with the following publication: Rooney, J., T. Hill, C. Qin, F. Sistare, and C. Corton. Adverse outcome pathway-driven identification of rat liver tumorigens in short-term assays. TOXICOLOGY AND APPLIED PHARMACOLOGY. Academic Press Incorporated, Orlando, FL, USA, 356: 99-113, (2018).

Gene expression data from the Fluidigm qRT-PCR arrays was analyzed in R (v3.6.1; R Foundation for Statistical Computing, 2019). Prior to processing through the tcpl package, each qRT-PCR primer set was annotated as an individual assay endpoint (aeid) for analyses. For each plate, well types were designated for test compound wells (t), positive controls (c), (that is phenobarbital) and neutral controls (n, DMSO). Fold-change in the number of amplification cycles needed to pass the background threshold (Ct) for 96 transcripts to (ftp://newftp.epa.gov/COMPTOX/CCTE_Publication_Data/CCED_Publication_Data/Wambaugh/ToxCast_LTEA, file LTEA_Level2_20191119.zip) were normalized to the geometric mean of three housekeeping genes (ACTB, GAPDH, POLR2A) to generate ΔCt values (cval). Prior to calculating the response values (rval), or ΔΔCt, for each transcript (n = 96) per well, the baseline value (bval), the plate-wise median of the neutral control wells, was generated for each plate (the normalization process is described in detail in supplemental file SupFile4-DeltaCTCalculation.docx). The bval was subtracted from the cval to yield the rval or log2 Fold Change per transcript. Gene expression data from the Fluidigm qRT-PCR arrays was analyzed in R (v3.6.1; R Foundation for Statistical Computing, 2019). Prior to processing through the tcpl package, each qRT-PCR primer set was annotated as an individual assay endpoint (aeid) for analyses. For each plate, well types were designated for test compound wells (t), positive controls (c), (that is phenobarbital) and neutral controls (n, DMSO). Fold-change in the number of amplification cycles needed to pass the background threshold (Ct) for 96 transcripts to (ftp://newftp.epa.gov/COMPTOX/CCTE_Publication_Data/CCED_Publication_Data/Wambaugh/ToxCast_LTEA, file LTEA_Level5_20191119.zip) were normalized to the geometric mean of three housekeeping genes (ACTB, GAPDH, POLR2A) to generate ΔCt values (cval). Prior to calculating the response values (rval), or ΔΔCt, for each transcript (n = 96) per well, the baseline value (bval), the plate-wise median of the neutral control wells, was generated for each plate (the normalization process is described in detail in supplemental file SupFile4-DeltaCTCalculation.docx). The bval was subtracted from the cval to yield the rval or log2 Fold Change per transcript. Supplemental File LTEA_Inucyte_Images.zip is comprised of 20,493 images totaling more than 15 gigabytes. Cell morphology images were acquired for each well/plate with an Essen IncuCyte™ FLR automated phase-contrast microscope located inside a tissue culture incubator. Six 96-well culture plates were loaded into the instrument and imaged for an elapsed time (~24 minutes). The IncuCyte™ software was used for image capturing and export of images in JPEG format. This dataset is associated with the following publication: Franzosa, J., J. Bonzo, J. Jack, N.C. Baker, P. Kothiya, R. Witek, P. Hurban, S. Siferd, S. Hester, I. Shah, S. Ferguson, K. Houck, and J. Wambaugh. High-throughput toxicogenomic screening of chemicals in the environment using metabolically competent hepatic cell cultures. npj Systems Biology and Applications. Springer Nature Group, New York, NY, 7: Article 7, (2021).

We introduce a new high-throughput transcriptomics (HTTr) platform comprised of a collagen sandwich primary rat hepatocyte culture and the TempO-Seq assay for screening and prioritizing potential hepatotoxicants. We selected 14 chemicals based on their risk of drug-induced liver injury (DILI) and tested them in hepatocytes at two treatment concentrations. HTTr data was generated using the TempO-Seq whole transcriptome and S1500+ assays. The HTTr platform exhibited high reproducibility between technical replicates (r>0.9) but biological replication was greater for TempO-Seq S1500+ (r>0.85) than for the whole transcriptome (r>0.7). Reproducibility between biological replicates was dependent on the strength of transcriptional effects induced by a chemical treatment. Despite targeting a smaller number of genes, the S1500+ assay clustered chemical treatments and produced gene set enrichment analysis (GSEA) scores comparable to those of the whole transcriptome. Connectivity mapping showed a high-level of reproducibility between TempO-Seq data and Affymetrix GeneChip data from the Open TG-GATES project with high concordance between the S1500+ gene set and whole transcriptome. Taken together, our results provide guidance on selecting the number of technical and biological replicates and support the use of TempO-Seq S1500+ assay for a high-throughput platform for screening hepatotoxicants. FASTQ files and read counts data have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) (GSE152128). This dataset is associated with the following publication: Lee, F., I. Shah, Y.T. Soong, J. Xing, I.C. Ng, F. Tasnim, and H. Yu. Reproducibility and Robustness of High-Throughput S1500+ Transcriptomics on Primary Rat Hepatocytes for Chemical-Induced Hepatotoxicity Assessment. Current Research in Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 2: 282-295, (2021).

This dataset was prepared from the US Environmental Protection Agency's (EPA) Toxicity Reference Database (ToxRefDB) that contains information for 1,142 chemicals and 5,960 studies. Curations include information regarding the study design, chemical identity, dosing, treatment group parameters, treatment-related (significantly different from control) and critical (adverse) effects for all dose treatment groups, as well as endpoint testing status according to guideline specifications. ToxRefDB data was examined for all subchronic (SUB) studies with complete curations, which included registrant-submitted toxicity studies from the US EPA’s Office of Pesticide Programs (OPP) and guideline studies sourced from the National Toxicology Program (NTP). Statistically significant differences between treatment and control group data at p<0.05 within the source documents was extracted and denoted with a “treatment-related” Boolean indicator “true”. Across the studies with absolute liver weights and relative-to-body (RLW) liver weights, the treatment-related mean effect values at the lowest effect (LE) dose levels as well as mean control liver weights were determined for all chemical-study-sex-species-exposure route groupings. The LE-ALW and LE-RLW changes were quantified as effect size differences from control using the following equation: Effect_size = 100 x (LE Effect_value – Control Effect_Value) / Control Effect_Value Any microscopic liver pathology effects occurring at the corresponding LE dose level of weight change were also identified and listed in the dataset. Histopathology terms were presented as they appeared in ToxRefDB without harmonizing different hierarchical levels and aggregating multiple terms used to depict the same lesions. The final dataset that includes chemical stressor information, study source identifiers, study type, sex, species, strain, administration route, administration method, dose level, mg/kg/day value, qualitative and quantitative effect information, effect size from control, and pathology effects if present. The dataset includes data from 389 subchronic studies on 273 chemicals. This dataset is associated with the following publication: Mezencev, R., M. Feshuk, L. Kolaczkowski, G. Peterson, Q. Zhao, S. Watford, and J. Weaver. The association between histopathologic effects and liver weight changes induced in mice and rats by chemical exposures: an analysis of the data from Toxicity Reference Database (ToxRefDB). TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 200(2): 404-413, (2024).

Dataset for Robarts et al., 'Hepatic Transcriptome Comparative In Silico Analysis Reveals Similar Pathways and Targets Altered by Legacy and Alternative Per- and Polyfluoroalkyl Substances in Mice' published in Toxics, DOI https://doi.org/10.3390/toxics11120963, PMCID 10748317. This dataset is associated with the following publication: Robarts, D., J. Dai, C. Lau, U. Apte, and J. Corton. Hepatic Transcriptome Comparative In Silico Analysis Reveals Similar Pathways and Targets Altered by Legacy and Alternative Per- and Polyfluoroalkyl Substances in Mice. Toxics. MDPI, Basel, SWITZERLAND, 11(12): 963, (2023).

This dataset contains model code and supporting analysis files necessary to evaluate the impact of variability in human hepatic scaling factors. Variation in scaling factor values impacts metabolic rate parameter estimates (Vmax) and hence estimates of internal dose used in dose response analysis and biomarkers of exposure that are important for interpretation of epidemiology studies. This dataset is associated with the following publication: Kenyon, E., C. Eklund, J. Lipscomb, and R. Pegram. The impact of variation in scaling factors on the estimation of internal dose metrics: a case study using bromodichloromethane (BDCM).1. Toxicology Mechanisms and Methods. Taylor & Francis, Inc., Philadelphia, PA, USA, 26(8): 620-626, (2016).

httk: High-Throughput Toxicokinetics Functions and data tables for simulation and statistical analysis of chemical toxicokinetics ("TK") using data obtained from relatively high throughput, in vitro studies. Both physiologically-based ("PBTK") and empirical (e.g., one compartment) "TK" models can be parameterized for several hundred chemicals and multiple species. These models are solved efficiently, often using compiled (C-based) code. A Monte Carlo sampler is included for simulating biological variability and measurement limitations. Functions are also provided for exporting "PBTK" models to "SBML" and "JARNAC" for use with other simulation software. These functions and data provide a set of tools for in vitro-in vivo extrapolation ("IVIVE") of high throughput screening data (e.g., ToxCast) to real-world exposures via reverse dosimetry (also known as "RTK"). This dataset is associated with the following publication: Pearce, R., W. Setzer, J. Davis, and J. Wambaugh. Evaluation and Calibration of High-Throughput Predictions of Chemical Distribution to Tissues. JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS. Springer, New York, NY, USA, 44(6): 549-565, (2017).

This dataset provides measured in vitro intrinsic clearance rates for 54 chemicals tested using isolated hepatocytes from humans, rats, and rainbow trout. The test chemicals were selected to provide broad coverage across the industrial and pesticidal chemical space while also prioritizing chemicals of interest to EPA’s Endocrine Disruptor Screening Program (EDSP). A data evaluation framework was developed to identify results suitable for rate reporting. Acceptable results were then used to evaluate the chemical domain of applicability of the applied methods, the influence of starting substrate concentration on measured rates of intrinsic clearance, and differences in metabolic activity among species. These findings provide data for chemicals of specific interest to the EDSP. More importantly, the results provide critical guidance on future use of in vitro biotransformation assays to support high-throughput chemical risks assessments. This dataset is associated with the following publication: Black, S., J. Nichols, K. Fay, S. Matten, and S. Lynn. Evaluation and comparison of in vitro intrinsic hepatic clearance rates measured using cryopreserved hepatocytes from humans, rats, and rainbow trout. TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 457: 152819, (2021).

Background: High-content imaging (HCI) allows simultaneous measurement of multiple cellular phenotypic changes and is an important tool for evaluating the biological activity of chemicals. Objectives: Our goal was to analyze dynamic cellular changes using HCI to identify the “tipping point” at which the cells did not show recovery towards a normal phenotypic state. Methods: HCI was used to evaluate the effects of 967 chemicals (in concentrations ranging from 0.4 to 200 μM) on HepG2 cells over a 72-hr exposure period. The HCI end points included p53, c-Jun, histone H2A.x, α-tubulin, histone H3, alpha tubulin, mitochondrial membrane potential, mitochondrial mass, cell cycle arrest, nuclear size, and cell number. A computational model was developed to interpret HCI responses as cell-state trajectories. Results: Analysis of cell-state trajectories showed that 336 chemicals produced tipping points and that HepG2 cells were resilient to the effects of 334 chemicals up to the highest concentration (200 μM) and duration (72 hr) tested. Tipping points were identified as concentration-dependent transitions in system recovery, and the corresponding critical concentrations were generally between 5 and 15 times (25th and 75th percentiles, respectively) lower than the concentration that produced any significant effect on HepG2 cells. The remaining 297 chemicals require more data before they can be placed in either of these categories. Conclusions: These findings show the utility of HCI data for reconstructing cell state trajectories and provide insight into the adaptation and resilience of in vitro cellular systems based on tipping points. Cellular tipping points could be used to define a point of departure for risk-based prioritization of environmental chemicals. This dataset is associated with the following publication: Shah , I., W. Setzer , J. Jack, K. Houck , R. Judson , T. Knudsen , J. Liu, M. Martin , D. Reif, A.M. Richard , R.S. Thomas , K. Crofton , D.J. Dix , and R.J. Kavlock. (Envir. Health Perspect.) Using ToxCast data to reconstruct dynamic cell state trajectories and estimate toxicological points of departure. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 1-33, (2015).