1
|
Bundy JL, Everett LJ, Rogers JD, Nyffeler J, Byrd G, Culbreth M, Haggard DE, Word LJ, Chambers BA, Davidson-Fritz S, Harris F, Willis C, Paul-Friedman K, Shah I, Judson R, Harrill JA. High-Throughput Transcriptomics Screen of ToxCast Chemicals in U-2 OS Cells. Toxicol Appl Pharmacol 2024; 491:117073. [PMID: 39159848 DOI: 10.1016/j.taap.2024.117073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 08/14/2024] [Accepted: 08/16/2024] [Indexed: 08/21/2024]
Abstract
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.
Collapse
Affiliation(s)
- Joseph L Bundy
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America.
| | - Logan J Everett
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Jesse D Rogers
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, 37831, United States of America
| | - Jo Nyffeler
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, 37831, United States of America
| | - Gabrielle Byrd
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Associated Universities (ORAU), Oak Ridge, TN, 37831, United States of America
| | - Megan Culbreth
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Derik E Haggard
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Laura J Word
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Bryant A Chambers
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Sarah Davidson-Fritz
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Felix Harris
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Associated Universities (ORAU), Oak Ridge, TN, 37831, United States of America
| | - Clinton Willis
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Katie Paul-Friedman
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Imran Shah
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Richard Judson
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Joshua A Harrill
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| |
Collapse
|
2
|
Meier MJ, Harrill J, Johnson K, Thomas RS, Tong W, Rager JE, Yauk CL. Progress in toxicogenomics to protect human health. Nat Rev Genet 2024:10.1038/s41576-024-00767-1. [PMID: 39223311 DOI: 10.1038/s41576-024-00767-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/23/2024] [Indexed: 09/04/2024]
Abstract
Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose-response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.
Collapse
Affiliation(s)
- Matthew J Meier
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada
| | - Joshua Harrill
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, USA
| | - Kamin Johnson
- Predictive Safety Center, Corteva Agriscience, Indianapolis, IN, USA
| | - Russell S Thomas
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, AR, USA
- Curriculum in Toxicology & Environmental Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Julia E Rager
- Curriculum in Toxicology & Environmental Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA
- The Center for Environmental Medicine, Asthma and Lung Biology, School of Medicine, The University of North Carolina, Chapel Hill, NC, USA
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Carole L Yauk
- Department of Biology, University of Ottawa, Ottawa, Ontario, Canada.
| |
Collapse
|
3
|
Barutcu AR. Assessment of TGx-DDI genes for genotoxicity in a comprehensive panel of chemicals. Toxicol Mech Methods 2024; 34:761-767. [PMID: 38538091 DOI: 10.1080/15376516.2024.2335966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 03/23/2024] [Indexed: 04/16/2024]
Abstract
BACKGROUND The TGx-DDI biomarker identifies transcripts specifically induced by primary DNA damage. Profiling similarity of TGx-DDI signatures can allow clustering compounds by genotoxic mechanism. This transcriptomics-based approach complements conventional toxicology testing by enhancing mechanistic resolution. METHODS Unsupervised hierarchical clustering and t-distributed stochastic neighbor embedding (tSNE) were utilized to assess similarity of publicly-available per- and polyfluoroalkyl substances (PFAS) and ToxCast chemicals based on TGx-DDI modulation. TempO-seq transcriptomic data after highest chemical concentrations were analyzed. RESULTS Clustering discriminated between genotoxic and non-genotoxic compounds while drawing similarity among chemicals with shared mechanisms. PFAS largely clustered distinctly from classical mutagens. However, dynamic range across PFAS types and durations indicated variable potential for DNA damage. tSNE visualization reinforced phenotypic groupings, with genotoxins clustering separately from non-DNA damaging agents. DISCUSSION Unsupervised learning approaches applied to TGx-DDI profiles effectively categorizes chemical genotoxicity potential, aiding elucidation of biological response pathways. This transcriptomics-based strategy gives further insight into the role and effect of individual TGx-DDI biomarker genes and complements existing assays by enhancing mechanistic resolution. Overall, TGx-DDI biomarker profiling holds promise for predictive safety screening.
Collapse
|
4
|
Barham K, Spencer R, Baker NC, Knudsen TB. Engineering a computable epiblast for in silico modeling of developmental toxicity. Reprod Toxicol 2024; 128:108625. [PMID: 38857815 DOI: 10.1016/j.reprotox.2024.108625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 05/23/2024] [Accepted: 05/25/2024] [Indexed: 06/12/2024]
Abstract
Developmental hazard evaluation is an important part of assessing chemical risks during pregnancy. Toxicological outcomes from prenatal testing in pregnant animals result from complex chemical-biological interactions, and while New Approach Methods (NAMs) based on in vitro bioactivity profiles of human cells offer promising alternatives to animal testing, most of these assays lack cellular positional information, physical constraints, and regional organization of the intact embryo. Here, we engineered a fully computable model of the embryonic disc in the CompuCell3D.org modeling environment to simulate epithelial-mesenchymal transition (EMT) of epiblast cells and self-organization of mesodermal domains (chordamesoderm, paraxial, lateral plate, posterior/extraembryonic). Mesodermal fate is modeled by synthetic activity of the BMP4-NODAL-WNT signaling axis. Cell position in the epiblast determines timing with respect to EMT for 988 computational cells in the computer model. An autonomous homeobox (Hox) clock hidden in the epiblast is driven by WNT-FGF4-CDX signaling. Executing the model renders a quantitative cell-level computation of mesodermal fate and consequences of perturbation based on known biology. For example, synthetic perturbation of the control network rendered altered phenotypes (cybermorphs) mirroring some aspects of experimental mouse embryology, with electronic knockouts, under-activation (hypermorphs) or over-activation (hypermorphs) particularly affecting the size and specification of the posterior mesoderm. This foundational model is trained on embryology but capable of performing a wide variety of toxicological tasks conversing through anatomical simulation to integrate in vitro chemical bioactivity data with known embryology. It is amenable to quantitative simulation for probabilistic prediction of early developmental toxicity.
Collapse
Affiliation(s)
- Kaitlyn Barham
- Oak Ridge Associated Universities, USA; USEPA, Center for Compuational Toxicology and Exposure.
| | | | | | | |
Collapse
|
5
|
Silva M, Capps S, London JK. Community-Engaged Research and the Use of Open Access ToxVal/ToxRef In Vivo Databases and New Approach Methodologies (NAM) to Address Human Health Risks From Environmental Contaminants. Birth Defects Res 2024; 116:e2395. [PMID: 39264239 DOI: 10.1002/bdr2.2395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 06/19/2024] [Accepted: 08/11/2024] [Indexed: 09/13/2024]
Abstract
BACKGROUND The paper analyzes opportunities for integrating Open access resources (Abstract Sifter, US EPA and NTP Toxicity Value and Toxicity Reference [ToxVal/ToxRefDB]) and New Approach Methodologies (NAM) integration into Community Engaged Research (CEnR). METHODS CompTox Chemicals Dashboard and Integrated Chemical Environment with in vivo ToxVal/ToxRef and NAMs (in vitro) databases are presented in three case studies to show how these resources could be used in Pilot Projects involving Community Engaged Research (CEnR) from the University of California, Davis, Environmental Health Sciences Center. RESULTS Case #1 developed a novel assay methodology for testing pesticide toxicity. Case #2 involved detection of water contaminants from wildfire ash and Case #3 involved contaminants on Tribal Lands. Abstract Sifter/ToxVal/ToxRefDB regulatory data and NAMs could be used to screen/prioritize risks from exposure to metals, PAHs and PFAS from wildfire ash leached into water and to investigate activities of environmental toxins (e.g., pesticides) on Tribal lands. Open access NAMs and computational tools can apply to detection of sensitive biological activities in potential or known adverse outcome pathways to predict points of departure (POD) for comparison with regulatory values for hazard identification. Open access Systematic Empirical Evaluation of Models or biomonitoring exposures are available for human subpopulations and can be used to determine bioactivity (POD) to exposure ratio to facilitate mitigation. CONCLUSIONS These resources help prioritize chemical toxicity and facilitate regulatory decisions and health protective policies that can aid stakeholders in deciding on needed research. Insights into exposure risks can aid environmental justice and health equity advocates.
Collapse
Affiliation(s)
- Marilyn Silva
- Co-Chair Community Stakeholders' Advisory Committee, University of California (UC Davis), Environmental Health Sciences Center (EHSC), Davis, California, USA
| | - Shosha Capps
- Co-Director Community Engagement Core, UC Davis EHSC, Davis, California, USA
| | - Jonathan K London
- Department of Human Ecology and Faculty Director Community Engagement Core, UC Davis EHSC, Sacramento, California, USA
| |
Collapse
|
6
|
Tsai HHD, Ford LC, Burnett SD, Dickey AN, Wright FA, Chiu WA, Rusyn I. Informing Hazard Identification and Risk Characterization of Environmental Chemicals by Combining Transcriptomic and Functional Data from Human-Induced Pluripotent Stem-Cell-Derived Cardiomyocytes. Chem Res Toxicol 2024; 37:1428-1444. [PMID: 39046974 DOI: 10.1021/acs.chemrestox.4c00193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
Environmental chemicals may contribute to the global burden of cardiovascular disease, but experimental data are lacking to determine which substances pose the greatest risk. Human-induced pluripotent stem cell (iPSC)-derived cardiomyocytes are a high-throughput cardiotoxicity model that is widely used to test drugs and chemicals; however, most studies focus on exploring electro-physiological readouts. Gene expression data may provide additional molecular insights to be used for both mechanistic interpretation and dose-response analyses. Therefore, we hypothesized that both transcriptomic and functional data in human iPSC-derived cardiomyocytes may be used as a comprehensive screening tool to identify potential cardiotoxicity hazards and risks of the chemicals. To test this hypothesis, we performed concentration-response analysis of 464 chemicals from 12 classes, including both pharmaceuticals and nonpharmaceutical substances. Functional effects (beat frequency, QT prolongation, and asystole), cytotoxicity, and whole transcriptome response were evaluated. Points of departure were derived from phenotypic and transcriptomic data, and risk characterization was performed. Overall, 244 (53%) substances were active in at least one phenotype; as expected, pharmaceuticals with known cardiac liabilities were the most active. Positive chronotropy was the functional phenotype activated by the largest number of tested chemicals. No chemical class was particularly prone to pose a potential hazard to cardiomyocytes; a varying proportion (10-44%) of substances in each class had effects on cardiomyocytes. Transcriptomic data showed that 69 (15%) substances elicited significant gene expression changes; most perturbed pathways were highly relevant to known key characteristics of human cardiotoxicants. The bioactivity-to-exposure ratios showed that phenotypic- and transcriptomic-based POD led to similar results for risk characterization. Overall, our findings demonstrate how the integrative use of in vitro transcriptomic and phenotypic data from iPSC-derived cardiomyocytes not only offers a complementary approach for hazard and risk prioritization, but also enables mechanistic interpretation of the in vitro test results to increase confidence in decision-making.
Collapse
Affiliation(s)
- Han-Hsuan D Tsai
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, United States
| | - Lucie C Ford
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, United States
| | - Sarah D Burnett
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, United States
| | - Allison N Dickey
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27603, United States
| | - Fred A Wright
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27603, United States
- Department of Statistics and Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27603, United States
| | - Weihsueh A Chiu
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, United States
| | - Ivan Rusyn
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, United States
| |
Collapse
|
7
|
Ollitrault G, Marzo M, Roncaglioni A, Benfenati E, Mombelli E, Taboureau O. Prediction of Endocrine-Disrupting Chemicals Related to Estrogen, Androgen, and Thyroid Hormone (EAT) Modalities Using Transcriptomics Data and Machine Learning. TOXICS 2024; 12:541. [PMID: 39195643 PMCID: PMC11360171 DOI: 10.3390/toxics12080541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Revised: 07/13/2024] [Accepted: 07/19/2024] [Indexed: 08/29/2024]
Abstract
Endocrine-disrupting chemicals (EDCs) are chemicals that can interfere with homeostatic processes. They are a major concern for public health, and they can cause adverse long-term effects such as cancer, intellectual impairment, obesity, diabetes, and male infertility. The endocrine system is a complex machinery, with the estrogen (E), androgen (A), and thyroid hormone (T) modes of action being of major importance. In this context, the availability of in silico models for the rapid detection of hazardous chemicals is an effective contribution to toxicological assessments. We developed Qualitative Gene expression Activity Relationship (QGexAR) models to predict the propensities of chemically induced disruption of EAT modalities. We gathered gene expression profiles from the LINCS database tested on two cell lines, i.e., MCF7 (breast cancer) and A549 (adenocarcinomic human alveolar basal epithelial). We optimized our prediction protocol by testing different feature selection methods and classification algorithms, including CATBoost, XGBoost, Random Forest, SVM, Logistic regression, AutoKeras, TPOT, and deep learning models. For each EAT endpoint, the final prediction was made according to a consensus prediction as a function of the best model obtained for each cell line. With the available data, we were able to develop a predictive model for estrogen receptor and androgen receptor binding and thyroid hormone receptor antagonistic effects with a consensus balanced accuracy on a validation set ranging from 0.725 to 0.840. The importance of each predictive feature was further assessed to identify known genes and suggest new genes potentially involved in the mechanisms of action of EAT perturbation.
Collapse
Affiliation(s)
| | - Marco Marzo
- Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 20156 Milano, Italy; (M.M.); (A.R.); (E.B.)
| | - Alessandra Roncaglioni
- Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 20156 Milano, Italy; (M.M.); (A.R.); (E.B.)
| | - Emilio Benfenati
- Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 20156 Milano, Italy; (M.M.); (A.R.); (E.B.)
| | - Enrico Mombelli
- Institut National de l’Environnement Industriel et des Risques (INERIS), 60550 Verneuil en Halatte, France;
| | - Olivier Taboureau
- Inserm U1133, CNRS UMR 8251, Université Paris Cité, 75013 Paris, France;
| |
Collapse
|
8
|
Fresnais L, Perin O, Riu A, Grall R, Ott A, Fromenty B, Gallardo JC, Stingl M, Frainay C, Jourdan F, Poupin N. A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques. BMC Bioinformatics 2024; 25:234. [PMID: 38992584 PMCID: PMC11238488 DOI: 10.1186/s12859-024-05845-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 06/14/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes. RESULTS To address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition-specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, a graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated condition-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone), bringing additional information from the network topology, or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA. CONCLUSION The proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. The presented strategy is freely available as a python module ( https://pypi.org/project/manamodeller/ ) and jupyter notebooks ( https://github.com/LouisonF/MANA ).
Collapse
Affiliation(s)
- Louison Fresnais
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France.
| | - Olivier Perin
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Anne Riu
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Romain Grall
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Alban Ott
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Bernard Fromenty
- Institut NUMECAN (Nutrition Metabolisms and Cancer) UMR_A 1317, UMR_S 1241, INSERM, Univ Rennes, INRAE, 35000, Rennes, France
| | - Jean-Clément Gallardo
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Maximilian Stingl
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Clément Frainay
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Fabien Jourdan
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Nathalie Poupin
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
| |
Collapse
|
9
|
Britton KN, Judson RS, Hill BN, Jarema KA, Olin JK, Knapp BR, Lowery M, Feshuk M, Brown J, Padilla S. Using Zebrafish to Screen Developmental Toxicity of Per- and Polyfluoroalkyl Substances (PFAS). TOXICS 2024; 12:501. [PMID: 39058153 PMCID: PMC11281043 DOI: 10.3390/toxics12070501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 07/01/2024] [Accepted: 07/02/2024] [Indexed: 07/28/2024]
Abstract
Per- and polyfluoroalkyl substances (PFAS) are found in many consumer and industrial products. While some PFAS, notably perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS), are developmentally toxic in mammals, the vast majority of PFAS have not been evaluated for developmental toxicity potential. A concentration-response study of 182 unique PFAS chemicals using the zebrafish medium-throughput, developmental vertebrate toxicity assay was conducted to investigate chemical structural identifiers for toxicity. Embryos were exposed to each PFAS compound (≤100 μM) beginning on the day of fertilization. At 6 days post-fertilization (dpf), two independent observers graded developmental landmarks for each larva (e.g., mortality, hatching, swim bladder inflation, edema, abnormal spine/tail, or craniofacial structure). Thirty percent of the PFAS were developmentally toxic, but there was no enrichment of any OECD structural category. PFOS was developmentally toxic (benchmark concentration [BMC] = 7.48 μM); however, other chemicals were more potent: perfluorooctanesulfonamide (PFOSA), N-methylperfluorooctane sulfonamide (N-MeFOSA), ((perfluorooctyl)ethyl)phosphonic acid, perfluoro-3,6,9-trioxatridecanoic acid, and perfluorohexane sulfonamide. The developmental toxicity profile for these more potent PFAS is largely unexplored in mammals and other species. Based on these zebrafish developmental toxicity results, additional screening may be warranted to understand the toxicity profile of these chemicals in other species.
Collapse
Affiliation(s)
- Katy N. Britton
- Oak Ridge Associated Universities Research Participation Program Hosted by EPA, Center for Computational Toxicology and Exposure, Biomolecular and Computational Toxicology Division, Rapid Assay Development Branch, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Richard S. Judson
- Center for Computational Toxicology and Exposure, Computational Toxicology and Bioinformatics Branch, Research Triangle Park, NC 27711, USA;
| | - Bridgett N. Hill
- Oak Ridge Institute for Science and Education Research Participation Program Hosted by EPA, Center for Computational Toxicology and Exposure, Biomolecular and Computational Toxicology Division, Rapid Assay Development Branch, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA; (B.N.H.); (B.R.K.)
| | - Kimberly A. Jarema
- Center for Public Health and Environmental Assessment, Immediate Office, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA;
| | - Jeanene K. Olin
- Center for Computational Toxicology and Exposure, Biomolecular and Computational Toxicology Division, Rapid Assay Development Branch, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA; (J.K.O.); (M.L.)
| | - Bridget R. Knapp
- Oak Ridge Institute for Science and Education Research Participation Program Hosted by EPA, Center for Computational Toxicology and Exposure, Biomolecular and Computational Toxicology Division, Rapid Assay Development Branch, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA; (B.N.H.); (B.R.K.)
| | - Morgan Lowery
- Center for Computational Toxicology and Exposure, Biomolecular and Computational Toxicology Division, Rapid Assay Development Branch, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA; (J.K.O.); (M.L.)
| | - Madison Feshuk
- Center for Computational Toxicology and Exposure, Scientific Computing and Data Curation Division, Data Extraction and Quality Evaluation Branch, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA;
| | - Jason Brown
- Center for Computational Toxicology and Exposure, Scientific Computing and Data Curation Division, Application Development Branch, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA;
| | - Stephanie Padilla
- Center for Computational Toxicology and Exposure, Biomolecular and Computational Toxicology Division, Rapid Assay Development Branch, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA; (J.K.O.); (M.L.)
| |
Collapse
|
10
|
Motsinger-Reif AA, Reif DM, Akhtari FS, House JS, Campbell CR, Messier KP, Fargo DC, Bowen TA, Nadadur SS, Schmitt CP, Pettibone KG, Balshaw DM, Lawler CP, Newton SA, Collman GW, Miller AK, Merrick BA, Cui Y, Anchang B, Harmon QE, McAllister KA, Woychik R. Gene-environment interactions within a precision environmental health framework. CELL GENOMICS 2024; 4:100591. [PMID: 38925123 PMCID: PMC11293590 DOI: 10.1016/j.xgen.2024.100591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 03/26/2024] [Accepted: 06/02/2024] [Indexed: 06/28/2024]
Abstract
Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.
Collapse
Affiliation(s)
- Alison A Motsinger-Reif
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA.
| | - David M Reif
- Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Farida S Akhtari
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - John S House
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - C Ryan Campbell
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kyle P Messier
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA; Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - David C Fargo
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Tiffany A Bowen
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Srikanth S Nadadur
- Exposure, Response, and Technology Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Charles P Schmitt
- Office of the Scientific Director, Office of Data Science, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kristianna G Pettibone
- Program Analysis Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - David M Balshaw
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA; Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Cindy P Lawler
- Genes, Environment, and Health Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Shelia A Newton
- Office of Scientific Coordination, Planning and Evaluation, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Gwen W Collman
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA; Office of Scientific Coordination, Planning and Evaluation, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Aubrey K Miller
- Office of Scientific Coordination, Planning and Evaluation, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - B Alex Merrick
- Mechanistic Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Yuxia Cui
- Exposure, Response, and Technology Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Benedict Anchang
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Quaker E Harmon
- Epidemiology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kimberly A McAllister
- Genes, Environment, and Health Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Rick Woychik
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
| |
Collapse
|
11
|
Wang X, Rowan-Carroll A, Meier MJ, Yauk CL, Wade MG, Robaire B, Hales BF. House dust-derived mixtures of organophosphate esters alter the phenotype, function, transcriptome, and lipidome of KGN human ovarian granulosa cells. Toxicol Sci 2024; 200:95-113. [PMID: 38603619 PMCID: PMC11199920 DOI: 10.1093/toxsci/kfae052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2024] Open
Abstract
Organophosphate esters (OPEs), used as flame retardants and plasticizers, are present ubiquitously in the environment. Previous studies suggest that exposure to OPEs is detrimental to female fertility in humans. However, no experimental information is available on the effects of OPE mixtures on ovarian granulosa cells, which play essential roles in female reproduction. We used high-content imaging to investigate the effects of environmentally relevant OPE mixtures on KGN human granulosa cell phenotypes. Perturbations to steroidogenesis were assessed using ELISA and qRT-PCR. A high-throughput transcriptomic approach, TempO-Seq, was used to identify transcriptional changes in a targeted panel of genes. Effects on lipid homeostasis were explored using a cholesterol assay and global lipidomic profiling. OPE mixtures altered multiple phenotypic features of KGN cells, with triaryl OPEs in the mixture showing higher potencies than other mixture components. The mixtures increased basal production of steroid hormones; this was mediated by significant changes in the expression of critical transcripts involved in steroidogenesis. Further, the total-OPE mixture disrupted cholesterol homeostasis and the composition of intracellular lipid droplets. Exposure to complex mixtures of OPEs, similar to those found in house dust, may adversely affect female reproductive health by altering a multitude of phenotypic and functional endpoints in granulosa cells. This study provides novel insights into the mechanisms of actions underlying the toxicity induced by OPEs and highlights the need to examine the effects of human relevant chemical mixtures.
Collapse
Affiliation(s)
- Xiaotong Wang
- Department of Pharmacology and Therapeutics, McGill University, Montréal, Quebec, H3G 1Y6, Canada
| | - Andrea Rowan-Carroll
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, K1A 0K9, Canada
| | - Matthew J Meier
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, K1A 0K9, Canada
| | - Carole L Yauk
- Department of Biology, University of Ottawa, Ottawa, Ontario, K1N 9A7, Canada
| | - Michael G Wade
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, K1A 0K9, Canada
| | - Bernard Robaire
- Department of Pharmacology and Therapeutics, McGill University, Montréal, Quebec, H3G 1Y6, Canada
- Department of Obstetrics and Gynecology, McGill University, Montréal, Quebec, H3G 1Y6, Canada
| | - Barbara F Hales
- Department of Pharmacology and Therapeutics, McGill University, Montréal, Quebec, H3G 1Y6, Canada
| |
Collapse
|
12
|
Sostare E, Bowen TJ, Lawson TN, Freier A, Li X, Lloyd GR, Najdekr L, Jankevics A, Smith T, Varshavi D, Ludwig C, Colbourne JK, Weber RJM, Crizer DM, Auerbach SS, Bucher JR, Viant MR. Metabolomics Simultaneously Derives Benchmark Dose Estimates and Discovers Metabolic Biotransformations in a Rat Bioassay. Chem Res Toxicol 2024; 37:923-934. [PMID: 38842447 PMCID: PMC11187623 DOI: 10.1021/acs.chemrestox.4c00002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 05/24/2024] [Accepted: 05/27/2024] [Indexed: 06/07/2024]
Abstract
Benchmark dose (BMD) modeling estimates the dose of a chemical that causes a perturbation from baseline. Transcriptional BMDs have been shown to be relatively consistent with apical end point BMDs, opening the door to using molecular BMDs to derive human health-based guidance values for chemical exposure. Metabolomics measures the responses of small-molecule endogenous metabolites to chemical exposure, complementing transcriptomics by characterizing downstream molecular phenotypes that are more closely associated with apical end points. The aim of this study was to apply BMD modeling to in vivo metabolomics data, to compare metabolic BMDs to both transcriptional and apical end point BMDs. This builds upon our previous application of transcriptomics and BMD modeling to a 5-day rat study of triphenyl phosphate (TPhP), applying metabolomics to the same archived tissues. Specifically, liver from rats exposed to five doses of TPhP was investigated using liquid chromatography-mass spectrometry and 1H nuclear magnetic resonance spectroscopy-based metabolomics. Following the application of BMDExpress2 software, 2903 endogenous metabolic features yielded viable dose-response models, confirming a perturbation to the liver metabolome. Metabolic BMD estimates were similarly sensitive to transcriptional BMDs, and more sensitive than both clinical chemistry and apical end point BMDs. Pathway analysis of the multiomics data sets revealed a major effect of TPhP exposure on cholesterol (and downstream) pathways, consistent with clinical chemistry measurements. Additionally, the transcriptomics data indicated that TPhP activated xenobiotic metabolism pathways, which was confirmed by using the underexploited capability of metabolomics to detect xenobiotic-related compounds. Eleven biotransformation products of TPhP were discovered, and their levels were highly correlated with multiple xenobiotic metabolism genes. This work provides a case study showing how metabolomics and transcriptomics can estimate mechanistically anchored points-of-departure. Furthermore, the study demonstrates how metabolomics can also discover biotransformation products, which could be of value within a regulatory setting, for example, as an enhancement of OECD Test Guideline 417 (toxicokinetics).
Collapse
Affiliation(s)
- Elena Sostare
- Michabo
Health Science Ltd., Union House, 111 New Union Street, Coventry CV1 2NT, U.K.
| | - Tara J. Bowen
- School
of Biosciences, University of Birmingham, Birmingham B15 2TT, U.K.
| | - Thomas N. Lawson
- Michabo
Health Science Ltd., Union House, 111 New Union Street, Coventry CV1 2NT, U.K.
| | - Anne Freier
- School
of Biosciences, University of Birmingham, Birmingham B15 2TT, U.K.
| | - Xiaojing Li
- School
of Biosciences, University of Birmingham, Birmingham B15 2TT, U.K.
| | - Gavin R. Lloyd
- Phenome
Centre Birmingham, University of Birmingham, Birmingham B15 2TT, U.K.
| | - Lukáš Najdekr
- Phenome
Centre Birmingham, University of Birmingham, Birmingham B15 2TT, U.K.
| | - Andris Jankevics
- Phenome
Centre Birmingham, University of Birmingham, Birmingham B15 2TT, U.K.
| | - Thomas Smith
- Phenome
Centre Birmingham, University of Birmingham, Birmingham B15 2TT, U.K.
| | - Dorsa Varshavi
- Phenome
Centre Birmingham, University of Birmingham, Birmingham B15 2TT, U.K.
| | - Christian Ludwig
- Phenome
Centre Birmingham, University of Birmingham, Birmingham B15 2TT, U.K.
| | - John K. Colbourne
- Michabo
Health Science Ltd., Union House, 111 New Union Street, Coventry CV1 2NT, U.K.
- School
of Biosciences, University of Birmingham, Birmingham B15 2TT, U.K.
| | - Ralf J. M. Weber
- Michabo
Health Science Ltd., Union House, 111 New Union Street, Coventry CV1 2NT, U.K.
- School
of Biosciences, University of Birmingham, Birmingham B15 2TT, U.K.
- Phenome
Centre Birmingham, University of Birmingham, Birmingham B15 2TT, U.K.
| | - David M. Crizer
- Division
of Translational Toxicology, National Institute
of Environmental Health Sciences, Research Triangle Park NC 27709, North Carolina, United
States
| | - Scott S. Auerbach
- Division
of Translational Toxicology, National Institute
of Environmental Health Sciences, Research Triangle Park NC 27709, North Carolina, United
States
| | - John R. Bucher
- Division
of Translational Toxicology, National Institute
of Environmental Health Sciences, Research Triangle Park NC 27709, North Carolina, United
States
| | - Mark R. Viant
- Michabo
Health Science Ltd., Union House, 111 New Union Street, Coventry CV1 2NT, U.K.
- School
of Biosciences, University of Birmingham, Birmingham B15 2TT, U.K.
- Phenome
Centre Birmingham, University of Birmingham, Birmingham B15 2TT, U.K.
| |
Collapse
|
13
|
Khadem S, Marles RJ. Biological activity of natural 2-quinolinones. Nat Prod Res 2024:1-15. [PMID: 38824680 DOI: 10.1080/14786419.2024.2359545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 05/18/2024] [Indexed: 06/04/2024]
Abstract
While natural products have undeniably played a crucial role in drug discovery, challenges such as limited availability and complex synthesis methods have hindered the identification of lead compounds. At the core of numerous natural and synthetic compounds, each displaying distinct biological behaviours, lies the foundational structure of 2-quinolinone. Compounds with this structural motif exhibit a broad range of effects in different tissues. Furthermore, specific members showcase therapeutic potential for various disorders. Despite the significance of these compounds, the current review literature has not provided a comprehensive overview, underscoring the essential contribution of this article in exploring their biological functions. This study examines the biological activity of selected 2-quinolinone alkaloids across diverse organisms, unveiling their potential as a source of innovative bioactive natural products.
Collapse
Affiliation(s)
- Shahriar Khadem
- Safe Environments Directorate, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
| | - Robin J Marles
- Retired Senior Scientific Advisor, Health Canada, Ottawa, Canada
| |
Collapse
|
14
|
Barutcu AR, Black MB, Samuel R, Slattery S, McMullen PD, Nong A. Integrating gene expression and splicing dynamics across dose-response oxidative modulators. Front Genet 2024; 15:1389095. [PMID: 38846964 PMCID: PMC11155298 DOI: 10.3389/fgene.2024.1389095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/06/2024] [Indexed: 06/09/2024] Open
Abstract
Toxicological risk assessment increasingly utilizes transcriptomics to derive point of departure (POD) and modes of action (MOA) for chemicals. One essential biological process that allows a single gene to generate several different RNA isoforms is called alternative splicing. To comprehensively assess the role of splicing dysregulation in toxicological evaluation and elucidate its potential as a complementary endpoint, we performed RNA-seq on A549 cells treated with five oxidative stress modulators across a wide dose range. Differential gene expression (DGE) showed limited pathway enrichment except at high concentrations. However, alternative splicing analysis revealed variable intron retention events affecting diverse pathways for all chemicals in the absence of significant expression changes. For instance, diazinon elicited negligible gene expression changes but progressive increase in the number of intron retention events, suggesting splicing alterations precede expression responses. Benchmark dose modeling of intron retention data highlighted relevant pathways overlooked by expression analysis. Systematic integration of splicing datasets should be a useful addition to the toxicogenomic toolkit. Combining both modalities paint a more complete picture of transcriptomic dose-responses. Overall, evaluating intron retention dynamics afforded by toxicogenomics may provide biomarkers that can enhance chemical risk assessment and regulatory decision making. This work highlights splicing-aware toxicogenomics as a possible additional tool for examining cellular responses.
Collapse
|
15
|
Corton JC, Matteo G, Chorley B, Liu J, Vallanat B, Everett L, Atlas E, Meier MJ, Williams A, Yauk CL. A 50-gene biomarker identifies estrogen receptor-modulating chemicals in a microarray compendium. Chem Biol Interact 2024; 394:110952. [PMID: 38570061 DOI: 10.1016/j.cbi.2024.110952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/01/2024] [Accepted: 03/09/2024] [Indexed: 04/05/2024]
Abstract
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.
Collapse
Affiliation(s)
- J Christopher Corton
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC, 27711, USA.
| | - Geronimo Matteo
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, K1A 0K9, Canada; Department of Biology, University of Ottawa, Ottawa, ON, K1N 6N5, Canada.
| | - Brian Chorley
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC, 27711, USA.
| | - Jie Liu
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC, 27711, USA.
| | - Beena Vallanat
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC, 27711, USA.
| | - Logan Everett
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC, 27711, USA.
| | - Ella Atlas
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, K1A 0K9, Canada.
| | - Matthew J Meier
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, K1A 0K9, Canada.
| | - Andrew Williams
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, K1A 0K9, Canada.
| | - Carole Lyn Yauk
- Department of Biology, University of Ottawa, Ottawa, ON, K1N 6N5, Canada.
| |
Collapse
|
16
|
Flynn K, Le M, Hazemi M, Biales A, Bencic DC, Blackwell BR, Bush K, Flick R, Hoang JX, Martinson J, Morshead M, Rodriguez KS, Stacy E, Villeneuve DL. Comparing Transcriptomic Points of Departure to Apical Effect Concentrations For Larval Fathead Minnow Exposed to Chemicals with Four Different Modes Of Action. ARCHIVES OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY 2024; 86:346-362. [PMID: 38743081 PMCID: PMC11305162 DOI: 10.1007/s00244-024-01064-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 04/04/2024] [Indexed: 05/16/2024]
Abstract
It is postulated that below a transcriptomic-based point of departure, adverse effects are unlikely to occur, thereby providing a chemical concentration to use in screening level hazard assessment. The present study extends previous work describing a high-throughput fathead minnow assay that can provide full transcriptomic data after exposure to a test chemical. One-day post-hatch fathead minnows were exposed to ten concentrations of three representatives of four chemical modes of action: organophosphates, ecdysone receptor agonists, plant photosystem II inhibitors, and estrogen receptor agonists for 24 h. Concentration response modeling was performed on whole body gene expression data from each exposure, using measured chemical concentrations when available. Transcriptomic points of departure in larval fathead minnow were lower than apical effect concentrations across fish species but not always lower than toxic effect concentrations in other aquatic taxa like crustaceans and insects. The point of departure was highly dependent on measured chemical concentration which were often lower than the nominal concentration. Differentially expressed genes between chemicals within modes of action were compared and often showed statistically significant overlap. In addition, reproducibility between identical exposures using a positive control chemical (CuSO4) and variability associated with the transcriptomic point of departure using in silico sampling were considered. Results extend a transcriptomic-compatible fathead minnow high-throughput assay for possible use in ecological hazard screening.
Collapse
Affiliation(s)
- Kevin Flynn
- Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, US EPA GLTED, 6201 Congdon Blvd, Duluth, MN, 55804, USA.
| | - Michelle Le
- Oak Ridge Institute for Science and Education (ORISE) Research Participant, Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, Duluth, MN, 55804, USA
| | - Monique Hazemi
- Oak Ridge Institute for Science and Education (ORISE) Research Participant, Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, Duluth, MN, 55804, USA
| | - Adam Biales
- Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, Cincinnati, OH, 45220, USA
| | - David C Bencic
- Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, Cincinnati, OH, 45220, USA
| | - Brett R Blackwell
- Biochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Kendra Bush
- Oak Ridge Institute for Science and Education (ORISE) Research Participant, Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, Duluth, MN, 55804, USA
| | - Robert Flick
- Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, Cincinnati, OH, 45220, USA
| | - John X Hoang
- Oak Ridge Institute for Science and Education (ORISE) Research Participant, Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, Duluth, MN, 55804, USA
| | - John Martinson
- Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, Cincinnati, OH, 45220, USA
| | - Mackenzie Morshead
- Oak Ridge Institute for Science and Education (ORISE) Research Participant, Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, Duluth, MN, 55804, USA
| | - Kelvin Santana Rodriguez
- Oak Ridge Institute for Science and Education (ORISE) Research Participant, Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, Duluth, MN, 55804, USA
| | - Emma Stacy
- Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, US EPA GLTED, 6201 Congdon Blvd, Duluth, MN, 55804, USA
| | - Daniel L Villeneuve
- Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, US EPA GLTED, 6201 Congdon Blvd, Duluth, MN, 55804, USA
| |
Collapse
|
17
|
Lee H, Stead JD, Williams A, Cortés Ramírez SA, Atlas E, Mennigen JA, O’Brien JM, Yauk C. Empirical Characterization of False Discovery Rates of Differentially Expressed Genes and Transcriptomic Benchmark Concentrations in Zebrafish Embryos. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:6128-6137. [PMID: 38530926 PMCID: PMC11008580 DOI: 10.1021/acs.est.3c10543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/08/2024] [Accepted: 03/11/2024] [Indexed: 03/28/2024]
Abstract
High-throughput transcriptomics (HTTr) is increasingly applied to zebrafish embryos to survey the toxicological effects of environmental chemicals. Before the adoption of this approach in regulatory testing, it is essential to characterize background noise in order to guide experimental designs. We thus empirically quantified the HTTr false discovery rate (FDR) across different embryo pool sizes, sample sizes, and concentration groups for toxicology studies. We exposed zebrafish embryos to 0.1% dimethyl sulfoxide (DMSO) for 5 days. Pools of 1, 5, 10, and 20 embryos were created (n = 24 samples for each pool size). Samples were sequenced on the TempO-Seq platform and then randomly assigned to mock treatment groups before differentially expressed gene (DEG), pathway, and benchmark concentration (BMC) analyses. Given that all samples were treated with DMSO, any significant DEGs, pathways, or BMCs are false positives. As expected, we found decreasing FDRs for DEG and pathway analyses with increasing pool and sample sizes. Similarly, FDRs for BMC analyses decreased with increasing pool size and concentration groups, with more stringent BMC premodel filtering reducing BMC FDRs. Our study provides foundational data for determining appropriate experiment designs for regulatory toxicity testing with HTTr in zebrafish embryos.
Collapse
Affiliation(s)
- Hyojin Lee
- Department
of Biology, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - John D.H. Stead
- Department
of Neuroscience, Carleton University, Ottawa, Ontario K1S 5B6, Canada
| | - Andrew Williams
- Environmental
Health Science and Research Bureau, Health
Canada, Ottawa, Ontario K1A 0K9, Canada
| | | | - Ella Atlas
- Environmental
Health Science and Research Bureau, Health
Canada, Ottawa, Ontario K1A 0K9, Canada
| | - Jan A. Mennigen
- Department
of Biology, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Jason M. O’Brien
- Ecotoxicology
and Wildlife Health Division, Environment
and Climate Change Canada, Ottawa, Ontario K1A 0H3, Canada
| | - Carole Yauk
- Department
of Biology, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| |
Collapse
|
18
|
Judson RS, Smith D, DeVito M, Wambaugh JF, Wetmore BA, Paul Friedman K, Patlewicz G, Thomas RS, Sayre RR, Olker JH, Degitz S, Padilla S, Harrill JA, Shafer T, Carstens KE. A Comparison of In Vitro Points of Departure with Human Blood Levels for Per- and Polyfluoroalkyl Substances (PFAS). TOXICS 2024; 12:271. [PMID: 38668494 PMCID: PMC11053643 DOI: 10.3390/toxics12040271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 03/29/2024] [Accepted: 03/30/2024] [Indexed: 04/29/2024]
Abstract
Per- and polyfluoroalkyl substances (PFAS) are widely used, and their fluorinated state contributes to unique uses and stability but also long half-lives in the environment and humans. PFAS have been shown to be toxic, leading to immunosuppression, cancer, and other adverse health outcomes. Only a small fraction of the PFAS in commerce have been evaluated for toxicity using in vivo tests, which leads to a need to prioritize which compounds to examine further. Here, we demonstrate a prioritization approach that combines human biomonitoring data (blood concentrations) with bioactivity data (concentrations at which bioactivity is observed in vitro) for 31 PFAS. The in vitro data are taken from a battery of cell-based assays, mostly run on human cells. The result is a Bioactive Concentration to Blood Concentration Ratio (BCBCR), similar to a margin of exposure (MoE). Chemicals with low BCBCR values could then be prioritized for further risk assessment. Using this method, two of the PFAS, PFOA (Perfluorooctanoic Acid) and PFOS (Perfluorooctane Sulfonic Acid), have BCBCR values < 1 for some populations. An additional 9 PFAS have BCBCR values < 100 for some populations. This study shows a promising approach to screening level risk assessments of compounds such as PFAS that are long-lived in humans and other species.
Collapse
Affiliation(s)
- Richard S. Judson
- US Environmental Protection Agency, Research Triangle Park, NC 27711, USA; (D.S.); (M.D.); (J.F.W.); (B.A.W.); (K.P.F.); (G.P.); (R.S.T.); (R.R.S.); (J.H.O.); (S.D.); (S.P.); (J.A.H.); (T.S.); (K.E.C.)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
19
|
Thienpont A, Cho E, Williams A, Meier MJ, Yauk CL, Rogiers V, Vanhaecke T, Mertens B. Unlocking the Power of Transcriptomic Biomarkers in Qualitative and Quantitative Genotoxicity Assessment of Chemicals. Chem Res Toxicol 2024; 37:465-475. [PMID: 38408751 PMCID: PMC10952014 DOI: 10.1021/acs.chemrestox.3c00318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 02/01/2024] [Accepted: 02/06/2024] [Indexed: 02/28/2024]
Abstract
To modernize genotoxicity assessment and reduce reliance on experimental animals, new approach methodologies (NAMs) that provide human-relevant dose-response data are needed. Two transcriptomic biomarkers, GENOMARK and TGx-DDI, have shown a high classification accuracy for genotoxicity. As these biomarkers were extracted from different training sets, we investigated whether combining the two biomarkers in a human-derived metabolically competent cell line (i.e., HepaRG) provides complementary information for the classification of genotoxic hazard identification and potency ranking. First, the applicability of GENOMARK to TempO-Seq, a high-throughput transcriptomic technology, was evaluated. HepaRG cells were exposed for 72 h to increasing concentrations of 10 chemicals (i.e., eight known in vivo genotoxicants and two in vivo nongenotoxicants). Gene expression data were generated using the TempO-Seq technology. We found a prediction performance of 100%, confirming the applicability of GENOMARK to TempO-Seq. Classification using TGx-DDI was then compared to GENOMARK. For the chemicals identified as genotoxic, benchmark concentration modeling was conducted to perform potency ranking. The high concordance observed for both hazard classification and potency ranking by GENOMARK and TGx-DDI highlights the value of integrating these NAMs in a weight of evidence evaluation of genotoxicity.
Collapse
Affiliation(s)
- Anouck Thienpont
- Department
of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel (VUB), 1090 Brussels, Belgium
- Department
of Chemical and Physical Health Risks, Sciensano, 1050 Brussels, Belgium
| | - Eunnara Cho
- Environmental
Health Science and Research Bureau, Health
Canada, Ottawa, ON K1A 0K9, Canada
| | - Andrew Williams
- Environmental
Health Science and Research Bureau, Health
Canada, Ottawa, ON K1A 0K9, Canada
| | - Matthew J. Meier
- Environmental
Health Science and Research Bureau, Health
Canada, Ottawa, ON K1A 0K9, Canada
| | - Carole L. Yauk
- Department
of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Vera Rogiers
- Department
of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel (VUB), 1090 Brussels, Belgium
| | - Tamara Vanhaecke
- Department
of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel (VUB), 1090 Brussels, Belgium
| | - Birgit Mertens
- Department
of Chemical and Physical Health Risks, Sciensano, 1050 Brussels, Belgium
| |
Collapse
|
20
|
Villeneuve DL, Blackwell BR, Bush K, Harrill J, Harris F, Hazemi M, Le M, Stacy E, Flynn KM. Transcriptomics-Based Points of Departure for Daphnia magna Exposed to 18 Per- and Polyfluoroalkyl Substances. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2024. [PMID: 38450772 DOI: 10.1002/etc.5838] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 01/08/2024] [Accepted: 01/27/2024] [Indexed: 03/08/2024]
Abstract
Per- and polyfluoroalkyl substances (PFAS) represent a large group of contaminants of concern based on their widespread use, environmental persistence, and potential toxicity. Many traditional models for estimating toxicity, bioaccumulation, and other toxicological properties are not well suited for PFAS. Consequently, there is a need to generate hazard information for PFAS in an efficient and cost-effective manner. In the present study, Daphnia magna were exposed to multiple concentrations of 22 different PFAS for 24 h in a 96-well plate format. Following exposure, whole-body RNA was extracted and extracts, each representing five exposed individuals, were subjected to RNA sequencing. Following analytical measurements to verify PFAS exposure concentrations and quality control on processed cDNA libraries for sequencing, concentration-response modeling was applied to the data sets for 18 of the tested compounds, and the concentration at which a concerted molecular response occurred (transcriptomic point of departure; tPOD) was calculated. The tPODs, based on measured concentrations of PFAS, generally ranged from 0.03 to 0.58 µM (9.9-350 µg/L; interquartile range). In most cases, these concentrations were two orders of magnitude lower than similarly calculated tPODs for human cell lines exposed to PFAS. They were also lower than apical effect concentrations reported for seven PFAS for which some crustacean or invertebrate toxicity data were available, although there were a few exceptions. Despite being lower than most other available hazard benchmarks, D. magna tPODs were, on average, four orders of magnitude greater than the maximum aqueous concentrations of PFAS measured in Great Lakes tributaries. Overall, this high-throughput transcriptomics assay with D. magna holds promise as a component of a tiered hazard evaluation strategy employing new approach methodologies. Environ Toxicol Chem 2024;00:1-16. © 2024 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
Collapse
Affiliation(s)
- Daniel L Villeneuve
- Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, Duluth, MN, USA
| | - Brett R Blackwell
- Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, Duluth, MN, USA
- Bioscience Division, Biochemistry and Biotechnology Group, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Kendra Bush
- Oak Ridge Institute for Science and Education Research Participant at US EPA, Great Lakes Toxicology and Ecology Division, Duluth, MN, USA
| | - Joshua Harrill
- Biomolecular and Computational Toxicology Division, United States Environmental Protection Agency, NC, USA
| | - Felix Harris
- Oak Ridge Institute for Science and Education Research Participant at US EPA, Biomolecular and Computational Toxicology Division, Oak Ridge, NC, USA
| | - Monique Hazemi
- Oak Ridge Institute for Science and Education Research Participant at US EPA, Great Lakes Toxicology and Ecology Division, Duluth, MN, USA
| | - Michelle Le
- Oak Ridge Institute for Science and Education Research Participant at US EPA, Great Lakes Toxicology and Ecology Division, Duluth, MN, USA
| | - Emma Stacy
- Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, Duluth, MN, USA
| | - Kevin M Flynn
- Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, Duluth, MN, USA
| |
Collapse
|
21
|
Tate T, Patlewicz G, Shah I. A Comparison of Machine Learning Approaches for predicting Hepatotoxicity potential using Chemical Structure and Targeted Transcriptomic Data. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2024; 29:1-14. [PMID: 38993502 PMCID: PMC11235188 DOI: 10.1016/j.comtox.2024.100301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
Animal toxicity testing is time and resource intensive, making it difficult to keep pace with the number of substances requiring assessment. Machine learning (ML) models that use chemical structure information and high-throughput experimental data can be helpful in predicting potential toxicity . However, much of the toxicity data used to train ML models is biased with an unequal balance of positives and negatives primarily since substances selected for in vivo testing are expected to elicit some toxicity effect. To investigate the impact this bias had on predictive performance, various sampling approaches were used to balance in vivo toxicity data as part of a supervised ML workflow to predict hepatotoxicity outcomes from chemical structure and/or targeted transcriptomic data. From the chronic, subchronic, developmental, multigenerational reproductive, and subacute repeat-dose testing toxicity outcomes with a minimum of 50 positive and 50 negative substances, 18 different study-toxicity outcome combinations were evaluated in up to 7 ML models. These included Artificial Neural Networks, Random Forests, Bernouilli Naïve Bayes, Gradient Boosting, and Support Vector classification algorithms which were compared with a local approach, Generalised Read-Across (GenRA), a similarity-weighted k-Nearest Neighbour (k-NN) method. The mean CV F1 performance for unbalanced data across all classifiers and descriptors for chronic liver effects was 0.735 (0.0395 SD). Mean CV F1 performance dropped to 0.639 (0.073 SD) with over-sampling approaches though the poorer performance of KNN approaches in some cases contributed to the observed decrease (mean CV F1 performance excluding KNN was 0.697 (0.072 SD)). With under-sampling approaches, the mean CV F1 was 0.523 (0.083 SD). For developmental liver effects, the mean CV F1 performance was much lower with 0.089 (0.111 SD) for unbalanced approaches and 0.149 (0.084 SD) for under-sampling. Over-sampling approaches led to an increase in mean CV F1 performance (0.234, (0.107 SD)) for developmental liver toxicity. Model performance was found to be dependent on dataset, model type, balancing approach and feature selection. Accordingly tailoring ML workflows for predicting toxicity should consider class imbalance and rely on simpler classifiers first.
Collapse
Affiliation(s)
- Tia Tate
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
| | - Imran Shah
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
| |
Collapse
|
22
|
Brooks BW, van den Berg S, Dreier DA, LaLone CA, Owen SF, Raimondo S, Zhang X. Towards Precision Ecotoxicology: Leveraging Evolutionary Conservation of Pharmaceutical and Personal Care Product Targets to Understand Adverse Outcomes Across Species and Life Stages. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2024; 43:526-536. [PMID: 37787405 PMCID: PMC11017229 DOI: 10.1002/etc.5754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 05/19/2023] [Accepted: 09/20/2023] [Indexed: 10/04/2023]
Abstract
Translation of environmental science to the practice aims to protect biodiversity and ecosystem services, and our future ability to do so relies on the development of a precision ecotoxicology approach wherein we leverage the genetics and informatics of species to better understand and manage the risks of global pollution. A little over a decade ago, a workshop focusing on the risks of pharmaceuticals and personal care products (PPCPs) in the environment identified a priority research question, "What can be learned about the evolutionary conservation of PPCP targets across species and life stages in the context of potential adverse outcomes and effects?" We review the activities in this area over the past decade, consider prospects of more recent developments, and identify future research needs to develop next-generation approaches for PPCPs and other global chemicals and waste challenges. Environ Toxicol Chem 2024;43:526-536. © 2023 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
Collapse
Affiliation(s)
- Bryan W Brooks
- Department of Environmental Science, Center for Reservoir and Aquatic Systems Research, Institute of Biomedical Studies, Baylor University, Waco, Texas, USA
| | | | - David A Dreier
- Syngenta Crop Protection, Greensboro, North Carolina, USA
| | - Carlie A LaLone
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Duluth, Minnesota
| | - Stewart F Owen
- Global Sustainability, Astra Zeneca, Macclesfield, Cheshire, UK
| | - Sandy Raimondo
- Gulf Ecosystem Measurement and Modeling Division, Office of Research and Development, US Environmental Protection Agency, Gulf Breeze, Florida
| | - Xiaowei Zhang
- School of the Environment, Nanjing University, Nanjing, China
| |
Collapse
|
23
|
Ford LC, Lin HC, Tsai HHD, Zhou YH, Wright FA, Sedykh A, Shah RR, Chiu WA, Rusyn I. Hazard and risk characterization of 56 structurally diverse PFAS using a targeted battery of broad coverage assays using six human cell types. Toxicology 2024; 503:153763. [PMID: 38423244 PMCID: PMC11214689 DOI: 10.1016/j.tox.2024.153763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/13/2024] [Accepted: 02/23/2024] [Indexed: 03/02/2024]
Abstract
Per- and poly-fluoroalkyl substances (PFAS) are extensively used in commerce leading to their prevalence in the environment. Due to their chemical stability, PFAS are considered to be persistent and bioaccumulative; they are frequently detected in both the environment and humans. Because of this, PFAS as a class (composed of hundreds to thousands of chemicals) are contaminants of very high concern. Little information is available for the vast majority of PFAS, and regulatory agencies lack safety data to determine whether exposure limits or restrictions are needed. Cell-based assays are a pragmatic approach to inform decision-makers on potential health hazards; therefore, we hypothesized that a targeted battery of human in vitro assays can be used to determine whether there are structure-bioactivity relationships for PFAS, and to characterize potential risks by comparing bioactivity (points of departure) to exposure estimates. We tested 56 PFAS from 8 structure-based subclasses in concentration response (0.1-100 μM) using six human cell types selected from target organs with suggested adverse effects of PFAS - human induced pluripotent stem cell (iPSC)-derived hepatocytes, neurons, and cardiomyocytes, primary human hepatocytes, endothelial and HepG2 cells. While many compounds were without effect; certain PFAS demonstrated cell-specific activity highlighting the necessity of using a compendium of in vitro models to identify potential hazards. No class-specific groupings were evident except for some chain length- and structure-related trends. In addition, margins of exposure (MOE) were derived using empirical and predicted exposure data. Conservative MOE calculations showed that most tested PFAS had a MOE in the 1-100 range; ∼20% of PFAS had MOE<1, providing tiered priorities for further studies. Overall, we show that a compendium of human cell-based models can be used to derive bioactivity estimates for a range of PFAS, enabling comparisons with human biomonitoring data. Furthermore, we emphasize that establishing structure-bioactivity relationships may be challenging for the tested PFAS.
Collapse
Affiliation(s)
- Lucie C Ford
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX 77843, USA
| | - Hsing-Chieh Lin
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX 77843, USA
| | - Han-Hsuan D Tsai
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX 77843, USA
| | - Yi-Hui Zhou
- Department of Biological Sciences and Statistics, North Carolina State University, Raleigh, NC 27695, USA; Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA
| | - Fred A Wright
- Department of Biological Sciences and Statistics, North Carolina State University, Raleigh, NC 27695, USA; Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA
| | | | | | - Weihsueh A Chiu
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX 77843, USA
| | - Ivan Rusyn
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX 77843, USA.
| |
Collapse
|
24
|
Villeneuve DL, Bush K, Hazemi M, Hoang JX, Le M, Blackwell BR, Stacy E, Flynn KM. Derivation of Transcriptomics-Based Points of Departure for 20 Per- or Polyfluoroalkyl Substances Using a Larval Fathead Minnow (Pimephales promelas) Reduced Transcriptome Assay. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2024. [PMID: 38415853 DOI: 10.1002/etc.5825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 12/18/2023] [Accepted: 01/08/2024] [Indexed: 02/29/2024]
Abstract
Traditional toxicity testing has been unable to keep pace with the introduction of new chemicals into commerce. Consequently, there are limited or no toxicity data for many chemicals to which fish and wildlife may be exposed. Per- and polyfluoroalkyl substances (PFAS) are emblematic of this issue in that ecological hazards of most PFAS remain uncharacterized. The present study employed a high-throughput assay to identify the concentration at which 20 PFAS, with diverse properties, elicited a concerted gene expression response (termed a transcriptomics-based point of departure [tPOD]) in larval fathead minnows (Pimephales promelas; 5-6 days postfertilization) exposed for 24 h. Based on a reduced transcriptome approach that measured whole-body expression of 1832 genes, the median tPOD for the 20 PFAS tested was 10 µM. Longer-chain carboxylic acids (12-13 C-F); an eight-C-F dialcohol, N-alkyl sulfonamide; and telomer sulfonic acid were among the most potent PFAS, eliciting gene expression responses at concentrations <1 µM. With a few exceptions, larval fathead minnow tPODs were concordant with those based on whole-transcriptome response in human cell lines. However, larval fathead minnow tPODs were often greater than those for Daphnia magna exposed to the same PFAS. The tPODs overlapped concentrations at which other sublethal effects have been reported in fish (available for 10 PFAS). Nonetheless, fathead minnow tPODs were orders of magnitude higher than aqueous PFAS concentrations detected in tributaries of the North American Great Lakes, suggesting a substantial margin of safety. Overall, results broadly support the use of a fathead minnow larval transcriptomics assay to derive screening-level potency estimates for use in ecological risk-based prioritization. Environ Toxicol Chem 2024;00:1-16. © 2024 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
Collapse
Affiliation(s)
- Daniel L Villeneuve
- Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, Duluth, Minnesota
| | - Kendra Bush
- Research Participant at Great Lakes Toxicology and Ecology Division, Oak Ridge Institute for Science and Education, Duluth, Minnesota, USA
| | - Monique Hazemi
- Research Participant at Great Lakes Toxicology and Ecology Division, Oak Ridge Institute for Science and Education, Duluth, Minnesota, USA
| | - John X Hoang
- Research Participant at Great Lakes Toxicology and Ecology Division, Oak Ridge Institute for Science and Education, Duluth, Minnesota, USA
| | - Michelle Le
- Research Participant at Great Lakes Toxicology and Ecology Division, Oak Ridge Institute for Science and Education, Duluth, Minnesota, USA
| | - Brett R Blackwell
- Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, Duluth, Minnesota
- Bioscience Division, Biochemistry and Biotechnology Group, Los Alamos National Laboratory, Los Alamos, Minnesota, USA
| | - Emma Stacy
- Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, Duluth, Minnesota
| | - Kevin M Flynn
- Great Lakes Toxicology and Ecology Division, US Environmental Protection Agency, Duluth, Minnesota
| |
Collapse
|
25
|
Tsai HHD, Ford LC, Chen Z, Dickey AN, Wright FA, Rusyn I. Risk-based prioritization of PFAS using phenotypic and transcriptomic data from human induced pluripotent stem cell-derived hepatocytes and cardiomyocytes. ALTEX 2024; 41:363-381. [PMID: 38429992 PMCID: PMC11305846 DOI: 10.14573/altex.2311031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 02/20/2024] [Indexed: 03/03/2024]
Abstract
Per- and polyfluoroalkyl substances (PFAS) are chemicals with important applications; they are persistent in the environment and may pose human health hazards. Regulatory agencies are considering restrictions and bans of PFAS; however, little data exists for informed decisions. Several prioritization strategies were proposed for evaluation of potential hazards of PFAS. Structure-based grouping could expedite the selection of PFAS for testing; still, the hypothesis that structure-effect relationships exist for PFAS requires confirmation. We tested 26 structurally diverse PFAS from 8 groups using human induced pluripotent stem cell-derived hepatocytes and cardiomyocytes, and tested concentration-response effects on cell function and gene expression. Few phenotypic effects were observed in hepatocytes, but negative chronotropy was observed in cardiomyocytes for 8 PFAS. Substance- and cell type-dependent transcriptomic changes were more prominent but lacked substantial group-specific effects. In hepatocytes, we found upregulation of stress-related and extracellular matrix organization pathways, and down-regulation of fat metabolism. In cardiomyocytes, contractility-related pathways were most affected. We derived phenotypic and transcriptomic points of departure and compared them to predicted PFAS exposures. Conservative estimates for bioactivity and exposure were used to derive a bioactivity-to-exposure ratio (BER) for each PFAS; 23 of 26 PFAS had BER > 1. Overall, these data suggest that structure-based PFAS grouping may not be sufficient to predict their biological effects. Testing of individual PFAS may be needed for scientifically-supported decision-making. Our proposed strategy of using two human cell types and considering phenotypic and transcriptomic effects, combined with dose-response analysis and calculation of BER, may be used for PFAS prioritization.
Collapse
Affiliation(s)
- Han-Hsuan D Tsai
- Interdisciplinary Faculty of Toxicology, College Station, TX, USA
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX, USA
| | - Lucie C Ford
- Interdisciplinary Faculty of Toxicology, College Station, TX, USA
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX, USA
| | - Zunwei Chen
- Interdisciplinary Faculty of Toxicology, College Station, TX, USA
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX, USA
- Current address: Program in Molecular and Integrative Physiological Sciences, Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Allison N Dickey
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Fred A Wright
- Interdisciplinary Faculty of Toxicology, College Station, TX, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
- Department of Statistics and Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Ivan Rusyn
- Interdisciplinary Faculty of Toxicology, College Station, TX, USA
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX, USA
| |
Collapse
|
26
|
Schumann P, Rivetti C, Houghton J, Campos B, Hodges G, LaLone C. Combination of computational new approach methodologies for enhancing evidence of biological pathway conservation across species. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:168573. [PMID: 37981146 PMCID: PMC10926110 DOI: 10.1016/j.scitotenv.2023.168573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 11/09/2023] [Accepted: 11/12/2023] [Indexed: 11/21/2023]
Abstract
The ability to predict which chemicals are of concern for environmental safety is dependent, in part, on the ability to extrapolate chemical effects across many species. This work investigated the complementary use of two computational new approach methodologies to support cross-species predictions of chemical susceptibility: the US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool and Unilever's recently developed Genes to Pathways - Species Conservation Analysis (G2P-SCAN) tool. These stand-alone tools rely on existing biological knowledge to help understand chemical susceptibility and biological pathway conservation across species. The utility and challenges of these combined computational approaches were demonstrated using case examples focused on chemical interactions with peroxisome proliferator activated receptor alpha (PPARα), estrogen receptor 1 (ESR1), and gamma-aminobutyric acid type A receptor subunit alpha (GABRA1). Overall, the biological pathway information enhanced the weight of evidence to support cross-species susceptibility predictions. Through comparisons of relevant molecular and functional data gleaned from adverse outcome pathways (AOPs) to mapped biological pathways, it was possible to gain a toxicological context for various chemical-protein interactions. The information gained through this computational approach could ultimately inform chemical safety assessments by enhancing cross-species predictions of chemical susceptibility. It could also help fulfill a core objective of the AOP framework by potentially expanding the biologically plausible taxonomic domain of applicability of relevant AOPs.
Collapse
Affiliation(s)
- Peter Schumann
- Oak Ridge Institute for Science and Education, Duluth, MN, USA
| | - Claudia Rivetti
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedfordshire, UK
| | - Jade Houghton
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedfordshire, UK
| | - Bruno Campos
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedfordshire, UK
| | - Geoff Hodges
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedfordshire, UK
| | - Carlie LaLone
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, MN, USA.
| |
Collapse
|
27
|
Silva MH. Investigating open access new approach methods (NAM) to assess biological points of departure: A case study with 4 neurotoxic pesticides. Curr Res Toxicol 2024; 6:100156. [PMID: 38404712 PMCID: PMC10891343 DOI: 10.1016/j.crtox.2024.100156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 12/28/2023] [Accepted: 02/09/2024] [Indexed: 02/27/2024] Open
Abstract
Open access new approach methods (NAM) in the US EPA ToxCast program and NTP Integrated Chemical Environment (ICE) were used to investigate activities of four neurotoxic pesticides: endosulfan, fipronil, propyzamide and carbaryl. Concordance of in vivo regulatory points of departure (POD) adjusted for interspecies extrapolation (AdjPOD) to modelled human Administered Equivalent Dose (AEDHuman) was assessed using 3-compartment or Adult/Fetal PBTK in vitro to in vivo extrapolation. Model inputs were from Tier 1 (High throughput transcriptomics: HTTr, high throughput phenotypic profiling: HTPP) and Tier 2 (single target: ToxCast) assays. HTTr identified gene expression signatures associated with potential neurotoxicity for endosulfan, propyzamide and carbaryl in non-neuronal MCF-7 and HepaRG cells. The HTPP assay in U-2 OS cells detected potent effects on DNA endpoints for endosulfan and carbaryl, and mitochondria with fipronil (propyzamide was inactive). The most potent ToxCast assays were concordant with specific components of each chemical mode of action (MOA). Predictive adult IVIVE models produced fold differences (FD) < 10 between the AEDHuman and the measured in vivo AdjPOD. The 3-compartment model was concordant (i.e., smallest FD) for endosulfan, fipronil and carbaryl, and PBTK was concordant for propyzamide. The most potent AEDHuman predictions for each chemical showed HTTr, HTPP and ToxCast were mainly concordant with in vivo AdjPODs but assays were less concordant with MOAs. This was likely due to the cell types used for testing and/or lack of metabolic capabilities and pathways available in vivo. The Fetal PBTK model had larger FDs than adult models and was less predictive overall.
Collapse
|
28
|
Rogers JD, Leusch FD, Chambers B, Daniels KD, Everett LJ, Judson R, Maruya K, Mehinto AC, Neale PA, Paul-Friedman K, Thomas R, Snyder SA, Harrill J. High-Throughput Transcriptomics of Water Extracts Detects Reductions in Biological Activity with Water Treatment Processes. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:2027-2037. [PMID: 38235672 PMCID: PMC11003563 DOI: 10.1021/acs.est.3c07525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
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.
Collapse
Affiliation(s)
- Jesse D. Rogers
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
- Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831, USA
| | - Frederic D.L. Leusch
- Australian Rivers Institute, School of Environment and Science, Griffith University, Southport Qld 4222, Australia
| | - Bryant Chambers
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | | | - Logan J. Everett
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Richard Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Keith Maruya
- Southern California Coastal Water Research Project Authority, 3535 Harbor Boulevard, Suite 110, Costa Mesa, CA 92626, USA
| | - Alvine C. Mehinto
- Southern California Coastal Water Research Project Authority, 3535 Harbor Boulevard, Suite 110, Costa Mesa, CA 92626, USA
| | - Peta A. Neale
- Australian Rivers Institute, School of Environment and Science, Griffith University, Southport Qld 4222, Australia
| | - Katie Paul-Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Russell Thomas
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Shane A. Snyder
- Nanyang Environment & Water Research Institute (NEWRI), Nanyang Technological University, 1 Cleantech Loop, CleanTech One, #06-08, 637141, Singapore
| | - Joshua Harrill
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| |
Collapse
|
29
|
Harrill JA, Everett LJ, Haggard DE, Bundy JL, Willis CM, Shah I, Friedman KP, Basili D, Middleton A, Judson RS. Exploring the effects of experimental parameters and data modeling approaches on in vitro transcriptomic point-of-departure estimates. Toxicology 2024; 501:153694. [PMID: 38043774 DOI: 10.1016/j.tox.2023.153694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/24/2023] [Accepted: 11/29/2023] [Indexed: 12/05/2023]
Abstract
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.
Collapse
Affiliation(s)
- Joshua A Harrill
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Logan J Everett
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Derik E Haggard
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA; Oak Ridge Institute for Science and Education (ORISE), USA
| | - Joseph L Bundy
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Clinton M Willis
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA; Oak Ridge Associated Universities (ORAU), USA
| | - Imran Shah
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Katie Paul Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Danilo Basili
- Unilever Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Alistair Middleton
- Unilever Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Richard S Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA.
| |
Collapse
|
30
|
Chen P, Li Y, Long Q, Zuo T, Zhang Z, Guo J, Xu D, Li K, Liu S, Li S, Yin J, Chang L, Kukic P, Liddell M, Tulum L, Carmichael P, Peng S, Li J, Zhang Q, Xu P. The phosphoproteome is a first responder in tiered cellular adaptation to chemical stress followed by proteomics and transcriptomics alteration. CHEMOSPHERE 2023; 344:140329. [PMID: 37783352 DOI: 10.1016/j.chemosphere.2023.140329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 09/20/2023] [Accepted: 09/28/2023] [Indexed: 10/04/2023]
Abstract
Next-generation risk assessment (NGRA) for environmental chemicals involves a weight of evidence (WoE) framework integrating a suite of new approach methodologies (NAMs) based on points of departure (PoD) obtained from in vitro assays. Among existing NAMs, the omic-based technologies are of particular importance based on the premise that any apical endpoint change indicative of impaired health must be underpinned by some alterations at the omics level, such as transcriptome, proteome, metabolome, epigenome and genome. Transcriptomic assay plays a leading role in providing relatively conservative PoDs compared with apical endpoints. However, it is unclear whether and how parameters measured with other omics techniques predict the cellular response to chemical perturbations, especially at exposure levels below the transcriptomically defined PoD. Multi-omics coverage may provide additional sensitive or confirmative biomarkers to complement and reduce the uncertainty in safety decisions made using targeted and transcriptomics assays. In the present study, we conducted multi-omics studies of transcriptomics, proteomics and phosphoproteomics on two prototype compounds, coumarin and 2,4-dichlorophenoxyacetic acid (2,4-D), with multiple chemical concentrations and time points, to understand the sensitivity of the three omics techniques in response to chemically-induced changes in HepG2. We demonstrated that, phosphoproteomics alterations occur not only earlier in time, but also more sensitive to lower concentrations than proteomics and transcriptomics when the HepG2 cells were exposed to various chemical treatments. The phosphoproteomics changes appear to approach maximum when the transcriptomics alterations begin to initiate. Therefore, it is proximal to the very early effects induced by chemical exposure. We concluded that phosphoproteomics can be utilized to provide a more complete coverage of chemical-induced cellular alteration and supplement transcriptomics-based health safety decision making.
Collapse
Affiliation(s)
- Peiru Chen
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China; Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, 071002, China
| | - Yuan Li
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China; Department of Biomedicine, Medical College, Guizhou University, Guiyang, 550025, China; Guizhou Provincial People's Hospital, Affiliated Hospital of Guizhou University, Guiyang, 550002, China
| | - Qi Long
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China; School of Basic Medicine, Anhui Medical University, Hefei, 230032, China
| | - Tao Zuo
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China
| | - Zhenpeng Zhang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China
| | - Jiabin Guo
- Evaluation and Research Centre for Toxicology, Institute of Disease Control and Prevention, Academy of Military Medical Sciences, Beijing, 100071, China
| | - Danyang Xu
- Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Kaixuan Li
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China; Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, 071002, China
| | - Shu Liu
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China
| | - Suzhen Li
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China; School of Basic Medicine, Anhui Medical University, Hefei, 230032, China
| | - Jian Yin
- Evaluation and Research Centre for Toxicology, Institute of Disease Control and Prevention, Academy of Military Medical Sciences, Beijing, 100071, China
| | - Lei Chang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China
| | - Predrag Kukic
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire, MK44 1LQ, UK
| | - Mark Liddell
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire, MK44 1LQ, UK
| | - Liz Tulum
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire, MK44 1LQ, UK
| | - Paul Carmichael
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire, MK44 1LQ, UK
| | - Shuangqing Peng
- Evaluation and Research Centre for Toxicology, Institute of Disease Control and Prevention, Academy of Military Medical Sciences, Beijing, 100071, China
| | - Jin Li
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire, MK44 1LQ, UK.
| | - Qiang Zhang
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, USA, GA, 30322.
| | - Ping Xu
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China; Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, 071002, China; Department of Biomedicine, Medical College, Guizhou University, Guiyang, 550025, China; School of Basic Medicine, Anhui Medical University, Hefei, 230032, China; Program of Environmental Toxicology, School of Public Health, China Medical University, Shenyang, 110122, China.
| |
Collapse
|
31
|
Barutcu AR, Black MB, Nong A. Mining toxicogenomic data for dose-responsive pathways: implications in advancing next-generation risk assessment. FRONTIERS IN TOXICOLOGY 2023; 5:1272364. [PMID: 38046401 PMCID: PMC10691261 DOI: 10.3389/ftox.2023.1272364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 11/02/2023] [Indexed: 12/05/2023] Open
Abstract
Introduction: While targeted investigation of key toxicity pathways has been instrumental for biomarker discovery, unbiased and holistic analysis of transcriptomic data provides a complementary systems-level perspective. However, in a systematic context, this approach has yet to receive comprehensive and methodical implementation. Methods: Here, we took an integrated bioinformatic approach by re-analyzing publicly available MCF7 cell TempO-seq data for 44 ToxCast chemicals using an alternative pipeline to demonstrate the power of this approach. The original study has focused on analyzing the gene signature approach and comparing them to in vitro biological pathway altering concentrations determined from ToxCast HTS assays. Our workflow, in comparison, involves sequential differential expression, gene set enrichment, benchmark dose modeling, and identification of commonly perturbed pathways by network visualization. Results: Using this approach, we identified dose-responsive molecular changes, biological pathways, and points of departure in an untargeted manner. Critically, benchmark dose modeling based on pathways recapitulated points of departure for apical endpoints, while also revealing additional perturbed mechanisms missed by single endpoint analyses. Discussion: This systems-toxicology approach provides multifaceted insights into the complex effects of chemical exposures. Our work highlights the importance of unbiased data-driven techniques, alongside targeted methods, for comprehensively evaluating molecular initiating events, dose-response relationships, and toxicity pathways. Overall, integrating omics assays with robust bioinformatics holds promise for improving chemical risk assessment and advancing new approach methodologies (NAMs).
Collapse
|
32
|
Wang X, Rowan-Carroll A, Meier MJ, Williams A, Yauk CL, Hales BF, Robaire B. Toxicological Mechanisms and Potencies of Organophosphate Esters in KGN Human Ovarian Granulosa Cells as Revealed by High-throughput Transcriptomics. Toxicol Sci 2023; 197:kfad114. [PMID: 37941476 PMCID: PMC10823774 DOI: 10.1093/toxsci/kfad114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023] Open
Abstract
Despite the growing number of studies reporting potential risks associated with exposure to organophosphate esters (OPEs), their molecular mechanisms of action remain poorly defined. We used the high-throughput TempO-Seq™ platform to investigate the effects of frequently detected OPEs on the expression of ∼3000 environmentally responsive genes in KGN human ovarian granulosa cells. Cells were exposed for 48 h to one of five OPEs (0.1 to 50 μM): tris(methylphenyl) phosphate (TMPP), isopropylated triphenyl phosphate (IPPP), tert-butylphenyl diphenyl phosphate (BPDP), triphenyl phosphate (TPHP), or tris(2-butoxyethyl) phosphate (TBOEP). The sequencing data indicate that four OPEs induced transcriptional changes, whereas TBOEP had no effect within the concentration range tested. Multiple pathway databases were used to predict alterations in biological processes based on differentially expressed genes. At lower concentrations, inhibition of the cholesterol biosynthetic pathway was the predominant effect of OPEs; this was likely a consequence of intracellular cholesterol accumulation. At higher concentrations, BPDP and TPHP had distinct effects, primarily affecting pathways involved in cell cycle progression and other stress responses. Benchmark concentration (BMC) modelling revealed that BPDP had the lowest transcriptomic point of departure. However, in vitro to in vivo extrapolation modeling indicated that TMPP was bioactive at lower concentrations than the other OPEs. We conclude that these new approach methodologies provide information on the mechanism(s) underlying the effects of data-poor compounds and assist in the derivation of protective points of departure for use in chemical read-across and decision-making.
Collapse
Affiliation(s)
- Xiaotong Wang
- Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec H3G 1Y6, Canada
| | - Andrea Rowan-Carroll
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario K2K 0K9, Canada
| | - Matthew J Meier
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario K2K 0K9, Canada
| | - Andrew Williams
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario K2K 0K9, Canada
| | - Carole L Yauk
- Department of Biology, University of Ottawa, Ottawa, Ontario K1N 9A7, Canada
| | - Barbara F Hales
- Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec H3G 1Y6, Canada
| | - Bernard Robaire
- Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec H3G 1Y6, Canada
- Department of Obstetrics and Gynecology, McGill University, Montréal, Québec H3G 1Y6, Canada
| |
Collapse
|
33
|
Sakolish C, Moyer HL, Tsai HHD, Ford LC, Dickey AN, Wright FA, Han G, Bajaj P, Baltazar MT, Carmichael PL, Stanko JP, Ferguson SS, Rusyn I. Analysis of reproducibility and robustness of a renal proximal tubule microphysiological system OrganoPlate 3-lane 40 for in vitro studies of drug transport and toxicity. Toxicol Sci 2023; 196:52-70. [PMID: 37555834 PMCID: PMC10613961 DOI: 10.1093/toxsci/kfad080] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2023] Open
Abstract
Microphysiological systems are an emerging area of in vitro drug development, and their independent evaluation is important for wide adoption and use. The primary goal of this study was to test reproducibility and robustness of a renal proximal tubule microphysiological system, OrganoPlate 3-lane 40, as an in vitro model for drug transport and toxicity studies. This microfluidic model was compared with static multiwell cultures and tested using several human renal proximal tubule epithelial cell (RPTEC) types. The model was characterized in terms of the functional transport for various tubule-specific proteins, epithelial permeability of small molecules (cisplatin, tenofovir, and perfluorooctanoic acid) versus large molecules (fluorescent dextrans, 60-150 kDa), and gene expression response to a nephrotoxic xenobiotic. The advantages offered by OrganoPlate 3-lane 40 as compared with multiwell cultures are the presence of media flow, albeit intermittent, and increased throughput compared with other microfluidic models. However, OrganoPlate 3-lane 40 model appeared to offer only limited (eg, MRP-mediated transport) advantages in terms of either gene expression or functional transport when compared with the multiwell plate culture conditions. Although OrganoPlate 3-lane 40 can be used to study cellular uptake and direct toxic effects of small molecules, it may have limited utility for drug transport studies. Overall, this study offers refined experimental protocols and comprehensive comparative data on the function of RPETCs in traditional multiwell culture and microfluidic OrganoPlate 3-lane 40, information that will be invaluable for the prospective end-users of in vitro models of the human proximal tubule.
Collapse
Affiliation(s)
- Courtney Sakolish
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, USA
| | - Haley L Moyer
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, USA
| | - Han-Hsuan D Tsai
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, USA
| | - Lucie C Ford
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, USA
| | - Allison N Dickey
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Fred A Wright
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695, USA
- Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, USA
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Gang Han
- Department of Epidemiology and Biostatistics, Texas A&M University, College Station, Texas 77843, USA
| | - Piyush Bajaj
- Global Investigative Toxicology, Preclinical Safety, Sanofi, Cambridge, Massachusetts 02141, USA
| | - Maria T Baltazar
- Safety & Environmental Assurance Centre (SEAC), Unilever, Bedfordshire MK44 1LQ, UK
| | - Paul L Carmichael
- Safety & Environmental Assurance Centre (SEAC), Unilever, Bedfordshire MK44 1LQ, UK
| | - Jason P Stanko
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709, USA
| | - Stephen S Ferguson
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709, USA
| | - Ivan Rusyn
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, USA
| |
Collapse
|
34
|
Garnovskaya M, Feshuk M, Stewart W, Friedman KP, Thomas RS, Deisenroth C. Evaluation of a high-throughput H295R homogenous time resolved fluorescence assay for androgen and estrogen steroidogenesis screening. Toxicol In Vitro 2023; 92:105659. [PMID: 37557933 PMCID: PMC10903741 DOI: 10.1016/j.tiv.2023.105659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 07/06/2023] [Accepted: 08/06/2023] [Indexed: 08/11/2023]
Abstract
The H295R test guideline assay evaluates the effect of test substances on synthesis of 17β-estradiol (E2) and testosterone (T). The objective of this study was to leverage commercial immunoassay technology to develop a more efficient H295R assay to measure E2 and T levels in 384-well format. The resulting Homogenous Time Resolved Fluorescence assay platform (H295R-HTRF) was evaluated against a training set of 36 chemicals derived from the OECD inter-laboratory validation study, EPA guideline 890.1200 aromatase assay, and azole fungicides active in the HT-H295R assay. Quality control performance criteria were met for all conditions except E2 synthesis inhibition where low basal hormone synthesis was observed. Five proficiency chemicals were active for both the E2 and T endpoints, consistent with guideline classifications. Of the nine OECD core reference chemicals, 9/9 were concordant with outcomes for E2 and 7/9 for T. Likewise, 9/13 and 11/13 OECD supplemental chemicals were concordant with anticipated effects for E2 and T, respectively. Of the 10 azole fungicides screened, 7/10 for E2 and 8/10 for T exhibited concordant outcomes for inhibition. Generally, all active chemicals in the training set demonstrated equivalent or greater potency in the H295R-HTRF assay, supporting the sensitivity of the platform. The adaptation of HTRF technology to the H295R model provides an efficient way to evaluate E2 and T modulators in accordance with guideline specifications.
Collapse
Affiliation(s)
- Maria Garnovskaya
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, United States
| | - Madison Feshuk
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, United States
| | - Wendy Stewart
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, United States
| | - Katie Paul Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, United States
| | - Russell S Thomas
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, United States
| | - Chad Deisenroth
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, United States.
| |
Collapse
|
35
|
Feshuk M, Kolaczkowski L, Dunham K, Davidson-Fritz SE, Carstens KE, Brown J, Judson RS, Paul Friedman K. The ToxCast pipeline: updates to curve-fitting approaches and database structure. FRONTIERS IN TOXICOLOGY 2023; 5:1275980. [PMID: 37808181 PMCID: PMC10552852 DOI: 10.3389/ftox.2023.1275980] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 09/08/2023] [Indexed: 10/10/2023] Open
Abstract
Introduction: The US Environmental Protection Agency Toxicity Forecaster (ToxCast) program makes in vitro medium- and high-throughput screening assay data publicly available for prioritization and hazard characterization of thousands of chemicals. The assays employ a variety of technologies to evaluate the effects of chemical exposure on diverse biological targets, from distinct proteins to more complex cellular processes like mitochondrial toxicity, nuclear receptor signaling, immune responses, and developmental toxicity. The ToxCast data pipeline (tcpl) is an open-source R package that stores, manages, curve-fits, and visualizes ToxCast data and populates the linked MySQL Database, invitrodb. Methods: Herein we describe major updates to tcpl and invitrodb to accommodate a new curve-fitting approach. The original tcpl curve-fitting models (constant, Hill, and gain-loss models) have been expanded to include Polynomial 1 (Linear), Polynomial 2 (Quadratic), Power, Exponential 2, Exponential 3, Exponential 4, and Exponential 5 based on BMDExpress and encoded by the R package dependency, tcplfit2. Inclusion of these models impacted invitrodb (beta version v4.0) and tcpl v3 in several ways: (1) long-format storage of generic modeling parameters to permit additional curve-fitting models; (2) updated logic for winning model selection; (3) continuous hit calling logic; and (4) removal of redundant endpoints as a result of bidirectional fitting. Results and discussion: Overall, the hit call and potency estimates were largely consistent between invitrodb v3.5 and 4.0. Tcpl and invitrodb provide a standard for consistent and reproducible curve-fitting and data management for diverse, targeted in vitro assay data with readily available documentation, thus enabling sharing and use of these data in myriad toxicology applications. The software and database updates described herein promote comparability across multiple tiers of data within the US Environmental Protection Agency CompTox Blueprint.
Collapse
Affiliation(s)
- M. Feshuk
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| | - L. Kolaczkowski
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
- National Student Services Contractor, Oak Ridge Associated Universities, Oak Ridge, TN, United States
| | - K. Dunham
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
- National Student Services Contractor, Oak Ridge Associated Universities, Oak Ridge, TN, United States
| | - S. E. Davidson-Fritz
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| | - K. E. Carstens
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| | - J. Brown
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| | - R. S. Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| | - K. Paul Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| |
Collapse
|
36
|
Nelson GM, Carswell GK, Swartz CD, Recio L, Yauk CL, Chorley BN. Early microRNA responses in rodent liver mediated by furan exposure establish dose thresholds for later adverse outcomes. Toxicol Lett 2023; 384:105-114. [PMID: 37517673 PMCID: PMC10530563 DOI: 10.1016/j.toxlet.2023.07.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 07/27/2023] [Indexed: 08/01/2023]
Abstract
To reduce reliance on long-term in vivo studies, short-term data linking early molecular-based measurements to later adverse health effects is needed. Although transcriptional-based benchmark dose (BMDT) modeling has been used to estimate potencies and stratify chemicals based on potential to induce later-life effects, dose-responsive epigenetic alterations have not been routinely considered. Here, we evaluated the utility of microRNA (miRNA) profiling in mouse liver and blood, as well as in mouse primary hepatocytes in vitro, to indicate mechanisms of liver perturbation due to short-term exposure of the known rodent liver hepatotoxicant and carcinogen, furan. Benchmark dose modeling of miRNA measurements (BMDmiR) were compared to the referent transcriptional (BMDT) and apical (BMDA) estimates. These analyses indicate a robust dose response for 34 miRNAs to furan and involvement of p53-linked pathways in furan-mediated hepatotoxicity, supporting mRNA and apical measurements. Liver-sourced miRNAs were also altered in the blood and primary hepatocytes. Overall, these results indicate mechanistic involvement of miRNA in furan carcinogenicity and provide evidence of their potential utility as accessible biomarkers of exposure and disease.
Collapse
Affiliation(s)
- Gail M Nelson
- US Environmental Protection Agency, Research Triangle Park, NC 27709, USA
| | - Gleta K Carswell
- US Environmental Protection Agency, Research Triangle Park, NC 27709, USA
| | - Carol D Swartz
- Inotiv Co., 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA
| | - Leslie Recio
- ScitoVation, 100 Capitola Drive Suite 106, Durham, NC 27713, USA
| | - Carole L Yauk
- Dept. Of Biology, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Brian N Chorley
- US Environmental Protection Agency, Research Triangle Park, NC 27709, USA.
| |
Collapse
|
37
|
Gant TW, Auerbach SS, Von Bergen M, Bouhifd M, Botham PA, Caiment F, Currie RA, Harrill J, Johnson K, Li D, Rouquie D, van Ravenzwaay B, Sistare F, Tralau T, Viant MR, van de Laan JW, Yauk C. Applying genomics in regulatory toxicology: a report of the ECETOC workshop on omics threshold on non-adversity. Arch Toxicol 2023; 97:2291-2302. [PMID: 37296313 PMCID: PMC10322787 DOI: 10.1007/s00204-023-03522-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 05/11/2023] [Indexed: 06/12/2023]
Abstract
In a joint effort involving scientists from academia, industry and regulatory agencies, ECETOC's activities in Omics have led to conceptual proposals for: (1) A framework that assures data quality for reporting and inclusion of Omics data in regulatory assessments; and (2) an approach to robustly quantify these data, prior to interpretation for regulatory use. In continuation of these activities this workshop explored and identified areas of need to facilitate robust interpretation of such data in the context of deriving points of departure (POD) for risk assessment and determining an adverse change from normal variation. ECETOC was amongst the first to systematically explore the application of Omics methods, now incorporated into the group of methods known as New Approach Methodologies (NAMs), to regulatory toxicology. This support has been in the form of both projects (primarily with CEFIC/LRI) and workshops. Outputs have led to projects included in the workplan of the Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) group of the Organisation for Economic Co-operation and Development (OECD) and to the drafting of OECD Guidance Documents for Omics data reporting, with potentially more to follow on data transformation and interpretation. The current workshop was the last in a series of technical methods development workshops, with a sub-focus on the derivation of a POD from Omics data. Workshop presentations demonstrated that Omics data developed within robust frameworks for both scientific data generation and analysis can be used to derive a POD. The issue of noise in the data was discussed as an important consideration for identifying robust Omics changes and deriving a POD. Such variability or "noise" can comprise technical or biological variation within a dataset and should clearly be distinguished from homeostatic responses. Adverse outcome pathways (AOPs) were considered a useful framework on which to assemble Omics methods, and a number of case examples were presented in illustration of this point. What is apparent is that high dimension data will always be subject to varying processing pipelines and hence interpretation, depending on the context they are used in. Yet, they can provide valuable input for regulatory toxicology, with the pre-condition being robust methods for the collection and processing of data together with a comprehensive description how the data were interpreted, and conclusions reached.
Collapse
Affiliation(s)
- Timothy W Gant
- United Kingdom Health Security Agency, Harwell Science Campus, Didcot, Oxfordshire, United Kingdom.
- Imperial College London School of Public Health, London, United Kingdom.
| | - Scott S Auerbach
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, RTP, Durham, NC, USA
| | - Martin Von Bergen
- Department for Molecular Systems Biology, Helmholtz Centre for Environmental Research, Leipzig, Germany
| | | | | | - Florian Caiment
- Department of Toxicogenomics, Maastricht University, Maastricht, The Netherlands
| | | | - Joshua Harrill
- Cellular and Molecular Toxicologist, Center for Computational Toxicology and Exposure (CCTE), U.S. Environmental Protection Agency, Durham, NC, USA
| | - Kamin Johnson
- Predictive Safety Center, Corteva Agriscience, Indianapolis, IN, USA
| | - Dongying Li
- National Center for Toxicological Research, U.S. Food and Drug Administration (FDA), 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - David Rouquie
- Bayer SAS, Bayer Crop Science, 355 Rue Dostoïevski, CS 90153, 06906, Valbonne Sophia-Antipolis, France
| | | | | | - Tewes Tralau
- Department of Pesticides Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Mark R Viant
- School of Biosciences, University of Birmingham, Birmingham, UK
| | | | - Carole Yauk
- Department of Biology, University of Ottawa, Ottawa, Canada
| |
Collapse
|
38
|
Tsai HHD, House JS, Wright FA, Chiu WA, Rusyn I. A tiered testing strategy based on in vitro phenotypic and transcriptomic data for selecting representative petroleum UVCBs for toxicity evaluation in vivo. Toxicol Sci 2023; 193:219-233. [PMID: 37079747 PMCID: PMC10230285 DOI: 10.1093/toxsci/kfad041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/22/2023] Open
Abstract
Hazard evaluation of substances of "unknown or variable composition, complex reaction products and biological materials" (UVCBs) remains a major challenge in regulatory science because their chemical composition is difficult to ascertain. Petroleum substances are representative UVCBs and human cell-based data have been previously used to substantiate their groupings for regulatory submissions. We hypothesized that a combination of phenotypic and transcriptomic data could be integrated to make decisions as to selection of group-representative worst-case petroleum UVCBs for subsequent toxicity evaluation in vivo. We used data obtained from 141 substances from 16 manufacturing categories previously tested in 6 human cell types (induced pluripotent stem cell [iPSC]-derived hepatocytes, cardiomyocytes, neurons, and endothelial cells, and MCF7 and A375 cell lines). Benchmark doses for gene-substance combinations were calculated, and both transcriptomic and phenotype-derived points of departure (PODs) were obtained. Correlation analysis and machine learning were used to assess associations between phenotypic and transcriptional PODs and to determine the most informative cell types and assays, thus representing a cost-effective integrated testing strategy. We found that 2 cell types-iPSC-derived-hepatocytes and -cardiomyocytes-contributed the most informative and protective PODs and may be used to inform selection of representative petroleum UVCBs for further toxicity evaluation in vivo. Overall, although the use of new approach methodologies to prioritize UVCBs has not been widely adopted, our study proposes a tiered testing strategy based on iPSC-derived hepatocytes and cardiomyocytes to inform selection of representative worst-case petroleum UVCBs from each manufacturing category for further toxicity evaluation in vivo.
Collapse
Affiliation(s)
- Han-Hsuan Doris Tsai
- Interdisciplinary Faculty of Toxicology, College Station, Texas 77843, USA
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, USA
| | - John S House
- National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709, USA
| | - Fred A Wright
- Interdisciplinary Faculty of Toxicology, College Station, Texas 77843, USA
- Department of Statistics and Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27603, USA
- Department of Biological Sciences and Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27603, USA
| | - Weihsueh A Chiu
- Interdisciplinary Faculty of Toxicology, College Station, Texas 77843, USA
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, USA
| | - Ivan Rusyn
- Interdisciplinary Faculty of Toxicology, College Station, Texas 77843, USA
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, USA
| |
Collapse
|
39
|
Reardon AJF, Farmahin R, Williams A, Meier MJ, Addicks GC, Yauk CL, Matteo G, Atlas E, Harrill J, Everett LJ, Shah I, Judson R, Ramaiahgari S, Ferguson SS, Barton-Maclaren TS. From vision toward best practices: Evaluating in vitro transcriptomic points of departure for application in risk assessment using a uniform workflow. FRONTIERS IN TOXICOLOGY 2023; 5:1194895. [PMID: 37288009 PMCID: PMC10242042 DOI: 10.3389/ftox.2023.1194895] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 05/03/2023] [Indexed: 06/09/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Anthony J. F. Reardon
- Existing Substances Risk Assessment Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
| | - Reza Farmahin
- Existing Substances Risk Assessment Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
| | - Andrew Williams
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
| | - Matthew J. Meier
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
| | - Gregory C. Addicks
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
| | - Carole L. Yauk
- Department of Biology, University of Ottawa, Ottawa, ON, Canada
| | - Geronimo Matteo
- Department of Biology, University of Ottawa, Ottawa, ON, Canada
| | - Ella Atlas
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
- Department of Biochemistry, University of Ottawa, Ottawa, ON, Canada
| | - Joshua Harrill
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Durham, NC, United States
| | - Logan J. Everett
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Durham, NC, United States
| | - Imran Shah
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Durham, NC, United States
| | - Richard Judson
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Durham, NC, United States
| | - Sreenivasa Ramaiahgari
- Division of Translational Toxicology, Mechanistic Toxicology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, NC, United States
| | - Stephen S. Ferguson
- Division of Translational Toxicology, Mechanistic Toxicology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, NC, United States
| | - Tara S. Barton-Maclaren
- Existing Substances Risk Assessment Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
| |
Collapse
|
40
|
Pruteanu LL, Bender A. Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice. ACS Med Chem Lett 2023; 14:386-395. [PMID: 37077392 PMCID: PMC10107910 DOI: 10.1021/acsmedchemlett.3c00015] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/10/2023] [Indexed: 04/21/2023] Open
Abstract
Gene expression and cell morphology data are high-dimensional biological readouts of much recent interest for drug discovery. They are able to describe biological systems in different states (e.g., healthy and diseased), as well as biological systems before and after compound treatment, and they are hence useful for matching both spaces (e.g., for drug repurposing) as well as for characterizing compounds with respect to efficacy and safety endpoints. This Microperspective describes recent advances in this direction with a focus on applied drug discovery and drug repurposing, as well as outlining what else is needed to advance further, with a particular focus on better understanding the applicability domain of readouts and their relevance for decision making, which is currently often still unclear.
Collapse
Affiliation(s)
- Lavinia-Lorena Pruteanu
- Department
of Chemistry and Biology, North University
Center at Baia Mare, Technical University of Cluj-Napoca, Victoriei 76, 430122 Baia Mare, Romania
- Research
Center for Functional Genomics, Biomedicine, and Translational Medicine, “Iuliu Haţieganu” University
of Medicine and Pharmacy, 400337 Cluj-Napoca, Romania
| | - Andreas Bender
- Centre
for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| |
Collapse
|
41
|
Silva M, Kwok RKH. Use of computational toxicology models to predict toxicological points of departure: A case study with triazine herbicides. Birth Defects Res 2023; 115:525-544. [PMID: 36584090 DOI: 10.1002/bdr2.2144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 12/31/2022]
Abstract
BACKGROUND Atrazine simazine and propazine, widely used triazine herbicides on food crops and in residential areas, disrupt the neuroendocrine system raising human health concerns. USEPA developed a PBPK model based on triazine common Mode of Action (MOA)-suppression of luteinizing hormone surge in female rats-to generate human regulatory points of departure (POD: mg/kg/day). We compared triazine Human Administered Equivalent Dose (AEDHuman mg/kg/day) predictions from open access computational tools to the PBPK PODs to assess concordance. METHODS Computational tools were the following: ToxCast/Tox21 in vitro assays; Toxicogenomic databases to assess concordance with ToxCast/Tox21 targets; integrated chemical environment (ICE) models with ToxCast/Tox21 inputs to predict AEDHuman PODs and population-based age-refined high throughput toxicokinetics (HTTK-Pop) to compare to age-related PBPK PODs. RESULTS ToxCast/Tox21 assays identified critical targets in the triazine common MOA and gene databases; ICE AEDHuman predictions were mainly concordant with the USEPA PBPK PODs quantitatively. Low fold-differences between PBPK POD and ICE AEDHuman predictions indicated that the ICE models are health-protective. HTTK-Pop age-refinements were within 10-fold of the USEPA PBPK PODs. CONCLUSIONS CompTox tools were used to identify assay targets in the MOA and identify potential molecular initiating targets in the adverse outcome pathway for potential use in risk assessment.
Collapse
Affiliation(s)
- Marilyn Silva
- Retired from the California Environmental Protection Agency, Sacramento, California, USA
| | | |
Collapse
|
42
|
Chauhan V, Yu J, Vuong N, Haber LT, Williams A, Auerbach SS, Beaton D, Wang Y, Stainforth R, Wilkins RC, Azzam EI, Richardson RB, Khan MGM, Jadhav A, Burtt JJ, Leblanc J, Randhawa K, Tollefsen KE, Yauk CL. Considerations for application of benchmark dose modeling in radiation research: workshop highlights. Int J Radiat Biol 2023; 99:1320-1331. [PMID: 36881459 DOI: 10.1080/09553002.2023.2181998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 01/18/2023] [Accepted: 02/06/2023] [Indexed: 02/24/2023]
Abstract
BACKGROUND Exposure to different forms of ionizing radiation occurs in diverse occupational, medical, and environmental settings. Improving the accuracy of the estimated health risks associated with exposure is therefore, essential for protecting the public, particularly as it relates to chronic low dose exposures. A key aspect to understanding health risks is precise and accurate modeling of the dose-response relationship. Toward this vision, benchmark dose (BMD) modeling may be a suitable approach for consideration in the radiation field. BMD modeling is already extensively used for chemical hazard assessments and is considered statistically preferable to identifying low and no observed adverse effects levels. BMD modeling involves fitting mathematical models to dose-response data for a relevant biological endpoint and identifying a point of departure (the BMD, or its lower bound). Recent examples in chemical toxicology show that when applied to molecular endpoints (e.g. genotoxic and transcriptional endpoints), BMDs correlate to points of departure for more apical endpoints such as phenotypic changes (e.g. adverse effects) of interest to regulatory decisions. This use of BMD modeling may be valuable to explore in the radiation field, specifically in combination with adverse outcome pathways, and may facilitate better interpretation of relevant in vivo and in vitro dose-response data. To advance this application, a workshop was organized on June 3rd, 2022, in Ottawa, Ontario that brought together BMD experts in chemical toxicology and the radiation scientific community of researchers, regulators, and policy-makers. The workshop's objective was to introduce radiation scientists to BMD modeling and its practical application using case examples from the chemical toxicity field and demonstrate the BMDExpress software using a radiation dataset. Discussions focused on the BMD approach, the importance of experimental design, regulatory applications, its use in supporting the development of adverse outcome pathways, and specific radiation-relevant examples. CONCLUSIONS Although further deliberations are needed to advance the use of BMD modeling in the radiation field, these initial discussions and partnerships highlight some key steps to guide future undertakings related to new experimental work.
Collapse
Affiliation(s)
- Vinita Chauhan
- Consumer and Clinical Radiation Protection Bureau, Health Canada, Ottawa, Canada
| | - Jihang Yu
- Radiobiology and Health Branch, Canadian Nuclear Laboratories, Chalk River, Canada
| | - Ngoc Vuong
- Radiation Protection Bureau, Health Canada, Ottawa, Canada
| | - Lynne T Haber
- Department of Environmental and Public Health Sciences, Risk Science Center, University of Cincinnati, Cincinnati, OH, USA
| | - Andrew Williams
- Environmental Health Science and Research Bureau, Health Science and Research Bureau, Health Canada, Ottawa, Canada
| | - Scott S Auerbach
- National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - Danielle Beaton
- Radiobiology and Health Branch, Canadian Nuclear Laboratories, Chalk River, Canada
| | - Yi Wang
- Radiobiology and Health Branch, Canadian Nuclear Laboratories, Chalk River, Canada
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, Canada
| | | | - Ruth C Wilkins
- Consumer and Clinical Radiation Protection Bureau, Health Canada, Ottawa, Canada
| | - Edouard I Azzam
- Radiobiology and Health Branch, Canadian Nuclear Laboratories, Chalk River, Canada
- Department of Radiology, New Jersey Medical School, Rutgers University, Newark, NJ, USA
| | - Richard B Richardson
- Radiobiology and Health Branch, Canadian Nuclear Laboratories, Chalk River, Canada
- Medical Physics Unit, McGill University, Montreal, QC, Canada
| | | | - Ashok Jadhav
- Radiobiology and Health Branch, Canadian Nuclear Laboratories, Chalk River, Canada
| | - Julie J Burtt
- Directorate of Environmental and Radiation Protection and Assessment, Canadian Nuclear Safety Commission, Ottawa, Canada
| | - Julie Leblanc
- Directorate of Environmental and Radiation Protection and Assessment, Canadian Nuclear Safety Commission, Ottawa, Canada
| | - Kristi Randhawa
- Directorate of Environmental and Radiation Protection and Assessment, Canadian Nuclear Safety Commission, Ottawa, Canada
| | - Knut Erik Tollefsen
- Norwegian Institute for Water Research (NIVA), Oslo, Norway
- Norwegian University of Life Sciences (NMBU), Ås, Norway
- Centre for Environmental Radioactivity, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Carole L Yauk
- Department of Biology, University of Ottawa, Ottawa, Canada
| |
Collapse
|
43
|
Wu S, Ellison C, Naciff J, Karb M, Obringer C, Yan G, Shan Y, Smith A, Wang X, Daston GP. Structure-activity relationship read-across and transcriptomics for branched carboxylic acids. Toxicol Sci 2023; 191:343-356. [PMID: 36583546 DOI: 10.1093/toxsci/kfac139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
The purpose of this study was to use chemical similarity evaluations, transcriptional profiling, in vitro toxicokinetic data, and physiologically based pharmacokinetic (PBPK) models to support read-across for a series of branched carboxylic acids using valproic acid (VPA), a known developmental toxicant, as a comparator. The chemicals included 2-propylpentanoic acid (VPA), 2-ethylbutanoic acid, 2-ethylhexanoic acid (EHA), 2-methylnonanoic acid, 2-hexyldecanoic acid, 2-propylnonanoic acid (PNA), dipentyl acetic acid or 2-pentylheptanoic acid, octanoic acid (a straight chain alkyl acid), and 2-ethylhexanol. Transcriptomics was evaluated in 4 cell types (A549, HepG2, MCF7, and iCell cardiomyocytes) 6 h after exposure to 3 concentrations of the compounds, using the L1000 platform. The transcriptional profiling data indicate that 2- or 3-carbon alkyl substituents at the alpha position of the carboxylic acid (EHA and PNA) elicit a transcriptional profile similar to the one elicited by VPA. The transcriptional profile is different for the other chemicals tested, which provides support for limiting read-across from VPA to much shorter and longer acids. Molecular docking models for histone deacetylases, the putative target of VPA, provide a possible mechanistic explanation for the activity cliff elucidated by transcriptomics. In vitro toxicokinetic data were utilized in a PBPK model to estimate internal dosimetry. The PBPK modeling data show that as the branched chain increases, predicted plasma Cmax decreases. This work demonstrates how transcriptomics and other mode of action-based methods can improve read-across.
Collapse
Affiliation(s)
- Shengde Wu
- Global Product Stewardship, The Procter and Gamble Company, Mason, Ohio 45040, USA
| | - Corie Ellison
- Global Product Stewardship, The Procter and Gamble Company, Mason, Ohio 45040, USA
| | - Jorge Naciff
- Global Product Stewardship, The Procter and Gamble Company, Mason, Ohio 45040, USA
| | - Michael Karb
- Global Product Stewardship, The Procter and Gamble Company, Mason, Ohio 45040, USA
| | - Cindy Obringer
- Global Product Stewardship, The Procter and Gamble Company, Mason, Ohio 45040, USA
| | - Gang Yan
- Global Product Stewardship, The Procter and Gamble Company, Mason, Ohio 45040, USA
| | - Yuqing Shan
- Global Product Stewardship, The Procter and Gamble Company, Mason, Ohio 45040, USA
| | - Alex Smith
- Global Product Stewardship, The Procter and Gamble Company, Mason, Ohio 45040, USA
| | - Xiaohong Wang
- Global Product Stewardship, The Procter and Gamble Company, Mason, Ohio 45040, USA
| | - George P Daston
- Global Product Stewardship, The Procter and Gamble Company, Mason, Ohio 45040, USA
| |
Collapse
|
44
|
Matteo G, Leingartner K, Rowan-Carroll A, Meier M, Williams A, Beal MA, Gagné M, Farmahin R, Wickramasuriya S, Reardon AJF, Barton-Maclaren T, Christopher Corton J, Yauk CL, Atlas E. In vitro transcriptomic analyses reveal pathway perturbations, estrogenic activities, and potencies of data-poor BPA alternative chemicals. Toxicol Sci 2023; 191:266-275. [PMID: 36534918 PMCID: PMC9936204 DOI: 10.1093/toxsci/kfac127] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Since initial regulatory action in 2010 in Canada, bisphenol A (BPA) has been progressively replaced by structurally related alternative chemicals. Unfortunately, many of these chemicals are data-poor, limiting toxicological risk assessment. We used high-throughput transcriptomics to evaluate potential hazards and compare potencies of BPA and 15 BPA alternative chemicals in cultured breast cancer cells. MCF-7 cells were exposed to BPA and 15 alternative chemicals (0.0005-100 µM) for 48 h. TempO-Seq (BioSpyder Inc) was used to examine global transcriptomic changes and estrogen receptor alpha (ERα)-associated transcriptional changes. Benchmark concentration (BMC) analysis was conducted to identify 2 global transcriptomic points of departure: (1) the lowest pathway median gene BMC and (2) the 25th lowest rank-ordered gene BMC. ERα activation was evaluated using a published transcriptomic biomarker and an ERα-specific transcriptomic point of departure was derived. Genes fitting BMC models were subjected to upstream regulator and canonical pathway analysis in Ingenuity Pathway Analysis. Biomarker analysis identified BPA and 8 alternative chemicals as ERα active. Global and ERα transcriptomic points of departure produced highly similar potency rankings with bisphenol AF as the most potent chemical tested, followed by BPA and bisphenol C. Further, BPA and transcriptionally active alternative chemicals enriched similar gene sets associated with increased cell division and cancer-related processes. These data provide support for future read-across applications of transcriptomic profiling for risk assessment of data-poor chemicals and suggest that several BPA alternative chemicals may cause hazards at similar concentrations to BPA.
Collapse
Affiliation(s)
- Geronimo Matteo
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch (HECSB) Health Canada, Ottawa, Ontario K2K 0K9, Canada.,Department of Biology, Faculty of Science, University of Ottawa, Ottawa, Ontario K1N 9A7, Canada
| | - Karen Leingartner
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch (HECSB) Health Canada, Ottawa, Ontario K2K 0K9, Canada.,Department of Biology, Faculty of Science, University of Ottawa, Ottawa, Ontario K1N 9A7, Canada
| | - Andrea Rowan-Carroll
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch (HECSB) Health Canada, Ottawa, Ontario K2K 0K9, Canada.,Department of Biology, Faculty of Science, University of Ottawa, Ottawa, Ontario K1N 9A7, Canada
| | - Matthew Meier
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch (HECSB) Health Canada, Ottawa, Ontario K2K 0K9, Canada.,Department of Biology, Faculty of Science, University of Ottawa, Ottawa, Ontario K1N 9A7, Canada
| | - Andrew Williams
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch (HECSB) Health Canada, Ottawa, Ontario K2K 0K9, Canada.,Department of Biology, Faculty of Science, University of Ottawa, Ottawa, Ontario K1N 9A7, Canada
| | - Marc A Beal
- Bureau of Chemical Safety, Health Canada.,Existing Substances Risk Assessment Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario K2K 0K9, Canada
| | - Matthew Gagné
- Existing Substances Risk Assessment Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario K2K 0K9, Canada
| | - Reza Farmahin
- Existing Substances Risk Assessment Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario K2K 0K9, Canada
| | - Shamika Wickramasuriya
- Existing Substances Risk Assessment Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario K2K 0K9, Canada
| | - Anthony J F Reardon
- Existing Substances Risk Assessment Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario K2K 0K9, Canada
| | - Tara Barton-Maclaren
- Existing Substances Risk Assessment Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario K2K 0K9, Canada
| | - J Christopher Corton
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Carole L Yauk
- Department of Biology, Faculty of Science, University of Ottawa, Ottawa, Ontario K1N 9A7, Canada
| | - Ella Atlas
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch (HECSB) Health Canada, Ottawa, Ontario K2K 0K9, Canada.,Department of Biochemistry, Faculty of Medicine, University of Ottawa, Ottawa, Ontario K1H 8M5, Canada
| |
Collapse
|
45
|
Cerisier N, Dafniet B, Badel A, Taboureau O. Linking chemicals, genes and morphological perturbations to diseases. Toxicol Appl Pharmacol 2023; 461:116407. [PMID: 36736439 DOI: 10.1016/j.taap.2023.116407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/13/2023] [Accepted: 01/28/2023] [Indexed: 02/05/2023]
Abstract
The progress in image-based high-content screening technology has facilitated high-throughput phenotypic profiling notably the quantification of cell morphology perturbation by chemicals. However, understanding the mechanism of action of a chemical and linking it to cell morphology and phenotypes remains a challenge in drug discovery. In this study, we intended to integrate molecules that induced transcriptomic perturbations and cellular morphological changes into a biological network in order to assess chemical-phenotypic relationships in humans. Such a network was enriched with existing disease information to suggest molecular and cellular profiles leading to phenotypes. Two datasets were used for this study. Firstly, we used the "Cell Painting morphological profiling assay" dataset, composed of 30,000 compounds tested on human osteosarcoma cells (named U2OS). Secondly, we used the "L1000 mRNA profiling assay" dataset, a collection of transcriptional expression data from cultured human cells treated with approximately 20,000 bioactive small molecules from the Library of Integrated Network-based Cellular Signatures (LINCS). Furthermore, pathways, gene ontology terms and disease enrichments were performed on the transcriptomics data. Overall, our study makes it possible to develop a biological network combining chemical-gene-pathway-morphological perturbation and disease relationships. It contains an ensemble of 9989 chemicals, 732 significant morphological features and 12,328 genes. Through diverse examples, we demonstrated that some drugs shared similar genes, pathways and morphological profiles that, taken together, could help in deciphering chemical-phenotype observations.
Collapse
Affiliation(s)
- Natacha Cerisier
- Université Paris Cité, INSERM U1133, CNRS UMR 8251, 75006 Paris, France
| | - Bryan Dafniet
- Université Paris Cité, INSERM U1133, CNRS UMR 8251, 75006 Paris, France
| | - Anne Badel
- Université Paris Cité, INSERM U1133, CNRS UMR 8251, 75006 Paris, France
| | - Olivier Taboureau
- Université Paris Cité, INSERM U1133, CNRS UMR 8251, 75006 Paris, France.
| |
Collapse
|
46
|
LaLone CA, Blatz DJ, Jensen MA, Vliet SM, Mayasich S, Mattingly KZ, Transue TR, Melendez W, Wilkinson A, Simmons CW, Ng C, Zhang C, Zhang Y. From Protein Sequence to Structure: The Next Frontier in Cross-Species Extrapolation for Chemical Safety Evaluations. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2023; 42:463-474. [PMID: 36524855 PMCID: PMC11265300 DOI: 10.1002/etc.5537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 12/02/2022] [Accepted: 12/06/2022] [Indexed: 06/17/2023]
Abstract
Computational screening for potentially bioactive molecules using advanced molecular modeling approaches including molecular docking and molecular dynamic simulation is mainstream in certain fields like drug discovery. Significant advances in computationally predicting protein structures from sequence information have also expanded the availability of structures for nonmodel species. Therefore, the objective of the present study was to develop an analysis pipeline to harness the power of these bioinformatics approaches for cross-species extrapolation for evaluating chemical safety. The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool compares protein-sequence similarity across species for conservation of known chemical targets, providing an initial line of evidence for extrapolation of toxicity knowledge. However, with the development of structural models from tools like the Iterative Threading ASSEmbly Refinement (ITASSER), analyses of protein structural conservation can be included to add further lines of evidence and generate protein models across species. Models generated through such a pipeline could then be used for advanced molecular modeling approaches in the context of species extrapolation. Two case examples illustrating this pipeline from SeqAPASS sequences to I-TASSER-generated protein structures were created for human liver fatty acid-binding protein (LFABP) and androgen receptor (AR). Ninety-nine LFABP and 268 AR protein models representing diverse species were generated and analyzed for conservation using template modeling (TM)-align. The results from the structural comparisons were in line with the sequence-based SeqAPASS workflow, adding further evidence of LFABL and AR conservation across vertebrate species. The present study lays the foundation for expanding the capabilities of the web-based SeqAPASS tool to include structural comparisons for species extrapolation, facilitating more rapid and efficient toxicological assessments among species with limited or no existing toxicity data. Environ Toxicol Chem 2023;42:463-474. © 2022 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
Collapse
Affiliation(s)
- Carlie A. LaLone
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, Minnesota, USA
| | - Donovan J. Blatz
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, Minnesota, USA
- Oak Ridge Institute for Science and Education, Duluth, Minnesota, USA
| | - Marissa A. Jensen
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, Minnesota, USA
- University of Minnesota Duluth, Swenson College of Science and Engineering, Department of Biology, Duluth, Minnesota, USA
| | - Sara M.F. Vliet
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Scientific Computing and Data Curation Division, Duluth, Minnesota, USA
| | - Sally Mayasich
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, Minnesota, USA
- University of Wisconsin-Madison Aquatic Sciences Center at U.S. EPA Great Lakes Toxicology and Ecology Division, Duluth, Minnesota, USA
| | - Kali Z. Mattingly
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, Minnesota, USA
- SpecPro Professional Services, Contractor to U.S. EPA Great Lakes Toxicology and Ecology Division, Duluth, Minnesota, USA
| | - Thomas R. Transue
- Congruence Therapeutics, Chapel Hill, NC, USA
- General Dynamics Information Technology, Research Triangle Park, North Carolina, USA
| | - Wilson Melendez
- General Dynamics Information Technology, Research Triangle Park, North Carolina, USA
| | - Audrey Wilkinson
- General Dynamics Information Technology, Research Triangle Park, North Carolina, USA
| | - Cody W. Simmons
- General Dynamics Information Technology, Research Triangle Park, North Carolina, USA
| | - Carla Ng
- Departments of Civil & Environmental Engineering and Environmental and Occupational Health, University of Pittsburgh
| | - Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109 USA
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109 USA
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
| |
Collapse
|
47
|
Li Y, Zhang Z, Jiang S, Xu F, Tulum L, Li K, Liu S, Li S, Chang L, Liddell M, Tu F, Gu X, Carmichael PL, White A, Peng S, Zhang Q, Li J, Zuo T, Kukic P, Xu P. Using transcriptomics, proteomics and phosphoproteomics as new approach methodology (NAM) to define biological responses for chemical safety assessment. CHEMOSPHERE 2023; 313:137359. [PMID: 36427571 DOI: 10.1016/j.chemosphere.2022.137359] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 11/19/2022] [Accepted: 11/21/2022] [Indexed: 06/16/2023]
Abstract
Omic-based technologies are of particular interest and importance for hazard identification and health risk characterization of chemicals. Their application in the new approach methodologies (NAMs) anchored on cellular toxicity pathways is based on the premise that any apical health endpoint change must be underpinned by some alterations at the omic levels. In the present study we examined the cellular responses to two chemicals, caffeine and coumarin, by generating and integrating multi-omic data from multi-dose and multi-time point transcriptomic, proteomic and phosphoproteomic experiments. We showed that the methodology presented here was able to capture the complete chain of events from the first chemical-induced changes at the phosphoproteome level, to changes in gene expression, and lastly to changes in protein abundance, each with vastly different points of departure (PODs). In HepG2 cells we found that the metabolism of lipids and general cellular stress response to be the dominant biological processes in response to caffeine and coumarin exposure, respectively. The phosphoproteomic changes were detected early in time, at very low doses and provided a fast, adaptive cellular response to chemical exposure with 7-37-fold lower points of departure comparing to the transcriptomics. Changes in protein abundance were found much less frequently than transcriptomic changes. While challenges remain, our study provides strong and novel evidence supporting the notion that these three omic technologies can be used in an integrated manner to facilitate a more complete understanding of pathway perturbations and POD determinations for risk assessment of chemical exposures.
Collapse
Affiliation(s)
- Yuan Li
- Department of Biomedicine, Medical College, Guizhou University, Guiyang, 550025, China; State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China
| | - Zhenpeng Zhang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China
| | - Songhao Jiang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China; Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, 071002, China
| | - Feng Xu
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China
| | - Liz Tulum
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire, MK44 1LQ, UK
| | - Kaixuan Li
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China
| | - Shu Liu
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China
| | - Suzhen Li
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China
| | - Lei Chang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China
| | - Mark Liddell
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire, MK44 1LQ, UK
| | - Fengjuan Tu
- Unilever Research & Development Centre Shanghai, Shanghai, 200335, China
| | - Xuelan Gu
- Unilever Research & Development Centre Shanghai, Shanghai, 200335, China
| | - Paul Lawford Carmichael
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire, MK44 1LQ, UK
| | - Andrew White
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire, MK44 1LQ, UK
| | - Shuangqing Peng
- Evaluation and Research Centre for Toxicology, Institute of Disease Control and Prevention, Academy of Military Medical Sciences, Beijing, 100071, China
| | - Qiang Zhang
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, USA
| | - Jin Li
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire, MK44 1LQ, UK
| | - Tao Zuo
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China.
| | - Predrag Kukic
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire, MK44 1LQ, UK.
| | - Ping Xu
- Department of Biomedicine, Medical College, Guizhou University, Guiyang, 550025, China; State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, 102206, China; Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, 071002, China; Program of Environmental Toxicology, School of Public Health, China Medical University, Shenyang, 110122, China.
| |
Collapse
|
48
|
Patlewicz G, Shah I. Towards systematic read-across using Generalised Read-Across (GenRA). COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2023; 25:1-15. [PMID: 37693774 PMCID: PMC10483627 DOI: 10.1016/j.comtox.2022.100258] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Read-across continues to be a popular data gap filling technique within category and analogue approaches. One of the main issues hindering read-across acceptance is the notion of addressing and reducing uncertainties. Frameworks and formats have been created to help facilitate read-across development, evaluation, and residual uncertainties. However, read-across remains an expert-driven approach with each assessment decided on its own merits with no objective means of evaluating performance or quantifying uncertainties. Here, the underlying motivation of creating an algorithmic approach to read-across, namely the Generalised Read-Across (GenRA) approach, is described. The overall objectives of the approach were to quantify performance and uncertainty. Progress made in quantifying the impact of each similarity context commonly relied upon as part of read-across assessment are discussed. The framework underpinning the approach, the software tools developed to date and how GenRA can be used to make and interpret predictions as part of a screening level hazard assessment decision context are illustrated. Future directions and some of the overarching issues still needed in this field and the extent to which GenRA might facilitate those needs are discussed.
Collapse
Affiliation(s)
- Grace Patlewicz
- Center for Computational Toxicology and Exposure (CCTE), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC 27711, USA
| | - Imran Shah
- Center for Computational Toxicology and Exposure (CCTE), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC 27711, USA
| |
Collapse
|
49
|
Fortin AMV, Long AS, Williams A, Meier MJ, Cox J, Pinsonnault C, Yauk CL, White PA. Application of a new approach methodology (NAM)-based strategy for genotoxicity assessment of data-poor compounds. FRONTIERS IN TOXICOLOGY 2023; 5:1098432. [PMID: 36756349 PMCID: PMC9899896 DOI: 10.3389/ftox.2023.1098432] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/02/2023] [Indexed: 01/24/2023] Open
Abstract
The conventional battery for genotoxicity testing is not well suited to assessing the large number of chemicals needing evaluation. Traditional in vitro tests lack throughput, provide little mechanistic information, and have poor specificity in predicting in vivo genotoxicity. New Approach Methodologies (NAMs) aim to accelerate the pace of hazard assessment and reduce reliance on in vivo tests that are time-consuming and resource-intensive. As such, high-throughput transcriptomic and flow cytometry-based assays have been developed for modernized in vitro genotoxicity assessment. This includes: the TGx-DDI transcriptomic biomarker (i.e., 64-gene expression signature to identify DNA damage-inducing (DDI) substances), the MicroFlow® assay (i.e., a flow cytometry-based micronucleus (MN) test), and the MultiFlow® assay (i.e., a multiplexed flow cytometry-based reporter assay that yields mode of action (MoA) information). The objective of this study was to investigate the utility of the TGx-DDI transcriptomic biomarker, multiplexed with the MicroFlow® and MultiFlow® assays, as an integrated NAM-based testing strategy for screening data-poor compounds prioritized by Health Canada's New Substances Assessment and Control Bureau. Human lymphoblastoid TK6 cells were exposed to 3 control and 10 data-poor substances, using a 6-point concentration range. Gene expression profiling was conducted using the targeted TempO-Seq™ assay, and the TGx-DDI classifier was applied to the dataset. Classifications were compared with those based on the MicroFlow® and MultiFlow® assays. Benchmark Concentration (BMC) modeling was used for potency ranking. The results of the integrated hazard calls indicate that five of the data-poor compounds were genotoxic in vitro, causing DNA damage via a clastogenic MoA, and one via a pan-genotoxic MoA. Two compounds were likely irrelevant positives in the MN test; two are considered possibly genotoxic causing DNA damage via an ambiguous MoA. BMC modeling revealed nearly identical potency rankings for each assay. This ranking was maintained when all endpoint BMCs were converted into a single score using the Toxicological Prioritization (ToxPi) approach. Overall, this study contributes to the establishment of a modernized approach for effective genotoxicity assessment and chemical prioritization for further regulatory scrutiny. We conclude that the integration of TGx-DDI, MicroFlow®, and MultiFlow® endpoints is an effective NAM-based strategy for genotoxicity assessment of data-poor compounds.
Collapse
Affiliation(s)
- Anne-Marie V. Fortin
- Department of Biology, University of Ottawa, Ottawa, ON, Canada,Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
| | - Alexandra S. Long
- Existing Substances Risk Assessment Bureau, Health Canada, Ottawa, ON, Canada
| | - Andrew Williams
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
| | - Matthew J. Meier
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
| | - Julie Cox
- Bureau of Gastroenterology, Infection and Viral Diseases, Health Canada, Ottawa, ON, Canada
| | - Claire Pinsonnault
- New Substances Assessment and Control Bureau, Health Canada, Ottawa, ON, Canada
| | - Carole L. Yauk
- Department of Biology, University of Ottawa, Ottawa, ON, Canada,*Correspondence: Carole L. Yauk, ; Paul A. White,
| | - Paul A. White
- Department of Biology, University of Ottawa, Ottawa, ON, Canada,Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada,*Correspondence: Carole L. Yauk, ; Paul A. White,
| |
Collapse
|
50
|
Transcriptomic-based evaluation of trichloroethylene glutathione and cysteine conjugates demonstrate phenotype-dependent stress responses in a panel of human in vitro models. Arch Toxicol 2023; 97:523-545. [PMID: 36576512 PMCID: PMC9859926 DOI: 10.1007/s00204-022-03436-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 12/14/2022] [Indexed: 12/29/2022]
Abstract
Environmental or occupational exposure of humans to trichloroethylene (TCE) has been associated with different extrahepatic toxic effects, including nephrotoxicity and neurotoxicity. Bioactivation of TCE via the glutathione (GSH) conjugation pathway has been proposed as underlying mechanism, although only few mechanistic studies have used cell models of human origin. In this study, six human derived cell models were evaluated as in vitro models representing potential target tissues of TCE-conjugates: RPTEC/TERT1 (kidney), HepaRG (liver), HUVEC/TERT2 (vascular endothelial), LUHMES (neuronal, dopaminergic), human induced pluripotent stem cells (hiPSC) derived peripheral neurons (UKN5) and hiPSC-derived differentiated brain cortical cultures containing all subtypes of neurons and astrocytes (BCC42). A high throughput transcriptomic screening, utilizing mRNA templated oligo-sequencing (TempO-Seq), was used to study transcriptomic effects after exposure to TCE-conjugates. Cells were exposed to a wide range of concentrations of S-(1,2-trans-dichlorovinyl)glutathione (1,2-DCVG), S-(1,2-trans-dichlorovinyl)-L-cysteine (1,2-DCVC), S-(2,2-dichlorovinyl)glutathione (2,2-DCVG), and S-(2,2-dichlorovinyl)-L-cysteine (2,2-DCVC). 1,2-DCVC caused stress responses belonging to the Nrf2 pathway and Unfolded protein response in all the tested models but to different extents. The renal model was the most sensitive model to both 1,2-DCVC and 1,2-DCVG, with an early Nrf2-response at 3 µM and hundreds of differentially expressed genes at higher concentrations. Exposure to 2,2-DCVG and 2,2-DCVC also resulted in the upregulation of Nrf2 pathway genes in RPTEC/TERT1 although at higher concentrations. Of the three neuronal models, both the LUHMES and BCC42 showed significant Nrf2-responses and at higher concentration UPR-responses, supporting recent hypotheses that 1,2-DCVC may be involved in neurotoxic effects of TCE. The cell models with the highest expression of γ-glutamyltransferase (GGT) enzymes, showed cellular responses to both 1,2-DCVG and 1,2-DCVC. Little to no effects were found in the neuronal models from 1,2-DCVG exposure due to their low GGT-expression. This study expands our knowledge on tissue specificity of TCE S-conjugates and emphasizes the value of human cell models together with transcriptomics for such mechanistic studies.
Collapse
|