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Thienpont A, Cho E, Williams A, Meier MJ, Yauk CL, Beal MA, Van Goethem F, Rogiers V, Vanhaecke T, Mertens B. In vitro to in vivo extrapolation modeling to facilitate the integration of transcriptomics data into genotoxicity assessment. Toxicology 2025; 515:154165. [PMID: 40288562 DOI: 10.1016/j.tox.2025.154165] [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: 02/04/2025] [Revised: 04/18/2025] [Accepted: 04/23/2025] [Indexed: 04/29/2025]
Abstract
In vitro transcriptomics holds promise for high-throughput, human-relevant data but is not yet integrated into regulatory decision-making due to the lack of standardized approaches. For genotoxicity assessment, transcriptomic biomarkers such as GENOMARK and TGx-DDI facilitate qualitative and quantitative analysis of complex in vitro transcriptomic datasets. However, advancing their use in quantitative testing requires standardized methods for deriving transcriptomic Points of Departure (tPoDs) and linking them to in vivo responses. Herein, we investigated different approaches to calculate tPoDs and applied in vitro to in vivo extrapolation to obtain administered equivalent doses (AEDs). Human HepaRG cells were exposed for 72 h to 10 known in vivo genotoxicants (glycidol, methyl methanesulfonate, nitrosodimethylamine, 4-nitroquinoline-N-oxide, aflatoxin B1, colchicine, cyclophosphamide, mitomycin C, ethyl methanesulfonate, and N-Nitroso-N-ethylurea) from the highest concentration that induces up to 50 % cytotoxicity through a range of lower concentrations. Gene expression data was generated using a customized version of the TempO-Seq® human S1500 + gene panel. The GENOMARK and TGx-DDI biomarkers produced genotoxic calls for all of these reference genotoxicants. Next, we performed benchmark concentration (BMC) modeling to generate both genotoxicity-specific biomarker (tPoDbiomarkers) and generic tPoDs (tPoD S1500+). High-throughput toxicokinetic models estimated the human AEDs for these tPoDs, which were compared with (a) previously reported genotoxicity-specific AEDs from other New Approach Methodologies, and (b) in vivo PoDs from animal studies. We found that the generic AEDs were more conservative than genotoxicity-specific biomarker AEDs. For six of the nine genotoxicants, transcriptomic AEDs were lower than the in vivo PoDs; refined kinetic models may improve predictions. Overall, in vitro transcriptomic data in HepaRG cells provide protective estimates of in vivo genotoxic concentrations, consistent with other in vitro genotoxicity testing systems.
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Affiliation(s)
- Anouck Thienpont
- Department of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel (VUB), Brussels 1090, Belgium; Department of Chemical and Physical Health Risks, Sciensano, Brussels 1050, 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
| | - Marc A Beal
- Bureau of Chemical Safety, Health Products and Food Branch, Health Canada, Ottawa, ON K1A 0K9, Canada
| | - Freddy Van Goethem
- Department of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel (VUB), Brussels 1090, Belgium
| | - Vera Rogiers
- Department of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel (VUB), Brussels 1090, Belgium
| | - Tamara Vanhaecke
- Department of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel (VUB), Brussels 1090, Belgium
| | - Birgit Mertens
- Department of Chemical and Physical Health Risks, Sciensano, Brussels 1050, Belgium.
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2
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Rowan-Carroll A, Meier MJ, Yauk CL, Williams A, Leingartner K, Bradford L, Lorusso L, Atlas E. Deciphering per- and polyfluoroalkyl substances mode of action: comparative gene expression analysis in human liver spheroids. Toxicol Sci 2025; 205:124-142. [PMID: 40037795 DOI: 10.1093/toxsci/kfaf023] [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: 03/06/2025] Open
Abstract
Understanding the mechanisms by which environmental chemicals cause toxicity is necessary for effective human health risk assessment. High-throughput transcriptomics (HTTr) can be used to inform risk assessment on toxicological mechanisms, hazards, and potencies. We applied HTTr to elucidate the molecular mechanisms by which per- and polyfluoroalkyl substances (PFAS) cause liver perturbations. We contrasted transcriptomic profiles of PFOA, PFBS, PFOS, and PFDS against transcriptomic profiles from established liver-toxic and non-toxic reference compounds, alongside peroxisome proliferator-activated receptors (PPARs) agonists. Our analysis was conducted on metabolically competent 3-D human liver spheroids produced from primary cells from 10 donors. Pathway analysis showed that PFOS and PFDS perturb many of the same pathways as the known liver-toxic compounds in the spheroids, and that the cholesterol biosynthesis pathways are significantly affected by exposure to these compounds. PFOA alters lipid metabolism-related pathways but its expression profile does not closely match reference compounds. PFBS upregulates many degradation-related pathways and targets many of the same pathways as the PPAR agonists and acetaminophen. Our transcriptional analysis does not support the claim that these PFAS are DNA-damaging in this model. A multidimensional scaling (MDS) analysis revealed that PFOS, PFOA, and PFDS cluster together in the same multidimensional space as liver-damaging compounds, whereas PFBS clusters more closely with the non-liver-damaging compounds. Benchmark concentration-response modeling predicts that all the PFAS are bioactive in the liver. Overall, our results show that these PFAS produce unique transcriptional changes but also alter pathways associated with established liver-toxic chemicals in this liver spheroid model.
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Affiliation(s)
- Andrea Rowan-Carroll
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch (HECSB), Health Canada, Ottawa, ON K1S 0K9, Canada
| | - Matthew J Meier
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch (HECSB), Health Canada, Ottawa, ON K1S 0K9, Canada
| | - Carole L Yauk
- Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Andrew Williams
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch (HECSB), Health Canada, Ottawa, ON K1S 0K9, Canada
| | - Karen Leingartner
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch (HECSB), Health Canada, Ottawa, ON K1S 0K9, Canada
| | - Lauren Bradford
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch (HECSB), Health Canada, Ottawa, ON K1S 0K9, Canada
| | - Luigi Lorusso
- Chemicals and Environmental Health Management Bureau, Healthy Environments and Consumer Safety Branch (HECSB), Health Canada, Ottawa, ON K1S 0K9, Canada
| | - Ella Atlas
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch (HECSB), Health Canada, Ottawa, ON K1S 0K9, Canada
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3
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Pannala VR, Hari A, AbdulHameed MDM, Balik-Meisner MR, Mav D, Phadke DP, Scholl EH, Shah RR, Auerbach SS, Wallqvist A. Quantifying liver-toxic responses from dose-dependent chemical exposures using a rat genome-scale metabolic model. Toxicol Sci 2025; 204:154-168. [PMID: 39821420 PMCID: PMC11939075 DOI: 10.1093/toxsci/kfaf005] [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: 01/19/2025] Open
Abstract
Because the liver plays a vital role in the clearance of exogenous chemical compounds, it is susceptible to chemical-induced toxicity. Animal-based testing is routinely used to assess the hepatotoxic potential of chemicals. Although large-scale high-throughput sequencing data can indicate the genes affected by chemical exposures, we need system-level approaches to interpret these changes. To this end, we developed an updated rat genome-scale metabolic model to integrate large-scale transcriptomics data and utilized a chemical structure similarity-based ToxProfiler tool to identify chemicals that bind to specific toxicity targets to understand the mechanisms of toxicity. We used high-throughput transcriptomics data from a 5-day in vivo study where rats were exposed to different non-toxic and hepatotoxic chemicals at increasing concentrations and investigated how liver metabolism was differentially altered between the non-toxic and hepatotoxic chemical exposures. Our analysis indicated that the genes identified via toxicity target analysis and those mapped to the metabolic model showed a distinct gene expression pattern, with the majority showing upregulation for hepatotoxicants compared with non-toxic chemicals. Similarly, when we mapped the metabolic genes at the pathway level, we identified several pathways in carbohydrate, amino acid, and lipid metabolism that were significantly upregulated for hepatotoxic chemicals. Furthermore, using our system-level integration of gene expression data with the rat metabolic model, we could differentiate metabolites in these pathways that were systematically elevated or suppressed due to hepatotoxic versus non-toxic chemicals. Thus, using our combined approach, we were able to identify a set of potential gene signatures that clearly differentiated liver toxic responses from non-toxic chemicals, which helped us identify potential metabolic pathways and metabolites that are systematically associated with the toxicant exposure.
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Affiliation(s)
- Venkat R Pannala
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, United States
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, United States
| | - Archana Hari
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, United States
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, United States
| | - Mohamed Diwan M AbdulHameed
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, United States
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, United States
| | | | - Deepak Mav
- Sciome LLC, Research Triangle Park, NC 27709, United States
| | | | | | - Ruchir R Shah
- Sciome LLC, Research Triangle Park, NC 27709, United States
| | - Scott S Auerbach
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, United States
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, United States
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4
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Mauge-Lewis K, Ramaiahgari SC, Auerbach SS, Roberts GK, Waidyanatha S, Fenton SE, Phadke DP, Balik-Meisner MR, Tandon A, Mav D, Howard B, Shah R, Sparrow B, Gorospe J, Ferguson SS. Unraveling Human Hepatocellular Responses to PFAS and Aqueous Film-Forming Foams (AFFFs) for Molecular Hazard Prioritization and In Vivo Translation. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2025; 59:2423-2435. [PMID: 39893674 PMCID: PMC11823446 DOI: 10.1021/acs.est.4c10595] [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: 10/03/2024] [Revised: 01/16/2025] [Accepted: 01/17/2025] [Indexed: 02/04/2025]
Abstract
Aqueous film-forming foams (AFFFs) are complex product mixtures that often contain per- and polyfluorinated alkyl substances (PFAS) to enhance fire suppression and protect firefighters. However, PFAS have been associated with a range of adverse health effects (e.g., liver and thyroid disease and cancer), and innovative approach methods to better understand their toxicity potential and identify safer alternatives are needed. In this study, we investigated a set of 30 substances (e.g., AFFF, PFAS, and clinical drugs) using differentiated cultures of human hepatocytes (HepaRG, 2D), high-throughput transcriptomics, deep learning of cell morphology images, and liver enzyme leakage assays with benchmark dose analysis to (1) predict the potency ranges for human liver injury, (2) delineate gene- and pathway-level transcriptomic points-of-departure for molecular hazard characterization and prioritization, (3) characterize human hepatocellular response similarities to inform regulatory read-across efforts, and (4) introduce an innovative approach to translate mechanistic hepatocellular response data to predict the potency ranges for PFAS-induced hepatomegaly in vivo. Collectively, these data fill important mechanistic knowledge gaps with PFAS/AFFF and represent a scalable platform to address the thousands of PFAS in commerce for greener chemistries and next-generation risk assessments.
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Affiliation(s)
- Kevin
A. Mauge-Lewis
- Division
of Translational Toxicology, National Institute
for Environmental Sciences, 111 TW Alexander Drive, Durham, North Carolina 27709, United States
| | - Sreenivasa C. Ramaiahgari
- Division
of Translational Toxicology, National Institute
for Environmental Sciences, 111 TW Alexander Drive, Durham, North Carolina 27709, United States
| | - Scott S. Auerbach
- Division
of Translational Toxicology, National Institute
for Environmental Sciences, 111 TW Alexander Drive, Durham, North Carolina 27709, United States
| | - Georgia K. Roberts
- Division
of Translational Toxicology, National Institute
for Environmental Sciences, 111 TW Alexander Drive, Durham, North Carolina 27709, United States
| | - Suramya Waidyanatha
- Division
of Translational Toxicology, National Institute
for Environmental Sciences, 111 TW Alexander Drive, Durham, North Carolina 27709, United States
| | - Suzanne E. Fenton
- Division
of Translational Toxicology, National Institute
for Environmental Sciences, 111 TW Alexander Drive, Durham, North Carolina 27709, United States
| | - Dhiral P. Phadke
- Sciome, 1920 NC-54 Suite 510 & 520, Durham, North Carolina 27713, United States
| | | | - Arpit Tandon
- Sciome, 1920 NC-54 Suite 510 & 520, Durham, North Carolina 27713, United States
| | - Deepak Mav
- Sciome, 1920 NC-54 Suite 510 & 520, Durham, North Carolina 27713, United States
| | - Brian Howard
- Sciome, 1920 NC-54 Suite 510 & 520, Durham, North Carolina 27713, United States
| | - Ruchir Shah
- Sciome, 1920 NC-54 Suite 510 & 520, Durham, North Carolina 27713, United States
| | - Barney Sparrow
- Battelle, 505 King Avenue, Columbus, Ohio 43201, United States
| | - Jenni Gorospe
- Battelle, 505 King Avenue, Columbus, Ohio 43201, United States
| | - Stephen S. Ferguson
- Division
of Translational Toxicology, National Institute
for Environmental Sciences, 111 TW Alexander Drive, Durham, North Carolina 27709, United States
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5
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O’Brien J, Mitchell C, Auerbach S, Doonan L, Ewald J, Everett L, Faranda A, Johnson K, Reardon A, Rooney J, Shao K, Stainforth R, Wheeler M, Dalmas Wilk D, Williams A, Yauk C, Costa E. Bioinformatic workflows for deriving transcriptomic points of departure: current status, data gaps, and research priorities. Toxicol Sci 2025; 203:147-159. [PMID: 39499193 PMCID: PMC11775421 DOI: 10.1093/toxsci/kfae145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2024] Open
Abstract
There is a pressing need to increase the efficiency and reliability of toxicological safety assessment for protecting human health and the environment. Although conventional toxicology tests rely on measuring apical changes in vertebrate models, there is increasing interest in the use of molecular information from animal and in vitro studies to inform safety assessment. One promising and pragmatic application of molecular information involves the derivation of transcriptomic points of departure (tPODs). Transcriptomic analyses provide a snapshot of global molecular changes that reflect cellular responses to stressors and progression toward disease. A tPOD identifies the dose level below which a concerted change in gene expression is not expected in a biological system in response to a chemical. A common approach to derive such a tPOD consists of modeling the dose-response behavior for each gene independently and then aggregating the gene-level data into a single tPOD. Although different implementations of this approach are possible, as discussed in this manuscript, research strongly supports the overall idea that reference doses produced using tPODs are health protective. An advantage of this approach is that tPODs can be generated in shorter term studies (e.g. days) compared with apical endpoints from conventional tests (e.g. 90-d subchronic rodent tests). Moreover, research strongly supports the idea that reference doses produced using tPODs are health protective. Given the potential application of tPODs in regulatory toxicology testing, rigorous and reproducible wet and dry laboratory methodologies for their derivation are required. This review summarizes the current state of the science regarding the study design and bioinformatics workflows for tPOD derivation. We identify standards of practice and sources of variability in tPOD generation, data gaps, and areas of uncertainty. We provide recommendations for research to address barriers and promote adoption in regulatory decision making.
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Affiliation(s)
- Jason O’Brien
- Ecotoxicology and Wildlife Health Division, Environment and Climate Change Canada, Ottawa, ON J8X 4C6, Canada
| | - Constance Mitchell
- Health and Environmental Sciences Institute, Washington, DC 22205, United States
| | - Scott Auerbach
- Predictive Toxicology Branch, Division of Translational Toxicology, NIEHS, Research Triangle Park, NC 27709, United States
| | - Liam Doonan
- Syngenta International Research Centre, Berkshire RG42 6EY, United Kingdom
| | - Jessica Ewald
- Institute of Parasitology, McGill University, Montreal, QC H3A 0G4, Canada
| | - Logan Everett
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC 27709, United States
| | - Adam Faranda
- FMC Agricultural Solutions, Newark, DE 19711, United States
| | - Kamin Johnson
- Corteva Agriscience, Indianapolis, IN 46268, United States
| | - Anthony Reardon
- Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON K1A 0K9, Canada
- Existing Substances Risk Assessment Bureau, Health Canada, Ottawa, ON K1A 0K9, Canada
| | - John Rooney
- Syngenta Crop Protection, LLC, Greensboro, NC 27409, United States
| | - Kan Shao
- Department of Environmental and Occupational Health, School of Public Health—Bloomington, Indiana University, Bloomington, IN 47405, United States
| | - Robert Stainforth
- Radiation Protection Bureau, Health Canada, Ottawa, ON K1A 0K9, Canada
| | - Matthew Wheeler
- Predictive Toxicology Branch, Division of Translational Toxicology, NIEHS, Research Triangle Park, NC 27709, United States
| | | | - Andrew Williams
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON K1A 0K9, Canada
| | - Carole Yauk
- Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada
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6
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Meier MJ, Harrill J, Johnson K, Thomas RS, Tong W, Rager JE, Yauk CL. Progress in toxicogenomics to protect human health. Nat Rev Genet 2025; 26:105-122. [PMID: 39223311 DOI: 10.1038/s41576-024-00767-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [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.
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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.
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7
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Zilber D, Messier KP, House J, Parham F, Auerbach SS, Wheeler MW. Bayesian gene set benchmark dose estimation for "omic" responses. Bioinformatics 2024; 41:btaf008. [PMID: 39786864 PMCID: PMC11783320 DOI: 10.1093/bioinformatics/btaf008] [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: 05/23/2024] [Revised: 11/22/2024] [Accepted: 01/07/2025] [Indexed: 01/12/2025] Open
Abstract
MOTIVATION Estimating a toxic reference point using tools like the benchmark dose (BMD) is a critical step in setting policy to regulate pollution and ensure safe environments. Toxicity can be measured for different endpoints, including changes in gene expression and histopathology for various tissues, and is typically explored one gene or tissue at a time in a univariate setting that ignores correlation. In this work, we develop a multivariate estimation procedure to estimate the BMD for specified gene sets. Our approach extends the foundational univariate approach by accounting for correlation in a statistically principled way. RESULTS We illustrate the method using data from a 5-day rat study and Hallmark gene sets and compare to existing BMD results computed by the EPA for both gene sets and apical histopathology endpoints. In contrast to previous ad-hoc methods, our principled approach provides the needed extension to bring the foundational univariate method into the multivariate world of transcriptomics. In addition to use in a regulatory setting, our method can provide hypothesis generation when gene sets correspond to mechanistic pathways. AVAILABILITY AND IMPLEMENTATION BS-BMD is implemented in R and C++ and available at https://github.com/NIEHS/BS-BMD.
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Affiliation(s)
- Daniel Zilber
- Division of Intramural Research, Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, United States
- Division of Translational Toxicology, Predictive Toxicology Branch, National Institute of Environmental Health Sciences, Durham, NC 27713, United States
| | - Kyle P Messier
- Division of Intramural Research, Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, United States
- Division of Translational Toxicology, Predictive Toxicology Branch, National Institute of Environmental Health Sciences, Durham, NC 27713, United States
| | - John House
- Division of Intramural Research, Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, United States
| | - Fred Parham
- Division of Translational Toxicology, Predictive Toxicology Branch, National Institute of Environmental Health Sciences, Durham, NC 27713, United States
| | - Scott S Auerbach
- Division of Translational Toxicology, Predictive Toxicology Branch, National Institute of Environmental Health Sciences, Durham, NC 27713, United States
| | - Matthew W Wheeler
- Division of Intramural Research, Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, United States
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8
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Dubreil E, Darney K, Delignette-Muller ML, Barranger A, Huet S, Hogeveen K, Léger T, Fessard V, Hégarat LL. Modeling HepaRG metabolome responses to pyrrolizidine alkaloid exposure for insight into points of departure and modes of action. JOURNAL OF HAZARDOUS MATERIALS 2024; 474:134721. [PMID: 38843629 DOI: 10.1016/j.jhazmat.2024.134721] [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: 01/29/2024] [Revised: 05/12/2024] [Accepted: 05/22/2024] [Indexed: 06/26/2024]
Abstract
The new challenges in toxicology demand novel and innovative in vitro approaches for deriving points of departure (PODs) and determining the mode of action (MOA) of chemicals. Therefore, the aim of this original study was to couple in vitro studies with untargeted metabolomics to model the concentration-response of extra- and intracellular metabolome data on human HepaRG cells treated for 48 h with three pyrrolizidine alkaloids (PAs): heliotrine, retrorsine and lasiocarpine. Modeling revealed that the three PAs induced various monotonic and, importantly, biphasic curves of metabolite content. Based on unannotated metabolites, the endometabolome was more sensitive than the exometabolome in terms of metabolomic effects, and benchmark concentrations (BMCs) confirmed that lasiocarpine was the most hepatotoxic PA. Regarding its MOA, impairment of lipid metabolism was highlighted at a very low BMC (first quartile, 0.003 µM). Moreover, results confirmed that lasiocarpine targets bile acids, as well as amino acid and steroid metabolisms. Analysis of the endometabolome, based on coupling concentration-response and PODs, gave encouraging results for ranking toxins according to their hepatotoxic effects. Therefore, this novel approach is a promising tool for next-generation risk assessment, readily applicable to a broad range of compounds and toxic endpoints.
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Affiliation(s)
- Estelle Dubreil
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Fougères Laboratory, Toxicology of Contaminants Unit, 10 B rue Claude Bourgelat, 35306 Fougères, France.
| | - Keyvin Darney
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Risk Assessment Department, 14 Rue Pierre et Marie Curie, 94701 Maisons-Alfort, France
| | - Marie-Laure Delignette-Muller
- University of Lyon 1, CNRS, VetAgro Sup, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, 69622 Villeurbanne, France
| | - Audrey Barranger
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Fougères Laboratory, Toxicology of Contaminants Unit, 10 B rue Claude Bourgelat, 35306 Fougères, France
| | - Sylvie Huet
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Fougères Laboratory, Toxicology of Contaminants Unit, 10 B rue Claude Bourgelat, 35306 Fougères, France
| | - Kevin Hogeveen
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Fougères Laboratory, Toxicology of Contaminants Unit, 10 B rue Claude Bourgelat, 35306 Fougères, France
| | - Thibaut Léger
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Fougères Laboratory, Toxicology of Contaminants Unit, 10 B rue Claude Bourgelat, 35306 Fougères, France
| | - Valérie Fessard
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Fougères Laboratory, Toxicology of Contaminants Unit, 10 B rue Claude Bourgelat, 35306 Fougères, France
| | - Ludovic Le Hégarat
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Fougères Laboratory, Toxicology of Contaminants Unit, 10 B rue Claude Bourgelat, 35306 Fougères, France
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9
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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.
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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
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10
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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.
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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
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11
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Carpi D, Liska R, Malinowska JM, Palosaari T, Bouhifd M, Whelan M. Investigating the dependency of in vitro benchmark concentrations on exposure time in transcriptomics experiments. Toxicol In Vitro 2024; 95:105761. [PMID: 38081393 PMCID: PMC10879918 DOI: 10.1016/j.tiv.2023.105761] [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: 09/18/2023] [Revised: 11/10/2023] [Accepted: 12/05/2023] [Indexed: 12/22/2023]
Abstract
There is increasing interest to employ in vitro transcriptomics experiments in toxicological testing, for example to determine a point-of-departure (PoD) for chemical safety assessment. However current practices to derive PoD tend to utilise a single exposure time despite the importance of exposure time on the manifestation of toxicity caused by a chemical. Therefore it is important to investigate both concentration and exposure time to determine how these factors affect biological responses, and as a consequence, the derivation of PoDs. In this study, metabolically competent HepaRG cells were exposed to five known toxicants over a range of concentrations and time points for subsequent gene expression analysis, using a targeted RNA expression assay (TempO-Seq). A non-parametric factor-modelling approach was used to model the collective response of all significant genes, which exploited the interdependence of differentially expressed gene responses. This in turn allowed the determination of an isobenchmark response (isoBMR) curve for each chemical in a reproducible manner. For 2 of the 5 chemicals tested, the PoD was observed to vary by 0.5-1 log-order within the 48-h timeframe of the experiment. The approach and findings presented here clearly demonstrate the need to take both concentration and exposure time into account when designing in vitro toxicogenomics experiments to determine PoD. Doing so also provides a means to use concentration-time-response modelling as a basis to extrapolate a PoD from shorter to longer exposure durations, and to identify chemicals of concern that can cause cumulative effects over time.
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Affiliation(s)
- Donatella Carpi
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Roman Liska
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | | | - Taina Palosaari
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Mounir Bouhifd
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Maurice Whelan
- European Commission, Joint Research Centre (JRC), Ispra, Italy.
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12
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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.
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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
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13
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Suciu I, Delp J, Gutbier S, Suess J, Henschke L, Celardo I, Mayer TU, Amelio I, Leist M. Definition of the Neurotoxicity-Associated Metabolic Signature Triggered by Berberine and Other Respiratory Chain Inhibitors. Antioxidants (Basel) 2023; 13:49. [PMID: 38247474 PMCID: PMC10812665 DOI: 10.3390/antiox13010049] [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] [Received: 10/24/2023] [Revised: 12/06/2023] [Accepted: 12/19/2023] [Indexed: 01/23/2024] Open
Abstract
To characterize the hits from a phenotypic neurotoxicity screen, we obtained transcriptomics data for valinomycin, diethylstilbestrol, colchicine, rotenone, 1-methyl-4-phenylpyridinium (MPP), carbaryl and berberine (Ber). For all compounds, the concentration triggering neurite degeneration correlated with the onset of gene expression changes. The mechanistically diverse toxicants caused similar patterns of gene regulation: the responses were dominated by cell de-differentiation and a triggering of canonical stress response pathways driven by ATF4 and NRF2. To obtain more detailed and specific information on the modes-of-action, the effects on energy metabolism (respiration and glycolysis) were measured. Ber, rotenone and MPP inhibited the mitochondrial respiratory chain and they shared complex I as the target. This group of toxicants was further evaluated by metabolomics under experimental conditions that did not deplete ATP. Ber (204 changed metabolites) showed similar effects as MPP and rotenone. The overall metabolic situation was characterized by oxidative stress, an over-abundance of NADH (>1000% increase) and a re-routing of metabolism in order to dispose of the nitrogen resulting from increased amino acid turnover. This unique overall pattern led to the accumulation of metabolites known as biomarkers of neurodegeneration (saccharopine, aminoadipate and branched-chain ketoacids). These findings suggest that neurotoxicity of mitochondrial inhibitors may result from an ensemble of metabolic changes rather than from a simple ATP depletion. The combi-omics approach used here provided richer and more specific MoA data than the more common transcriptomics analysis alone. As Ber, a human drug and food supplement, mimicked closely the mode-of-action of known neurotoxicants, its potential hazard requires further investigation.
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Affiliation(s)
- Ilinca Suciu
- In Vitro Toxicology and Biomedicine, Department Inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, 78464 Konstanz, Germany
- Graduate School of Chemical Biology, University of Konstanz, 78464 Konstanz, Germany
| | - Johannes Delp
- In Vitro Toxicology and Biomedicine, Department Inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, 78464 Konstanz, Germany
| | - Simon Gutbier
- In Vitro Toxicology and Biomedicine, Department Inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, 78464 Konstanz, Germany
| | - Julian Suess
- In Vitro Toxicology and Biomedicine, Department Inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, 78464 Konstanz, Germany
| | - Lars Henschke
- Graduate School of Chemical Biology, University of Konstanz, 78464 Konstanz, Germany
- Department of Molecular Genetics, University of Konstanz, 78464 Konstanz, Germany
| | - Ivana Celardo
- In Vitro Toxicology and Biomedicine, Department Inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, 78464 Konstanz, Germany
| | - Thomas U. Mayer
- Department of Molecular Genetics, University of Konstanz, 78464 Konstanz, Germany
| | - Ivano Amelio
- Division for Systems Toxicology, Department of Biology, University of Konstanz, 78464 Konstanz, Germany
| | - Marcel Leist
- In Vitro Toxicology and Biomedicine, Department Inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, 78464 Konstanz, Germany
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14
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Pannala VR, Wallqvist A. High-Throughput Transcriptomics Differentiates Toxic versus Non-Toxic Chemical Exposures Using a Rat Liver Model. Int J Mol Sci 2023; 24:17425. [PMID: 38139254 PMCID: PMC10743995 DOI: 10.3390/ijms242417425] [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: 11/14/2023] [Revised: 12/07/2023] [Accepted: 12/10/2023] [Indexed: 12/24/2023] Open
Abstract
To address the challenge of limited throughput with traditional toxicity testing, a newly developed high-throughput transcriptomics (HTT) platform, together with a 5-day in vivo rat model, offers an alternative approach to estimate chemical exposures and provide reasonable estimates of toxicological endpoints. This study contains an HTT analysis of 18 environmental chemicals with known liver toxicity. They were evaluated using male Sprague Dawley rats exposed to various concentrations daily for five consecutive days via oral gavage, with data collected on the sixth day. Here, we further explored the 5-day rat model to identify potential gene signatures that can differentiate between toxic and non-toxic liver responses and provide us with a potential histopathological endpoint of chemical exposure. We identified a distinct gene expression pattern that differentiated non-hepatotoxic compounds from hepatotoxic compounds in a dose-dependent manner, and an analysis of the significantly altered common genes indicated that toxic chemicals predominantly upregulated most of the genes and several pathways in amino acid and lipid metabolism. Finally, our liver injury module analysis revealed that several liver-toxic compounds showed similarities in the key injury phenotypes of cellular inflammation and proliferation, indicating potential molecular initiating processes that may lead to a specific end-stage liver disease.
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Affiliation(s)
- Venkat R. Pannala
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Frederick, MD 21702, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Frederick, MD 21702, USA
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15
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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.
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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
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16
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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: 22] [Impact Index Per Article: 11.0] [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.
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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
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17
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Louisse J, Fragki S, Rijkers D, Janssen A, van Dijk B, Leenders L, Staats M, Bokkers B, Zeilmaker M, Piersma A, Luijten M, Hoogenboom R, Peijnenburg A. Determination of in vitro hepatotoxic potencies of a series of perfluoroalkyl substances (PFASs) based on gene expression changes in HepaRG liver cells. Arch Toxicol 2023; 97:1113-1131. [PMID: 36864359 PMCID: PMC10025204 DOI: 10.1007/s00204-023-03450-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 01/24/2023] [Indexed: 03/04/2023]
Abstract
Per- and polyfluoroalkyl substances (PFASs) are omnipresent and have been shown to induce a wide range of adverse health effects, including hepatotoxicity, developmental toxicity, and immunotoxicity. The aim of the present work was to assess whether human HepaRG liver cells can be used to obtain insight into differences in hepatotoxic potencies of a series of PFASs. Therefore, the effects of 18 PFASs on cellular triglyceride accumulation (AdipoRed assay) and gene expression (DNA microarray for PFOS and RT-qPCR for all 18 PFASs) were studied in HepaRG cells. BMDExpress analysis of the PFOS microarray data indicated that various cellular processes were affected at the gene expression level. From these data, ten genes were selected to assess the concentration-effect relationship of all 18 PFASs using RT-qPCR analysis. The AdipoRed data and the RT-qPCR data were used for the derivation of in vitro relative potencies using PROAST analysis. In vitro relative potency factors (RPFs) could be obtained for 8 PFASs (including index chemical PFOA) based on the AdipoRed data, whereas for the selected genes, in vitro RPFs could be obtained for 11-18 PFASs (including index chemical PFOA). For the readout OAT5 expression, in vitro RPFs were obtained for all PFASs. In vitro RPFs were found to correlate in general well with each other (Spearman correlation) except for the PPAR target genes ANGPTL4 and PDK4. Comparison of in vitro RPFs with RPFs obtained from in vivo studies in rats indicate that best correlations (Spearman correlation) were obtained for in vitro RPFs based on OAT5 and CXCL10 expression changes and external in vivo RPFs. HFPO-TA was found to be the most potent PFAS tested, being around tenfold more potent than PFOA. Altogether, it may be concluded that the HepaRG model may provide relevant data to provide insight into which PFASs are relevant regarding their hepatotoxic effects and that it can be applied as a screening tool to prioritize other PFASs for further hazard and risk assessment.
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Affiliation(s)
- Jochem Louisse
- Wageningen Food Safety Research (WFSR), Wageningen, The Netherlands.
| | - Styliani Fragki
- Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Deborah Rijkers
- Wageningen Food Safety Research (WFSR), Wageningen, The Netherlands
| | - Aafke Janssen
- Wageningen Food Safety Research (WFSR), Wageningen, The Netherlands
| | - Bas van Dijk
- Wageningen Food Safety Research (WFSR), Wageningen, The Netherlands
| | - Liz Leenders
- Wageningen Food Safety Research (WFSR), Wageningen, The Netherlands
| | - Martijn Staats
- Wageningen Food Safety Research (WFSR), Wageningen, The Netherlands
| | - Bas Bokkers
- Centre for Safety of Substances and Products, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Marco Zeilmaker
- Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Aldert Piersma
- Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
| | - Mirjam Luijten
- Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Ron Hoogenboom
- Wageningen Food Safety Research (WFSR), Wageningen, The Netherlands
| | - Ad Peijnenburg
- Wageningen Food Safety Research (WFSR), Wageningen, The Netherlands
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18
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Li J, Zagorski JW, Kaminski NE. Establishment of a point of departure for CBD hepatotoxicity employing human HepaRG spheroids. Toxicology 2023; 488:153469. [PMID: 36863504 DOI: 10.1016/j.tox.2023.153469] [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: 01/27/2023] [Revised: 02/17/2023] [Accepted: 02/26/2023] [Indexed: 03/04/2023]
Abstract
The United States Food and Drug Administration recently approved the use of Cannabis sativa derived cannabidiol (CBD) in the treatment of Dravet Syndrome and Lennox-Gastaut Syndrome, under the trade name, Epidiolex. In double-blinded, placebo-controlled clinical trials, elevated ALT levels were observed in some patients, but these findings could not be uncoupled from the confounds of potential drug-drug interactions with co-administration of valproate and clobazam. Given the uncertainty of the potential hepatatoxic effects of CBD, the objective of the present study was to determine a point of departure for CBD, using human HepaRG spheroid cultures, followed by transcriptomic benchmark dose analysis. Treatment of HepaRG spheroids with CBD for 24 and 72 h, resulted in EC50 concentrations for cytotoxicity of 86.27 µM and 58.04 µM, respectively. Subsequent transcriptomic analysis at these timepoints demonstrated little alteration of gene and pathway data sets at a CBD concentration at or below 10 µM. Although this current analysis was conducted using liver cells, interestingly the findings at 72 h post CBD treatment showed suppression of many genes more commonly associated with immune regulation. Indeed, the immune system is a well-established target for CBD based on immune function assays. Collectively, in the present studies a point of departure was derived using transcriptomic changes produced by CBD in a human cell-based model system, which has been shown to accurately translate to human hepatotoxicity modeling.
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Affiliation(s)
- Jinpeng Li
- Center for Research on Ingredient Safety, Michigan State University, East Lansing, MI 48824, United States; Institute for Integrative Toxicology, Michigan State University, East Lansing, MI 48824, United States
| | - Joseph W Zagorski
- Center for Research on Ingredient Safety, Michigan State University, East Lansing, MI 48824, United States; Institute for Integrative Toxicology, Michigan State University, East Lansing, MI 48824, United States
| | - Norbert E Kaminski
- Center for Research on Ingredient Safety, Michigan State University, East Lansing, MI 48824, United States; Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI 48824, United States; Institute for Integrative Toxicology, Michigan State University, East Lansing, MI 48824, United States.
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19
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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.
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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
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20
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Wheeler MW, Lim S, House J, Shockley K, Bailer AJ, Fostel J, Yang L, Talley D, Raghuraman A, Gift JS, Davis JA, Auerbach SS, Motsinger-Reif AA. ToxicR: A computational platform in R for computational toxicology and dose-response analyses. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2023; 25:100259. [PMID: 36909352 PMCID: PMC9997717 DOI: 10.1016/j.comtox.2022.100259] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The need to analyze the complex relationships observed in high-throughput toxicogenomic and other omic platforms has resulted in an explosion of methodological advances in computational toxicology. However, advancements in the literature often outpace the development of software researchers can implement in their pipelines, and existing software is frequently based on pre-specified workflows built from well-vetted assumptions that may not be optimal for novel research questions. Accordingly, there is a need for a stable platform and open-source codebase attached to a programming language that allows users to program new algorithms. To fill this gap, the Biostatistics and Computational Biology Branch of the National Institute of Environmental Health Sciences, in cooperation with the National Toxicology Program (NTP) and US Environmental Protection Agency (EPA), developed ToxicR, an open-source R programming package. The ToxicR platform implements many of the standard analyses used by the NTP and EPA, including dose-response analyses for continuous and dichotomous data that employ Bayesian, maximum likelihood, and model averaging methods, as well as many standard tests the NTP uses in rodent toxicology and carcinogenicity studies, such as the poly-K and Jonckheere trend tests. ToxicR is built on the same codebase as current versions of the EPA's Benchmark Dose software and NTP's BMDExpress software but has increased flexibility because it directly accesses this software. To demonstrate ToxicR, we developed a custom workflow to illustrate its capabilities for analyzing toxicogenomic data. The unique features of ToxicR will allow researchers in other fields to add modules, increasing its functionality in the future.
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Affiliation(s)
- Matthew W. Wheeler
- Biostatistics and Computational Biology Branch Division of Intramural Research, National Institute of Environmental Health Sciences Durham, NC
| | - Sooyeong Lim
- Miami University Department of Statistics Oxford, OH
| | - John House
- Biostatistics and Computational Biology Branch Division of Intramural Research, National Institute of Environmental Health Sciences Durham, NC
| | - Keith Shockley
- Biostatistics and Computational Biology Branch Division of Intramural Research, National Institute of Environmental Health Sciences Durham, NC
| | | | - Jennifer Fostel
- Contractor, Division of the National Toxicology Program Durham, NC
| | - Longlong Yang
- Contractor, Division of the National Toxicology Program Durham, NC
| | - Dawan Talley
- Contractor, Division of the National Toxicology Program Durham, NC
| | | | - Jeffery S. Gift
- US Environmental Protection Agency (B243-01), National Center for Environmental Assessment, Durham, NC
| | - J. Allen Davis
- National Center for Environmental Assessment, US Environmental Protection Agency, Cincinnati, OH
| | - Scott S. Auerbach
- Predictive Toxicology Branch, Division of the National Toxicology Program National Institute of Environmental Health Sciences Durham, NC
| | - Alison A. Motsinger-Reif
- Biostatistics and Computational Biology Branch Division of Intramural Research, National Institute of Environmental Health Sciences Durham, NC
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21
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Drake C, Wehr MM, Zobl W, Koschmann J, De Lucca D, Kühne BA, Hansen T, Knebel J, Ritter D, Boei J, Vrieling H, Bitsch A, Escher SE. Substantiate a read-across hypothesis by using transcriptome data-A case study on volatile diketones. FRONTIERS IN TOXICOLOGY 2023; 5:1155645. [PMID: 37206915 PMCID: PMC10188990 DOI: 10.3389/ftox.2023.1155645] [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: 01/31/2023] [Accepted: 04/17/2023] [Indexed: 05/21/2023] Open
Abstract
This case study explores the applicability of transcriptome data to characterize a common mechanism of action within groups of short-chain aliphatic α-, β-, and γ-diketones. Human reference in vivo data indicate that the α-diketone diacetyl induces bronchiolitis obliterans in workers involved in the preparation of microwave popcorn. The other three α-diketones induced inflammatory responses in preclinical in vivo animal studies, whereas beta and gamma diketones in addition caused neuronal effects. We investigated early transcriptional responses in primary human bronchiolar (PBEC) cell cultures after 24 h and 72 h of air-liquid exposure. Differentially expressed genes (DEGs) were assessed based on transcriptome data generated with the EUToxRisk gene panel of Temp-O-Seq®. For each individual substance, genes were identified displaying a consistent differential expression across dose and exposure duration. The log fold change values of the DEG profiles indicate that α- and β-diketones are more active compared to γ-diketones. α-diketones in particular showed a highly concordant expression pattern, which may serve as a first indication of the shared mode of action. In order to gain a better mechanistic understanding, the resultant DEGs were submitted to a pathway analysis using ConsensusPathDB. The four α-diketones showed very similar results with regard to the number of activated and shared pathways. Overall, the number of signaling pathways decreased from α-to β-to γ-diketones. Additionally, we reconstructed networks of genes that interact with one another and are associated with different adverse outcomes such as fibrosis, inflammation or apoptosis using the TRANSPATH-database. Transcription factor enrichment and upstream analyses with the geneXplain platform revealed highly interacting gene products (called master regulators, MRs) per case study compound. The mapping of the resultant MRs on the reconstructed networks, visualized similar gene regulation with regard to fibrosis, inflammation and apoptosis. This analysis showed that transcriptome data can strengthen the similarity assessment of compounds, which is of particular importance, e.g., in read-across approaches. It is one important step towards grouping of compounds based on biological profiles.
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Affiliation(s)
- Christina Drake
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Hannover, Germany
- *Correspondence: Christina Drake,
| | - Matthias M. Wehr
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Hannover, Germany
| | - Walter Zobl
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Hannover, Germany
| | | | | | - Britta A. Kühne
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Hannover, Germany
| | - Tanja Hansen
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Hannover, Germany
| | - Jan Knebel
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Hannover, Germany
| | - Detlef Ritter
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Hannover, Germany
| | - Jan Boei
- Leiden University Medical Center, Leiden, Netherlands
| | | | - Annette Bitsch
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Hannover, Germany
| | - Sylvia E. Escher
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Hannover, Germany
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22
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Derivation of metabolic point of departure using high-throughput in vitro metabolomics: investigating the importance of sampling time points on benchmark concentration values in the HepaRG cell line. Arch Toxicol 2023; 97:721-735. [PMID: 36683062 PMCID: PMC9968698 DOI: 10.1007/s00204-022-03439-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 12/21/2022] [Indexed: 01/23/2023]
Abstract
Amongst omics technologies, metabolomics should have particular value in regulatory toxicology as the measurement of the molecular phenotype is the closest to traditional apical endpoints, whilst offering mechanistic insights into the biological perturbations. Despite this, the application of untargeted metabolomics for point-of-departure (POD) derivation via benchmark concentration (BMC) modelling is still a relatively unexplored area. In this study, a high-throughput workflow was applied to derive PODs associated with a chemical exposure by measuring the intracellular metabolome of the HepaRG cell line following treatment with one of four chemicals (aflatoxin B1, benzo[a]pyrene, cyclosporin A, or rotenone), each at seven concentrations (aflatoxin B1, benzo[a]pyrene, cyclosporin A: from 0.2048 μM to 50 μM; rotenone: from 0.04096 to 10 μM) and five sampling time points (2, 6, 12, 24 and 48 h). The study explored three approaches to derive PODs using benchmark concentration modelling applied to single features in the metabolomics datasets or annotated metabolites or lipids: (1) the 1st rank-ordered unannotated feature, (2) the 1st rank-ordered putatively annotated feature (using a recently developed HepaRG-specific library of polar metabolites and lipids), and (3) 25th rank-ordered feature, demonstrating that for three out of four chemical datasets all of these approaches led to relatively consistent BMC values, varying less than tenfold across the methods. In addition, using the 1st rank-ordered unannotated feature it was possible to investigate temporal trends in the datasets, which were shown to be chemical specific. Furthermore, a possible integration of metabolomics-driven POD derivation with the liver steatosis adverse outcome pathway (AOP) was demonstrated. The study highlights that advances in technologies enable application of in vitro metabolomics at scale; however, greater confidence in metabolite identification is required to ensure PODs are mechanistically anchored.
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23
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Oku Y, Madia F, Lau P, Paparella M, McGovern T, Luijten M, Jacobs MN. Analyses of Transcriptomics Cell Signalling for Pre-Screening Applications in the Integrated Approach for Testing and Assessment of Non-Genotoxic Carcinogens. Int J Mol Sci 2022; 23:ijms232112718. [PMID: 36361516 PMCID: PMC9659232 DOI: 10.3390/ijms232112718] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/18/2022] [Accepted: 10/18/2022] [Indexed: 12/03/2022] Open
Abstract
With recent rapid advancement of methodological tools, mechanistic understanding of biological processes leading to carcinogenesis is expanding. New approach methodologies such as transcriptomics can inform on non-genotoxic mechanisms of chemical carcinogens and can be developed for regulatory applications. The Organisation for the Economic Cooperation and Development (OECD) expert group developing an Integrated Approach to the Testing and Assessment (IATA) of Non-Genotoxic Carcinogens (NGTxC) is reviewing the possible assays to be integrated therein. In this context, we review the application of transcriptomics approaches suitable for pre-screening gene expression changes associated with phenotypic alterations that underlie the carcinogenic processes for subsequent prioritisation of downstream test methods appropriate to specific key events of non-genotoxic carcinogenesis. Using case studies, we evaluate the potential of gene expression analyses especially in relation to breast cancer, to identify the most relevant approaches that could be utilised as (pre-) screening tools, for example Gene Set Enrichment Analysis (GSEA). We also consider how to address the challenges to integrate gene panels and transcriptomic assays into the IATA, highlighting the pivotal omics markers identified for assay measurement in the IATA key events of inflammation, immune response, mitogenic signalling and cell injury.
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Affiliation(s)
- Yusuke Oku
- The Organisation for Economic Cooperation and Development (OECD), 2 Rue Andre Pascal, 75016 Paris, France
- Correspondence: (Y.O.); (M.N.J.)
| | - Federica Madia
- European Commission, Joint Research Centre (JRC), Via Enrico Fermi, 2749, 21027 Ispra, Italy
| | - Pierre Lau
- Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Martin Paparella
- Institute of Medical Biochemistry, Biocenter, Medical University of Innsbruck, Innrain 80, 6020 Innbruck, Austria
| | - Timothy McGovern
- US Food and Drug Administration (FDA), 10903 New Hampshire Avenue, Silver Spring, MD 20901, USA
| | - Mirjam Luijten
- Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, Bilthoven, 3721 MA Utrecht, The Netherlands
| | - Miriam N. Jacobs
- Centre for Radiation, Chemical and Environmental Hazard (CRCE), Public Health England (PHE), Chilton OX11 0RQ, Oxfordshire, UK
- Correspondence: (Y.O.); (M.N.J.)
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24
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Adam N, Vuong NQ, Adams H, Kuo B, Beheshti A, Yauk C, Wilkins R, Chauhan V. Evaluating the Influences of Confounding Variables on Benchmark Dose using a Case Study in the Field of Ionizing Radiation. Int J Radiat Biol 2022; 98:1845-1855. [PMID: 35939396 DOI: 10.1080/09553002.2022.2110303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Purpose A vast amount of data regarding the effects of radiation stressors on transcriptional changes has been produced over the past few decades. These data have shown remarkable consistency across platforms and experimental design, enabling increased understanding of early molecular effects of radiation exposure. However, the value of transcriptomic data in the context of risk assessment is not clear and represents a gap that is worthy of further consideration. Recently, benchmark dose (BMD) modeling has shown promise in correlating a transcriptional point of departure (POD) to that derived using phenotypic outcomes relevant to human health risk assessment. Although frequently applied in chemical toxicity evaluation, our group has recently demonstrated application within the field of radiation research. This approach allows the possibility to quantitatively compare radiation-induced gene and pathway alterations across various datasets using BMD values and derive meaningful biological effects. However, before BMD modeling can confidently be used, an understanding of the impact of confounding variables on BMD outputs is needed. Methods: To this end, BMD modeling was applied to a publicly available microarray dataset (Gene Expression Omnibus #GSE23515) that used peripheral blood ex-vivo gamma-irradiated at 0.82 Gy/min, at doses of 0, 0.1, 0.5 or 2 Gy, and assessed 6 hours post-exposure. The dataset comprised six female smokers (F-S), six female non-smokers (F-NS), six male smokers (M-S), and six male non-smokers (M-NS). Results: A combined total of 412 genes were fit to models and the BMD distribution was noted to be bi-modal across the four groups. A total of 74, 41, 62 and 62 genes were unique to the F-NS, M-NS, F-S and M-S groups. Sixty-two BMD modeled genes and nine pathways were common across all four groups. There were no differential sensitivity of responses in the robust common genes and pathways. Conclusion: For radiation-responsive genes and pathways common across the study groups, the BMD distribution of transcriptional activity was unaltered by sex and smoking status. Although further validation of the data is needed, these initial findings suggest BMD values for radiation relevant genes and pathways are robust and could be explored further in future studies.
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Affiliation(s)
- Nadine Adam
- Consumer and Clinical Radiation Protection Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Ngoc Q Vuong
- Radiation Protection Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Hailey Adams
- Consumer and Clinical Radiation Protection Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Byron Kuo
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
| | - Afshin Beheshti
- KBR, Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, 94035, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Carole Yauk
- University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Ruth Wilkins
- Consumer and Clinical Radiation Protection Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Vinita Chauhan
- Consumer and Clinical Radiation Protection Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
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25
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Stainforth R, Vuong N, Adam N, Kuo B, Wilkins RC, Yauk C, Beheshti A, Chauhan V. Benchmark dose modeling of transcriptional data: a systematic approach to identify best practices for study designs used in radiation research. Int J Radiat Biol 2022; 98:1832-1844. [PMID: 35939275 DOI: 10.1080/09553002.2022.2110300] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
PURPOSE Benchmark dose (BMD) modeling is a method commonly used in chemical toxicology to identify the point of departure (POD) from a dose-response curve linked to a health-related outcome. Recently, it is being explored on transcriptional data and in adverse outcome pathways (AOPs). As AOPs are informed by diverse data types, it is important to understand the impact of study parameters such as dose selection, number of replicates and dose range on BMD outputs for radiation induced genes and pathways. MATERIALS AND METHODS Data were selected from the Gene Expression Omnibus (GSE52403) that featured gene expression profiles of peripheral blood samples from C57BL/6 mice 6 hours post-exposure to 137Cs gamma-radiation at 0, 1, 2, 3, 4.5, 6, 8 and 10.5 Gy. The dataset comprised a broad dose-range over multiple dose-points with consistent dose spacing and multiple biological replicates. This dataset was ideal for systematically transforming across three categories: (1) dose-range, (2) dose-spacing and (3) number of controls/replicates. Across these categories, 29 transformed datasets were compared to the original dataset to determine the impact of each transformation on the BMD outputs. RESULTS Most of the experimental changes did not impact the BMD outputs. The transformed datasets were largely consistent with the original dataset in terms of number of reproduced genes modeled and absolute BMD values for genes and pathways. Variations in dose selection identified the importance of the absolute value of the lowest and second dose. It was determined that dose selection should include at least two doses <1 Gy and two >5 Gy to achieve meaningful BMD outputs. Changes to the number of biological replicates in the control and non-zero dose groups impacted the overall accuracy and precision of the BMD outputs as well as the ability to fit dose-response models consistent with the original dataset. CONCLUSION Successful application of transcriptomic BMD modeling for radiation datasets requires considerations of the exposure dose and the number of biological replicates. Most important is the selection of the lowest doses and dose spacing. Reflections on these parameters in experimental design will provide meaningful BMD outputs that could correlate well to apical endpoints of relevance to radiation exposure assessment.
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Affiliation(s)
| | - Ngoc Vuong
- Radiation Protection Bureau, Health Canada, Ottawa, Canada
| | - Nadine Adam
- Consumer and Clinical Radiation Protection Bureau, Health Canada, Ottawa, Canada
| | - Byron Kuo
- Exposure and Biomonitoring Division, Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch
| | - Ruth C Wilkins
- Consumer and Clinical Radiation Protection Bureau, Health Canada, Ottawa, Canada
| | - Carole Yauk
- Department of Biology, University of Ottawa, Ottawa, ON, Canada
| | - Afshin Beheshti
- KBR, Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, 94035, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Vinita Chauhan
- Consumer and Clinical Radiation Protection Bureau, Health Canada, Ottawa, Canada
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26
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ALOHA: Aggregated local extrema splines for high-throughput dose-response analysis. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2022; 21:100196. [PMID: 35083394 PMCID: PMC8785973 DOI: 10.1016/j.comtox.2021.100196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Computational methods for genomic dose-response integrate dose-response modeling with bioinformatics tools to evaluate changes in molecular and cellular functions related to pathogenic processes. These methods use parametric models to describe each gene's dose-response, but such models may not adequately capture expression changes. Additionally, current approaches do not consider gene co-expression networks. When assessing co-expression networks, one typically does not consider the dose-response relationship, resulting in 'co-regulated' gene sets containing genes having different dose-response patterns. To avoid these limitations, we develop an analysis pipeline called Aggregated Local Extrema Splines for High-throughput Analysis (ALOHA), which computes individual genomic dose-response functions using a flexible class Bayesian shape constrained splines and clusters gene co-regulation based upon these fits. Using splines, we reduce information loss due to parametric lack-of-fit issues, and because we cluster on dose-response relationships, we better identify co-regulation clusters for genes that have co-expressed dose-response patterns from chemical exposure. The clustered pathways can then be used to estimate a dose associated with a pre-specified biological response, i.e., the benchmark dose (BMD), and approximate a point of departure dose corresponding to minimal adverse response in the whole tissue/organism. We compare our approach to current parametric methods and our biologically enriched gene sets to cluster on normalized expression data. Using this methodology, we can more effectively extract the underlying structure leading to more cohesive estimates of gene set potency.
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27
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Tandon A, Howard B, Ramaiahgari S, Maharana A, Ferguson S, Shah R, Merrick BA. Deep Learning Image Analysis of High-Throughput Toxicology Assay Images. SLAS DISCOVERY : ADVANCING LIFE SCIENCES R & D 2022; 27:29-38. [PMID: 35058173 PMCID: PMC8955414 DOI: 10.1016/j.slasd.2021.10.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
High-throughput chemical screening approaches often employ microscopy to capture photomicrographs from multi-well cell culture plates, generating thousands of images that require time-consuming human analysis. To automate this subjective and time-consuming manual process, we have developed a method that uses deep learning to automatically classify digital assay images. We have trained a convolutional neural network (CNN) to perform binary and multi-class classification. The binary classifier binned assay images into healthy (comparable to untreated controls) and altered (not comparable to untreated-control) classes with >98% accuracy; the multi-class classifier assigned "Healthy," "Intermediate" and "Altered" labels to assay images with >95% accuracy. Our dataset comprised high-resolution assay images from primary human hepatocytes and undifferentiated (proliferating) and differentiated 2D cultures of HepaRG cells. In this study we have focused on testing and fine-tuning various CNN architectures, including ResNet 34, 50 and 101. To visualize regions in the images that the CNN model used for classification, we employed Class Activation Maps (CAM). This allowed us to better understand the inner workings of the neural network and led to additional optimizations of the algorithm. The results indicate a strong correspondence between dosage and classifier-predicted scores, suggesting that these scores might be useful in further characterizing benchmark dose. Together, these results clearly demonstrate that deep-learning based automated image classification of cell morphology changes upon chemical-induced stress can yield highly accurate and reproducible assessments of cytotoxicity across a variety of cell types.
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Affiliation(s)
| | | | - Sreenivasa Ramaiahgari
- National Toxicology Program Division, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | | | - Stephen Ferguson
- National Toxicology Program Division, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - Ruchir Shah
- Sciome LLC, Research Triangle Park, NC, USA.
| | - B Alex Merrick
- National Toxicology Program Division, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
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28
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Reardon AJF, Rowan-Carroll A, Ferguson SS, Leingartner K, Gagne R, Kuo B, Williams A, Lorusso L, Bourdon-Lacombe JA, Carrier R, Moffat I, Yauk CL, Atlas E. Potency Ranking of Per- and Polyfluoroalkyl Substances Using High-Throughput Transcriptomic Analysis of Human Liver Spheroids. Toxicol Sci 2021; 184:154-169. [PMID: 34453843 DOI: 10.1093/toxsci/kfab102] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Per- and polyfluoroalkyl substances (PFAS) are some of the most prominent organic contaminants in human blood. Although the toxicological implications of human exposure to perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA) are well established, data on lesser-understood PFAS are limited. New approach methodologies (NAMs) that apply bioinformatic tools to high-throughput data are being increasingly considered to inform risk assessment for data-poor chemicals. The aim of this study was to compare the potencies (ie, benchmark concentrations: BMCs) of PFAS in primary human liver microtissues (3D spheroids) using high-throughput transcriptional profiling. Gene expression changes were measured using TempO-seq, a templated, multiplexed RNA-sequencing platform. Spheroids were exposed for 1 or 10 days to increasing concentrations of 23 PFAS in 3 subgroups: carboxylates (PFCAs), sulfonates (PFSAs), and fluorotelomers and sulfonamides. PFCAs and PFSAs exhibited trends toward increased transcriptional potency with carbon chain-length. Specifically, longer-chain compounds (7-10 carbons) were more likely to induce changes in gene expression and have lower transcriptional BMCs. The combined high-throughput transcriptomic and bioinformatic analyses support the capability of NAMs to efficiently assess the effects of PFAS in liver microtissues. The data enable potency ranking of PFAS for human liver cell spheroid cytotoxicity and transcriptional changes, and assessment of in vitro transcriptomic points of departure. These data improve our understanding of the possible health effects of PFAS and will be used to inform read-across for human health risk assessment.
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Affiliation(s)
- Anthony J F Reardon
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Andrea Rowan-Carroll
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Stephen S Ferguson
- Biomolecular Screening Branch, Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA
| | - Karen Leingartner
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Remi Gagne
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Byron Kuo
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Andrew Williams
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Luigi Lorusso
- Chemicals and Environmental Health Management Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Julie A Bourdon-Lacombe
- Water and Air Quality Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Richard Carrier
- Water and Air Quality Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Ivy Moffat
- Water and Air Quality Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Carole L Yauk
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada.,Department of Biology, University of Ottawa, Ottawa, Ontario, Canada
| | - Ella Atlas
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada.,Department of Biochemistry, University of Ottawa, Ottawa, Ontario, Canada
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29
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Reardon AJF, Rowan-Carroll A, Ferguson SS, Leingartner K, Gagne R, Kuo B, Williams A, Lorusso L, Bourdon-Lacombe JA, Carrier R, Moffat I, Yauk CL, Atlas E. Potency Ranking of Per- and Polyfluoroalkyl Substances Using High-Throughput Transcriptomic Analysis of Human Liver Spheroids. Toxicol Sci 2021; 184:154-169. [PMID: 34453843 DOI: 10.1101/2020.10.20.347328] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023] Open
Abstract
Per- and polyfluoroalkyl substances (PFAS) are some of the most prominent organic contaminants in human blood. Although the toxicological implications of human exposure to perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA) are well established, data on lesser-understood PFAS are limited. New approach methodologies (NAMs) that apply bioinformatic tools to high-throughput data are being increasingly considered to inform risk assessment for data-poor chemicals. The aim of this study was to compare the potencies (ie, benchmark concentrations: BMCs) of PFAS in primary human liver microtissues (3D spheroids) using high-throughput transcriptional profiling. Gene expression changes were measured using TempO-seq, a templated, multiplexed RNA-sequencing platform. Spheroids were exposed for 1 or 10 days to increasing concentrations of 23 PFAS in 3 subgroups: carboxylates (PFCAs), sulfonates (PFSAs), and fluorotelomers and sulfonamides. PFCAs and PFSAs exhibited trends toward increased transcriptional potency with carbon chain-length. Specifically, longer-chain compounds (7-10 carbons) were more likely to induce changes in gene expression and have lower transcriptional BMCs. The combined high-throughput transcriptomic and bioinformatic analyses support the capability of NAMs to efficiently assess the effects of PFAS in liver microtissues. The data enable potency ranking of PFAS for human liver cell spheroid cytotoxicity and transcriptional changes, and assessment of in vitro transcriptomic points of departure. These data improve our understanding of the possible health effects of PFAS and will be used to inform read-across for human health risk assessment.
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Affiliation(s)
- Anthony J F Reardon
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Andrea Rowan-Carroll
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Stephen S Ferguson
- Biomolecular Screening Branch, Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA
| | - Karen Leingartner
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Remi Gagne
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Byron Kuo
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Andrew Williams
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Luigi Lorusso
- Chemicals and Environmental Health Management Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Julie A Bourdon-Lacombe
- Water and Air Quality Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Richard Carrier
- Water and Air Quality Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Ivy Moffat
- Water and Air Quality Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Carole L Yauk
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
- Department of Biology, University of Ottawa, Ottawa, Ontario, Canada
| | - Ella Atlas
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
- Department of Biochemistry, University of Ottawa, Ottawa, Ontario, Canada
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30
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Buick JK, Williams A, Meier MJ, Swartz CD, Recio L, Gagné R, Ferguson SS, Engelward BP, Yauk CL. A Modern Genotoxicity Testing Paradigm: Integration of the High-Throughput CometChip® and the TGx-DDI Transcriptomic Biomarker in Human HepaRG™ Cell Cultures. Front Public Health 2021; 9:694834. [PMID: 34485225 PMCID: PMC8416458 DOI: 10.3389/fpubh.2021.694834] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 07/14/2021] [Indexed: 12/14/2022] Open
Abstract
Higher-throughput, mode-of-action-based assays provide a valuable approach to expedite chemical evaluation for human health risk assessment. In this study, we combined the high-throughput alkaline DNA damage-sensing CometChip® assay with the TGx-DDI transcriptomic biomarker (DDI = DNA damage-inducing) using high-throughput TempO-Seq®, as an integrated genotoxicity testing approach. We used metabolically competent differentiated human HepaRG™ cell cultures to enable the identification of chemicals that require bioactivation to cause genotoxicity. We studied 12 chemicals (nine DDI, three non-DDI) in increasing concentrations to measure and classify chemicals based on their ability to damage DNA. The CometChip® classified 10/12 test chemicals correctly, missing a positive DDI call for aflatoxin B1 and propyl gallate. The poor detection of aflatoxin B1 adducts is consistent with the insensitivity of the standard alkaline comet assay to bulky lesions (a shortcoming that can be overcome by trapping repair intermediates). The TGx-DDI biomarker accurately classified 10/12 agents. TGx-DDI correctly identified aflatoxin B1 as DDI, demonstrating efficacy for combined used of these complementary methodologies. Zidovudine, a known DDI chemical, was misclassified as it inhibits transcription, which prevents measurable changes in gene expression. Eugenol, a non-DDI chemical known to render misleading positive results at high concentrations, was classified as DDI at the highest concentration tested. When combined, the CometChip® assay and the TGx-DDI biomarker were 100% accurate in identifying chemicals that induce DNA damage. Quantitative benchmark concentration (BMC) modeling was applied to evaluate chemical potencies for both assays. The BMCs for the CometChip® assay and the TGx-DDI biomarker were highly concordant (within 4-fold) and resulted in identical potency rankings. These results demonstrate that these two assays can be integrated for efficient identification and potency ranking of DNA damaging agents in HepaRG™ cell cultures.
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Affiliation(s)
- Julie K Buick
- Environmental Health Science and Research 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
| | - Carol D Swartz
- Integrated Laboratory Systems Inc. (ILS), Research Triangle Park, Durham, NC, United States
| | - Leslie Recio
- Integrated Laboratory Systems Inc. (ILS), Research Triangle Park, Durham, NC, United States
| | - Rémi Gagné
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
| | - Stephen S Ferguson
- National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, NC, United States
| | - Bevin P Engelward
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Carole L Yauk
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada.,Department of Biology, University of Ottawa, Ottawa, ON, Canada
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31
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Lee F, Shah I, Soong YT, Xing J, Ng IC, Tasnim F, Yu H. Reproducibility and robustness of high-throughput S1500+ transcriptomics on primary rat hepatocytes for chemical-induced hepatotoxicity assessment. Curr Res Toxicol 2021; 2:282-295. [PMID: 34467220 PMCID: PMC8384775 DOI: 10.1016/j.crtox.2021.07.003] [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: 03/22/2021] [Revised: 07/15/2021] [Accepted: 07/31/2021] [Indexed: 11/06/2022] Open
Abstract
TempO-Seq assays of rat hepatocytes in collagen sandwich are highly reproducible. Gene expression analysis shows S1500+ is representative of the whole transcriptome. Connectivity mapping shows consistency between TempO-Seq and Affymetrix data. Gene set enrichment shows consistency between S1500+ and the whole transcriptome. Gene set enrichment using hallmark gene sets informs hepatotoxicity.
Cell-based in vitro models coupled with high-throughput transcriptomics (HTTr) are increasingly utilized as alternative methods to animal-based toxicity testing. Here, using a panel of 14 chemicals with different risks of human drug-induced liver injury (DILI) and two dosing concentrations, we evaluated an HTTr platform comprised of collagen sandwich primary rat hepatocyte culture and the TempO-Seq surrogate S1500+ (ST) assay. First, the HTTr platform was found to exhibit high reproducibility between technical and biological replicates (r greater than 0.85). Connectivity mapping analysis further demonstrated a high level of inter-platform reproducibility between TempO-Seq data and Affymetrix GeneChip data from the Open TG-GATES project. Second, the TempO-Seq ST assay was shown to be a robust surrogate to the whole transcriptome (WT) assay in capturing chemical-induced changes in gene expression, as evident from correlation analysis, PCA and unsupervised hierarchical clustering. Gene set enrichment analysis (GSEA) using the Hallmark gene set collection also demonstrated consistency in enrichment scores between ST and WT assays. Lastly, unsupervised hierarchical clustering of hallmark enrichment scores broadly divided the samples into hepatotoxic, intermediate, and non-hepatotoxic groups. Xenobiotic metabolism, bile acid metabolism, apoptosis, p53 pathway, and coagulation were found to be the key hallmarks driving the clustering. Taken together, our results established the reproducibility and performance of collagen sandwich culture in combination with TempO-Seq S1500+ assay, and demonstrated the utility of GSEA using the hallmark gene set collection to identify potential hepatotoxicants for further validation.
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Affiliation(s)
- Fan Lee
- Innovations in Food & Chemical Safety Program (IFCS), Institute of Bioengineering and Bioimaging (IBB), Agency for Science Technology and Research, Singapore
| | - Imran Shah
- Center for Computational Toxicology & Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, United States
| | - Yun Ting Soong
- Innovations in Food & Chemical Safety Program (IFCS), Institute of Bioengineering and Bioimaging (IBB), Agency for Science Technology and Research, Singapore
| | - Jiangwa Xing
- Innovations in Food & Chemical Safety Program (IFCS), Institute of Bioengineering and Bioimaging (IBB), Agency for Science Technology and Research, Singapore
| | - Inn Chuan Ng
- Department of Physiology and Mechanobiology Institute, National University of Singapore, Singapore
| | - Farah Tasnim
- Innovations in Food & Chemical Safety Program (IFCS), Institute of Bioengineering and Bioimaging (IBB), Agency for Science Technology and Research, Singapore
| | - Hanry Yu
- Innovations in Food & Chemical Safety Program (IFCS), Institute of Bioengineering and Bioimaging (IBB), Agency for Science Technology and Research, Singapore.,Department of Physiology and Mechanobiology Institute, National University of Singapore, Singapore.,Critical Analytics for Manufacturing Personalized-Medicine, Singapore-MIT Alliance for Research and Technology, Singapore
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32
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Wang H, Brown PC, Chow EC, Ewart L, Ferguson SS, Fitzpatrick S, Freedman BS, Guo GL, Hedrich W, Heyward S, Hickman J, Isoherranen N, Li AP, Liu Q, Mumenthaler SM, Polli J, Proctor WR, Ribeiro A, Wang J, Wange RL, Huang S. 3D cell culture models: Drug pharmacokinetics, safety assessment, and regulatory consideration. Clin Transl Sci 2021; 14:1659-1680. [PMID: 33982436 PMCID: PMC8504835 DOI: 10.1111/cts.13066] [Citation(s) in RCA: 112] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 04/15/2021] [Accepted: 04/16/2021] [Indexed: 12/12/2022] Open
Abstract
Nonclinical testing has served as a foundation for evaluating potential risks and effectiveness of investigational new drugs in humans. However, the current two-dimensional (2D) in vitro cell culture systems cannot accurately depict and simulate the rich environment and complex processes observed in vivo, whereas animal studies present significant drawbacks with inherited species-specific differences and low throughput for increased demands. To improve the nonclinical prediction of drug safety and efficacy, researchers continue to develop novel models to evaluate and promote the use of improved cell- and organ-based assays for more accurate representation of human susceptibility to drug response. Among others, the three-dimensional (3D) cell culture models present physiologically relevant cellular microenvironment and offer great promise for assessing drug disposition and pharmacokinetics (PKs) that influence drug safety and efficacy from an early stage of drug development. Currently, there are numerous different types of 3D culture systems, from simple spheroids to more complicated organoids and organs-on-chips, and from single-cell type static 3D models to cell co-culture 3D models equipped with microfluidic flow control as well as hybrid 3D systems that combine 2D culture with biomedical microelectromechanical systems. This article reviews the current application and challenges of 3D culture systems in drug PKs, safety, and efficacy assessment, and provides a focused discussion and regulatory perspectives on the liver-, intestine-, kidney-, and neuron-based 3D cellular models.
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Affiliation(s)
- Hongbing Wang
- Department of Pharmaceutical SciencesUniversity of Maryland School of PharmacyBaltimoreMarylandUSA
| | - Paul C. Brown
- Center for Drug Evaluation and ResearchUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
| | - Edwin C.Y. Chow
- Office of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
| | | | - Stephen S. Ferguson
- Division of the National Toxicology ProgramNational Institute of Environmental Health SciencesResearch Triangle ParkNorth CarolinaUSA
| | - Suzanne Fitzpatrick
- Office of the Center DirectorCenter for Food Safety and Applied NutritionUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
| | - Benjamin S. Freedman
- Division of NephrologyDepartment of PathologyKidney Research Institute, and Institute for Stem Cell and Regenerative MedicineUniversity of WashingtonSeattleWashingtonUSA
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Grace L. Guo
- Department of Pharmacology and ToxicologyErnest Mario School of PharmacyRutgers UniversityPiscatawayNew JerseyUSA
| | - William Hedrich
- Pharmaceutical Candidate Optimization, Metabolism and PharmacokineticsBristol‐Myers Squibb CompanyPrincetonNew JerseyUSA
| | | | - James Hickman
- NanoScience Technology CenterUniversity of Central FloridaOrlandoFloridaUSA
| | - Nina Isoherranen
- Department of PharmaceuticsSchool of PharmacyUniversity of WashingtonSeattleWashingtonUSA
| | - Albert P. Li
- In Vitro ADMET LaboratoriesColumbiaMarylandUSA
- In Vitro ADMET LaboratoriesMaldenMassachusettsUSA
| | - Qi Liu
- Office of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
| | - Shannon M. Mumenthaler
- Lawrence J. Ellison Institute for Transformative MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - James Polli
- Department of Pharmaceutical SciencesUniversity of Maryland School of PharmacyBaltimoreMarylandUSA
| | - William R. Proctor
- Predictive Toxicology, Safety AssessmentGenentech, IncSouth San FranciscoCaliforniaUSA
| | - Alexandre Ribeiro
- Office of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
| | - Jian‐Ying Wang
- Department of SurgeryCell Biology GroupUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Ronald L. Wange
- Center for Drug Evaluation and ResearchUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
| | - Shiew‐Mei Huang
- Office of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
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33
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Harrill JA, Viant MR, Yauk CL, Sachana M, Gant TW, Auerbach SS, Beger RD, Bouhifd M, O'Brien J, Burgoon L, Caiment F, Carpi D, Chen T, Chorley BN, Colbourne J, Corvi R, Debrauwer L, O'Donovan C, Ebbels TMD, Ekman DR, Faulhammer F, Gribaldo L, Hilton GM, Jones SP, Kende A, Lawson TN, Leite SB, Leonards PEG, Luijten M, Martin A, Moussa L, Rudaz S, Schmitz O, Sobanski T, Strauss V, Vaccari M, Vijay V, Weber RJM, Williams AJ, Williams A, Thomas RS, Whelan M. Progress towards an OECD reporting framework for transcriptomics and metabolomics in regulatory toxicology. Regul Toxicol Pharmacol 2021; 125:105020. [PMID: 34333066 DOI: 10.1016/j.yrtph.2021.105020] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 07/26/2021] [Indexed: 12/12/2022]
Abstract
Omics methodologies are widely used in toxicological research to understand modes and mechanisms of toxicity. Increasingly, these methodologies are being applied to questions of regulatory interest such as molecular point-of-departure derivation and chemical grouping/read-across. Despite its value, widespread regulatory acceptance of omics data has not yet occurred. Barriers to the routine application of omics data in regulatory decision making have been: 1) lack of transparency for data processing methods used to convert raw data into an interpretable list of observations; and 2) lack of standardization in reporting to ensure that omics data, associated metadata and the methodologies used to generate results are available for review by stakeholders, including regulators. Thus, in 2017, the Organisation for Economic Co-operation and Development (OECD) Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) launched a project to develop guidance for the reporting of omics data aimed at fostering further regulatory use. Here, we report on the ongoing development of the first formal reporting framework describing the processing and analysis of both transcriptomic and metabolomic data for regulatory toxicology. We introduce the modular structure, content, harmonization and strategy for trialling this reporting framework prior to its publication by the OECD.
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Affiliation(s)
- Joshua A Harrill
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, United States.
| | - Mark R Viant
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom; Michabo Health Science, University of Birmingham Enterprise, Birmingham Research Park, Vincent Drive, Birmingham, B15 2SQ, United Kingdom.
| | - Carole L Yauk
- Department of Biology, University of Ottawa, Ottawa, ON, K1N 6N5, Canada.
| | - Magdalini Sachana
- Organisation for Economic Co-operation and Development (OECD), Environment Health and Safety Division, Paris, France
| | - Timothy W Gant
- Centre for Radiation, Chemical and Environmental Hazards (CRCE), Public Health England (PHE), Harwell Science Campus, Oxfordshire, United Kingdom
| | - Scott S Auerbach
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Richard D Beger
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | | | - Jason O'Brien
- Ecotoxicology and Wildlife Health Division, Environment and Climate Change Canada, Ottawa, ON, K1A 0H3, Canada
| | - Lyle Burgoon
- US Army Engineer Research and Development Center, 3909 Halls Ferry Rd, Vicksburg, MS, 39180, USA
| | - Florian Caiment
- Department of Toxicogenomics, Maastricht University, Universiteitssingel 50, 6229, ER, Maastricht, the Netherlands
| | - Donatella Carpi
- European Commission, Joint Research Centre (JRC), 21027, Ispra, Italy
| | - Tao Chen
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Brian N Chorley
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, United States
| | - John Colbourne
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom; Michabo Health Science, University of Birmingham Enterprise, Birmingham Research Park, Vincent Drive, Birmingham, B15 2SQ, United Kingdom
| | - Raffaella Corvi
- European Commission, Joint Research Centre (JRC), 21027, Ispra, Italy
| | - Laurent Debrauwer
- Toxalim (Research Centre in Food Toxicology), INRAE UMR 1331, ENVT, INP-Purpan, Paul Sabatier University (UPS), Toulouse, France; MetaToul-AXIOM Platform, MetaboHUB, National Infrastructure for Metabolomics and Fluxomics, Toulouse, France
| | - Claire O'Donovan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Timothy M D Ebbels
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, SW7 2AZ, United Kingdom
| | - Drew R Ekman
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Athens, GA, 30605, United States
| | | | - Laura Gribaldo
- European Commission, Joint Research Centre (JRC), 21027, Ispra, Italy
| | - Gina M Hilton
- PETA Science Consortium International e.V., Friolzheimer Str. 3, 70499, Stuttgart, Germany
| | - Stephanie P Jones
- Ecotoxicology and Wildlife Health Division, Environment and Climate Change Canada, Ottawa, ON, K1A 0H3, Canada
| | - Aniko Kende
- Syngenta Jealott's Hill International Research Centre, Bracknell, RG42 6EY, United Kingdom
| | - Thomas N Lawson
- Michabo Health Science, University of Birmingham Enterprise, Birmingham Research Park, Vincent Drive, Birmingham, B15 2SQ, United Kingdom
| | - Sofia B Leite
- European Commission, Joint Research Centre (JRC), 21027, Ispra, Italy
| | - Pim E G Leonards
- Department of Environment and Health, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081HV, Amsterdam, the Netherlands
| | - Mirjam Luijten
- Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | | | - Laura Moussa
- US Food and Drug Administration, Center for Veterinary Medicine, Rockville, MD, United States
| | - Serge Rudaz
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland; Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland; Swiss Centre for Applied Human Toxicology (SCAHT), Switzerland
| | - Oliver Schmitz
- BASF Metabolome Solutions, Metabolome Data Science, Tegeler Weg 33, 10589, Berlin, Germany
| | | | - Volker Strauss
- BASF SE, Toxicology and Ecology, 67056, Ludwigshafen, Germany
| | - Monica Vaccari
- Center for Environmental Health and Prevention, Regional Agency for Prevention, Environment and Energy of Emilia-Romagna, Bologna, Italy
| | - Vikrant Vijay
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Ralf J M Weber
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom; Michabo Health Science, University of Birmingham Enterprise, Birmingham Research Park, Vincent Drive, Birmingham, B15 2SQ, United Kingdom
| | - Antony J Williams
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, United States
| | - Andrew Williams
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, K1A 0K9, Canada
| | - 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
| | - Maurice Whelan
- European Commission, Joint Research Centre (JRC), 21027, Ispra, Italy
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34
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Shah I, Antonijevic T, Chambers B, Harrill J, Thomas R. Estimating Hepatotoxic Doses Using High-Content Imaging in Primary Hepatocytes. Toxicol Sci 2021; 183:285-301. [PMID: 34289070 DOI: 10.1093/toxsci/kfab091] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Using in vitro data to estimate point of departure (POD) values is an essential component of new approach methodologies (NAM)-based chemical risk assessments. In this case study, we evaluated a NAM for hepatotoxicity based on rat primary hepatocytes, high-content imaging (HCI), and toxicokinetic modeling. First, we treated rat primary hepatocytes with 10 concentrations (0.2 to 100 µM) of 51 chemicals that produced hepatotoxicity in repeat-dose subchronic and chronic exposures. Second, we used HCI to measure endoplasmic reticulum stress, mitochondrial function, lysosomal mass, steatosis, apoptosis, DNA texture, nuclear size, and cell number at 24, 48, and 72 h and calculated concentrations at 50% maximal activity (AC50). Third, we estimated administered equivalent doses (AEDs) from AC50 values using toxicokinetic modeling. AEDs using physiologically-based toxicokinetic models were 4.1-fold (SD 6.3) and 8.1-fold (SD 15.5) lower than subchronic and chronic lowest observed adverse effect levels (LOAELs), respectively. In contrast, AEDs from ToxCast and Tox21 assays were 89.8-fold (SD 149.5) and 168-fold (SD 323.7) lower than subchronic and chronic LOAELs. Individual HCI end-points also estimated AEDs for specific hepatic lesions that were lower than in vivo PODs. Lastly, AEDs were similar for different in vitro exposure durations, but steady-state toxicokinetic models produced 7.6-fold lower estimates than dynamic physiologically-based ones. Our findings suggest that NAMs from diverse cell types provide conservative estimates of PODs. In contrast, NAMs based on the same species and cell type as the adverse outcome may produce estimates closer to the traditional in vivo PODs.
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Affiliation(s)
- Imran Shah
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Todor Antonijevic
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA.,Oak Ridge Institute for Science and Education (ORISE), USA
| | - Bryant Chambers
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Joshua Harrill
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Russell Thomas
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
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Crizer DM, Ramaiahgari SC, Ferguson SS, Rice JR, Dunlap PE, Sipes NS, Auerbach SS, Merrick BA, DeVito MJ. Benchmark Concentrations for Untargeted Metabolomics Versus Transcriptomics for Liver Injury Compounds in In Vitro Liver Models. Toxicol Sci 2021; 181:175-186. [PMID: 33749773 PMCID: PMC8163038 DOI: 10.1093/toxsci/kfab036] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Interpretation of untargeted metabolomics data from both in vivo and physiologically relevant in vitro model systems continues to be a significant challenge for toxicology research. Potency-based modeling of toxicological responses has served as a pillar of interpretive context and translation of testing data. In this study, we leverage the resolving power of concentration-response modeling through benchmark concentration (BMC) analysis to interpret untargeted metabolomics data from differentiated cultures of HepaRG cells exposed to a panel of reference compounds and integrate data in a potency-aligned framework with matched transcriptomic data. For this work, we characterized biological responses to classical human liver injury compounds and comparator compounds, known to not cause liver injury in humans, at 10 exposure concentrations in spent culture media by untargeted liquid chromatography-mass spectrometry analysis. The analyte features observed (with limited metabolites identified) were analyzed using BMC modeling to derive compound-induced points of departure. The results revealed liver injury compounds produced concentration-related increases in metabolomic response compared to those rarely associated with liver injury (ie, sucrose, potassium chloride). Moreover, the distributions of altered metabolomic features were largely comparable with those observed using high throughput transcriptomics, which were further extended to investigate the potential for in vitro observed biological responses to be observed in humans with exposures at therapeutic doses. These results demonstrate the utility of BMC modeling of untargeted metabolomics data as a sensitive and quantitative indicator of human liver injury potential.
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Affiliation(s)
- David M Crizer
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709, USA
| | - Sreenivasa C Ramaiahgari
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709, USA
| | - Stephen S Ferguson
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709, USA
| | - Julie R Rice
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709, USA
| | - Paul E Dunlap
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709, USA
| | - Nisha S Sipes
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709, USA
| | - Scott S Auerbach
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709, USA
| | - Bruce Alex Merrick
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709, USA
| | - Michael J DeVito
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709, USA
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36
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Harrill JA, Everett LJ, Haggard DE, Sheffield T, Bundy JL, Willis CM, Thomas RS, Shah I, Judson RS. High-Throughput Transcriptomics Platform for Screening Environmental Chemicals. Toxicol Sci 2021; 181:68-89. [PMID: 33538836 DOI: 10.1093/toxsci/kfab009] [Citation(s) in RCA: 104] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
New approach methodologies (NAMs) that efficiently provide information about chemical hazard without using whole animals are needed to accelerate the pace of chemical risk assessments. Technological advancements in gene expression assays have made in vitro high-throughput transcriptomics (HTTr) a feasible option for NAMs-based hazard characterization of environmental chemicals. In this study, we evaluated the Templated Oligo with Sequencing Readout (TempO-Seq) assay for HTTr concentration-response screening of a small set of chemicals in the human-derived MCF7 cell model. Our experimental design included a variety of reference samples and reference chemical treatments in order to objectively evaluate TempO-Seq assay performance. To facilitate analysis of these data, we developed a robust and scalable bioinformatics pipeline using open-source tools. We also developed a novel gene expression signature-based concentration-response modeling approach and compared the results to a previously implemented workflow for concentration-response analysis of transcriptomics data using BMDExpress. Analysis of reference samples and reference chemical treatments demonstrated highly reproducible differential gene expression signatures. In addition, we found that aggregating signals from individual genes into gene signatures prior to concentration-response modeling yielded in vitro transcriptional biological pathway altering concentrations (BPACs) that were closely aligned with previous ToxCast high-throughput screening assays. Often these identified signatures were associated with the known molecular target of the chemicals in our test set as the most sensitive components of the overall transcriptional response. This work has resulted in a novel and scalable in vitro HTTr workflow that is suitable for high-throughput hazard evaluation of environmental chemicals.
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Affiliation(s)
- Joshua A Harrill
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
| | - Logan J Everett
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
| | - Derik E Haggard
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA.,Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, Tennessee, USA
| | - Thomas Sheffield
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA.,Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, Tennessee, USA
| | - Joseph L Bundy
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
| | - Clinton M Willis
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA.,Oak Ridge Associated Universities (ORAU), Oak Ridge, Tennessee, USA
| | - Russell S Thomas
- 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
| | - Richard S Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
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Systematic transcriptome-based comparison of cellular adaptive stress response activation networks in hepatic stem cell-derived progeny and primary human hepatocytes. Toxicol In Vitro 2021; 73:105107. [PMID: 33545341 DOI: 10.1016/j.tiv.2021.105107] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 01/12/2021] [Accepted: 01/30/2021] [Indexed: 12/12/2022]
Abstract
Various adaptive cellular stress response pathways are critical in the pathophysiology of liver disease and drug-induced liver injury. Human-induced pluripotent stem cell (hiPSC)-derived hepatocyte-like cells (HLCs) provide a promising tool to study cellular stress response pathways, but in this context there is limited insight on how HLCs compare to other in vitro liver models. Here, we systematically compared the transcriptomic profiles upon chemical activation in HLCs, hiPSC, primary human hepatocytes (PHH) and HepG2 liver cancer cells. We used targeted RNA-sequencing to map concentration transcriptional response using benchmark concentration modeling for the various stress responses in the different test systems. We found that HLCs are very sensitive towards oxidative stress and inflammation conditions as corresponding genes were activated at over 3 fold lower concentrations of the corresponding pathway inducing compounds as compared to PHH. PHH were the most sensitive model when studying UPR related effects. Due to the non-proliferative nature of PHH and HLCs, these do not pose a good/sensitive model to pick up DNA damage responses, while hiPSC and HepG2 were more sensitive in these conditions. We envision that this study contributes to a better understanding on how HLCs can contribute to the assessment of cell physiological stress response activation to predict hepatotoxic events.
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Franzosa JA, Bonzo JA, Jack J, Baker NC, Kothiya P, Witek RP, Hurban P, Siferd S, Hester S, Shah I, Ferguson SS, Houck KA, Wambaugh JF. High-throughput toxicogenomic screening of chemicals in the environment using metabolically competent hepatic cell cultures. NPJ Syst Biol Appl 2021; 7:7. [PMID: 33504769 PMCID: PMC7840683 DOI: 10.1038/s41540-020-00166-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 10/15/2020] [Indexed: 01/30/2023] Open
Abstract
The ToxCast in vitro screening program has provided concentration-response bioactivity data across more than a thousand assay endpoints for thousands of chemicals found in our environment and commerce. However, most ToxCast screening assays have evaluated individual biological targets in cancer cell lines lacking integrated physiological functionality (such as receptor signaling, metabolism). We evaluated differentiated HepaRGTM cells, a human liver-derived cell model understood to effectively model physiologically relevant hepatic signaling. Expression of 93 gene transcripts was measured by quantitative polymerase chain reaction using Fluidigm 96.96 dynamic arrays in response to 1060 chemicals tested in eight-point concentration-response. A Bayesian framework quantitatively modeled chemical-induced changes in gene expression via six transcription factors including: aryl hydrocarbon receptor, constitutive androstane receptor, pregnane X receptor, farnesoid X receptor, androgen receptor, and peroxisome proliferator-activated receptor alpha. For these chemicals the network model translates transcriptomic data into Bayesian inferences about molecular targets known to activate toxicological adverse outcome pathways. These data also provide new insights into the molecular signaling network of HepaRGTM cell cultures.
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Affiliation(s)
- Jill A Franzosa
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. EPA, Research Triangle Park, NC, 27711, USA
| | - Jessica A Bonzo
- Cell Biology, Biosciences Division, Thermo Fisher Scientific, Frederick, MD, 21703, USA
| | - John Jack
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. EPA, Research Triangle Park, NC, 27711, USA
| | | | - Parth Kothiya
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. EPA, Research Triangle Park, NC, 27711, USA
| | - Rafal P Witek
- Cell Biology, Biosciences Division, Thermo Fisher Scientific, Frederick, MD, 21703, USA
| | | | | | - Susan Hester
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. EPA, Research Triangle Park, NC, 27711, USA
| | - Imran Shah
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. EPA, Research Triangle Park, NC, 27711, USA
| | - Stephen S Ferguson
- Division of National Toxicology Program, National Institutes of Environmental Health Sciences of National Institutes of Health, Durham, NC, 27709, USA
| | - Keith A Houck
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. EPA, Research Triangle Park, NC, 27711, USA
| | - John F Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. EPA, Research Triangle Park, NC, 27711, USA.
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Mav D, Phadke DP, Balik-Meisner MR, Merrick BA, Auerbach S, Niemeijer M, Huppelschoten S, Baze A, Parmentier C, Richert L, van de Water B, Shah RR, Paules RS. Utility of Extrapolating Human S1500+ Genes to the Whole Transcriptome: Tunicamycin Case Study. Bioinform Biol Insights 2020; 14:1177932220952742. [PMID: 33088175 PMCID: PMC7545517 DOI: 10.1177/1177932220952742] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 08/04/2020] [Indexed: 01/07/2023] Open
Abstract
The TempO-Seq S1500+ platform(s), now available for human, mouse, rat, and zebrafish, measures a discrete number of genes that are representative of biological and pathway co-regulation across the entire genome in a given species. While measurement of these genes alone provides a direct assessment of gene expression activity, extrapolating expression values to the whole transcriptome (~26 000 genes in humans) can estimate measurements of non-measured genes of interest and increases the power of pathway analysis algorithms by using a larger background gene expression space. Here, we use data from primary hepatocytes of 54 donors that were treated with the endoplasmic reticulum (ER) stress inducer tunicamycin and then measured on the human S1500+ platform containing ~3000 representative genes. Measurements for the S1500+ genes were then used to extrapolate expression values for the remaining human transcriptome. As a case study of the improved downstream analysis achieved by extrapolation, the “measured only” and “whole transcriptome” (measured + extrapolated) gene sets were compared. Extrapolation increased the number of significant genes by 49%, bringing to the forefront many that are known to be associated with tunicamycin exposure. The extrapolation procedure also correctly identified established tunicamycin-related functional pathways reflected by coordinated changes in interrelated genes while maintaining the sample variability observed from the “measured only” genes. Extrapolation improved the gene- and pathway-level biological interpretations for a variety of downstream applications, including differential expression analysis, gene set enrichment pathway analysis, DAVID keyword analysis, Ingenuity Pathway Analysis, and NextBio correlated compound analysis. The extrapolated data highlight the role of metabolism/metabolic pathways, the ER, immune response, and the unfolded protein response, each of which are key activities associated with tunicamycin exposure that were unrepresented or underrepresented in one or more of the analyses of the original “measured only” dataset. Furthermore, the inclusion of the extrapolated genes raised “tunicamycin” from third to first upstream regulator in Ingenuity Pathway Analysis and from sixth to second most correlated compound in NextBio analysis. Therefore, our case study suggests an approach to extend and enhance data from the S1500+ platform for improved insight into biological mechanisms and functional outcomes of diseases, drugs, and other perturbations.
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Affiliation(s)
- Deepak Mav
- Sciome LLC, Research Triangle Park, NC, USA
| | | | | | - B Alex Merrick
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Scott Auerbach
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Marije Niemeijer
- Division of Toxicology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Suzanna Huppelschoten
- Division of Toxicology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | | | | | | | - Bob van de Water
- Division of Toxicology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | | | - Richard S Paules
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
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Jackson AC, Liu J, Vallanat B, Jones C, Nelms MD, Patlewicz G, Corton JC. Identification of novel activators of the metal responsive transcription factor (MTF-1) using a gene expression biomarker in a microarray compendium. Metallomics 2020; 12:1400-1415. [PMID: 32661532 PMCID: PMC10776036 DOI: 10.1039/d0mt00071j] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
Environmental exposure to metals is known to cause a number of human toxicities including cancer. Metal-responsive transcription factor 1 (MTF-1) is an important component of metal regulation systems in mammalian cells. Here, we describe a novel method to identify chemicals that activate MTF-1 based on microarray profiling data. MTF-1 biomarker genes were identified that exhibited consistent, robust expression across 10 microarray comparisons examining the effects of metals (zinc, nickel, lead, arsenic, mercury, and silver) on gene expression in human cells. A subset of the resulting 81 biomarker genes was shown to be altered by knockdown of the MTF1 gene including metallothionein family members and a zinc transporter. The ability to correctly identify treatment conditions that activate MTF-1 was determined by comparing the biomarker to microarray comparisons from cells exposed to reference metal activators of MTF-1 using the rank-based Running Fisher algorithm. The balanced accuracy for prediction was 93%. The biomarker was then used to identify organic chemicals that activate MTF-1 from a compendium of 11 725 human gene expression comparisons representing 2582 chemicals. There were 700 chemicals identified that included those known to interact with cellular metals, such as clioquinol and disulfiram, as well as a set of novel chemicals. All nine of the novel chemicals selected for validation were confirmed to activate MTF-1 biomarker genes in MCF-7 cells and to lesser extents in MTF1-null cells by qPCR and targeted RNA-Seq. Overall, our work demonstrates that the biomarker for MTF-1 coupled with the Running Fisher test is a reliable strategy to identify novel chemical modulators of metal homeostasis using gene expression profiling.
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Affiliation(s)
- Abigail C Jackson
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, 109 T.W. Alexander Dr. MD-B105-3, Research Triangle Park, NC 27711, USA. and Department of Chemistry, Duke University, Durham, NC 27708, USA
| | - Jie Liu
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, 109 T.W. Alexander Dr. MD-B105-3, Research Triangle Park, NC 27711, USA.
| | - Beena Vallanat
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, 109 T.W. Alexander Dr. MD-B105-3, Research Triangle Park, NC 27711, USA.
| | - Carlton Jones
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, 109 T.W. Alexander Dr. MD-B105-3, Research Triangle Park, NC 27711, USA.
| | - Mark D Nelms
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, 109 T.W. Alexander Dr. MD-B105-3, Research Triangle Park, NC 27711, USA. and Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, 109 T.W. Alexander Dr. MD-B105-3, Research Triangle Park, NC 27711, USA.
| | - J Christopher Corton
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, 109 T.W. Alexander Dr. MD-B105-3, Research Triangle Park, NC 27711, USA.
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Nyffeler J, Haggard DE, Willis C, Setzer RW, Judson R, Paul-Friedman K, Everett LJ, Harrill JA. Comparison of Approaches for Determining Bioactivity Hits from High-Dimensional Profiling Data. SLAS DISCOVERY 2020; 26:292-308. [PMID: 32862757 DOI: 10.1177/2472555220950245] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Phenotypic profiling assays are untargeted screening assays that measure a large number (hundreds to thousands) of cellular features in response to a stimulus and often yield diverse and unanticipated profiles of phenotypic effects, leading to challenges in distinguishing active from inactive treatments. Here, we compare a variety of different strategies for hit identification in imaging-based phenotypic profiling assays using a previously published Cell Painting data set. Hit identification strategies based on multiconcentration analysis involve curve fitting at several levels of data aggregation (e.g., individual feature level, aggregation of similarly derived features into categories, and global modeling of all features) and on computed metrics (e.g., Euclidean and Mahalanobis distance metrics and eigenfeatures). Hit identification strategies based on single-concentration analysis included measurement of signal strength (e.g., total effect magnitude) and correlation of profiles among biological replicates. Modeling parameters for each approach were optimized to retain the ability to detect a reference chemical with subtle phenotypic effects while limiting the false-positive rate to 10%. The percentage of test chemicals identified as hits was highest for feature-level and category-based approaches, followed by global fitting, whereas signal strength and profile correlation approaches detected the fewest number of active hits at the fixed false-positive rate. Approaches involving fitting of distance metrics had the lowest likelihood for identifying high-potency false-positive hits that may be associated with assay noise. Most of the methods achieved a 100% hit rate for the reference chemical and high concordance for 82% of test chemicals, indicating that hit calls are robust across different analysis approaches.
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Affiliation(s)
- Johanna Nyffeler
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA.,Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, USA
| | - Derik E Haggard
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA.,Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, USA
| | - Clinton Willis
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA.,Oak Ridge Associated Universities (ORAU), Oak Ridge, TN, USA
| | - R Woodrow Setzer
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA
| | - Richard Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA
| | - Katie Paul-Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA
| | - Logan J Everett
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA
| | - Joshua A Harrill
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA
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Gwinn WM, Auerbach SS, Parham F, Stout MD, Waidyanatha S, Mutlu E, Collins B, Paules RS, Merrick BA, Ferguson S, Ramaiahgari S, Bucher JR, Sparrow B, Toy H, Gorospe J, Machesky N, Shah RR, Balik-Meisner MR, Mav D, Phadke DP, Roberts G, DeVito MJ. Evaluation of 5-day In Vivo Rat Liver and Kidney With High-throughput Transcriptomics for Estimating Benchmark Doses of Apical Outcomes. Toxicol Sci 2020; 176:343-354. [PMID: 32492150 PMCID: PMC7416315 DOI: 10.1093/toxsci/kfaa081] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
A 5-day in vivo rat model was evaluated as an approach to estimate chemical exposures that may pose minimal risk by comparing benchmark dose (BMD) values for transcriptional changes in the liver and kidney to BMD values for toxicological endpoints from traditional toxicity studies. Eighteen chemicals, most having been tested by the National Toxicology Program in 2-year bioassays, were evaluated. Some of these chemicals are potent hepatotoxicants (eg, DE71, PFOA, and furan) in rodents, some exhibit toxicity but have minimal hepatic effects (eg, acrylamide and α,β-thujone), and some exhibit little overt toxicity (eg, ginseng and milk thistle extract) based on traditional toxicological evaluations. Male Sprague Dawley rats were exposed once daily for 5 consecutive days by oral gavage to 8-10 dose levels for each chemical. Liver and kidney were collected 24 h after the final exposure and total RNA was assayed using high-throughput transcriptomics (HTT) with the rat S1500+ platform. HTT data were analyzed using BMD Express 2 to determine transcriptional gene set BMD values. BMDS was used to determine BMD values for histopathological effects from chronic or subchronic toxicity studies. For many of the chemicals, the lowest transcriptional BMDs from the 5-day assays were within a factor of 5 of the lowest histopathological BMDs from the toxicity studies. These data suggest that using HTT in a 5-day in vivo rat model provides reasonable estimates of BMD values for traditional apical endpoints. This approach may be useful to prioritize chemicals for further testing while providing actionable data in a timely and cost-effective manner.
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Affiliation(s)
- William M Gwinn
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina 27709
| | - Scott S Auerbach
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina 27709
| | - Fred Parham
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina 27709
| | - Matthew D Stout
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina 27709
| | - Suramya Waidyanatha
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina 27709
| | - Esra Mutlu
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina 27709
| | - Brad Collins
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina 27709
| | - Richard S Paules
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina 27709
| | - Bruce Alex Merrick
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina 27709
| | - Stephen Ferguson
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina 27709
| | - Sreenivasa Ramaiahgari
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina 27709
| | - John R Bucher
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina 27709
| | | | | | | | | | | | | | | | | | - Georgia Roberts
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina 27709
| | - Michael J DeVito
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina 27709
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Hatherell S, Baltazar MT, Reynolds J, Carmichael PL, Dent M, Li H, Ryder S, White A, Walker P, Middleton AM. Identifying and Characterizing Stress Pathways of Concern for Consumer Safety in Next-Generation Risk Assessment. Toxicol Sci 2020; 176:11-33. [PMID: 32374857 PMCID: PMC7357173 DOI: 10.1093/toxsci/kfaa054] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Many substances for which consumer safety risk assessments need to be conducted are not associated with specific toxicity modes of action, but rather exhibit nonspecific toxicity leading to cell stress. In this work, a cellular stress panel is described, consisting of 36 biomarkers representing mitochondrial toxicity, cell stress, and cell health, measured predominantly using high content imaging. To evaluate the panel, data were generated for 13 substances at exposures consistent with typical use-case scenarios. These included some that have been shown to cause adverse effects in a proportion of exposed humans and have a toxicological mode-of-action associated with cellular stress (eg, doxorubicin, troglitazone, and diclofenac), and some that are not associated with adverse effects due to cellular stress at human-relevant exposures (eg, caffeine, niacinamide, and phenoxyethanol). For each substance, concentration response data were generated for each biomarker at 3 timepoints. A Bayesian model was then developed to quantify the evidence for a biological response, and if present, a credibility range for the estimated point of departure (PoD) was determined. PoDs were compared with the plasma Cmax associated with the typical substance exposures, and indicated a clear differentiation between "low" risk and "high" risk chemical exposure scenarios. Developing robust methods to characterize the in vitro bioactivity of xenobiotics is an important part of non-animal safety assessment. The results presented in this work show that the cellular stress panel can be used, together with other new approach methodologies, to identify chemical exposures that are protective of consumer health.
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Affiliation(s)
- Sarah Hatherell
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Maria T Baltazar
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Joe Reynolds
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Paul L Carmichael
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Matthew Dent
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Hequn Li
- 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
| | - Paul Walker
- Cyprotex Discovery Ltd, Macclesfield, Cheshire SK10 4TG, UK
| | - Alistair M Middleton
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
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Baltazar MT, Cable S, Carmichael PL, Cubberley R, Cull T, Delagrange M, Dent MP, Hatherell S, Houghton J, Kukic P, Li H, Lee MY, Malcomber S, Middleton AM, Moxon TE, Nathanail AV, Nicol B, Pendlington R, Reynolds G, Reynolds J, White A, Westmoreland C. A Next-Generation Risk Assessment Case Study for Coumarin in Cosmetic Products. Toxicol Sci 2020; 176:236-252. [PMID: 32275751 PMCID: PMC7357171 DOI: 10.1093/toxsci/kfaa048] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Next-Generation Risk Assessment is defined as an exposure-led, hypothesis-driven risk assessment approach that integrates new approach methodologies (NAMs) to assure safety without the use of animal testing. These principles were applied to a hypothetical safety assessment of 0.1% coumarin in face cream and body lotion. For the purpose of evaluating the use of NAMs, existing animal and human data on coumarin were excluded. Internal concentrations (plasma Cmax) were estimated using a physiologically based kinetic model for dermally applied coumarin. Systemic toxicity was assessed using a battery of in vitro NAMs to identify points of departure (PoDs) for a variety of biological effects such as receptor-mediated and immunomodulatory effects (Eurofins SafetyScreen44 and BioMap Diversity 8 Panel, respectively), and general bioactivity (ToxCast data, an in vitro cell stress panel and high-throughput transcriptomics). In addition, in silico alerts for genotoxicity were followed up with the ToxTracker tool. The PoDs from the in vitro assays were plotted against the calculated in vivo exposure to calculate a margin of safety with associated uncertainty. The predicted Cmax values for face cream and body lotion were lower than all PoDs with margin of safety higher than 100. Furthermore, coumarin was not genotoxic, did not bind to any of the 44 receptors tested and did not show any immunomodulatory effects at consumer-relevant exposures. In conclusion, this case study demonstrated the value of integrating exposure science, computational modeling and in vitro bioactivity data, to reach a safety decision without animal data.
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Affiliation(s)
- Maria T Baltazar
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Sophie Cable
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Paul L Carmichael
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Richard Cubberley
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Tom Cull
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Mona Delagrange
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Matthew P Dent
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Sarah Hatherell
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Jade Houghton
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Predrag Kukic
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Hequn Li
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Mi-Young Lee
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Sophie Malcomber
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Alistair M Middleton
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Thomas E Moxon
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Alexis V Nathanail
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Beate Nicol
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Ruth Pendlington
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Georgia Reynolds
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Joe Reynolds
- 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
| | - Carl Westmoreland
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
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Bushel PR, Ferguson SS, Ramaiahgari SC, Paules RS, Auerbach SS. Comparison of Normalization Methods for Analysis of TempO-Seq Targeted RNA Sequencing Data. Front Genet 2020; 11:594. [PMID: 32655620 PMCID: PMC7325690 DOI: 10.3389/fgene.2020.00594] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 05/15/2020] [Indexed: 11/30/2022] Open
Abstract
Analysis of bulk RNA sequencing (RNA-Seq) data is a valuable tool to understand transcription at the genome scale. Targeted sequencing of RNA has emerged as a practical means of assessing the majority of the transcriptomic space with less reliance on large resources for consumables and bioinformatics. TempO-Seq is a templated, multiplexed RNA-Seq platform that interrogates a panel of sentinel genes representative of genome-wide transcription. Nuances of the technology require proper preprocessing of the data. Various methods have been proposed and compared for normalizing bulk RNA-Seq data, but there has been little to no investigation of how the methods perform on TempO-Seq data. We simulated count data into two groups (treated vs. untreated) at seven-fold change (FC) levels (including no change) using control samples from human HepaRG cells run on TempO-Seq and normalized the data using seven normalization methods. Upper Quartile (UQ) performed the best with regard to maintaining FC levels as detected by a limma contrast between treated vs. untreated groups. For all FC levels, specificity of the UQ normalization was greater than 0.84 and sensitivity greater than 0.90 except for the no change and +1.5 levels. Furthermore, K-means clustering of the simulated genes normalized by UQ agreed the most with the FC assignments [adjusted Rand index (ARI) = 0.67]. Despite having an assumption of the majority of genes being unchanged, the DESeq2 scaling factors normalization method performed reasonably well as did simple normalization procedures counts per million (CPM) and total counts (TCs). These results suggest that for two class comparisons of TempO-Seq data, UQ, CPM, TC, or DESeq2 normalization should provide reasonably reliable results at absolute FC levels ≥2.0. These findings will help guide researchers to normalize TempO-Seq gene expression data for more reliable results.
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Affiliation(s)
- Pierre R Bushel
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States.,Massive Genome Informatics Group, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States.,Biomolecular Screening Branch, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States
| | - Stephen S Ferguson
- Biomolecular Screening Branch, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States
| | - Sreenivasa C Ramaiahgari
- Biomolecular Screening Branch, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States
| | - Richard S Paules
- Biomolecular Screening Branch, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States
| | - Scott S Auerbach
- Biomolecular Screening Branch, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States
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Willis C, Nyffeler J, Harrill J. Phenotypic Profiling of Reference Chemicals across Biologically Diverse Cell Types Using the Cell Painting Assay. SLAS DISCOVERY 2020; 25:755-769. [PMID: 32546035 DOI: 10.1177/2472555220928004] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Cell Painting is a high-throughput phenotypic profiling assay that uses fluorescent cytochemistry to visualize a variety of organelles and high-content imaging to derive a large number of morphological features at the single-cell level. Most Cell Painting studies have used the U-2 OS cell line for chemical or functional genomics screening. The Cell Painting assay can be used with many other human-derived cell types, given that the assay is based on the use of fluoroprobes that label organelles that are present in most (if not all) human cells. Questions remain, however, regarding the optimization steps required and overall ease of deployment of the Cell Painting assay to novel cell types. Here, we used the Cell Painting assay to characterize the phenotypic effects of 14 phenotypic reference chemicals in concentration-response screening mode across six biologically diverse human-derived cell lines (U-2 OS, MCF7, HepG2, A549, HTB-9 and ARPE-19). All cell lines were labeled using the same cytochemistry protocol, and the same set of phenotypic features was calculated. We found it necessary to optimize image acquisition settings and cell segmentation parameters for each cell type, but did not adjust the cytochemistry protocol. For some reference chemicals, similar subsets of phenotypic features corresponding to a particular organelle were associated with the highest-effect magnitudes in each affected cell type. Overall, for certain chemicals, the Cell Painting assay yielded qualitatively similar biological activity profiles among a group of diverse, morphologically distinct human-derived cell lines without the requirement for cell type-specific optimization of cytochemistry protocols.
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Affiliation(s)
- Clinton Willis
- Center for Computational Toxicology and Exposure (CCTE), Office of Research and Development, U.S. Environmental Protection Agency, Durham, NC, USA.,Oak Ridge Associated Universities (ORAU), Oak Ridge, TN, USA
| | - Johanna Nyffeler
- Center for Computational Toxicology and Exposure (CCTE), Office of Research and Development, U.S. Environmental Protection Agency, Durham, NC, USA.,Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, USA
| | - Joshua Harrill
- Center for Computational Toxicology and Exposure (CCTE), Office of Research and Development, U.S. Environmental Protection Agency, Durham, NC, USA
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Transcriptomics in Toxicogenomics, Part I: Experimental Design, Technologies, Publicly Available Data, and Regulatory Aspects. NANOMATERIALS 2020; 10:nano10040750. [PMID: 32326418 PMCID: PMC7221878 DOI: 10.3390/nano10040750] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 04/02/2020] [Accepted: 04/03/2020] [Indexed: 02/07/2023]
Abstract
The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the need for a better understanding of the molecular changes occurring in exposed biological systems. Transcriptomics enables the exploration of organisms' responses to environmental, chemical, and physical agents by observing the molecular alterations in more detail. Toxicogenomics integrates classical toxicology with omics assays, thus allowing the characterization of the mechanism of action (MOA) of chemical compounds, novel small molecules, and engineered nanomaterials (ENMs). Lack of standardization in data generation and analysis currently hampers the full exploitation of toxicogenomics-based evidence in risk assessment. To fill this gap, TGx methods need to take into account appropriate experimental design and possible pitfalls in the transcriptomic analyses as well as data generation and sharing that adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. In this review, we summarize the recent advancements in the design and analysis of DNA microarray, RNA sequencing (RNA-Seq), and single-cell RNA-Seq (scRNA-Seq) data. We provide guidelines on exposure time, dose and complex endpoint selection, sample quality considerations and sample randomization. Furthermore, we summarize publicly available data resources and highlight applications of TGx data to understand and predict chemical toxicity potential. Additionally, we discuss the efforts to implement TGx into regulatory decision making to promote alternative methods for risk assessment and to support the 3R (reduction, refinement, and replacement) concept. This review is the first part of a three-article series on Transcriptomics in Toxicogenomics. These initial considerations on Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects, are the starting point for further rigorous and reliable data preprocessing and modeling, described in the second and third part of the review series.
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48
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Afantitis A, Melagraki G, Isigonis P, Tsoumanis A, Varsou DD, Valsami-Jones E, Papadiamantis A, Ellis LJA, Sarimveis H, Doganis P, Karatzas P, Tsiros P, Liampa I, Lobaskin V, Greco D, Serra A, Kinaret PAS, Saarimäki LA, Grafström R, Kohonen P, Nymark P, Willighagen E, Puzyn T, Rybinska-Fryca A, Lyubartsev A, Alstrup Jensen K, Brandenburg JG, Lofts S, Svendsen C, Harrison S, Maier D, Tamm K, Jänes J, Sikk L, Dusinska M, Longhin E, Rundén-Pran E, Mariussen E, El Yamani N, Unger W, Radnik J, Tropsha A, Cohen Y, Leszczynski J, Ogilvie Hendren C, Wiesner M, Winkler D, Suzuki N, Yoon TH, Choi JS, Sanabria N, Gulumian M, Lynch I. NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment. Comput Struct Biotechnol J 2020; 18:583-602. [PMID: 32226594 PMCID: PMC7090366 DOI: 10.1016/j.csbj.2020.02.023] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 02/18/2020] [Accepted: 02/29/2020] [Indexed: 01/26/2023] Open
Abstract
Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational 'safe-by-design' approaches to facilitate NM commercialization.
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Key Words
- (quantitative) Structure–activity relationships
- AI, Artificial Intelligence
- AOPs, Adverse Outcome Pathways
- API, Application Programming interface
- CG, coarse-grained (model)
- CNTs, carbon nanotubes
- Computational toxicology
- Engineered nanomaterials
- FAIR, Findable Accessible Inter-operable and Re-usable
- GUI, Graphical Processing Unit
- HOMO-LUMO, Highest Occupied Molecular Orbital Lowest Unoccupied Molecular Orbital
- Hazard assessment
- IATA, Integrated Approaches to Testing and Assessment
- Integrated approach for testing and assessment
- KE, key events
- MIE, molecular initiating events
- ML, machine learning
- MOA, mechanism (mode) of action
- MWCNT, multi-walled carbon nanotubes
- Machine learning
- NMs, nanomaterials
- Nanoinformatics
- OECD, Organisation for Economic Co-operation and Development
- PBPK, Physiologically Based PharmacoKinetics
- PC, Protein Corona
- PChem, Physicochemical
- PTGS, Predictive Toxicogenomics Space
- Predictive modelling
- QC, quantum-chemical
- QM, quantum-mechanical
- QSAR, quantitative structure-activity relationship
- QSPR, quantitative structure-property relationship
- RA, risk assessment
- REST, Representational State Transfer
- ROS, reactive oxygen species
- Read across
- SAR, structure-activity relationship
- SMILES, Simplified Molecular Input Line Entry System
- SOPs, standard operating procedures
- Safe-by-design
- Toxicogenomics
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Affiliation(s)
| | | | | | | | | | - Eugenia Valsami-Jones
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Anastasios Papadiamantis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Laura-Jayne A. Ellis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Philip Doganis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Pantelis Karatzas
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Periklis Tsiros
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Irene Liampa
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Vladimir Lobaskin
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland
| | - Dario Greco
- Faculty of Medicine and Health Technology, University of Tampere, FI-33014, Finland
| | - Angela Serra
- Faculty of Medicine and Health Technology, University of Tampere, FI-33014, Finland
| | | | | | - Roland Grafström
- Misvik Biology OY, Itäinen Pitkäkatu 4, 20520 Turku, Finland
- Karolinska Institute, Institute of Environmental Medicine, Nobels väg 13, 17177 Stockholm, Sweden
| | - Pekka Kohonen
- Misvik Biology OY, Itäinen Pitkäkatu 4, 20520 Turku, Finland
- Karolinska Institute, Institute of Environmental Medicine, Nobels väg 13, 17177 Stockholm, Sweden
| | - Penny Nymark
- Misvik Biology OY, Itäinen Pitkäkatu 4, 20520 Turku, Finland
- Karolinska Institute, Institute of Environmental Medicine, Nobels väg 13, 17177 Stockholm, Sweden
| | - Egon Willighagen
- Department of Bioinformatics – BiGCaT, School of Nutrition and Translational Research in Metabolism, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, the Netherlands
| | - Tomasz Puzyn
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland
- University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
| | | | - Alexander Lyubartsev
- Institutionen för material- och miljökemi, Stockholms Universitet, 106 91 Stockholm, Sweden
| | - Keld Alstrup Jensen
- The National Research Center for the Work Environment, Lersø Parkallé 105, 2100 Copenhagen, Denmark
| | - Jan Gerit Brandenburg
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Germany
- Chief Digital Organization, Merck KGaA, Frankfurter Str. 250, 64293 Darmstadt, Germany
| | - Stephen Lofts
- UK Centre for Ecology and Hydrology, Library Ave, Bailrigg, Lancaster LA1 4AP, UK
| | - Claus Svendsen
- UK Centre for Ecology and Hydrology, MacLean Bldg, Benson Ln, Crowmarsh Gifford, Wallingford OX10 8BB, UK
| | - Samuel Harrison
- UK Centre for Ecology and Hydrology, Library Ave, Bailrigg, Lancaster LA1 4AP, UK
| | - Dieter Maier
- Biomax Informatics AG, Robert-Koch-Str. 2, 82152 Planegg, Germany
| | - Kaido Tamm
- Department of Chemistry, University of Tartu, Ülikooli 18, 50090 Tartu, Estonia
| | - Jaak Jänes
- Department of Chemistry, University of Tartu, Ülikooli 18, 50090 Tartu, Estonia
| | - Lauri Sikk
- Department of Chemistry, University of Tartu, Ülikooli 18, 50090 Tartu, Estonia
| | - Maria Dusinska
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Eleonora Longhin
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Elise Rundén-Pran
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Espen Mariussen
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Naouale El Yamani
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Wolfgang Unger
- Federal Institute for Material Testing and Research (BAM), Unter den Eichen 44-46, 12203 Berlin, Germany
| | - Jörg Radnik
- Federal Institute for Material Testing and Research (BAM), Unter den Eichen 44-46, 12203 Berlin, Germany
| | - Alexander Tropsha
- Eschelman School of Pharmacy, University of North Carolina at Chapel Hill, 100K Beard Hall, CB# 7568, Chapel Hill, NC 27955-7568, USA
| | - Yoram Cohen
- Samueli School Of Engineering, University of California, Los Angeles, 5531 Boelter Hall, Los Angeles, CA 90095, USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Jackson State University, 1400 J. R. Lynch Street, Jackson, MS 39217, USA
| | - Christine Ogilvie Hendren
- Center for Environmental Implications of Nanotechnologies, Duke University, 121 Hudson Hall, Durham, NC 27708-0287, USA
| | - Mark Wiesner
- Center for Environmental Implications of Nanotechnologies, Duke University, 121 Hudson Hall, Durham, NC 27708-0287, USA
| | - David Winkler
- La Trobe Institute of Molecular Sciences, La Trobe University, Plenty Rd & Kingsbury Dr, Bundoora, VIC 3086, Australia
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville 3052, Australia
- CSIRO Data61, Clayton 3168, Australia
- School of Pharmacy, University of Nottingham, Nottingham, UK
| | - Noriyuki Suzuki
- National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-0053, Japan
| | - Tae Hyun Yoon
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Republic of Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Republic of Korea
| | - Jang-Sik Choi
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Republic of Korea
| | - Natasha Sanabria
- National Health Laboratory Services, 1 Modderfontein Rd, Sandringham, Johannesburg 2192, South Africa
| | - Mary Gulumian
- National Health Laboratory Services, 1 Modderfontein Rd, Sandringham, Johannesburg 2192, South Africa
- Haematology and Molecular Medicine, University of the Witwatersrand, Johannesburg, South Africa
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
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Xia P, Zhang H, Peng Y, Shi W, Zhang X. Pathway-based assessment of single chemicals and mixtures by a high-throughput transcriptomics approach. ENVIRONMENT INTERNATIONAL 2020; 136:105455. [PMID: 31945694 DOI: 10.1016/j.envint.2019.105455] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 12/12/2019] [Accepted: 12/26/2019] [Indexed: 05/23/2023]
Abstract
The ever-increasing number of chemicals and complex mixtures demands a high-throughput and cost-effective approach for chemical safety assessment. High-throughput transcriptomics (HTT) is promising in investigating genome-scale perturbation of chemical exposure in concentration-dependent manner. However, the application of HTT has been limited due to lack of methodology for single chemicals and mixture assessment. This study aimed to evaluate the ability of a newly-developed human reduced transcriptomics (RHT) approach to assess pathway-based profiles of single chemicals, and to develop a biological pathway-based approach for benchmarking mixture potency using single chemical-based prediction model. First, concentration-dependent RHT were used to qualitatively and quantitatively differentiate pathway-based patterns of different chemicals, using three model toxicants, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), triclosan (TCS) and 5-Chloro-6-hydroxy-2,2',4,4'-tetrabromodiphenyl ether (5-Cl-6-OH-BDE-47). AHR-regulated genes and pathways were most sensitively induced by TCDD, while TCS and 5-Cl-6-OH-BDE-47 were much less potent in AHR-associated activation, which was concordant with known MoA of each single chemical. Second, two artificial mixtures and their components of twelve individual chemicals were performed with concentration-dependent RHT. Concentration addition (CA) and independent action (IA) models were used to predict transcriptional potency of mixtures from transcriptomics of individual chemicals. For overall bioactivity, CA and IA models can both predict potency of observed responses within 95% confidence interval. For specific biological processes, multiple biological processes such as hormone signaling and DNA damage can be predicted using CA models for mixtures. The concentration-dependent RHT can provide a powerful approach for qualitative and quantitative assessment of biological pathway perturbated by environment chemical and mixtures.
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Affiliation(s)
- Pu Xia
- State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, PR China
| | - Hanxin Zhang
- State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, PR China
| | - Ying Peng
- State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, PR China
| | - Wei Shi
- State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, PR China
| | - Xiaowei Zhang
- State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, PR China.
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Buick JK, Williams A, Gagné R, Swartz CD, Recio L, Ferguson SS, Yauk CL. Flow cytometric micronucleus assay and TGx-DDI transcriptomic biomarker analysis of ten genotoxic and non-genotoxic chemicals in human HepaRG™ cells. Genes Environ 2020; 42:5. [PMID: 32042365 PMCID: PMC7001283 DOI: 10.1186/s41021-019-0139-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 11/27/2019] [Indexed: 11/10/2022] Open
Abstract
Background Modern testing paradigms seek to apply human-relevant cell culture models and integrate data from multiple test systems to accurately inform potential hazards and modes of action for chemical toxicology. In genetic toxicology, the use of metabolically competent human hepatocyte cell culture models provides clear advantages over other more commonly used cell lines that require the use of external metabolic activation systems, such as rat liver S9. HepaRG™ cells are metabolically competent cells that express Phase I and II metabolic enzymes and differentiate into mature hepatocyte-like cells, making them ideal for toxicity testing. We assessed the performance of the flow cytometry in vitro micronucleus (MN) test and the TGx-DDI transcriptomic biomarker to detect DNA damage-inducing (DDI) chemicals in human HepaRG™ cells after a 3-day repeat exposure. The biomarker, developed for use in human TK6 cells, is a panel of 64 genes that accurately classifies chemicals as DDI or non-DDI. Herein, the TGx-DDI biomarker was analyzed by Ion AmpliSeq whole transcriptome sequencing to assess its classification accuracy using this more modern gene expression technology as a secondary objective. Methods HepaRG™ cells were exposed to increasing concentrations of 10 test chemicals (six genotoxic chemicals, including one aneugen, and four non-genotoxic chemicals). Cytotoxicity and genotoxicity were measured using the In Vitro MicroFlow® kit, which was run in parallel with the TGx-DDI biomarker. Results A concentration-related decrease in relative survival and a concomitant increase in MN frequency were observed for genotoxic chemicals in HepaRG™ cells. All five DDI and five non-DDI agents were correctly classified (as genotoxic/non-genotoxic and DDI/non-DDI) by pairing the test methods. The aneugenic agent (colchicine) yielded the expected positive result in the MN test and negative (non-DDI) result by TGx-DDI. Conclusions This next generation genotoxicity testing strategy is aligned with the paradigm shift occurring in the field of genetic toxicology. It provides mechanistic insight in a human-relevant cell-model, paired with measurement of a conventional endpoint, to inform the potential for adverse health effects. This work provides support for combining these assays in an integrated test strategy for accurate, higher throughput genetic toxicology testing in this metabolically competent human progenitor cell line.
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Affiliation(s)
- Julie K Buick
- 1Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario K1A 0K9 Canada
| | - Andrew Williams
- 1Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario K1A 0K9 Canada
| | - Rémi Gagné
- 1Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario K1A 0K9 Canada
| | - Carol D Swartz
- 2Integrated Laboratory Systems Inc. (ILS), Research Triangle Park, Durham, North Carolina 27709 USA
| | - Leslie Recio
- 2Integrated Laboratory Systems Inc. (ILS), Research Triangle Park, Durham, North Carolina 27709 USA
| | - Stephen S Ferguson
- 3National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, North Carolina 27709 USA
| | - Carole L Yauk
- 1Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario K1A 0K9 Canada.,4Health Canada, Environmental Health Centre, 50 Colombine Driveway, PL 0803A, Ottawa, Ontario K1A 0K9 Canada
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