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Villeneuve DL, Le M, Hazemi M, Biales A, Bencic DC, Bush K, Flick R, Martinson J, Morshead M, Rodriguez KS, Vitense K, Flynn K. Pilot testing and optimization of a larval fathead minnow high throughput transcriptomics assay. Curr Res Toxicol 2022; 4:100099. [PMID: 36619288 PMCID: PMC9816907 DOI: 10.1016/j.crtox.2022.100099] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 11/03/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
Concentrations at which global gene expression profiles in cells or animals exposed to a test substance start to differ significantly from those of controls have been proposed as an alternative point of departure for use in screening level hazard assessment. The present study describes pilot testing of a high throughput compatible transcriptomics assay with larval fathead minnows. One day post hatch fathead minnows were exposed to eleven different concentrations of three metals, three selective serotonin reuptake inhibitors, and four neonicotinoid-like compounds for 24 h and concentration response modeling was applied to whole body gene expression data. Transcriptomics-based points of departure (tPODs) were consistently lower than effect concentrations reported in apical endpoint studies in fish. However, larval fathead minnow-based tPODs were not always lower than concentrations reported to elicit apical toxicity in other aquatic organisms like crustaceans or insects. Random in silico subsampling of data from the pilot assays was used to evaluate various assay design and acceptance considerations such as transcriptome coverage, number of replicate individuals to sequence per treatment, and minimum number of differentially expressed genes to produce a reliable tPOD estimate. Results showed a strong association between the total number of genes for which a concentration response relationship could be derived and the overall variability in the resulting tPOD estimates. We conclude that, for our current assay design and analysis pipeline, tPODs based on fewer than 15 differentially expressed genes are likely to be unreliable for screening and that interindividual variability in gene expression profiles appears to be a more significant driver of tPOD variability than sample size alone. Results represent initial steps toward developing high throughput transcriptomics assays for use in ecological hazard screening.
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Key Words
- BMD, Benchmark dose
- Benchmark dose
- Computational toxicology
- DEGs, Differentially expressed genes
- ECOTOX knowledgebase
- Fish
- HTTr, High throughput transcriptomics
- RIN, RNA integrity number
- RNA sequencing
- RNAseq, RNA sequencing
- SSRI, Selective serotonin reuptake inhibitor
- ToxCast, US EPA Toxicity Forecaster
- Transcriptomics-based point of departure
- cDNA, Complementary DNA
- eco-HTTr, Ecological high throughput transcriptomics
- tPOD, Transcriptomics-based point of departure
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Affiliation(s)
- Daniel L. Villeneuve
- US Environmental Protection Agency, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804, USA,Corresponding author at: U.S. EPA Great Lakes Toxicology and Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804-2595, USA.
| | - Michelle Le
- Oak Ridge Institute for Science and Education (ORISE) Research Participant, US Environmental Protection Agency Great Lakes Toxicology and Ecology Division, Duluth, MN 55804, USA
| | - Monique Hazemi
- Oak Ridge Institute for Science and Education (ORISE) Research Participant, US Environmental Protection Agency Great Lakes Toxicology and Ecology Division, Duluth, MN 55804, USA
| | - Adam Biales
- US Environmental Protection Agency, Great Lakes Toxicology and Ecology Division, Cincinnati, OH 45220, USA
| | - David C. Bencic
- US Environmental Protection Agency, Great Lakes Toxicology and Ecology Division, Cincinnati, OH 45220, USA
| | - Kendra Bush
- Oak Ridge Institute for Science and Education (ORISE) Research Participant, US Environmental Protection Agency Great Lakes Toxicology and Ecology Division, Duluth, MN 55804, USA
| | - Robert Flick
- US Environmental Protection Agency, Great Lakes Toxicology and Ecology Division, Cincinnati, OH 45220, USA
| | - John Martinson
- US Environmental Protection Agency, Great Lakes Toxicology and Ecology Division, Cincinnati, OH 45220, USA
| | - Mackenzie Morshead
- Oak Ridge Institute for Science and Education (ORISE) Research Participant, US Environmental Protection Agency Great Lakes Toxicology and Ecology Division, Duluth, MN 55804, USA
| | - Kelvin Santana Rodriguez
- Oak Ridge Institute for Science and Education (ORISE) Research Participant, US Environmental Protection Agency Great Lakes Toxicology and Ecology Division, Duluth, MN 55804, USA
| | - Kelsey Vitense
- US Environmental Protection Agency, Scientific Computing and Data Curation Division, Duluth, MN 55804, USA
| | - Kevin Flynn
- US Environmental Protection Agency, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804, USA
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Naciff JM, Shan YK, Wang X, Daston GP. Article title: Transcriptional profiling efficacy to define biological activity similarity for cosmetic ingredients' safety assessment based on next-generation read-across. FRONTIERS IN TOXICOLOGY 2022; 4:1082222. [PMID: 36618549 PMCID: PMC9811170 DOI: 10.3389/ftox.2022.1082222] [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: 10/27/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
The objective of this work was to use transcriptional profiling to assess the biological activity of structurally related chemicals to define their biological similarity and with that, substantiate the validity of a read-across approach usable in risk assessment. Two case studies are presented, one with 4 short alkyl chain parabens: methyl (MP), ethyl (EP), butyl (BP), and propylparaben (PP), as well as their main metabolite, p-hydroxybenzoic acid (pHBA) with the assumption that propylparaben was the target chemical; and a second one with caffeine and its main metabolites theophylline, theobromine and paraxanthine where CA was the target chemical. The comprehensive transcriptional response of MCF7, HepG2, A549 and ICell cardiomyocytes was evaluated (TempO-Seq) after exposure to vehicle-control, each paraben or pHBA, CA or its metabolites, at 3 non-cytotoxic concentrations, for 6 h. Differentially expressed genes (FDR ≥0.05, and fold change ±1.2≥) were identified for each chemical, at each concentration, and used to determine similarities. Each of the chemicals is able to elicit changes in the expression of a number of genes, as compared to controls. Importantly, the transcriptional profile elicited by each of the parabens shares a high degree of similarity across the group. The highest number of genes commonly affected was between butylparaben and PP. The transcriptional profile of the parabens is similar to the one elicited by estrogen receptor agonists, with BP being the closest structural and biological analogue for PP. In the CA case, the transcriptional profile elicited of all four methylxanthines had a high degree of similarity across the cell types, with CA and theophylline being the most active. The most robust response was obtained in the cardiomyocytes with the highest transcriptional profile similarity between CA and TP. The transcriptional profile of the methylxanthines is similar to the one elicited by inhibitors of phosphatidylinositol 3-kinase as well as other kinase inhibitors. Overall, our results support the approach of incorporating transcriptional profiling in well-designed in vitro tests as one robust stream of data to support biological similarity driven read-across procedures and strengthening the traditional structure-based approaches useful in risk assessment.
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Yang C, Lee D, Zhang MS, Tse AP, Wei L, Bao MH, Wong BP, Chan CY, Yuen VW, Chen Y, Wong CC. Genome-Wide CRISPR/Cas9 Library Screening Revealed Dietary Restriction of Glutamine in Combination with Inhibition of Pyruvate Metabolism as Effective Liver Cancer Treatment. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2202104. [PMID: 36310121 PMCID: PMC9731711 DOI: 10.1002/advs.202202104] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 08/05/2022] [Indexed: 06/16/2023]
Abstract
Hepatocellular carcinoma (HCC) is the second most lethal cancer worldwide. Glutamine is an essential, extracellular nutrient which supports HCC growth. Dietary glutamine deficiency may be a potential therapeutic approach for HCC. HCC cells overcome metabolic challenges by rewiring their metabolic pathways for rapid adaptations. The efficiency of dietary glutamine deficiency as HCC treatment is examined and the adaptation machinery under glutamine depletion in HCC cells is unraveled. Using genome-wide CRISPR/Cas9 knockout library screening, this study identifies that pyruvate dehydrogenase α (PDHA), pyruvate dehydrogenase β (PDHB), and pyruvate carboxylase (PC) in pyruvate metabolism are crucial to the adaptation of glutamine depletion in HCC cells. Knockout of either PDHA, PDHB or PC induced metabolic reprogramming of the tricarboxylic acid (TCA) cycle, disrupts mitochondrial function, leading to the suppression of HCC cell proliferation under glutamine depletion. Surprisingly, dietary glutamine restriction improves therapeutic responses of HCC to PDH or PC inhibitor in mouse HCC models. Stable isotope carbon tracing confirms that PDH or PC inhibitors further disrupt the metabolic rewiring of the TCA cycle induced by dietary glutamine depletion in HCC. In summary, the results demonstrate that pyruvate metabolism acts as novel targetable metabolic vulnerabilities for HCC treatment in combination with a glutamine-deficient diet.
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Affiliation(s)
- Chunxue Yang
- Department of PathologyThe University of Hong KongHong KongP. R. China
- State Key Laboratory of Liver ResearchThe University of Hong KongHong KongP. R. China
- School of Public Health (Shenzhen)Sun Yat‐sen UniversityGuangzhou510275P. R. China
| | - Derek Lee
- Department of PathologyThe University of Hong KongHong KongP. R. China
- State Key Laboratory of Liver ResearchThe University of Hong KongHong KongP. R. China
- Centre for Oncology and ImmunologyHong Kong Science ParkHong KongP. R. China
| | - Misty Shuo Zhang
- Department of PathologyThe University of Hong KongHong KongP. R. China
- State Key Laboratory of Liver ResearchThe University of Hong KongHong KongP. R. China
- Centre for Oncology and ImmunologyHong Kong Science ParkHong KongP. R. China
| | - Aki Pui‐Wah Tse
- Department of PathologyThe University of Hong KongHong KongP. R. China
- State Key Laboratory of Liver ResearchThe University of Hong KongHong KongP. R. China
- Centre for Oncology and ImmunologyHong Kong Science ParkHong KongP. R. China
| | - Lai Wei
- Department of PathologyThe University of Hong KongHong KongP. R. China
- State Key Laboratory of Liver ResearchThe University of Hong KongHong KongP. R. China
| | - Macus Hao‐Ran Bao
- Department of PathologyThe University of Hong KongHong KongP. R. China
- State Key Laboratory of Liver ResearchThe University of Hong KongHong KongP. R. China
- Centre for Oncology and ImmunologyHong Kong Science ParkHong KongP. R. China
| | - Bowie Po‐Yee Wong
- Department of PathologyThe University of Hong KongHong KongP. R. China
- State Key Laboratory of Liver ResearchThe University of Hong KongHong KongP. R. China
- Centre for Oncology and ImmunologyHong Kong Science ParkHong KongP. R. China
| | - Cerise Yuen‐Ki Chan
- Department of PathologyThe University of Hong KongHong KongP. R. China
- State Key Laboratory of Liver ResearchThe University of Hong KongHong KongP. R. China
- Centre for Oncology and ImmunologyHong Kong Science ParkHong KongP. R. China
| | - Vincent Wai‐Hin Yuen
- Department of PathologyThe University of Hong KongHong KongP. R. China
- State Key Laboratory of Liver ResearchThe University of Hong KongHong KongP. R. China
- Centre for Oncology and ImmunologyHong Kong Science ParkHong KongP. R. China
| | - Yiling Chen
- Department of PathologyThe University of Hong KongHong KongP. R. China
- State Key Laboratory of Liver ResearchThe University of Hong KongHong KongP. R. China
- Centre for Oncology and ImmunologyHong Kong Science ParkHong KongP. R. China
| | - Carmen Chak‐Lui Wong
- Department of PathologyThe University of Hong KongHong KongP. R. China
- State Key Laboratory of Liver ResearchThe University of Hong KongHong KongP. R. China
- Centre for Oncology and ImmunologyHong Kong Science ParkHong KongP. R. China
- Guangdong‐Hong Kong Joint Laboratory for RNA MedicineSun Yat‐sen UniversityGuangzhou510120P. R. China
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Shah I, Bundy J, Chambers B, Everett LJ, Haggard D, Harrill J, Judson RS, Nyffeler J, Patlewicz G. Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities. Chem Res Toxicol 2022; 35:1929-1949. [PMID: 36301716 PMCID: PMC10483698 DOI: 10.1021/acs.chemrestox.2c00245] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Screening new compounds for potential bioactivities against cellular targets is vital for drug discovery and chemical safety. Transcriptomics offers an efficient approach for assessing global gene expression changes, but interpreting chemical mechanisms from these data is often challenging. Connectivity mapping is a potential data-driven avenue for linking chemicals to mechanisms based on the observation that many biological processes are associated with unique gene expression signatures (gene signatures). However, mining the effects of a chemical on gene signatures for biological mechanisms is challenging because transcriptomic data contain thousands of noisy genes. New connectivity mapping approaches seeking to distinguish signal from noise continue to be developed, spurred by the promise of discovering chemical mechanisms, new drugs, and disease targets from burgeoning transcriptomic data. Here, we analyze these approaches in terms of diverse transcriptomic technologies, public databases, gene signatures, pattern-matching algorithms, and statistical evaluation criteria. To navigate the complexity of connectivity mapping, we propose a harmonized scheme to coherently organize and compare published workflows. We first standardize concepts underlying transcriptomic profiles and gene signatures based on various transcriptomic technologies such as microarrays, RNA-Seq, and L1000 and discuss the widely used data sources such as Gene Expression Omnibus, ArrayExpress, and MSigDB. Next, we generalize connectivity mapping as a pattern-matching task for finding similarity between a query (e.g., transcriptomic profile for new chemical) and a reference (e.g., gene signature of known target). Published pattern-matching approaches fall into two main categories: vector-based use metrics like correlation, Jaccard index, etc., and aggregation-based use parametric and nonparametric statistics (e.g., gene set enrichment analysis). The statistical methods for evaluating the performance of different approaches are described, along with comparisons reported in the literature on benchmark transcriptomic data sets. Lastly, we review connectivity mapping applications in toxicology and offer guidance on evaluating chemical-induced toxicity with concentration-response transcriptomic data. In addition to serving as a high-level guide and tutorial for understanding and implementing connectivity mapping workflows, we hope this review will stimulate new algorithms for evaluating chemical safety and drug discovery using transcriptomic data.
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Affiliation(s)
- Imran Shah
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Joseph Bundy
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Bryant Chambers
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Logan J. Everett
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Derik Haggard
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Joshua Harrill
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Richard S. Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Johanna Nyffeler
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
- Oak Ridge Institute for Science and Education (ORISE) Postdoctoral Fellow, Oak Ridge, Tennessee, 37831, US
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
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55
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Ewald JD, Basu N, Crump D, Boulanger E, Head J. Characterizing Variability and Uncertainty Associated with Transcriptomic Dose-Response Modeling. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:15960-15968. [PMID: 36268973 DOI: 10.1021/acs.est.2c04665] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Transcriptomics dose-response analysis (TDRA) has emerged as a promising approach for integrating toxicogenomics data into a risk assessment context; however, variability and uncertainty associated with experimental design are not well understood. Here, we evaluated n = 55 RNA-seq profiles derived from Japanese quail liver tissue following exposure to chlorpyrifos (0, 0.04, 0.1, 0.2, 0.4, 1, 2, 4, 10, 20, and 40 μg/g; n = 5 replicates per group) via egg injection. The full dataset was subsampled 637 times to generate smaller datasets with different dose ranges and spacing (designs A-E) and number of replicates (n = 2-5). TDRA of the 637 datasets revealed substantial variability in the gene and pathway benchmark doses, but relative stability in overall transcriptomic point-of-departure (tPOD) values when tPODs were calculated with the "pathway" and "mode" methods. Further, we found that tPOD values were more dependent on the dose range and spacing than on the number of replicates, suggesting that optimal experimental designs should use fewer replicates (n = 2 or 3) and more dose groups to reduce uncertainty in the results. Finally, tPOD values ranged by over ten times for all surveyed experimental designs and tPOD types, suggesting that tPODs should be interpreted as order-of-magnitude estimates.
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Affiliation(s)
- Jessica D Ewald
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue H9X 3V9, Canada
| | - Niladri Basu
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue H9X 3V9, Canada
| | - Doug Crump
- Ecotoxicology and Wildlife Health Division, National Wildlife Research Centre, Environment and Climate Change Canada, Ottawa K1A 0H3, Canada
| | - Emily Boulanger
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue H9X 3V9, Canada
| | - Jessica Head
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue H9X 3V9, Canada
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56
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Patlewicz G, Richard AM, Williams AJ, Judson RS, Thomas RS. Towards reproducible structure-based chemical categories for PFAS to inform and evaluate toxicity and toxicokinetic testing. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2022; 24:10.1016/j.comtox.2022.100250. [PMID: 36969381 PMCID: PMC10031514 DOI: 10.1016/j.comtox.2022.100250] [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: 11/08/2022]
Abstract
Per- and Polyfluoroalkyl substances (PFAS) are a class of synthetic chemicals that are in widespread use and present concerns for persistence, bioaccumulation and toxicity. Whilst a handful of PFAS have been characterised for their hazard profiles, the vast majority of PFAS have not been studied. The US Environmental Protection Agency (EPA) undertook a research project to screen ~150 PFAS through an array of different in vitro high throughput toxicity and toxicokinetic tests in order to inform chemical category and read-across approaches. A previous publication described the rationale behind the selection of an initial set of 75 PFAS, whereas herein, we describe how various category approaches were applied and extended to inform the selection of a second set of 75 PFAS from our library of approximately 430 commercially procured PFAS. In particular, we focus on the challenges in grouping PFAS for prospective analysis and how we have sought to develop and apply objective structure-based categories to profile the testing library and other PFAS inventories. We additionally illustrate how these categories can be enriched with other information to facilitate read-across inferences once experimental data become available. The availability of flexible, objective, reproducible and chemically intuitive categories to explore PFAS constitutes an important step forward in prioritising PFAS for further testing and assessment.
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Affiliation(s)
- Grace Patlewicz
- Center for Computational Toxicology and Exposure (CCTE), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC 27711, USA
| | - Ann M. Richard
- Center for Computational Toxicology and Exposure (CCTE), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC 27711, USA
| | - Antony J. Williams
- Center for Computational Toxicology and Exposure (CCTE), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC 27711, USA
| | - Richard S. Judson
- Center for Computational Toxicology and Exposure (CCTE), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC 27711, USA
| | - Russell S. Thomas
- Center for Computational Toxicology and Exposure (CCTE), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC 27711, USA
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57
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Wlodkowic D, Jansen M. High-throughput screening paradigms in ecotoxicity testing: Emerging prospects and ongoing challenges. CHEMOSPHERE 2022; 307:135929. [PMID: 35944679 DOI: 10.1016/j.chemosphere.2022.135929] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 06/09/2022] [Accepted: 07/31/2022] [Indexed: 06/15/2023]
Abstract
The rapidly increasing number of new production chemicals coupled with stringent implementation of global chemical management programs necessities a paradigm shift towards boarder uses of low-cost and high-throughput ecotoxicity testing strategies as well as deeper understanding of cellular and sub-cellular mechanisms of ecotoxicity that can be used in effective risk assessment. The latter will require automated acquisition of biological data, new capabilities for big data analysis as well as computational simulations capable of translating new data into in vivo relevance. However, very few efforts have been so far devoted into the development of automated bioanalytical systems in ecotoxicology. This is in stark contrast to standardized and high-throughput chemical screening and prioritization routines found in modern drug discovery pipelines. As a result, the high-throughput and high-content data acquisition in ecotoxicology is still in its infancy with limited examples focused on cell-free and cell-based assays. In this work we outline recent developments and emerging prospects of high-throughput bioanalytical approaches in ecotoxicology that reach beyond in vitro biotests. We discuss future importance of automated quantitative data acquisition for cell-free, cell-based as well as developments in phytotoxicity and in vivo biotests utilizing small aquatic model organisms. We also discuss recent innovations such as organs-on-a-chip technologies and existing challenges for emerging high-throughput ecotoxicity testing strategies. Lastly, we provide seminal examples of the small number of successful high-throughput implementations that have been employed in prioritization of chemicals and accelerated environmental risk assessment.
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Affiliation(s)
- Donald Wlodkowic
- The Neurotox Lab, School of Science, RMIT University, Melbourne, VIC, 3083, Australia.
| | - Marcus Jansen
- LemnaTec GmbH, Nerscheider Weg 170, 52076, Aachen, Germany
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Escher BI, Lamoree M, Antignac JP, Scholze M, Herzler M, Hamers T, Jensen TK, Audebert M, Busquet F, Maier D, Oelgeschläger M, Valente MJ, Boye H, Schmeisser S, Dervilly G, Piumatti M, Motteau S, König M, Renko K, Margalef M, Cariou R, Ma Y, Treschow AF, Kortenkamp A, Vinggaard AM. Mixture Risk Assessment of Complex Real-Life Mixtures-The PANORAMIX Project. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:12990. [PMID: 36293571 PMCID: PMC9602166 DOI: 10.3390/ijerph192012990] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/15/2022] [Accepted: 09/20/2022] [Indexed: 05/06/2023]
Abstract
Humans are involuntarily exposed to hundreds of chemicals that either contaminate our environment and food or are added intentionally to our daily products. These complex mixtures of chemicals may pose a risk to human health. One of the goals of the European Union's Green Deal and zero-pollution ambition for a toxic-free environment is to tackle the existent gaps in chemical mixture risk assessment by providing scientific grounds that support the implementation of adequate regulatory measures within the EU. We suggest dealing with this challenge by: (1) characterising 'real-life' chemical mixtures and determining to what extent they are transferred from the environment to humans via food and water, and from the mother to the foetus; (2) establishing a high-throughput whole-mixture-based in vitro strategy for screening of real-life complex mixtures of organic chemicals extracted from humans using integrated chemical profiling (suspect screening) together with effect-directed analysis; (3) evaluating which human blood levels of chemical mixtures might be of concern for children's development; and (4) developing a web-based, ready-to-use interface that integrates hazard and exposure data to enable component-based mixture risk estimation. These concepts form the basis of the Green Deal project PANORAMIX, whose ultimate goal is to progress mixture risk assessment of chemicals.
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Affiliation(s)
- Beate I. Escher
- Department of Cell Toxicology, Helmholtz Centre for Environmental Research—UFZ, DE-04318 Leipzig, Germany
- Environmental Toxicology, Department of Geoscience, Eberhard Karls University Tübingen, DE-72076 Tübingen, Germany
| | - Marja Lamoree
- Department Environment & Health, Faculty of Science, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | | | - Martin Scholze
- Centre for Pollution Research and Policy, Environmental Sciences Division, Brunel University London, Kingston Lane, Uxbridge UB8 3PH, UK
| | - Matthias Herzler
- German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | - Timo Hamers
- Department Environment & Health, Faculty of Science, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Tina Kold Jensen
- Department of Environmental Medicine, University of Southern Denmark, DK-5000 Odense, Denmark
| | - Marc Audebert
- Toxalim, UMR1331, INRAE, 31027 Toulouse, France
- PrediTox, 31100 Toulouse, France
| | | | | | | | - Maria João Valente
- National Food Institute, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Henriette Boye
- Odense Child Cohort, Hans Christian Andersen Hospital for Children, Odense University Hospital, DK-5000 Odense, Denmark
| | | | | | | | | | - Maria König
- Department of Cell Toxicology, Helmholtz Centre for Environmental Research—UFZ, DE-04318 Leipzig, Germany
- Environmental Toxicology, Department of Geoscience, Eberhard Karls University Tübingen, DE-72076 Tübingen, Germany
| | - Kostja Renko
- German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | - Maria Margalef
- Department Environment & Health, Faculty of Science, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | | | - Yanying Ma
- National Food Institute, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | | | - Andreas Kortenkamp
- Centre for Pollution Research and Policy, Environmental Sciences Division, Brunel University London, Kingston Lane, Uxbridge UB8 3PH, UK
| | - Anne Marie Vinggaard
- National Food Institute, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
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59
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Jeong J, Kim D, Choi J. Application of ToxCast/Tox21 data for toxicity mechanism-based evaluation and prioritization of environmental chemicals: Perspective and limitations. Toxicol In Vitro 2022; 84:105451. [PMID: 35921976 DOI: 10.1016/j.tiv.2022.105451] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 07/28/2022] [Indexed: 01/28/2023]
Abstract
In response to the need to minimize the use of experimental animals, new approach methodologies (NAMs) using advanced technology have emerged in the 21st century. ToxCast/Tox21 aims to evaluate the adverse effects of chemicals quickly and efficiently using a high-throughput screening and to transform the paradigm of toxicity assessment into mechanism-based toxicity prediction. The ToxCast/Tox21 database, which contains extensive data from over 1400 assays with numerous biological targets and activity data for over 9000 chemicals, can be used for various purposes in the field of chemical prioritization and toxicity prediction. In this study, an overview of the database was explored to aid mechanism-based chemical prioritization and toxicity prediction. Implications for the utilization of the ToxCast/Tox21 database in chemical prioritization and toxicity prediction were derived. The research trends in ToxCast/Tox21 assay data were reviewed in the context of toxicity mechanism identification, chemical priority, environmental monitoring, assay development, and toxicity prediction. Finally, the potential applications and limitations of using ToxCast/Tox21 assay data in chemical risk assessment were discussed. The analysis of the toxicity mechanism-based assays of ToxCast/Tox21 will help in chemical prioritization and regulatory applications without the use of laboratory animals.
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Affiliation(s)
- Jaeseong Jeong
- School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea
| | - Donghyeon Kim
- School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea
| | - Jinhee Choi
- School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea.
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60
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Colbourne JK, Shaw JR, Sostare E, Rivetti C, Derelle R, Barnett R, Campos B, LaLone C, Viant MR, Hodges G. Toxicity by descent: A comparative approach for chemical hazard assessment. ENVIRONMENTAL ADVANCES 2022; 9:100287. [PMID: 39228468 PMCID: PMC11370884 DOI: 10.1016/j.envadv.2022.100287] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Toxicology is traditionally divided between human and eco-toxicology. In the shared pursuit of environmental health, this separation does not account for discoveries made in the comparative studies of animal genomes. Here, we provide evidence on the feasibility of understanding the health impact of chemicals on all animals, including ecological keystone species and humans, based on a significant number of conserved genes and their functional associations to health-related outcomes across much of animal diversity. We test four conditions to understand the value of comparative genomics data to inform mechanism-based human and environmental hazard assessment: (1) genes that are most fundamental for health evolved early during animal evolution; (2) the molecular functions of pathways are better conserved among distantly related species than the individual genes that are members of these pathways; (3) the most conserved pathways among animals are those that cause adverse health outcomes when disrupted; (4) gene sets that serve as molecular signatures of biological processes or disease-states are largely enriched by evolutionarily conserved genes across the animal phylogeny. The concept of homology is applied in a comparative analysis of gene families and pathways among invertebrate and vertebrate species compared with humans. Results show that over 70% of gene families associated with disease are shared among the greatest variety of animal species through evolution. Pathway conservation between invertebrates and humans is based on the degree of conservation within vertebrates and the number of interacting genes within the human network. Human gene sets that already serve as biomarkers are enriched by evolutionarily conserved genes across the animal phylogeny. By implementing a comparative method for chemical hazard assessment, human and eco-toxicology converge towards a more holistic and mechanistic understanding of toxicity disrupting biological processes that are important for health and shared among animals (including humans).
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Affiliation(s)
- John K. Colbourne
- Michabo Health Science Ltd, Coventry CV1 2NT, UK
- School of Biosciences, University of Birmingham, Edgbaston B15 2TT, UK
| | - Joseph R. Shaw
- O’Neill School of Public and Environmental Affairs, Indiana University, Bloomington 47405, USA
| | | | - Claudia Rivetti
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook MK44 1LQ, UK
| | - Romain Derelle
- School of Biosciences, University of Birmingham, Edgbaston B15 2TT, UK
| | | | - Bruno Campos
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook MK44 1LQ, UK
| | - Carlie LaLone
- US Environmental Protection Agency, Duluth 55804, USA
| | - Mark R. Viant
- Michabo Health Science Ltd, Coventry CV1 2NT, UK
- School of Biosciences, University of Birmingham, Edgbaston B15 2TT, UK
| | - Geoff Hodges
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook MK44 1LQ, UK
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61
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Buick JK, Rowan-Carroll A, Gagné R, Williams A, Chen R, Li HH, Fornace AJ, Chao C, Engelward BP, Frötschl R, Ellinger-Ziegelbauer H, Pettit SD, Aubrecht J, Yauk CL. Integrated Genotoxicity Testing of three anti-infective drugs using the TGx-DDI transcriptomic biomarker and high-throughput CometChip® assay in TK6 cells. FRONTIERS IN TOXICOLOGY 2022; 4:991590. [PMID: 36211197 PMCID: PMC9540394 DOI: 10.3389/ftox.2022.991590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 08/10/2022] [Indexed: 11/21/2022] Open
Abstract
Genotoxicity testing relies on the detection of gene mutations and chromosome damage and has been used in the genetic safety assessment of drugs and chemicals for decades. However, the results of standard genotoxicity tests are often difficult to interpret due to lack of mode of action information. The TGx-DDI transcriptomic biomarker provides mechanistic information on the DNA damage-inducing (DDI) capability of chemicals to aid in the interpretation of positive in vitro genotoxicity data. The CometChip® assay was developed to assess DNA strand breaks in a higher-throughput format. We paired the TGx-DDI biomarker with the CometChip® assay in TK6 cells to evaluate three model agents: nitrofurantoin (NIT), metronidazole (MTZ), and novobiocin (NOV). TGx-DDI was analyzed by two independent labs and technologies (nCounter® and TempO-Seq®). Although these anti-infective drugs are, or have been, used in human and/or veterinary medicine, the standard genotoxicity testing battery showed significant genetic safety findings. Specifically, NIT is a mutagen and causes chromosome damage, and MTZ and NOV cause chromosome damage in conventional in vitro tests. Herein, the TGx-DDI biomarker classified NIT and MTZ as non-DDI at all concentrations tested, suggesting that NIT’s mutagenic activity is bacterial specific and that the observed chromosome damage by MTZ might be a consequence of in vitro test conditions. In contrast, NOV was classified as DDI at the second highest concentration tested, which is in line with the fact that NOV is a bacterial DNA-gyrase inhibitor that also affects topoisomerase II at high concentrations. The lack of DNA damage for NIT and MTZ was confirmed by the CometChip® results, which were negative for all three drugs except at overtly cytotoxic concentrations. This case study demonstrates the utility of combining the TGx-DDI biomarker and CometChip® to resolve conflicting genotoxicity data and provides further validation to support the reproducibility of the biomarker.
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Affiliation(s)
- Julie K. Buick
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
| | - Andrea Rowan-Carroll
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
| | - Rémi Gagné
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
| | - Andrew Williams
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
| | - Renxiang Chen
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, United States
- Department of Biochemistry and Molecular and Cellular Biology, Georgetown University, Washington, DC, United States
| | - Heng-Hong Li
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, United States
- Department of Biochemistry and Molecular and Cellular Biology, Georgetown University, Washington, DC, United States
| | - Albert J. Fornace
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, United States
- Department of Biochemistry and Molecular and Cellular Biology, Georgetown University, Washington, DC, United States
| | - Christy Chao
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Bevin P. Engelward
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Roland Frötschl
- Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany
| | | | - Syril D. Pettit
- Health and Environmental Sciences Institute, Washington, DC, United States
| | - Jiri Aubrecht
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, 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
- *Correspondence: Carole L. Yauk,
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62
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El-Masri H, Paul Friedman K, Isaacs K, Wetmore BA. Advances in computational methods along the exposure to toxicological response paradigm. Toxicol Appl Pharmacol 2022; 450:116141. [PMID: 35777528 PMCID: PMC9619339 DOI: 10.1016/j.taap.2022.116141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/27/2022] [Accepted: 06/23/2022] [Indexed: 10/17/2022]
Abstract
Human health risk assessment is a function of chemical toxicity, bioavailability to reach target biological tissues, and potential environmental exposure. These factors are complicated by many physiological, biochemical, physical and lifestyle factors. Furthermore, chemical health risk assessment is challenging in view of the large, and continually increasing, number of chemicals found in the environment. These challenges highlight the need to prioritize resources for the efficient and timely assessment of those environmental chemicals that pose greatest health risks. Computational methods, either predictive or investigative, are designed to assist in this prioritization in view of the lack of cost prohibitive in vivo experimental data. Computational methods provide specific and focused toxicity information using in vitro high throughput screening (HTS) assays. Information from the HTS assays can be converted to in vivo estimates of chemical levels in blood or target tissue, which in turn are converted to in vivo dose estimates that can be compared to exposure levels of the screened chemicals. This manuscript provides a review for the landscape of computational methods developed and used at the U.S. Environmental Protection Agency (EPA) highlighting their potentials and challenges.
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Affiliation(s)
- Hisham El-Masri
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA.
| | - Katie Paul Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kristin Isaacs
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Barbara A Wetmore
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA
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Middleton AM, Reynolds J, Cable S, Baltazar MT, Li H, Bevan S, Carmichael PL, Dent MP, Hatherell S, Houghton J, Kukic P, Liddell M, Malcomber S, Nicol B, Park B, Patel H, Scott S, Sparham C, Walker P, White A. Are Non-animal Systemic Safety Assessments Protective? A Toolbox and Workflow. Toxicol Sci 2022; 189:124-147. [PMID: 35822611 PMCID: PMC9412174 DOI: 10.1093/toxsci/kfac068] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
An important question in toxicological risk assessment is whether non-animal new approach methodologies (NAMs) can be used to make safety decisions that are protective of human health, without being overly conservative. In this work, we propose a core NAM toolbox and workflow for conducting systemic safety assessments for adult consumers. We also present an approach for evaluating how protective and useful the toolbox and workflow are by benchmarking against historical safety decisions. The toolbox includes physiologically based kinetic (PBK) models to estimate systemic Cmax levels in humans, and 3 bioactivity platforms, comprising high-throughput transcriptomics, a cell stress panel, and in vitro pharmacological profiling, from which points of departure are estimated. A Bayesian model was developed to quantify the uncertainty in the Cmax estimates depending on how the PBK models were parameterized. The feasibility of the evaluation approach was tested using 24 exposure scenarios from 10 chemicals, some of which would be considered high risk from a consumer goods perspective (eg, drugs that are systemically bioactive) and some low risk (eg, existing food or cosmetic ingredients). Using novel protectiveness and utility metrics, it was shown that up to 69% (9/13) of the low risk scenarios could be identified as such using the toolbox, whilst being protective against all (5/5) the high-risk ones. The results demonstrated how robust safety decisions could be made without using animal data. This work will enable a full evaluation to assess how protective and useful the toolbox and workflow are across a broader range of chemical-exposure scenarios.
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Affiliation(s)
| | - Joe Reynolds
- Unilever Safety and Environmental Assurance Centre, Bedfordshire MK44 1LQ, UK
| | - Sophie Cable
- Unilever Safety and Environmental Assurance Centre, Bedfordshire MK44 1LQ, UK
| | | | - Hequn Li
- Unilever Safety and Environmental Assurance Centre, Bedfordshire MK44 1LQ, UK
| | | | - Paul L Carmichael
- Unilever Safety and Environmental Assurance Centre, Bedfordshire MK44 1LQ, UK
| | - Matthew Philip Dent
- Unilever Safety and Environmental Assurance Centre, Bedfordshire MK44 1LQ, UK
| | - Sarah Hatherell
- Unilever Safety and Environmental Assurance Centre, Bedfordshire MK44 1LQ, UK
| | - Jade Houghton
- Unilever Safety and Environmental Assurance Centre, Bedfordshire MK44 1LQ, UK
| | - Predrag Kukic
- Unilever Safety and Environmental Assurance Centre, Bedfordshire MK44 1LQ, UK
| | - Mark Liddell
- Unilever Safety and Environmental Assurance Centre, Bedfordshire MK44 1LQ, UK
| | - Sophie Malcomber
- Unilever Safety and Environmental Assurance Centre, Bedfordshire MK44 1LQ, UK
| | - Beate Nicol
- Unilever Safety and Environmental Assurance Centre, Bedfordshire MK44 1LQ, UK
| | | | - Hiral Patel
- Charles River Laboratories, Cambridgeshire, CB10 1XL, UK
| | - Sharon Scott
- Unilever Safety and Environmental Assurance Centre, Bedfordshire MK44 1LQ, UK
| | - Chris Sparham
- Unilever Safety and Environmental Assurance Centre, Bedfordshire MK44 1LQ, UK
| | - Paul Walker
- Cyprotex Discovery Ltd, Cheshire SK10 4TG, UK
| | - Andrew White
- Unilever Safety and Environmental Assurance Centre, Bedfordshire MK44 1LQ, UK
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64
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House JS, Grimm FA, Klaren WD, Dalzell A, Kuchi S, Zhang SD, Lenz K, Boogaard PJ, Ketelslegers HB, Gant TW, Rusyn I, Wright FA. Grouping of UVCB substances with dose-response transcriptomics data from human cell-based assays. ALTEX 2022; 39:388–404. [PMID: 35288757 PMCID: PMC9344966 DOI: 10.14573/altex.2107051] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 02/22/2022] [Indexed: 12/18/2022]
Abstract
The application of in vitro biological assays as new approach methodologies (NAMs) to support grouping of UVCB (unknown or variable composition, complex reaction products, and biological materials) substances has recently been demonstrated. In addition to cell-based phenotyping as NAMs, in vitro transcriptomic profiling is used to gain deeper mechanistic understanding of biological responses to chemicals and to support grouping and read-across. However, the value of gene expression profiling for characterizing complex substances like UVCBs has not been explored. Using 141 petroleum substance extracts, we performed dose-response transcriptomic profiling in human induced pluripotent stem cell (iPSC)-derived hepatocytes, cardiomyocytes, neurons, and endothelial cells, as well as cell lines MCF7 and A375. The goal was to determine whether transcriptomic data can be used to group these UVCBs and to further characterize the molecular basis for in vitro biological responses. We found distinct transcriptional responses for petroleum substances by manufacturing class. Pathway enrichment informed interpretation of effects of substances and UVCB petroleum-class. Transcriptional activity was strongly correlated with concentration of polycyclic aromatic compounds (PAC), especially in iPSC-derived hepatocytes. Supervised analysis using transcriptomics, alone or in combination with bioactivity data collected on these same substances/cells, suggest that transcriptomics data provide useful mechanistic information, but only modest additional value for grouping. Overall, these results further demonstrate the value of NAMs for grouping of UVCBs, identify informative cell lines, and provide data that could be used for justifying selection of substances for further testing that may be required for registration.
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Affiliation(s)
- John S House
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.,Biostatistics & Computational Biology Branch, National Institute of Environmental Health Sciences, RTP, NC, USA
| | - Fabian A Grimm
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA
| | - William D Klaren
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA.,current address: ToxStrategies, Inc., Asheville, NC, USA
| | - Abigail Dalzell
- Public Health England, Centre for Radiation, Chemical and Environmental Hazards, Harwell Science Campus, Oxon, UK
| | - Srikeerthana Kuchi
- Northern Ireland Centre for Stratified Medicine, Ulster University, L/Derry, Northern Ireland, UK.,current address: MRC-University of Glasgow Centre for Virus Research, Glasgow, Scotland, UK
| | - Shu-Dong Zhang
- Northern Ireland Centre for Stratified Medicine, Ulster University, L/Derry, Northern Ireland, UK
| | - Klaus Lenz
- SYNCOM Forschungs und Entwicklungsberatung GmbH, Ganderkesee, Germany
| | | | | | - Timothy W Gant
- Public Health England, Centre for Radiation, Chemical and Environmental Hazards, Harwell Science Campus, Oxon, UK
| | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA
| | - Fred A Wright
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
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65
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Corton JC, Mitchell CA, Auerbach S, Bushel P, Ellinger-Ziegelbauer H, Escobar PA, Froetschl R, Harrill AH, Johnson K, Klaunig JE, Pandiri AR, Podtelezhnikov AA, Rager JE, Tanis KQ, van der Laan JW, Vespa A, Yauk CL, Pettit SD, Sistare FD. A Collaborative Initiative to Establish Genomic Biomarkers for Assessing Tumorigenic Potential to Reduce Reliance on Conventional Rodent Carcinogenicity Studies. Toxicol Sci 2022; 188:4-16. [PMID: 35404422 PMCID: PMC9238304 DOI: 10.1093/toxsci/kfac041] [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: 11/24/2022] Open
Abstract
There is growing recognition across broad sectors of the scientific community that use of genomic biomarkers has the potential to reduce the need for conventional rodent carcinogenicity studies of industrial chemicals, agrochemicals, and pharmaceuticals through a weight-of-evidence approach. These biomarkers fall into 2 major categories: (1) sets of gene transcripts that can identify distinct tumorigenic mechanisms of action; and (2) cancer driver gene mutations indicative of rapidly expanding growth-advantaged clonal cell populations. This call-to-action article describes a collaborative approach launched to develop and qualify biomarker gene expression panels that measure widely accepted molecular pathways linked to tumorigenesis and their activation levels to predict tumorigenic doses of chemicals from short-term exposures. Growing evidence suggests that application of such biomarker panels in short-term exposure rodent studies can identify both tumorigenic hazard and tumorigenic activation levels for chemical-induced carcinogenicity. In the future, this approach will be expanded to include methodologies examining mutations in key cancer driver gene mutation hotspots as biomarkers of both genotoxic and nongenotoxic chemical tumor risk. Analytical, technical, and biological validation studies of these complementary genomic tools are being undertaken by multisector and multidisciplinary collaborative teams within the Health and Environmental Sciences Institute. Success from these efforts will facilitate the transition from current heavy reliance on conventional 2-year rodent carcinogenicity studies to more rapid animal- and resource-sparing approaches for mechanism-based carcinogenicity evaluation supporting internal and regulatory decision-making.
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Affiliation(s)
- J Christopher Corton
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Constance A Mitchell
- Health and Environmental Sciences Institute, Washington, District of Columbia, USA
| | - Scott Auerbach
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Pierre Bushel
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, North Carolina, USA
| | | | - Patricia A Escobar
- Safety Assessment and Laboratory Animal Resources, Merck Sharp & Dohme Corp, West Point, Pennsylvania, USA
| | - Roland Froetschl
- BfArM-Bundesinstitut für Arzneimittel und Medizinprodukte, Federal Institute for Drugs and Medical Devices, Bonn, Germany
| | - Alison H Harrill
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | | | - James E Klaunig
- Laboratory of Investigative Toxicology and Pathology, Department of Environmental and Occupational Health, Indiana School of Public Health, Indiana University, Bloomington, Indiana, USA
| | - Arun R Pandiri
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | | | - Julia E Rager
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Keith Q Tanis
- Safety Assessment and Laboratory Animal Resources, Merck Sharp & Dohme Corp, West Point, Pennsylvania, USA
| | - Jan Willem van der Laan
- Section on Pharmacology, Toxicology and Kinetics, Medicines Evaluation Board, Utrecht, The Netherlands
| | - Alisa Vespa
- Therapeutic Products Directorate, Health Canada, Ottawa, Ontario, Canada
| | - Carole L Yauk
- Department of Biology, University of Ottawa, Ottawa, Ontario, Canada
| | - Syril D Pettit
- Health and Environmental Sciences Institute, Washington, District of Columbia, USA
| | - Frank D Sistare
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
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66
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Corton JC, Liu J, Williams A, Cho E, Yauk CL. A gene expression biomarker identifies inhibitors of two classes of epigenome effectors in a human microarray compendium. Chem Biol Interact 2022; 365:110032. [PMID: 35777453 DOI: 10.1016/j.cbi.2022.110032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 11/03/2022]
Abstract
Biomarkers predictive of molecular and toxicological effects are needed to interpret emerging high-throughput transcriptomics (HTTr) data streams. To address the limited approaches available for identifying epigenotoxicants, we previously developed and validated an 81-gene biomarker that accurately predicts histone deacetylase inhibition (HDACi) in transcript profiles derived from chemically-treated TK6 cells. In the present study, we sought to determine if this biomarker (TGx-HDACi) could be used to identify HDACi chemicals in other cell lines using the Running Fisher correlation test. Using microarray comparisons derived from human cells exposed to HDACi, we found considerable heterogeneity in correlation with the TGx-HDACi biomarker dependent on chemical exposure conditions and tissue from which the cell line was derived. Using a defined set of conditions that overlapped with our earlier study, the biomarker was able to accurately identify HDACi chemicals (90-100% balanced accuracy). In an in silico screen of 2427 chemicals in 9660 chemical versus control comparisons, the biomarker coupled with the Running Fisher test was able to identify 14 additional HDACi chemicals as well as other chemicals not previously associated with HDACi. Most notable were 12 inhibitors of bromodomain (BRD) and extraterminal (BET) family proteins including BRD4 that bind to acetylated histones. The BET protein inhibitors could be distinguished from the HDACi based on differences in the expression of a small set of biomarker genes. Our results indicate that the TGx-HDACi biomarker will be useful for identifying inhibitors of two classes of epigenome effectors in HTTr screening studies.
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Affiliation(s)
- J Christopher Corton
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC, 27711, USA.
| | - Jie Liu
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC, 27711, USA.
| | - Andrew Williams
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada.
| | - Eunnara Cho
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada.
| | - Carole L Yauk
- Department of Biology, University of Ottawa, Ottawa, Ontario, Canada.
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67
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Corton JC, Liu J, Kleinstreuer N, Gwinn MR, Ryan N. Towards replacement of animal tests with in vitro assays: a gene expression biomarker predicts in vitro and in vivo estrogen receptor activity. Chem Biol Interact 2022; 363:109995. [PMID: 35697134 DOI: 10.1016/j.cbi.2022.109995] [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: 04/05/2022] [Revised: 05/02/2022] [Accepted: 05/24/2022] [Indexed: 11/18/2022]
Abstract
High-throughput transcriptomics (HTTr) has the potential to support efforts to reduce or replace some animal tests. In past studies, we described a computational approach utilizing a gene expression biomarker consisting of 46 genes to predict estrogen receptor (ER) activity after chemical exposure in ER-positive human breast cancer cells including the MCF-7 cell line. We hypothesized that the biomarker model could identify ER activities of chemicals examined by Endocrine Disruptor Screening Program (EDSP) Tier 1 screening assays in which transcript profiles of the same chemicals were examined in MCF-7 cells. For the 62 chemicals examined including 5 chemicals examined in this study using RNA-Seq, the ER biomarker model accuracy was 1) 97% for in vitro reference chemicals, 2) 76-85% for guideline uterotrophic assays, and 3) 87-88% for guideline and nonguideline uterotrophic assays. For the same chemicals, these accuracies were similar or slightly better than those of the ToxCast ER model based on 18 in vitro assays. The performance of the ER biomarker model indicates that HTTr interpreted using the ER biomarker correctly identifies active and inactive ER reference chemicals. As part of the HTTr screening program the approach could rapidly identify chemicals with potential ER bioactivities for additional screening and testing.
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Affiliation(s)
- J Christopher Corton
- Center for Computational Toxicology and Exposure, US-EPA, Research Triangle Park, NC, 27711, USA.
| | - Jie Liu
- Center for Computational Toxicology and Exposure, US-EPA, Research Triangle Park, NC, 27711, USA.
| | - Nicole Kleinstreuer
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27711, USA.
| | - Maureen R Gwinn
- Center for Computational Toxicology and Exposure, US-EPA, Research Triangle Park, NC, 27711, USA.
| | - Natalia Ryan
- Oak Ridge Institute for Science and Education (ORISE), Research Triangle Park, NC, 27711, USA.
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68
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Long Q, Feng L, Li Y, Zuo T, Chang L, Zhang Z, Xu P. Time-resolved quantitative phosphoproteomics reveals cellular responses induced by caffeine and coumarin. Toxicol Appl Pharmacol 2022; 449:116115. [PMID: 35691368 DOI: 10.1016/j.taap.2022.116115] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 05/27/2022] [Accepted: 06/06/2022] [Indexed: 11/30/2022]
Abstract
Protein phosphorylation is a critical way that cells respond to external signals and environmental stresses. However, the patterns of cellular response to chemicals at different times were largely unknown. Here, we used quantitative phosphoproteomics to analyze the cellular response of kinases and signaling pathways, as well as pattern change of phosphorylated substrates in HepG2 cells that were exposed to caffeine and coumarin for 10 min and 24 h. Comparing the 10 min and 24 h groups, 33 kinases were co-responded and 32 signaling pathways were co-enriched in caffeine treated samples, while 48 kinases and 34 signaling pathways were co-identified in coumarin treated samples. Instead, the percentage of co-identified phosphorylated substrates only accounted for 4.31% and 9.57% between 10 min and 24 h in caffeine and coumarin treated samples, respectively. The results showed that specific chemical exposure led to a bunch of the same kinases and signaling pathways changed in HepG2 cells, while the phosphorylated substrates were different. In addition, it was found that insulin signaling pathway was significantly enriched by both the caffeine and coumarin treatment. The pattern changes in phosphorylation of protein substrates, kinases and signaling pathways with varied chemicals and different time course shed light on the potential mechanism of cellular responses to endless chemical stimulation.
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Affiliation(s)
- Qi Long
- School of Basic Medicine, Anhui Medical University, Hefei 230032, China; State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Lijie Feng
- School of Basic Medicine, Anhui Medical University, Hefei 230032, China
| | - Yuan Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China; School of Medicine, Guizhou University, Guiyang 550025, China
| | - Tao Zuo
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Lei Chang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Zhenpeng Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China.
| | - Ping Xu
- School of Basic Medicine, Anhui Medical University, Hefei 230032, China; State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China; School of Medicine, Guizhou University, Guiyang 550025, China; School of Public Health, China Medical University, Shenyang 110122, China; Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding 071002, China.
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Nyffeler J, Willis C, Harris FR, Taylor LW, Judson R, Everett LJ, Harrill JA. Combining phenotypic profiling and targeted RNA-Seq reveals linkages between transcriptional perturbations and chemical effects on cell morphology: Retinoic acid as an example. Toxicol Appl Pharmacol 2022; 444:116032. [PMID: 35483669 PMCID: PMC10894461 DOI: 10.1016/j.taap.2022.116032] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 04/11/2022] [Accepted: 04/12/2022] [Indexed: 10/18/2022]
Abstract
The United States Environmental Protection Agency has proposed a tiered testing strategy for chemical hazard evaluation based on new approach methods (NAMs). The first tier includes in vitro profiling assays applicable to many (human) cell types, such as high-throughput transcriptomics (HTTr) and high-throughput phenotypic profiling (HTPP). The goals of this study were to: (1) harmonize the seeding density of U-2 OS human osteosarcoma cells for use in both assays; (2) compare HTTr- versus HTPP-derived potency estimates for 11 mechanistically diverse chemicals; (3) identify candidate reference chemicals for monitoring assay performance in future screens; and (4) characterize the transcriptional and phenotypic changes in detail for all-trans retinoic acid (ATRA) as a model compound known for its adverse effects on osteoblast differentiation. The results of this evaluation showed that (1) HTPP conducted at low (400 cells/well) and high (3000 cells/well) seeding densities yielded comparable potency estimates and similar phenotypic profiles for the tested chemicals; (2) HTPP and HTTr resulted in comparable potency estimates for changes in cellular morphology and gene expression, respectively; (3) three test chemicals (etoposide, ATRA, dexamethasone) produced concentration-dependent effects on cellular morphology and gene expression that were consistent with known modes-of-action, demonstrating their suitability for use as reference chemicals for monitoring assay performance; and (4) ATRA produced phenotypic changes that were highly similar to other retinoic acid receptor activators (AM580, arotinoid acid) and some retinoid X receptor activators (bexarotene, methoprene acid). This phenotype was observed concurrently with autoregulation of the RARB gene. Both effects were prevented by pre-treating U-2 OS cells with pharmacological antagonists of their respective receptors. Thus, the observed phenotype could be considered characteristic of retinoic acid pathway activation in U-2 OS cells. These findings lay the groundwork for combinatorial screening of chemicals using HTTr and HTPP to generate complementary information for the first tier of a NAM-based chemical hazard evaluation strategy.
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Affiliation(s)
- Johanna Nyffeler
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Institute for Science and Education (ORISE) Postdoctoral Fellow, Oak Ridge, TN 37831, United States of America
| | - Clinton Willis
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Felix R Harris
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Associated Universities (ORAU) National Student Services Contractor, Oak Ridge, TN 37831, United States of America
| | - Laura W Taylor
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Richard Judson
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Logan J Everett
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Joshua A Harrill
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America.
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70
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Crofton KM, Bassan A, Behl M, Chushak YG, Fritsche E, Gearhart JM, Marty MS, Mumtaz M, Pavan M, Ruiz P, Sachana M, Selvam R, Shafer TJ, Stavitskaya L, Szabo DT, Szabo ST, Tice RR, Wilson D, Woolley D, Myatt GJ. Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2022; 22:100223. [PMID: 35844258 PMCID: PMC9281386 DOI: 10.1016/j.comtox.2022.100223] [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: 04/27/2023]
Abstract
Neurotoxicology is the study of adverse effects on the structure or function of the developing or mature adult nervous system following exposure to chemical, biological, or physical agents. The development of more informative alternative methods to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics is critically needed. The use of such alternative methods including in silico approaches that predict DNT or NT from chemical structure (e.g., statistical-based and expert rule-based systems) is ideally based on a comprehensive understanding of the relevant biological mechanisms. This paper discusses known mechanisms alongside the current state of the art in DNT/NT testing. In silico approaches available today that support the assessment of neurotoxicity based on knowledge of chemical structure are reviewed, and a conceptual framework for the integration of in silico methods with experimental information is presented. Establishing this framework is essential for the development of protocols, namely standardized approaches, to ensure that assessments of NT and DNT based on chemical structures are generated in a transparent, consistent, and defendable manner.
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Affiliation(s)
| | - Arianna Bassan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova,
Italy
| | - Mamta Behl
- Division of the National Toxicology Program, National
Institutes of Environmental Health Sciences, Durham, NC 27709, USA
| | - Yaroslav G. Chushak
- Henry M Jackson Foundation for the Advancement of Military
Medicine, Wright-Patterson AFB, OH 45433, USA
| | - Ellen Fritsche
- IUF – Leibniz Research Institute for Environmental
Medicine & Medical Faculty Heinrich-Heine-University, Düsseldorf,
Germany
| | - Jeffery M. Gearhart
- Henry M Jackson Foundation for the Advancement of Military
Medicine, Wright-Patterson AFB, OH 45433, USA
| | | | - Moiz Mumtaz
- Agency for Toxic Substances and Disease Registry, US
Department of Health and Human Services, Atlanta, GA, USA
| | - Manuela Pavan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova,
Italy
| | - Patricia Ruiz
- Agency for Toxic Substances and Disease Registry, US
Department of Health and Human Services, Atlanta, GA, USA
| | - Magdalini Sachana
- Environment Health and Safety Division, Environment
Directorate, Organisation for Economic Co-Operation and Development (OECD), 75775
Paris Cedex 16, France
| | - Rajamani Selvam
- Office of Clinical Pharmacology, Office of Translational
Sciences, Center for Drug Evaluation and Research (CDER), U.S. Food and Drug
Administration (FDA), Silver Spring, MD 20993, USA
| | - Timothy J. Shafer
- Biomolecular and Computational Toxicology Division, Center
for Computational Toxicology and Exposure, US EPA, Research Triangle Park, NC,
USA
| | - Lidiya Stavitskaya
- Office of Clinical Pharmacology, Office of Translational
Sciences, Center for Drug Evaluation and Research (CDER), U.S. Food and Drug
Administration (FDA), Silver Spring, MD 20993, USA
| | | | | | | | - Dan Wilson
- The Dow Chemical Company, Midland, MI 48667, USA
| | | | - Glenn J. Myatt
- Instem, Columbus, OH 43215, USA
- Corresponding author.
(G.J. Myatt)
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71
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Everett LJ, Mav D, Phadke DP, Balik-Meisner MR, Shah RR. Impact of Aligner, Normalization Method, and Sequencing Depth on TempO-seq Accuracy. Bioinform Biol Insights 2022; 16:11779322221095216. [PMID: 35515009 PMCID: PMC9067045 DOI: 10.1177/11779322221095216] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/29/2022] [Indexed: 11/17/2022] Open
Abstract
High-throughput transcriptomics has advanced through the introduction of TempO-seq, a targeted alternative to traditional RNA-seq. TempO-seq platforms use 50 nucleotide probes, each specifically designed to target a known transcript, thus allowing for reduced sequencing depth per sample compared with RNA-seq without compromising the accuracy of results. Thus far, studies using the TempO-seq method have relied on existing tools for processing the resulting short read data. However, these tools were originally designed for other data types. While they have been used for processing of early TempO-seq data, they have not been systematically assessed for accuracy or compared to determine an optimal framework for processing and analyzing TempO-seq data. In this work, we re-analyze several publicly available TempO-seq data sets covering a range of experimental designs and use corresponding RNA-seq data sets as a gold standard to rigorously assess accuracy at multiple levels. We compare 6 aligners and 5 normalization methods across various accuracy and performance metrics. Our results demonstrate the overall robust accuracy of the TempO-seq platform, independent of data processing methods. Complex aligners and advanced normalization methods do not appear to have any general advantage over simpler methods when it comes to analyzing TempO-seq data. The reduced complexity of the sequencing space, and the fact that TempO-seq probes are all equal length, appears to reduce the need for elaborate bioinformatic or statistical methods used to address these factors in RNA-seq data.
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Affiliation(s)
| | - Deepak Mav
- Sciome LLC, Research Triangle Park, NC, USA
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72
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Basili D, Reynolds J, Houghton J, Malcomber S, Chambers B, Liddell M, Muller I, White A, Shah I, Everett LJ, Middleton A, Bender A. Latent Variables Capture Pathway-Level Points of Departure in High-Throughput Toxicogenomic Data. Chem Res Toxicol 2022; 35:670-683. [PMID: 35333521 PMCID: PMC9019810 DOI: 10.1021/acs.chemrestox.1c00444] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Indexed: 11/28/2022]
Abstract
Estimation of points of departure (PoDs) from high-throughput transcriptomic data (HTTr) represents a key step in the development of next-generation risk assessment (NGRA). Current approaches mainly rely on single key gene targets, which are constrained by the information currently available in the knowledge base and make interpretation challenging as scientists need to interpret PoDs for thousands of genes or hundreds of pathways. In this work, we aimed to address these issues by developing a computational workflow to investigate the pathway concentration-response relationships in a way that is not fully constrained by known biology and also facilitates interpretation. We employed the Pathway-Level Information ExtractoR (PLIER) to identify latent variables (LVs) describing biological activity and then investigated in vitro LVs' concentration-response relationships using the ToxCast pipeline. We applied this methodology to a published transcriptomic concentration-response data set for 44 chemicals in MCF-7 cells and showed that our workflow can capture known biological activity and discriminate between estrogenic and antiestrogenic compounds as well as activity not aligning with the existing knowledge base, which may be relevant in a risk assessment scenario. Moreover, we were able to identify the known estrogen activity in compounds that are not well-established ER agonists/antagonists supporting the use of the workflow in read-across. Next, we transferred its application to chemical compounds tested in HepG2, HepaRG, and MCF-7 cells and showed that PoD estimates are in strong agreement with those estimated using a recently developed Bayesian approach (cor = 0.89) and in weak agreement with those estimated using a well-established approach such as BMDExpress2 (cor = 0.57). These results demonstrate the effectiveness of using PLIER in a concentration-response scenario to investigate pathway activity in a way that is not fully constrained by the knowledge base and to ease the biological interpretation and support the development of an NGRA framework with the ability to improve current risk assessment strategies for chemicals using new approach methodologies.
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Affiliation(s)
- Danilo Basili
- Department
of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K.
- Unilever,
Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K.
| | - Joe Reynolds
- Unilever,
Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K.
| | - Jade Houghton
- Unilever,
Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K.
| | - Sophie Malcomber
- Unilever,
Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K.
| | - Bryant Chambers
- Center
for Computational Toxicology and Exposure, Office of Research and
Development, U.S. Environmental Protection
Agency, Research Triangle Park, North Carolina 27711, United States
| | - Mark Liddell
- Unilever,
Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K.
| | - Iris Muller
- Unilever,
Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K.
| | - Andrew White
- Unilever,
Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K.
| | - Imran Shah
- Center
for Computational Toxicology and Exposure, Office of Research and
Development, U.S. Environmental Protection
Agency, Research Triangle Park, North Carolina 27711, United States
| | - Logan J. Everett
- Center
for Computational Toxicology and Exposure, Office of Research and
Development, U.S. Environmental Protection
Agency, Research Triangle Park, North Carolina 27711, United States
| | - Alistair Middleton
- Unilever,
Safety and Environmental Assurance Centre (SEAC), Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K.
| | - Andreas Bender
- Department
of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K.
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73
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Speen AM, Murray JR, Krantz QT, Davies D, Evansky P, Harrill JA, Everett LJ, Bundy JL, Dailey LA, Hill J, Zander W, Carlsten E, Monsees M, Zavala J, Higuchi MA. Benchmark Dose Modeling Approaches for Volatile Organic Chemicals using a Novel Air-Liquid Interface In Vitro Exposure System. Toxicol Sci 2022; 188:88-107. [PMID: 35426944 DOI: 10.1093/toxsci/kfac040] [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: 11/14/2022] Open
Abstract
Inhalation is the most relevant route of volatile organic chemical (VOC) exposure; however, due to unique challenges posed by their chemical properties and poor solubility in aqueous solutions, in vitro chemical safety testing is predominantly performed using direct application dosing/submerged exposures. To address the difficulties in screening toxic effects of VOCs, our cell culture exposure system permits cells to be exposed to multiple concentrations at air-liquid interface (ALI) in a 24-well format. ALI exposure methods permit direct chemical-to-cell interaction with the test article at physiological conditions. In the present study, BEAS-2B and primary normal human bronchial epithelial cells (pHBEC) are used to assess gene expression, cytotoxicity, and cell viability responses to a variety of volatile chemicals including acrolein, formaldehyde, 1,3-butadiene, acetaldehyde, 1-bromopropane, carbon tetrachloride, dichloromethane, and trichloroethylene. BEAS-2B cells were exposed to all the test agents, while pHBECs were only exposed to the latter four listed above. The VOC concentrations tested elicited only slight cell viability changes in both cell types. Gene expression changes were analyzed using benchmark dose (BMD) modeling. The BMD for the most sensitive gene set was within one order of magnitude of the threshold-limit value reported by the American Conference of Governmental Industrial Hygienists, and the most sensitive gene sets impacted by exposure correlate to known adverse health effects recorded in epidemiologic and in vivo exposure studies. Overall, our study outlines a novel in vitro approach for evaluating molecular-based points-of-departure in human airway epithelial cell exposure to volatile chemicals.
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Affiliation(s)
- Adam M Speen
- Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, Tennessee 37830, USA
- CPHEA, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
| | - Jessica R Murray
- CPHEA, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
| | - Quentin Todd Krantz
- CPHEA, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
| | - David Davies
- CPHEA, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
| | - Paul Evansky
- CPHEA, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
| | - Joshua A Harrill
- CCTE, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
| | - Logan J Everett
- CCTE, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
| | - Joseph L Bundy
- CCTE, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
| | - Lisa A Dailey
- CPHEA, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
| | - Jazzlyn Hill
- CPHEA, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
- Oak Ridge Associated Universities (ORAU), Oak Ridge, Tennessee 37830, USA
| | - Wyatt Zander
- CPHEA, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
- Oak Ridge Associated Universities (ORAU), Oak Ridge, Tennessee 37830, USA
| | - Elise Carlsten
- CPHEA, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
- Oak Ridge Associated Universities (ORAU), Oak Ridge, Tennessee 37830, USA
| | - Michael Monsees
- CPHEA, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
- Oak Ridge Associated Universities (ORAU), Oak Ridge, Tennessee 37830, USA
| | - Jose Zavala
- MedTec BioLab Inc., Hillsborough, North Carolina 27278, USA
| | - Mark A Higuchi
- CPHEA, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA
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74
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Daston GP, Mahony C, Thomas RS, Vinken M. Assessing Safety without Animal Testing: The Road Ahead. Toxicol Sci 2022; 187:214-218. [PMID: 35357465 DOI: 10.1093/toxsci/kfac039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
| | | | - Russell S Thomas
- US Environmental Protection Agency, Research Triangle Park, NC, USA
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75
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Bundy JL, Judson R, Williams AJ, Grulke C, Shah I, Everett LJ. Predicting molecular initiating events using chemical target annotations and gene expression. BioData Min 2022; 15:7. [PMID: 35246223 PMCID: PMC8895536 DOI: 10.1186/s13040-022-00292-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 02/10/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The advent of high-throughput transcriptomic screening technologies has resulted in a wealth of publicly available gene expression data associated with chemical treatments. From a regulatory perspective, data sets that cover a large chemical space and contain reference chemicals offer utility for the prediction of molecular initiating events associated with chemical exposure. Here, we integrate data from a large compendium of transcriptomic responses to chemical exposure with a comprehensive database of chemical-protein associations to train binary classifiers that predict mechanism(s) of action from transcriptomic responses. First, we linked reference chemicals present in the LINCS L1000 gene expression data collection to chemical identifiers in RefChemDB, a database of chemical-protein interactions. Next, we trained binary classifiers on MCF7 human breast cancer cell line derived gene expression profiles and chemical-protein labels using six classification algorithms to identify optimal analysis parameters. To validate classifier accuracy, we used holdout data sets, training-excluded reference chemicals, and empirical significance testing of null models derived from permuted chemical-protein associations. To identify classifiers that have variable predicting performance across training data derived from different cellular contexts, we trained a separate set of binary classifiers on the PC3 human prostate cancer cell line. RESULTS We trained classifiers using expression data associated with chemical treatments linked to 51 molecular initiating events. This analysis identified and validated 9 high-performing classifiers with empirical p-values lower than 0.05 and internal accuracies ranging from 0.73 to 0.94 and holdout accuracies of 0.68 to 0.92. High-ranking predictions for training-excluded reference chemicals demonstrating that predictive accuracy extends beyond the set of chemicals used in classifier training. To explore differences in classifier performance as a function of training data cellular context, MCF7-trained classifier accuracies were compared to classifiers trained on the PC3 gene expression data for the same molecular initiating events. CONCLUSIONS This methodology can offer insight in prioritizing candidate perturbagens of interest for targeted screens. This approach can also help guide the selection of relevant cellular contexts for screening classes of candidate perturbagens using cell line specific model performance.
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Affiliation(s)
- Joseph L Bundy
- Biomolecular and Computational Toxicology Division, Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Durham, NC, 27709, USA
| | - Richard Judson
- Biomolecular and Computational Toxicology Division, Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Durham, NC, 27709, USA
| | - Antony J Williams
- Chemical Characterization and Exposure Division, Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Durham, NC, 27709, USA
| | - Chris Grulke
- Chemical Characterization and Exposure Division, Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Durham, NC, 27709, USA
| | - Imran Shah
- Biomolecular and Computational Toxicology Division, Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Durham, NC, 27709, USA
| | - Logan J Everett
- Biomolecular and Computational Toxicology Division, Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Durham, NC, 27709, USA.
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76
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R-ODAF: Omics data analysis framework for regulatory application. Regul Toxicol Pharmacol 2022; 131:105143. [PMID: 35247516 DOI: 10.1016/j.yrtph.2022.105143] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 12/20/2021] [Accepted: 02/14/2022] [Indexed: 12/12/2022]
Abstract
Despite the widespread use of transcriptomics technologies in toxicology research, acceptance of the data by regulatory agencies to support the hazard assessment is still limited. Fundamental issues contributing to this are the lack of reproducibility in transcriptomics data analysis arising from variance in the methods used to generate data and differences in the data processing. While research applications are flexible in the way the data are generated and interpreted, this is not the case for regulatory applications where an unambiguous answer, possibly later subject to legal scrutiny, is required. A reference analysis framework would give greater credibility to the data and allow the practitioners to justify their use of an alternative bioinformatic process by referring to a standard. In this publication, we propose a method called omics data analysis framework for regulatory application (R-ODAF), which has been built as a user-friendly pipeline to analyze raw transcriptomics data from microarray and next-generation sequencing. In the R-ODAF, we also propose additional statistical steps to remove the number of false positives obtained from standard data analysis pipelines for RNA-sequencing. We illustrate the added value of R-ODAF, compared to a standard workflow, using a typical toxicogenomics dataset of hepatocytes exposed to paracetamol.
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77
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Barton-Maclaren TS, Wade M, Basu N, Bayen S, Grundy J, Marlatt V, Moore R, Parent L, Parrott J, Grigorova P, Pinsonnault-Cooper J, Langlois VS. Innovation in regulatory approaches for endocrine disrupting chemicals: The journey to risk assessment modernization in Canada. ENVIRONMENTAL RESEARCH 2022; 204:112225. [PMID: 34666016 DOI: 10.1016/j.envres.2021.112225] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 10/12/2021] [Accepted: 10/13/2021] [Indexed: 06/13/2023]
Abstract
Globally, regulatory authorities grapple with the challenge of assessing the hazards and risks to human and ecosystem health that may result from exposure to chemicals that disrupt the normal functioning of endocrine systems. Rapidly increasing number of chemicals in commerce, coupled with the reliance on traditional, costly animal experiments for hazard characterization - often with limited sensitivity to many important mechanisms of endocrine disruption -, presents ongoing challenges for chemical regulation. The consequence is a limited number of chemicals for which there is sufficient data to assess if there is endocrine toxicity and hence few chemicals with thorough hazard characterization. To address this challenge, regulatory assessment of endocrine disrupting chemicals (EDCs) is benefiting from a revolution in toxicology that focuses on New Approach Methodologies (NAMs) to more rapidly identify, prioritize, and assess the potential risks from exposure to chemicals using novel, more efficient, and more mechanistically driven methodologies and tools. Incorporated into Integrated Approaches to Testing and Assessment (IATA) and guided by conceptual frameworks such as Adverse Outcome Pathways (AOPs), emerging approaches focus initially on molecular interactions between the test chemical and potentially vulnerable biological systems instead of the need for animal toxicity data. These new toxicity testing methods can be complemented with in silico and computational toxicology approaches, including those that predict chemical kinetics. Coupled with exposure data, these will inform risk-based decision-making approaches. Canada is part of a global network collaborating on building confidence in the use of NAMs for regulatory assessment of EDCs. Herein, we review the current approaches to EDC regulation globally (mainly from the perspective of human health), and provide a perspective on how the advances for regulatory testing and assessment can be applied and discuss the promises and challenges faced in adopting these novel approaches to minimize risks due to EDC exposure in Canada, and our world.
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Affiliation(s)
- T S Barton-Maclaren
- Existing Substances Risk Assessment Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Canada.
| | - M Wade
- Environmental Health Centre, Environmental Health, Science and Research Bureau, Health Canada, Ottawa, ON, Canada
| | - N Basu
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste Anne de Bellevue, QC, Canada
| | - S Bayen
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste Anne de Bellevue, QC, Canada
| | - J Grundy
- New Substances Assessment and Control Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Canada
| | - V Marlatt
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - R Moore
- New Substances Assessment and Control Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Canada
| | - L Parent
- Département Science et Technologie, Université TÉLUQ, Montréal, QC, Canada
| | - J Parrott
- Water Science and Technology Directorate, Environment and Climate Change Canada, Burlington, ON, Canada
| | - P Grigorova
- Département Science et Technologie, Université TÉLUQ, Montréal, QC, Canada
| | - J Pinsonnault-Cooper
- New Substances Assessment and Control Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Canada
| | - V S Langlois
- Institut National de la Recherche Scientifique (INRS), Centre Eau Terre Environnement, Quebec City, QC, Canada
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78
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Sheffield T, Brown J, Davidson S, Friedman KP, Judson R. tcplfit2: an R-language general purpose concentration-response modeling package. Bioinformatics 2022; 38:1157-1158. [PMID: 34791027 DOI: 10.1093/bioinformatics/btab779] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/14/2021] [Accepted: 11/09/2021] [Indexed: 02/03/2023] Open
Abstract
SUMMARY Many applications of chemical screening are performed in concentration or dose-response mode, and it is necessary to extract appropriate parameters, including whether the chemical/assay pair is active and if so, what are concentrations where activity is seen. Typically, multiple mathematical models or curve shapes are tested against the data to assess the best fit. There are several commercial programs used for this purpose as well as open-source libraries. A widely used system for managing high-throughput screening (HTS) concentration-response data is tcpl (ToxCast Pipeline). The current implementation of tcpl has the concentration-response modeling code tightly integrated with the data management and databasing aspects of HTS data processing. Tcplfit2 is a stand-alone version of the curve-fitting and hitcalling core of tcpl that has been extended to include a large number of standard curve classes and to use benchmark dose modeling. This package will be useful for HTS concentration-response data such as high-throughput whole genome transcriptomics. AVAILABILITY AND IMPLEMENTATION tcplfit2 is written in R and is available from CRAN.
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Affiliation(s)
- Thomas Sheffield
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA
| | - Jason Brown
- US Environmental Protection Agency, RTP NC USA
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Scholz S, Nichols JW, Escher BI, Ankley GT, Altenburger R, Blackwell B, Brack W, Burkhard L, Collette TW, Doering JA, Ekman D, Fay K, Fischer F, Hackermüller J, Hoffman JC, Lai C, Leuthold D, Martinovic-Weigelt D, Reemtsma T, Pollesch N, Schroeder A, Schüürmann G, von Bergen M. The Eco-Exposome Concept: Supporting an Integrated Assessment of Mixtures of Environmental Chemicals. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2022; 41:30-45. [PMID: 34714945 PMCID: PMC9104394 DOI: 10.1002/etc.5242] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 10/20/2021] [Accepted: 10/26/2021] [Indexed: 05/04/2023]
Abstract
Organisms are exposed to ever-changing complex mixtures of chemicals over the course of their lifetime. The need to more comprehensively describe this exposure and relate it to adverse health effects has led to formulation of the exposome concept in human toxicology. Whether this concept has utility in the context of environmental hazard and risk assessment has not been discussed in detail. In this Critical Perspective, we propose-by analogy to the human exposome-to define the eco-exposome as the totality of the internal exposure (anthropogenic and natural chemicals, their biotransformation products or adducts, and endogenous signaling molecules that may be sensitive to an anthropogenic chemical exposure) over the lifetime of an ecologically relevant organism. We describe how targeted and nontargeted chemical analyses and bioassays can be employed to characterize this exposure and discuss how the adverse outcome pathway concept could be used to link this exposure to adverse effects. Available methods, their limitations, and/or requirement for improvements for practical application of the eco-exposome concept are discussed. Even though analysis of the eco-exposome can be resource-intensive and challenging, new approaches and technologies make this assessment increasingly feasible. Furthermore, an improved understanding of mechanistic relationships between external chemical exposure(s), internal chemical exposure(s), and biological effects could result in the development of proxies, that is, relatively simple chemical and biological measurements that could be used to complement internal exposure assessment or infer the internal exposure when it is difficult to measure. Environ Toxicol Chem 2022;41:30-45. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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Affiliation(s)
- Stefan Scholz
- Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany
- Address correspondence to
| | - John W. Nichols
- Office of Research and Development, Great Lakes Ecology and Toxicology Division, US Environmental Protection Agency, Duluth, Minnesota
| | - Beate I. Escher
- Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany
- Environmental Toxicology, Center for Applied Geoscience, Eberhard Karls University Tubingen, Tubingen, Germany
| | - Gerald T. Ankley
- Office of Research and Development, Great Lakes Ecology and Toxicology Division, US Environmental Protection Agency, Duluth, Minnesota
| | - Rolf Altenburger
- Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany
- Institute for Environmental Research, Biologie V, RWTH Aachen University, Aachen, Germany
| | - Brett Blackwell
- Office of Research and Development, Great Lakes Ecology and Toxicology Division, US Environmental Protection Agency, Duluth, Minnesota
| | - Werner Brack
- Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany
- Department of Evolutionary Ecology and Environmental Toxicology, Faculty of Biological Sciences, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Lawrence Burkhard
- Office of Research and Development, Great Lakes Ecology and Toxicology Division, US Environmental Protection Agency, Duluth, Minnesota
| | - Timothy W. Collette
- Office of Research and Development, Ecosystem Processes Division, US Environmental Protection Agency, Athens, Georgia
| | - Jon A. Doering
- National Research Council, US Environmental Protection Agency, Duluth, Minnesota
| | - Drew Ekman
- Office of Research and Development, Ecosystem Processes Division, US Environmental Protection Agency, Athens, Georgia
| | - Kellie Fay
- Office of Pollution Prevention and Toxics, Risk Assessment Division, US Environmental Protection Agency, Washington, DC
| | - Fabian Fischer
- Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany
| | | | - Joel C. Hoffman
- Office of Research and Development, Great Lakes Ecology and Toxicology Division, US Environmental Protection Agency, Duluth, Minnesota
| | - Chih Lai
- College of Arts and Sciences, University of Saint Thomas, St. Paul, Minnesota, USA
| | - David Leuthold
- Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany
| | | | | | - Nathan Pollesch
- Office of Research and Development, Great Lakes Ecology and Toxicology Division, US Environmental Protection Agency, Duluth, Minnesota
| | | | - Gerrit Schüürmann
- Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany
- Institute of Organic Chemistry, Technische Universitat Bergakademie Freiberg, Freiberg, Germany
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80
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Beal MA, Gagne M, Kulkarni SA, Patlewicz G, Thomas RS, Barton-Maclaren TS. Implementing in vitro bioactivity data to modernize priority setting of chemical inventories. ALTEX 2022; 39:123-139. [PMID: 34818430 PMCID: PMC8973434 DOI: 10.14573/altex.2106171] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 11/22/2021] [Indexed: 01/03/2023]
Abstract
Internationally, there are thousands of existing and newly introduced chemicals in commerce, highlighting the ongoing importance of innovative approaches to identify emerging chemicals of concern. For many chemicals, there is a paucity of hazard and exposure data. Thus, there is a crucial need for efficient and robust approaches to address data gaps and support risk-based prioritization. Several studies have demonstrated the utility of in vitro bioactivity data from the ToxCast program in deriving points of departure (PODs). ToxCast contains data for nearly 1,400 endpoints per chemical, and the bioactivity concentrations, indicative of potential adverse outcomes, can be converted to human-equivalent PODs using high-throughput toxicokinetics (HTTK) modeling. However, data gaps need to be addressed for broader application: the limited chemical space of HTTK and quantitative high-throughput screening data. Here we explore the applicability of in silico models to address these data needs. Specifically, we used ADMET predictor for HTTK predictions and a generalized read-across approach to predict ToxCast bioactivity potency. We applied these models to profile 5,801 chemicals on Canada’s Domestic Substances List (DSL). To evaluate the approach’s performance, bioactivity PODs were compared with in vivo results from the EPA Toxicity Values database for 1,042 DSL chemicals. Comparisons demonstrated that the bioactivity PODs, based on ToxCast data or read-across, were conservative for 95% of the chemicals. Comparing bioactivity PODs to human exposure estimates supports the identification of chemicals of potential interest for further work. The bioactivity workflow shows promise as a powerful screening tool to support effective triaging of chemical inventories.
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Affiliation(s)
- Marc A. Beal
- Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Canada
| | - Matthew Gagne
- Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Canada
| | - Sunil A. Kulkarni
- Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Canada
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Russell S. Thomas
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
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81
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Desaulniers D, Vasseur P, Jacobs A, Aguila MC, Ertych N, Jacobs MN. Integration of Epigenetic Mechanisms into Non-Genotoxic Carcinogenicity Hazard Assessment: Focus on DNA Methylation and Histone Modifications. Int J Mol Sci 2021; 22:10969. [PMID: 34681626 PMCID: PMC8535778 DOI: 10.3390/ijms222010969] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/30/2021] [Accepted: 10/04/2021] [Indexed: 12/15/2022] Open
Abstract
Epigenetics involves a series of mechanisms that entail histone and DNA covalent modifications and non-coding RNAs, and that collectively contribute to programing cell functions and differentiation. Epigenetic anomalies and DNA mutations are co-drivers of cellular dysfunctions, including carcinogenesis. Alterations of the epigenetic system occur in cancers whether the initial carcinogenic events are from genotoxic (GTxC) or non-genotoxic (NGTxC) carcinogens. NGTxC are not inherently DNA reactive, they do not have a unifying mode of action and as yet there are no regulatory test guidelines addressing mechanisms of NGTxC. To fil this gap, the Test Guideline Programme of the Organisation for Economic Cooperation and Development is developing a framework for an integrated approach for the testing and assessment (IATA) of NGTxC and is considering assays that address key events of cancer hallmarks. Here, with the intent of better understanding the applicability of epigenetic assays in chemical carcinogenicity assessment, we focus on DNA methylation and histone modifications and review: (1) epigenetic mechanisms contributing to carcinogenesis, (2) epigenetic mechanisms altered following exposure to arsenic, nickel, or phenobarbital in order to identify common carcinogen-specific mechanisms, (3) characteristics of a series of epigenetic assay types, and (4) epigenetic assay validation needs in the context of chemical hazard assessment. As a key component of numerous NGTxC mechanisms of action, epigenetic assays included in IATA assay combinations can contribute to improved chemical carcinogen identification for the better protection of public health.
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Affiliation(s)
- Daniel Desaulniers
- Environmental Health Sciences and Research Bureau, Hazard Identification Division, Health Canada, AL:2203B, Ottawa, ON K1A 0K9, Canada
| | - Paule Vasseur
- CNRS, LIEC, Université de Lorraine, 57070 Metz, France;
| | - Abigail Jacobs
- Independent at the Time of Publication, Previously US Food and Drug Administration, Rockville, MD 20852, USA;
| | - M. Cecilia Aguila
- Toxicology Team, Division of Human Food Safety, Center for Veterinary Medicine, US Food and Drug Administration, Department of Health and Human Services, Rockville, MD 20852, USA;
| | - Norman Ertych
- German Centre for the Protection of Laboratory Animals (Bf3R), German Federal Institute for Risk Assessment, Diedersdorfer Weg 1, 12277 Berlin, Germany;
| | - Miriam N. Jacobs
- Centre for Radiation, Chemical and Environmental Hazards, Public Health England, Chilton OX11 0RQ, UK;
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82
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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: 147] [Impact Index Per Article: 49.0] [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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83
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Fentem J, Malcomber I, Maxwell G, Westmoreland C. Upholding the EU's Commitment to 'Animal Testing as a Last Resort' Under REACH Requires a Paradigm Shift in How We Assess Chemical Safety to Close the Gap Between Regulatory Testing and Modern Safety Science. Altern Lab Anim 2021; 49:122-132. [PMID: 34461762 DOI: 10.1177/02611929211040824] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Animal use for testing chemicals under REACH continues to increase, despite advances in non-animal safety science during the past 15 years. The application of modern science and technology, and the use of 'next generation' weight-of-evidence assessment approaches, are embedded in EU guidance for establishing the safety of cosmetics and foods - and of the ingredients used in these products. However, this is still not the case for the regulation of chemicals. Under the new Chemicals Strategy for Sustainability, thought leaders in human health and environmental protection are calling on the European Commission to quickly embrace the benefits of modern and innovative non-animal safety science, in place of outdated animal testing, if the EU is to be a leader in safe and sustainable innovation under the European Green Deal transformational change ambitions. The European Commission also needs to enable companies to meet their legal obligation to only conduct animal testing as a last resort, by providing a more flexible, science-based and consistent regulatory framework for assuring chemical safety, which supports the integration of data from different sources. We are at a tipping point for closing the gap between regulatory chemicals testing and modern safety science. It is time to join forces, across policy makers, scientists, regulators and lawyers, to lead the paradigm shift needed to deliver what EU citizens want - namely, chemicals and products that are safe and sustainable, without resorting to animal testing.
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Affiliation(s)
- Julia Fentem
- Unilever Safety & Environmental Assurance Centre (SEAC), 3099Unilever Plc, Bedfordshire, UK
| | - Ian Malcomber
- Unilever Safety & Environmental Assurance Centre (SEAC), 3099Unilever Plc, Bedfordshire, UK
| | - Gavin Maxwell
- Unilever Safety & Environmental Assurance Centre (SEAC), 3099Unilever Plc, Bedfordshire, UK
| | - Carl Westmoreland
- Unilever Safety & Environmental Assurance Centre (SEAC), 3099Unilever Plc, Bedfordshire, UK
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84
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Tate T, Wambaugh J, Patlewicz G, Shah I. Repeat-dose toxicity prediction with Generalized Read-Across (GenRA) using targeted transcriptomic data: A proof-of-concept case study. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2021; 19:1-12. [PMID: 37309449 PMCID: PMC10259651 DOI: 10.1016/j.comtox.2021.100171] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Read-across is a data gap filling technique utilized to predict the toxicity of a target chemical using data from similar analogues. Recent efforts such as Generalized Read-Across (GenRA) facilitate automated read-across predictions for untested chemicals. GenRA makes predictions of toxicity outcomes based on "neighboring" chemicals characterized by chemical and bioactivity fingerprints. Here we investigated the impact of biological similarities on neighborhood formation and read-across performance in predicting hazard (based on repeat-dose testing outcomes from US EPA ToxRefDB v2.0). We used targeted transcriptomic data on 93 genes for 1060 chemicals in HepaRG™ cells that measure nuclear receptor activation, xenobiotic metabolism, cellular stress, cell cycle progression, and apoptosis. Transcriptomic similarity between chemicals was calculated using binary hit-calls from concentration-response data for each gene. We evaluated GenRA performance in predicting ToxRefDB v2.0 hazard outcomes using the area under the Receiver Operating Characteristic (ROC) curve (AUC) for the baseline approach (chemical fingerprints) versus transcriptomic fingerprints and a combination of both (hybrid). For all endpoints, there were significant but only modest improvements in ROC AUC scores of 0.01 (2.1%) and 0.04 (7.3%) with transcriptomic and hybrid descriptors, respectively. However, for liver-specific toxicity endpoints, ROC AUC scores improved by 10% and 17% for transcriptomic and hybrid descriptors, respectively. Our findings suggest that using hybrid descriptors formed by combining chemical and targeted transcriptomic information can improve in vivo toxicity predictions in the right context.
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Affiliation(s)
| | | | | | - Imran Shah
- Corresponding author at: U.S. Environmental
Protection Agency, 109 TW Alexander Drive (D130A), Research Triangle Park, NC
27711, USA. (I. Shah)
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