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Dilger M, Armant O, Ramme L, Mülhopt S, Sapcariu SC, Schlager C, Dilger E, Reda A, Orasche J, Schnelle-Kreis J, Conlon TM, Yildirim AÖ, Hartwig A, Zimmermann R, Hiller K, Diabaté S, Paur HR, Weiss C. Systems toxicology of complex wood combustion aerosol reveals gaseous carbonyl compounds as critical constituents. ENVIRONMENT INTERNATIONAL 2023; 179:108169. [PMID: 37688811 DOI: 10.1016/j.envint.2023.108169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 07/19/2023] [Accepted: 08/22/2023] [Indexed: 09/11/2023]
Abstract
Epidemiological studies identified air pollution as one of the prime causes for human morbidity and mortality, due to harmful effects mainly on the cardiovascular and respiratory systems. Damage to the lung leads to several severe diseases such as fibrosis, chronic obstructive pulmonary disease and cancer. Noxious environmental aerosols are comprised of a gas and particulate phase representing highly complex chemical mixtures composed of myriads of compounds. Although some critical pollutants, foremost particulate matter (PM), could be linked to adverse health effects, a comprehensive understanding of relevant biological mechanisms and detrimental aerosol constituents is still lacking. Here, we employed a systems toxicology approach focusing on wood combustion, an important source for air pollution, and demonstrate a key role of the gas phase, specifically carbonyls, in driving adverse effects. Transcriptional profiling and biochemical analysis of human lung cells exposed at the air-liquid-interface determined DNA damage and stress response, as well as perturbation of cellular metabolism, as major key events. Connectivity mapping revealed a high similarity of gene expression signatures induced by wood smoke and agents prompting DNA-protein crosslinks (DPCs). Indeed, various gaseous aldehydes were detected in wood smoke, which promote DPCs, initiate similar genomic responses and are responsible for DNA damage provoked by wood smoke. Hence, systems toxicology enables the discovery of critical constituents of complex mixtures i.e. aerosols and highlights the role of carbonyls on top of particulate matter as an important health hazard.
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Affiliation(s)
- Marco Dilger
- HICE - Helmholtz Virtual Institute of Complex Molecular Systems in Environmental Health - Aerosols and Health, Germany(1); Institute of Biological and Chemical Systems, Biological Information Processing, Karlsruhe Institute of Technology, Campus North, Eggenstein-Leopoldshafen, Germany
| | - Olivier Armant
- Institute of Biological and Chemical Systems, Biological Information Processing, Karlsruhe Institute of Technology, Campus North, Eggenstein-Leopoldshafen, Germany; Institut de Radioprotection et de Sureté Nucléaire (IRSN), PSE-ENV/SRTE/LECO, Cadarache, Saint-Paul-lez-Durance 13115, France
| | - Larissa Ramme
- HICE - Helmholtz Virtual Institute of Complex Molecular Systems in Environmental Health - Aerosols and Health, Germany(1); Institute of Biological and Chemical Systems, Biological Information Processing, Karlsruhe Institute of Technology, Campus North, Eggenstein-Leopoldshafen, Germany
| | - Sonja Mülhopt
- HICE - Helmholtz Virtual Institute of Complex Molecular Systems in Environmental Health - Aerosols and Health, Germany(1); Institute for Technical Chemistry, Karlsruhe Institute of Technology, Campus North, Eggenstein-Leopoldshafen, Germany
| | - Sean C Sapcariu
- HICE - Helmholtz Virtual Institute of Complex Molecular Systems in Environmental Health - Aerosols and Health, Germany(1); Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4362 Esch-Belval, Luxembourg
| | - Christoph Schlager
- HICE - Helmholtz Virtual Institute of Complex Molecular Systems in Environmental Health - Aerosols and Health, Germany(1); Institute for Technical Chemistry, Karlsruhe Institute of Technology, Campus North, Eggenstein-Leopoldshafen, Germany
| | - Elena Dilger
- Institute of Applied Biosciences, Department of Food Chemistry and Toxicology, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Ahmed Reda
- HICE - Helmholtz Virtual Institute of Complex Molecular Systems in Environmental Health - Aerosols and Health, Germany(1); Joint Mass Spectrometry Centre, Chair of Analytical Chemistry, Institute of Chemistry, University Rostock, Germany; Joint Mass Spectrometry Centre, CMA - Comprehensive Molecular Analytics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jürgen Orasche
- HICE - Helmholtz Virtual Institute of Complex Molecular Systems in Environmental Health - Aerosols and Health, Germany(1); Joint Mass Spectrometry Centre, Chair of Analytical Chemistry, Institute of Chemistry, University Rostock, Germany; Joint Mass Spectrometry Centre, CMA - Comprehensive Molecular Analytics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jürgen Schnelle-Kreis
- HICE - Helmholtz Virtual Institute of Complex Molecular Systems in Environmental Health - Aerosols and Health, Germany(1); Joint Mass Spectrometry Centre, CMA - Comprehensive Molecular Analytics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Thomas M Conlon
- Institute of Lung Health and Immunity (LHI), Comprehensive Pneumology Center (CPC), Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Neuherberg, Germany
| | - Ali Önder Yildirim
- HICE - Helmholtz Virtual Institute of Complex Molecular Systems in Environmental Health - Aerosols and Health, Germany(1); Institute of Lung Health and Immunity (LHI), Comprehensive Pneumology Center (CPC), Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Neuherberg, Germany
| | - Andrea Hartwig
- Institute of Applied Biosciences, Department of Food Chemistry and Toxicology, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Ralf Zimmermann
- HICE - Helmholtz Virtual Institute of Complex Molecular Systems in Environmental Health - Aerosols and Health, Germany(1); Joint Mass Spectrometry Centre, Chair of Analytical Chemistry, Institute of Chemistry, University Rostock, Germany; Joint Mass Spectrometry Centre, CMA - Comprehensive Molecular Analytics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Karsten Hiller
- HICE - Helmholtz Virtual Institute of Complex Molecular Systems in Environmental Health - Aerosols and Health, Germany(1); Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4362 Esch-Belval, Luxembourg
| | - Silvia Diabaté
- HICE - Helmholtz Virtual Institute of Complex Molecular Systems in Environmental Health - Aerosols and Health, Germany(1); Institute of Biological and Chemical Systems, Biological Information Processing, Karlsruhe Institute of Technology, Campus North, Eggenstein-Leopoldshafen, Germany
| | - Hanns-Rudolf Paur
- HICE - Helmholtz Virtual Institute of Complex Molecular Systems in Environmental Health - Aerosols and Health, Germany(1); Institute for Technical Chemistry, Karlsruhe Institute of Technology, Campus North, Eggenstein-Leopoldshafen, Germany
| | - Carsten Weiss
- Institute of Biological and Chemical Systems, Biological Information Processing, Karlsruhe Institute of Technology, Campus North, Eggenstein-Leopoldshafen, Germany.
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2
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He H, Duo H, Hao Y, Zhang X, Zhou X, Zeng Y, Li Y, Li B. Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications. Comput Biol Med 2023; 155:106671. [PMID: 36805225 DOI: 10.1016/j.compbiomed.2023.106671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/05/2023] [Accepted: 02/10/2023] [Indexed: 02/18/2023]
Abstract
De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.
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Affiliation(s)
- Hao He
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, 200032, PR China
| | - Hongrui Duo
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Xiaoxi Zhang
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Xinyi Zhou
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Yujie Zeng
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Yinghong Li
- The Key Laboratory on Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, PR China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China.
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3
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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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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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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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Toth GP, Bencic DC, Martinson JW, Flick RW, Lattier DL, Kostich MS, Huang W, Biales AD. Development of omics biomarkers for estrogen exposure using mRNA, miRNA and piRNAs. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2021; 235:105807. [PMID: 33838496 DOI: 10.1016/j.aquatox.2021.105807] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 03/03/2021] [Accepted: 03/05/2021] [Indexed: 06/12/2023]
Abstract
The number of chemicals requiring risk evaluation exceeds our capacity to provide the underlying data using traditional methodology. This has led to an increased focus on the development of novel approach methodologies. This work aimed to expand the panel of gene expression-based biomarkers to include responses to estrogens, to identify training strategies that maximize the range of applicable concentrations, and to evaluate the potential for two classes of small non-coding RNAs (sncRNAs), microRNA (miRNA) and piwi-interacting RNA (piRNA), as biomarkers. To this end larval Pimephales promelas (96 hpf +/- 1h) were exposed to 5 concentrations of 17α- ethinylestradiol (0.12, 1.25, 2.5, 5.0, 10.0 ng/L) for 48 h. For mRNA-based biomarker development, RNA-seq was conducted across all concentrations. For sncRNA biomarkers, small RNA libraries were prepared only for the control and 10.0 ng/L EE2 treatment. In order to develop an mRNA classifier that remained accurate over the range of exposure concentrations, three different training strategies were employed that focused on 10 ng/L, 2.5 ng/L or a combination of both. Classifiers were tested against an independent test set of individuals exposed to the same concentrations used in training and subsequently against concentrations not included in model training. Both random forest (RF) and logistic regression with elastic net regularizations (glmnet) models trained on 10 ng/L EE2 performed poorly when applied to lower concentrations. RF models trained with either the 2.5 ng/L or combination (2.5 + 10 ng/L) treatments were able to accurately discriminate exposed vs. non-exposed across all but the lowest concentrations. glmnet models were unable to accurately classify below 5 ng/L. With the exception of the 10 ng/L treatment, few mRNA differentially expressed genes (DEG) were observed, however, there was marked overlap of DEGs across treatments. Overlapping DEGs have well established linkages to estrogen and several of the 81 DEGs identified in the 10 ng/L treatment have been previously utilized as estrogenic biomarkers (vitellogenin, estrogen receptor-β). Following multiple test correction, no sncRNAs were found to be differentially expressed, however, both miRNA and piRNA classifiers were able to accurately discriminate control and 10 ng/L exposed organisms with AUCs of 0.83 and 1.0 respectively. We have developed a highly discriminative estrogenic mRNA biomarker that is accurate over a range of concentrations likely to be found in real-world exposures. We have demonstrated that both miRNA and piRNA are responsive to estrogenic exposure, suggesting the need to further investigate their regulatory roles in the estrogenic response.
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Affiliation(s)
- Gregory P Toth
- US Environmental Protection Agency, Office of Research and Development, 26 W. Martin Luther King Dr., Cincinnati, OH 45268, United States
| | - David C Bencic
- US Environmental Protection Agency, Office of Research and Development, 26 W. Martin Luther King Dr., Cincinnati, OH 45268, United States
| | - John W Martinson
- US Environmental Protection Agency, Office of Research and Development, 26 W. Martin Luther King Dr., Cincinnati, OH 45268, United States
| | - Robert W Flick
- US Environmental Protection Agency, Office of Research and Development, 26 W. Martin Luther King Dr., Cincinnati, OH 45268, United States
| | - David L Lattier
- US Environmental Protection Agency, Office of Research and Development, 26 W. Martin Luther King Dr., Cincinnati, OH 45268, United States
| | - Mitchell S Kostich
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Dr, Farmington, CT 06032, United States
| | - Weichun Huang
- US Environmental Protection Agency, Office of Research and Development, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Adam D Biales
- US Environmental Protection Agency, Office of Research and Development, 26 W. Martin Luther King Dr., Cincinnati, OH 45268, United States.
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Ewald JD, Soufan O, Crump D, Hecker M, Xia J, Basu N. EcoToxModules: Custom Gene Sets to Organize and Analyze Toxicogenomics Data from Ecological Species. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:4376-4387. [PMID: 32106671 DOI: 10.1021/acs.est.9b06607] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Traditional results from toxicogenomics studies are complex lists of significantly impacted genes or gene sets, which are challenging to synthesize down to actionable results with a clear interpretation. Here, we defined two sets of 21 custom gene sets, called the functional and statistical EcoToxModules, in fathead minnow (Pimephales promelas) to (1) re-cast predefined molecular pathways into a toxicological framework and (2) provide a data-driven, unsupervised grouping of genes impacted by exposure to environmental contaminants. The functional EcoToxModules were identified by re-organizing KEGG pathways into biological processes that are more relevant to ecotoxicology based on the input from expert scientists and regulators. The statistical EcoToxModules were identified using co-expression analysis of publicly available microarray data (n = 303 profiles) measured in livers of fathead minnows after exposure to 38 different conditions. Potential applications of the EcoToxModules were demonstrated with two case studies that represent exposure to a pure chemical and to environmental wastewater samples. In comparisons to differential expression and gene set analysis, we found that EcoToxModule responses were consistent with these traditional results. Additionally, they were easier to visualize and quantitatively compare across different conditions, which facilitated drawing conclusions about the relative toxicity of the exposures within each case study.
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Affiliation(s)
- Jessica D Ewald
- Faculty of Agricultural and Environmental Sciences, McGill University, Sainte-Anne-de-Bellevue H9X 3V9, Canada
| | - Othman Soufan
- Faculty of Agricultural and Environmental Sciences, McGill University, Sainte-Anne-de-Bellevue H9X 3V9, Canada
| | - Doug Crump
- Ecotoxicology and Wildlife Health Division, Environment and Climate Change Canada, National Wildlife Research Centre, Ottawa K1A 0H3, Canada
| | - Markus Hecker
- School of the Environment & Sustainability and Toxicology Centre, University of Saskatchewan, Saskatoon S7N 5B3, Canada
| | - Jianguo Xia
- Faculty of Agricultural and Environmental Sciences, McGill University, Sainte-Anne-de-Bellevue H9X 3V9, Canada
| | - Niladri Basu
- Faculty of Agricultural and Environmental Sciences, McGill University, Sainte-Anne-de-Bellevue H9X 3V9, Canada
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8
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Noyes PD, Friedman KP, Browne P, Haselman JT, Gilbert ME, Hornung MW, Barone S, Crofton KM, Laws SC, Stoker TE, Simmons SO, Tietge JE, Degitz SJ. Evaluating Chemicals for Thyroid Disruption: Opportunities and Challenges with in Vitro Testing and Adverse Outcome Pathway Approaches. ENVIRONMENTAL HEALTH PERSPECTIVES 2019; 127:95001. [PMID: 31487205 PMCID: PMC6791490 DOI: 10.1289/ehp5297] [Citation(s) in RCA: 103] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 07/01/2019] [Accepted: 08/13/2019] [Indexed: 05/19/2023]
Abstract
BACKGROUND Extensive clinical and experimental research documents the potential for chemical disruption of thyroid hormone (TH) signaling through multiple molecular targets. Perturbation of TH signaling can lead to abnormal brain development, cognitive impairments, and other adverse outcomes in humans and wildlife. To increase chemical safety screening efficiency and reduce vertebrate animal testing, in vitro assays that identify chemical interactions with molecular targets of the thyroid system have been developed and implemented. OBJECTIVES We present an adverse outcome pathway (AOP) network to link data derived from in vitro assays that measure chemical interactions with thyroid molecular targets to downstream events and adverse outcomes traditionally derived from in vivo testing. We examine the role of new in vitro technologies, in the context of the AOP network, in facilitating consideration of several important regulatory and biological challenges in characterizing chemicals that exert effects through a thyroid mechanism. DISCUSSION There is a substantial body of knowledge describing chemical effects on molecular and physiological regulation of TH signaling and associated adverse outcomes. Until recently, few alternative nonanimal assays were available to interrogate chemical effects on TH signaling. With the development of these new tools, screening large libraries of chemicals for interactions with molecular targets of the thyroid is now possible. Measuring early chemical interactions with targets in the thyroid pathway provides a means of linking adverse outcomes, which may be influenced by many biological processes, to a thyroid mechanism. However, the use of in vitro assays beyond chemical screening is complicated by continuing limits in our knowledge of TH signaling in important life stages and tissues, such as during fetal brain development. Nonetheless, the thyroid AOP network provides an ideal tool for defining causal linkages of a chemical exerting thyroid-dependent effects and identifying research needs to quantify these effects in support of regulatory decision making. https://doi.org/10.1289/EHP5297.
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Affiliation(s)
- Pamela D Noyes
- National Center for Environmental Assessment, Office of Research and Development (ORD), U.S. Environmental Protection Agency (EPA), Washington, DC, USA
| | - Katie Paul Friedman
- National Center for Computational Toxicology, ORD, U.S. EPA, Research Triangle Park, North Carolina, USA
| | - Patience Browne
- Environment Health and Safety Division, Environment Directorate, Organisation for Economic Co-operation and Development (OECD), Paris, France
| | - Jonathan T Haselman
- Mid-Continent Ecology Division, National Health and Environmental Effects Research Laboratory (NHEERL), ORD, U.S. EPA, Duluth, Minnesota, USA
| | - Mary E Gilbert
- Toxicity Assessment Division, NHEERL, ORD, U.S. EPA, Research Triangle Park, North Carolina, USA
| | - Michael W Hornung
- Mid-Continent Ecology Division, National Health and Environmental Effects Research Laboratory (NHEERL), ORD, U.S. EPA, Duluth, Minnesota, USA
| | - Stan Barone
- Office of Pollution Prevention and Toxics, Office of Chemical Safety and Pollution Prevention, U.S. EPA, Washington, DC, USA
| | - Kevin M Crofton
- National Center for Computational Toxicology, ORD, U.S. EPA, Research Triangle Park, North Carolina, USA
| | - Susan C Laws
- Toxicity Assessment Division, NHEERL, ORD, U.S. EPA, Research Triangle Park, North Carolina, USA
| | - Tammy E Stoker
- Toxicity Assessment Division, NHEERL, ORD, U.S. EPA, Research Triangle Park, North Carolina, USA
| | - Steven O Simmons
- National Center for Computational Toxicology, ORD, U.S. EPA, Research Triangle Park, North Carolina, USA
| | - Joseph E Tietge
- Mid-Continent Ecology Division, National Health and Environmental Effects Research Laboratory (NHEERL), ORD, U.S. EPA, Duluth, Minnesota, USA
| | - Sigmund J Degitz
- Mid-Continent Ecology Division, National Health and Environmental Effects Research Laboratory (NHEERL), ORD, U.S. EPA, Duluth, Minnesota, USA
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9
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Keenan AB, Wojciechowicz ML, Wang Z, Jagodnik KM, Jenkins SL, Lachmann A, Ma'ayan A. Connectivity Mapping: Methods and Applications. Annu Rev Biomed Data Sci 2019. [DOI: 10.1146/annurev-biodatasci-072018-021211] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.
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Affiliation(s)
- Alexandra B. Keenan
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Megan L. Wojciechowicz
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Zichen Wang
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kathleen M. Jagodnik
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sherry L. Jenkins
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Alexander Lachmann
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Avi Ma'ayan
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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10
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Schüttler A, Altenburger R, Ammar M, Bader-Blukott M, Jakobs G, Knapp J, Krüger J, Reiche K, Wu GM, Busch W. Map and model-moving from observation to prediction in toxicogenomics. Gigascience 2019; 8:giz057. [PMID: 31140561 PMCID: PMC6539241 DOI: 10.1093/gigascience/giz057] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 03/13/2019] [Accepted: 04/22/2019] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Chemicals induce compound-specific changes in the transcriptome of an organism (toxicogenomic fingerprints). This provides potential insights about the cellular or physiological responses to chemical exposure and adverse effects, which is needed in assessment of chemical-related hazards or environmental health. In this regard, comparison or connection of different experiments becomes important when interpreting toxicogenomic experiments. Owing to lack of capturing response dynamics, comparability is often limited. In this study, we aim to overcome these constraints. RESULTS We developed an experimental design and bioinformatic analysis strategy to infer time- and concentration-resolved toxicogenomic fingerprints. We projected the fingerprints to a universal coordinate system (toxicogenomic universe) based on a self-organizing map of toxicogenomic data retrieved from public databases. Genes clustering together in regions of the map indicate functional relation due to co-expression under chemical exposure. To allow for quantitative description and extrapolation of the gene expression responses we developed a time- and concentration-dependent regression model. We applied the analysis strategy in a microarray case study exposing zebrafish embryos to 3 selected model compounds including 2 cyclooxygenase inhibitors. After identification of key responses in the transcriptome we could compare and characterize their association to developmental, toxicokinetic, and toxicodynamic processes using the parameter estimates for affected gene clusters. Furthermore, we discuss an association of toxicogenomic effects with measured internal concentrations. CONCLUSIONS The design and analysis pipeline described here could serve as a blueprint for creating comparable toxicogenomic fingerprints of chemicals. It integrates, aggregates, and models time- and concentration-resolved toxicogenomic data.
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Affiliation(s)
- Andreas Schüttler
- Department Bioanalytical Ecotoxicology, Helmholtz-Centre for Environmental Research – UFZ, Permoserstr. 15, 04318 Leipzig, Germany
- Institute for Environmental Research, RWTH Aachen, Worringerweg 1, 52074 Aachen, Germany
| | - Rolf Altenburger
- Department Bioanalytical Ecotoxicology, Helmholtz-Centre for Environmental Research – UFZ, Permoserstr. 15, 04318 Leipzig, Germany
- Institute for Environmental Research, RWTH Aachen, Worringerweg 1, 52074 Aachen, Germany
| | - Madeleine Ammar
- Department Bioanalytical Ecotoxicology, Helmholtz-Centre for Environmental Research – UFZ, Permoserstr. 15, 04318 Leipzig, Germany
| | - Marcella Bader-Blukott
- Department Bioanalytical Ecotoxicology, Helmholtz-Centre for Environmental Research – UFZ, Permoserstr. 15, 04318 Leipzig, Germany
| | - Gianina Jakobs
- Department Bioanalytical Ecotoxicology, Helmholtz-Centre for Environmental Research – UFZ, Permoserstr. 15, 04318 Leipzig, Germany
| | - Johanna Knapp
- Department Bioanalytical Ecotoxicology, Helmholtz-Centre for Environmental Research – UFZ, Permoserstr. 15, 04318 Leipzig, Germany
| | - Janet Krüger
- Department Bioanalytical Ecotoxicology, Helmholtz-Centre for Environmental Research – UFZ, Permoserstr. 15, 04318 Leipzig, Germany
| | - Kristin Reiche
- Bioinformatics Unit, Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Perlickstr. 1, 04103 Leipzig, Germany
| | - Gi-Mick Wu
- DEVELOP, Helmholtz-Centre for Environmental Research – UFZ, Permoserstr. 15, 04318 Leipzig, Germany
| | - Wibke Busch
- Department Bioanalytical Ecotoxicology, Helmholtz-Centre for Environmental Research – UFZ, Permoserstr. 15, 04318 Leipzig, Germany
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11
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Campos B, Colbourne JK. How omics technologies can enhance chemical safety regulation: perspectives from academia, government, and industry: The Perspectives column is a regular series designed to discuss and evaluate potentially competing viewpoints and research findings on current environmental issues. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2018; 37:1252-1259. [PMID: 29697867 DOI: 10.1002/etc.4079] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 06/19/2017] [Accepted: 12/29/2017] [Indexed: 06/08/2023]
Affiliation(s)
- Bruno Campos
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA, CSIC), Jordi Girona, Barcelona, Spain
| | - John K Colbourne
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
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12
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Connon RE, Jeffries KM, Komoroske LM, Todgham AE, Fangue NA. The utility of transcriptomics in fish conservation. ACTA ACUST UNITED AC 2018; 221:221/2/jeb148833. [PMID: 29378879 DOI: 10.1242/jeb.148833] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
There is growing recognition of the need to understand the mechanisms underlying organismal resilience (i.e. tolerance, acclimatization) to environmental change to support the conservation management of sensitive and economically important species. Here, we discuss how functional genomics can be used in conservation biology to provide a cellular-level understanding of organismal responses to environmental conditions. In particular, the integration of transcriptomics with physiological and ecological research is increasingly playing an important role in identifying functional physiological thresholds predictive of compensatory responses and detrimental outcomes, transforming the way we can study issues in conservation biology. Notably, with technological advances in RNA sequencing, transcriptome-wide approaches can now be applied to species where no prior genomic sequence information is available to develop species-specific tools and investigate sublethal impacts that can contribute to population declines over generations and undermine prospects for long-term conservation success. Here, we examine the use of transcriptomics as a means of determining organismal responses to environmental stressors and use key study examples of conservation concern in fishes to highlight the added value of transcriptome-wide data to the identification of functional response pathways. Finally, we discuss the gaps between the core science and policy frameworks and how thresholds identified through transcriptomic evaluations provide evidence that can be more readily used by resource managers.
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Affiliation(s)
- Richard E Connon
- Department of Anatomy, Physiology & Cell Biology, School of Veterinary Medicine, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Ken M Jeffries
- Department of Biological Sciences, University of Manitoba, 50 Sifton Road, Winnipeg, Manitoba, Canada R3T 2N2
| | - Lisa M Komoroske
- Marine Mammal and Turtle Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, La Jolla, CA 92037, USA.,Department of Environmental Conservation, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - Anne E Todgham
- Department of Animal Science, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Nann A Fangue
- Wildlife, Fish & Conservation Biology, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
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13
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Abstract
As manufacturing processes and development of new synthetic compounds increase to keep pace with the expanding global demand, environmental health, and the effects of toxicant exposure are emerging as critical public health concerns. Additionally, chemicals that naturally occur in the environment, such as metals, have profound effects on human and animal health. Many of these compounds are in the news: lead, arsenic, and endocrine disruptors such as bisphenol A have all been widely publicized as causing disease or damage to humans and wildlife in recent years. Despite the widespread appreciation that environmental toxins can be harmful, there is limited understanding of how many toxins cause disease. Zebrafish are at the forefront of toxicology research; this system has been widely used as a tool to detect toxins in water samples and to investigate the mechanisms of action of environmental toxins and their related diseases. The benefits of zebrafish for studying vertebrate development are equally useful for studying teratogens. Here, we review how zebrafish are being used both to detect the presence of some toxins as well as to identify how environmental exposures affect human health and disease. We focus on areas where zebrafish have been most effectively used in ecotoxicology and in environmental health, including investigation of exposures to endocrine disruptors, industrial waste byproducts, and arsenic.
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Affiliation(s)
- Kathryn Bambino
- Icahn School of Medicine at Mount Sinai, New York, United States
| | - Jaime Chu
- Icahn School of Medicine at Mount Sinai, New York, United States.
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