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Map and model—moving from observation to prediction in toxicogenomics

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...

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Published in:Gigascience 2019-06, Vol.8 (6)
Main Authors: Schüttler, Andreas, Altenburger, Rolf, Ammar, Madeleine, Bader-Blukott, Marcella, Jakobs, Gianina, Knapp, Johanna, Krüger, Janet, Reiche, Kristin, Wu, Gi-Mick, Busch, Wibke
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cited_by cdi_FETCH-LOGICAL-c468t-e5d70e6f3f093399eb6f9b4e2c65f7d5ce2f90b872078f6a2128ee53de077d983
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container_issue 6
container_start_page
container_title Gigascience
container_volume 8
creator Schüttler, Andreas
Altenburger, Rolf
Ammar, Madeleine
Bader-Blukott, Marcella
Jakobs, Gianina
Knapp, Johanna
Krüger, Janet
Reiche, Kristin
Wu, Gi-Mick
Busch, Wibke
description 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.
doi_str_mv 10.1093/gigascience/giz057
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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.</description><identifier>ISSN: 2047-217X</identifier><identifier>EISSN: 2047-217X</identifier><identifier>DOI: 10.1093/gigascience/giz057</identifier><identifier>PMID: 31140561</identifier><language>eng</language><publisher>United States: Oxford University Press</publisher><subject>Animals ; Case studies ; Clustering ; Computational Biology - methods ; Coordinates ; Design analysis ; Design of experiments ; DNA microarrays ; Drug-Related Side Effects and Adverse Reactions ; Embryos ; Exposure ; Fingerprints ; Gene clusters ; Gene expression ; Hazard assessment ; Health hazards ; Models, Biological ; Parameter estimation ; Physiological effects ; Physiological responses ; Pipeline design ; Process parameters ; Prostaglandin endoperoxide synthase ; Regression models ; Risk Assessment ; Self organizing maps ; Toxicogenetics - methods ; Transcriptome ; Transcriptomes ; Zebrafish ; Zebrafish - genetics</subject><ispartof>Gigascience, 2019-06, Vol.8 (6)</ispartof><rights>The Author(s) 2019. 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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. 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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. 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source Oxford University Press Open Access; PubMed Central
subjects Animals
Case studies
Clustering
Computational Biology - methods
Coordinates
Design analysis
Design of experiments
DNA microarrays
Drug-Related Side Effects and Adverse Reactions
Embryos
Exposure
Fingerprints
Gene clusters
Gene expression
Hazard assessment
Health hazards
Models, Biological
Parameter estimation
Physiological effects
Physiological responses
Pipeline design
Process parameters
Prostaglandin endoperoxide synthase
Regression models
Risk Assessment
Self organizing maps
Toxicogenetics - methods
Transcriptome
Transcriptomes
Zebrafish
Zebrafish - genetics
title Map and model—moving from observation to prediction in toxicogenomics
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