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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) |
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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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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.</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. Published by Oxford University Press. 2019</rights><rights>The Author(s) 2019. Published by Oxford University Press.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c468t-e5d70e6f3f093399eb6f9b4e2c65f7d5ce2f90b872078f6a2128ee53de077d983</citedby><cites>FETCH-LOGICAL-c468t-e5d70e6f3f093399eb6f9b4e2c65f7d5ce2f90b872078f6a2128ee53de077d983</cites><orcidid>0000-0002-4093-0400 ; 0000-0002-5497-6266 ; 0000-0002-2477-783X ; 0000-0003-2929-2978 ; 0000-0002-4452-4872 ; 0000-0003-3823-1667</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539241/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539241/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,1604,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31140561$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Schüttler, Andreas</creatorcontrib><creatorcontrib>Altenburger, Rolf</creatorcontrib><creatorcontrib>Ammar, Madeleine</creatorcontrib><creatorcontrib>Bader-Blukott, Marcella</creatorcontrib><creatorcontrib>Jakobs, Gianina</creatorcontrib><creatorcontrib>Knapp, Johanna</creatorcontrib><creatorcontrib>Krüger, Janet</creatorcontrib><creatorcontrib>Reiche, Kristin</creatorcontrib><creatorcontrib>Wu, Gi-Mick</creatorcontrib><creatorcontrib>Busch, Wibke</creatorcontrib><title>Map and model—moving from observation to prediction in toxicogenomics</title><title>Gigascience</title><addtitle>Gigascience</addtitle><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.</description><subject>Animals</subject><subject>Case studies</subject><subject>Clustering</subject><subject>Computational Biology - methods</subject><subject>Coordinates</subject><subject>Design analysis</subject><subject>Design of experiments</subject><subject>DNA microarrays</subject><subject>Drug-Related Side Effects and Adverse Reactions</subject><subject>Embryos</subject><subject>Exposure</subject><subject>Fingerprints</subject><subject>Gene clusters</subject><subject>Gene expression</subject><subject>Hazard assessment</subject><subject>Health hazards</subject><subject>Models, Biological</subject><subject>Parameter estimation</subject><subject>Physiological effects</subject><subject>Physiological responses</subject><subject>Pipeline design</subject><subject>Process parameters</subject><subject>Prostaglandin endoperoxide synthase</subject><subject>Regression models</subject><subject>Risk Assessment</subject><subject>Self organizing maps</subject><subject>Toxicogenetics - methods</subject><subject>Transcriptome</subject><subject>Transcriptomes</subject><subject>Zebrafish</subject><subject>Zebrafish - genetics</subject><issn>2047-217X</issn><issn>2047-217X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><recordid>eNqNkc1u1DAUhS0EolXpC7BAkbphE-qfOHY2SFUFQ6WpugGJneU418FVYgc7GRVWfYg-IU-CO1OqaVd443vt7x7do4PQW4I_ENyw0971OhkH3kCuf2MuXqBDiitRUiK-v9yrD9BxStc4HyGkFOw1OmCEVJjX5BCtLvVUaN8VY-hg-HN7N4aN831hYxiL0CaIGz274Is5FFOEzplt5-4fbpwJPfgwOpPeoFdWDwmOH-4j9O3zp6_nX8r11eri_GxdmqqWcwm8Exhqy2z2wJoG2to2bQXU1NyKjhugtsGtFBQLaWtNCZUAnHWQl-8ayY7Qx53utLQjdAb8HPWgpuhGHX-poJ16-uPdD9WHjao5a2hFssD7B4EYfi6QZjW6ZGAYtIewJEUpI5KTBuOMnjxDr8MSfbanqCBcYsG2gnRHmRhSimAflyFY3Uel9qJSu6jy0Lt9G48j_4LJQLkDwjL9j-Bf1Celfw</recordid><startdate>20190601</startdate><enddate>20190601</enddate><creator>Schüttler, Andreas</creator><creator>Altenburger, Rolf</creator><creator>Ammar, Madeleine</creator><creator>Bader-Blukott, Marcella</creator><creator>Jakobs, Gianina</creator><creator>Knapp, Johanna</creator><creator>Krüger, Janet</creator><creator>Reiche, Kristin</creator><creator>Wu, Gi-Mick</creator><creator>Busch, Wibke</creator><general>Oxford University Press</general><scope>TOX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope><scope>K9.</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-4093-0400</orcidid><orcidid>https://orcid.org/0000-0002-5497-6266</orcidid><orcidid>https://orcid.org/0000-0002-2477-783X</orcidid><orcidid>https://orcid.org/0000-0003-2929-2978</orcidid><orcidid>https://orcid.org/0000-0002-4452-4872</orcidid><orcidid>https://orcid.org/0000-0003-3823-1667</orcidid></search><sort><creationdate>20190601</creationdate><title>Map and model—moving from observation to prediction in toxicogenomics</title><author>Schüttler, Andreas ; Altenburger, Rolf ; Ammar, Madeleine ; Bader-Blukott, Marcella ; Jakobs, Gianina ; Knapp, Johanna ; Krüger, Janet ; Reiche, Kristin ; Wu, Gi-Mick ; Busch, Wibke</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c468t-e5d70e6f3f093399eb6f9b4e2c65f7d5ce2f90b872078f6a2128ee53de077d983</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Animals</topic><topic>Case studies</topic><topic>Clustering</topic><topic>Computational Biology - methods</topic><topic>Coordinates</topic><topic>Design analysis</topic><topic>Design of experiments</topic><topic>DNA microarrays</topic><topic>Drug-Related Side Effects and Adverse Reactions</topic><topic>Embryos</topic><topic>Exposure</topic><topic>Fingerprints</topic><topic>Gene clusters</topic><topic>Gene expression</topic><topic>Hazard assessment</topic><topic>Health hazards</topic><topic>Models, Biological</topic><topic>Parameter estimation</topic><topic>Physiological effects</topic><topic>Physiological responses</topic><topic>Pipeline design</topic><topic>Process parameters</topic><topic>Prostaglandin endoperoxide synthase</topic><topic>Regression models</topic><topic>Risk Assessment</topic><topic>Self organizing maps</topic><topic>Toxicogenetics - methods</topic><topic>Transcriptome</topic><topic>Transcriptomes</topic><topic>Zebrafish</topic><topic>Zebrafish - genetics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Schüttler, Andreas</creatorcontrib><creatorcontrib>Altenburger, Rolf</creatorcontrib><creatorcontrib>Ammar, Madeleine</creatorcontrib><creatorcontrib>Bader-Blukott, Marcella</creatorcontrib><creatorcontrib>Jakobs, Gianina</creatorcontrib><creatorcontrib>Knapp, Johanna</creatorcontrib><creatorcontrib>Krüger, Janet</creatorcontrib><creatorcontrib>Reiche, Kristin</creatorcontrib><creatorcontrib>Wu, Gi-Mick</creatorcontrib><creatorcontrib>Busch, Wibke</creatorcontrib><collection>Oxford University Press Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Gigascience</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Schüttler, Andreas</au><au>Altenburger, Rolf</au><au>Ammar, Madeleine</au><au>Bader-Blukott, Marcella</au><au>Jakobs, Gianina</au><au>Knapp, Johanna</au><au>Krüger, Janet</au><au>Reiche, Kristin</au><au>Wu, Gi-Mick</au><au>Busch, Wibke</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Map and model—moving from observation to prediction in toxicogenomics</atitle><jtitle>Gigascience</jtitle><addtitle>Gigascience</addtitle><date>2019-06-01</date><risdate>2019</risdate><volume>8</volume><issue>6</issue><issn>2047-217X</issn><eissn>2047-217X</eissn><abstract>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.</abstract><cop>United States</cop><pub>Oxford University Press</pub><pmid>31140561</pmid><doi>10.1093/gigascience/giz057</doi><orcidid>https://orcid.org/0000-0002-4093-0400</orcidid><orcidid>https://orcid.org/0000-0002-5497-6266</orcidid><orcidid>https://orcid.org/0000-0002-2477-783X</orcidid><orcidid>https://orcid.org/0000-0003-2929-2978</orcidid><orcidid>https://orcid.org/0000-0002-4452-4872</orcidid><orcidid>https://orcid.org/0000-0003-3823-1667</orcidid><oa>free_for_read</oa></addata></record> |
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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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