Map and model—moving from observation to prediction in toxicogenomics
| Autor: | Madeleine Ammar, Rolf Altenburger, Janet Krüger, Gi-Mick Wu, Marcella Bader-Blukott, Gianina Jakobs, Johanna Knapp, Kristin Reiche, Wibke Busch, Andreas Schüttler |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0106 biological sciences
Toxicodynamics Drug-Related Side Effects and Adverse Reactions Computer science Health Informatics Computational biology 01 natural sciences Models Biological Toxicogenetics Functional relation Chemical exposure Transcriptome 03 medical and health sciences mode of action dose response naproxen Animals ddc:610 Cluster analysis Organism Zebrafish 030304 developmental biology environmental monitoring ’omics time course 0303 health sciences Research risk assessment Computational Biology Physiological responses 3. Good health Computer Science Applications adverse outcome pathway diuron diclofenac machine learning Toxicogenomics 010606 plant biology & botany |
| Zdroj: | GigaScience GigaScience 8(6), giz057 (2019). doi:10.1093/gigascience/giz057 |
| ISSN: | 2047-217X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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