Feasibility study on integrating morphological and transcriptomic data for identifying teratogenic molecular markers in zebrafish embryo toxicity testing
Matthias M. Wehr , Hilda Witters , Silvie Remy , Bruce Schultz , Marc Jacobs , Sylvia E. Escher
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引用次数: 0
Abstract
To create a resource for the integration of developmental toxicity new approach methodologies (NAM) data, zebrafish embryo toxicity test (ZET) data were curated and the zeTera database was created. To capture observations of the morphological alteration potential of chemicals, the zeTera database contains experimental study designs and morphological observation data from the literature. Observations of alterations recorded in zeTera were mapped to ontologies and terms were harmonized. In addition, public transcriptomics repositories were mined for data on zebrafish embryos under chemically induced stress. The re-analyzed datasets were compiled into the zetOmics database for the identification of biomarkers of teratogenicity. To identify data-rich compounds, an overlap of both databases was formed, and compounds were grouped based on structural similarities.
To identify the molecular drivers of teratogenic toxicants, Triadimefon was chosen as model compound for its well-documented teratogenic effects and known mode of action (MOA). We have compiled existing data about Triadimefon from zeTera and conducted additional testing using the ZET, with additional gene expression measurements for data gap filling. From the literature search we identified the adverse outcome pathway (AOP) of triadimefon leading to craniofacial malformations by disruption of retinoic acid metabolism.
Transcriptomic response in a concentration dependent manner was observed as early as 24 h post fertilization (hpf) with consistent, statistically significant, differential expression spanning the later timepoints. A set of 5 genes (cyp26a1, dhrs3b, cyp26b1, cthrc1a, and cd248b) were selected for their differential expression pattern across time and concentration. These biomarkers were further confirmed using read across approach including data from related structures.
期刊介绍:
Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs