Implementation of Zebrafish Ontologies for Toxicology Screening

The NTP’s SEAZIT undertook a collaborative exercise to evaluate how the application of standardized phenotype terminology improved data consistency.

Anne E. Thessen1*, Skylar Marvel2, J. C. Achenbach3, Stephan Fischer4Melissa A. Haendel1, Kimberly Hayward5, Nils Klüver6, Sarah Könemann7, Jessica Legradi8Pamela Lein9, Connor Leong6J. Erik Mylroie10Stephanie Padilla11Dante Perone6Antonio Planchart12, Rafael Miñana Prieto13, Arantza Muriana14, Celia Quevedo15David Reif2, Kristen Ryan16, Evelyn Stinckens17, Lisa Truong6Lucia Vergauwen18Colette Vom Berg5, Mitch Wilbanks12, Bianca Yaghoobi10 and Jon Hamm19

1Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, United States

2Department of Biological Sciences, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, United States

3Aquatic and Crop Resource Development Research Center, National Research Council of Canada, Halifax, NS, Canada

4aQuaTox-Solutions Ltd, Wallisellen, Switzerland

5Department of Environmental and Molecular Toxicology and the Sinnhuber Aquatic Research Laboratory, Oregon State University, Corvallis, OR, United States

6Department Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany

7Environmental Toxicology, Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dübendorf, Switzerland

8Environment & Health, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

9Department of Molecular Biosciences, University of California, Davis, Davis, CA, United States

10Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS, United States

11Center for Computational Toxicology and Exposure, Biomolecular and Computational Toxicology Division, U.S. Environmental Protection Agency, Research Triangle Park, NC, United States

12Center for Human Health and the Environment, and Center for Environmental and Health Effects of PFAS, Biological Sciences, NC State University, Raleigh, NC, United States

13ZeClinics SL, Badalona, Spain

14Biobide USA, Cambridge, MA, United States

15Viralgen Vector Core, Donostia-San Sebastián, Spain

16Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Durham, NC, United States

17Zebrafishlab, Department of Veterinary Sciences, University of Antwerp, Wilrijk, Belgium

18Molecular Toxicology, Environmental Toxicology, Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dübendorf, Switzerland

19Integrated Laboratory Systems, LLC, Contractor supporting the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Durham, NC, United States

 

ABSTRACT

Toxicological evaluation of chemicals using early-life stage zebrafish (Danio rerio) involves the observation and recording of altered phenotypes. Substantial variability has been observed among researchers in phenotypes reported from similar studies, as well as a lack of consistent data annotation, indicating a need for both terminological and data harmonization. When examined from a data science perspective, many of these apparent differences can be parsed into the same or similar endpoints whose measurements differ only in time, methodology, or nomenclature. Ontological knowledge structures can be leveraged to integrate diverse data sets across terminologies, scales, and modalities. Building on this premise, the National Toxicology Program’s Systematic Evaluation of the Application of Zebrafish in Toxicology undertook a collaborative exercise to evaluate how the application of standardized phenotype terminology improved data consistency. To accomplish this, zebrafish researchers were asked to assess images of zebrafish larvae for morphological malformations in two surveys. In the first survey, researchers were asked to annotate observed malformations using their own terminology. In the second survey, researchers were asked to annotate the images from a list of terms and definitions from the Zebrafish Phenotype Ontology. Analysis of the results suggested that the use of ontology terms increased consistency and decreased ambiguity, but a larger study is needed to confirm. We conclude that utilizing a common data standard will not only reduce the heterogeneity of reported terms but increases agreement and repeatability between different laboratories. Thus, we advocate for the development of a zebrafish phenotype atlas to help laboratories create interoperable, computable data.


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