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 Fischer⁴, Melissa A. Haendel¹, Kimberly Hayward⁵, Nils Klüver⁶, Sarah Könemann⁷, Jessica Legradi⁸, Pamela Lein⁹, Connor Leong⁶, J. Erik Mylroie¹⁰, Stephanie Padilla¹¹, Dante Perone⁶, Antonio Planchart¹², Rafael Miñana Prieto¹³, Arantza Muriana¹⁴, Celia Quevedo¹⁵, David Reif², Kristen Ryan¹⁶, Evelyn Stinckens¹⁷, Lisa Truong⁶, Lucia Vergauwen¹⁸, Colette 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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