Time-dependent behavioral data from zebrafish reveals novel signatures of chemical toxicity using point of departure analysis
High-content imaging of embryonic zebrafish behavior can be used as a screening approach to rapidly evaluate the relative potential for chemicals to cause toxicity. However, most statistical methods applied to these data so far transform movement values to incidence-based “hits” and calculate lowest effect levels (LELs), which loses individual fish resolution of behavior and defies hazard ranking due to reliance on applied dose levels. Here, we have developed and parallelized a workflow to calculate benchmark dose (BMD) values from dynamic, high-content zebrafish behavior data that scales for high-throughput chemical screening. To appropriately capture the dynamics of zebrafish response from light to dark stimulus, we summarized time-dependent data using both area under the curve and immediate change at the transition point into two novel metrics that characterized abnormal behavior as a function of chemical concentration. The BMD workflow was applied to calculate BMD10 values of 1,060 ToxCast chemicals for 24 zebrafish endpoints, including behavior, mortality and morphology. The BMD10 values provided better precision and separation than LELs for clustering chemicals since they were derived from models that best-fit their concentration-response curves. Clustering analysis revealed behavioral signatures as the most sensitive endpoints. High concordance in chemical activity was observed between ToxCast assays and zebrafish behavioral endpoints, however ToxPi analysis indicated that rankings based on in vitro data was not a reliable predictor of in vivo rankings for lower potency chemicals. The methods developed in this work will enable the use of high-content zebrafish behavioral screening data for BMD analysis in toxicological hazard assessment.
- Research Organization:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- National Institute of Environmental Health Sciences (NIEHS); USDOE; USEPA
- Grant/Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1773362
- Report Number(s):
- PNNL-SA--124559
- Journal Information:
- Computational Toxicology, Journal Name: Computational Toxicology Vol. 9; ISSN 2468-1113
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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