TitleTime-dependent behavioral data from zebrafish reveals novel signatures of chemical toxicity using point of departure analysis.
Publication TypeJournal Article
Year of Publication2019
AuthorsThomas, DG, Shankaran, H, Truong, L, Tanguay, RL, Waters, KM
JournalComput Toxicol
Date Published2019 Feb

High-content imaging of larval 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 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. We developed a parallelizable workflow to calculate benchmark dose (BMD) values from dynamic, high-content zebrafish behavior data that scales for high-throughput chemical screening. To capture the zebrafish movement response from light to dark stimulus, we summarized time-dependent data using both area under the curve and the 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 BMD values of 1,060 ToxCast chemicals for 24 zebrafish endpoints, including behavior, mortality and morphology. The BMD values provided better precision and separation than LELs for clustering chemicals since they were derived from models that best-fit their concentration-response curves. Analysis of BMD values revealed behavioral signatures as the most sensitive endpoints. High concordance in chemical activity was observed between ToxCast data and zebrafish behavioral data, however ToxPi analysis indicated that rankings based on data were not a reliable predictor of rankings for lower potency chemicals. This analysis method will enable the use of high-content zebrafish behavioral screening data for BMD analysis in toxicological hazard assessment.

Alternate JournalComput Toxicol
PubMed ID31485548
PubMed Central IDPMC6726397
Grant ListP30 ES000210 / ES / NIEHS NIH HHS / United States
P42 ES016465 / ES / NIEHS NIH HHS / United States