期刊
TOXICOLOGY AND APPLIED PHARMACOLOGY
卷 314, 期 -, 页码 109-117出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.taap.2016.11.010
关键词
Zebrafish; High-dimensional; Bayesian; Developmental cascade; ToxRefDB; Risk assessment; Multiple endpoints; Multivariate; Scoring
资金
- NIEHS [R01 ES19604, R01 ES023788, P42 ES005948, P30 ES025128, RC4 ES019764 P30, P30 ES000210, P42 ES016465, 5T32ES007329]
- Environmental Protection Agency (EPA) STAR [835168, 83579601]
- NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES [P42ES016465, T32ES007329, P42ES005948, P30ES025128, RC4ES019764, P30ES000210, R01ES019604, U01ES027294, R01ES023788] Funding Source: NIH RePORTER
Zebrafish have become a key alternative model for studying health effects of environmental stressors, partly due to their genetic similarity to humans, fast generation time, and the efficiency of generating high-dimensional systematic data. Studies aiming to characterize adverse health effects in zebrafish typically include several phenotypic measurements (endpoints). While there is a solid biomedical basis for capturing a comprehensive set of endpoints, making summary judgments regarding health effects requires thoughtful integration across endpoints. Here, we introduce a Bayesian method to quantify the informativeness of 17 distinct zebrafish endpoints as a data-driven weighting scheme for a multi-endpoint summary measure, called weighted Aggregate Entropy (wAggE). We implement wAggE using high-throughput screening (HTS) data from zebrafish exposed to five concentrations of all 1060 ToxCast chemicals. Our results show that our empirical weighting scheme provides better performance in terms of the Receiver Operating Characteristic (ROC) curve for identifying significant morphological effects and improves robustness over traditional curve-fitting approaches. From a biological perspective, our results suggest that developmental cascade effects triggered by chemical exposure can be recapitulated by analyzing the relationships among endpoints. Thus, wAggE offers a powerful approach for analysis of multivariate phenotypes that can reveal underlying etiological processes. (C) 2016 Elsevier Inc. All rights reserved.
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