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Prediction of zebrafish embryonic developmental toxicity by integrating omics with adverse outcome pathway

不良结局途径 斑马鱼 发育毒性 组学 毒性 结果(博弈论) 胚胎干细胞 计算生物学 生物 不利影响 生物信息学 药理学 医学 遗传学 基因 内科学 数理经济学 妊娠期 怀孕 数学
作者
Xiao Gou,Cong Ma,Huimin Ji,Lu Yan,Pingping Wang,Zhihao Wang,Yishan Lin,Nivedita Chatterjee,Hongxia Yu,Xiaowei Zhang
出处
期刊:Journal of Hazardous Materials [Elsevier BV]
卷期号:448: 130958-130958 被引量:13
标识
DOI:10.1016/j.jhazmat.2023.130958
摘要

New approach methodologies (NAMs), especially omics-based high-throughput bioassays have been developed rapidly, providing rich mechanistic information such as molecular initiation events (MIEs) and (sub)cellular key events (KEs) in adverse outcome pathways (AOPs). However, how to apply the knowledge of MIEs/KEs to predict adverse outcomes (AOs) induced by chemicals represents a new challenge for computational toxicology. Here, an integrated method named ScoreAOP was developed and evaluated to predict chemicals’ developmental toxicity for zebrafish embryos by integrating four related AOPs and dose-dependent reduced zebrafish transcriptome (RZT). The rules of ScoreAOP included 1) sensitivity of responsive KEs demonstrated by point of departure of KEs (PODKE), 2) evidence reliability and 3) distance between KEs and AOs. Moreover, eleven chemicals with different modes of action (MoAs) were tested to evaluate ScoreAOP. Results showed that eight of the eleven chemicals caused developmental toxicity at tested concentration in apical tests. All the tested chemicals’ developmental defects were predicted using ScoreAOP, whereas eight out of the eleven chemicals predicted by ScoreMIE which was developed to score MIEs disturbed by chemicals based on in vitro bioassays data. Finally, in terms of mechanism explanation, ScoreAOP clustered chemicals with different MoAs while ScoreMIE failed, and ScoreAOP revealed the activation of aryl hydrocarbon receptor (AhR) plays a significant role in dysfunction of cardiovascular system, resulting in zebrafish developmental defects and mortality. In conclusion, ScoreAOP represents a promising approach to apply mechanism information obtained from omics to predict AOs induced by chemicals.

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