Abstract：
Surfactants as synergistic agents are necessary to improve the stability and utilization of pesticides, while their use is often accompanied by unexpected release into the environment. However, there are no efficient strategies available for screening low-toxicity surfactants, and traditional toxicity studies rely on extensive experimentation which are not predictive. Herein, a commonly used agricultural adjuvant Triton X (TX) series was selected to study the function of amphipathic structure to their toxicity in zebrafish. Molecular dynamics (MD) simulations, transcriptomics, metabolomics and machine learning (ML) were used to study the toxic effects and predict the toxicity of various TX. The results showed that TX with a relatively short hydrophilic chain was highly toxic to zebrafish with LC50 of 1.526 mg/L. However, TX with a longer hydrophilic chain was more likely to damage the heart, liver and gonads of zebrafish through the arachidonic acid metabolic network, suggesting that the effect of surfactants on membrane permeability is the key to determine toxic results. Moreover, biomarkers were screened through machine learning, and other hydrophilic chain lengths were predicted to affect zebrafish heart health potentially. Our study provides an advanced adjuvants screening method to improve the bioavailability of pesticides while reducing environmental impacts.
摘要：
表面活性剂作为增效剂是提高农药的稳定性和利用率所必需的,而它们的使用往往伴随着意外释放到环境中。然而,没有有效的策略来筛选低毒性表面活性剂，和传统的毒性研究依赖于广泛的实验，这是不可预测的。在这里，选择常用的农业佐剂TritonX（TX）系列来研究两亲结构对斑马鱼毒性的功能。分子动力学（MD）模拟，转录组学，代谢组学和机器学习(ML)用于研究各种TX的毒性作用和预测毒性.结果表明，亲水链相对较短的TX对斑马鱼具有高毒性，LC50为1.526mg/L。然而,具有较长亲水链的TX更有可能损害心脏，斑马鱼的肝脏和性腺通过花生四烯酸代谢网络，表明表面活性剂对膜渗透性的影响是确定毒性结果的关键。此外，通过机器学习筛选生物标志物，和其他亲水链长度被预测可能会影响斑马鱼的心脏健康。我们的研究提供了一种先进的佐剂筛选方法，以提高农药的生物利用度，同时减少对环境的影响。
