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整合RNA测序与基于深度学习的代谢毒性预测:对选优先液晶单体的新视角

Wenjin Zhao¹, Jiatong Li¹, Ning Hao¹

¹Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130021, China; College of New Energy and Environment, Jilin University, Changchun 130012, China.

Journal of hazardous materials

|November 14, 2025

概括

一个新的深度学习模型,LCMsT-MTP,快速选液晶单体 (LCM) 在水生生物中的代谢毒性. 这种方法优先考虑LCMs,确定约95%为高或中等风险,解决生态毒理学中的重大数据缺口.

科学领域:

环境毒理学环境毒理学
计算化学的计算化学
生态毒理学生态毒理学

背景情况:

对于液晶单体 (LCM) 存在显著的毒理学数据缺口,特别是对于水生生物,这阻碍了环境风险评估.
目前用于LCM毒性查的方法通常很慢,缺乏高通量能力,无法解决大量的LCM.
代谢干扰是LCMs诱导的主要毒性机制,影响许多生物通路.

研究的目的:

开发一种快速和高通量选方法,根据其在水生环境中的潜在代谢毒性来优先考虑LCM.
创建一个预测深度学习模型,用于识别诱导代谢干扰的LCM.
为LCMs生成一个全面的代谢毒性优先清单.

主要方法:

用RNA测序来识别暴露于化LCM (LCMs^F) 的斑马鱼中的代谢障碍和差异表达基因.
开发了一个深度学习模型,LCMsT-MTP,整合了LCM结构和代谢毒性标序列,用于预测查.
使用分子动力学模拟验证了LCMsT-MTP模型,并应用于化 (LCMs^F) 和非化 (LCMs^{None F}) 的LCM.

主要成果:

RNA测序揭示了28个基因和基因组京都百科全书 (KEGG) 途径异常在498个由于LCMs^F暴露而差异表达的基因.
LCMsT-MTP模型成功地同时确定了21种代谢毒性,并应用于LCMs^没有F.
一个优先清单将大约95%的LCM归类为高或中等的代谢毒性风险,具有由分子动力学证实的明显相互作用模式.

结论:

开发的LCMsT-MTP深度学习模型提供了一种快速,高通量和准确的方法来选LCM的代谢毒性.
这种方法显著推进了对LCMs环境风险的评估,解决了水生生物的关键数据缺口.
生成的代谢毒性优先清单有助于识别危险的LCMs,用于监管和环境管理目的.

关键词:

深度学习是一种深度学习.液晶单体是液晶的单体.代谢性毒性代谢性毒性分子动力学模拟模型有关RNA测序的RNA测序

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