A Refined Nontarget Workflow for the Investigation of Metabolites through the Prioritization by in Silico Prediction Tools
A Refined Nontarget Workflow for the Investigation of Metabolites through the Prioritization by in Silico Prediction Tools
复制标题
DOI：
10.1021/acs.analchem.9b01218
复制
发表时间：
2019-05-07
影响因子：
7.4
通讯作者：
Merel, Sylvain
中科院分区：
文献类型：
--
作者：
Bijlsma, Lubertus;Berntssen, Marc H. G.;Merel, Sylvain
基于高分辨率质谱法（HRMS）的非靶向策略的应用是一个有趣的替代真正的非靶向筛选的发现已知污染物在鱼的代谢物。为了减少长时间和昂贵的实验室实验，计算能力的最新进展已经允许开发基于知识的综合软件来预测化学品的代谢命运。此外，基于机器的学习工具允许在使用离子迁移谱（IMS）时预测色谱保留时间（RT）或碰撞截面（CCS）值。这些工具可以简化数据评估并增强化合物鉴定的信心。目前的工作探讨的能力，在硅片上的预测工具，通过使用RT和CCS预测，优先考虑和促进非目标液相色谱（LC）-IMS-HRMS数据处理。农药甲基吡螨酮（PM）在养殖大西洋鲑鱼暴露于受污染的饲料的命运被用作案例研究。60个潜在相关的生物PM代谢物的理论预测允许在不同的鲑鱼组织（肝脏，肾脏，胆汁，肌肉和脂肪）的已知和未知的PM代谢物的筛选的优先顺序。基于质子化分子的精确质量（质量误差），在样品基质中发现平均43个潜在阳性
The application of nontargeted strategies based on high-resolution mass spectrometry (HRMS) directed toward the discovery of metabolites of known contaminants in fish is an interesting alternative to true nontarget screening. To reduce prolonged and costly laboratory experiments, recent advances in computing power have permitted the development of comprehensive knowledge-based software to predict the metabolic fate of chemicals. In addition, machine-based learning tools allow the prediction of chromatographic retention times (RT) or collision cross section (CCS) values when using ion mobility spectrometry (IMS). These tools can ease data evaluation and strengthen the confidence in the identification of compounds. The current work explores the capabilities of in silico prediction tools, refined by the use of RT and CCS prediction, to prioritize and facilitate nontarget liquid chromatography (LC)-IMS-HRMS data processing. The fate of the insecticide pirimiphos-methyl (PM) in farmed Atlantic salmon exposed to contaminated feed was used as a case study. The theoretical prediction of 60 potentially relevant biological PM metabolites permitted the prioritization of screening in different salmon tissues (liver, kidney, bile, muscle, and fat) of known and unknown PM metabolites. An average of 43 potential positives was found in the sample matrixes based on the accurate mass of protonated molecules (mass error