Research Institute for Pesticides and Water , University Jaume I , Avenida Sos Baynat s/n , E-12071 Castellón , Spain.
Institute of Marine Research , P.O. Box 2029 Nordness, N-5817 Bergen , Norway.
Anal Chem. 2019 May 7;91(9):6321-6328. doi: 10.1021/acs.analchem.9b01218. Epub 2019 Apr 22.
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 ≤5 ppm). The application of different tolerance filters for RT (Δ ≤ 2 min) and CCS (Δ ≤ 6%) based on predicted values permitted us to reduce this number up to 66% of the features. Finally, five PM metabolites could be identified; two known metabolites (2-DAMP and N-desethyl PM) were confirmed with a standard, whereas three previously unknown metabolites (2-DAMP glucuronide, didesethyl PM, and hydroxy-2-DAMP glucuronide) were tentatively identified in different matrixes, allowing the first proposition of a metabolic pathway in fish.
基于高分辨率质谱 (HRMS) 的非靶向策略在发现鱼类中已知污染物的代谢物方面是一种很有前途的替代真正非靶向筛选的方法。为了减少漫长而昂贵的实验室实验，计算能力的最新进展使得开发全面的基于知识的软件来预测化学物质的代谢命运成为可能。此外，基于机器的学习工具允许在使用离子淌度谱 (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对化合物鉴定的信心。当前的工作探讨了通过使用 RT 和 CCS 预测进行优化和简化非靶向液相色谱 (LC)-IMS-HRMS 数据处理的计算预测工具的能力。将杀虫剂吡虫磷 (PM) 在暴露于受污染饲料的养殖大西洋鲑鱼中的命运作为案例研究。对 60 种潜在相关生物 PM 代谢物的理论预测允许对已知和未知 PM 代谢物在不同的鲑鱼组织（肝、肾、胆、肌肉和脂肪）中的筛选进行优先级排序。基于质子化分子的精确质量（质量误差≤5 ppm），在样品基质中发现了平均 43 个潜在阳性物质。根据预测值应用不同的 RT（Δ≤2 min）和 CCS（Δ≤6%）容忍滤波器，可以将特征数量减少高达 66%。最后，鉴定出五种 PM 代谢物；两种已知代谢物（2-DAMP 和 N-去乙基 PM）用标准品确认，而三种以前未知的代谢物（2-DAMP 葡萄糖醛酸、双去乙基 PM 和羟基-2-DAMP 葡萄糖醛酸）在不同的基质中被初步鉴定，从而首次提出了鱼类的代谢途径。
