Lu Dasheng, Zhang Suhui, Wang Dongli, Feng Chao, Liu Shihong, Jin Yu 'e, Xu Qian, Lin Yuanjie, Wu Chunhua, Tang Liming, She Jianwen, Wang Guoquan, Zhou Zhijun
School of Public Health/MOE Key Lab for Public Health/Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai 200032, China; Shanghai Municipal Center for Disease Control and Prevention, 1380 Zhongshan West Road, Shanghai 200336, China.
School of Public Health/MOE Key Lab for Public Health/Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai 200032, China; Pharmacology and Toxicology Department, Shanghai Institute for Food and Drug Control, Shanghai 201203, China.
J Chromatogr A. 2016 May 6;1445:80-92. doi: 10.1016/j.chroma.2016.03.080. Epub 2016 Mar 31.
It is of great interest to develop strategic methods to enable chemicals' metabolites to be accurately and rapidly screened and identified. To screen and identify a category of metabolites with distinct isotopic distribution, this study proposed a generic strategy using in silico metabolite prediction plus accurate-mass-based isotopic pattern recognition (AMBIPR) and library identification on the data acquired via the data dependent MS/MS scan of LC-Q Exactive Orbitrap mass spectrometry. The proposed method was evaluated by the analysis of flurochloridone (FLC) metabolites in rat urine sample collected from toxicity tests. Different from the traditional isotopic pattern recognition (IPR) approach, AMBIPR here was performed based on the potential metabolites predicted via in silico metabolite prediction tools. Thus, the AMBIPR treated FLC data was only associated with FLC metabolites, consequently not only avoiding great efforts made to remove FLC-unrelated information and reveal FLC metabolites, but also increasing the percent of positive hits. Among the FLC metabolite peaks screened using AMBIPR, 87% of them (corresponding 97 metabolites and 49 biotransformation) were successfully identified via multiple MS identification techniques packaged in an established FLC's metabolites library based on Mass Frontier. Noteworthy, 34 metabolites (89%) were identified without distinct naturally isotopic distribution. The universal strategic approach based on background subtraction (BS) and mass defect filtering (MDF) was used to evaluate the AMBIPR and no more false positive and negative metabolites were detected. Furthermore, our results revealed that AMBIPR is very effective, inherently sensitive and accurate, and is easily automated for the rapidly screening and profiling chemicals related metabolites.
