In Silico Prediction and Automatic LC鈥揗Sn Annotation of Green Tea Metabolites in Urine

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• 作者：Lars Ridder ; Justin J. J. van der Hooft ; Stefan Verhoeven ; Ric C. H. de Vos ; Jacques Vervoort ; Raoul J. Bino
• 刊名：Analytical Chemistry
• 出版年：2014
• 出版时间：May 20, 2014
• 年：2014
• 卷：86
• 期：10
• 页码：4767-4774
• 全文大小：437K
• 年卷期：v.86,no.10(May 20, 2014)
• ISSN：1520-6882

文摘

The colonic breakdown and human biotransformation of small molecules present in food can give rise to a large variety of potentially bioactive metabolites in the human body. However, the absence of reference data for many of these components limits their identification in complex biological samples, such as plasma and urine. We present an in silico workflow for automatic chemical annotation of metabolite profiling data from liquid chromatography coupled with multistage accurate mass spectrometry (LC鈭扢Sⁿ), which we used to systematically screen for the presence of tea-derived metabolites in human urine samples after green tea consumption. Reaction rules for intestinal degradation and human biotransformation were systematically applied to chemical structures of 75 green tea components, resulting in a virtual library of 27鈥?45 potential metabolites. All matching precursor ions in the urine LC鈥揗Sⁿ data sets, as well as the corresponding fragment ions, were automatically annotated by in silico generated (sub)structures. The results were evaluated based on 74 previously identified urinary metabolites and lead to the putative identification of 26 additional green tea-derived metabolites. A total of 77% of all annotated metabolites were not present in the Pubchem database, demonstrating the benefit of in silico metabolite prediction for the automatic annotation of yet unknown metabolites in LC鈥揗Sⁿ data from nutritional metabolite profiling experiments.