Ridder Lars, van der Hooft Justin J J, Verhoeven Stefan, de Vos Ric C H, Vervoort Jacques, Bino Raoul J
Laboratory of Biochemistry, Wageningen University , Dreijenlaan 3, 6703 HA, Wageningen, The Netherlands.
Anal Chem. 2014 May 20;86(10):4767-74. doi: 10.1021/ac403875b. Epub 2014 Apr 29.
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-MS(n)), 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 27,245 potential metabolites. All matching precursor ions in the urine LC-MS(n) 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-MS(n) data from nutritional metabolite profiling experiments.
食物中存在的小分子在结肠中的分解以及人体的生物转化会在人体内产生大量潜在的生物活性代谢物。然而，许多这些成分缺乏参考数据，限制了它们在血浆和尿液等复杂生物样品中的鉴定。我们提出了一种计算机模拟工作流程，用于对液相色谱与多级精确质谱联用（LC-MS(n)）的代谢物谱数据进行自动化学注释，我们用该工作流程系统地筛查了饮用绿茶后人体尿液样本中茶源代谢物的存在情况。将肠道降解和人体生物转化的反应规则系统地应用于75种绿茶成分的化学结构，生成了一个包含27245种潜在代谢物的虚拟库。尿液LC-MS(n)数据集中所有匹配的前体离子以及相应的碎片离子，都通过计算机模拟生成的（亚）结构进行自动注释。基于74种先前鉴定出的尿液代谢物对结果进行评估，从而推定鉴定出另外26种茶源代谢物。所有注释的代谢物中，共有77%不存在于Pubchem数据库中，这表明计算机模拟代谢物预测对于营养代谢物谱实验的LC-MS(n)数据中未知代谢物的自动注释具有重要作用。
