Deep Annotation of Hydroxycinnamic Acid Amides in Plants Based on Ultra-High-Performance Liquid Chromatography-High-Resolution Mass Spectrometry and Its In Silico Database
Deep Annotation of Hydroxycinnamic Acid Amides in Plants Based on Ultra-High-Performance Liquid Chromatography-High-Resolution Mass Spectrometry and Its In Silico Database
复制标题
基于超高效液相色谱-高分辨率质谱及其计算机数据库的植物中羟基肉桂酰胺的深度注释
DOI：
10.1021/acs.analchem.8b03654
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发表时间：
2018-12-18
影响因子：
7.4
通讯作者：
Xu, Guowang
中科院分区：
文献类型：
--
作者：
Li, Zaifang;Zhao, Chunxia;Xu, Guowang
羟基肉桂酸酰胺（HCAAs）是一类广泛分布于植物体内的次生代谢产物，在植物生长发育过程中起着重要作用。目前大多数方法可用于分析给定工厂中的一些已知HCAAs。因此，迫切需要一种新的植物HCAAs检测方法。本研究基于超高效液相色谱-高分辨质谱（UHPLC HRMS）及其HCAAs电子数据库，提出了一种HCAAs的深度注释方法。为了构建计算机UHPLC-HRMS HCAAs数据库，根据可能的生物合成反应，从最常见的酚酸和多胺/芳香族单胺底物中产生了总共846个HCAAs，其代表了植物特异性HCAAs的结构。从参比混合物中提取HCAAs的特征MS/MS裂解模式。建立了四种定量结构-保留关系（QSRR）模型，用于预测单反式HCAAs（芳香胺缀合物）、单反式HCAAs（脂肪胺缀合物）、双HCAAs和三HCAAs的保留时间。所开发的方法被应用于确定HCAAs在种子（玉米，小麦和水稻），根（水稻），叶（水稻和烟草）。共检测到79种HCAAs，其中42种为首次在这些植物中鉴定，20种从未报道过植物中存在。结果表明，所开发的方法可以用来识别HCAA在植物中没有HCAA分布的先验知识。据我们所知，这是第一个开发的UHPLC HRMS数据库，用于从非目标UHPLC HRMS数据中有效地深度注释HCAAs。这对植物中新的HCAAs的鉴定是有用的。
Hydroxycinnamic acid amides (HCAAs), diversely distributed secondary metabolites in plants, play essential roles in plant growth and developmental processes. Most current approaches can be used to analyze a few known HCAAs in a given plant. A novel method for comprehensive detection of plant HCAAs is urgently needed. In this study, a deep annotation method of HCAAs was proposed on the basis of ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC HRMS) and its in silico database of HCAAs. To construct an in silico UHPLC-HRMS HCAAs database, a total of 846 HCAAs were generated from the most common phenolic acid and polyamine/aromatic monoamine substrates according to possible biosynthesis reactions, which represent the structures of plant-specialized HCAAs. The characteristic MS/MS fragmentation patterns of HCAAs were extracted from reference mixtures. Four quantitative structure-retention relationship (QSRR) models were developed to predict retention times of mono-trans-HCAAs (aromatic amines conjugates), mono-trans-HCAAs (aliphatic amines conjugates), bis-HCAAs, and tris-HCAAs. The developed method was applied for identifying HCAAs in seeds (maize, wheat, and rice), roots (rice), and leaves (rice and tobacco). A total of 79 HCAAs were detected: 42 of them were identified in these plants for the first time, and 20 of them have never been reported to exist in plants. The results showed that the developed method can be used to identify HCAAs in a plant without prior knowledge of HCAA distributions. To the best of our knowledge, it is the first UHPLC HRMS database developed for effective deep annotation of HCAAs from nontargeted UHPLC HRMS data. It is useful for the identification of novel HCAAs in plants.