School of Pharmacy, Fudan University, Shanghai 201203, PR China.
School of Pharmacy, East China University of Science and Technology, Shanghai 200237, PR China.
J Pharm Biomed Anal. 2023 Mar 20;226:115247. doi: 10.1016/j.jpba.2023.115247. Epub 2023 Jan 11.
LC-MS has been a widely used analytical technique for identification of natural compounds. However, sophisticated and laborious data analysis is required to identify chemical components, especially new compounds, from a large LC-MS dataset. The aim of this study is to develop an integrated data-mining strategy that combines molecular networking (MN), in-house polygonal mass defect filtering (MDF), and diagnostic fragment ion filtering (DFIF) to identify phytochemicals in Stephania tetrandra based on LC-MS data. S. tetrandra samples were prepared by matrix solid-phase dispersion extraction methods and then raw MS spectra were acquired using LC-QTOF-MS/MS. MN and in-house polygonal MDF classified the compounds roughly. Modified DFIF were then used in succession to place each spectrum into a specific class. Finally, the exact structures were deduced by fragmentation pathways and related botanical biogenesis, with the help of the narrowed classification from MN and MDF. The total workflow was a combination of data filtering and identification methods for rapid characterization of known compounds (dereplication) and discovery of new compounds. Consequently, 144 compounds were identified or tentatively identified in the aerial parts and roots of S. tetrandra, including 11 potentially new compounds and 63 compounds first identified in this species. Among 144 compounds, 61 were from the aerial parts exclusively, 8 were from the roots exclusively, and 75 were found in both parts. Furthermore, two new biflavonoids were isolated with the guide of LC-MS analysis and structurally elucidated by spectroscopic methods. In conclusion, the proposed data-mining strategy based on LC-MS can be used to profile chemical constituents with high efficiency and guide the isolation of new compounds from medicinal plants. The comparison of the components of the aerial parts and roots of S. tetrandra would be helpful for the rational utilization of the medicinal plant.
LC-MS 已广泛应用于天然化合物的鉴定分析技术。然而，要从大量的 LC-MS 数据集中鉴定化学组分，特别是新化合物，需要进行复杂且繁琐的数据处理。本研究旨在开发一种集成的数据挖掘策略，该策略结合了分子网络（MN）、内部多边形质量缺陷过滤（MDF）和诊断碎片离子过滤（DFIF），基于 LC-MS 数据鉴定蛇根草中的植物化学物质。采用基质固相分散萃取方法制备蛇根草样品，然后使用 LC-QTOF-MS/MS 采集原始 MS 光谱。MN 和内部多边形 MDF 对化合物进行大致分类。然后，连续使用修改后的 DFIF 将每个光谱放入特定的类别中。最后，通过片段化途径和相关的植物生源学推断确切的结构，借助 MN 和 MDF 的分类缩小范围。总工作流程是数据过滤和鉴定方法的组合，用于快速表征已知化合物（去重）和发现新化合物。因此，在蛇根草的地上部分和根部鉴定或初步鉴定了 144 种化合物，包括 11 种潜在的新化合物和 63 种首次在该种中鉴定的化合物。在 144 种化合物中，61 种来自地上部分，8 种来自根部，75 种来自地上部分和根部。此外，根据 LC-MS 分析的指导，分离得到了两种新的双黄酮类化合物，并通过光谱方法对其结构进行了阐明。总之，基于 LC-MS 的这种数据挖掘策略可用于高效地分析化学成分，并指导从药用植物中分离新化合物。比较蛇根草地上部分和根部的成分有助于该药用植物的合理利用。
