文献类型：期刊文献

英文题名：An integrated strategy for characterization of chemical constituents in Stephania tetrandra using LC-QTOF-MS/MS and the target isolation of two new biflavonoids

作者：Xue, Jiayun[1];Liu, Shun[2];Kang, Yun[1];Wang, Yaqin[1];Weng, Weiyu[2];Yang, Ping[1];Huang, Jianming[1]

机构：[1]Fudan Univ, Sch Pharm, Shanghai 201203, Peoples R China;[2]East China Univ Sci & Technol, Sch Pharm, Shanghai 200237, Peoples R China

年份：2023

卷号：226

外文期刊名：JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000991145000001)】；

语种：英文

外文关键词：Stephania tetrandra; Molecular networking; Mass defect filtering; Diagnostic fragment ion filtering; Biflavonoid; LC-QTOF-MS

摘要：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.