基于分子网络的导向发现，用于区分五种药用巴黎草属植物的物种特异性标记物。
Molecular-networking-guided discovery of species-specific markers for discriminating five medicinal Paris herbs.
机构信息
State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Lane, Nanjing 210009, China.
College of chemical engineering, Nanjing Forestry University, Nanjing, 210037, China.
出版信息
Phytomedicine. 2021 May;85:153542. doi: 10.1016/j.phymed.2021.153542. Epub 2021 Mar 10.
BACKGROUND
Paridis Rhizoma (PR) is a famous traditional herbal medicine. Apart from two officially recorded species, viz. Paris polyphylla Smith var. yunnanensis (Franch.) Hand. - Mazz. (PPY) and P. polyphylla Smith var. chinensis (Franch.) Hara (PPC), there are still many other species used as folk medicine. It is necessary to understand the metabolic differences among Paris species.
PURPOSE
To establish a strategy that can discover species-specific steroidal saponin markers to distinguish closely-related Paris herbs for quality and safety control.
METHODS
A new strategy of molecular-networking-guided discovery of species-specific markers was proposed. Firstly, the ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) was applied to obtain the MS and MS/MS data of all samples. Then, molecular networking (MN) was created using MS/MS data to prescreen the steroidal saponins for subsequent analysis. Next, the principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLS-DA) models were established to discover potential markers. Finally, the verification, identification and distribution of chemical markers were performed.
RESULTS
A total of 126 steroidal saponins were screened out from five species using MN. Five species were classified successfully by OPLS-DA model, and 18 species-specific markers were discovered combining the variable importance in the projection (VIP) value, P value (one-way ANOVA) and their relative abundance. These markers could predict the species of Paris herbs correctly.
CONCLUSION
These results revealed that this new strategy could be an efficient way for chemical discrimination of medicinal herbs with close genetic relationship.
背景
重楼属（Paridis Rhizoma）植物是一种著名的传统草药。除了两种官方记录的物种，即云南重楼（Paris polyphylla Smith var. yunnanensis（Franch.）Hand.-Mazz.）和中华重楼（P. polyphylla Smith var. chinensis（Franch.）Hara）外，还有许多其他物种被用作民间药物。有必要了解重楼属物种之间的代谢差异。
目的
建立一种可以发现种特异性甾体皂苷标志物的策略，以区分亲缘关系密切的重楼属草药，用于质量和安全性控制。
方法
提出了一种基于分子网络引导的发现种特异性标志物的新策略。首先，采用超高效液相色谱-四极杆飞行时间质谱联用（UPLC-QTOF/MS）获得所有样品的 MS 和 MS/MS 数据。然后，使用 MS/MS 数据创建分子网络（MN），以预筛选后续分析用的甾体皂苷。接下来，建立主成分分析（PCA）和正交偏最小二乘判别分析（OPLS-DA）模型，以发现潜在标志物。最后，对化学标志物进行验证、鉴定和分布分析。
结果
利用 MN 从 5 个物种中筛选出 126 种甾体皂苷。利用 OPLS-DA 模型成功对 5 个物种进行分类，并结合 VIP 值、P 值（单因素方差分析）及其相对丰度，发现了 18 种种特异性标志物。这些标志物可以正确预测重楼属草药的物种。
结论
这些结果表明，这种新策略可能是一种有效的化学鉴别亲缘关系密切的草药的方法。
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