Abstract：
BACKGROUND: The genus Salvia L., a member of the family Lamiaceae, is a keystone genus with a wide range of medicinal properties. It possesses a rich metabolite source that has long been used to treat different disorders.
OBJECTIVE: Due to a deficiency of untargeted metabolomic profiling in the genus Salvia, this work attempts to investigate a comprehensive mass spectral library matching, computational data annotations, exclusive biomarkers, specific chemotypes, intraspecific metabolite profile variation, and metabolite enrichment by a case study of five medicinal species of Salvia.
METHODS: Aerial parts of each species were subjected to QTRAP liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis workflow based on untargeted metabolites. A comprehensive and multivariate analysis was acquired on the metabolite dataset utilizing MetaboAnalyst 6.0 and the Global Natural Products Social Molecular Networking (GNPS) Web Platform.
RESULTS: The untargeted approach empowered the identification of 117 metabolites by library matching and 92 nodes annotated by automated matching. A machine learning algorithm as substructural topic modeling, MS2LDA, was further implemented to explore the metabolite substructures, resulting in four Mass2Motifs. The automated library newly discovered a total of 23 metabolites. In addition, 87 verified biomarkers of library matching, 58 biomarkers of GNPS annotations, and 11 specific chemotypes were screened.
CONCLUSIONS: Integrative spectral library matching and automated annotation by the GNPS platform provide comprehensive metabolite profiling through a workflow. In addition, QTRAP LC-MS/MS with multivariate analysis unveiled reliable information about inter and intraspecific levels of differentiation. The rigorous investigation of metabolite profiling presents a large-scale overview and new insights for chemotaxonomy and pharmaceutical studies.
OBJECTIVE: Due to a deficiency of untargeted metabolomic profiling in the genus Salvia, this work attempts to investigate a comprehensive mass spectral library matching, computational data annotations, exclusive biomarkers, specific chemotypes, intraspecific metabolite profile variation, and metabolite enrichment by a case study of five medicinal species of Salvia.
METHODS: Aerial parts of each species were subjected to QTRAP liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis workflow based on untargeted metabolites. A comprehensive and multivariate analysis was acquired on the metabolite dataset utilizing MetaboAnalyst 6.0 and the Global Natural Products Social Molecular Networking (GNPS) Web Platform.
RESULTS: The untargeted approach empowered the identification of 117 metabolites by library matching and 92 nodes annotated by automated matching. A machine learning algorithm as substructural topic modeling, MS2LDA, was further implemented to explore the metabolite substructures, resulting in four Mass2Motifs. The automated library newly discovered a total of 23 metabolites. In addition, 87 verified biomarkers of library matching, 58 biomarkers of GNPS annotations, and 11 specific chemotypes were screened.
CONCLUSIONS: Integrative spectral library matching and automated annotation by the GNPS platform provide comprehensive metabolite profiling through a workflow. In addition, QTRAP LC-MS/MS with multivariate analysis unveiled reliable information about inter and intraspecific levels of differentiation. The rigorous investigation of metabolite profiling presents a large-scale overview and new insights for chemotaxonomy and pharmaceutical studies.
摘要：
背景：丹参属，唇形科的一员,是具有广泛药用特性的基石属。它具有丰富的代谢物来源，长期以来一直用于治疗不同的疾病。
目的：由于丹参属的非靶向代谢组学缺乏，这项工作试图研究一个全面的质谱库匹配，计算数据注释，排他性生物标志物，特定的化学型，种内代谢物谱变异，以丹参的五种药用物种为例进行了代谢产物富集研究。
方法：对每个物种的地上部分进行基于非靶向代谢物的QTRAP液相色谱-串联质谱（LC-MS/MS）分析工作流程。利用MetaboAnalyst6.0和全球天然产物社会分子网络（GNPS）网络平台对代谢物数据集进行了全面和多变量分析。
结果：非目标方法通过文库匹配和通过自动匹配注释的92个节点，增强了117个代谢物的鉴定能力。一种机器学习算法作为子结构主题建模，MS2LDA,进一步实施以探索代谢物亚结构，导致四个Mass2Motifs。该自动化文库新发现了总共23种代谢物。此外,87个验证的生物标志物的文库匹配，GNPS的58个生物标志物注解，筛选出11种特定的化学型。
结论：GNPS平台的综合光谱库匹配和自动注释通过工作流程提供了全面的代谢物分析。此外,具有多变量分析的QTRAPLC-MS/MS揭示了有关种间和种内分化水平的可靠信息。代谢物谱分析的严格调查为化学分类学和药物研究提供了大规模的概述和新的见解。
目的：由于丹参属的非靶向代谢组学缺乏，这项工作试图研究一个全面的质谱库匹配，计算数据注释，排他性生物标志物，特定的化学型，种内代谢物谱变异，以丹参的五种药用物种为例进行了代谢产物富集研究。
方法：对每个物种的地上部分进行基于非靶向代谢物的QTRAP液相色谱-串联质谱（LC-MS/MS）分析工作流程。利用MetaboAnalyst6.0和全球天然产物社会分子网络（GNPS）网络平台对代谢物数据集进行了全面和多变量分析。
结果：非目标方法通过文库匹配和通过自动匹配注释的92个节点，增强了117个代谢物的鉴定能力。一种机器学习算法作为子结构主题建模，MS2LDA,进一步实施以探索代谢物亚结构，导致四个Mass2Motifs。该自动化文库新发现了总共23种代谢物。此外,87个验证的生物标志物的文库匹配，GNPS的58个生物标志物注解，筛选出11种特定的化学型。
结论：GNPS平台的综合光谱库匹配和自动注释通过工作流程提供了全面的代谢物分析。此外,具有多变量分析的QTRAPLC-MS/MS揭示了有关种间和种内分化水平的可靠信息。代谢物谱分析的严格调查为化学分类学和药物研究提供了大规模的概述和新的见解。
