UHPLC-HRMS/MS on untargeted metabolomics: a case study with Copaifera (Fabaceae)

IntroductionFructus Psoraleae (FP) is a well‐known traditional Chinese medicine for the treatment of osteoporosis. However, major quality differences were witnessed owing to its various origins, thus influencing its safety and efficacy.ObjectivesThe study aimed to evaluate the quality of FP from different origins and predict its quality evaluation markers.MethodsUltra‐high‐performance liquid chromatography coupled with quadrupole time‐of‐flight tandem mass spectrometry was employed for tentative characterisation of the constituents in 10 batches of FP, followed by the utilisation of multivariate statistical analysis methods including principal component analysis and orthogonal partial least squares discriminant analysis for quality evaluation. Network pharmacology approaches were utilised to explore the underlying mechanism of the screened chemotaxonomic markers in treating osteoporosis.ResultsForty‐one components in FP including, chalcones, coumarins, coumestans, flavonoids, iso‐flavonoids, and phenolics, were characterised based on their fragmentation pathways. Ten batches of FP were basically divided into three categories, and eight chemotaxonomic markers including isopsoralen, calamenene, bakuchiol, psoralen, bavachinin, isoneobavaisoflavone, corylifol C, and neobavaisoflavone were screened. Network pharmacology revealed that the chemotaxonomic markers can act on targets such as AKT1, HSP90AA1, and EGFR and possess effects mainly through glycolysis and wnt/β‐catenin signalling to alleviate osteoporosis. Molecular docking and molecular dynamic simulation confirmed the good binding affinity and stability between proteins and selected markers. So, eight chemotaxonomic markers were all preferentially recommended as quality evaluation markers.ConclusionThe study not only provides a reference for the improvement of quality control of FP but also offers a theoretical basis for its further in‐depth research in osteoporosis.

Section: Introductionmentioning

confidence: 99%

Exploring quality evaluation markers of Fructus Psoraleae based on chemometric analysis integrated with network pharmacology

Zhao,

Guo,

et al. 2023

“…Metabolomics is a comprehensive metabolic network approach which assists as a valuable method for an extensive range of goals. Different analytical platfoms such as LC‐MS/MS based on untargeted metabolomics are a potent, efficient, and comprehensive technique applied for pharmacological purposes, detection of chemical or biomarkers, clarification of differential metabolic profiles, metabolic variations, chemotaxonomy, chemodiversity, and phytochemical screening without any specific compounds on account of their high sensitivity and accuracy 16 . In recent years, a tremendous metabolomic platform has widely emerged to explore different metabolites and their pathways, screen specific metabolite levels, and conduct omics analysis for plant species 17 …”

Section: Introductionmentioning

confidence: 99%

Untargeted metabolomics study and identification of potential biomarkers in the six sections of the genus Stachys L. (Lamiaceae) using HPLC‐MQ‐API‐MS/MS

Mohammadi

Kharazian

2022

Introduction The genus Stachys L., belonging to the family Lamiaceae, is one of the largest genera with remarkable medicinal properties. Plants of this genus produce a broad range of secondary metabolites. Objectives Due to the incomplete comprehensive assessment of chemical profiles in Stachys species, we conducted an untargeted metabolomics study and identified potential biomarkers in the six sections of Stachys with chemotaxonomic importance. Material and Methods Dried leaves of 17 taxa were utilized for analysis of all the constituents using HPLC‐MQ‐API‐MS. The obtained data were processed and analyzed using multivariate statistical methods, including heatmaps, PLS‐DA score plots, functional analysis of metabolic pathways, metabolite set enrichment analysis, and biomarker and network analysis. Results Among the 129 metabolites, 111 flavonoids and 18 non‐flavonoids were recognized. The most represented flavonoids, including 41 flavones and 20 flavonols, displayed remarkable abundance. In non‐flavonoid compounds, a total of six coumarins and six phenolic acids were present at high levels. In terms of approved markers in six sections, 76 chemical compounds, mainly flavonoids, coumarins, quinic acids, and cinnamic acids, were identified as potential biomarkers or chemotaxonomic indicators. Accordingly, the taxonomic complexities of some Stachys species in sections Fragilicaulis, Aucheriana, and Setifolia were properly resolved. Conclusion An HPLC‐MS/MS‐based metabolomics approach integrated with multivariate statistical methods was employed to identify (1) valuable markers and analyze metabolic diversity and (2) predict the pharmaceutical properties of Stachys species. The obtained chemical profiles provide a new perspective for investigation of the Stachys genus.

Metabolomics‐based profiling of five Salvia L. (Lamiaceae) species using untargeted data analysis workflow

Kharazian,

Dehkordi,

Xiang

2024

IntroductionThe 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.ObjectivesDue 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.Material and methodsAerial 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.ResultsThe 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.ConclusionIntegrative 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.