A Mass Spectrometry Based Metabolite Profiling Workflow for Selecting Abundant Specific Markers and Their Structurally Related Multi-Component Signatures in Traditional Chinese Medicine Multi-Herb Formulae.

Joëlle Houriet,Kenny Kuchta,Arnaud Gaudry,Laurence Marcourt,Yu Lin,Emerson Ferreira Queiroz,Ruwei Wang,Songhua Li,Jean-Luc Wolfender,Pierre-Marie Allard,Lennie Vallin

doi:10.3389/fphar.2020.578346

Abstract

In Traditional Chinese Medicine (TCM), herbal preparations often consist of a mixture of herbs. Their quality control is challenging because every single herb contains hundreds of components (secondary metabolites). A typical 10 herb TCM formula was selected to develop an innovative strategy for its comprehensive chemical characterization and to study the specific contribution of each herb to the formula in an exploratory manner. Metabolite profiling of the TCM formula and the extract of each single herb were acquired with liquid chromatography coupled to high-resolution mass spectrometry for qualitative analyses, and to evaporative light scattering detection (ELSD) for semi-quantitative evaluation. The acquired data were organized as a feature-based molecular network (FBMN) which provided a comprehensive view of all types of secondary metabolites and their occurrence in the formula and all single herbs. These features were annotated by combining MS/MS-based in silico spectral match, manual evaluation of the structural consistency in the FBMN clusters, and taxonomy information. ELSD detection was used as a filter to select the most abundant features. At least one marker per herb was highlighted based on its specificity and abundance. A single large-scale fractionation from the enriched formula enabled the isolation and formal identification of most of them. The obtained markers allowed an improved annotation of associated features by manually propagating this information through the FBMN. These data were incorporated in the high-resolution metabolite profiling of the formula, which highlighted specific series of related components to each individual herb markers. These series of components, named multi-component signatures, may serve to improve the traceability of each herb in the formula. Altogether, the strategy provided highly informative compositional data of the TCM formula and detailed visualizations of the contribution of each herb by FBMN, filtered feature maps, and reconstituted chromatogram traces of all components linked to each specific marker. This comprehensive MS-based analytical workflow allowed a generic and unbiased selection of specific and abundant markers and the identification of multiple related sub-markers. This exploratory approach could serve as a starting point to develop more simple and targeted quality control methods with adapted marker specificity selection criteria to given TCM formula.

Highlights

Multi-herb mixtures are used in many traditional medicines, such as Traditional Chinese Medicine (TCM), Japanese Kampo medicine, traditional European phytomedicine, and in modern evidence-based herbal medicinal products (AbdelAziz et al, 2017)
The formula selected for this study contains 10 various herbal drugs from eight botanical families: Angelica sinensis (Oliv.) Diels (Apiaceae), Chrysanthemum indicum L. (Asteraceae), Glycyrrhiza uralensis Fisch (Fabaceae), Isatis tinctoria L. (Brassicaceae), Oldenlandia diffusa (Willd.) Roxb. (Rubiaceae), Reynoutria japonica Houtt. (Polygonaceae), whose synonym, Polygonum cuspidatum Siebold & Zucc. was used in this study to stay in line with (Li et al, 2013), Prunella vulgaris L. (Lamiaceae), Scutellaria baicalensis Georgi (Lamiaceae), Smilax glabra Roxb. (Smilacaceae) and Sophora flavescens Aiton (Fabaceae) (Table 2)
A wide variety of different chemical classes has been described for these 10 herbal drugs, ranging from the usual flavonoids to alkaloids (S. flavescens (He et al, 2015), I. tinctoria (Mohn et al, 2009), A. sinensis (Jin et al, 2012; Ma et al, 2015), O. diffusa (Chen et al, 2016)), triterpenoids (G. uralensis (Wang et al, 2013), P. vulgaris (Bai et al, 2016), S. flavescens (He et al, 2015)), quinones (P. cuspidatum (Peng et al, 2013)), iridoids (O. diffusa (Chen et al, 2016)), S. flavescens (He et al, 2015), and phtalides (A. sinensis (Jin et al, 2012; Ma et al, 2015)), among others

Summary

Introduction

Multi-herb mixtures are used in many traditional medicines, such as Traditional Chinese Medicine (TCM), Japanese Kampo medicine, traditional European phytomedicine, and in modern evidence-based herbal medicinal products (AbdelAziz et al, 2017). Modern pharmacopoeial monographs propose methods to check multiple chemical components both in terms of presence and relative quantification Such methods, called single standard to determine multi-components methods (SSDMC), have the advantage of reducing the number of required standards, but remain limited to the verification of a single plant (Gao et al, 2009; Liang et al, 2013; Hou et al, 2019). These SSDMC methods address the limitations encountered with quality controls (QC) restricted to a single marker per herbal drug. In the food supplement market, which is not submitted to specific QC, illegal additions of potentially dangerous pure substances were observed in herbal preparations (SkalickaWozniak et al, 2017; Kee et al, 2018)

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