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Plant metabolomics: a new strategy and tool for quality evaluation of Chinese medicinal materials

Chinese Medicine volume 17, Article number: 45 (2022) Cite this article

Abstract

The present quality control method of Chinese medicinal materials (CMM) has obvious deficiency, which cannot be compatible with the multi-target and multi-component characteristics and production process of CMM. Plant metabolomics with a huge impetus to comprehensively characterize the metabolites and clarify the complexity and integrity of CMM, has been widely used in the research of CMM. This article comprehensively reviewed the application of plant metabolomics in the quality control of CMM. It introduced the concept, technique, and application examples, discussed the prospects, limitations, improvements of plant metabolomics. MS and NMR, as important techniques for plant metabolomics, are mainly highlighted in the case references. The purpose of this article is to clarify the advantage of plants metabolomics for promoting the optimization of the CMM quality control system and proposing a system approach to realize the overall quality control of CMM based on plant metabolomics combined with multidisciplinary method.

Introduction

Chinese medicinal materials (CMM) from plant sources are plants with preventive and therapeutic effects in diseases. China has more than 10,000 kinds of medicinal plants, about 87% of the total number of CMM resources. Different therapeutically active ingredients, from plant sources, have long been used in China. Some CMM with dual-purpose of drug and food, such as peach, jujube and plum, have been recorded as early as 2600 years ago. The Chinese pharmacopoeia (ChP) (2020 edition) has documented 499 categories of CMM from plant sources. In recent years, especially in fighting against COVID-19, CMM has played a significant role, such as forsythia, honeysuckle and ephedra in Lianhua Qingwen capsules (granules) [1, 2]. In so that, CMM attracted a growing amount of attention as the choice of clinical treatment and the source of new drug discovery. A controlled quality evaluation standard is a national strategy for the development of scientific industry, the modernization and internationalization of CMM, as well as the indispensable guarantee of safety and effectiveness in clinical treatment [3, 4].

The quality of CMM is closely related to their metabolites. CMM biosynthetic secondary metabolites with changeable structure and diverse activities are usually the pharmacodynamic basis of CMM. During the production, these metabolites are prone to interference and produce changes, affecting the quality of CMM [5, 6]. It is difficult to achieve the scientific evaluation of the quality of CMM by current quality evaluation mode which is mainly consisted of determination of chemical markers and the fingerprint evaluation system. The former takes the single component as the evaluation mark, which cannot comprehensively represent the components of CMM [7], while the latter focuses on the similarity between samples and is therefore difficult to evaluate the differences [8, 9] (Fig. 1).

Fig. 1
figure 1

Comparison of existing CMM quality control system and metabolomics methods

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Plant metabolomics is well suited for the quality control of CMM due to it can conduct a comprehensive detection of the metabolites of living organisms. It can be used to qualitatively and quantitatively analyze all small molecular metabolites of plants with different species, genotypes and ecological types, and find the difference between samples [10]. At present, plant metabolomic has been mainly applied to the source identification, authenticity identification, geoherbalism analysis, processing method evaluation and other quality control links of CMM [11, 12]. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) are important analytic techniques, widely used in metabolomics studies. We introduce the principles, the technical composition of plant metabolomics, and collect its application in the quality evaluation of CMM in recent years. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) are important detection techniques of plant metabolomics, their application were highlighted in the paper. Moreover, advantages and disadvantages of current analytical strategies in the innovations of CMM quality were discussed, and a systems strategy to substantially improve the performance of current strategies towards a new-horizon solution to systems-level quality evaluation of CMM was proposed.

The concept and major technology of plant metabolomics

The concept of plant metabolomics

Plant Metabolomics, a branch of metabolomics, is designed to study the overall changes in a large number of metabolites in plant samples and then conduct deep data mining and bioinformation analysis. Metabolites, components of defense systems developed by plants in response to pathogen attacks and other environmental stresses, are an important source of many natural pharmacological activity products which are often designated as specific biological activities associated with their biochemical structures [5, 6].

The main techniques of plant metabolomics

Complete plant metabolomics research process includes experimental design, sample collection, sample processing, sample preparation, detection and analysis, data processing, and metabolic pathway analysis/metabolic network analysis [13]. Analytical techniques and data processing techniques are the two important parts of metabolomics and metabolomics strategy is based on three main techniques: NMR, MS and chromatographic techniques [11, 12]. The common techniques applied to the studies of plant metabolomics are described below. Additionally, MS and NMR are the most advanced detection technology in recent years, with great application potential, and we mainly discuss their advantages and disadvantages.

Separation technology

Chromatography is mainly applied to the separation of metabolites GC is mostly used in the study of CMM rich in volatile oils, such as Cyperi Rhizoma, Myristica fragrans Houtt, and Rhizoma Chuanxiong [14,15,16]. High performance liquid chromatography (HPLC) and ultra performance liquid chromatography (UPLC) are appropriate for the separation and analysis of substances with high boiling points, macromolecules, strong polarity, and poor thermal stability [17,18,19,20]. CE achieves a breakthrough in analytical chemistry from microrise to nanorise, making a single cell or even single molecule analysis possible [21, 22]. CE is particularly suitable for the analysis of (highly) polarity and charged metabolites such as amino acids, nucleotides, small organic acids and phosphoric sugars [23,24,25,26,27].

Detection technology

The detection techniques of plant metabolomics mainly include UV, IR, MS, NMR. MS is characterised by extreme sensitivity and can provide qualitative and quantitative information according to the molecular weight [28,29,30]. Electrospray ionization (DESI) can achieve open environmental detection real-time analysis, which is more suitable for the analysis of drugs and endogenous small molecules. Orbitrap analyzer, time-of-flight (TOF) analyzer, and fourier transform ion cyclotron resonance (FTICR) analyzer for high resolution MS, enables the determination of fine structure [31]. The disadvantages of MS include inaccurate identification of unknown compounds, quantitative analysis depending on the reference substance, and the need of purity of the mixture and prior separation. MS is often coupled with a separation technique, such as liquid chromatography (LC), gas chromatography (GC) or capillary electrophoresis (CE) [32].

NMR, especially 1H-NMR, 1C-NMR and 31P-NMR, in plant metabolomics, commonly used in identification of species and growth years, metabolic pathways research, pharmacodynamics study, and methodology evaluation of CMM. NMR shows the absolute advantage in the identification for unknown compounds, which is of value in new compound discovery [33]. The sample preparation of NMR is simple, 1H high resolution magic angle rotation (HRMAS) can even be used for direct sample analysis [34]. In addition, sample detection of NMR is non-destructive and can be associated with downstream detection to achieve high-throughput determination [35]. However, even if new probes such as cryogenically cooled probes and microcoil probes appear, it is difficult to surpass the MS in sensitivity. In recent years, some studies have shown that the small high-temperature superconducting coils and ultra poltechnology can improve NMR sensitivity, but not widely used [36].

In addition, ultraviolet spectrum (UV) as a fundamental analysis technique has been widely used in the research of CMM [37, 38]. In recent years, infrared (IR) was applied to the study of enzyme activity, grease and fat, species difference, harvesting time, and source of CMM [39,40,41,42,43].

Data processing techniques

The pattern recognition technology is mainly for data processing, including unsupervised analysis and supervised analysis [44,45,46,47]. Principal components analysis (PCA) can clearly demonstrates repeatability within data group and differences between data groups [49], describing the data in a minimum dimensions but guarantee reproducibility. Partial least squares discriminative analysis (PLS-DA) and orthogonal projections to latent structures (OPLS-DA) reduce the data dimensionality and analyse them with a regression model [50]. Compared to PCA, PLS-DA/OPLS-DA establishes the model between metabolic expression and grouping relations, which better obtains intergroup difference information and predicts the grouping of samples [51]. In a practical application, several statistical methods are often combined according to the data characteristics and research requirement. Necessarily, these detection and analysis techniques depend on existing software and databases. (Fig. 2).

Fig. 2
figure 2

Process, technology and methods of plant metabolomics: A The process of plant metabolomics. B Contents included in the data processing [111,112,113]. C The classification of multivariate statistical methods and figure of PCA and OPLS-DA [114,115,116,117,118]

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The application of plant metabolomics in quality evaluation of Chinese medicinal materials

Unlike chemosynthetic drugs, CMM is a complex system composed of multiple chemical components. It is characterized by diverse chemical compositions, diverse structures and huge content differences [51, 52]. In addition, the species and contents of chemical components are susceptible to be influenced by factors such as germplasm resources, growth conditions, cultivation and processing methods, etc. [53,54,55]. MS-and NMR-based metabolomic techniques can be used to monitor key indicators of the production link of CMM by analyzing changes in the type and content of metabolites. Below we mainly discuss the application of MS-and MNR-based metabolomics techniques in five production links of CMM: sources, planting, harvesting, processing and market sales (Fig. 3).

Fig. 3
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The application of plant metabolomics in the quality control of CMM

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Herbal source

The types and contents of the active metabolites in CMM are highly variable depending on the species, parts of the plant, cultivated geographic region, and planting period involved.

Species identification

Species genetically determine the difference in types and contents of plant metabolites, leading to differences in clinical effects. The current ChP stipulates that the original plant of licorice herbs is Glycyrrhiza Uralensis, Glycyrrhiza inflata or Glycyrrhiza glabra. The chemical composition and efficacy of the three kinds of licorice herbs differ significantly, but the mixing use of same generic plants often occurs frequently. Fukuda et al. used direct analysis in real time (DART)-MS to detect the metabolites of several licorice species, the peak at m/z 339 was mainly derived from [M + H]+ of licochalcone A, which is present in Glycyrrhiza inflata and is hardly detected in Glycyrrhiza glabra and Glycyrrhiza Uralensis. Therefore, licochalcone A can serve as a candidate biomarker to distinguish these three licorice species, which has great implications for ensuring the clinical efficacy of licorice [56]. In one study, in vitro activity experiments and NMR based metabolomic were combined to comprehensively measure the activity differences of Artemisia afra and Artemisia annua, two antimalarial drugs come from Africa and China. The antiplasmodial activity of Artemisia afra and Artemisia annua extracts were tested for activity against Plasmodiam falciparum 3D7, and the activity was only found in polar extracts of Artemisia annua. 1D- and 2D-NMR spectroscopy identified 24 semi-polar components including three novel phenylpropane for this species: caffeic acid, chlorogenic acid and 3,5-dicaffeoyl quinic acid, which can be seen as the biomarkers of this species. Combined with the in vitro experiments, metabolomics allows for a more comprehensive evaluation of the quality of CMM based on the compounds and activity [57]. The combination of metabolomics and molecular techniques shows the unique advantages of species identification. GC and TOF–MS combined with DNA molecular markers was applied to distinguish Astragalus mongolica and Astragalus membranaceus. In a result, three markers and eight metabolites were identified as candidate DNA and metabolic markers [58]. 1H-NMR combined with DNA barcoding technique ssuccessfully demonstrated that different genetic variation and chemical constituents existed among 3 different Seabuckthorns, Hippopahe gyantsensis., Hippopahe Neurocarpa and Hippopahe tibetana [59]. The combination of molecular techniques and MS and/or NMR based metabolomics reveals the nature of metabolite changes at the genetic level, which is conducive to the exploration of metabolic rules and functional compound synthesis using molecular technology.

Habitat identification

The variety, quantity and quality of CMM resources are restricted by the natural conditions of the cultivated geographic region. The habitat of ginseng has an important impact on the type and number of ginsenosides which are often used as biomarkers to evaluate the quality of Panax ginseng. Song et al. successfully identified the difference between Korean white ginseng (KWG) and Chinese white ginseng (CWG) using UPLC-Q-TOF–MS and OPLS-DA. Ginsenoside Rf and notoginsenoside R3 isomer in KWG can be used as biomarkers to make a distinction with the CWG which is rich in ginsenoside Ro and chikusetsusaponin IVa [60]. Dae et al. distinguished Panax ginseng grown in Korea, China and Japan by using rapid resolution liquid chromatography (RRLC)-QTOF/MS combined with PCA and PLS-DA. In the PCA score plot, ginseng samples from the three regions were significantly separated, revealing the obvious effect of the planting region on the metabolites [61]. The LC–MS based metabolomics technology has been widely used in the identification of Panax ginseng from different habitat, providing a powerful method to ensure the quality of valuable CMM. Astragalus membranaceus and Paeonia albiflora with different origins in Korea and China were analysed by 1H NMR and inductively coupled plasma atomic emission spectro/inductively coupled plasma-mass spectrometry (ICP-AES/ICP-MS). The results indicated that the complementary multielement and metabolomic data were more suitable for determining the geographical origin than the use of each individual data set alone [62]. HPLC coupled with Q-TOF–MS was used to analyze metabolites from dandelions (Taraxacum mongolicum) from four different geographical regions in China, namely Gansu, Henan, Shanxi, and Jiangsu. OPLS-DAD was applied to analysis metabolite and MBRoleto was used to identify potential metabolic pathways. This study found that the differential metabolites of dandelion in these four regions were mostly phenolic, and the biosynthesis of phenylpropanoids and flavonoids were main metabolic pathways involved. Environmental stress generates an enhancement of these metabolic pathways, causing elevated metabolite concentrations, so the samples from Gansu has higher quality relatly. This study not only analyze the metabolite differences between samples, but also conducted the exploration of metabolic pathways to speculate on the association between climate factors and metabolites, which can be used to guide production [63].

Herbal planting

The higher market demand and the sharp decrease of wild herbal resources encourage the development of herbal planting, which can ease the current contradiction between medicinal resources supply and demand. The problems such as evaluating and comparing the quality of the cultivated and wild, residues of pesticide and herbicides, and excessive use of chemical fertilizers have aroused widespread concern.

Cultivated CMM quality evaluation

Although artificially cultivated CMM are booming, cultivated products and wild products often differ in the content of active compounds, which may affect the clinical efficacy of CMM. Agarwood, an aromatic medicinal materials mainly formed from species of Aquilaria, is mainly rich in 2-(2-phenylethyl) chromones. Based on characterization of the 2-(2-phenylethyl) chromones via ultra-performance liquid chromatography coupled with electrospray ionization mass spectrometry (UPLC-ESI-QTOF-MS), 14 biomarkers were found from wild and cultivated agarwood. Unfortunately, some chromone isomers were difficult to distinguish clearly due to the most of the compounds have the same molecular formula and similar fragment ions, which requires more sensitive detection methods [64]. Cultivated Panax quinquefolius, a very common analogue of wild products, is hard to distinguish in the traditional way due to complex ingredients. 1H NMR-PCA was applied to study 37 batches of sample from 15 pharmacy stores over 5 provinces in China, which built a model which excises the most abundant metabolites of Panax quinquefolius extract from the NMR spectra before PCA, and correlated the differences between Panax quinquefolius types to the less abundant metabolites. It is the first time for the 1H NMR used in Panax quinquefolius and large sample size studies, which provided a new method for other CMM study [65]. According to the growth environment and the cultivation method, ginseng can be divided into three types: cultivated ginseng (CG), mountain-wild ginseng (MWG), and mountain-cultivated ginseng (MCG). The ChP also classified ginseng into CG and MCG groups, and there was no a viable way to distinguish them before. Xu et al. identified 40 ginsenosides in MCG and CG using a plant metabolomics method consisted of UPLC-QTOF-MS/MS and multivariate statistical methods. This is the first observed chemical composition differences between MCG and CG, providing theoretical guidance for the subsequent cultivation of high-quality ginseng [66]. The root of wild Salvia miltiorrhiza is red owing to the periderm is rich in tanshinones while the root in cultivated fields showed orange. In a study, metabolome and transcriptome analyses were integrated to analyze the differences of metabolites between two Danshen (Salvia miltiorrhiza) in different colors. UPLC/Q-TOf–MS based metabolomic detected 40 lipophilic components and 7 of them including tanshinone IIA and tanshinone I were obviously decreased in the orange one. The results of transcriptome analysis indicated that decreases in the content of dehydrogenated furanring tanshinones such as tanshinone IIA resulted in phenotypic change and quality degradation of Danshen. These changes may be related to environmental deterioration and biological stress. The combination of the metabolome and transcriptome can excavate the factors affecting the quality from the source while conducting the quality evaluation of CMM, which has the guiding significance of the actual production [67].

Heavy metals and agricultural residues detection

Heavy metals and agricultural residues are the safety control index of cultivation and the hotspot of quality control of CMM. The accumulation of secondary metabolites in many CMM is related to external environmental stress, such as heavy metals, drought, waterlogging. The research and analysis of stress resistance of CMM in extreme environments are the research trend of future CMM cultivation, which is the research basis for improving the quality of cultivated products. Luo et al. used GC–MS technique, combined with PCA and OPLS-DA, to explore metabolite differences in Sebum Alfredii root exudates in different environments. The result displayed that 12 compounds as biomarkers to distinguish different treatment conditions, indicating that Sedum Alfredii could tolerate super-rich heavy metal cadmium by regulating their secretion. With the deterioration of the ecological environment, the research on tolerating extreme environmental CMM will inevitably become a hotspot, and these studies will bring the cultivation of CMM to a more promising direction [68]. Moreover, the use of pesticides, including herbicides, fungicides, insecticides, and plant growth regulators, could also affect the secondary metabolism of CMM. UPLC/Q-Orbitrap-Full MS scan method combined with multivariate statistical analysis were employed to evaluate effect of two insecticides, imidacloprid and compound flonicamid and acetamiprid, on the overall composition of Lonicerae Japonicae Flos (LJF). The experiment obtained and characterized 29 metabolic markers including iridoids and carried out relative quantitative assay to monitor their changes at different times of flowering developments. These decreased iridoids should be considered as important markers in the holistic quality assessment of LJF. This is the first time to study the impact of pesticides on metabolites of LJF, which provides a basis of pesticide indicators for the quality control of bulk cultivated CMM [69]. The use of pesticides is a necessary link for the mass production of CMM. With the application of plant metabolomics technology, we can find pesticides friendly to plant growth or with less influence, which will promote quality improvement of CMM cultivation (Table 1).

Table 1 Application of plant metabolomics in the quality evaluation of CMM
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Herbal harvesting

The growth age and harvest season directly affect the quality, yield and harvest rate of CMM. Moreover, the types and content of active metabolites in different parts of CMM are different, which determines the clinical selection.

Growth age identification

Growth age has a significant impact on the contents of metabolites in plants. Ginseng is easily affected by differences in growth years, and 4, 5 and 6 years old Ginseng is in high demand in the market due to have higher levels of active substances. UPLC-Q-TOF–MS combined with PCA and HCA methods was developed to evaluate various ginsenosides in Panax ginseng roots between 1 and 6 years old. The results show that 30 ginsenosides profiling was carried out and 14 ginsenosides in P. ginseng roots cultivated for 4, 5, and 6 years can be clearly identified [70]. MALDI-MSI is a powerful tool to localize the components distribution and its cross sections showed the distribution of ginsenosides from P. ginseng roots cultivated for 4, 5, and 6 years, which provides the indicator of growth age for the quality control of ginseng [32]. The active compounds of Polygala tenufolia is apt to be affected by its growth age, and the market generally considers Polygala tenufolia aged 2–3 years of better quality. In Chp, Tenuifolin, Polygalaxanthone III, (3-sinapoyl) fructofuranosyl-(6-sinapoyl) glucopyranoside were measured as the detection index for quality of Polygala tenufolia. UPLC-QTOF-MS and NMR based metabolomic technology has found that 1 year-old Polygala tenufolia was significantly different from those of 2 and 3 years old due to differences between primary and secondary metabolites, and a portion of those index compounds will decrease accordingly as age grows. This result shows that those primary or secondary metabolites, with marked changes, are perhaps more appropriate quality markers [71]. Growth age is an important problem in CMM, which determines its quality. The UPLC-Q-TOF–MS and NMR methods were applied to the study of growth age of CMM, which effectively solves the problem of distinguishing different years.

Harvest season identification

Harvesting in different seasons affects the quality of CMM, because of the formation and enrichment of compounds are affected by seasonal factors. Lee et al. used 1H NMR metabolomics to explore the relationship between ginseng metabolites and climate. The results show that the growing season from March to October is of great importance for the synthesis of ginseng metabolites. Especially, it has been shown that March to May may be the optimal synthesis time for ginsenosides, arginine, steroids, fatty acids and uracil diphosphate glucose-sugar, a process accompanied by accelerating sucrose catabolism that may be associated with climate change, such as sunshine time and rainfall [72]. One study was designed to investigate the appropriate harvest time of Piper Sarmentosum fruits to maintain consistency of efficacy and batch reproducibility. Plant metabolomics was applied to detect, quantify and catalogue the time related metabolic processes of the samples. The Fourier transform infrared spectroscopy (FTIR) spectral data were analyzed by stoichiometry and PCA, and identification, classification and differentiation (ICD) evaluation were obtained using PerkinElmer software. These results indicated that the metabolites in the samples varied in all the batches. FTIR fingerprint profiles of the samples in combination with chemometrics is an effective tool. It is concluded from the results of this study that plant metabolomics in combination with chemometrics is an effective tool of fast and easy assessment of similarity of different samples and may be applied as an analytical tool in quality evaluation [73]. Pericarpium Citri Reticulatae (PCR) and Pericarpium Citri Reticulatae Viride (PCRV), two different maturation stages of the same plant source, have different clinical effects. Based the HPLC fingerprints, heuristic evolving latent projection (HELP) method was applied to obtain two-dimensional data and chemometrics. The growth footprint of Tangerine peels was given by PCA score chart, which show that July may be the best harvest time for PCRV, and November and December are better for PCR [74].

Different parts identification

Due to the differences in the distribution of the active compounds, different plant parts have been regarded as different drugs. The Chp also makes detailed requirements for the medicinal parts of medicinal plants. The succulent stem is the medicinal site of Cistanche deserticola, and few studies have focused on other parts. In one study, an UPLC-PA-QTOF-MS based metabolomic method was applied to identify the chemical constituents and screen the antioxidant activity profiles of 6 different parts from cultivated Cistanche deserticola. An obvious difference was observed between the chemical profiles and content distribution of phenylethanoid glycosides (PhGs). The results showed a significant decreasing trend from the bottom to the top of cultivated Cistanche deserticola and the highest content in the stems. This combination of metabolomics and antioxidant activity detection promotes the link between composition and activity, which will characterize the quality of CMM by a more comprehensive way and provide a guarantee for clinical efficacy [75]. Chang et al. utilized UHPLC-QTOF/MS combined with PCA and OPLS-DA analysis methods to identify different parts of ginseng, showing significant differences in chemical composition. A total of 81 major metabolites were detected from 4 different sites of the ginseng, and 70 metabolites was finally identified or preliminarily inferred. The plant metabolomics analysis of different parts of ginseng is beneficial to clarify the differences in pharmacodynamic substances of different parts of ginseng for treating different diseases and this method is fast, accurate, and reliable [76]. UPLC-ESI-HRMSn based metabolomic identified 36 biomarkers from different parts of Gentiana crasicaulis. The result shows that iridoids are mainly enriched in the root, while flavonoids and triterpenes were mainly concentrated in the aerial part. Through the detection of different compounds, different plant sites can be clearly distinguished [77]. Additionally, 1HNMR combined with PCA and SIMCA was successfully used to identify the differences on compounds from 6 sites of mulberry leaf [78]. In recent years, a chemical profiling of seven parts of Clausena lansium was built by a non‐targeted UPLC‐Q‐Orbitrap‐MS metabolomic method. 364 metabolites including 62 potential biomarkers were selected by the multivariate statistical analysis. Heat map and KEGG annotation showed a significant enrichment of the “Flavone and flavonol synthesis” and “Isoquinoline alkaloid biosynthesis” pathway. This investigation could provide a foundation for the isolation and identification of new constituents from Clausena lansium and further clarified the biosynthetic pathway of differential metabolites among various tissues of Clausena lansium, which can be used for reference by other studies of CMM [79].

Herbal processing

Processing of drugs necessarily leads to changes in metabolites that affect efficacy. The processing of CMM is called paozhi, an indispensable production process. The traditional processing methods are based primarily on the theory and the character of CMM which includes various methods, like parching, roasting, moistening, burning. The purpose of processing is eliminating or reducing the toxicity of drugs, strengthening the curative effect, facilitating the preparation or storage, and making the drugs pure.

Metabolite change detection

Changes in drug composition caused by processing will alter their clinical effects. UPLC-QTOF-MS based metabolomic paired with novel informatics UNIFI platform have been undertaken for evaluating the changes in chemical components of Astragali Radix after processing. PCA scores and OPLS-DAS maps indicate that there are some changes in compound composition of Astragali Radix processed by heating and auxiliary materials. For example, the content of Calycosin-7-O-β-Dglucopyranoside varies significantly after processing, while several unknown compounds are newly generated after processing, which can applied as the biomarkers. The present study provided a basis of chemical components for revealing connotation of different processing techniques on Astragali Radix [80]. Similarly, different processed and raw products of Radix Polygala were studied by NMR and UPLC. PCA analysis results showed that the metabolic composition of Radix Polygala was obviously different. Moreover, there are also differences in metabolomics between Radix Polygala honey products and licorice products [81]. Ginsenoside, a biomarker of raw and steamed Panax notoginseng, also was discoveried by UPLC/TOF–MS [82]. Through plant metabolomics, changes in metabolites of CMM before and after processing can be more intuitively found, and the relationship between processing and efficacy can be more directly clarified if combined with corresponding pharmacodynamic studies. The purpose of processing toxic drugs is often to reduce toxicity and increase efficiency, which can also be tested by plant metabolomics. Pre-use process is necessary due to long-term usage of Rheum palmatum may bring liver and kidney injury. In one study, UPLC/Q-TOF–MS based metabolomic was applied to study differential chemical composition of R. palmatum samples before and after processing. The results showed that Emodin-8-O-glucoside and gallic acid-3-O-glucoside can distinguish raw and processed products as biomarkers [83]. The detoxification effects of processing of Fuzi (the lateral root of Aconitum carmichaelii Debx) were evaluated via RPLC-Q-TOF/MS coupled with PCA, which found 19 key biomarkers associated with detoxification [84]. In addition, an UPLC-MS method was established to analyze the chemical composition before and after the processing of Euphorbia pekinensis root. The change of chemical composition caused by processing is obviously altered, which is supposed to be the material basis of the detoxification effect [85]. The reduction of toxic for CMM is an important target for processing, and mining attenuated related metabolites of toxic CMM with metabolomics provides criteria for clinical drug safety.

Processing methods evaluation

The evaluation of processing methods is advantageous to standardize the production process and excavate suitable and efficient processing methods of CMM. The processing method of ginseng is mainly steaming. Chan et al. utilized a UHPLC/TOF–MS based metabolomic strategy to discriminate the difference of steaming Panax notoginseng [86]. Moreover, Kim et al. compared the metabolite changes of steamed ginseng at 100 °C, 110 °C and 120 °C, and found that 120 °C is more conducive to its efficacy. At the same time, it was also noted that steaming caused the formation of new ginsenoside [87]. The quality of the drying method will affect the content of the active compounds in CMM. Chromatography-mass spectrometry coupled with targeted and untargeted analyses was applied to study on the relationship between drying method and chemical concentration of Notopterygium franchetii. 30 differentiated compounds are detected in seven drying methods. The results suggested that hot air drying is the best processing method, with minimal chemical changes at the lowest cost, and the largest chemical change caused by shadow drying [88]. Dai et al. used NMR and LC-DAD-MS method to study the effect of stress caused by water loss on metabolites in root of Salvia miltiorrhiza. Using freeze drying as reference, the effects of sun drying and air drying methods were compared. The results showed that water stress resulted in a significant change in the profile of metabolites in salvia miltiorrhiza, and content of tanshinone was significantly increased by air-drying [89]. Plant metabolomics has been widely used to evaluat the drying methods of CMM, but less research in other processing methods, which is also the need to be expanded later, such as stir-frying with bran, stir-frying with honey, steaming with wine and so on. Furthermore, evaluation of the postharvest peeling process of Astragalus root using 1HNMR and UPLC-MS has been demonstrated that there was a clear distinction between exfoliated and undelayed root of astragalus. Peeling can cause significant loss of several primary metabolites, while the content of xylem-related compounds were higher [90]. Postharvest processing often determines the quality of herbal medicines and rhizomes plants, but an full analysis using several complementary methods has not been developed. Plant metabolomics coupled with chemometric analysis can be applied to ensure proper postharvest processing by offering information of primary and secondary metabolites.

Herbal marketing

In drug sales, adulteration, staining, excessive sulfur fumigation will affect the changes of small molecular compounds in CMM, and finally reflect the difference of product quality. Plant metabolomics can be used to detect the indexes and evaluate the quality problems in the sales of CMM.

In recent years, UPLC-MS based metabolomics were applied to analysis 35 commercial products of Hydrastis canadensis. The obtained results demonstrate the potential for untargeted metabolomics to discriminate between multiple unknown products and predict possible adulteration [91]. While Panax ginseng adulteration is rampant, Nguyen et al. used 1H NMR combined with OPLS-DA to distinguish 60 Panax ginseng samples from Korea and China. 21 mixed samples of numerous Korea/China ratios were tested, showing satisfactory separation according to the proportion of mixing [92]. It is noteworthy that sulfur fumigation bleaching is also a common adulteration method of ginseng in the market which requires strict control. UPLC-QTOF-MS/MS-based metabolomics approach was developed to evaluate the holistic qualities of commercial white ginseng (WG) and red ginseng (RG) from herbal markets. 43 compounds including 3 sulfur-containing compounds were identified within 24 min suggested the inconsistencies in the quality of commercial WG and RG due to heat treatment and sulfur fumigation [93]. UPLC-ESI-QTOF-MS/MS-based non-targeted metabolomics and UPLC-ESI-QTRAP-MS/MS-based widely targeted metabolomics identified five characteristic sulfur fumigation markers of Trichosanthis Radix (TR) samples. At the same time, the different sulfur fumigation degree of TR samples was tested by chemical transformation analysis and sulfur dioxide residue test. Furthermore, 20 non-sulfur labeled metabolites were tested to evaluate the quality of samples of TR before and after sulfur fumigation. Sulfur fumigation of CMM may cause harm to human body or affect clinical effects and needs to be strictly controlled. The plant metabolome has provided technical support for the detection of sulfur-fumigated drugs by providing information about sulfur fumigation markers [94]. The scarcity of wild Rhodiola officinalis resources, coupled with strong market demand, led to the emergence of artificial cultivation of low quality varieties as adulterated drugs, seriously affected the CMM market. Booker et al. combined with NMR and high performance thin layer chromatography (HPTLC) technology to study the chemical composition differences of five varieties of Rhodiola officinalis in the market, proved that salidroside and tyrosine were the biomarkers, and established the method of content identification in mixed components to provide a methodological basis for distinguishing adulterated medicinal materials [95]. Metabolomics approaches have been applied to evaluate the quality and quantity of characteristic molecules in three different Rhodiola species, Rhodiola rosea, Rhodiola kirilowii and Rhodiola crenulata. Main molecules were identified by one and two-dimensional NMR metabolomics and quantified by HPLC–UV assay, and then OPLS-DA reveals specific patterns in the metabolite profiles. Therefore, plant metabolomics can be utilized to study the chemical diversity of different species, identify unique metabolites and identify adulterated products [96]. Degree of Angelica acutiloba root was evaluated by GC TOF–MS combined with PCA as the good, the moderate and the bad. Among the 22 metabolites identified, phosphoric acid, proline, malic acid, and citric acid showed an association with high-quality roots, while fructose and glucose are mostly found in bad roots. Moreover, PCA shows that malic acid is the most differentiated for medium quality roots [97]. The market price of different levels of CMM varies greatly. The adulteration of CMM occurs repeatedly due to the evaluation methods of different levels are not unified. Plant metabolomics can be applied as a evaluation methodology to provide the theoretical support for sales based on quality (Fig. 4).

Fig. 4
figure 4

Schematic diagram of experimental steps of plant metabolomic in quality evaluation of CMM

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Concluding and future remarks

CMM is integrity due to complex composition, and various components interact to produce unique efficacy. Each link of CMM production may cause changes in metabolites and affect the efficacy. A quality control system to respectively evaluate production link from source, planting, harvesting, processing and marketing is urgently needed. Plant metabolomics technique can be used to analyze the composition and variation of metabolites from the view of the whole, which coincides with the “holistic” and “dynamic” views of CMM. In recent year, plant metabolomics have been applied for metabolites analysis to identify species, habitats, growth age, harvest season, parts and adulteration of CMM, which show an enormously potential for quality evaluation [98]. Although plant metabolomics develops rapidly, it is still in the early stage of development. There are still some limitations of plant metabolomics due to the diversity and complication of metabolites, and performance flaw of the instruments [99, 100]. First, the metabolites of CMM are complex and are vulnerable to impact by many factors, including heredity, temperature, humidity, light, chemical substances, etc., which poses challenges to metabolomic analysis [101]. Second, the application conditions of numerous technologies have limitations, where there is not an untargeted approach can profile all the metabolites of CMM. Third, the existing databases are not perfect because many studies on metabolism of medicinal plants remain blank, which limits the analysis and identification of chemical components. Therefore, breaking technical barriers and building more complete databases for comprehensively profiling metabolites are the primary problem of plant metabolomics. Systematically excavating and processing mass data is still the technical bottleneck of current plant metabolomics, which need a high coverage data acquisition strategy to min information of low abundance metabolites. Moreover, establishing a new approach for sample acquisition and preparation is beneficial to improve the experimental reproducibility and stability.

The application of plant metabolomics in the field of quality control will have more development space with the improvement of new systems and multidisciplinary integration applications. Establishing a comprehensive quality control system needs to improve the relationship of environment-metabolites and metabolites-activity. Combining multi-omics technology and bioinformatics technology, the influence of external factors on plant metabolism can be explored from the gene level and a comprehensive metabolic network can be established [102,103,104,105]. Based on the differential metabolites found by plant metabolomics, the network pharmacology can be used to predict the disease and target, and finally discover the therapeutic material basis and the predict the active mechanism of CMM. The relationship between different metabolites and their activity can be clarified by plant metabolomics combined with in vivo and in vitro efficacy experiments, then active markers of CMM can be discovered [106,107,108,109,110]. Ultimately, an associative network between environment-metabolite-efficacy will be established for a standardized cultivation-harvesting-processing system and a quality evaluation method of CMM. This system will substantially promote the production and application of CMM towards scientification and modernization. (Fig. 5).

Fig. 5
figure 5

Schematic diagram of multi-disciplinary application based on plant metabolomics to establish a comprehensive CMM quality control system

Full size image

Availability of data and materials

Not applicable.

Abbreviations

CMM:

Chinese Medicinal materials

ChP:

Chinese pharmacopoeia

NMR:

Nuclear magnetic resonance

MS:

Mass spectrometry

LC:

Liquid chromatography

GC:

Gas chromatography

CE:

Capillary electrophoresis

HPLC:

High performance liquid chromatography

UPLC:

Ultra performance liquid chromatography

UV:

Ultraviolet spectrum

IR:

Infrared

EI:

Electron impact ion source

CI:

Chemical ionization

ESI:

Electrospray ionization

APCI:

Atmospheric pressure chemical ionization

MALDI:

Matrix-assisted laser desorption ionization

DESI:

Desorption electrospray ionization

Q:

Quadrupole

IT:

Ion trap

QQQ:

Triple quadrupole

TOF:

Time-of-flight

FTICR:

Fourier transform ion cyclotron resonance

HRMAS:

1H high resolution magic angle rotation

PCA:

Principal components analysis

PLS-DA:

Partial least squares discriminative analysis

OPLS-DA:

Orthogonal projections to latent structures

DART:

Direct Analysis in Real Time

KWG:

Korean white ginseng

CWG:

Chinese white ginseng

RRLC:

Rapid resolution liquid chromatography

ICP-AES:

Plasma atomic emission spectro

ICP-MS:

Inductively coupled plasma-mass spectrometry

FTIR:

Fourier Transform infrared spectroscopy

ICD:

Identification, classification and differentiation

PCR:

Pericarpium Citri Reticulatae

PCRV:

Pericarpium Citri Reticulatae Viride

HELP:

Heuristic evolving latent projection

WG:

White ginseng

RG:

Red ginseng

TR:

T richosanthis Radix

HPTLC:

High performance thin layer chromatography

Q-markers:

Quality marker

References

  1. Wang Y, Fan X, Qu H, Gao X, Cheng Y. Strategies and techniques for multi-component drug design from medicinal herbs and traditional Chinese medicine. Curr Top Med Chem. 2012;12(12):1356–62.

    CAS  PubMed  Article  Google Scholar 

  2. Wang C, Sun S, Ding X. The therapeutic effects of traditional chinese medicine on COVID-19: a narrative review. Int J Clin Pharm. 2020;89:56.

    Google Scholar 

  3. Liu ZJ. Innovation and development of quality control and evaluation pattern of CMM. Clin J Chin Med. 2014;89:567.

    Google Scholar 

  4. Kunle OF, Egharevba HO, Ahmadu PO. Standardization of herbal medicines-A review. Int J Biodiver Conserv. 2012;4(3):101–12.

    Article  Google Scholar 

  5. Guy C, Kopka J, Moritz T. Plant metabolomics coming of age. Physiol Plant. 2010;132(2):113–6.

    Article  CAS  Google Scholar 

  6. Chunmei LU, Cui X. Research Progress in TCM Quality Control and Toxicity Evaluation Based on Metabolomics Technology. Chin J Inform Tradit Chin Med. 2019;8:78.

    Google Scholar 

  7. Heyman HM, Meyer JJM. NMR-based metabolomics as a quality control tool for herbal products. S Afr J Bot. 2012;82:21–32.

    CAS  Article  Google Scholar 

  8. Tu XM, Wu H, Wu KY. Research and Application on Traditional Chinese Medicine Fingerprint. Strait Pharma J. 2016;9:7.

    Google Scholar 

  9. Xie PS. A Feasible Strategy for Applying Chromatography Fingerprint to Assess Quality of Chinese Herbal Medicine. Tradit Chin Drug Res Clin Pharmacol. 2001;98:6799.

    Google Scholar 

  10. Hegeman AD. Plant metabolomics—meeting the analytical challenges of comprehensive metabolite analysis. Brief Funct Genomics. 2010;9(2):139–48.

    CAS  PubMed  Article  Google Scholar 

  11. Barding GA, Salditos R, Larive CK. Quantitative NMR for bioanalysis and metabolomics. Anal Bioanal Chem. 2012;404(4):1165–79.

    CAS  PubMed  Article  Google Scholar 

  12. Dettmer K, Aronov PA, Hammock BD. Mass spectrometry-based metabolomics. Mass Spectrom Rev. 2007;26(1):51–78.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. Kim HK, Verpoorte R. Sample preparation for plant metabolomics. Phytochem Anal. 2010;21(1):4–13.

    CAS  PubMed  Article  Google Scholar 

  14. Kanani H, Chrysanthopoulos PK, Klapa MI. Standardizing GC-MS metabolomics. J Chromatogr B. 2008;871(2):191–201.

    CAS  Article  Google Scholar 

  15. Covington BC, Mclean JA, Bachmann BO. Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites. Nat Prod Rep. 2016;6:78.

    Google Scholar 

  16. Ren JL, Zhang AH, Ling K, Wang XJ. Advances in mass spectrometry-based metabolomics for investigation of metabolites. RSC Adv. 2018;8(40):22335–50.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. Yin Q, Wang P, Zhang A, Sun H, Wu X, Wang X. Ultra-performance LC-ESI/quadrupole-TOF MS for rapid analysis of chemical constituents of Shaoyao-Gancao decoction. J Sep Sci. 2014;36(7):1238–46.

    Article  CAS  Google Scholar 

  18. Shi Q, Zhang A, Zhang T, Hui S, Wang X. Dissect new mechanistic insights for geniposide efficacy on the hepatoprotection using multiomics approach. Oncotarget. 2017;8:65.

    Google Scholar 

  19. Klimczak I, Gliszczyńska-Świgło A. Comparison of UPLC and HPLC methods for determination of vitamin C. Food Chem. 2015;2015(75):100–5.

    Article  CAS  Google Scholar 

  20. Nováková L, Matysová L, Solich P. Advantages of application of UPLC in pharmaceutical analysis - ScienceDirect. Talanta. 2006;68(3):908–18.

    PubMed  Article  CAS  Google Scholar 

  21. Ramautar R, Somsen GW, Jong G. CE-MS in metabolomics. Electrophoresis. 2010;30(1):276–91.

    Article  CAS  Google Scholar 

  22. Ramautar R, Somsen GW, Jong GD. CE-MS for metabolomics: developments and applications in the period 2012–2014. Electrophoresis. 2015;34(1):86–98.

    Article  CAS  Google Scholar 

  23. Ramautar R. CE-MS in metabolomics: Status quo and the way forward. Bioanalysis. 2016;8:5.

    Google Scholar 

  24. Fu X, Liu Y, Li W, Pang N, Nie H, Liu H, Cai Z. Analysis of aristolochic acids by CE-MS with carboxymethyl chitosan-coated capillary. Electrophoresis. 2010;30(10):1783–9.

    Article  CAS  Google Scholar 

  25. Liu JX, Zhang YW, Yuan F, Chen HX, Zhang XX. Differential detection of Rhizoma coptidisby capillary electrophoresis electrospray ionization mass spectrometry with a nanospray interface. Electrophoresis. 2014;35(21–22):3258–63.

    CAS  PubMed  Article  Google Scholar 

  26. Liu Y, Yu B, Pang N, Liao Y, Liu H. Applications of LC-MS and CE-MS in the analysis of traditional Chinese medicine. Sciencepaper Online. 2009;89:6.

    Google Scholar 

  27. Sun Y, Sun G, Yu J. Quality control of traditional Chinese medicines by the capillary electrophoresis fingerprint and capillary electrophoresis-mass spectrometry. Chin J Chromatogr. 2008;26(2):160.

    CAS  Article  Google Scholar 

  28. Sun H, Liu C, Zhang A. Rapid discovery and global characterization of multiple constituents from Kai-Xin-San using an integrated MS E data acquisition mode strategy based on ultra-performance liquid chromatography coupled to electrospray ionization/quadrupole-time-of-flight mass spectrometry. Anal Methods. 2015;7(1):279–86.

    Article  Google Scholar 

  29. Sun H, Wang H, Zhang A. Berberine ameliorates nonbacterial prostatitis via multi-target metabolic network regulation. OMICS. 2015;19(3):186.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. Wang XJ, Ren JL, Zhang AH, Sun H, Yan GL, Han Y, Liu L. Novel applications of mass spectrometry-based metabolomics in herbal medicines and its active ingredients: current evidence. Mass Spectrom Rev. 2019;38:380–402.

    CAS  PubMed  Article  Google Scholar 

  31. Ren J, Zhang A, Kong LA, Han YA, Yan G, Sun HA, Wang XJ. Analytical strategies for the discovery and validation of quality-markers of traditional Chinese medicine. Phytomedicine. 2020;67:153165.

    CAS  PubMed  Article  Google Scholar 

  32. Hu C, Xu G. Metabolomics and traditional Chinese medicine. TrAC, Trends Anal Chem. 2014;61:207–14.

    CAS  Article  Google Scholar 

  33. Markley JL, Brüschweiler R, Edison AS, Eghbalnia HR, Powers R, Raftery D, Wishart DS. The future of NMR-based metabolomics. Curr Opin Biotechnol. 2017;43:34–40.

    CAS  PubMed  Article  Google Scholar 

  34. Beckonert O, Coen M, Keun HC, Wang Y, Ebbels TM, Holmes E, Lindon JC, Nicholson JK. High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues. Nat Protoc. 2010;5(6):1019–32.

    CAS  PubMed  Article  Google Scholar 

  35. Alessia V, Veronica G, Gaia M, Cristina L, Takis PG, Leonardo T, Paola T, Claudio L. High-throughput metabolomics by 1D NMR. Angew Chem Int Ed. 2018;8:243.

    Google Scholar 

  36. Ramaswamy V, Hooker JW, Withers RS, Nast RE, Brey WW, Edison AS. Development of a 13C-optimized 15-mm high temperature superconducting NMR probe. J Magn Reson. 2013;235:58–65.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. Rahman A, Fayyadh MZ. Uv-Visible Metabolomics Approach For The Determination Of Selected Adulterants .In: Claimed Premixed Coffee, 2015.

  38. Ke L, Yan Z, Yang J, Liang X. Simple quality assessment approach for herbal extracts using high performance liquid chromatography-UV based metabolomics platform. J Chromatogr A. 2010;1217(8):1414–8.

    Article  CAS  Google Scholar 

  39. Saleh M, Siddiqui MJ, So'Ad SZM, Khatib A. FT-IR- based metabolomics approach to characterize the α-glucosidase inhibitory activity of Salak fruit; 2017.

  40. Vlachos N, Skopelitis Y, Psaroudaki M, Konstantinidou V, Chatzilazarou A, Tegou E. Applications of Fourier transform-infrared spectroscopy to edible oils. Anal Chim Acta. 2006;573–574:459–65.

    PubMed  Article  CAS  Google Scholar 

  41. Kokalj M, Kolar J, Trafela T, Kreft S. Differences among Epilobium and Hypericum species revealed by four IR spectroscopy modes: transmission. KBr tablet, diffuse reflectance and ATR, Phytochem Anal. 2011;22:541–6.

    CAS  PubMed  Google Scholar 

  42. Yusof NA, Isha A, Ismail IS. IR-metabolomics aprroach in detecting the possible changes of metabolites due to different harvesting ages and times. Open Conf Proc J. 2013;4(1):234–234.

    Article  Google Scholar 

  43. Consonni R, Tsimidou M, Ordoudi S, Cagliani L. On the Traceability of Commercial Saffron Samples Using 1H-NMR and FT-IR Metabolomics. Molecules. 2016;21(3):286.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  44. Wishart DS. Metabolomics: applications to food science and nutrition research. Trends Food Sci Technol. 2008;19(9):482–93.

    CAS  Article  Google Scholar 

  45. Leenders J, Frédérich M, Tullio PD. Nuclear magnetic resonance: A key metabolomics platform in the drug discovery process. Drug Discovery Today Technol. 2015;13:39–46.

    Article  Google Scholar 

  46. Ning Z, Lu C, Zhang Y, Zhao S, Liu B, Xu X, Liu Y. Application of Plant Metabonomics in Quality Assessment for Large-Scale Production of Traditional Chinese Medicine. Planta Med. 2013;79(11):897–908.

    CAS  PubMed  Article  Google Scholar 

  47. Pan W, Wu M, Zheng Z, Guo L, Qiu B. Rapid authentication of Pseudostellaria heterophylla (Taizishen) from different regions by near infrared spectroscopy combined with chemometric methods. J Food Sci. 2020;85:11.

    Google Scholar 

  48. Xia JM, Wu XJ, Yuan YJ. Integration of wavelet transform with PCA and ANN for metabolomics data-mining. Metabolomics. 2007;3(4):531–7.

    CAS  Article  Google Scholar 

  49. Kalivodová A, Hron K, Filzmoser P, Najdekr L, Janečková H, Adam T. PLS-DA for compositional data with application to metabolomics. J Chemom. 2015;29(1):21–8.

    Article  CAS  Google Scholar 

  50. Triba MN, Moyec LL, Amathieu R. PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters. Mol BioSyst. 2014;11(1):13–9.

    PubMed  Article  CAS  Google Scholar 

  51. Chen YB, Tong XF, Ren J, Yu CQ, Cui YL. Current research trends in traditional Chinese medicine formula: a bibliometric review from 2000 to 2016. Evid Based Complement Alternat Med. 2019;2019:3961395.

    PubMed  PubMed Central  Google Scholar 

  52. Liu CX. Recognizing healthy development of Chinese medicine industry from resourcesquality-quality markers of Chinese medicine. Chin Tradition Herb Drugs. 2016;47:3149–54.

    Google Scholar 

  53. Zhang T, Bai G, Han Y, Xu J, Gong S, Li Y, Zhang H, Liu C. The method of quality marker research and quality evaluation of traditional Chinese medicine based on drug properties and effffect characteristics. Phytomedicine. 2018;44:204–11.

    CAS  PubMed  Article  Google Scholar 

  54. Sun H, Zhang A, Zou D. Metabolomics coupled with pattern recognition and pathway analysis on potential biomarkers in liver injury and hepatoprotective effffects of yinchenhao. Appl Biochem Biotechnol. 2014;173(4):857–69.

    CAS  PubMed  Article  Google Scholar 

  55. Yang X, Jia S, Zhan W. Study on Quality Influencing Factor and Preparation of CMM Hard Capsules. Western J Tradit Chin Med. 2013;98:5682.

    Google Scholar 

  56. Fukuda E, Baba M, Iwasaki N, Uesawa Y, Arifuku K, Kamoe O, Tsubono K, Okada Y. Identification of Glycyrrhiza species by direct analysis in real time mass spectrometry. Nat Prod Commun. 2010;5(11):1755–8.

    CAS  PubMed  Google Scholar 

  57. Ning QL, Cao M, Frédérich M, Choi YH, Verpoorte R. FVD Kooy, Metabolomic investigation of the ethnopharmacological use of Artemisia afra with NMR spectroscopy and multivariate data analysis. J Ethnopharmacol. 2010;128(1):230–5.

    Article  Google Scholar 

  58. Qi X. Use of the Metabolomics Approach to Characterize Chinese Medicinal Material Huangqi. Mol Plant. 2012;5(2):376–86.

    PubMed  Article  CAS  Google Scholar 

  59. Yue L, Liu C, Tan E, Gang F, Yi Z. Genetic and chemical discrimination of traditional Tibetan medicine seabuckthorn based on DNA barcode and 1H-NMR metabolic method. China J Chin Materia Med. 2016;41(4):578–85.

    Google Scholar 

  60. Song HH, Moon JY, Ryu HW, Noh BS, Kim JH, Lee HK, Oh SR. Discrimination of white ginseng origins using multivariate statistical analysis of data sets. J Ginseng Res. 2014;38:3.

    Google Scholar 

  61. Dae-Young L, Jae K, Sabina S, Kyeong-Hwa S, Youn-Hyung L, Hyung-Jun N, Geum-Soog K, Yong-Bum K, Seung-Yu K, Nam-In B. Quality Evaluation of Panax ginseng Roots Using a Rapid Resolution LC-QTOF/MS-Based Metabolomics Approach. Molecules. 2013;18(12):14849–61.

    Article  CAS  Google Scholar 

  62. Shihao, H.U., Xinyue, Y.U., Ruiting, L.I., Xiaoxia, W.U., Yang, X., Han, Z., Huang, Y., Department, P. Analysis of the differential compounds in Astragalus membranaceus from different geographical origins by UFLC-IT-TOF/MS. J China Pharma Univer. 2019;89:78.

    Google Scholar 

  63. Zhang S, Li C, Gu W, Qiu R, Tian R. Metabolomics analysis of dandelions from different geographical regions in china. Phytochem Anayl. 2021;19:78.

    CAS  Google Scholar 

  64. Li Y, Sheng N, Wang L, Shijie L, Jiannan C, Xiaoping L. Analysis of 2-(2-phenylethyl) chromones by UPLC-ESI-QTOF-MS and multivariate statistical methods in wild and cultivated agarwood. Int J Mol Sci. 2016;17(5):771.

    PubMed Central  Article  CAS  Google Scholar 

  65. Zhao H, Xu J, Ghebrezadik H, Hylands PJ. Metabolomic quality control of commercial Asian ginseng, and cultivated and wild American ginseng using 1H NMR and multi-step PCA. J Pharm Biomed Anal. 2015;114:113–20.

    CAS  PubMed  Article  Google Scholar 

  66. Xu XF, Cheng XL, Lin QH, Li SS, Jia Z, Han T. Identification of mountain-cultivated ginseng and cultivated ginsengusing UPLC/oa-TOF MSE with a multivariate statistical sample-profiling strategy. J Ginseng Res. 2016;40(4):344–50.

    CAS  PubMed  Article  Google Scholar 

  67. Zhan Z, Fang W, Ma X, Chen T, Huang L. Metabolome and transcriptome analyses reveal quality change in the orange-rooted salvia miltiorrhiza (danshen) from cultivated field. Chin Med. 2019;14:1.

    Article  Google Scholar 

  68. Lu Q, Sun LN, Hu XM. Metabonomics Study on Root Exudates of Cadmium Hyperaccumulator Sedum Alfredii. Chin J Anal Chem. 2015;43(1):7–12.

    Article  Google Scholar 

  69. Pan HQ, Zhou H, Miao S, Guo DA, Shen JI. Plant metabolomics for studying the effect of two insecticides on comprehensive constituents of Lonicerae Japonicae Flos. Chin J Nat Med. 2021;19:1.

    Google Scholar 

  70. Kim N, Kim K, Choi BY, Lee DH, Shin YS, Bang KH, Cha SW, Lee JW, Choi HK, Jang DS. Metabolomic approach for age discrimination of Panax ginseng using UPLC-Q-TOF-MS. J Agric Food Chem. 2011;59:10435–41.

    CAS  PubMed  Article  Google Scholar 

  71. Xue Y, Li XW, Li ZY, Zeng ZP, Peng B. UPLC/Q-TOF MS and NMR plant metabolomics approach in studying the effect of growth year on the quality of Polygala tenuifolia. Acta pharmaceutica Sinica. 2015;50(3):340.

    CAS  PubMed  Google Scholar 

  72. Lee HJ, Jeong J, Alves AC, Han ST, Hong YS. Metabolomic understanding of intrinsic physiology in Panax ginseng during whole growing seasons. J Ginseng Research. 2019;43:4.

    Google Scholar 

  73. Hussain K, Ismail Z, Sadikun A, Ibrahim P. Evaluation of Metabolic Changes in Fruit of Piper Sarmentosum in Various Seasons by Metabolomics Using Fourier Transform Infrared (FTIR) Spectroscopy. Int J Pharm Clin Res. 2009;1(2):68–71.

    Google Scholar 

  74. Yi L, Yuan D, Liang Y, Xie P, Zhao Y. Fingerprinting alterations of secondary metabolites of tangerine peels during growth by HPLC–DAD and chemometric methods. Anal Chim Acta. 2009;649:43–51.

    CAS  PubMed  Article  Google Scholar 

  75. Wang X, Wang J, Guan H. Comparison of the Chemical Profiles and Antioxidant Activities of Different Parts of Cultivated Cistanche deserticola Using Ultra Performance Liquid Chromatography-Quadrupole Te-of-Flight Mass Spectrometry and a 1,1-Diphenyl-2-picrylhydrazyl-Based Assay. Molecules. 2017;22:11.

    Google Scholar 

  76. Chang XW, Li D, Wang T, Wu YC, Ye ZL. Application of metabolomics approach to study of different parts of Mountain Cultivated Ginseng using UHPLC-QTOF/MS. Acta Pharmaceutica Sinica. 2016;51(10):1609–15.

    PubMed  Google Scholar 

  77. Chen, J.K., Zeng, R., University, S.M. Application of metabolomics approach to study on chemical constituents in different parts of Gentiana crasicaulis based on UPLC-ESI-HRMS~n. Chin Tradit Herbal Drugs. 2018;49(10):2328–35.

    Google Scholar 

  78. Fukuda E, Yoshida M, Baba M, Uesawa Y, Ad AY. Application to classification of mulberry leaves using multivariate analysis of proton NMR metabolomic data. Nat Prod Commun. 2011;6(11):1621–5.

    CAS  PubMed  Google Scholar 

  79. Fan RY, Peng C, Zhang XX, Qiu DY, Mao GL, Lu YS, Zeng JW. A comparative UPLC-Q-Orbitrap-MS untargeted metabolomics investigation of different parts of Clausena lansium (Lour Skeels. Food Sci Nutr. 2020;8:5811–22.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  80. Liu PP, Shan GS, Zhang F, Zhang F, Chen JN, Jia TZ. Metabolomics analysis and rapid identification of changes in chemical ingredients in crude and processed Astragali Radix by UPLC-QTOF-MS combined with novel informatics UNIFI platform. Chin J Nat Med. 2018;16(09):82–8.

    Google Scholar 

  81. Meng Y, Peng WU, Zhang XL, Jiang HQ, Hui-Fen LI, Zhang QQ, Wang JX. Rapid Identification of Chemical Components in Raw and Processed Products of Polygalae Radix by HPLC-TOF/MS. Chin J Exp Tradit Med Formulae. 2015;34:5.

    Google Scholar 

  82. Sun BS, Xu MY, Zheng L, Wang YB, Sung CK. UPLC-Q-TOF-MS/MS Analysis for Steaming Times-dependent Profiling of Steamed Panax quinquefolius and Its Ginsenosides Transformations Induced by Repetitious Steaming. PubMed. 2012;36(3):277–90.

    CAS  Google Scholar 

  83. Wang Z, Wang D, Zheng SH. Ultra-performance liquid chromatography-quadrupole\time-of- flight mass spectrometry with multivariate statistical analysis for exploring potential chemical markers to distinguish between raw and processed Rheum palmatum. BMC Complement Altern Med. 2014;14(1):302.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  84. Sun B, Li Y, Zhang Q, Ma K, Gao Y, Yan X. Metabonomic study on the toxicity of Hei-Shun-Pian, the processed lateral root of Aconitum carmichaelii Debx (Ranunculaceae). J Ethnopharmacol. 2008;116(3):561–8.

    PubMed  Article  Google Scholar 

  85. Zeng Y, Hou PY, Chen XH. Investigation of Variations of Components in Euphorbia pekinensis Root and Its Processed Products Based on Plant Metabolomics. J Chin Med Mater. 2016;39(3):530–3.

    Google Scholar 

  86. Toh DF, New LS, Koh HL, Chan CY. Ultra-high performance liquid chromatography/time-of-flight mass spectrometry (UHPLC/TOFMS) for time-dependent profiling of raw and steamed Panax notoginseng. J Pharm Biomed Anal. 2010;52(1):43–50.

    CAS  PubMed  Article  Google Scholar 

  87. Shin JH, Park YJ, Kim W, Kim DO, Kim BY, Lee H, Baik MY. Change of Ginsenoside Profiles in Processed Ginseng by Drying, Steaming, and Puffing. J Microbiol Biotechnol. 2019: 29(2):222–229.

  88. Su X, Wu Y, Li Y, Huang Y, Liu Y, Luo P, Zhang Z. Molecules. 2019;24:17.

    Google Scholar 

  89. Dai H, Xiao C, Liu H, Tang H. Combined NMR and LC-MS analysis reveals the metabonomic changes in Salvia miltiorrhiza Bunge induced by water depletionJournal of Proteome Research. J Proteome Res. 2010;9(3):1460–75.

    CAS  PubMed  Article  Google Scholar 

  90. Jung JY, Jung Y, Kim JS, Ryu DH, Hwang GS. Assessment of Peeling of Astragalus Roots Using 1H NMR- and UPLC-MS-Based Metabolite Profiling. J Agric Food Chem. 2013;61(43):10398–407.

    CAS  PubMed  Article  Google Scholar 

  91. Wallace ED, Oberlies NH, Cech NB, Kellogg JJ. Detection of adulteration in Hydrastis canadensis (goldenseal) dietary supplements via untargeted mass spectrometry-based metabolomics. Food Chem Toxicol. 2018;120:439–47.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  92. Nguyen HT, Lee DK, Choi YG, Choi YG, Min JE, Yoon SJ, Yu YH, Lim J, Lee J, Kwon SW, Park JH. A 1H NMR-based metabolomics approach to evaluate the geographical authenticity of herbal medicine and its application in building a model effectively assessing the mixing proportion of intentional admixtures: a case study of Panax ginseng: metabolomics for the authenticity of herbal medicine, Journal of pharmaceutical and biomedical analysis. J Pharm Biomed Anal. 2016;124:120–8.

    CAS  PubMed  Article  Google Scholar 

  93. Zhang HM, Li SL, Zhang H, Wang Y, Zhao ZL, Chen SL. Holistic quality evaluation of commercial white and red ginseng using a UPLC-QTOF-MS/MS-based metabolomics approach. J Pharm Biomed Anal. 2012;62:258–73.

    CAS  PubMed  Article  Google Scholar 

  94. Kang C, Lv C, Yang J, Kang L, Guo L. A Practical Protocol for a Comprehensive Evaluation of Sulfur Fumigation of Trichosanthis Radix Based on Both Non-Targeted and Widely Targeted Metabolomics. Front Plant Sci. 2020;11:578086.

    PubMed  PubMed Central  Article  Google Scholar 

  95. Booker A, Zhai LX, Gkouva C, Li S, Michael H. From traditional resource to global commodities:a comparison of Rhodiola species using NMR spectroscopy-metabolomics and HPTLC. Front Pharmacol. 2016;7:254.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  96. Marchev AS, Koycheva IK, Aneva IY, Georgiev MI. Authenticity and quality evaluation of different Rhodiola species and commercial products based on NMR-spectroscopy and HPLC. Phytochem Analy. 2020;31:6.

    Google Scholar 

  97. Tianniam S, Tarachiwin L, Bamba T, Kobayashi A, Fukusaki E. Metabolic profiling of Angelica acutiloba roots utilizing gas chromatography-time-of-flight-mass spectrometry for quality assessment based on cultivation area and cultivar via multivariate pattern recognition. J Biosci Bioeng. 2008;105(6):655–9.

    CAS  PubMed  Article  Google Scholar 

  98. Gonulalan EM, Nemutlu E, Demirezer LO. A new perspective on evaluation of medicinal plant biological activities: The correlation between phytomics and matrix metalloproteinases activities of some medicinal plant. Saudi Pharmac J. 2019;27(3):446–52.

    Article  Google Scholar 

  99. Wolfender JL, Rudaz S, Hae Choi Y, Kim HK. Plant metabolomics: from holistic data to relevant biomarkers. Curr Med Chem. 2013;20(8):1056–90.

    CAS  PubMed  Google Scholar 

  100. Duan LX, Dai YT, Sun C, Chen SL. Metabolomics research of medicinal plants. China J Chin Materia Med. 2016;41(22):4090–5.

    Google Scholar 

  101. Ferry-Dumazet H, Gil L, Deborde C, et al. MeRy-B: a web knowledgebase for the storage, visualization, analysis and annotation of plant NMR metabolomic profiles. BMC Plant Biol. 2011;11:104.

    PubMed  PubMed Central  Article  Google Scholar 

  102. Zeng TX, Sun X, Miao YJ, Gu SJ, Tian LX, Zheng Y, Jiang Y, Zhang XK, Feng Z, Pei J, Huang LF. Integrating bioclimatic factors and secondary metabolism to predict the suitable producing area of plants with high specific metabolite content in a real-world environment - a case of Carthamus tinctorius L. Ind Crops Products. 2022;177:114545.

    Article  Google Scholar 

  103. Tokimatsu T, Sakurai N, Suzuki H, Shibata D. KaPPA-View: A Tool for Integrating Transcriptomic and Metabolomic Data on Plant Metabolic Pathway Maps. Biotechnol Agric For. 2006;57:209.

    Google Scholar 

  104. Acharya S, Hedda GV, Kankariya AJ, et al. Hairy roots of ‘dashmula’ plant Uraria picta as a promising alternative to its medicinally valued true roots-functional and metabolomic analysis. Plant Cell Tiss Organ Cult. 2021;145:533–44.

    CAS  Article  Google Scholar 

  105. Villiers F, et al. Exploring the plant response to cadmium exposure by transcriptomic, proteomic and metabolomic approaches: potentiality of high-throughput methods. In: Promises of Integrative Biology, 2012.

  106. Nkomo MM, Katerere DD, Vismer HH, et al. Fusarium inhibition by wild populations of the medicinal plant Salvia africana-lutea L linked to metabolomic profiling. BMC Complement Altern Med. 2014;14:99.

    PubMed  PubMed Central  Article  Google Scholar 

  107. Acharya S, Hedda GV, Kankariya AJ, et al. Hairy roots of ‘dashmula’ plant Uraria picta as a promising alternative to its medicinally valued true roots - functional and metabolomic analysis. Plant Cell Tiss Organ Cult. 2021;145:533–44.

    CAS  Article  Google Scholar 

  108. Pares RB, Alves DS, Alves LFA, et al. Acaricidal Activity of Annonaceae Plants for Dermanyssus gallinae (Acari: Dermanyssidae) and Metabolomic Profile by HPLC-MS/MS. Neotrop Entomol. 2021;50:662–72.

    CAS  PubMed  Article  Google Scholar 

  109. Prinsloo G, Vervoort J. Identifying anti-HSV compounds from unrelated plants using NMR and LC–MS metabolomic analysis. Metabolomics. 2018;14:134.

    PubMed  Article  CAS  Google Scholar 

  110. Niazian M, Sabbatini P. Traditional in vitro strategies for sustainable production of bioactive compounds and manipulation of metabolomic profile in medicinal, aromatic and ornamental plants. Planta. 2021;254:111.

    CAS  PubMed  Article  Google Scholar 

  111. Andreev VP, Rejtar T, Chen HS, Moskovets EV, Ivanov AR, Karger BL. A universal denoising and peak picking algorithm for LC-MS based on matched filtration in the chromatographic time domain. Anal Chem. 2003;75(22):6314–26.

    CAS  PubMed  Article  Google Scholar 

  112. Urban J, Štys D. Noise and Baseline Filtration in Mass Spectrometry. In: International Conference on Bioinformatics and Biomedical Engineering, 2015.

  113. Andreev, V.P., Rejtar, T., Karger, B.L., Matched Filtration with Experimental Noise Determination for Denoising, Peak Picking and Quantitation in LC-MS (MEND).

  114. Warmenhoven J, Coffey N, Liebl D, Harrison A, Hooker G. PCA of Waveforms and Functional PCA: A Primer for Biomechanics. J Biomech, 2020;89:12

  115. Uarrota VG, Moresco R, Coelho B, Nunes E, Peruch L, Neubert E, Rocha M, Maraschin M. Metabolomics combined with chemometric tools (PCA, HCA, PLS-DA and SVM) for screening cassava (Manihot esculenta Crantz) roots during postharvest physiological deterioration. Food Chem. 2014;161(6):67–78.

    CAS  PubMed  Article  Google Scholar 

  116. Rukundo IR, Danao M. Identifying Turmeric Powder by Source and Metanil Yellow Adulteration Levels Using Near-Infrared Spectra and PCA-SIMCA Modeling. J Food Protection. 2020;83:6.

    Article  Google Scholar 

  117. Boccard J, Rutledge DN. A consensus orthogonal partial least squares discriminant analysis (OPLS-DA) strategy for multiblock Omics data fusion. Anal Chim Acta. 2013;769:30–9.

    CAS  PubMed  Article  Google Scholar 

  118. Bylesj M, Eriksson D, Kusano M, Moritz T, Trygg J. Data integration in plant biology: the O2PLS method for combined modeling of transcript and metabolite data. Plant J. 2010;52(6):1181–91.

    Article  CAS  Google Scholar 

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Acknowledgements

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Funding

The ability establishment of sustainable use for valuable Chinese medicine resources (No. 2060302-2002-05), and the National Natural Science Foundation of China (No. 81872965).

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Authors and Affiliations

  1. Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine (Peking Union Medical College), Ministry of Education, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100193, China

    Qi Xiao, Xinlu Mu, Jiushi Liu, Bin Li, Haitao Liu, Bengang Zhang & Peigen Xiao

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  1. Qi Xiao
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  2. Xinlu Mu
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  3. Jiushi Liu
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  4. Bin Li
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  5. Haitao Liu
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  6. Bengang Zhang
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  7. Peigen Xiao
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Contributions

QX: Investigation, Writing-original draft, Writing-review and editing; XLM: Visualization, Data Curation; JSL: Writing-review and editing, Funding acquisition; BL: Validation; HTL: Funding acquisition, Supervision, Conceptualization, Project administration; BGZ: Methodology; PGX: Writing-Review and Editing. All authors read and approved the final manuscript.

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Correspondence to Haitao Liu.

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Xiao, Q., Mu, X., Liu, J. et al. Plant metabolomics: a new strategy and tool for quality evaluation of Chinese medicinal materials. Chin Med 17, 45 (2022). https://doi.org/10.1186/s13020-022-00601-y

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  • DOI: https://doi.org/10.1186/s13020-022-00601-y

Keywords

  • Chinese medicinal materials
  • Plant metabolomics
  • Quality control
  • Biomarkers
  • Multivariate statistical analysis

Chinese Medicine

ISSN: 1749-8546

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