Comparative metabolite profiling and fingerprinting of medicinal licorice roots using a multiplex approach of GC–MS, LC–MS and 1D NMR techniques

Abstract

Glycyrrhiza glabra, commonly known as licorice, is a popular herbal supplement used for the treatment of chronic inflammatory conditions and possesses anticancer and antiviral activities. This species contains a plethora of phytochemicals including terpenoids, saponins, flavonoids, polyamines and polysaccharides. The full complement of bioactive compounds has yet to be elucidated, a step necessary in order to explain its medicinal use. There are over 30 species in the Glycyrrhiza genus world-wide, most of which have been little characterized in terms of phytochemical or pharmacological properties. Here, large scale multi-targeted metabolic profiling and fingerprinting techniques were utilized to help gain a broader insight into Glycyrrhiza species chemical composition. UV, MS and NMR spectra of extracted components were connected with NMR, MS, and multivariate analyses data from Glycyrrhiza glabra, Glycyrrhiza uralensis, Glycyrrhiza inflata and Glycyrrhiza echinata. Major peaks in ¹H NMR and MS spectra contributing to the discrimination among species were assigned as those of glycyrrhizin, 4-hydroxyphenyl acetic acid, and glycosidic conjugates of liquiritigenin/isoliquiritigenin. Primary metabolites profiling using GC–MS revealed the presence of cadaverine, an amino acid, exclusively found in G. inflata roots. Both LC–MS and NMR were found effective techniques in sample classification based on genetic and or geographical origin as revealed from derived PCA analysis.

Introduction

Licorice is the dried root of Glycyrrhiza glabra, a member of legumes endogenous to Asia and Southern Europe. Licorice is widely used as flavoring and sweetening agent, but has also been proposed for various clinical applications (Fiore et al., 2005). Traditions from ancient Assyrian, Egyptian, Chinese and Indian cultures have documented its extensive medicinal use as demulcent, expectorant and in ulcer healing (Armanini et al., 2002). Pharmacological effects of licorice including inhibition of gastric acid secretion, anti-inflammatory, antiviral and anti-atherogenic properties have been well verified (Fiore et al., 2008, Tanaka et al., 2008). An anti-carcinogenic effect of licorice has also recently been demonstrated (Csuk et al., 2010, Kobayashi et al., 2002, Lee et al., 2007). These biological effects are attributed to a variety of biologically active constituents: terpenoids, alkaloids, polysaccharides, polyamines, saponins and flavonoids (Fenwick et al., 1990, Simons et al., 2009, Zhang and Ye, 2009). The most important constituent of licorice is glycyrrhizin (G) reported in quantities of 3.63–13.06% from dried roots. G is a saponin of the pentacyclic triterpene derivative of the oleanane type (Maatooq et al., 2010). Following hydrolysis, it releases two molecules of D-glucuronic acid and the aglycone 18 β-glycyrrhetinic acid (GA), also called glycyrrhetic acid (Fig. 1). G exhibits potent hydrocortisone-like anti-inflammatory, antiulcer, antiviral and antihepatotoxic activities (Cinatl et al., 2003, Sasaki et al., 2002) whereas glycyrrhetinic acid is a potent antibiotic against ulcer causing Helicobacter pylori (Krausse et al., 2004). Also GA and its derivatives have cytotoxic and apoptosis inducing effects (Csuk et al., 2010). In addition to triterpene saponins, numerous polyphenols in quantities of 1–5% have been isolated from Glycyrrhiza species including phenolic acids, flavones, flavans, chalcones, and isoflavonoids (Vaya et al., 1997). The main phenolic compounds are liquiritin and liquiritigenin and their chalcone-type derivatives isoliquiritigenin and isoliquiritin (Fig. 1). Licorice polyphenols are mostly responsible for its antioxidant and antitumor activities (Wang and Nixon, 2001). Glabridin, an isoprenylated flavonoid, inhibits P450 enzyme activities in tumor cells whereas licochalcone A, another flavonoid, induces autophagy by suppression of Bcl-2 expression in prostate cancer cells (Tamir et al., 2000, Yo et al., 2009).

Despite licorice widespread medicinal and culinary uses, the authentication of ground licorice samples and standardization of its extracts poses a problem due to heterogeneity of the plant material and contamination or purposeful adulteration with other Glycyrrhiza species. Glycyrrhiza species can be differentiated based on the morphologic features of their aerial part, i.e. leaf and fruit morphologies, but not on their root morphology, even though that is the medicinal part. Chemical investigation in Glycyrrhiza has mostly focused on G. glabra and G. uralensis with little information on other species’ chemical composition (Montoro et al., 2011, Tanaka et al., 2010, Zhang and Ye, 2009).

Quality control analyses in licorice mostly target single compound examination, of which glycyrrhizin appears the most-frequently examined compound (Montoro et al., 2011, Ong and Len, 2003). Simultaneous LC–UV analysis of flavonoids such as liquiritin, isoliquiritigenin and liquiritigenin has also been recently employed for quality control assessment (Ma et al., 2005). Several specific compounds responsible for licorice pharmacological use have yet to be fully elucidated. Also, not all pharmacological properties could yet be attributed to defined phytoconstituents of licorice. With an increasing demand for accuracy and consistency of phytomedicine bioactivity, and the knowledge that a pharmacological effect may originate from more than one sole constituent, these targeted techniques become insufficient tools for standardization and request better means to profile plant extracts in a multi-targeted or even a holistic untargeted manner (Liang et al., 2010, Wang et al., 2004, Wolfender et al., 2010).

Metabolic profiling techniques are established to help us gain a broader insight into the biochemical composition of (living) organisms at a certain time in a semi-automated and essentially, untargeted manner. Metabolomic studies make use mostly of hyphenated techniques which rely on chromatographic separation of metabolites using either gas chromatography (GC) or liquid chromatography (LC) coupled to mass spectrometry (MS) to analyze complex mixtures of extracted metabolites. While nuclear magnetic resonance spectroscopy (NMR) metabolite fingerprinting approaches provide a valuable metabolite signature of complex plant extracts combined with unbiased quantitative accuracy, LC–MS metabolic profiling resolves individual chemical components into separate peaks, enhancing the opportunity to uncover novel or minor metabolites. The profiling of secondary metabolites of Arabidopsis thaliana into the metabolomics approach (Roepenack-Lahaye et al., 2004) preceded applications in food and medicinal plants that include authentication of medicinal ginger (Jiang et al., 2006) quality control analysis of chamomile flowers (Wang et al., 2004) green tea (Pongsuwan et al., 2007), Ginko biloba (Agnolet et al., 2010) and hops (Farag et al., 2012). Contemporary with our work, possible effects of the geographical location on G. glabra (Montoro et al., 2011) and G. uralensis (Tanaka et al., 2010) saponins and flavonoids composition using LC–MS was investigated.

The major goal of the current study was to further investigate licorice global bioactive secondary and primary metabolism in the context of both environmental and genetic diversity represented by four different Glycyrrhiza species (Glycyrrhiza glabra, Glycyrrhiza uralensis, Glycyrrhiza inflata and Glycyrrhiza echinata) from different localities (see Table 1) so as to set a framework for its quality control. The adopted approaches focused on evaluating the capability of developing fast and effective analytical methods for metabolomic fingerprinting of licorice extracts by direct analysis of extract solutions using ¹H NMR and 2D NMR techniques, ideally without any preliminary chromatographic separation step in parallel with chromatographic hyphenated GC and LC–MS techniques. Owing to the complexity of acquired data, as reflected in the complexity of spectral data, multivariate analyses e.g. hierarchical cluster analysis (HCA) and principal component analysis (PCA) were performed to ensure good analytical reliability and to define both similarities and differences.