UHPLC-Q-Orbitrap-MS-Based Metabolomics Reveals Chemical Variations of Two Types of Rhizomes of Polygonatum sibiricum

Abstract

The rhizomes of Polygonatum sibiricum are commonly consumed as food and also used as medicine. However, the metabolic profile of P. sibiricum has not been fully revealed yet. Recently, we developed a novel evergreen species of P. sibiricum. The objectives of this study were to compare the metabolic profiles of two types of P. sibiricum, i.e., the newly developed evergreen type (Gtype) and a wide-type (Wtype), by using UHPLC-Q-Orbitrap-MS-based untargeted metabolomics approach. A total of 263 and 258 compounds in the positive and negative modes of the mass spectra were tentatively identified. Distinctively different metabolomic profiles of these two types of P. sibiricum were also revealed by principal component analysis (PCA) and principal coordinates analysis (PCoA). Furthermore, by using partial least squares discriminant analysis (PLS-DA) modeling, it was found that, as compared with Wtype, Gtype samples had significantly higher content of oxyberberine, proliferin, alpinetin, and grandisin. On the other hand, 15 compounds, including herniarin, kaempferol 7-neohesperidoside, benzyl beta-primeveroside, vanillic acid, biochanin A, neoschaftoside, benzyl gentiobioside, cornuside, hydroxytyrosol-glucuronide, apigenin-pentosyl-glucoside, obacunone, 13-alpha-(21)-epoxyeurycomanone, vulgarin, digitonin, and 3-formylindole, were discovered to have higher abundance in Wtype samples. These distinguishing metabolites suggest the different beneficial health potentials and flavor attributes of the two types of P. sibiricum rhizomes.

Influence of cultivation substrate on antioxidant activities and triterpenoid profiles of the fruiting body of Ganoderma lucidum

“…Untargeted metabolomic methodologies, involving high-resolution mass spectrometry and chemometric tools, have been effectively employed to analyze the chemical composition of diverse botanical specimens, such as tea leaves ( 26 ) and rhizomes of Polygonatum sibiricum ( 27 ). In this study, an untargeted metabolomic approach utilizing UHPLC-Q-Orbitrap-MS was adopted to comprehensively profile the chemical constituents of ten samples of G. lucidum fruiting bodies.…”

Metabolome and transcriptome signatures shed light on the anti-obesity effect of Polygonatum sibiricum

“…Untargeted metabolomics aims to identify the vast majority of metabolites in the specimen, and has been widely used in plant metabolite profiling to explore the mechanisms causing particular phenotypes. By using untargeted metabolomics, a recent report identified that a number of differentially accumulated metabolites account for the different beneficial health potentials and flavor of two types of P. sibiricum rhizomes ( Luo et al., 2022 ). Besides, combining this approach with high-throughput transcriptome profiling could not only provide detailed description of the gene-to-metabolite network, but also enable the identification of novel genes involved in the regulation of metabolic features.…”

“…Previous studies have reported diverse pharmacological effects of P. sibiricum , such as anti-aging, anti-diabetic, anti- inflammatory, and anti-tumor effects ( Zhao et al., 2018 ). These beneficial effects could be attributed to the diverse biologically-active compounds accumulated in the rhizome, such as polysaccharides, alkaloids, saponins, and phenolics ( Luo et al., 2022 ). For example, polysaccharides derived from P. sibiricum were shown to significantly alleviate several memory and cognitive impairments as well as aging characteristics in rats fed with the aging reagent D-galactose ( Zheng, 2020 ).…”

Carbon and nitrogen metabolism affects kentucky bluegrass rhizome expansion

“…Metabolomics is widely applied for better understanding phenotypic variation [ 27 , 28 ], investigating nutritive values [ 29 ], and stress resistance [ 30 , 31 ]. Luo et al [ 32 ] reported 19 significantly different metabolites causing flavor attributes of the two types of P. sibiricum rhizomes. Masson et al [ 33 ] successfully utilized metabolomics to discriminate 22 Iris rhizomes, and tentatively identified 32 metabolite discriminating markers to distinguish origins of these plants.…”