Metabolomics Analysis Reveals the Differences Between Bupleurum chinense DC. and Bupleurum scorzonerifolium Willd

Front Plant Sci. 2022 Jul 13:13:933849. doi: 10.3389/fpls.2022.933849. eCollection 2022.

Abstract

Bupleurum chinense DC. and Bupleurum scorzonerifolium Willd. are two varieties of Bupleuri Radix in Chinese Pharmacopoeia 2020. The clinical efficacy of the two bupleurum species is different. The difference in clinical efficacy is closely related to the composition of plant metabolites. In order to analyze the difference in metabolites, we used liquid chromatography coupled with mass spectrometry (LC-MS) for untargeted metabolome and gas chromatography coupled with mass spectrometry (GC-MS) for widely targeted metabolome to detect the roots (R), stems (S), leaves (L), and flowers (F) of two varieties, and detected 1,818 metabolites in 25 classes. We performed a statistical analysis of metabolites. Differential metabolites were screened by fold-change and variable importance in the projection values of the OPLS-DA model, and significant differences were found among different groups. The content of active components (triterpenoid saponins) was found to be high in the BcR group than in the BsR group. Other pharmacological metabolites were significantly different. By Kyoto Encyclopedia of Genes and Genomes annotation and enrichment analysis, we found that differential metabolites of the aboveground parts mainly concentrated in monoterpenoid biosynthesis, while the differential metabolites of the root mainly concentrated in sesquiterpenoid and triterpenoid biosynthesis. Differences in metabolic networks may indirectly affect the metabolic profile of Bc and Bs, leading to differences in clinical efficacy. Our study provides a scientific basis for subsequent biosynthesis pathway and related bioactivity research, and provides a reference for developing non-medicinal parts and guiding the clinical application of Bupleuri Radix.

Keywords: Bupleurum chinense DC.; Bupleurum scorzonerifolium Willd.; differential metabolites; saikosaponins; statistical analysis.