Geographical variation in metabolite profiles and bioactivity of Thesium chinense Turcz. revealed by UPLC-Q-TOF-MS-based metabolomics

Abstract

Introduction: This study aims to investigate the impact of geographical origin on the metabolite composition and bioactivity of Thesium chinense Turcz. (TCT), a member of the Apiaceae family renowned for its wide range of pharmacological properties, including antioxidant, antimicrobial, and anti-inflammatory effects. In this study, we investigated the whole plants of TCT from different regions in China, aiming to explore the geographical variation of TCT.

Methods: A non-targeted metabolomics approach was employed using ultra-high-performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were utilized to identify and differentiate the metabolite profiles. We investigated the bioactivity, antioxidant activity, total flavonoid content (TFC), and the content of characteristic compounds from TCT sourced from different regions. This aims to further explore the metabolic differences and quality characteristics of TCT from various origins.

Results: PCA and PLS-DA analyses indicated that samples from different origins could be clearly distinguished. The analysis revealed 54 differential metabolites, predominantly flavonoids and alkaloids. KEGG pathway analysis indicated significant variations in the biosynthesis pathways of flavonoids and flavanols among the samples. TCT from Anhui province exhibited the highest TFC and strongest antioxidant and anti-inflammatory activities, while samples from Jilin province showed the lowest.

Discussion: A strong correlation was observed between metabolite content and geographical origins, suggesting that the bioactivity of TCT is significantly influenced by its provenance. Additionally, the antioxidant and anti-inflammatory activities of TCT were validated, showing a strong predictive relationship with TFC. This research highlights the potential of metabolomics in discerning the subtleties of plant metabolomes, contributing to the advancement of traditional Chinese medicine and its integration into modern healthcare practices.

Keywords: Thesium chinense Turcz.; UPLC-Q-TOF-MS/MS; anti-inflammatory; antioxidant; geographical variations; non-targeted metabolomics; total flavonoid content.

Figure 1

Metabolite profiling of TCT samples from different geographical origins. (A) Basepeak diagrams of 15 batches of TCT samples and QC samples. (B) Heatmap of mass spectral data from TCT samples across seven regions. The heatmap, with distinct colors representing samples from different geographical origins.

Figure 2

Multivariate analysis of TCT samples from various geographical regions. (A) PCA score plot of TCT samples in POS and NEG ion modes. (B) Hierarchical clustering dendrogram of TCT samples in POS and NEG ion modes, based on Euclidean distance and Ward’s clustering algorithm. (C) PLS-DA score plot of TCT samples, demonstrating clear differentiation among samples from seven regions. (D) Permutation test results (n=200) for the PLS-DA model.

Figure 3

Heatmap of the 54 differential metabolites from fifteen batches of TCT samples.

Figure 4

Enriched metabolic pathways of different TCT samples from various origins.

Figure 5

Regional variation in antioxidant and anti-inflammatory activities of TCT correlates with TFC. (A) DPPH radical scavenging activity of TCT extracts from different regions. (B) Hydroxyl radical scavenging activity of TCT extracts from different regions. (C) The expression levels of mRNA for inflammatory markers (IL-6, IL-1β, and TNF-α) in LPS-induced inflammatory response assays. *p < 0.05, **p < 0.01 M vs. CON; #p < 0.05, ##p < 0.01 different treatment group vs. M; (D) TFC of TCT extracts from different regions.

Figure 6

HPLC analysis of characteristic chemical components in TCT. (A) HPLC chromatograms of the four major components (Afzelin, Kaempferol, Astragalin, and Rutin) in TCT. (B) Chromatographic profiles of TCT samples from different regions.

Figure 7

Overlapping targets of characteristic chemical components in TCT and their enrichment analysis. (A) Venn diagram illustrating the overlapping targets of four characteristic chemical components (Afzelin, Kaempferol, Rutin, and Astragalin) in TCT. The numbers within the overlapping regions indicate the shared targets among the components. (B) Enrichment analysis of the overlapping targets using the DAVID Knowledgebase platform. The size of the bubbles represents the count of genes, and the color gradient indicates the significance level (p-value).