Using widely targeted metabolomics profiling to explore differences in constituents of three Bletilla species

Abstract

Bletilla striata has been used in traditional Chinese medicine for thousands of years to treat a variety of health diseases. Currently, metabolic causes of differences in medicinal values are unknown, due to the lack of a large-scale and comprehensive investigation of metabolites in Bletilla species. In order to gain a better understanding of the major chemical constituents responsible for the medicinal value, this study aimed to explore the metabolomic differences among three Bletilla species (Bletilla striata: Bs, Bletilla ochracea: Bo and Bletilla formosana: Bf). There were 258 different metabolites between 'Bo' and 'Bf', the contents of 109 metabolites had higher abundance, while 149 metabolites showed less accumulation. There were 165 different metabolites between the 'Bs' and 'Bf', content of 72 metabolites was increased and content of 93 metabolites was decreased. There were 239 different metabolites between the 'Bs' and 'Bo', content of 145 metabolites was increased and content of 94 metabolites was decreased. In the Bo_vs_Bf, Bs_vs_Bf and Bs_vs_Bo groups, the major differential categories were flavonoids, phenolic acids, organic acids and alkaloids. Moreover, the differential metabolites were clustered into clear and distinct profiles via K-means analysis. In addition, the major differential categories were flavonoids, phenolic acids, organic acids and alkaloids. The 'Flavonoid biosynthesis' (ko00941) and 'Phenylalanine metabolism' (ko00360) pathways were significantly enriched in Bo_vs_Bf, Bs_vs_Bf and Bs_vs_Bo comparisons. These results clarify the metabolomics in different Bletilla species, as well as providing basis for the phamaceutical value of novel species of Bletilla.

Keywords: Bletilla formosana; Bletilla ochracea; Bletilla striata; Chinese traditional medicine; Metabolites.

Fig. 1

Phenotype of Bletilla striata, Bletilla ochracea and Bletilla formosana. (A) Bletilla striata, (B) Bletilla formosana, (C) Bletilla ochracea.

Fig. 2

Total ion current (TIC) chromatograms and multiple reaction monitoring (MRM) metabolite detection multi-peak diagram (XIC of multi-substance extraction), abscess is Retention time (Rt) of metabolite detection, ordinate is ion current intensity of ion detection (cps, count per second) obtained from Bletilla samples. (A. sample in negative mode, B. sample in positive mode).

Fig. 3

The PCA and heatmap clustering between ‘Bs’, ‘Bo’ and ‘Bf’. (A) PCA analysis of all metabolites identified from ‘Bs’, ‘Bo’ and ‘Bf’. (B) Heatmap clustering showing correlation among ‘Bs’, ‘Bo’ and ‘Bf’. The color indicates the level of accumulation of each metabolite, from low (green) to high (red).

Fig. 4

Differential metabolite analyses in Bo_vs_Bf, Bs_vs_Bf and Bs_vs_Bo comparisons (A–C): Venn diagram showing the overlapping and species-specific metabolites in Bo_vs_Bf, Bs_vs_Bf and Bs_vs_Bo comparisons. (D–F): Volcano plots for differentially accumulated metabolite levels between Bo_vs_Bf, Bs_vs_Bf and Bs_vs_Bo comparisons, red dots represent upregulated differentially accumulated metabolites, green dots represent downregulated differentially accumulated metabolites. (G–I): The heatmap of differentially accumulated metabolite between Bo_vs_Bf, Bs_vs_Bf and Bs_vs_Bo comparisons. Red color represented upregulated differentially accumulated metabolites, green color represented downregulated differentially accumulated metabolites.

Fig. 5

K-means analysis of all differential metabolites and pathway enrichment of cluster 1, 2, 3, 4, 5 and 6. (A) K-means clustering analysis based on differential metabolites from all comparison groups. (B) Subclass 1, 2, 3, 4, 5 and 6 were enriched by KEGG.