2.1 Metabolomics profiling of four Angelica species

Widely targeted metabolomics provides a promising approach for the chemical screening of volatile metabolites, enabling the characterization of new metabolites in Angelica [12]. In this study, the root metabolomics data were generated to investigate the differences in volatile metabolites among four Angelica species. A total of 698 non-redundant volatile metabolites were qualified and quantified based on GC–MS (Table S1), with 616, 536, 576, and 545 metabolites detected in A. sinensis, A. dahurica, A. biserrata, A. keiskei, respectively. Of these, 391 metabolites were commonly detected in the roots of all four species (Fig. 1a).

Fig. 1

An overview of volatile metabolites among four Angelica species. a Venn diagram showing the number of common and specific metabolites in the four species. b PCA of volatile metabolites for the four species with three biological replicates. c Heatmap clustering of volatile metabolites identified from the four species. Volatile metabolite abundance was Z-score transformed. The color-coded scale grading from green to red corresponds to the content of volatile metabolites shifting from low to high

Principal component analysis (PCA) of the metabolome data, transformed with Hellinger transformation method, divided the samples into four distinct groups corresponding to the four species. The three biological replicates were clustered closely, indicating high reproducibility and reliability. The PCA plot showed that PC1 and PC2 explained 48.09% and 30.62% of the total variance, respectively. Of the four clusters, PC1 mainly differentiated A. sinensis from the other Angelica species, while PC2 primarily segregated A. dahurica from the other Angelica species (Fig. 1b).

The abundances of volatile metabolites were normalized using Z-score and subjected to hierarchical clustering analysis (Fig. 1c). The results showed that significant differences among the four species. Of these, the abundance of volatile metabolites in A. keiskei was the highest.

2.2 Identification of differential metabolites in four Angelica species

To explore the metabolite composition of the four species, 698 volatile metabolites were classified into 15 different categories, including terpenoids, ester, heterocyclic, aromatics and 11 others (Fig. 2). Terpenoids represented the largest proportion of all volatile metabolites across all four Angelica species, followed by heterocyclic compounds, eater, and aromatics. Notably, A. sinensis contained a relatively lower proportion of terpenoids (43.34%) compared to the other Angelica species, exhibiting a more balanced metabolite composition. In contrast, terpenoids amount accounted for over half of the total volatile metabolites in A. dahurica, A. biserrata, and A. keiskei, especially in A. dahurica, its proportion reached up to 75.64%.

Fig. 2

Classification and proportion of volatile metabolites detected in the four Angelica species. a A. biserrata, b A. dahurica, c A. keiskei, d A. sinensis

In addition, the relative abundance of each metabolite category in the four species were compared with Kruskal-Wallis test. Bonferroni-corrected p-values indicated significant difference in eight categories among the four species, including alcohol, aldehyde, aromatics, ester, heterocyclic compounds, hydrocarbons, ketone and terpenoids (Fig. 3, Figure S3; p-values were shown in the Table S2). Among these, terpenoids showed the most pronounced variation between species. In each of the eight categories, A. sinensis and A. keiskei differed significantly from at least one other species. These findings suggest that while the overall qualitative composition of volatile metabolites is similar, the individual components vary significantly between the Angelica species.

Fig. 3

Comparison for the relative abundance of seven categories (alcohol, aromatics, aldehyde, ester, heterocyclic compounds, ketone and terpenoids) with significant differences in the four Angelica species

In this study, hierarchical clustering analysis was conducted using Bray–Curtis’s dissimilarity distances to assess the composition and abundance of volatile metabolites across the four Angelica species. The resulting dendrogram (Fig. 4a) showed high correspondence with the phylogenetic tree (Fig. 4b) based on chloroplast sequences, indicating a correlation relationship between the volatile metabolites and the phylogenetic relationships.

Fig. 4

Hierarchical clustering based on the similarity of volatile metabolites (a) and phylogenetic tree of the four Angelica species and H. sibthorpioides (b). The chloroplast sequences above were available in GenBank of NCBI

2.3 Differential metabolites between A. sinensis and the three other Angelica species

To further identify the metabolites responsible for differences among the four Angelica species, significantly different accumulated metabolites between groups were screened by |Log2FC|≥ 1 and VIP ≥ 1. A. sinensis belongs to Sinodielsia clade in Angelica genus, that was phylogenetically distant from core Angelica group, including A. biserrata, A. dahurica, A. keiskei [2, 23]. Moreover, PC1 mainly differentiated A. sinensis from the other three species (Fig. 1b), therefore, A. sinensis was used to comparison to other three species. Interestingly, A. sinensis exhibited fewer up-regulated metabolites compared to the other species. Additionally, no significantly enriched pathways were detected in the KEGG enrichment results of these differential metabolites, which could be a bias caused by the small dataset. In comparison to A. biserrata, 446 significantly differential metabolites were screened in A. sinensis, with 123 up-regulated and 323 down-regulated (Fig. 5a). The top 3 enrichment pathways were metabolic pathways (23 metabolites with p = 0.19), tyrosine metabolism (3 metabolites with p = 0.22) and limonene and pinene degradation (5 metabolites with p = 0.24) (Fig. 5d). When compared with A. dahurica, 429 significantly differential metabolites were detected in A. sinensis, including 169 up-regulated and 260 down-regulated (Fig. 5b). The top 3 enrichment pathways were tyrosine metabolism (3 metabolites with p = 0.21), limonene and pinene degradation (5 metabolites with p = 0.22) and metabolic pathways (22 metabolites with p = 0.26) (Fig. 5e). Finally, in comparison to A. keiskei, 502 significantly differential metabolites were identified in A. sinensis, with 105 up-regulated and 397 down-regulated (Fig. 5c), representing the largest number of differential metabolites among the groups. The top 3 enrichment pathways of these metabolites were metabolic pathways (25 metabolites with p = 0.09), biosynthesis of various plant secondary metabolites (5 metabolites with p = 0.10), and tyrosine metabolism (3 metabolites with p = 0.26) (Fig. 5f).

Fig. 5

The overall distribution and KEGG enrichment analysis of differential metabolites between A. sinensis and the three other Angelica species. a–c Volcano plots for differential metabolites between A. sinensis and the three other Angelica species. a A. sinensis vs A. biserrata. b A. sinensis vs A. dahurica. c A. sinensis vs A. keiskei. Colors of metabolites indicated significant differences (red, upregulated; green, downregulated). d–f KEGG pathway enrichment analysis of differential metabolites for A. sinensis vs A. biserrata (d), A. sinensis vs A. dahurica (e) and A. sinensis vs A. keiskei (f). Color of the bubbles represented statistical significance of the enriched terms, and the size of the bubbles represented number of differentially enriched metabolites. The pathway of “Biosynthesis of various plant secondary metabolites” including: crocin biosynthesis, cannabidiol biosynthesis, mugineic acid biosynthesis, pentagalloylglucose biosynthesis, benzoxazinoid biosynthesis, gramine biosynthesis, coumarin biosynthesis, furanocoumarin biosynthesis, hordatine biosynthesis, podophyllotoxin biosynthesis

In order to delve into the details of the volatile metabolite difference between A. sinensis and the other three species, the most significantly twenty metabolites (the top 10 for up-regulation and down-regulation, respectively) were selected (Fig. 6). Hippuric acid, 7-hydroxycoumarin and 7-ethoxycoumarin were found to be more abundant in A. sinensis than the three other Angelica species. In addition, the abundance of 3-butylisobenzofuran-1(3H)-one was substantially higher A. sinensis than in A. dahurica and A. keiskei (log2FC > 19). In contrast, γ-terpinene and bornyl acetate were present in high abundance in A. dahurica, A. keiskei and A. biserrata, but were found in lower concentrations A. sinensis.

Fig. 6

The top 20 metabolites of significantly differential volatiles between A. sinensis and three other Angelica species. Red indicates the more abundant metabolites in A. sinensis compared to A. biserrata (a), A. dahurica (b), A. keiskei (c). Green indicates the lower levels of metabolites in A. sinensis than that in other species

2.4 Differential metabolites between A. keiskei and the three other Angelica species

The abundance of volatile metabolites in the roots of A. keiskei was the highest among the four species, suggesting that even the non-medicinal parts of A. keiskei have potential for practical applications. To further investigate, the differences of metabolites between A. keiskei and the other Angelica species were compared. The volcanic map visually showed the overall distribution of differential metabolites in each comparison. In the comparison between A. keiskei and A. biserrata, 401 significantly different metabolites were detected, with 308 up-regulated and 93 down-regulated (Fig. 7a). These metabolites were associated with phenylpropanoid biosynthesis (2 metabolites with p = 0.21), metabolic pathways (17 metabolites with p = 0.38) and tyrosine metabolism (3 metabolites with p = 0.45) (Fig. 7c). In the comparison between A. keiskei and A. dahurica, 473 significantly different metabolites were detected, with 421 up-regulated and 52 down-regulated (Fig. 7b), which were associated with sesquiterpenoid and triterpenoid biosynthesis (8 metabolites with p = 0.14), monoterpenoid biosynthesis (9 metabolites with p = 0.23) and biosynthesis of secondary metabolites (22 metabolites with p = 0.39) (Fig. 7d).

Fig. 7

The overall distribution and KEGG enrichment analysis of differential metabolites between A. keiskei and A. biserrata (a, c), A. keiskei and A. dahurica (b, d). a, b Volcano plots for differential metabolites. The colors of metabolites indicated significant differences (red, upregulated; green, downregulated). c, d KEGG pathway enrichment analysis of differential metabolites. Color of the bubbles represented statistical significance of the enriched terms, and the size of the bubbles represented number of differential metabolites

Moreover, to further investigate the differences of volatile metabolites between A. keiskei and other Angelica species, twenty of the most differentiated metabolites were selected for comparison (Fig. 8). The terpenoids metabolites, carvenone and cedrene were more abundant in A. keiskei than in A. biserrate, while carene, bornyl acetate and isobornyl acetate were the most enriched in A. keiskei compared to A. dahurica. Conversely, β-pinene was found in higher concentrations in A. dahurica and A. biserrata than in A. keiskei.

Fig. 8

Top 20 metabolites with significant difference between A. keiskei and A. biserrata (a), A. keiskei and A. dahurica (b). Red and green represent up-regulated and down-regulated metabolites in A. keiskei, respectively