New Results

Widely targeted metabolomics analysis reveals differences in volatile metabolites among four Angelica species

Jiaojiao Ji, Lanlan Zang, Tingting Lu, Cheng Li, Xiaoxu Han, Soo-Rang Lee, Li Wang
doi: https://doi.org/10.1101/2022.12.01.518649

Abstract

Angelica L. has attracted global interest for its traditional medicinal uses and commercial values. However, few studies have focused on the metabolomic differences among the Angelica species. In this study, employing the widely targeted metabolomics based on gas chromatography-tandem mass spectrometry, the metabolomes of four Angelica species were analyzed (Angelica sinensis (Oliv.) Diels (A. sinensis), Angelica biserrata (R.H.Shan & Yuan) C.Q.Yuan & R.H.Shan (A. biserrata), Angelica dahurica (Hoffm.) Benth. & Hook.f. ex Franch. & Sav. (A. dahurica), Angelica keiskei Koidz. (A. keiskei)). A total of 698 volatile metabolites were identified and classified into fifteen different categories. The metabolomic analysis indicated that 7-hydroxycoumarin and Z-ligustilide were accumulated at significantly higher levels in A. sinensis, whereas the opposite pattern was observed for bornyl acetate. In addition, a high correspondence between the dendrogram of metabolite contents and phylogenetic positions was detected in the four species. This study provides a biochemical map for the exploitation, application and development of the Angelica species as medicinal plants or health-related dietary supplements.

1. Introduction

Angelica L., a genus in the family Apiaceae, is comprised of 90 species of herbs that are widespread in north-temperate regions, especially Eurasia (Feng et al., 2009; Sowndhararajan et al., 2017). Many plants in the genus have long been used in traditional Chinese medicine (TCM) (Sarker & Nahar, 2004), in particular, the dried roots of Angelica have been widely used for nourishing blood, regulating menstruation, and analgesic (Dong et al., 2022; Sowndhararajan et al., 2017). Various herbal preparations containing Angelica species are available over the counter, not only in China, but also in Europe and American countries (Hook, 2014; Wei et al., 2016). Besides its medicinal value, Angelica is also highly appreciated in various industrial applications such as the dietary supplements, perfumery, and cosmetics (Alkan Turkucar et al., 2021; Sowndhararajan et al., 2017; Zhang et al., 2012). A previous study demonstrated that the pharmacological activity of aromatic and medicinal plants is attributed to its effective volatile components (Pandey et al., 2020). Plants in Angelica are extremely rich in secondary metabolites, including coumarins, flavonoids, terpenoids, as well as volatiles oils (VOs) (Sarker & Nahar, 2004; Sowndhararajan et al., 2017). Modern medical research has revealed that the VOs composition is mainly responsible for the medicinal properties of the genus Angelica (Kumar et al., 2022). VOs are complex mixture of low molecular weight volatile compounds that are isolated from the raw plant material by distillation (Sadgrove et al., 2022), which have been reported to treat serious health diseases, involving gynecological diseases, fever, and arthritis (Perveen et al., 2020; Sowndhararajan et al., 2017). There are a couple of good examples showing the proven effects of VOs in Angelica species. Phthalides of Angelica sinensis (Oliv.) Diels (A. sinensis) are one of the highly effective VOs to analgesic and sedative activities (Du et al., 2006; Wei et al., 2016). Angelica biserrata (R.H.Shan & Yuan) C.Q.Yuan & R.H.Shan (A. biserrata) also contains active ingredients such as oxygenates, terpenoids, ketones and esters with analgesic and anti-inflammatory effects (Ma et al., 2019). However, most of current studies only focused on several targeted compounds in a single Angelica species. There have been no comprehensive and comparative studies examining the volatile metabolites of multiple Angelica species. It has posed a major obstacle to the application and exploitation of the medicinal plants in Angelica species.

With the development of metabolomics, high-throughput and high-resolution methods such as headspace solid phase micro-extraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) have been widely used to identify metabolite profiles and detect differences in the biochemical compositions of aromatic and medicinal plants (Chen et al., 2021; Hua et al., 2019; Kumar et al., 2022). The four species A. biserrata, Angelica dahurica (Hoffm.) Benth. & Hook.f. ex Franch. & Sav. (A. dahurica), Angelica keiskei Koidz. (A. keiskei) and A. sinensis are the representative medicinal plants in Angelica, and it is noteworthy that roots of A. sinensis are one of the most widely prescribed medicine in China owing to its rich VOs (Wei et al., 2016). In this study, volatile metabolites of four Angelica species were identified and quantified using widely targeted metabolomics. The aim was to reveal the differed accumulation of medicinally important metabolites among the four species. This study provides useful information for the chemical composition of Angelica plants and may help the identification of the biologically active substances responsible for the pharmacological activity of Angelica plants.

2. Materials and methods

2.1. Plant samples

Four species in genus Angelica, including A. sinensis, A. dahurica, A. biserrata, and A. keiskei, were analyzed in this study. The A. sinensis plants were collected from Minxian County, Gansu Province, China. The A. dahurica, A. biserrata, and A. keiskei plants were collected from Shenzhen City, Guangdong Province, China. The specimens of the four species were deposited at Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences and identified by Prof. Li Wang (A. sinensis (202011002), A. dahurica (202008001), A. biserrata (202106003) and A. keiskei (202109004)).

Roots of each species were sampled with three biological replicates. The collected roots were washed, naturally dried, frozen in liquid nitrogen, and then stored at −80°C for further analysis.

2.2. Solid phase microextraction (SPEM) extraction

The samples were ground into powder in liquid nitrogen. Powdered samples (1 g) were weighed and transferred immediately to a 20 mL head-space vial (Agilent, Palo Alto, CA, USA), containing NaCl saturated solution to inhibit potential enzyme reactions. The headspace vials were sealed using crimp-top caps. As for SPME analysis, each vial was placed in 60°C for 5 min, and then a 120 µm DVB/CWR/PDMS fiber (Agilent, Palo Alto, CA, USA) was exposed to the headspace of the sample for 15 min at 100°C. DVB/CWR/PDMS are three-phase fiber heads, which were confirmed to be able to extract more volatile metabolites than other fiber headers (Bertuzzi et al., 2018). The quality control (mix) sample was prepared by mixing equal volumes of samples into a single tube, and during the instrumental analysis, a quality control sample was inserted into each of the 10 test samples to check the repeatability of the analysis process (Figure S1 and Table S1).

2.3. GC-MS analysis

After the extraction procedure, the fiber was transferred to the injection port of the GC-MS system (Model 8890; Agilent, Palo Alto, CA, USA). The SPME fiber was desorbed and maintained in the injection port at 250°C for 5 min in the split-less mode. The identification and quantification of volatile metabolites was carried out using an Agilent Model 8890 GC and a 7000 D mass spectrometer (Agilent, Palo Alto, CA, USA), equipped with a 30 m × 0.25 mm × 0.25 μm DB-5MS (5% phenyl-polymethylsiloxane) capillary column. Helium was used as the carrier gas at a linear velocity of 1.2 mL/min. The injector temperature was kept at 250°C and the detector at 280°C. The oven temperature was programmed as followings: 40°C (3.5 min), increasing at 10°C min−1 to 100°C, 7°C min−1 to 180°C, 25°C min−1 to 280°C and hold for 5 min. Mass spectra was recorded in electron impact ionization mode at 70 eV. The quadrupole mass detector, ion source and transfer line temperatures were set, respectively, at 150, 230 and 280°C. For the identification and quantification of analytes, the MS was selected ion monitoring mode.

2.4. Qualitative and quantitative analysis

After the mass spectrometry analysis, all raw data were analyzed with the software Qualitative Analysis Workflows B.08.00 (Agilent, Palo Alto, CA, USA). The qualitative analysis of primary and secondary mass spectrometry data was annotated based on the self-built database MWDB (Metware Biotechnology Co., Ltd. Wuhan, China) and the publicly available metabolite databases.

2.5 Statistical analysis

After the metabolite data was transformed with Hellinger transformation, principal component analysis (PCA) was performed using the function rda in the R package vegan v 2.6-2 (Oksanen et al., 2013). In addition, the data set that was log10 transform and mean centering were imported into the R package MetaboAnalystR v5.0 (Chong & Xia, 2018) to conduct orthogonal partial least squares-discriminant analysis (OPLS-DA) and extract the variable important in projection (VIP) value from the analysis results to evaluate the variance importance of compounds. The values of R2X, R2Y, and Q2 for OPLS-DA models were showed in Figure S2. In order to avoid overfitting, a permutation test (100 permutations) was performed. Based on Bray-Curtis’s dissimilarity distances of the composition and abundance of all volatile metabolites after ln transform, which were calculated using the function vegdist built in vegan, hierarchical clustering was visualized with the R package factoextra v.1.0.7 (Lê et al., 2008).

The chloroplast sequence alignments of A. sinensis, A. dahurica, A. biserrata, and A. keiskei were generated using MAFFT v7.475 (Katoh & Standley, 2013). Phylogenetic trees were constructed by maximum likelihood using IQ-TREE v 2.1.2 (Nguyen et al., 2015) with Hydrocotyle sibthorpioides Lam. as an outgroup.

All identified metabolites were annotated with KEGG database (http://www.kegg.jp/kegg/compound/) and further subjected to KEGG enrichment analyses with the R package clusterProfiler v. 4.4.4 (Wu et al., 2021).

3. Results and discussion

3.1. Metabolomics profiling of four Angelica species

Widely targeted metabolomics offered a promising way for the chemical screening of volatile metabolites and allowed the characterization of new metabolites in Angelica (Kumar et al., 2022). In this study, to get insight into differences of volatile metabolites among four Angelica species, the root metabolomics data were generated. A total of 698 non-redundant volatile metabolites were qualified and quantified based on GC-MS (Table S1). Among them, 616, 536, 576, and 545 volatile metabolites in A. sinensis, A. dahurica, A. biserrata, A. keiskei, respectively. Three hundred and ninety-one metabolites were commonly detected in the roots of all four species (Figure 1a).

Figure 1.
Figure 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.

PCA of the metabolome data, transformed with Hellinger transformation method, the samples were divided into four distinct groups corresponding to the four species. And the three biological replicates were clustered together, suggesting that the data are reproducible and reliable. Based on the PCA plot, where 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 (Figure 1b).

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

3.2. 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 (Figure 2). Consistent with the previous reports, terpenoids were the largest and most diverse class of volatile metabolites in the four Angelica species (Sowndhararajan et al., 2017), followed by heterocyclic compounds, eater, and aromatics. Notably, A. sinensis contained a relatively lower proportion (43.34%) of terpenoids than other Angelica species, but exhibited a more balanced metabolite composition in volatile metabolites. In contrast, the amount of terpenoids accounted for more than 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%.

Figure 2.
Figure 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. The p values corrected by the bonferroni method showed significant difference in eight categories among the four species, including alcohol, aldehyde, aromatics, ester, heterocyclic compounds, hydrocarbons, ketone and terpenoids (Figure 3, Figure S3; p-values were shown in the Table S2). Terpenoids was the most different metabolites among the four species. In each of the eight categories, A. sinensis and A. keiskei were significantly different from at least one species. These results indicated that the difference between the presented Angelica species lies in the representation of individual components in the metabolomic profiles of the samples, while the qualitative composition is approximately the same.

Figure 3.
Figure 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.

Previous studies have verified that plants with closer phylogenetic relationship are not only similar in morphology but also in chemical composition and curative effects (Hao & Xiao, 2020; Kang et al., 2019; Saslis-Lagoudakis et al., 2011). Here, this study performed hierarchical clustering analysis based on Bray-Curtis’s dissimilarity distances of the composition and abundance of volatile metabolites in the four Angelica species. The dendrogram (Figure 4a) showed high correspondence with the phylogenetic tree (Figure 4b) based on chloroplast sequences, suggesting a correlation relationship between the volatile metabolites and the phylogenetic relationships. Although more extensive sampling and deeper investigations would be necessary to reveal more reliable correlations, the study implied that phylogenetic relationships could serve as a window to coarsely apprehend the unknown biochemical diversity of some plants based on the known biochemical map of phylogenetically related species. This finding may offer a great tool for searching replacements of medicinal plant resources that are endangered with closely related non-endangered species.

Figure 4.
Figure 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 at [https://www.ncbi.nlm.nih.gov].

3.3. 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 also known as “female ginseng” is a traditional herb, which has long been used to treat various gynecological conditions (Hook, 2014; Wei et al., 2016; Yeh et al., 2011). A. sinensis belongs to Sinodielsia clade in Angelica genus, that was phylogenetically distant from core Angelica group, including A. biserrata, A. dahurica, A. keiskei (Feng et al., 2009; Liao et al., 2022). Moreover, PC1 mainly differentiated A. sinensis from the other three species (Figure 1b), therefore, A. sinensis was used to comparison to other three species. Interestingly, there were fewer up-regulated metabolites in A. sinensis when compared with the other species. And no significantly enriched pathway was detected in the KEGG enrichment results of these differential metabolites, which could be a bias caused by the small dataset. Compared with A. biserrata, 446 significantly differential metabolites (123 up-regulated and 323 down-regulated) were screened in A. sinensis (Figure 5a), and the top 3 enrichment pathways of these substance 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) (Figure 5d). Compared with A. dahurica, 429 significantly differential metabolites (169 up-regulated and 260 down-regulated) were detected in A. sinensis (Figure 5b), and the top 3 enrichment pathways of these metabolites 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) (Figure 5e). When compared with A. keiskei, 502 significantly differential metabolites (105 up-regulated and 397 down-regulated) were identified in A. sinensis (Figure 5c), which were the most abundant compared with the other two group, and 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) (Figure 5f).

Figure 5.
Figure 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 (Figure 6). It was discovered that hippuric acid, 7-hydroxycoumarin and 7-ethoxycoumarin were more enriched in A. sinensis than the three other Angelica species. In addition, the abundance of 3-butylisobenzofuran-1(3H)-one in A. sinensis was also substantially higher than that in A. dahurica and A. keiskei (log2FC > 19). Meanwhile, the metabolites γ-terpinene and bornyl acetate in A. dahurica, A. keiskei and A. biserrata were in high abundance, but the metabolites were lower in A. sinensis.

Figure 6.
Figure 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.

Phthalides is believed to be responsible for the bioactivities of A. sinensis (Chen et al., 2013; Wei et al., 2016). This study shows that 3-butylisobenzofuran-1(3H)-one and Z-ligustilide were detected in the four species and its contents were relatively higher in A. sinensis, which is consistent with previous studies (Hook, 2014). In addition, coumarin and its derivatives are one of the important heterocyclic metabolites (Wu et al., 2009), which is mainly used as anti-HIV, anticancer activity agents, and anticoagulant activities (Kim et al., 2023; Zhou et al., 2016). The results show that the contents of 7-hydroxycoumarin and 7-ethoxycoumarin in A. sinensis were significantly higher than A. dahurica, A. biserrata, and A. keiskei (Figure 2). By virtue of its structural simplicity, 7-hydroxycoumarin has been generally accepted as the parent metabolites for the furocoumarins and pyranocoumarins and is widely used as a synthon for a wide variety of coumarin-heterocycles (Han et al., 2022; Mazimba, 2017; Vanholme et al., 2019). Its higher abundance in A. sinensis was probably associated with biosynthesis of furocoumarins and pyranocoumarins, which were reported as one of the main active components influencing the pharmaceutical activity of the herb (Pandey et al., 2020). Nevertheless, in ancient Chinese medical systems, the pharmacological effect of medicinal plants depends not only on the high abundance of a single compound, but also on the synergy of multiple active ingredients (Liu et al., 2014; Song et al., 2016). Furthermore, this study also found that the proportion of various components in volatile metabolites was more balanced in A. sinensis (Figure 2). This might explain the wide and common applications of A. sinensis in TCM.

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

A. keiskei is called ashitaba in Japanese. Its leaves have been used as the medicinal part. And it is economically used as herbs, food and spices (Kil et al., 2017; Rong et al., 2021). However, the abundance of volatile metabolites in the root of the A. keiskei was the highest among the four species. It showed that the non-medicinal parts of A. keiskei also have potential to be exploited for practical uses. Therefore, the differences of metabolites between A. keiskei and the other Angelica species were further compared. The volcanic map visually showed the overall distribution of differential metabolites in each comparison. Four hundred and one significantly different metabolites (308 up-regulated and 93 down-regulated) were detected in the comparison between A. keiskei and A. biserrata (Figure 7a), which were related to 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) (Figure 7c). Four hundred seventy-three significantly different metabolites (421 up-regulated and 52 down-regulated) were detected in the comparison between A. keiskei and A. dahurica (Figure 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) (Figure 7d).

Figure 7.
Figure 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 in A. keiskei and other Angelica species, twenty metabolites that were differentiated the most between the two species were subsampled (Figure 8). From the comparison, the terpenoids metabolites, carvenone and cedrene were more abundant in A. keiskei than that in A. biserrata; and carene, bornyl acetate and isobornyl acetate were the most enriched in A. keiskei compared with A. dahurica. Additionally, the β-pinene was more enriched in A. dahurica and A. biserrata than in A. keiskei.

Figure 8.
Figure 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.

Angelica keiskei has been used as a medicine and food owing to its abundant pharmacological effects, including anti-cancer, lowering blood sugar and blood lipids, and improving human immunity (Guiné & Gonçalves, 2016; Kil et al., 2017). However, these pharmacological effects have not been validated in scientific research. To date, it is only found in the form of raw materials in tea and cosmetics, which has limited its medicinal and clinical applications (Kim et al., 2014; Rong et al., 2021). Interestingly, bornyl acetate, previously unmentioned terpenoid substances was detected with high expression levels in the root of A. keiskei, and it has been reported that bornyl acetate has antibacterial, insecticidal, and anesthetic effects symbiotically with other aromatic metabolites in the VOs (Liang et al., 2022). This discovery provides a basis for the development and utilization of active ingredients in A. keiskei for health-related dietary supplements. Taken together, this study greatly enriches the database of chemical composition in A. keiskei and imply that A. keiskei exhibited benign potential to be exploited as medicinal materials and health-related dietary supplements.

4. Conclusions

This study investigated the metabolites of four Angelica species by using widely targeted metabolomics, and found the differed accumulation of medicinally important metabolites among species. For example, high levels of bornyl acetate metabolites accumulated in A. keiskei, whereas coumarins and phthalides were significantly lower in A. keiskei than in A. sinensis. Moreover, the high correspondence between the dendrogram of metabolite contents and the phylogenetic tree suggested a potential correlation between the volatile metabolites and the phylogenetic relationships. Taken all together, the present study provides a biochemical map for the exploitation, application, and development of the Angelica species as TCM or health-related dietary supplements.

Supplementary Materials

Figure S1: TIC chromatogram of quality control samples; Figure S2: Permutation test of OPLS-DA model; Figure S3: The violin plot of relative abundance of 15 classes in the four Angelica species. Table S1: The volatile metabolites detected; Table S2: The p-values of the t-test for 15 classes in four species.

Author Contributions

Conceptualization, L.W.; methodology, J.J., L.Z. and T.L.; formal analysis, L.Z., J.J. and L.W.; original manuscript writing, J.J. and L.Z.; revision and supervision, J.J., L.Z., X.H., C.L., S.L. and L.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 32070242), the National Key Research and Development Program of China (Grant No. 2020YFA0907900), the Science Technology and Innovation Commission of Shenzhen Municipality of China (ZDSYS 20200811142605017), the Shenzhen Science and Technology Program (Grant No. KQTD2016113010482651), special funds for Science Technology Innovation and Industrial Development of Shenzhen Dapeng New District (Grant Nos. RC201901-05 and PT201901-19), the China Postdoctoral Science Foundation (Grant No. 2020M672904), and the Basic and Applied Basic Research Fund of Guangdong (Grant No. 2020A1515110912).

Institutional Review Board Statement

Not applicable.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

  • Based on existing contributions and future work plans, we have uptated the list of authors and their affiliations.

References

  1. ↵
    Alkan Turkucar, S., Aktas Karacelik, A., & Karakose, M. (2021). Phenolic compounds, essential oil composition, and antioxidant activity of Angelica purpurascens (Ave-Lall.) Gill. Turk J Chem, 45(3), 956–966. doi:10.3906/kim-2101-28
    OpenUrlCrossRef
  2. ↵
    Bertuzzi, A. S., McSweeney, P. L. H., Rea, M. C., & Kilcawley, K. N. (2018). Detection of Volatile Compounds of Cheese and Their Contribution to the Flavor Profile of Surface-Ripened Cheese. Compr Rev Food Sci Food Saf, 17(2), 371–390. doi:10.1111/1541-4337.12332
    OpenUrlCrossRef
  3. ↵
    Chen, Q., Hu, Y., Wen, R., Wang, Y., Qin, L., & Kong, B. (2021). Characterisation of the flavour profile of dry fermented sausages with different NaCl substitutes using HS-SPME-GC-MS combined with electronic nose and electronic tongue. Meat Science, 172, 108338. doi:10.1016/j.meatsci.2020.108338
    OpenUrlCrossRef
  4. ↵
    Chen, X.-P., Li, W., Xiao, X.-F., Zhang, L.-L., & Liu, C.-X. (2013). Phytochemical and pharmacological studies on Radix Angelica sinensis. Chinese Journal of Natural Medicines, 11(6), 577–587. doi:10.1016/s1875-5364(13)60067-9
    OpenUrlCrossRef
  5. ↵
    Chong, J., & Xia, J. (2018). MetaboAnalystR: an R package for flexible and reproducible analysis of metabolomics data. Bioinformatics, 34(24), 4313–4314. doi:10.1093/bioinformatics/bty528
    OpenUrlCrossRefPubMed
  6. ↵
    Dong, H., Li, M., Jin, L., Xie, X., Li, M., & Wei, J. (2022). Cool Temperature Enhances Growth, Ferulic Acid and Flavonoid Biosynthesis While Inhibiting Polysaccharide Biosynthesis in Angelica sinensis. Molecules, 27(1), 320. doi:10.3390/molecules27010320
    OpenUrlCrossRef
  7. ↵
    Du, J., Bai, B., Kuang, X., Yu, Y., Wang, C., Ke, Y., Xu, Y., Tzang, A. H., & Qian, Z. M. (2006). Ligustilide inhibits spontaneous and agonists-or K+ depolarization-induced contraction of rat uterus. Journal of ethnopharmacology, 108(1), 54–58.
    OpenUrlCrossRefPubMed
  8. ↵
    Feng, T., Downie, S. R., Yu, Y., Zhang, X., Chen, W., He, X., & Liu, S. (2009). Molecular systematics of Angelica and allied genera (Apiaceae) from the Hengduan Mountains of China based on nrDNA ITS sequences: phylogenetic affinities and biogeographic implications. J Plant Res, 122(4), 403–414. doi:10.1007/s10265-009-0238-4
    OpenUrlCrossRefPubMedWeb of Science
  9. ↵
    Guiné, R. P., & Gonçalves, F. J. (2016). Bioactive Compounds in Some Culinary Aromatic Herbs and Their Effects on Human Health. Mini Rev Med Chem, 16(11), 855–866. doi:10.2174/1389557516666160211120540
    OpenUrlCrossRef
  10. ↵
    Han, X., Li, C., Sun, S., Ji, J., Nie, B., Maker, G., Ren, Y., & Wang, L. (2022). The chromosome-level genome of female ginseng (Angelica sinensis) provides insights into molecular mechanisms and evolution of coumarin biosynthesis. Plant J. doi:10.1111/tpj.16007
    OpenUrlCrossRef
  11. ↵
    Hao, D.-c., & Xiao, P.-g. (2020). Pharmaceutical resource discovery from traditional medicinal plants: Pharmacophylogeny and pharmacophylogenomics. Chinese Herbal Medicines, 12(2), 104–117.
    OpenUrl
  12. ↵
    Hook, I. L. I. (2014). Danggui to Angelica sinensis root: Are potential benefits to European women lost in translation? A review. Journal of Ethnopharmacology, 152(1), 1–13. doi:10.1016/j.jep.2013.12.018
    OpenUrlCrossRefPubMed
  13. ↵
    Hua, Y. L., Ma, Q., Zhang, X. S., Yao, W. L., Ji, P., Hu, J. J., & Wei, Y. M. (2019). Urinary metabolomics analysis reveals the effect of volatile oil from Angelica sinensis on LPS-induced inflammation rats. Biomed Chromatogr, 33(2), e4402. doi:10.1002/bmc.4402
    OpenUrlCrossRef
  14. ↵
    Kang, K. B., Ernst, M., van der Hooft, J. J., da Silva, R. R., Park, J., Medema, M. H., Sung, S. H., & Dorrestein, P. C. (2019). Comprehensive mass spectrometry-guided phenotyping of plant specialized metabolites reveals metabolic diversity in the cosmopolitan plant family Rhamnaceae. The Plant Journal, 98(6), 1134–1144.
    OpenUrl
  15. ↵
    Katoh, K., & Standley, D. M. (2013). MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol, 30(4), 772–780. doi:10.1093/molbev/mst010
    OpenUrlCrossRefPubMedWeb of Science
  16. ↵
    Kil, Y. S., Pham, S. T., Seo, E. K., & Jafari, M. (2017). Angelica keiskei, an emerging medicinal herb with various bioactive constituents and biological activities. Arch Pharm Res, 40(6), 655–675. doi:10.1007/s12272-017-0892-3
    OpenUrlCrossRef
  17. ↵
    Kim, C. Y., Mitchell, A. J., Kastner, D. W., Albright, C. E., Gutierrez, M. A., Glinkerman, C. M., Kulik, H. J., & Weng, J.-K. (2023). Emergence of a proton exchange-based isomerization and lactonization mechanism in the plant coumarin synthase COSY. Nature Communications, 14(1), 597. doi:10.1038/s41467-023-36299-1
    OpenUrlCrossRef
  18. ↵
    Kim, D. W., Curtis-Long, M. J., Yuk, H. J., Wang, Y., Song, Y. H., Jeong, S. H., & Park, K. H. (2014). Quantitative analysis of phenolic metabolites from different parts of Angelica keiskei by HPLC-ESI MS/MS and their xanthine oxidase inhibition. Food Chem, 153, 20–27. doi:10.1016/j.foodchem.2013.12.026
    OpenUrlCrossRef
  19. ↵
    Kumar, P., Rana, V., & Singh, A. N. (2022). Angelica glauca Edgew. - A comprehensive review. Journal of Applied Research on Medicinal and Aromatic Plants, 31, 100397. doi:10.1016/j.jarmap.2022.100397
    OpenUrlCrossRef
  20. ↵
    Lê, S., Josse, J., & Husson, F. (2008). FactoMineR: An R Package for Multivariate Analysis. Journal of statistical software, 25(1), 1–18. doi:10.18637/jss.v025.i01
    OpenUrlCrossRef
  21. ↵
    Liang, H., Lin, X., Yang, P., Sun, Y., Wu, Q., Alimujiang, S., Zhao, H., Ma, D., Zhan, R., & Yang, J. (2022). Genome-Wide Identification of BAHD Superfamily and Functional Characterization of Bornyl Acetyltransferases Involved in the Bornyl Acetate Biosynthesis in Wurfbainia villosa. Frontiers in Plant Science, 13, 860152. doi:10.3389/fpls.2022.860152
    OpenUrlCrossRef
  22. ↵
    Liao, C. Y., Gao, Q., Katz-Downie, D. S., & Downie, S. R. (2022). A systematic study of North American Angelica species (Apiaceae) based on nrDNA ITS and cpDNA sequences and fruit morphology. Journal of Systematics and Evolution, 60(4), 789–808.
    OpenUrl
  23. ↵
    Liu, P., Yang, H., Long, F., Hao, H. P., Xu, X., Liu, Y., Shi, X. W., Zhang, D. D., Zheng, H. C., Wen, Q. Y., Li, W. W., Ji, H., Jiang, X. J., Zhang, B. L., Qi, L. W., & Li, P. (2014). Bioactive equivalence of combinatorial components identified in screening of an herbal medicine. Pharm Res, 31(7), 1788–1800. doi:10.1007/s11095-013-1283-1
    OpenUrlCrossRefPubMed
  24. ↵
    Ma, J., Huang, J., Hua, S., Zhang, Y., Zhang, Y., Li, T., Dong, L., Gao, Q., & Fu, X. (2019). The ethnopharmacology, phytochemistry and pharmacology of Angelica biserrata - A review. J Ethnopharmacol, 231, 152–169. doi:10.1016/j.jep.2018.10.040
    OpenUrlCrossRef
  25. ↵
    Mazimba, O. (2017). Umbelliferone: Sources, chemistry and bioactivities review. Bulletin of Faculty of Pharmacy, Cairo University, 55(2), 223–232. doi:10.1016/j.bfopcu.2017.05.001
    OpenUrlCrossRef
  26. ↵
    Nguyen, L.-T., Schmidt, H. A., Von Haeseler, A., & Minh, B. Q. (2015). IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Molecular biology and evolution, 32(1), 268–274.
    OpenUrlCrossRefPubMed
  27. ↵
    Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., O’Hara, R. G., Simpson, G., Solymos, P., Stevens, H., & Wagner, H. (2013). Multivariate analysis of ecological communities in R: vegan tutorial. R package version 1.7.
    OpenUrl
  28. ↵
    Pandey, A. K., Kumar, P., Saxena, M., & Maurya, P. (2020). Distribution of aromatic plants in the world and their properties. Elsevier Science Publishing Co Inc.
  29. ↵
    Perveen, A., Ijaz, S., & Ghaffar, N. (2020). Comparative phytochemical and physicochemical study of seeds of the genus Angelica L. from Neelum valley Azad Kashmir, Pakistan. Pakistan Journal of Botany, 52(1), 257–260. doi:10.30848/pjb2020-1(44)
    OpenUrlCrossRef
  30. ↵
    Rong, Y., Gu, X., Li, D., Chen, L., Zhang, Y., & Wang, Z. (2021). Characterization of aroma, sensory and taste properties of Angelica keiskei tea. European Food Research and Technology, 247(7), 1665–1677. doi:10.1007/s00217-021-03737-7
    OpenUrlCrossRef
  31. ↵
    Sadgrove, N. J., Padilla-Gonzalez, G. F., & Phumthum, M. (2022). Fundamental Chemistry of Essential Oils and Volatile Organic Compounds, Methods of Analysis and Authentication. Plants (Basel), 11(6), 789. doi:10.3390/plants11060789
    OpenUrlCrossRef
  32. ↵
    Sarker, S., & Nahar, L. (2004). Natural medicine: the genus Angelica. Current medicinal chemistry, 11(11), 1479–1500.
    OpenUrlCrossRefPubMedWeb of Science
  33. ↵
    Saslis-Lagoudakis, C. H., Klitgaard, B. B., Forest, F., Francis, L., Savolainen, V., Williamson, E. M., & Hawkins, J. A. (2011). The use of phylogeny to interpret cross-cultural patterns in plant use and guide medicinal plant discovery: an example from Pterocarpus (Leguminosae). PLoS ONE, 6(7), e22275.
    OpenUrlCrossRefPubMed
  34. ↵
    Song, H. P., Wu, S. Q., Hao, H., Chen, J., Lu, J., Xu, X., Li, P., & Yang, H. (2016). A chemical family-based strategy for uncovering hidden bioactive molecules and multicomponent interactions in herbal medicines. Sci Rep, 6, 23840. doi:10.1038/srep23840
    OpenUrlCrossRef
  35. ↵
    Sowndhararajan, K., Deepa, P., Kim, M., Park, S. J., & Kim, S. (2017). A Review of the Composition of the Essential Oils and Biological Activities of Angelica Species. Sci Pharm, 85(3), 33. doi:10.3390/scipharm85030033
    OpenUrlCrossRef
  36. ↵
    Vanholme, R., Sundin, L., Seetso, K. C., Kim, H., Liu, X., Li, J., De Meester, B., Hoengenaert, L., Goeminne, G., Morreel, K., Haustraete, J., Tsai, H. H., Schmidt, W., Vanholme, B., Ralph, J., & Boerjan, W. (2019). COSY catalyses trans-cis isomerization and lactonization in the biosynthesis of coumarins. Nat Plants, 5(10), 1066–1075. doi:10.1038/s41477-019-0510-0
    OpenUrlCrossRef
  37. ↵
    Wei, W. L., Zeng, R., Gu, C. M., Qu, Y., & Huang, L. F. (2016). Angelica sinensis in China-A review of botanical profile, ethnopharmacology, phytochemistry and chemical analysis. J Ethnopharmacol, 190, 116–141. doi:10.1016/j.jep.2016.05.023
    OpenUrlCrossRefPubMed
  38. ↵
    Wu, L., Wang, X., Xu, W., Farzaneh, F., & Xu, R. (2009). The structure and pharmacological functions of coumarins and their derivatives. Curr Med Chem, 16(32), 4236–4260. doi:10.2174/092986709789578187
    OpenUrlCrossRefPubMed
  39. ↵
    Wu, T., Hu, E., Xu, S., Chen, M., Guo, P., Dai, Z., Feng, T., Zhou, L., Tang, W., & Zhan, L. (2021). clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation, 2(3), 100141.
    OpenUrl
  40. ↵
    Yeh, J. C., Cindrova-Davies, T., Belleri, M., Morbidelli, L., Miller, N., Cho, C. W., Chan, K., Wang, Y. T., Luo, G. A., Ziche, M., Presta, M., Charnock-Jones, D. S., & Fan, T. P. (2011). The natural compound n-butylidenephthalide derived from the volatile oil of Radix Angelica sinensis inhibits angiogenesis in vitro and in vivo. Angiogenesis, 14(2), 187–197. doi:10.1007/s10456-011-9202-8
    OpenUrlCrossRef
  41. ↵
    Zhang, H. Y., Bi, W. G., Yu, Y., & Liao, W. B. (2012). Angelica sinensis (Oliv.) Diels in China: distribution, cultivation, utilization and variation. Genetic Resources and Crop Evolution, 59(4), 607–613. doi:10.1007/s10722-012-9795-9
    OpenUrlCrossRef
  42. ↵
    Zhou, D., Li, N., Zhang, Y., Yan, C., Jiao, K., Sun, Y., Ni, H., Lin, B., & Hou, Y. (2016). Biotransformation of neuro-inflammation inhibitor kellerin using Angelica sinensis (Oliv.) Diels callus. RSC Advances, 6(99), 97302–97312. doi:10.1039/c6ra22502k
    OpenUrlCrossRef
Back to top
Posted June 17, 2024.
Download PDF

Supplementary Material

Email
Widely targeted metabolomics analysis reveals differences in volatile metabolites among four Angelica species
Jiaojiao Ji, Lanlan Zang, Tingting Lu, Cheng Li, Xiaoxu Han, Soo-Rang Lee, Li Wang
bioRxiv 2022.12.01.518649; doi: https://doi.org/10.1101/2022.12.01.518649
Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools

Subject Area